diff --git a/.travis.yml b/.travis.yml index 39a1edcab3..98fafcdc36 100644 --- a/.travis.yml +++ b/.travis.yml @@ -19,24 +19,24 @@ jobs: - env: TEST_SUITE=activitysim/core - stage: Examples - name: "MTC Example" - env: TEST_SUITE=activitysim/examples/example_mtc/test - - name: "MTC Extended Example" - env: TEST_SUITE=activitysim/examples/example_mtc_extended/test + name: "Prototype MTC" + env: TEST_SUITE=activitysim/examples/prototype_mtc/test + - name: "Prototype MTC (Extended)" + env: TEST_SUITE=activitysim/examples/prototype_mtc_extended/test - name: "Multizone Example" - env: TEST_SUITE=activitysim/examples/example_multiple_zone/test + env: TEST_SUITE=activitysim/examples/placeholder_multiple_zone/test - name: "Marin Example" - env: TEST_SUITE=activitysim/examples/example_marin/test + env: TEST_SUITE=activitysim/examples/prototype_marin/test - name: "ARC Example" - env: TEST_SUITE=activitysim/examples/example_arc/test + env: TEST_SUITE=activitysim/examples/prototype_arc/test - name: "SEMCOG Example" - env: TEST_SUITE=activitysim/examples/example_semcog/test + env: TEST_SUITE=activitysim/examples/prototype_semcog/test - name: "PSRC Example" - env: TEST_SUITE=activitysim/examples/example_psrc/test + env: TEST_SUITE=activitysim/examples/placeholder_psrc/test - name: "SANDAG Example" - env: TEST_SUITE=activitysim/examples/example_sandag/test + env: TEST_SUITE=activitysim/examples/placeholder_sandag/test - name: "SANDAG Cross-Border Example" - env: TEST_SUITE=activitysim/examples/example_sandag_xborder/test + env: TEST_SUITE=activitysim/examples/prototype_sandag_xborder/test - stage: Estimation Mode name: "Larch Test" @@ -94,12 +94,12 @@ install: script: # This is the "default" script used for each test suite, unless overridden with a "script" in the jobs above. # build 2 and 3 zone test data twice since the Python test code on Linux sees these as different locations -- python activitysim/examples/example_multiple_zone/scripts/two_zone_example_data.py -- python activitysim/examples/example_multiple_zone/scripts/three_zone_example_data.py -- python /home/travis/miniconda/envs/asimtest/lib/python$TRAVIS_PYTHON_VERSION/site-packages/activitysim/examples/example_multiple_zone/scripts/two_zone_example_data.py -- python /home/travis/miniconda/envs/asimtest/lib/python$TRAVIS_PYTHON_VERSION/site-packages/activitysim/examples/example_multiple_zone/scripts/three_zone_example_data.py +- python activitysim/examples/placeholder_multiple_zone/scripts/two_zone_example_data.py +- python activitysim/examples/placeholder_multiple_zone/scripts/three_zone_example_data.py +- python /home/travis/miniconda/envs/asimtest/lib/python$TRAVIS_PYTHON_VERSION/site-packages/activitysim/examples/placeholder_multiple_zone/scripts/two_zone_example_data.py +- python /home/travis/miniconda/envs/asimtest/lib/python$TRAVIS_PYTHON_VERSION/site-packages/activitysim/examples/placeholder_multiple_zone/scripts/three_zone_example_data.py # pycodestyle - pycodestyle activitysim # run specific TEST_SUITE job on travis to avoid job max time - travis_wait 50 py.test $TEST_SUITE --cov activitysim --cov-report term-missing --durations=0 -- coveralls +# coveralls # disable coveralls service, which errors frequently diff --git a/HOW_TO_RELEASE.md b/HOW_TO_RELEASE.md index eba38de1c1..0c51962267 100644 --- a/HOW_TO_RELEASE.md +++ b/HOW_TO_RELEASE.md @@ -60,8 +60,8 @@ regular test suite takes some time to run, between about half an hour and two hours depending on the specs of your machine. ```sh - python activitysim/examples/example_multiple_zone/scripts/two_zone_example_data.py - python activitysim/examples/example_multiple_zone/scripts/three_zone_example_data.py + python activitysim/examples/placeholder_multiple_zone/scripts/two_zone_example_data.py + python activitysim/examples/placeholder_multiple_zone/scripts/three_zone_example_data.py pytest . ``` @@ -83,7 +83,7 @@ python ../activitysim/examples/scan_examples_for_errors.py . ``` -00. Test the notebooks in `activitysim/examples/example_mtc/notebooks`. +00. Test the notebooks in `activitysim/examples/prototype_mtc/notebooks`. There are also demo notebooks for estimation, but their functionality is completely tested in the unit tests run previously. diff --git a/activitysim/abm/models/util/vectorize_tour_scheduling.py b/activitysim/abm/models/util/vectorize_tour_scheduling.py index 199059e2af..0884e640cb 100644 --- a/activitysim/abm/models/util/vectorize_tour_scheduling.py +++ b/activitysim/abm/models/util/vectorize_tour_scheduling.py @@ -574,7 +574,7 @@ def _schedule_tours( if not RUN_ALTS_PREPROCESSOR_BEFORE_MERGE: # Note: Clint was running alts_preprocessor here on tdd_interaction_dataset instead of on raw (unmerged) alts # and he was using logsum_tour_purpose as selector, although logically it should be the spec_segment - # It just happened to work for example_arc.mandatory_tour_scheduling because, in that model, (unlike semcog) + # It just happened to work for prototype_arc.mandatory_tour_scheduling because, in that model, (unlike semcog) # logsum_tour_purpose and spec_segments are aligned (both logsums and spec are segmented on work, school, univ) # In any case, I don't see any benefit to doing this here - at least not for any existing implementations # but if we do, it will require passing spec_segment to schedule_tours and _schedule_tours diff --git a/activitysim/abm/test/test_misc/setup_utils.py b/activitysim/abm/test/test_misc/setup_utils.py index bfee82afc0..a7042f5a31 100644 --- a/activitysim/abm/test/test_misc/setup_utils.py +++ b/activitysim/abm/test/test_misc/setup_utils.py @@ -34,7 +34,7 @@ def example_path(dirname): - resource = os.path.join('examples', 'example_mtc', dirname) + resource = os.path.join('examples', 'prototype_mtc', dirname) return pkg_resources.resource_filename('activitysim', resource) diff --git a/activitysim/abm/test/test_misc/test_load_cached_accessibility.py b/activitysim/abm/test/test_misc/test_load_cached_accessibility.py index 762fdd705c..7c189f2ea3 100644 --- a/activitysim/abm/test/test_misc/test_load_cached_accessibility.py +++ b/activitysim/abm/test/test_misc/test_load_cached_accessibility.py @@ -37,7 +37,7 @@ def example_path(dirname): - resource = os.path.join('examples', 'example_mtc', dirname) + resource = os.path.join('examples', 'prototype_mtc', dirname) return pkg_resources.resource_filename('activitysim', resource) diff --git a/activitysim/abm/test/test_pipeline/test_pipeline.py b/activitysim/abm/test/test_pipeline/test_pipeline.py index 7f8986f8c5..924f7989cf 100644 --- a/activitysim/abm/test/test_pipeline/test_pipeline.py +++ b/activitysim/abm/test/test_pipeline/test_pipeline.py @@ -34,7 +34,7 @@ def example_path(dirname): - resource = os.path.join('examples', 'example_mtc', dirname) + resource = os.path.join('examples', 'prototype_mtc', dirname) return pkg_resources.resource_filename('activitysim', resource) diff --git a/activitysim/benchmarking/benchmarks/mtc1full.py b/activitysim/benchmarking/benchmarks/mtc1full.py index bad2dcb247..39115a9922 100644 --- a/activitysim/benchmarking/benchmarks/mtc1full.py +++ b/activitysim/benchmarking/benchmarks/mtc1full.py @@ -3,7 +3,7 @@ template_setup_cache, ) -EXAMPLE_NAME = "example_mtc_full" +EXAMPLE_NAME = "prototype_mtc_full" CONFIGS_DIRS = ("configs",) DYNAMIC_CONFIG_DIR = "bench_configs" DATA_DIR = "data" diff --git a/activitysim/benchmarking/benchmarks/mtc1mp4.py b/activitysim/benchmarking/benchmarks/mtc1mp4.py index 917f730495..b760f5dd57 100644 --- a/activitysim/benchmarking/benchmarks/mtc1mp4.py +++ b/activitysim/benchmarking/benchmarks/mtc1mp4.py @@ -7,7 +7,7 @@ import numpy as np PRETTY_NAME = "MTC1_MP4" -EXAMPLE_NAME = "example_mtc_full" +EXAMPLE_NAME = "prototype_mtc_full" NUM_PROCESSORS = int(np.clip(multiprocessing.cpu_count() - 2, 2, 4)) CONFIGS_DIRS = ("configs_mp", "configs") DYNAMIC_CONFIG_DIR = "bench_configs_mp" diff --git a/activitysim/benchmarking/benchmarks/sandag1example.py b/activitysim/benchmarking/benchmarks/sandag1example.py index fb5f45bf2c..66e296b38f 100644 --- a/activitysim/benchmarking/benchmarks/sandag1example.py +++ b/activitysim/benchmarking/benchmarks/sandag1example.py @@ -5,8 +5,8 @@ from .sandag_example import * -EXAMPLE_NAME = "example_sandag_1_zone" -CONFIGS_DIRS = ("configs_1_zone", "example_mtc/configs") +EXAMPLE_NAME = "placeholder_sandag_1_zone" +CONFIGS_DIRS = ("configs_1_zone", "prototype_mtc/configs") DYNAMIC_CONFIG_DIR = "bench_configs" DATA_DIR = "data_1" OUTPUT_DIR = "output_1" diff --git a/activitysim/benchmarking/benchmarks/sandag1full.py b/activitysim/benchmarking/benchmarks/sandag1full.py index b892d2771e..37e5372384 100644 --- a/activitysim/benchmarking/benchmarks/sandag1full.py +++ b/activitysim/benchmarking/benchmarks/sandag1full.py @@ -5,8 +5,8 @@ from .sandag_full import * -EXAMPLE_NAME = "example_sandag_1_zone_full" -CONFIGS_DIRS = ("configs_benchmarking", "configs_1_zone", "example_mtc/configs") +EXAMPLE_NAME = "placeholder_sandag_1_zone_full" +CONFIGS_DIRS = ("configs_benchmarking", "configs_1_zone", "prototype_mtc/configs") DYNAMIC_CONFIG_DIR = "bench_configs" DATA_DIR = "data_1" OUTPUT_DIR = "output_1" diff --git a/activitysim/benchmarking/benchmarks/sandag2example.py b/activitysim/benchmarking/benchmarks/sandag2example.py index 9903d0ec0e..bd4916c5a3 100644 --- a/activitysim/benchmarking/benchmarks/sandag2example.py +++ b/activitysim/benchmarking/benchmarks/sandag2example.py @@ -5,8 +5,8 @@ from .sandag_example import * -EXAMPLE_NAME = "example_sandag_2_zone" -CONFIGS_DIRS = ("configs_2_zone", "example_psrc/configs") +EXAMPLE_NAME = "placeholder_sandag_2_zone" +CONFIGS_DIRS = ("configs_2_zone", "placeholder_psrc/configs") DYNAMIC_CONFIG_DIR = "bench_configs" DATA_DIR = "data_2" OUTPUT_DIR = "output_2" diff --git a/activitysim/benchmarking/benchmarks/sandag2full.py b/activitysim/benchmarking/benchmarks/sandag2full.py index 974cb3f219..3e11f81232 100644 --- a/activitysim/benchmarking/benchmarks/sandag2full.py +++ b/activitysim/benchmarking/benchmarks/sandag2full.py @@ -5,8 +5,8 @@ from .sandag_full import * -EXAMPLE_NAME = "example_sandag_2_zone_full" -CONFIGS_DIRS = ("configs_benchmarking", "configs_2_zone", "example_psrc/configs") +EXAMPLE_NAME = "placeholder_sandag_2_zone_full" +CONFIGS_DIRS = ("configs_benchmarking", "configs_2_zone", "placeholder_psrc/configs") DYNAMIC_CONFIG_DIR = "bench_configs" DATA_DIR = "data_2" OUTPUT_DIR = "output_2" diff --git a/activitysim/benchmarking/benchmarks/sandag3example.py b/activitysim/benchmarking/benchmarks/sandag3example.py index aa040b1448..5bc22de7a0 100644 --- a/activitysim/benchmarking/benchmarks/sandag3example.py +++ b/activitysim/benchmarking/benchmarks/sandag3example.py @@ -5,8 +5,8 @@ from .sandag_example import * -EXAMPLE_NAME = "example_sandag_3_zone" -CONFIGS_DIRS = ("configs_3_zone", "example_mtc/configs") +EXAMPLE_NAME = "placeholder_sandag_3_zone" +CONFIGS_DIRS = ("configs_3_zone", "prototype_mtc/configs") DYNAMIC_CONFIG_DIR = "bench_configs" DATA_DIR = "data_3" OUTPUT_DIR = "output_3" diff --git a/activitysim/benchmarking/benchmarks/sandag3full.py b/activitysim/benchmarking/benchmarks/sandag3full.py index 727f66a32b..a2f9b67ee2 100644 --- a/activitysim/benchmarking/benchmarks/sandag3full.py +++ b/activitysim/benchmarking/benchmarks/sandag3full.py @@ -5,8 +5,8 @@ from .sandag_full import * -EXAMPLE_NAME = "example_sandag_3_zone_full" -CONFIGS_DIRS = ("configs_benchmarking", "configs_3_zone", "example_mtc/configs") +EXAMPLE_NAME = "placeholder_sandag_3_zone_full" +CONFIGS_DIRS = ("configs_benchmarking", "configs_3_zone", "prototype_mtc/configs") DYNAMIC_CONFIG_DIR = "bench_configs" DATA_DIR = "data_3" OUTPUT_DIR = "output_3" diff --git a/activitysim/cli/test/test_cli.py b/activitysim/cli/test/test_cli.py index b96044a3a0..f6dc6f1b82 100644 --- a/activitysim/cli/test/test_cli.py +++ b/activitysim/cli/test/test_cli.py @@ -30,15 +30,14 @@ def test_create_list(): cp = subprocess.run(['activitysim', 'create', '--list'], capture_output=True) assert 'Available examples' in str(cp.stdout) - assert "name: example_mtc" in str(cp.stdout) - assert "name: example_test" in str(cp.stdout) + assert "name: prototype_mtc" in str(cp.stdout) def test_create_copy(): target = os.path.join(os.path.dirname(__file__), 'test_example') cp = subprocess.run(['activitysim', 'create', - '--example', 'example_test', + '--example', 'prototype_mtc', '--destination', target], capture_output=True) assert 'copying data ...' in str(cp.stdout) diff --git a/activitysim/examples/example_estimation/build_example_data/build_test.txt b/activitysim/examples/example_estimation/build_example_data/build_test.txt index ed83e09337..6a45cbe1b1 100644 --- a/activitysim/examples/example_estimation/build_example_data/build_test.txt +++ b/activitysim/examples/example_estimation/build_example_data/build_test.txt @@ -4,24 +4,19 @@ MTC_SF_DATA_DIR=/Users/jeff.doyle/work/activitysim-data/mtc_tm1_sf/data MTC_FULL_DATA_DIR=/Users/jeff.doyle/work/activitysim-data/mtc_tm1/data EXAMPLES_DIR=$ACTIVITYSIM_BASE_DIR/activitysim/examples -MTC_EXAMPLE_DIR=$EXAMPLES_DIR/example_mtc +MTC_EXAMPLE_DIR=$EXAMPLES_DIR/prototype_mtc ESTIMATION_DIR=$EXAMPLES_DIR/example_estimation BUILD_DIR=$ESTIMATION_DIR/build_example_data -# build example using example_mtc test data set (25 zones) +# build example using prototype_mtc test data set (25 zones) # TAG=test # BUILD_DATA=$MTC_EXAMPLE_DIR/data -# build example using example_mtc sf data set (190 zones) +# build example using prototype_mtc_sf data set (190 zones) # data not in activitysim repo/distro TAG=sf BUILD_DATA=$MTC_SF_DATA_DIR -# build example using example_mtc sf data set (190 zones) -# data not in activitysim repo/distro -# TAG=full -# BUILD_DATA=$MTC_FULL_DATA_DIR - # output dirs for run to create fake survey data BUILD_OUTPUT=$BUILD_DIR/output_build_$TAG @@ -77,7 +72,7 @@ cp $BUILD_OUTPUT/trace/school_location*university*.csv $BUILD_DIR/scratch/$TAG/b cp $BUILD_OUTPUT/log/activitysim.log $BUILD_DIR/scratch/$TAG/build TAG=sf -python simulation.py -c override_configs -c configs -c ../example_mtc/configs -d data_$TAG -o output_$TAG +python simulation.py -c override_configs -c configs -c ../prototype_mtc/configs -d data_$TAG -o output_$TAG EST_OUTPUT=output_$TAG diff --git a/activitysim/examples/example_manifest.yaml b/activitysim/examples/example_manifest.yaml index 7ea94cd49d..e6a6a94d3b 100644 --- a/activitysim/examples/example_manifest.yaml +++ b/activitysim/examples/example_manifest.yaml @@ -1,38 +1,26 @@ -- name: example_mtc - description: 25-zone example for the MTC region - # activitysim create -e example_mtc -d test_example_mtc - # cd test_example_mtc +- name: prototype_mtc + description: 25-zone example extracted from the prototype MTC model + # activitysim create -e prototype_mtc -d test_prototype_mtc + # cd test_prototype_mtc # activitysim run -c configs -o output -d data # cd .. include: - - example_mtc/data - - example_mtc/configs - - example_mtc/configs_mp - - example_mtc/output - - example_mtc/README.MD + - prototype_mtc/data + - prototype_mtc/configs + - prototype_mtc/configs_mp + - prototype_mtc/output + - prototype_mtc/README.MD -- name: example_test - description: data and configs for the ActivitySim test system - # activitysim create -e example_test -d test_example_test - # cd test_example_test - # activitysim run -c configs -o output -d data - # cd .. - include: - - example_mtc/data - - example_mtc/configs - - example_mtc/configs_mp - - example_mtc/output - -- name: example_mtc_full - description: Full 1475-zone dataset for the MTC region with 2.8M households and 7.5M persons - # activitysim create -e example_mtc_full -d test_example_mtc_full - # cd test_example_mtc_full +- name: prototype_mtc_full + description: Prototype MTC example model using data from the full 1475-zone MTC region with 2.8M households and 7.5M persons + # activitysim create -e prototype_mtc_full -d test_prototype_mtc_full + # cd test_prototype_mtc_full # activitysim run -c configs_mp -c configs -o output -d data # cd .. include: - - example_mtc/configs - - example_mtc/configs_mp - - example_mtc/output + - prototype_mtc/configs + - prototype_mtc/configs_mp + - prototype_mtc/output - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/mtc_data_full/skims.omx data/skims.omx 04bddb2dd6b829a2ce25a27369d3276143fa9a354989ebd30ed9bba92f8e9bfb @@ -46,35 +34,35 @@ data/land_use.csv fac71207925a34c32b956632fe375814e42860624a99f88401c42317af0fc203 -- name: example_mtc_extended - description: 25-zone example for the MTC region with extended models - # activitysim create -e example_mtc_extended -d test_example_mtc_extended - # cd test_example_mtc_extended +- name: prototype_mtc_extended + description: 25-zone prototype MTC example with extended models + # activitysim create -e prototype_mtc_extended -d test_prototype_mtc_extended + # cd test_prototype_mtc_extended # activitysim run -c configs_extended/configs -c configs -o output -d data # cd .. include: - - example_mtc/data - - example_mtc_extended/configs + - prototype_mtc/data + - prototype_mtc_extended/configs configs_extended - - example_mtc/configs - - example_mtc_extended/configs_mp - - example_mtc_extended/output - - example_mtc_extended/README.MD + - prototype_mtc/configs + - prototype_mtc_extended/configs_mp + - prototype_mtc_extended/output + - prototype_mtc_extended/README.MD -- name: example_mtc_full_extended - description: Full 1475-zone dataset for the MTC region with 2.8M households and 7.5M persons with extended models - # activitysim create -e example_mtc_full_extended -d test_example_mtc_full_extended - # cd test_example_mtc_full_extended +- name: prototype_mtc_extended_full + description: Prototype MTC example model using data from the full 1475-zone MTC region with 2.8M households and 7.5M persons + # activitysim create -e prototype_mtc_extended_full -d test_prototype_mtc_extended_full + # cd test_prototype_mtc_extended_full # activitysim run -c configs_mp -c configs_extended/configs -c configs -o output -d data # cd .. include: - - example_mtc/data - - example_mtc/configs - - example_mtc_extended/configs + - prototype_mtc/data + - prototype_mtc/configs + - prototype_mtc_extended/configs configs_extended - - example_mtc_extended/configs_mp - - example_mtc_extended/output - - example_mtc_extended/README.MD + - prototype_mtc_extended/configs_mp + - prototype_mtc_extended/output + - prototype_mtc_extended/README.MD - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/mtc_data_full/skims.omx data/skims.omx - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/mtc_data_full/households.csv @@ -84,17 +72,17 @@ - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/mtc_data_full/land_use.csv data/land_use.csv -- name: example_mtc_sf +- name: prototype_mtc_sf description: San Francisco MTC dataset with 190 zones, 400k households and 900k persons - # activitysim create -e example_mtc_sf -d test_example_mtc_sf - # cd test_example_mtc_sf + # activitysim create -e prototype_mtc_sf -d test_prototype_mtc_sf + # cd test_prototype_mtc_sf # activitysim run -c configs -o output -d data # cd .. include: - - example_mtc/configs - - example_mtc/configs_mp - - example_mtc/data - - example_mtc/output + - prototype_mtc/configs + - prototype_mtc/configs_mp + - prototype_mtc/data + - prototype_mtc/output - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/mtc_data_sf/skims.omx data/skims.omx 579d6007266db3b055d0f9e4814004f4d5ccfae27a36e40f4881e3662bc3d3f1 @@ -115,13 +103,13 @@ # activitysim run -c configs_estimation/configs -c configs -o output -d data_test # cd .. include: - - example_mtc/configs + - prototype_mtc/configs - example_estimation/configs configs_estimation - example_estimation/data_test - - example_mtc/data/skims.omx + - prototype_mtc/data/skims.omx data_test/skims.omx - - example_mtc/output + - prototype_mtc/output - name: example_estimation_sf description: Estimation mode San Francisco MTC dataset with 190 zones, 2k households and 8k persons @@ -130,26 +118,26 @@ # activitysim run -c configs_estimation/configs -c configs -o output -d data_sf # cd .. include: - - example_mtc/configs + - prototype_mtc/configs - example_estimation/configs configs_estimation - example_estimation/data_sf - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/mtc_data_sf/skims.omx data_sf/skims.omx 579d6007266db3b055d0f9e4814004f4d5ccfae27a36e40f4881e3662bc3d3f1 - - example_mtc/output + - prototype_mtc/output -- name: example_2_zone - description: 2 zone system test example based on TM1 - # activitysim create -e example_2_zone -d test_example_2_zone - # cd test_example_2_zone +- name: placeholder_2_zone + description: 2 zone system test example based on prototype MTC + # activitysim create -e placeholder_2_zone -d test_placeholder_2_zone + # cd test_placeholder_2_zone # activitysim run -c configs_2_zone -c configs -d data_2 -o output_2 # cd .. include: - - example_mtc/configs - - example_multiple_zone/configs_2_zone - - example_multiple_zone/data_2 - - example_multiple_zone/output_2 + - prototype_mtc/configs + - placeholder_multiple_zone/configs_2_zone + - placeholder_multiple_zone/data_2 + - placeholder_multiple_zone/output_2 - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/data_2/households.csv data_2/households.csv 0e247da9105b152730573e782e7b5f1d62040e3b381f683286fe1bb22f949529 @@ -175,22 +163,22 @@ data_2/taz_skims.omx 773c89369f748e26309786b42aa4c62b5dd329a931214dd63a11007001de3829 -- name: example_3_zone - description: 3 zone system test example based on TM1 - # activitysim create -e example_3_zone -d test_example_3_zone - # cd test_example_3_zone +- name: placeholder_3_zone + description: 3 zone system test example based on prototype MTC + # activitysim create -e placeholder_3_zone -d test_placeholder_3_zone + # cd test_placeholder_3_zone # activitysim run -c configs_3_zone -c configs -d data_3 -o output_3 -s settings_static.yaml # cd .. - # activitysim create -e example_3_zone -d test_example_3_zone_mp - # cd test_example_3_zone_mp + # activitysim create -e placeholder_3_zone -d test_placeholder_3_zone_mp + # cd test_placeholder_3_zone_mp # activitysim run -c configs_3_zone -c configs -d data_3 -o output_3 -s settings_mp.yaml # cd .. include: - - example_mtc/configs - - example_multiple_zone/configs_3_zone - - example_multiple_zone/configs_local - - example_multiple_zone/data_3 - - example_multiple_zone/output_3 + - prototype_mtc/configs + - placeholder_multiple_zone/configs_3_zone + - placeholder_multiple_zone/configs_local + - placeholder_multiple_zone/data_3 + - placeholder_multiple_zone/output_3 - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/data_3/households.csv data_3/households.csv 0e247da9105b152730573e782e7b5f1d62040e3b381f683286fe1bb22f949529 @@ -231,37 +219,37 @@ data_3/taz_skims.omx d3f89e29878d3384318b15db26bfddb48d63c9a8d5187aa02797178cfead0394 -- name: example_marin +- name: prototype_marin description: Marin TM2 work tour mode choice cropped to Downtown SF for testing - # activitysim create -e example_marin -d test_example_marin - # cd test_example_marin + # activitysim create -e prototype_marin -d test_prototype_marin + # cd test_prototype_marin # activitysim run -c configs -d data -o output # cd .. - # activitysim create -e example_marin -d test_example_marin_mp - # cd test_example_marin_mp + # activitysim create -e prototype_marin -d test_prototype_marin_mp + # cd test_prototype_marin_mp # activitysim run -c configs -d data -o output -s settings_mp.yaml # cd .. include: - - example_marin/configs - - example_marin/data - - example_marin/output - - example_marin/README.MD + - prototype_marin/configs + - prototype_marin/data + - prototype_marin/output + - prototype_marin/README.MD -- name: example_marin_sf +- name: prototype_marin_sf description: Marin TM2 work tour mode choice cropped to marin and sf counties for testing (2054 MAZ - # activitysim create -e example_marin_sf -d test_example_marin_sf - # cd test_example_marin_sf + # activitysim create -e prototype_marin_sf -d test_prototype_marin_sf + # cd test_prototype_marin_sf # activitysim run -c configs -d data -o output # cd .. - # activitysim create -e example_marin_sf -d test_example_marin_sf_mp - # cd test_example_marin_sf_mp + # activitysim create -e prototype_marin_sf -d test_prototype_marin_sf_mp + # cd test_prototype_marin_sf_mp # activitysim run -c configs -d data -o output -s settings_mp.yaml # cd .. include: - - example_marin/configs - - example_marin/data - - example_marin/output - - example_marin/README.MD + - prototype_marin/configs + - prototype_marin/data + - prototype_marin/output + - prototype_marin/README.MD - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/marin_data_marin_sf/highway_skims_AM.omx data/highway_skims_AM.omx 2d5d21266a0cd29f174858bc2c47a26067c7af35e12793a78a3dc673df9f13df @@ -323,17 +311,17 @@ data/work_tours.csv a88082ead7ace00f1b2d8ed3997aa3d27c0bc36c9deeee8b8e4c6cf85d6a7004 -- name: example_3_marin_full +- name: prototype_3_marin_full description: Marin TM2 work tour mode choice for the 9 county MTC region - # activitysim create -e example_3_marin_full -d test_example_3_marin_full - # cd test_example_3_marin_full + # activitysim create -e prototype_3_marin_full -d test_prototype_3_marin_full + # cd test_prototype_3_marin_full # activitysim run -c configs -d data -o output -s settings_mp.yaml # cd .. include: - - example_marin/configs - - example_marin/data - - example_marin/output - - example_marin/README.MD + - prototype_marin/configs + - prototype_marin/data + - prototype_marin/output + - prototype_marin/README.MD - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/marin_data_full/highway_skims_AM.omx data/highway_skims_AM.omx e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855 @@ -395,32 +383,32 @@ data/work_tours.csv 4d5accd2ed87faa70551cd0763b2a84a630f7dbdeeb09959c8300178cebedc8c -- name: example_psrc +- name: placeholder_psrc description: 27-TAZ 487-MAZ example for the PSRC region, with 14871 Households - # activitysim create -e example_psrc -d test_example_psrc - # cd test_example_psrc + # activitysim create -e placeholder_psrc -d test_placeholder_psrc + # cd test_placeholder_psrc # activitysim run -c configs -o output -d data # cd .. include: - - example_psrc/configs - - example_psrc/configs_skip_accessibility - - example_psrc/data - - example_psrc/output - - example_psrc/README.MD + - placeholder_psrc/configs + - placeholder_psrc/configs_skip_accessibility + - placeholder_psrc/data + - placeholder_psrc/output + - placeholder_psrc/README.MD -- name: example_psrc_seattle +- name: placeholder_psrc_seattle description: 781-TAZ 8400-MAZ example for the PSRC region, with 336,725 Households - # activitysim create -e example_psrc_seattle -d test_example_seattle - # cd test_example_seattle + # activitysim create -e placeholder_psrc_seattle -d test_placeholder_seattle + # cd test_placeholder_seattle # activitysim run -c configs -o output -d data # cd .. include: - - example_psrc/configs - - example_psrc/configs_skip_accessibility - - example_psrc/configs_accessibility - - example_psrc/data - - example_psrc/output - - example_psrc/README.MD + - placeholder_psrc/configs + - placeholder_psrc/configs_skip_accessibility + - placeholder_psrc/configs_accessibility + - placeholder_psrc/data + - placeholder_psrc/output + - placeholder_psrc/README.MD - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/psrc_data_seattle/skims.omx data/skims.omx bbbdc70d4eceb71d3be0d025a21fb83d18a69a1806bad9cb85d66dba136b07df @@ -446,23 +434,23 @@ data/maz_to_maz_bike.csv 6c11e712abe91d6112f9bd85a7a5a01f4ce7642d44c86c3352c978ef1bb00b20 -- name: example_psrc_full +- name: placeholder_psrc_full description: 3444-TAZ 43589-MAZ example for the PSRC region - # activitysim create -e example_psrc_full -d test_example_psrc_full - # cd test_example_psrc_full + # activitysim create -e placeholder_psrc_full -d test_placeholder_psrc_full + # cd test_placeholder_psrc_full # activitysim run -c configs -o output -d data -s settings_mp.yaml # cd .. - # activitysim create -e example_psrc_full -d test_example_psrc_full_skip_access - # cd test_example_psrc_full_skip_access + # activitysim create -e placeholder_psrc_full -d test_placeholder_psrc_full_skip_access + # cd test_placeholder_psrc_full_skip_access # activitysim run -c configs_skip_accessibility -c configs -o output -d data -s settings_mp.yaml # cd .. include: - - example_psrc/configs - - example_psrc/configs_skip_accessibility - - example_psrc/configs_accessibility - - example_psrc/data - - example_psrc/output - - example_psrc/README.MD + - placeholder_psrc/configs + - placeholder_psrc/configs_skip_accessibility + - placeholder_psrc/configs_accessibility + - placeholder_psrc/data + - placeholder_psrc/output + - placeholder_psrc/README.MD - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/psrc_data_full/skims.omx data/skims.omx 7e3746501632b1912ce4cc642f7401bf70141f121a71ba63d444e8d42197a4f7 @@ -500,35 +488,35 @@ data/final_accessibility.csv 949994345c9965054be260589fd64079e4032d2e4079e46b9eb9c758a40527c3 -- name: example_semcog +- name: prototype_semcog description: 67 zone test example for the SEMCOG region - # activitysim create -e example_semcog -d test_example_semcog - # cd test_example_semcog + # activitysim create -e prototype_semcog -d test_prototype_semcog + # cd test_prototype_semcog # python simulation.py -c configs -o output -d data # cd .. include: - - example_semcog/extensions - - example_semcog/data - - example_semcog/configs - - example_semcog/configs_mp - - example_semcog/output - - example_semcog/README.MD - - example_semcog/simulation.py + - prototype_semcog/extensions + - prototype_semcog/data + - prototype_semcog/configs + - prototype_semcog/configs_mp + - prototype_semcog/output + - prototype_semcog/README.MD + - prototype_semcog/simulation.py -- name: example_semcog_z500 +- name: prototype_semcog_z500 description: 500 zone intermediate size example for the SEMCOG region - # activitysim create -e example_semcog_z500 -d test_example_semcog_z500 - # cd test_example_semcog_z500 + # activitysim create -e prototype_semcog_z500 -d test_prototype_semcog_z500 + # cd test_prototype_semcog_z500 # python simulation.py -c configs -o output -d data # cd .. include: - - example_semcog/extensions - - example_semcog/data - - example_semcog/configs - - example_semcog/configs_mp - - example_semcog/output - - example_semcog/README.MD - - example_semcog/simulation.py + - prototype_semcog/extensions + - prototype_semcog/data + - prototype_semcog/configs + - prototype_semcog/configs_mp + - prototype_semcog/output + - prototype_semcog/README.MD + - prototype_semcog/simulation.py - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/semcog_data_z500/skims.omx data/skims.omx 7c824032940d1006e731d715bcee8771572e03558eef40ca1ca5f820bfc071ca @@ -542,20 +530,20 @@ data/land_use.csv d4ddb6f5fc7cd844e3e9149d03d32c841c5cb641a77aea9174045ddb85f27a3a -- name: example_semcog_full +- name: prototype_semcog_full description: 2899 zone full size example for the SEMCOG region - # activitysim create -e example_semcog_full -d test_example_semcog_full - # cd test_example_semcog_full + # activitysim create -e prototype_semcog_full -d test_prototype_semcog_full + # cd test_prototype_semcog_full # python simulation.py -c configs_mp -c configs -o output -d data # cd .. include: - - example_semcog/extensions - - example_semcog/data - - example_semcog/configs - - example_semcog/configs_mp - - example_semcog/output - - example_semcog/README.MD - - example_semcog/simulation.py + - prototype_semcog/extensions + # prototype_semcog/data # data loaded from activitysim_resources + - prototype_semcog/configs + - prototype_semcog/configs_mp + - prototype_semcog/output + - prototype_semcog/README.MD + - prototype_semcog/simulation.py - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/semcog_data_full/skims.omx data/skims.omx efa1f87395df354ccfad7cfe4e67ace96c7ac04fa0290e5e6f8e2593fc0ba6a3 @@ -572,29 +560,29 @@ data/land_use.csv 8f9146993bff315afc6ada52182cb3ea719a6a1ed07095b51535bc7c026285f6 -- name: example_arc +- name: prototype_arc description: 36 zone test example for the ARC region - # activitysim create -e example_arc -d test_example_arc - # cd test_example_arc + # activitysim create -e prototype_arc -d test_prototype_arc + # cd test_prototype_arc # activitysim run -c configs -o output -d data # cd .. include: - - example_arc/data - - example_arc/configs - - example_arc/output - - example_arc/README.MD + - prototype_arc/data + - prototype_arc/configs + - prototype_arc/output + - prototype_arc/README.MD -- name: example_arc_fulton +- name: prototype_arc_fulton description: 1296 zone fulton county example for the ARC region - # activitysim create -e example_arc_fulton -d test_example_arc_fulton - # cd test_example_arc_fulton + # activitysim create -e prototype_arc_fulton -d test_prototype_arc_fulton + # cd test_prototype_arc_fulton # activitysim run -c configs -o output -d data # cd .. include: - - example_arc/data - - example_arc/configs - - example_arc/output - - example_arc/README.MD + - prototype_arc/data + - prototype_arc/configs + - prototype_arc/output + - prototype_arc/README.MD - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/arc_data_fulton/skims.omx data/skims.omx 335be57a0b89eac8b51874cc5d8a2c0a0b19472f7ac77c99fd8afd54f223712b @@ -608,17 +596,17 @@ data/land_use.csv 09a8c0cd0771c4ec3713940fc3d5b9e9443cc104668f7403c2caefb75c4fbb08 -- name: example_arc_full +- name: prototype_arc_full description: 5922 zone full example for the ARC region - # activitysim create -e example_arc_full -d test_example_arc_full - # cd test_example_arc_full + # activitysim create -e prototype_arc_full -d test_prototype_arc_full + # cd test_prototype_arc_full # activitysim run -c configs -o output -d data -s settings_mp.yaml # cd .. include: - - example_arc/data - - example_arc/configs - - example_arc/output - - example_arc/README.MD + # - prototype_arc/data # data is provided below + - prototype_arc/configs + - prototype_arc/output + - prototype_arc/README.MD - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/arc_data_full/skims.omx data/skims.omx 55af7b893f13b5275fd7d9721a07f3e09d98a2660fa5fa6f5ff99f8fa8ffbcdf @@ -641,31 +629,31 @@ data/land_use.csv 36383b4c9dacdd7dd10c84b4479aa9f53ced23e155b6d354c468decc6710c80e -- name: example_sandag_1_zone +- name: placeholder_sandag_1_zone description: 90-TAZ (includes univ) 1-zone test example for the SANDAG region - # activitysim create -e example_sandag_1_zone -d test_example_sandag_1_zone - # cd test_example_sandag_1_zone - # activitysim run -c configs_1_zone -c example_mtc/configs -d data_1 -o output_1 -s settings_mp.yaml + # activitysim create -e placeholder_sandag_1_zone -d test_placeholder_sandag_1_zone + # cd test_placeholder_sandag_1_zone + # activitysim run -c configs_1_zone -c prototype_mtc/configs -d data_1 -o output_1 -s settings_mp.yaml # cd .. include: - - example_sandag/../example_mtc/configs - example_mtc - - example_sandag/configs_1_zone - - example_sandag/data_1 - - example_sandag/output_1 + - placeholder_sandag/../prototype_mtc/configs + prototype_mtc + - placeholder_sandag/configs_1_zone + - placeholder_sandag/data_1 + - placeholder_sandag/output_1 -- name: example_sandag_1_zone_full +- name: placeholder_sandag_1_zone_full description: full 1-zone example for the SANDAG region - # activitysim create -e example_sandag_1_zone_full -d test_example_sandag_1_zone_full - # cd test_example_sandag_1_zone_full - # activitysim run -c configs_1_zone -c example_mtc/configs -d data_1 -o output_1 -s settings_mp.yaml + # activitysim create -e placeholder_sandag_1_zone_full -d test_placeholder_sandag_1_zone_full + # cd test_placeholder_sandag_1_zone_full + # activitysim run -c configs_1_zone -c prototype_mtc/configs -d data_1 -o output_1 -s settings_mp.yaml # cd .. include: - - example_sandag/../example_mtc/configs - example_mtc - - example_sandag/configs_1_zone - - example_sandag/data_1 - - example_sandag/output_1 + - placeholder_sandag/../prototype_mtc/configs + prototype_mtc + - placeholder_sandag/configs_1_zone + - placeholder_sandag/data_1 + - placeholder_sandag/output_1 - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_1_zone_data_full/households.csv data_1/households.csv 2b0b19a1a0b00901e80a533fc6418a4dbeb3d0b017d9feadae71324f9145dcec @@ -694,31 +682,31 @@ data_1/skims6.omx e3f7fb1e0b334fc83dc69985dce8b925e3660149c5de5635c7d8464e306074b9 -- name: example_sandag_2_zone +- name: placeholder_sandag_2_zone description: 90-TAZ 609-MAZ (includes univ) 2-zone test example for the SANDAG region - # activitysim create -e example_sandag_2_zone -d test_example_sandag_2_zone - # cd test_example_sandag_2_zone - # activitysim run -c configs_2_zone -c example_psrc/configs -d data_2 -o output_2 -s settings_mp.yaml + # activitysim create -e placeholder_sandag_2_zone -d test_placeholder_sandag_2_zone + # cd test_placeholder_sandag_2_zone + # activitysim run -c configs_2_zone -c placeholder_psrc/configs -d data_2 -o output_2 -s settings_mp.yaml # cd .. include: - - example_sandag/../example_psrc/configs - example_psrc - - example_sandag/configs_2_zone - - example_sandag/data_2 - - example_sandag/output_2 + - placeholder_sandag/../placeholder_psrc/configs + placeholder_psrc + - placeholder_sandag/configs_2_zone + - placeholder_sandag/data_2 + - placeholder_sandag/output_2 -- name: example_sandag_2_zone_full +- name: placeholder_sandag_2_zone_full description: full 2-zone example for the SANDAG region - # activitysim create -e example_sandag_2_zone_full -d test_example_sandag_2_zone_full - # cd test_example_sandag_2_zone_full - # activitysim run -c configs_2_zone -c example_psrc/configs -d data_2 -o output_2 -s settings_mp.yaml + # activitysim create -e placeholder_sandag_2_zone_full -d test_placeholder_sandag_2_zone_full + # cd test_placeholder_sandag_2_zone_full + # activitysim run -c configs_2_zone -c placeholder_psrc/configs -d data_2 -o output_2 -s settings_mp.yaml # cd .. include: - - example_sandag/../example_psrc/configs - example_psrc - - example_sandag/configs_2_zone - - example_sandag/data_2 - - example_sandag/output_2 + - placeholder_sandag/../placeholder_psrc/configs + placeholder_psrc + - placeholder_sandag/configs_2_zone + - placeholder_sandag/data_2 + - placeholder_sandag/output_2 - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_2_zone_data_full/households.csv data_2/households.csv 4afe40dfbdb89f17cd150fd5b32245364d538cdebb91fd323e18c6f570d1a087 @@ -759,33 +747,33 @@ data_2/skims6.omx 77a82afa09ee1f4c48aa7928e96756525d170402ca99da04cdf676e45cb6fbcf -- name: example_sandag_3_zone +- name: placeholder_sandag_3_zone description: 90-TAZ 609-MAZ (includes univ) 3-zone test example for the SANDAG region - # activitysim create -e example_sandag_3_zone -d test_example_sandag_3_zone - # cd test_example_sandag_3_zone - # activitysim run -c configs_3_zone -c example_mtc/configs -d data_3 -o output_3 -s settings_mp.yaml + # activitysim create -e placeholder_sandag_3_zone -d test_placeholder_sandag_3_zone + # cd test_placeholder_sandag_3_zone + # activitysim run -c configs_3_zone -c prototype_mtc/configs -d data_3 -o output_3 -s settings_mp.yaml # cd .. include: - - example_sandag/data_3 - - example_sandag/../example_mtc/configs - example_mtc - - example_sandag/configs_3_zone - - example_sandag/configs_skip_accessibility - - example_sandag/output_3 + - placeholder_sandag/data_3 + - placeholder_sandag/../prototype_mtc/configs + prototype_mtc + - placeholder_sandag/configs_3_zone + - placeholder_sandag/configs_skip_accessibility + - placeholder_sandag/output_3 -- name: example_sandag_3_zone_full +- name: placeholder_sandag_3_zone_full description: full 3-zone example for the SANDAG region - # activitysim create -e example_sandag_3_zone_full -d test_example_sandag_3_zone_full - # cd test_example_sandag_3_zone_full - # activitysim run -c configs_3_zone -c example_mtc/configs -d data_3 -o output_3 -s settings_mp.yaml + # activitysim create -e placeholder_sandag_3_zone_full -d test_placeholder_sandag_3_zone_full + # cd test_placeholder_sandag_3_zone_full + # activitysim run -c configs_3_zone -c prototype_mtc/configs -d data_3 -o output_3 -s settings_mp.yaml # cd .. include: - - example_sandag/data_3 - - example_sandag/../example_mtc/configs - example_mtc - - example_sandag/configs_3_zone - - example_sandag/configs_skip_accessibility - - example_sandag/output_3 + - placeholder_sandag/data_3 + - placeholder_sandag/../prototype_mtc/configs + prototype_mtc + - placeholder_sandag/configs_3_zone + - placeholder_sandag/configs_skip_accessibility + - placeholder_sandag/output_3 - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_3_zone_data_full/taz_skims1.omx data_3/taz_skims1.omx 5b56d0e79ec671e37f8c71f7fedd741d7bf32d2bced866ab1f03f3973fccce8c @@ -844,30 +832,30 @@ data_3/tap_lines.csv 0e1b2c532e5e85b48e2ac77b2836be7ec0cc7cba79907c6f5fb11d2ba171230a -- name: example_sandag_xborder +- name: prototype_sandag_xborder description: SANDAG cross border travel model - # activitysim create -e example_sandag_xborder -d test_example_sandag_xborder - # cd test_example_sandag_xborder + # activitysim create -e prototype_sandag_xborder -d test_prototype_sandag_xborder + # cd test_prototype_sandag_xborder # python simulation.py # cd .. include: - - example_sandag_xborder/configs - - example_sandag_xborder/data - - example_sandag_xborder/extensions - - example_sandag_xborder/output - - example_sandag_xborder/simulation.py + - prototype_sandag_xborder/configs + - prototype_sandag_xborder/data + - prototype_sandag_xborder/extensions + - prototype_sandag_xborder/output + - prototype_sandag_xborder/simulation.py -- name: example_sandag_xborder_full +- name: prototype_sandag_xborder_full description: full scale SANDAG cross border travel model - # activitysim create -e example_sandag_xborder_full -d test_example_sandag_xborder_full - # cd test_example_sandag_xborder_full + # activitysim create -e prototype_sandag_xborder_full -d test_prototype_sandag_xborder_full + # cd test_prototype_sandag_xborder_full # python simulation.py # cd .. include: - - example_sandag_xborder/configs - - example_sandag_xborder/extensions - - example_sandag_xborder/output - - example_sandag_xborder/simulation.py + - prototype_sandag_xborder/configs + - prototype_sandag_xborder/extensions + - prototype_sandag_xborder/output + - prototype_sandag_xborder/simulation.py - https://raw.githubusercontent.com/ActivitySim/activitysim_resources/master/sandag_xborder/households_xborder.csv data/households_xborder.csv - https://media.githubusercontent.com/media/activitysim/activitysim_resources/master/sandag_xborder/maz_maz_walk.csv diff --git a/activitysim/examples/example_mtc_extended/README.MD b/activitysim/examples/example_mtc_extended/README.MD deleted file mode 100644 index 826cd33804..0000000000 --- a/activitysim/examples/example_mtc_extended/README.MD +++ /dev/null @@ -1,6 +0,0 @@ - -### MTC Extended Example - -The MTC example with the following additional models: -* vehicle type model -* vehicle allocation model diff --git a/activitysim/examples/example_psrc/test/simulation.py b/activitysim/examples/example_psrc/test/simulation.py deleted file mode 100755 index ec6a1181b1..0000000000 --- a/activitysim/examples/example_psrc/test/simulation.py +++ /dev/null @@ -1,15 +0,0 @@ -# ActivitySim -# See full license in LICENSE.txt. - -import sys -import argparse - -from activitysim.cli.run import add_run_args, run - -if __name__ == '__main__': - - parser = argparse.ArgumentParser() - add_run_args(parser) - args = parser.parse_args() - - sys.exit(run(args)) diff --git a/activitysim/examples/example_sandag/configs_3_zone/notes.txt b/activitysim/examples/example_sandag/configs_3_zone/notes.txt deleted file mode 100644 index a1add69b8e..0000000000 --- a/activitysim/examples/example_sandag/configs_3_zone/notes.txt +++ /dev/null @@ -1,10 +0,0 @@ - -# test -# activitysim run -c configs_local -c configs -c configs_3_zone -c ../example_mtc/configs -o output_test -d data_test -# activitysim run -c configs_local -c configs -c configs_3_zone -c ../example_mtc/configs -o output_test -d data_test -s settings_mp.yaml - -# full dataset single-process 100K HH skip_accessibility -# activitysim run -c configs_local -c configs_skip_accessibility -c configs -c configs_3_zone -c ../example_mtc/configs -o output_full -d data_full - -# full run multiprocess -# activitysim run -c configs -c configs_3_zone -c ../example_mtc/configs -o output_full -d data_full -s settings_mp.yaml diff --git a/activitysim/examples/example_sandag/run_sandag.txt b/activitysim/examples/example_sandag/run_sandag.txt deleted file mode 100644 index 2c277d96b3..0000000000 --- a/activitysim/examples/example_sandag/run_sandag.txt +++ /dev/null @@ -1,14 +0,0 @@ -### -### 1 Zone -### -activitysim run -c configs_1_zone -c example_mtc/configs -d data_1 -o output_1 -s settings_mp.yaml - -### -### 2 Zone -### -activitysim run -c configs_2_zone -c example_psrc/configs -d data_2 -o output_2 -s settings_mp.yaml - -### -### 3 Zone -### -activitysim run -c configs_3_zone -c example_mtc/configs -d data_3 -o output_3 -s settings_mp.yaml \ No newline at end of file diff --git a/activitysim/examples/example_sandag/test/simulation.py b/activitysim/examples/example_sandag/test/simulation.py deleted file mode 100755 index ec6a1181b1..0000000000 --- a/activitysim/examples/example_sandag/test/simulation.py +++ /dev/null @@ -1,15 +0,0 @@ -# ActivitySim -# See full license in LICENSE.txt. - -import sys -import argparse - -from activitysim.cli.run import add_run_args, run - -if __name__ == '__main__': - - parser = argparse.ArgumentParser() - add_run_args(parser) - args = parser.parse_args() - - sys.exit(run(args)) diff --git a/activitysim/examples/example_multiple_zone/.gitignore b/activitysim/examples/placeholder_multiple_zone/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/.gitignore rename to activitysim/examples/placeholder_multiple_zone/.gitignore diff --git a/activitysim/examples/example_marin/README.MD b/activitysim/examples/placeholder_multiple_zone/README.MD similarity index 100% rename from activitysim/examples/example_marin/README.MD rename to activitysim/examples/placeholder_multiple_zone/README.MD diff --git a/activitysim/examples/example_multiple_zone/configs_1_zone/network_los.yaml b/activitysim/examples/placeholder_multiple_zone/configs_1_zone/network_los.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_1_zone/network_los.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_1_zone/network_los.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_1_zone/non_mandatory_tour_destination.yaml b/activitysim/examples/placeholder_multiple_zone/configs_1_zone/non_mandatory_tour_destination.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_1_zone/non_mandatory_tour_destination.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_1_zone/non_mandatory_tour_destination.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_1_zone/settings.yaml b/activitysim/examples/placeholder_multiple_zone/configs_1_zone/settings.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_1_zone/settings.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_1_zone/settings.yaml diff --git a/activitysim/examples/example_mtc/configs/accessibility.csv b/activitysim/examples/placeholder_multiple_zone/configs_2_zone/accessibility.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/accessibility.csv rename to activitysim/examples/placeholder_multiple_zone/configs_2_zone/accessibility.csv diff --git a/activitysim/examples/example_multiple_zone/configs_2_zone/network_los.yaml b/activitysim/examples/placeholder_multiple_zone/configs_2_zone/network_los.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_2_zone/network_los.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_2_zone/network_los.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_2_zone/settings.yaml b/activitysim/examples/placeholder_multiple_zone/configs_2_zone/settings.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_2_zone/settings.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_2_zone/settings.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/_bugs.txt b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/_bugs.txt similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone/_bugs.txt rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/_bugs.txt diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/accessibility.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/accessibility.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone/accessibility.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/accessibility.csv diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/annotate_households_workplace.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/annotate_households_workplace.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone/annotate_households_workplace.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/annotate_households_workplace.csv diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/annotate_persons_workplace.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/annotate_persons_workplace.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone/annotate_persons_workplace.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/annotate_persons_workplace.csv diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/auto_ownership.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/auto_ownership.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone/auto_ownership.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/auto_ownership.csv diff --git a/activitysim/examples/example_marin/configs/destination_choice_size_terms.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/destination_choice_size_terms.csv similarity index 100% rename from activitysim/examples/example_marin/configs/destination_choice_size_terms.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/destination_choice_size_terms.csv diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/network_los.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/network_los.yaml similarity index 96% rename from activitysim/examples/example_multiple_zone/configs_3_zone/network_los.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/network_los.yaml index a4a250a53a..76a7aadb36 100644 --- a/activitysim/examples/example_multiple_zone/configs_3_zone/network_los.yaml +++ b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/network_los.yaml @@ -1,157 +1,157 @@ -inherit_settings: True - -zone_system: 3 - -skim_dict_factory: NumpyArraySkimFactory -#skim_dict_factory: MemMapSkimFactory - -# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) -read_skim_cache: True -# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs -write_skim_cache: True - -# rebuild and overwrite existing tap_tap_utilities cache -rebuild_tvpb_cache: True - -# write a csv version of tvpb cache for tracing when checkpointing cache. -# (writes csv file when writing/checkpointing cache i.e. when cached changed) -# (n.b. csv file could be quite large if cache is STATIC!) -trace_tvpb_cache_as_csv: False - -taz_skims: taz_skims.omx - -# we require that skims for all tap_tap sets have unique names -# and can therefor share a single skim_dict without name collision -# e.g. TRN_XWAIT_FAST__AM, TRN_XWAIT_SHORT__AM, TRN_XWAIT_CHEAP__AM -tap_skims: tap_skims.omx - -maz: maz.csv - -tap: tap.csv - -maz_to_maz: - tables: - - maz_to_maz_walk.csv - - maz_to_maz_bike.csv - - # maz_to_maz blending distance (missing or 0 means no blending) - max_blend_distance: - DIST: 5 - # blend distance of 0 means no blending - DISTBIKE: 0 - DISTWALK: 1 - - # missing means use the skim value itself rather than DIST skim (e.g. DISTBIKE) - blend_distance_skim_name: DIST - -maz_to_tap: - walk: - table: maz_to_tap_walk.csv - drive: - table: maz_to_tap_drive.csv - - -skim_time_periods: - time_window: 1440 - period_minutes: 60 - periods: [0, 6, 11, 16, 20, 24] - labels: &skim_time_period_labels ['EA', 'AM', 'MD', 'PM', 'EV'] - -demographic_segments: &demographic_segments - - &low_income_segment_id 0 - - &high_income_segment_id 1 - -# transit virtual path builder settings -TVPB_SETTINGS: - - tour_mode_choice: - units: utility - path_types: - WTW: - access: walk - egress: walk - max_paths_across_tap_sets: 3 - max_paths_per_tap_set: 1 - DTW: - access: drive - egress: walk - max_paths_across_tap_sets: 3 - max_paths_per_tap_set: 1 - WTD: - access: walk - egress: drive - max_paths_across_tap_sets: 3 - max_paths_per_tap_set: 1 - tap_tap_settings: - SPEC: tvpb_utility_tap_tap.csv - PREPROCESSOR: - SPEC: tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv - DF: df - # FIXME this has to be explicitly specified, since e.g. attribute columns are assigned in expression files - attribute_segments: - demographic_segment: *demographic_segments - tod: *skim_time_period_labels - access_mode: ['drive', 'walk'] - attributes_as_columns: - - demographic_segment - - tod - maz_tap_settings: - walk: - SPEC: tvpb_utility_walk_maz_tap.csv - CHOOSER_COLUMNS: - #- demographic_segment - - walk_time - drive: - SPEC: tvpb_utility_drive_maz_tap.csv - CHOOSER_COLUMNS: - #- demographic_segment - - drive_time - - DIST - CONSTANTS: - C_LOW_INCOME_SEGMENT_ID: *low_income_segment_id - C_HIGH_INCOME_SEGMENT_ID: *high_income_segment_id - TVPB_demographic_segments_by_income_segment: - 1: *low_income_segment_id - 2: *low_income_segment_id - 3: *high_income_segment_id - 4: *high_income_segment_id - c_ivt_high_income: -0.028 - c_ivt_low_income: -0.0175 - c_cost_high_income: -0.00112 - c_cost_low_income: -0.00112 - c_wait: 1.5 - c_walk: 1.7 - c_drive: 1.5 - c_auto_operating_cost_per_mile: 18.29 - C_UNAVAILABLE: -999 - C_FASTEST_IVT_MULTIPLIER: 2 - C_FASTEST_COST_MULTIPLIER: 1 - C_CHEAPEST_IVT_MULTIPLIER: 1 - C_CHEAPEST_COST_MULTIPLIER: 500 - C_SHORTEST_IVT_MULTIPLIER: 1 - C_SHORTEST_COST_MULTIPLIER: 1 - C_SHORTEST_DIST_MULTIPLIER: 1 - # illustrate using access mode in tat-tap expressions files - C_DRIVE_TRANSFER_PENALTY: -1 - - accessibility: - units: time - path_types: - WTW: - access: walk - egress: walk - max_paths_across_tap_sets: 1 - max_paths_per_tap_set: 1 - tap_tap_settings: - SPEC: tvpb_accessibility_tap_tap_.csv - attributes_as_columns: - - tod - maz_tap_settings: - walk: - SPEC: tvpb_accessibility_walk_maz_tap.csv - CHOOSER_COLUMNS: - - walk_time - CONSTANTS: - out_of_vehicle_walk_time_weight: 1.5 - out_of_vehicle_wait_time_weight: 2.0 - +inherit_settings: True + +zone_system: 3 + +skim_dict_factory: NumpyArraySkimFactory +#skim_dict_factory: MemMapSkimFactory + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: True +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: True + +# rebuild and overwrite existing tap_tap_utilities cache +rebuild_tvpb_cache: True + +# write a csv version of tvpb cache for tracing when checkpointing cache. +# (writes csv file when writing/checkpointing cache i.e. when cached changed) +# (n.b. csv file could be quite large if cache is STATIC!) +trace_tvpb_cache_as_csv: False + +taz_skims: taz_skims.omx + +# we require that skims for all tap_tap sets have unique names +# and can therefor share a single skim_dict without name collision +# e.g. TRN_XWAIT_FAST__AM, TRN_XWAIT_SHORT__AM, TRN_XWAIT_CHEAP__AM +tap_skims: tap_skims.omx + +maz: maz.csv + +tap: tap.csv + +maz_to_maz: + tables: + - maz_to_maz_walk.csv + - maz_to_maz_bike.csv + + # maz_to_maz blending distance (missing or 0 means no blending) + max_blend_distance: + DIST: 5 + # blend distance of 0 means no blending + DISTBIKE: 0 + DISTWALK: 1 + + # missing means use the skim value itself rather than DIST skim (e.g. DISTBIKE) + blend_distance_skim_name: DIST + +maz_to_tap: + walk: + table: maz_to_tap_walk.csv + drive: + table: maz_to_tap_drive.csv + + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 6, 11, 16, 20, 24] + labels: &skim_time_period_labels ['EA', 'AM', 'MD', 'PM', 'EV'] + +demographic_segments: &demographic_segments + - &low_income_segment_id 0 + - &high_income_segment_id 1 + +# transit virtual path builder settings +TVPB_SETTINGS: + + tour_mode_choice: + units: utility + path_types: + WTW: + access: walk + egress: walk + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + DTW: + access: drive + egress: walk + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + WTD: + access: walk + egress: drive + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + tap_tap_settings: + SPEC: tvpb_utility_tap_tap.csv + PREPROCESSOR: + SPEC: tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv + DF: df + # FIXME this has to be explicitly specified, since e.g. attribute columns are assigned in expression files + attribute_segments: + demographic_segment: *demographic_segments + tod: *skim_time_period_labels + access_mode: ['drive', 'walk'] + attributes_as_columns: + - demographic_segment + - tod + maz_tap_settings: + walk: + SPEC: tvpb_utility_walk_maz_tap.csv + CHOOSER_COLUMNS: + #- demographic_segment + - walk_time + drive: + SPEC: tvpb_utility_drive_maz_tap.csv + CHOOSER_COLUMNS: + #- demographic_segment + - drive_time + - DIST + CONSTANTS: + C_LOW_INCOME_SEGMENT_ID: *low_income_segment_id + C_HIGH_INCOME_SEGMENT_ID: *high_income_segment_id + TVPB_demographic_segments_by_income_segment: + 1: *low_income_segment_id + 2: *low_income_segment_id + 3: *high_income_segment_id + 4: *high_income_segment_id + c_ivt_high_income: -0.028 + c_ivt_low_income: -0.0175 + c_cost_high_income: -0.00112 + c_cost_low_income: -0.00112 + c_wait: 1.5 + c_walk: 1.7 + c_drive: 1.5 + c_auto_operating_cost_per_mile: 18.29 + C_UNAVAILABLE: -999 + C_FASTEST_IVT_MULTIPLIER: 2 + C_FASTEST_COST_MULTIPLIER: 1 + C_CHEAPEST_IVT_MULTIPLIER: 1 + C_CHEAPEST_COST_MULTIPLIER: 500 + C_SHORTEST_IVT_MULTIPLIER: 1 + C_SHORTEST_COST_MULTIPLIER: 1 + C_SHORTEST_DIST_MULTIPLIER: 1 + # illustrate using access mode in tat-tap expressions files + C_DRIVE_TRANSFER_PENALTY: -1 + + accessibility: + units: time + path_types: + WTW: + access: walk + egress: walk + max_paths_across_tap_sets: 1 + max_paths_per_tap_set: 1 + tap_tap_settings: + SPEC: tvpb_accessibility_tap_tap_.csv + attributes_as_columns: + - tod + maz_tap_settings: + walk: + SPEC: tvpb_accessibility_walk_maz_tap.csv + CHOOSER_COLUMNS: + - walk_time + CONSTANTS: + out_of_vehicle_walk_time_weight: 1.5 + out_of_vehicle_wait_time_weight: 2.0 + diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/settings.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/settings.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone/settings.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/settings.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/settings_mp.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/settings_mp.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone/settings_mp.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/settings_mp.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/settings_static.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/settings_static.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone/settings_static.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/settings_static.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/stop_frequency.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/stop_frequency.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone/stop_frequency.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/stop_frequency.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tour_mode_choice.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/tour_mode_choice.csv similarity index 99% rename from activitysim/examples/example_multiple_zone/configs_3_zone/tour_mode_choice.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/tour_mode_choice.csv index 623001ea5b..5aeba76ced 100644 --- a/activitysim/examples/example_multiple_zone/configs_3_zone/tour_mode_choice.csv +++ b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/tour_mode_choice.csv @@ -1,189 +1,189 @@ -Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_TRANSIT,DRIVE_TRANSIT,TAXI,TNC_SINGLE,TNC_SHARED -#,Drive alone no toll,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,auto_ownership == 0,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['SOV_TIME'] + dot_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_Terminal_time,DRIVEALONEFREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']),coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOV_BTOLL'] + dot_skims['SOV_BTOLL']),coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_Person_is_between_16_and_19_years_old,DRIVEALONEFREE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da_multiplier,,,,,,,,,,,, -#,Drive alone toll,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,auto_ownership == 0,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,age < 16,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['SOVTOLL_TIME'] + dot_skims['SOVTOLL_TIME'],,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Terminal_time,DRIVEALONEPAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOVTOLL_DIST'] + dot_skims['SOVTOLL_DIST']),,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOVTOLL_BTOLL'] + dot_skims['SOVTOLL_BTOLL']),,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOVTOLL_VTOLL'] + dot_skims['SOVTOLL_VTOLL']),,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Person_is_between_16_and_19_years_old,DRIVEALONEPAY - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),,coef_age1619_da_multiplier,,,,,,,,,,, -#,Shared ride 2,,,,,,,,,,,,,, -util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,, -util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,, -util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_Terminal_time,SHARED2FREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']),,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']) / costShareSr2,,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_One_person_household,SHARED2FREE - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr_multiplier,,,,,,,,,, -util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr_multiplier,,,,,,,,,, -util_SHARED2FREE_Person_is_16_years_old_or_older,SHARED2FREE - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr_multiplier,,,,,,,,,, -#,Shared ride 2 toll,,,,,,,,,,,,,, -util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,, -util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,, -util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Terminal_time,SHARED2PAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']),,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']) / costShareSr2,,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']) / costShareSr2,,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_One_person_household,SHARED2PAY - One person household,@(df.hhsize == 1),,,,coef_hhsize1_sr_multiplier,,,,,,,,, -util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.hhsize == 2),,,,coef_hhsize2_sr_multiplier,,,,,,,,, -util_SHARED2PAY_Person_is_16_years_old_or_older,SHARED2PAY - Person is 16 years old or older,@(df.age >= 16),,,,coef_age16p_sr_multiplier,,,,,,,,, -#,Shared ride 3+,,,,,,,,,,,,,, -util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,, -util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@(odt_skims['HOV3_TIME'] + dot_skims['HOV3_TIME']),,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_Terminal_time,SHARED3FREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3_DIST'] + dot_skims['HOV3_DIST']),,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3_BTOLL'] + dot_skims['HOV3_BTOLL']) / costShareSr3,,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_One_person_household,SHARED3FREE - One person household,@(df.hhsize == 1),,,,,coef_hhsize1_sr_multiplier,,,,,,,, -util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.hhsize == 2),,,,,coef_hhsize2_sr_multiplier,,,,,,,, -util_SHARED3FREE_Person_is_16_years_old_or_older,SHARED3FREE - Person is 16 years old or older,@(df.age >= 16),,,,,coef_age16p_sr_multiplier,,,,,,,, -#,Shared ride 3+ toll,,,,,,,,,,,,,, -util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,, -util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@(odt_skims['HOV3TOLL_TIME'] + dot_skims['HOV3TOLL_TIME']),,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Terminal_time,SHARED3PAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3TOLL_DIST'] + dot_skims['HOV3TOLL_DIST']),,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3TOLL_BTOLL'] + dot_skims['HOV3TOLL_BTOLL']) / costShareSr3,,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3TOLL_VTOLL'] + dot_skims['HOV3TOLL_VTOLL']) / costShareSr3,,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_One_person_household,SHARED3PAY - One person household,@(df.hhsize == 1),,,,,,coef_hhsize1_sr_multiplier,,,,,,, -util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.hhsize == 2),,,,,,coef_hhsize2_sr_multiplier,,,,,,, -util_SHARED3PAY_Person_is_16_years_old_or_older,SHARED3PAY - Person is 16 years old or older,@(df.age >= 16),,,,,,coef_age16p_sr_multiplier,,,,,,, -#,Walk,,,,,,,,,,,,,, -#,FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,, -util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@walktimeshort_multiplier * (od_skims['DISTWALK'].clip(upper=walkThresh) + od_skims.reverse('DISTWALK').clip(upper=walkThresh))*60/walkSpeed,,,,,,,coef_ivt,,,,,, -util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * ((od_skims['DISTWALK'] - walkThresh).clip(lower=0) + (od_skims.reverse('DISTWALK') - walkThresh).clip(lower=0))*60/walkSpeed,,,,,,,coef_ivt,,,,,, -util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,coef_ivt,,,,,, -util_WALK_Topology,WALK - Topology,@coef_topology_walk_multiplier * df.dest_topology,,,,,,,coef_ivt,,,,,, -#,Bike,,,,,,,,,,,,,, -#,FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,, -util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,, -util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@biketimeshort_multiplier * (od_skims['DISTBIKE'].clip(upper=bikeThresh) + od_skims.reverse('DISTBIKE').clip(upper=bikeThresh))*60/bikeSpeed,,,,,,,,coef_ivt,,,,, -util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@biketimelong_multiplier * ((od_skims['DISTBIKE']-bikeThresh).clip(lower=0) + (od_skims.reverse('DISTBIKE')-bikeThresh).clip(lower=0))*60/bikeSpeed,,,,,,,,coef_ivt,,,,, -util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,coef_ivt,,,,, -util_BIKE_Topology,BIKE - Topology,@coef_topology_bike_multiplier * df.dest_topology,,,,,,,,coef_ivt,,,,, -#,Walk to Local,,,,,,,,,,,,,, -#util_WALK_TRANSIT_Unavailable,WALK_TRANSIT - Unavailable,walk_transit_available == False,,,,,,,,,-999,,,, -util_WALK_TRANSIT_Paths_logsums,WALK_TRANSIT - Path logsums,"@tvpb_logsum_odt['WTW'] + tvpb_logsum_dot['WTW']",,,,,,,,,coef_one,,,, -util_WALK_TRANSIT_Destination_zone_densityIndex,WALK_TRANSIT - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,coef_ivt,,,, -util_WALK_TRANSIT_Topology,WALK_TRANSIT - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,coef_ivt,,,, -util_WALK_TRANSIT_Person_is_less_than_10_years_old,WALK_TRANSIT - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,coef_age010_trn_multiplier,,,, -#,Drive to Local,,,,,,,,,,,,,, -#util_DRIVE_TRANSIT_Unavailable,DRIVE_TRANSIT - Unavailable,drive_transit_available == False,,,,,,,,,,-999,,, -util_DRIVE_TRANSIT_Unavailable_for_zero_auto_households,DRIVE_TRANSIT - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,-999,,, -util_DRIVE_TRANSIT_Unavailable_for_persons_less_than_16,DRIVE_TRANSIT - Unavailable for persons less than 16,age < 16,,,,,,,,,,-999,,, -util_DRIVE_TRANSIT_Paths_logsums,DRIVE_TRANSIT - Path logsums,"@tvpb_logsum_odt['DTW'] + tvpb_logsum_dot['WTD']",,,,,,,,,,coef_one,,, -util_DRIVE_TRANSIT_Destination_zone_densityIndex,DRIVE_TRANSIT - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,coef_ivt,,, -util_DRIVE_TRANSIT_Topology,DRIVE_TRANSIT - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,coef_ivt,,, -util_DRIVE_TRANSIT_Person_is_less_than_10_years_old,DRIVE_TRANSIT - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,coef_age010_trn_multiplier,,, -#,Taxi,,,,,,,,,,,,,, -util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,,,,,,,,coef_ivt,, -#, FIXME magic constant 1.5,,,,,,,,,,,,,, -util_Taxi_Wait_time,Taxi - Wait time,@1.5 * df.totalWaitTaxi,,,,,,,,,,,coef_ivt,, -util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,coef_ivt,, -util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,coef_ivt,, -util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * Taxi_costPerMile +(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * Taxi_costPerMinute)*100,,,,,,,,,,,coef_ivt,, -#,TNC Single,,,,,,,,,,,,,, -util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,,,,,,,,,coef_ivt, -util_TNC_Single_Wait_time,TNC Single - Wait time,@1.5 * df.totalWaitSingleTNC,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,coef_ivt, -util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,coef_ivt, -util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * TNC_single_costPerMile + (odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,coef_ivt, -#,TNC Shared,,,,,,,,,,,,,, -util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_shared_IVTFactor,,,,,,,,,,,,,coef_ivt -#, FIXME magic constant 1.5,,,,,,,,,,,,,, -util_TNC_Shared_Wait_time,TNC Shared - Wait time,@1.5 * df.totalWaitSharedTNC,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * TNC_shared_costPerMile + (odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,coef_ivt -#,indiv tour ASCs,,,,,,,,,,,,,, -util_Walk_ASC_Zero_auto,Walk ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,walk_ASC_no_auto,,,,,, -util_Walk_ASC_Auto_deficient,Walk ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,walk_ASC_auto_deficient,,,,,, -util_Walk_ASC_Auto_sufficient,Walk ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,walk_ASC_auto_sufficient,,,,,, -util_Bike_ASC_Zero_auto,Bike ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,bike_ASC_no_auto,,,,, -util_Bike_ASC_Auto_deficient,Bike ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,bike_ASC_auto_deficient,,,,, -util_Bike_ASC_Auto_sufficient,Bike ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,bike_ASC_auto_sufficient,,,,, -util_Shared_ride_2_ASC_Zero_auto,Shared ride 2 ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,sr2_ASC_no_auto,sr2_ASC_no_auto,,,,,,,,, -util_Shared_ride_2_ASC_Auto_deficient,Shared ride 2 ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,sr2_ASC_auto_deficient,sr2_ASC_auto_deficient,,,,,,,,, -util_Shared_ride_2_ASC_Auto_sufficient,Shared ride 2 ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient,,,,,,,,, -util_Shared_ride_3p_Zero_auto,Shared ride 3+ - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,sr3p_ASC_no_auto,sr3p_ASC_no_auto,,,,,,, -util_Shared_ride_3p_Auto_deficient,Shared ride 3+ - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient,,,,,,, -util_Shared_ride_3p_Auto_sufficient,Shared ride 3+ - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient,,,,,,, -util_Walk_to_Transit_Zero_auto,Walk to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,walk_transit_ASC_no_auto,,,, -util_Walk_to_Transit_Auto_deficient,Walk to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,walk_transit_ASC_auto_deficient,,,, -util_Walk_to_Transit_Auto_sufficient,Walk to Transit - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,walk_transit_ASC_auto_sufficient,,,, -util_Drive_to_Transit_Zero_auto,Drive to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,drive_transit_ASC_no_auto,,, -util_Drive_to_Transit_Auto_deficient,Drive to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,drive_transit_ASC_auto_deficient,,, -util_Drive_to_Transit_Auto_sufficient,Drive to Transit - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,drive_transit_ASC_auto_sufficient,,, -util_Taxi_Zero_auto,Taxi - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,taxi_ASC_no_auto,, -util_Taxi_Auto_deficient,Taxi - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,taxi_ASC_auto_deficient,, -util_Taxi_Auto_sufficient,Taxi - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,taxi_ASC_auto_sufficient,, -util_TNC_Single_Zero_auto,TNC Single - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,tnc_single_ASC_no_auto, -util_TNC_Single_Auto_deficient,TNC Single - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,tnc_single_ASC_auto_deficient, -util_TNC_Single_Auto_sufficient,TNC Single - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,tnc_single_ASC_auto_sufficient, -util_TNC_Shared_Zero_auto,TNC Shared - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,tnc_shared_ASC_no_auto -util_TNC_Shared_Auto_deficient,TNC Shared - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,tnc_shared_ASC_auto_deficient -util_TNC_Shared_Auto_sufficient,TNC Shared - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,tnc_shared_ASC_auto_sufficient -#,joint tour ASCs,,,,,,,,,,,,,, -util_Joint_Walk_ASC_Zero_auto,Joint - Walk ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,joint_walk_ASC_no_auto,,,,,, -util_Joint_Walk_ASC_Auto_deficient,Joint - Walk ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,joint_walk_ASC_auto_deficient,,,,,, -util_Joint_Walk_ASC_Auto_sufficient,Joint - Walk ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,joint_walk_ASC_auto_sufficient,,,,,, -util_Joint_Bike_ASC_Zero_auto,Joint - Bike ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,joint_bike_ASC_no_auto,,,,, -util_Joint_Bike_ASC_Auto_deficient,Joint - Bike ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,joint_bike_ASC_auto_deficient,,,,, -util_Joint_Bike_ASC_Auto_sufficient,Joint - Bike ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,joint_bike_ASC_auto_sufficient,,,,, -util_Joint_Shared_ride_2_ASC_Zero_auto,Joint - Shared ride 2 ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto,,,,,,,,, -util_Joint_Shared_ride_2_ASC_Auto_deficient,Joint - Shared ride 2 ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient,,,,,,,,, -util_Joint_Shared_ride_2_ASC_Auto_sufficient,Joint - Shared ride 2 ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient,,,,,,,,, -util_Joint_Shared_ride_3p_Zero_auto,Joint - Shared ride 3+ - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto,,,,,,, -util_Joint_Shared_ride_3p_Auto_deficient,Joint - Shared ride 3+ - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient,,,,,,, -util_Joint_Shared_ride_3p_Auto_sufficient,Joint - Shared ride 3+ - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient,,,,,,, -util_Joint_Walk_to_Transit_Zero_auto,Joint - Walk to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,joint_walk_transit_ASC_no_auto,,,, -util_Joint_Walk_to_Transit_Auto_deficient,Joint - Walk to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,joint_walk_transit_ASC_auto_deficient,,,, -util_Joint_Walk_to_Transit_Auto_sufficient,Joint - Walk to Transit - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,joint_walk_transit_ASC_auto_sufficient,,,, -util_Joint_Drive_to_Transit_Zero_auto,Joint - Drive to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,joint_drive_transit_ASC_no_auto,,, -util_Joint_Drive_to_Transit_Auto_deficient,Joint - Drive to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,joint_drive_transit_ASC_auto_deficient,,, -util_Joint_Drive_to_Transit_Auto_sufficient,Joint - Drive to Transit - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,joint_drive_transit_ASC_auto_sufficient,,, -util_Joint_Taxi_Zero_auto,Joint - Taxi - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,joint_taxi_ASC_no_auto,, -util_Joint_Taxi_Auto_deficient,Joint - Taxi - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,joint_taxi_ASC_auto_deficient,, -util_Joint_Taxi_Auto_sufficient,Joint - Taxi - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,joint_taxi_ASC_auto_sufficient,, -util_Joint_TNC_Single_Zero_auto,Joint - TNC Single - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,joint_tnc_single_ASC_no_auto, -util_Joint_TNC_Single_Auto_deficient,Joint - TNC Single - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,joint_tnc_single_ASC_auto_deficient, -util_Joint_TNC_Single_Auto_sufficient,Joint - TNC Single - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,joint_tnc_single_ASC_auto_sufficient, -util_Joint_TNC_Shared_Zero_auto,Joint - TNC Shared - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,joint_tnc_shared_ASC_no_auto -util_Joint_TNC_Shared_Auto_deficient,Joint - TNC Shared - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,joint_tnc_shared_ASC_auto_deficient -util_Joint_TNC_Shared_Auto_sufficient,Joint - TNC Shared - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,joint_tnc_shared_ASC_auto_sufficient -util_Local_bus_ASC,Local bus ASC,1,,,,,,,,,local_bus_ASC,local_bus_ASC,,, -#util_Walk_to_Light_Rail_ASC,Walk to Light Rail ASC,@(df.walk_ferry_available == False),,,,,,,,,,walk_light_rail_ASC,,,,,,,,,,, -#util_Drive_to_Light_Rail_ASC,Drive to Light Rail ASC,@(df.drive_ferry_available == False),,,,,,,,,,,,,,,drive_light_rail_ASC,,,,,, -#util_Walk_to_Ferry_ASC,Walk to Ferry ASC,@df.walk_ferry_available,,,,,,,,,,walk_ferry_ASC,,,,,,,,,,, -#util_Drive_to_Ferry_ASC,Drive to Ferry ASC,@df.drive_ferry_available,,,,,,,,,,,,,,,drive_ferry_ASC,,,,,, -#util_Express_Bus_ASC,Express Bus ASC,1,,,,,,,,,,,express_bus_ASC,,,,,express_bus_ASC,,,,, -#util_Heavy_Rail_ASC,Heavy Rail ASC,1,,,,,,,,,,,,heavy_rail_ASC,,,,,heavy_rail_ASC,,,, -#util_Commuter_Rail,Commuter Rail,1,,,,,,,,,,,,,commuter_rail_ASC,,,,,commuter_rail_ASC,,, -util_Walk_to_Transit_dest_CBD,Walk to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,walk_transit_CBD_ASC,,,, -util_Drive_to_Transit_dest_CBD,Drive to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,,drive_transit_CBD_ASC,,, -util_Drive_to_Transit_distance_penalty,Drive to Transit - distance penalty,@drvtrn_distpen_0_multiplier * (1-od_skims['DIST']/drvtrn_distpen_max).clip(lower=0),,,,,,,,,,coef_ivt,,, -#, FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,, -util_Walk_not_available_for_long_distances,Walk not available for long distances,@od_skims.max('DISTWALK') > 3,,,,,,,-999,,,,,, -util_Bike_not_available_for_long_distances,Bike not available for long distances,@od_skims.max('DISTBIKE') > 8,,,,,,,,-999,,,,, -util_Drive_alone_not_available_for_escort_tours,Drive alone not available for escort tours,is_escort,-999,-999,,,,,,,,,,, -#, max(c_densityIndexOrigin*originDensityIndex,originDensityIndexMax),,,,,,,,,1,1,,, +Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_TRANSIT,DRIVE_TRANSIT,TAXI,TNC_SINGLE,TNC_SHARED +#,Drive alone no toll,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,auto_ownership == 0,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['SOV_TIME'] + dot_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Terminal_time,DRIVEALONEFREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']),coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOV_BTOLL'] + dot_skims['SOV_BTOLL']),coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Person_is_between_16_and_19_years_old,DRIVEALONEFREE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da_multiplier,,,,,,,,,,,, +#,Drive alone toll,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,auto_ownership == 0,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,age < 16,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['SOVTOLL_TIME'] + dot_skims['SOVTOLL_TIME'],,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Terminal_time,DRIVEALONEPAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOVTOLL_DIST'] + dot_skims['SOVTOLL_DIST']),,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOVTOLL_BTOLL'] + dot_skims['SOVTOLL_BTOLL']),,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOVTOLL_VTOLL'] + dot_skims['SOVTOLL_VTOLL']),,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Person_is_between_16_and_19_years_old,DRIVEALONEPAY - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),,coef_age1619_da_multiplier,,,,,,,,,,, +#,Shared ride 2,,,,,,,,,,,,,, +util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,, +util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,, +util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Terminal_time,SHARED2FREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']),,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']) / costShareSr2,,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_One_person_household,SHARED2FREE - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr_multiplier,,,,,,,,,, +util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr_multiplier,,,,,,,,,, +util_SHARED2FREE_Person_is_16_years_old_or_older,SHARED2FREE - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr_multiplier,,,,,,,,,, +#,Shared ride 2 toll,,,,,,,,,,,,,, +util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,, +util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,, +util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Terminal_time,SHARED2PAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']),,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']) / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']) / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_One_person_household,SHARED2PAY - One person household,@(df.hhsize == 1),,,,coef_hhsize1_sr_multiplier,,,,,,,,, +util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.hhsize == 2),,,,coef_hhsize2_sr_multiplier,,,,,,,,, +util_SHARED2PAY_Person_is_16_years_old_or_older,SHARED2PAY - Person is 16 years old or older,@(df.age >= 16),,,,coef_age16p_sr_multiplier,,,,,,,,, +#,Shared ride 3+,,,,,,,,,,,,,, +util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,, +util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@(odt_skims['HOV3_TIME'] + dot_skims['HOV3_TIME']),,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Terminal_time,SHARED3FREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3_DIST'] + dot_skims['HOV3_DIST']),,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3_BTOLL'] + dot_skims['HOV3_BTOLL']) / costShareSr3,,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_One_person_household,SHARED3FREE - One person household,@(df.hhsize == 1),,,,,coef_hhsize1_sr_multiplier,,,,,,,, +util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.hhsize == 2),,,,,coef_hhsize2_sr_multiplier,,,,,,,, +util_SHARED3FREE_Person_is_16_years_old_or_older,SHARED3FREE - Person is 16 years old or older,@(df.age >= 16),,,,,coef_age16p_sr_multiplier,,,,,,,, +#,Shared ride 3+ toll,,,,,,,,,,,,,, +util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,, +util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@(odt_skims['HOV3TOLL_TIME'] + dot_skims['HOV3TOLL_TIME']),,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Terminal_time,SHARED3PAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3TOLL_DIST'] + dot_skims['HOV3TOLL_DIST']),,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3TOLL_BTOLL'] + dot_skims['HOV3TOLL_BTOLL']) / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3TOLL_VTOLL'] + dot_skims['HOV3TOLL_VTOLL']) / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_One_person_household,SHARED3PAY - One person household,@(df.hhsize == 1),,,,,,coef_hhsize1_sr_multiplier,,,,,,, +util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.hhsize == 2),,,,,,coef_hhsize2_sr_multiplier,,,,,,, +util_SHARED3PAY_Person_is_16_years_old_or_older,SHARED3PAY - Person is 16 years old or older,@(df.age >= 16),,,,,,coef_age16p_sr_multiplier,,,,,,, +#,Walk,,,,,,,,,,,,,, +#,FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,, +util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@walktimeshort_multiplier * (od_skims['DISTWALK'].clip(upper=walkThresh) + od_skims.reverse('DISTWALK').clip(upper=walkThresh))*60/walkSpeed,,,,,,,coef_ivt,,,,,, +util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * ((od_skims['DISTWALK'] - walkThresh).clip(lower=0) + (od_skims.reverse('DISTWALK') - walkThresh).clip(lower=0))*60/walkSpeed,,,,,,,coef_ivt,,,,,, +util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,coef_ivt,,,,,, +util_WALK_Topology,WALK - Topology,@coef_topology_walk_multiplier * df.dest_topology,,,,,,,coef_ivt,,,,,, +#,Bike,,,,,,,,,,,,,, +#,FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,, +util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,, +util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@biketimeshort_multiplier * (od_skims['DISTBIKE'].clip(upper=bikeThresh) + od_skims.reverse('DISTBIKE').clip(upper=bikeThresh))*60/bikeSpeed,,,,,,,,coef_ivt,,,,, +util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@biketimelong_multiplier * ((od_skims['DISTBIKE']-bikeThresh).clip(lower=0) + (od_skims.reverse('DISTBIKE')-bikeThresh).clip(lower=0))*60/bikeSpeed,,,,,,,,coef_ivt,,,,, +util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,coef_ivt,,,,, +util_BIKE_Topology,BIKE - Topology,@coef_topology_bike_multiplier * df.dest_topology,,,,,,,,coef_ivt,,,,, +#,Walk to Local,,,,,,,,,,,,,, +#util_WALK_TRANSIT_Unavailable,WALK_TRANSIT - Unavailable,walk_transit_available == False,,,,,,,,,-999,,,, +util_WALK_TRANSIT_Paths_logsums,WALK_TRANSIT - Path logsums,"@tvpb_logsum_odt['WTW'] + tvpb_logsum_dot['WTW']",,,,,,,,,coef_one,,,, +util_WALK_TRANSIT_Destination_zone_densityIndex,WALK_TRANSIT - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,coef_ivt,,,, +util_WALK_TRANSIT_Topology,WALK_TRANSIT - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,coef_ivt,,,, +util_WALK_TRANSIT_Person_is_less_than_10_years_old,WALK_TRANSIT - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,coef_age010_trn_multiplier,,,, +#,Drive to Local,,,,,,,,,,,,,, +#util_DRIVE_TRANSIT_Unavailable,DRIVE_TRANSIT - Unavailable,drive_transit_available == False,,,,,,,,,,-999,,, +util_DRIVE_TRANSIT_Unavailable_for_zero_auto_households,DRIVE_TRANSIT - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,-999,,, +util_DRIVE_TRANSIT_Unavailable_for_persons_less_than_16,DRIVE_TRANSIT - Unavailable for persons less than 16,age < 16,,,,,,,,,,-999,,, +util_DRIVE_TRANSIT_Paths_logsums,DRIVE_TRANSIT - Path logsums,"@tvpb_logsum_odt['DTW'] + tvpb_logsum_dot['WTD']",,,,,,,,,,coef_one,,, +util_DRIVE_TRANSIT_Destination_zone_densityIndex,DRIVE_TRANSIT - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,coef_ivt,,, +util_DRIVE_TRANSIT_Topology,DRIVE_TRANSIT - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,coef_ivt,,, +util_DRIVE_TRANSIT_Person_is_less_than_10_years_old,DRIVE_TRANSIT - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,coef_age010_trn_multiplier,,, +#,Taxi,,,,,,,,,,,,,, +util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,,,,,,,,coef_ivt,, +#, FIXME magic constant 1.5,,,,,,,,,,,,,, +util_Taxi_Wait_time,Taxi - Wait time,@1.5 * df.totalWaitTaxi,,,,,,,,,,,coef_ivt,, +util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,coef_ivt,, +util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,coef_ivt,, +util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * Taxi_costPerMile +(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * Taxi_costPerMinute)*100,,,,,,,,,,,coef_ivt,, +#,TNC Single,,,,,,,,,,,,,, +util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,,,,,,,,,coef_ivt, +util_TNC_Single_Wait_time,TNC Single - Wait time,@1.5 * df.totalWaitSingleTNC,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,coef_ivt, +util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,coef_ivt, +util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * TNC_single_costPerMile + (odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,coef_ivt, +#,TNC Shared,,,,,,,,,,,,,, +util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_shared_IVTFactor,,,,,,,,,,,,,coef_ivt +#, FIXME magic constant 1.5,,,,,,,,,,,,,, +util_TNC_Shared_Wait_time,TNC Shared - Wait time,@1.5 * df.totalWaitSharedTNC,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * TNC_shared_costPerMile + (odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,coef_ivt +#,indiv tour ASCs,,,,,,,,,,,,,, +util_Walk_ASC_Zero_auto,Walk ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,walk_ASC_no_auto,,,,,, +util_Walk_ASC_Auto_deficient,Walk ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,walk_ASC_auto_deficient,,,,,, +util_Walk_ASC_Auto_sufficient,Walk ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,walk_ASC_auto_sufficient,,,,,, +util_Bike_ASC_Zero_auto,Bike ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,bike_ASC_no_auto,,,,, +util_Bike_ASC_Auto_deficient,Bike ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,bike_ASC_auto_deficient,,,,, +util_Bike_ASC_Auto_sufficient,Bike ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,bike_ASC_auto_sufficient,,,,, +util_Shared_ride_2_ASC_Zero_auto,Shared ride 2 ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,sr2_ASC_no_auto,sr2_ASC_no_auto,,,,,,,,, +util_Shared_ride_2_ASC_Auto_deficient,Shared ride 2 ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,sr2_ASC_auto_deficient,sr2_ASC_auto_deficient,,,,,,,,, +util_Shared_ride_2_ASC_Auto_sufficient,Shared ride 2 ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient,,,,,,,,, +util_Shared_ride_3p_Zero_auto,Shared ride 3+ - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,sr3p_ASC_no_auto,sr3p_ASC_no_auto,,,,,,, +util_Shared_ride_3p_Auto_deficient,Shared ride 3+ - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient,,,,,,, +util_Shared_ride_3p_Auto_sufficient,Shared ride 3+ - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient,,,,,,, +util_Walk_to_Transit_Zero_auto,Walk to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,walk_transit_ASC_no_auto,,,, +util_Walk_to_Transit_Auto_deficient,Walk to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,walk_transit_ASC_auto_deficient,,,, +util_Walk_to_Transit_Auto_sufficient,Walk to Transit - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,walk_transit_ASC_auto_sufficient,,,, +util_Drive_to_Transit_Zero_auto,Drive to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,drive_transit_ASC_no_auto,,, +util_Drive_to_Transit_Auto_deficient,Drive to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,drive_transit_ASC_auto_deficient,,, +util_Drive_to_Transit_Auto_sufficient,Drive to Transit - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,drive_transit_ASC_auto_sufficient,,, +util_Taxi_Zero_auto,Taxi - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,taxi_ASC_no_auto,, +util_Taxi_Auto_deficient,Taxi - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,taxi_ASC_auto_deficient,, +util_Taxi_Auto_sufficient,Taxi - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,taxi_ASC_auto_sufficient,, +util_TNC_Single_Zero_auto,TNC Single - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,tnc_single_ASC_no_auto, +util_TNC_Single_Auto_deficient,TNC Single - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,tnc_single_ASC_auto_deficient, +util_TNC_Single_Auto_sufficient,TNC Single - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,tnc_single_ASC_auto_sufficient, +util_TNC_Shared_Zero_auto,TNC Shared - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,tnc_shared_ASC_no_auto +util_TNC_Shared_Auto_deficient,TNC Shared - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,tnc_shared_ASC_auto_deficient +util_TNC_Shared_Auto_sufficient,TNC Shared - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,tnc_shared_ASC_auto_sufficient +#,joint tour ASCs,,,,,,,,,,,,,, +util_Joint_Walk_ASC_Zero_auto,Joint - Walk ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,joint_walk_ASC_no_auto,,,,,, +util_Joint_Walk_ASC_Auto_deficient,Joint - Walk ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,joint_walk_ASC_auto_deficient,,,,,, +util_Joint_Walk_ASC_Auto_sufficient,Joint - Walk ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,joint_walk_ASC_auto_sufficient,,,,,, +util_Joint_Bike_ASC_Zero_auto,Joint - Bike ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,joint_bike_ASC_no_auto,,,,, +util_Joint_Bike_ASC_Auto_deficient,Joint - Bike ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,joint_bike_ASC_auto_deficient,,,,, +util_Joint_Bike_ASC_Auto_sufficient,Joint - Bike ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,joint_bike_ASC_auto_sufficient,,,,, +util_Joint_Shared_ride_2_ASC_Zero_auto,Joint - Shared ride 2 ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Auto_deficient,Joint - Shared ride 2 ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Auto_sufficient,Joint - Shared ride 2 ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient,,,,,,,,, +util_Joint_Shared_ride_3p_Zero_auto,Joint - Shared ride 3+ - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto,,,,,,, +util_Joint_Shared_ride_3p_Auto_deficient,Joint - Shared ride 3+ - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient,,,,,,, +util_Joint_Shared_ride_3p_Auto_sufficient,Joint - Shared ride 3+ - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient,,,,,,, +util_Joint_Walk_to_Transit_Zero_auto,Joint - Walk to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,joint_walk_transit_ASC_no_auto,,,, +util_Joint_Walk_to_Transit_Auto_deficient,Joint - Walk to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,joint_walk_transit_ASC_auto_deficient,,,, +util_Joint_Walk_to_Transit_Auto_sufficient,Joint - Walk to Transit - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,joint_walk_transit_ASC_auto_sufficient,,,, +util_Joint_Drive_to_Transit_Zero_auto,Joint - Drive to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,joint_drive_transit_ASC_no_auto,,, +util_Joint_Drive_to_Transit_Auto_deficient,Joint - Drive to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,joint_drive_transit_ASC_auto_deficient,,, +util_Joint_Drive_to_Transit_Auto_sufficient,Joint - Drive to Transit - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,joint_drive_transit_ASC_auto_sufficient,,, +util_Joint_Taxi_Zero_auto,Joint - Taxi - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,joint_taxi_ASC_no_auto,, +util_Joint_Taxi_Auto_deficient,Joint - Taxi - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,joint_taxi_ASC_auto_deficient,, +util_Joint_Taxi_Auto_sufficient,Joint - Taxi - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,joint_taxi_ASC_auto_sufficient,, +util_Joint_TNC_Single_Zero_auto,Joint - TNC Single - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,joint_tnc_single_ASC_no_auto, +util_Joint_TNC_Single_Auto_deficient,Joint - TNC Single - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,joint_tnc_single_ASC_auto_deficient, +util_Joint_TNC_Single_Auto_sufficient,Joint - TNC Single - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,joint_tnc_single_ASC_auto_sufficient, +util_Joint_TNC_Shared_Zero_auto,Joint - TNC Shared - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,joint_tnc_shared_ASC_no_auto +util_Joint_TNC_Shared_Auto_deficient,Joint - TNC Shared - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,joint_tnc_shared_ASC_auto_deficient +util_Joint_TNC_Shared_Auto_sufficient,Joint - TNC Shared - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,joint_tnc_shared_ASC_auto_sufficient +util_Local_bus_ASC,Local bus ASC,1,,,,,,,,,local_bus_ASC,local_bus_ASC,,, +#util_Walk_to_Light_Rail_ASC,Walk to Light Rail ASC,@(df.walk_ferry_available == False),,,,,,,,,,walk_light_rail_ASC,,,,,,,,,,, +#util_Drive_to_Light_Rail_ASC,Drive to Light Rail ASC,@(df.drive_ferry_available == False),,,,,,,,,,,,,,,drive_light_rail_ASC,,,,,, +#util_Walk_to_Ferry_ASC,Walk to Ferry ASC,@df.walk_ferry_available,,,,,,,,,,walk_ferry_ASC,,,,,,,,,,, +#util_Drive_to_Ferry_ASC,Drive to Ferry ASC,@df.drive_ferry_available,,,,,,,,,,,,,,,drive_ferry_ASC,,,,,, +#util_Express_Bus_ASC,Express Bus ASC,1,,,,,,,,,,,express_bus_ASC,,,,,express_bus_ASC,,,,, +#util_Heavy_Rail_ASC,Heavy Rail ASC,1,,,,,,,,,,,,heavy_rail_ASC,,,,,heavy_rail_ASC,,,, +#util_Commuter_Rail,Commuter Rail,1,,,,,,,,,,,,,commuter_rail_ASC,,,,,commuter_rail_ASC,,, +util_Walk_to_Transit_dest_CBD,Walk to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,walk_transit_CBD_ASC,,,, +util_Drive_to_Transit_dest_CBD,Drive to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,,drive_transit_CBD_ASC,,, +util_Drive_to_Transit_distance_penalty,Drive to Transit - distance penalty,@drvtrn_distpen_0_multiplier * (1-od_skims['DIST']/drvtrn_distpen_max).clip(lower=0),,,,,,,,,,coef_ivt,,, +#, FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,, +util_Walk_not_available_for_long_distances,Walk not available for long distances,@od_skims.max('DISTWALK') > 3,,,,,,,-999,,,,,, +util_Bike_not_available_for_long_distances,Bike not available for long distances,@od_skims.max('DISTBIKE') > 8,,,,,,,,-999,,,,, +util_Drive_alone_not_available_for_escort_tours,Drive alone not available for escort tours,is_escort,-999,-999,,,,,,,,,,, +#, max(c_densityIndexOrigin*originDensityIndex,originDensityIndexMax),,,,,,,,,1,1,,, diff --git a/activitysim/examples/example_sandag/configs_3_zone/tour_mode_choice.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/tour_mode_choice.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/tour_mode_choice.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/tour_mode_choice.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tour_mode_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/tour_mode_choice_annotate_choosers_preprocessor.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone/tour_mode_choice_annotate_choosers_preprocessor.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/tour_mode_choice_annotate_choosers_preprocessor.csv diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/trip_mode_choice.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/trip_mode_choice.csv similarity index 99% rename from activitysim/examples/example_multiple_zone/configs_3_zone/trip_mode_choice.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/trip_mode_choice.csv index 390b966d90..454846f78b 100644 --- a/activitysim/examples/example_multiple_zone/configs_3_zone/trip_mode_choice.csv +++ b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/trip_mode_choice.csv @@ -1,195 +1,195 @@ -Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_TRANSIT,DRIVE_TRANSIT,TAXI,TNC_SINGLE,TNC_SHARED -#,Drive alone no toll,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,auto_ownership == 0,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_Terminal_time,DRIVEALONEFREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['SOV_DIST'],coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost,coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOV_BTOLL'],coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_Person_is_between_16_and_19_years_old,DRIVEALONEFREE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da,,,,,,,,,,,, -#,Drive alone toll,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,auto_ownership == 0,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,age < 16,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['SOVTOLL_TIME'],,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Terminal_time,DRIVEALONEPAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['SOVTOLL_DIST'],,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost,,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOVTOLL_BTOLL'],,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOVTOLL_VTOLL'],,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Person_is_between_16_and_19_years_old,DRIVEALONEPAY - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),,coef_age1619_da,,,,,,,,,,, -#,Shared ride 2,,,,,,,,,,,,,, -util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,, -util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,, -util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@odt_skims['HOV2_TIME'],,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_Terminal_time,SHARED2FREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV2_DIST'],,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2_BTOLL'] / costShareSr2,,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_One_person_household,SHARED2FREE - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr,,,,,,,,,, -util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr,,,,,,,,,, -util_SHARED2FREE_Person_is_16_years_old_or_older,SHARED2FREE - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr,,,,,,,,,, -#,Shared ride 2 toll,,,,,,,,,,,,,, -util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,, -util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,, -util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@odt_skims['HOV2TOLL_TIME'],,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Terminal_time,SHARED2PAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV2TOLL_DIST'],,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_BTOLL'] / costShareSr2,,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'] / costShareSr2,,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_One_person_household,SHARED2PAY - One person household,@(df.hhsize == 1),,,,coef_hhsize1_sr,,,,,,,,, -util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.hhsize == 2),,,,coef_hhsize2_sr,,,,,,,,, -util_SHARED2PAY_Person_is_16_years_old_or_older,SHARED2PAY - Person is 16 years old or older,@(df.age >= 16),,,,coef_age16p_sr,,,,,,,,, -#,Shared ride 3+,,,,,,,,,,,,,, -util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,, -util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@odt_skims['HOV3_TIME'],,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_Terminal_time,SHARED3FREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV3_DIST'],,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3_BTOLL'] / costShareSr3,,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_One_person_household,SHARED3FREE - One person household,@(df.hhsize == 1),,,,,coef_hhsize1_sr,,,,,,,, -util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.hhsize == 2),,,,,coef_hhsize2_sr,,,,,,,, -util_SHARED3FREE_Person_is_16_years_old_or_older,SHARED3FREE - Person is 16 years old or older,@(df.age >= 16),,,,,coef_age16p_sr,,,,,,,, -#,Shared ride 3+ toll,,,,,,,,,,,,,, -util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,, -util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@odt_skims['HOV3TOLL_TIME'],,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Terminal_time,SHARED3PAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV3TOLL_DIST'],,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3TOLL_BTOLL'] / costShareSr3,,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3TOLL_VTOLL'] / costShareSr3,,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_One_person_household,SHARED3PAY - One person household,@(df.hhsize == 1),,,,,,coef_hhsize1_sr,,,,,,, -util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.hhsize == 2),,,,,,coef_hhsize2_sr,,,,,,, -util_SHARED3PAY_Person_is_16_years_old_or_older,SHARED3PAY - Person is 16 years old or older,@(df.age >= 16),,,,,,coef_age16p_sr,,,,,,, -#,Walk,,,,,,,,,,,,,, -util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@coef_walktimeshort_multiplier * od_skims['DISTWALK'].clip(upper=walkThresh) * 60/walkSpeed,,,,,,,coef_ivt,,,,,, -util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * (od_skims['DISTWALK'] - walkThresh).clip(lower=0) * 60/walkSpeed,,,,,,,coef_ivt,,,,,, -util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,coef_ivt,,,,,, -util_WALK_Topology,WALK - Topology,@topology_walk_multiplier * df.trip_topology,,,,,,,coef_ivt,,,,,, -#,Bike,,,,,,,,,,,,,, -util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,, -util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@coef_biketimeshort_multiplier * od_skims['DISTBIKE'].clip(upper=bikeThresh)*60/bikeSpeed,,,,,,,,coef_ivt,,,,, -util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@coef_biketimeshort_multiplier * biketimelong_multiplier * (od_skims['DISTBIKE']-bikeThresh).clip(lower=0)*60/bikeSpeed,,,,,,,,coef_ivt,,,,, -util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier*df.density_index,,,,,,,,coef_ivt,,,,, -util_BIKE_Topology,BIKE - Topology,@topology_bike_multiplier * df.trip_topology,,,,,,,,coef_ivt,,,,, -#,Walk to Transit,,,,,,,,,,,,,, -#util_WALK_TRANSIT_Unavailable,WALK_TRANSIT - Unavailable,walk_transit_available == False,,,,,,,,,-999,,,, -util_WALK_TRANSIT_Paths_logsums,WALK_TRANSIT - Path logsums,@tvpb_logsum_odt['WTW'],,,,,,,,,coef_one,,,, -util_WALK_TRANSIT_Destination_zone_densityIndex,WALK_TRANSIT - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,coef_ivt,,,, -util_WALK_TRANSIT_Topology,WALK_TRANSIT - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,coef_ivt,,,, -util_WALK_TRANSIT_Person_is_less_than_10_years_old,WALK_TRANSIT - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,coef_age010_trn,,,, -#,Drive to Transit,,,,,,,,,,,,,, -#util_DRIVE_TRANSIT_Unavailable,DRIVE_TRANSIT - Unavailable,drive_transit_available == False,,,,,,,,,,-999,,, -util_DRIVE_TRANSIT_Unavailable_for_zero_auto_households,DRIVE_TRANSIT - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,-999,,, -util_DRIVE_TRANSIT_Unavailable_for_persons_less_than_16,DRIVE_TRANSIT - Unavailable for persons less than 16,age < 16,,,,,,,,,,-999,,, -util_DRIVE_TRANSIT_Paths_logsums,DRIVE_TRANSIT - Path logsums,@tvpb_logsum_odt['DTW'],,,,,,,,,,coef_one,,, -util_DRIVE_TRANSIT_Destination_zone_densityIndex,DRIVE_TRANSIT - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,coef_ivt,,, -util_DRIVE_TRANSIT_Topology,DRIVE_TRANSIT - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,coef_ivt,,, -util_DRIVE_TRANSIT_Person_is_less_than_10_years_old,DRIVE_TRANSIT - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,coef_age010_trn,,, -#,Taxi,,,,,,,,,,,,,, -util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@odt_skims['HOV2TOLL_TIME'],,,,,,,,,,,coef_ivt,, -util_Taxi_Wait_time,Taxi - Wait time,@ridehail_wait_time_multiplier * df.origTaxiWaitTime,,,,,,,,,,,coef_ivt,, -util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,coef_ivt,, -util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_BTOLL'],,,,,,,,,,,coef_ivt,, -util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare + odt_skims['HOV2TOLL_DIST'] * Taxi_costPerMile + odt_skims['HOV2TOLL_TIME'] * Taxi_costPerMinute)*100,,,,,,,,,,,coef_ivt,, -#,TNC Single,,,,,,,,,,,,,, -util_TNC_Single_In_vehipasim -cle_time,TNC Single - In-vehicle time,@odt_skims['HOV2TOLL_TIME'] ,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Wait_time,TNC Single - Wait time,@ridehail_wait_time_multiplier * df.origSingleTNCWaitTime,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,coef_ivt, -util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,coef_ivt, -util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare + odt_skims['HOV2TOLL_DIST'] * TNC_single_costPerMile + odt_skims['HOV2TOLL_TIME'] * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,coef_ivt, -#,#TNC Shared,,,,,,,,,,,,,, -util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@odt_skims['HOV2TOLL_TIME'] * TNC_shared_IVTFactor,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Wait_time,TNC Shared - Wait time,@ridehail_wait_time_multiplier * df.origSharedTNCWaitTime,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare + odt_skims['HOV2TOLL_DIST'] * TNC_shared_costPerMile + odt_skims['HOV2TOLL_TIME']* TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,coef_ivt -#,,,,,,,,,,,,,,, -util_tour_mode_is_auto,Auto tour mode availability,tour_mode_is_auto,,,,,,,,-999,-999,-999,,, -util_tour_mode_is_walk,Walk tour mode availability,tour_mode_is_walk,-999,-999,-999,-999,-999,-999,,-999,-999,-999,,, -util_tour_mode_is_bike,Bike tour mode availability,tour_mode_is_bike,-999,-999,-999,-999,-999,-999,,,-999,-999,,, -util_tour_mode_is_walk_transit,Walk to Transit tour mode availability,tour_mode_is_walk_transit,-999,-999,,,,,,-999,,,,, -util_tour_mode_is_drive_transit,Drive to Transit tour modes availability,tour_mode_is_drive_transit,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, -util_tour_mode_is_ride_hail,Ride hail tour modes availability,tour_mode_is_ride_hail,-999,-999,,,,,,-999,,,,, -,#indiv tour ASCs,,,,,,,,,,,,,, -util_Drive_Alone_tour_mode_ASC_shared_ride_2_df_is_indiv,Drive Alone tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,sov_ASC_sr2,sov_ASC_sr2,,,,,,,,, -util_Drive_Alone_tour_mode_ASC_shared_ride_3_plus,Drive Alone tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,sov_ASC_sr3p,sov_ASC_sr3p,,,,,,, -util_Drive_Alone_tour_mode_ASC_walk,Drive Alone tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,sov_ASC_walk,,,,,, -util_Drive_Alone_tour_mode_ASC_ride_hail,Drive Alone tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,,,,,sov_ASC_rh,sov_ASC_rh,sov_ASC_rh -util_Shared_Ride_2_tour_mode_ASC_shared_ride_2,Shared Ride 2 tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,sr2_ASC_sr2,sr2_ASC_sr2,,,,,,,,, -util_Shared_Ride_2_tour_mode_ASC_shared_ride_3_plus,Shared Ride 2 tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,sr2_ASC_sr3p,sr2_ASC_sr3p,,,,,,, -util_Shared_Ride_2_tour_mode_ASC_walk,Shared Ride 2 tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,sr2_ASC_walk,,,,,, -util_Shared_Ride_2_tour_mode_ASC_ride_hail,Shared Ride 2 tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,,,,,sr2_ASC_rh,sr2_ASC_rh,sr2_ASC_rh -util_Shared_Ride_3_tour_mode_ASC_shared_ride_2,Shared Ride 3+ tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,sr3p_ASC_sr2,sr3p_ASC_sr2,,,,,,,,, -util_Shared_Ride_3_tour_mode_ASC_shared_ride_3_plus,Shared Ride 3+ tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,sr3p_ASC_sr3p,sr3p_ASC_sr3p,,,,,,, -util_Shared_Ride_3_tour_mode_ASC_walk,Shared Ride 3+ tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,sr3p_ASC_walk,,,,,, -util_Shared_Ride_3_tour_mode_ASC_ride_hail,Shared Ride 3+ tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,,,,,sr3p_ASC_rh,sr3p_ASC_rh,sr3p_ASC_rh -util_Walk_tour_mode_ASC_ride_hail,Walk tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_WALK_MODE),,,,,,,,,,,walk_ASC_rh,walk_ASC_rh,walk_ASC_rh -util_Bike_tour_mode_ASC_walk,Bike tour mode ASC -- walk,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,,,,bike_ASC_walk,,,,,, -util_Bike_tour_mode_ASC_ride_hail,Bike tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,bike_ASC_rh,bike_ASC_rh,bike_ASC_rh -#util_Walk_to_Transit_tour_mode_ASC_light_rail,Walk to Transit tour mode ASC -- light rail,@(df.is_indiv & df.tour_mode_is_walk_transit & ~df.walk_ferry_available),,,,,,,,,,walk_transit_ASC_lightrail,,, -#util_Walk_to_Transit_tour_mode_ASC_ferry,Walk to Transit tour mode ASC -- ferry,@(df.is_indiv & df.tour_mode_is_walk_transit & df.walk_ferry_available),,,,,,,,,,walk_transit_ASC_ferry,,, -#util_Walk_to_Transit_tour_mode_ASC_express_bus,Walk to Transit tour mode ASC -- express bus,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,, -#util_Walk_to_Transit_tour_mode_ASC_heavy_rail,Walk to Transit tour mode ASC -- heavy rail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,, -#util_Walk_to_Transit_tour_mode_ASC_commuter_rail,Walk to Transit tour mode ASC -- commuter rail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,, -#util_Walk_to_Transit_tour_mode_ASC_shared_ride_2,Walk to Transit tour mode ASC -- shared ride 2,@(df.is_indiv & df.tour_mode_is_walk_transit),,,walk_transit_ASC_sr2,walk_transit_ASC_sr2,,,,,,,,, -#util_Walk_to_Transit_tour_mode_ASC_shared_ride_3_plus,Walk to Transit tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,walk_transit_ASC_sr3p,walk_transit_ASC_sr3p,,,,,,, -#util_Walk_to_Transit_tour_mode_ASC_walk,Walk to Transit tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,walk_transit_ASC_walk,,,,,, -#util_Walk_to_Transit_tour_mode_ASC_ride_hail,Walk to Transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,walk_transit_ASC_rh,walk_transit_ASC_rh,walk_transit_ASC_rh -#util_Drive_to_Transit_tour_mode_ASC_light_rail_skims_differ,Drive to Transit tour mode ASC -- light rail (higher b/c loc d-trn skims differ),@(df.is_indiv & df.tour_mode_is_drive_transit & ~df.drive_ferry_available),,,,,,,,,,,,, -#util_Drive_to_Transit_tour_mode_ASC_ferry,Drive to Transit tour mode ASC -- ferry,@(df.is_indiv & df.tour_mode_is_drive_transit & df.drive_ferry_available),,,,,,,,,,,,, -#util_Drive_to_Transit_tour_mode_ASC_express_bus,Drive to Transit tour mode ASC -- express bus,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,, -#util_Drive_to_Transit_tour_mode_ASC_heavy_rail,Drive to Transit tour mode ASC -- heavy rail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,, -#util_Drive_to_Transit_tour_mode_ASC_commuter_rail,Drive to Transit tour mode ASC -- commuter rail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,, -#util_Drive_to_Transit_tour_mode_ASC_ride_hail,Drive to Transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,drive_transit_ASC_rh,drive_transit_ASC_rh,drive_transit_ASC_rh -util_Ride_Hail_tour_mode_ASC_shared_ride_2,Ride Hail tour mode ASC -- shared ride 2,@(df.is_indiv & df.tour_mode_is_ride_hail),,,ride_hail_ASC_sr2,ride_hail_ASC_sr2,,,,,,,,, -util_Ride_Hail_tour_mode_ASC_shared_ride_3_plus,Ride Hail tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,ride_hail_ASC_sr3p,ride_hail_ASC_sr3p,,,,,,, -util_Ride_Hail_tour_mode_ASC_walk,Ride Hail tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,ride_hail_ASC_walk,,,,,, -util_Ride_Hail_tour_mode_ASC_walk_to_transit,Ride Hail tour mode ASC -- walk to transit,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,,,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,,, -util_Ride_Hail_tour_mode_ASC_ride_hail_taxi,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,ride_hail_ASC_taxi,, -util_Ride_Hail_tour_mode_ASC_ride_hail_single,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,ride_hail_ASC_tnc_single, -util_Ride_Hail_tour_mode_ASC_ride_hail_shared,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,ride_hail_ASC_tnc_shared -#,joint tour ASCs,,,,,,,,,,,,,, -util_joint_auto_tour_mode_ASC_shared_ride_2,joint - auto tour mode ASC -- shared ride 2,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,joint_auto_ASC_sr2,joint_auto_ASC_sr2,,,,,,,,, -util_joint_auto_tour_mode_ASC_shared_ride_3_,joint - auto tour mode ASC -- shared ride 3+,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,,joint_auto_ASC_sr3p,joint_auto_ASC_sr3p,,,,,,, -util_joint_auto_tour_mode_ASC_walk,joint - auto tour mode ASC -- walk,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,,,,joint_auto_ASC_walk,,,,,, -util_joint_auto_tour_mode_ASC_ride_hail,joint - auto tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,joint_auto_ASC_rh,joint_auto_ASC_rh,joint_auto_ASC_rh -util_joint_Walk_tour_mode_ASC_ride_hail,joint - Walk tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,joint_walk_ASC_rh,,,,,, -util_joint_Bike_tour_mode_ASC_walk,joint - Bike tour mode ASC -- walk,@df.is_joint & (df.i_tour_mode == I_BIKE_MODE),,,,,,,joint_bike_ASC_walk,,,,,, -util_joint_Bike_tour_mode_ASC_ride_hail,joint - Bike tour mode ASC -- ride hail,@df.is_joint & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,joint_bike_ASC_rh,joint_bike_ASC_rh,joint_bike_ASC_rh -# util_joint_Walk_to_Transit_tour_mode_ASC_light_rail,joint - Walk to Transit tour mode ASC -- light rail,@(df.is_joint & df.tour_mode_is_walk_transit & ~df.walk_ferry_available),,,,,,,,,,joint_walk_transit_ASC_lightrail,,, -# util_joint_Walk_to_Transit_tour_mode_ASC_ferry,joint - Walk to Transit tour mode ASC -- ferry,@(df.is_joint & df.tour_mode_is_walk_transit & df.walk_ferry_available),,,,,,,,,,joint_walk_transit_ASC_ferry,,, -# util_joint_Walk_to_Transit_tour_mode_ASC_express_bus,joint - Walk to Transit tour mode ASC -- express bus,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,, -# util_joint_Walk_to_Transit_tour_mode_ASC_heavy_rail,joint - Walk to Transit tour mode ASC -- heavy rail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,, -# util_joint_Walk_to_Transit_tour_mode_ASC_commuter_rail,joint - Walk to Transit tour mode ASC -- commuter rail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,, -# util_joint_Walk_to_Transit_tour_mode_ASC_shared_ride_2,joint - Walk to Transit tour mode ASC -- shared ride 2,@(df.is_joint & df.tour_mode_is_walk_transit),,,joint_walk_transit_ASC_sr2,joint_walk_transit_ASC_sr2,,,,,,,,, -# util_joint_Walk_to_Transit_tour_mode_ASC_shared_ride_3_plus,joint - Walk to Transit tour mode ASC -- shared ride 3+,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,joint_walk_transit_ASC_sr3p,joint_walk_transit_ASC_sr3p,,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_walk,joint - Walk to Transit tour mode ASC -- walk,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,joint_walk_transit_ASC_walk,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_ride_hail,joint - Walk to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,joint_walk_transit_ASC_rh,joint_walk_transit_ASC_rh,joint_walk_transit_ASC_rh -# util_joint_Drive_to_Transit_tour_mode_ASC_light_rail_skims_differ,joint - Drive to Transit tour mode ASC -- light rail (higher b/c loc d-trn skims differ),@(df.is_joint & df.tour_mode_is_drive_transit & ~df.drive_ferry_available),,,,,,,,,,,,, -# util_joint_Drive_to_Transit_tour_mode_ASC_ferry,joint - Drive to Transit tour mode ASC -- ferry,@(df.is_joint & df.tour_mode_is_drive_transit & df.drive_ferry_available),,,,,,,,,,,,, -# util_joint_Drive_to_Transit_tour_mode_ASC_express_bus,joint - Drive to Transit tour mode ASC -- express bus,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,, -# util_joint_Drive_to_Transit_tour_mode_ASC_heavy_rail,joint - Drive to Transit tour mode ASC -- heavy rail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,, -# util_joint_Drive_to_Transit_tour_mode_ASC_commuter_rail,joint - Drive to Transit tour mode ASC -- commuter rail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,, -util_joint_Drive_to_Transit_tour_mode_ASC_ride_hail,joint - Drive to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,joint_drive_transit_ASC_rh,joint_drive_transit_ASC_rh,joint_drive_transit_ASC_rh -util_joint_Ride_Hail_tour_mode_ASC_shared_ride_2,joint - Ride Hail tour mode ASC -- shared ride 2,@(df.is_joint & df.tour_mode_is_ride_hail),,,joint_ride_hail_ASC_sr2,joint_ride_hail_ASC_sr2,,,,,,,,, -util_joint_Ride_Hail_tour_mode_ASC_shared_ride_3_plus,joint - Ride Hail tour mode ASC -- shared ride 3+,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,joint_ride_hail_ASC_sr3p,joint_ride_hail_ASC_sr3p,,,,,,, -util_joint_Ride_Hail_tour_mode_ASC_walk,joint - Ride Hail tour mode ASC -- walk,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,joint_ride_hail_ASC_walk,,,,,, -util_joint_Ride_Hail_tour_mode_ASC_walk_to_transit,joint - Ride Hail tour mode ASC -- walk to transit,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,,,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,,, -util_joint_Ride_Hail_tour_mode_ASC_ride_hail_taxi,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,joint_ride_hail_ASC_taxi,, -util_joint_Ride_Hail_tour_mode_ASC_ride_hail_single,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,joint_ride_hail_ASC_tnc_single, -util_joint_Ride_Hail_tour_mode_ASC_ride_hail_shared,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,joint_ride_hail_ASC_tnc_shared -#,#,,,,,,,,,,,,,, -util_Walk_not_available_for_long_distances,Walk not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTWALK'] > 3),,,,,,,-999,,,,,, -util_Bike_not_available_for_long_distances,Bike not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTBIKE'] > 8),,,,,,,,-999,,,,, -util_origin_density_index,Origin density index,@origin_density_applied*(origin_density_index_multiplier*df.origin_density_index).clip(origin_density_index_max),,,,,,,coef_ivt,coef_ivt,coef_ivt,coef_ivt,,coef_ivt,coef_ivt -util_walk_express_penalty,Walk-express penalty for intermediate stops,@walk_express_penalty * ~(df.first_trip | df.first_trip),,,,,,,,,,,,, -util_adjust_tnc_shared,TNC shared adjustment,@adjust_tnc_shared,,,,,,,,,,,,,coef_ivt +Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_TRANSIT,DRIVE_TRANSIT,TAXI,TNC_SINGLE,TNC_SHARED +#,Drive alone no toll,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,auto_ownership == 0,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Terminal_time,DRIVEALONEFREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['SOV_DIST'],coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost,coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOV_BTOLL'],coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Person_is_between_16_and_19_years_old,DRIVEALONEFREE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da,,,,,,,,,,,, +#,Drive alone toll,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,auto_ownership == 0,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,age < 16,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['SOVTOLL_TIME'],,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Terminal_time,DRIVEALONEPAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['SOVTOLL_DIST'],,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost,,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOVTOLL_BTOLL'],,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOVTOLL_VTOLL'],,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Person_is_between_16_and_19_years_old,DRIVEALONEPAY - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),,coef_age1619_da,,,,,,,,,,, +#,Shared ride 2,,,,,,,,,,,,,, +util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,, +util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,, +util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@odt_skims['HOV2_TIME'],,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Terminal_time,SHARED2FREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV2_DIST'],,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2_BTOLL'] / costShareSr2,,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_One_person_household,SHARED2FREE - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr,,,,,,,,,, +util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr,,,,,,,,,, +util_SHARED2FREE_Person_is_16_years_old_or_older,SHARED2FREE - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr,,,,,,,,,, +#,Shared ride 2 toll,,,,,,,,,,,,,, +util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,, +util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,, +util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@odt_skims['HOV2TOLL_TIME'],,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Terminal_time,SHARED2PAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV2TOLL_DIST'],,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_BTOLL'] / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'] / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_One_person_household,SHARED2PAY - One person household,@(df.hhsize == 1),,,,coef_hhsize1_sr,,,,,,,,, +util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.hhsize == 2),,,,coef_hhsize2_sr,,,,,,,,, +util_SHARED2PAY_Person_is_16_years_old_or_older,SHARED2PAY - Person is 16 years old or older,@(df.age >= 16),,,,coef_age16p_sr,,,,,,,,, +#,Shared ride 3+,,,,,,,,,,,,,, +util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,, +util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@odt_skims['HOV3_TIME'],,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Terminal_time,SHARED3FREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV3_DIST'],,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3_BTOLL'] / costShareSr3,,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_One_person_household,SHARED3FREE - One person household,@(df.hhsize == 1),,,,,coef_hhsize1_sr,,,,,,,, +util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.hhsize == 2),,,,,coef_hhsize2_sr,,,,,,,, +util_SHARED3FREE_Person_is_16_years_old_or_older,SHARED3FREE - Person is 16 years old or older,@(df.age >= 16),,,,,coef_age16p_sr,,,,,,,, +#,Shared ride 3+ toll,,,,,,,,,,,,,, +util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,, +util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@odt_skims['HOV3TOLL_TIME'],,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Terminal_time,SHARED3PAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV3TOLL_DIST'],,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3TOLL_BTOLL'] / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3TOLL_VTOLL'] / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_One_person_household,SHARED3PAY - One person household,@(df.hhsize == 1),,,,,,coef_hhsize1_sr,,,,,,, +util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.hhsize == 2),,,,,,coef_hhsize2_sr,,,,,,, +util_SHARED3PAY_Person_is_16_years_old_or_older,SHARED3PAY - Person is 16 years old or older,@(df.age >= 16),,,,,,coef_age16p_sr,,,,,,, +#,Walk,,,,,,,,,,,,,, +util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@coef_walktimeshort_multiplier * od_skims['DISTWALK'].clip(upper=walkThresh) * 60/walkSpeed,,,,,,,coef_ivt,,,,,, +util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * (od_skims['DISTWALK'] - walkThresh).clip(lower=0) * 60/walkSpeed,,,,,,,coef_ivt,,,,,, +util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,coef_ivt,,,,,, +util_WALK_Topology,WALK - Topology,@topology_walk_multiplier * df.trip_topology,,,,,,,coef_ivt,,,,,, +#,Bike,,,,,,,,,,,,,, +util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,, +util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@coef_biketimeshort_multiplier * od_skims['DISTBIKE'].clip(upper=bikeThresh)*60/bikeSpeed,,,,,,,,coef_ivt,,,,, +util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@coef_biketimeshort_multiplier * biketimelong_multiplier * (od_skims['DISTBIKE']-bikeThresh).clip(lower=0)*60/bikeSpeed,,,,,,,,coef_ivt,,,,, +util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier*df.density_index,,,,,,,,coef_ivt,,,,, +util_BIKE_Topology,BIKE - Topology,@topology_bike_multiplier * df.trip_topology,,,,,,,,coef_ivt,,,,, +#,Walk to Transit,,,,,,,,,,,,,, +#util_WALK_TRANSIT_Unavailable,WALK_TRANSIT - Unavailable,walk_transit_available == False,,,,,,,,,-999,,,, +util_WALK_TRANSIT_Paths_logsums,WALK_TRANSIT - Path logsums,@tvpb_logsum_odt['WTW'],,,,,,,,,coef_one,,,, +util_WALK_TRANSIT_Destination_zone_densityIndex,WALK_TRANSIT - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,coef_ivt,,,, +util_WALK_TRANSIT_Topology,WALK_TRANSIT - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,coef_ivt,,,, +util_WALK_TRANSIT_Person_is_less_than_10_years_old,WALK_TRANSIT - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,coef_age010_trn,,,, +#,Drive to Transit,,,,,,,,,,,,,, +#util_DRIVE_TRANSIT_Unavailable,DRIVE_TRANSIT - Unavailable,drive_transit_available == False,,,,,,,,,,-999,,, +util_DRIVE_TRANSIT_Unavailable_for_zero_auto_households,DRIVE_TRANSIT - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,-999,,, +util_DRIVE_TRANSIT_Unavailable_for_persons_less_than_16,DRIVE_TRANSIT - Unavailable for persons less than 16,age < 16,,,,,,,,,,-999,,, +util_DRIVE_TRANSIT_Paths_logsums,DRIVE_TRANSIT - Path logsums,@tvpb_logsum_odt['DTW'],,,,,,,,,,coef_one,,, +util_DRIVE_TRANSIT_Destination_zone_densityIndex,DRIVE_TRANSIT - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,coef_ivt,,, +util_DRIVE_TRANSIT_Topology,DRIVE_TRANSIT - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,coef_ivt,,, +util_DRIVE_TRANSIT_Person_is_less_than_10_years_old,DRIVE_TRANSIT - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,coef_age010_trn,,, +#,Taxi,,,,,,,,,,,,,, +util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@odt_skims['HOV2TOLL_TIME'],,,,,,,,,,,coef_ivt,, +util_Taxi_Wait_time,Taxi - Wait time,@ridehail_wait_time_multiplier * df.origTaxiWaitTime,,,,,,,,,,,coef_ivt,, +util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,coef_ivt,, +util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_BTOLL'],,,,,,,,,,,coef_ivt,, +util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare + odt_skims['HOV2TOLL_DIST'] * Taxi_costPerMile + odt_skims['HOV2TOLL_TIME'] * Taxi_costPerMinute)*100,,,,,,,,,,,coef_ivt,, +#,TNC Single,,,,,,,,,,,,,, +util_TNC_Single_In_vehipasim +cle_time,TNC Single - In-vehicle time,@odt_skims['HOV2TOLL_TIME'] ,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Wait_time,TNC Single - Wait time,@ridehail_wait_time_multiplier * df.origSingleTNCWaitTime,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,coef_ivt, +util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,coef_ivt, +util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare + odt_skims['HOV2TOLL_DIST'] * TNC_single_costPerMile + odt_skims['HOV2TOLL_TIME'] * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,coef_ivt, +#,#TNC Shared,,,,,,,,,,,,,, +util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@odt_skims['HOV2TOLL_TIME'] * TNC_shared_IVTFactor,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Wait_time,TNC Shared - Wait time,@ridehail_wait_time_multiplier * df.origSharedTNCWaitTime,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare + odt_skims['HOV2TOLL_DIST'] * TNC_shared_costPerMile + odt_skims['HOV2TOLL_TIME']* TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,coef_ivt +#,,,,,,,,,,,,,,, +util_tour_mode_is_auto,Auto tour mode availability,tour_mode_is_auto,,,,,,,,-999,-999,-999,,, +util_tour_mode_is_walk,Walk tour mode availability,tour_mode_is_walk,-999,-999,-999,-999,-999,-999,,-999,-999,-999,,, +util_tour_mode_is_bike,Bike tour mode availability,tour_mode_is_bike,-999,-999,-999,-999,-999,-999,,,-999,-999,,, +util_tour_mode_is_walk_transit,Walk to Transit tour mode availability,tour_mode_is_walk_transit,-999,-999,,,,,,-999,,,,, +util_tour_mode_is_drive_transit,Drive to Transit tour modes availability,tour_mode_is_drive_transit,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, +util_tour_mode_is_ride_hail,Ride hail tour modes availability,tour_mode_is_ride_hail,-999,-999,,,,,,-999,,,,, +,#indiv tour ASCs,,,,,,,,,,,,,, +util_Drive_Alone_tour_mode_ASC_shared_ride_2_df_is_indiv,Drive Alone tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,sov_ASC_sr2,sov_ASC_sr2,,,,,,,,, +util_Drive_Alone_tour_mode_ASC_shared_ride_3_plus,Drive Alone tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,sov_ASC_sr3p,sov_ASC_sr3p,,,,,,, +util_Drive_Alone_tour_mode_ASC_walk,Drive Alone tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,sov_ASC_walk,,,,,, +util_Drive_Alone_tour_mode_ASC_ride_hail,Drive Alone tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,,,,,sov_ASC_rh,sov_ASC_rh,sov_ASC_rh +util_Shared_Ride_2_tour_mode_ASC_shared_ride_2,Shared Ride 2 tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,sr2_ASC_sr2,sr2_ASC_sr2,,,,,,,,, +util_Shared_Ride_2_tour_mode_ASC_shared_ride_3_plus,Shared Ride 2 tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,sr2_ASC_sr3p,sr2_ASC_sr3p,,,,,,, +util_Shared_Ride_2_tour_mode_ASC_walk,Shared Ride 2 tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,sr2_ASC_walk,,,,,, +util_Shared_Ride_2_tour_mode_ASC_ride_hail,Shared Ride 2 tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,,,,,sr2_ASC_rh,sr2_ASC_rh,sr2_ASC_rh +util_Shared_Ride_3_tour_mode_ASC_shared_ride_2,Shared Ride 3+ tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,sr3p_ASC_sr2,sr3p_ASC_sr2,,,,,,,,, +util_Shared_Ride_3_tour_mode_ASC_shared_ride_3_plus,Shared Ride 3+ tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,sr3p_ASC_sr3p,sr3p_ASC_sr3p,,,,,,, +util_Shared_Ride_3_tour_mode_ASC_walk,Shared Ride 3+ tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,sr3p_ASC_walk,,,,,, +util_Shared_Ride_3_tour_mode_ASC_ride_hail,Shared Ride 3+ tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,,,,,sr3p_ASC_rh,sr3p_ASC_rh,sr3p_ASC_rh +util_Walk_tour_mode_ASC_ride_hail,Walk tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_WALK_MODE),,,,,,,,,,,walk_ASC_rh,walk_ASC_rh,walk_ASC_rh +util_Bike_tour_mode_ASC_walk,Bike tour mode ASC -- walk,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,,,,bike_ASC_walk,,,,,, +util_Bike_tour_mode_ASC_ride_hail,Bike tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,bike_ASC_rh,bike_ASC_rh,bike_ASC_rh +#util_Walk_to_Transit_tour_mode_ASC_light_rail,Walk to Transit tour mode ASC -- light rail,@(df.is_indiv & df.tour_mode_is_walk_transit & ~df.walk_ferry_available),,,,,,,,,,walk_transit_ASC_lightrail,,, +#util_Walk_to_Transit_tour_mode_ASC_ferry,Walk to Transit tour mode ASC -- ferry,@(df.is_indiv & df.tour_mode_is_walk_transit & df.walk_ferry_available),,,,,,,,,,walk_transit_ASC_ferry,,, +#util_Walk_to_Transit_tour_mode_ASC_express_bus,Walk to Transit tour mode ASC -- express bus,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,, +#util_Walk_to_Transit_tour_mode_ASC_heavy_rail,Walk to Transit tour mode ASC -- heavy rail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,, +#util_Walk_to_Transit_tour_mode_ASC_commuter_rail,Walk to Transit tour mode ASC -- commuter rail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,, +#util_Walk_to_Transit_tour_mode_ASC_shared_ride_2,Walk to Transit tour mode ASC -- shared ride 2,@(df.is_indiv & df.tour_mode_is_walk_transit),,,walk_transit_ASC_sr2,walk_transit_ASC_sr2,,,,,,,,, +#util_Walk_to_Transit_tour_mode_ASC_shared_ride_3_plus,Walk to Transit tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,walk_transit_ASC_sr3p,walk_transit_ASC_sr3p,,,,,,, +#util_Walk_to_Transit_tour_mode_ASC_walk,Walk to Transit tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,walk_transit_ASC_walk,,,,,, +#util_Walk_to_Transit_tour_mode_ASC_ride_hail,Walk to Transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,walk_transit_ASC_rh,walk_transit_ASC_rh,walk_transit_ASC_rh +#util_Drive_to_Transit_tour_mode_ASC_light_rail_skims_differ,Drive to Transit tour mode ASC -- light rail (higher b/c loc d-trn skims differ),@(df.is_indiv & df.tour_mode_is_drive_transit & ~df.drive_ferry_available),,,,,,,,,,,,, +#util_Drive_to_Transit_tour_mode_ASC_ferry,Drive to Transit tour mode ASC -- ferry,@(df.is_indiv & df.tour_mode_is_drive_transit & df.drive_ferry_available),,,,,,,,,,,,, +#util_Drive_to_Transit_tour_mode_ASC_express_bus,Drive to Transit tour mode ASC -- express bus,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,, +#util_Drive_to_Transit_tour_mode_ASC_heavy_rail,Drive to Transit tour mode ASC -- heavy rail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,, +#util_Drive_to_Transit_tour_mode_ASC_commuter_rail,Drive to Transit tour mode ASC -- commuter rail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,, +#util_Drive_to_Transit_tour_mode_ASC_ride_hail,Drive to Transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,drive_transit_ASC_rh,drive_transit_ASC_rh,drive_transit_ASC_rh +util_Ride_Hail_tour_mode_ASC_shared_ride_2,Ride Hail tour mode ASC -- shared ride 2,@(df.is_indiv & df.tour_mode_is_ride_hail),,,ride_hail_ASC_sr2,ride_hail_ASC_sr2,,,,,,,,, +util_Ride_Hail_tour_mode_ASC_shared_ride_3_plus,Ride Hail tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,ride_hail_ASC_sr3p,ride_hail_ASC_sr3p,,,,,,, +util_Ride_Hail_tour_mode_ASC_walk,Ride Hail tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,ride_hail_ASC_walk,,,,,, +util_Ride_Hail_tour_mode_ASC_walk_to_transit,Ride Hail tour mode ASC -- walk to transit,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,,,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,,, +util_Ride_Hail_tour_mode_ASC_ride_hail_taxi,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,ride_hail_ASC_taxi,, +util_Ride_Hail_tour_mode_ASC_ride_hail_single,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,ride_hail_ASC_tnc_single, +util_Ride_Hail_tour_mode_ASC_ride_hail_shared,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,ride_hail_ASC_tnc_shared +#,joint tour ASCs,,,,,,,,,,,,,, +util_joint_auto_tour_mode_ASC_shared_ride_2,joint - auto tour mode ASC -- shared ride 2,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,joint_auto_ASC_sr2,joint_auto_ASC_sr2,,,,,,,,, +util_joint_auto_tour_mode_ASC_shared_ride_3_,joint - auto tour mode ASC -- shared ride 3+,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,,joint_auto_ASC_sr3p,joint_auto_ASC_sr3p,,,,,,, +util_joint_auto_tour_mode_ASC_walk,joint - auto tour mode ASC -- walk,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,,,,joint_auto_ASC_walk,,,,,, +util_joint_auto_tour_mode_ASC_ride_hail,joint - auto tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,joint_auto_ASC_rh,joint_auto_ASC_rh,joint_auto_ASC_rh +util_joint_Walk_tour_mode_ASC_ride_hail,joint - Walk tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,joint_walk_ASC_rh,,,,,, +util_joint_Bike_tour_mode_ASC_walk,joint - Bike tour mode ASC -- walk,@df.is_joint & (df.i_tour_mode == I_BIKE_MODE),,,,,,,joint_bike_ASC_walk,,,,,, +util_joint_Bike_tour_mode_ASC_ride_hail,joint - Bike tour mode ASC -- ride hail,@df.is_joint & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,joint_bike_ASC_rh,joint_bike_ASC_rh,joint_bike_ASC_rh +# util_joint_Walk_to_Transit_tour_mode_ASC_light_rail,joint - Walk to Transit tour mode ASC -- light rail,@(df.is_joint & df.tour_mode_is_walk_transit & ~df.walk_ferry_available),,,,,,,,,,joint_walk_transit_ASC_lightrail,,, +# util_joint_Walk_to_Transit_tour_mode_ASC_ferry,joint - Walk to Transit tour mode ASC -- ferry,@(df.is_joint & df.tour_mode_is_walk_transit & df.walk_ferry_available),,,,,,,,,,joint_walk_transit_ASC_ferry,,, +# util_joint_Walk_to_Transit_tour_mode_ASC_express_bus,joint - Walk to Transit tour mode ASC -- express bus,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,, +# util_joint_Walk_to_Transit_tour_mode_ASC_heavy_rail,joint - Walk to Transit tour mode ASC -- heavy rail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,, +# util_joint_Walk_to_Transit_tour_mode_ASC_commuter_rail,joint - Walk to Transit tour mode ASC -- commuter rail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,, +# util_joint_Walk_to_Transit_tour_mode_ASC_shared_ride_2,joint - Walk to Transit tour mode ASC -- shared ride 2,@(df.is_joint & df.tour_mode_is_walk_transit),,,joint_walk_transit_ASC_sr2,joint_walk_transit_ASC_sr2,,,,,,,,, +# util_joint_Walk_to_Transit_tour_mode_ASC_shared_ride_3_plus,joint - Walk to Transit tour mode ASC -- shared ride 3+,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,joint_walk_transit_ASC_sr3p,joint_walk_transit_ASC_sr3p,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_walk,joint - Walk to Transit tour mode ASC -- walk,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,joint_walk_transit_ASC_walk,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_ride_hail,joint - Walk to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,joint_walk_transit_ASC_rh,joint_walk_transit_ASC_rh,joint_walk_transit_ASC_rh +# util_joint_Drive_to_Transit_tour_mode_ASC_light_rail_skims_differ,joint - Drive to Transit tour mode ASC -- light rail (higher b/c loc d-trn skims differ),@(df.is_joint & df.tour_mode_is_drive_transit & ~df.drive_ferry_available),,,,,,,,,,,,, +# util_joint_Drive_to_Transit_tour_mode_ASC_ferry,joint - Drive to Transit tour mode ASC -- ferry,@(df.is_joint & df.tour_mode_is_drive_transit & df.drive_ferry_available),,,,,,,,,,,,, +# util_joint_Drive_to_Transit_tour_mode_ASC_express_bus,joint - Drive to Transit tour mode ASC -- express bus,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,, +# util_joint_Drive_to_Transit_tour_mode_ASC_heavy_rail,joint - Drive to Transit tour mode ASC -- heavy rail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,, +# util_joint_Drive_to_Transit_tour_mode_ASC_commuter_rail,joint - Drive to Transit tour mode ASC -- commuter rail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,, +util_joint_Drive_to_Transit_tour_mode_ASC_ride_hail,joint - Drive to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,joint_drive_transit_ASC_rh,joint_drive_transit_ASC_rh,joint_drive_transit_ASC_rh +util_joint_Ride_Hail_tour_mode_ASC_shared_ride_2,joint - Ride Hail tour mode ASC -- shared ride 2,@(df.is_joint & df.tour_mode_is_ride_hail),,,joint_ride_hail_ASC_sr2,joint_ride_hail_ASC_sr2,,,,,,,,, +util_joint_Ride_Hail_tour_mode_ASC_shared_ride_3_plus,joint - Ride Hail tour mode ASC -- shared ride 3+,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,joint_ride_hail_ASC_sr3p,joint_ride_hail_ASC_sr3p,,,,,,, +util_joint_Ride_Hail_tour_mode_ASC_walk,joint - Ride Hail tour mode ASC -- walk,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,joint_ride_hail_ASC_walk,,,,,, +util_joint_Ride_Hail_tour_mode_ASC_walk_to_transit,joint - Ride Hail tour mode ASC -- walk to transit,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,,,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,,, +util_joint_Ride_Hail_tour_mode_ASC_ride_hail_taxi,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,joint_ride_hail_ASC_taxi,, +util_joint_Ride_Hail_tour_mode_ASC_ride_hail_single,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,joint_ride_hail_ASC_tnc_single, +util_joint_Ride_Hail_tour_mode_ASC_ride_hail_shared,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,joint_ride_hail_ASC_tnc_shared +#,#,,,,,,,,,,,,,, +util_Walk_not_available_for_long_distances,Walk not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTWALK'] > 3),,,,,,,-999,,,,,, +util_Bike_not_available_for_long_distances,Bike not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTBIKE'] > 8),,,,,,,,-999,,,,, +util_origin_density_index,Origin density index,@origin_density_applied*(origin_density_index_multiplier*df.origin_density_index).clip(origin_density_index_max),,,,,,,coef_ivt,coef_ivt,coef_ivt,coef_ivt,,coef_ivt,coef_ivt +util_walk_express_penalty,Walk-express penalty for intermediate stops,@walk_express_penalty * ~(df.first_trip | df.first_trip),,,,,,,,,,,,, +util_adjust_tnc_shared,TNC shared adjustment,@adjust_tnc_shared,,,,,,,,,,,,,coef_ivt diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/trip_mode_choice.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/trip_mode_choice.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone/trip_mode_choice.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/trip_mode_choice.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/trip_mode_choice_annotate_trips_preprocessor.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/trip_mode_choice_annotate_trips_preprocessor.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone/trip_mode_choice_annotate_trips_preprocessor.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/trip_mode_choice_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_accessibility_tap_tap_.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/tvpb_accessibility_tap_tap_.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_accessibility_tap_tap_.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/tvpb_accessibility_tap_tap_.csv diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_accessibility_walk_maz_tap.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/tvpb_accessibility_walk_maz_tap.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_accessibility_walk_maz_tap.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/tvpb_accessibility_walk_maz_tap.csv diff --git a/activitysim/examples/example_sandag/configs_3_zone/tvpb_utility_drive_maz_tap.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/tvpb_utility_drive_maz_tap.csv similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/tvpb_utility_drive_maz_tap.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/tvpb_utility_drive_maz_tap.csv diff --git a/activitysim/examples/example_sandag/configs_3_zone/tvpb_utility_tap_tap.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/tvpb_utility_tap_tap.csv similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/tvpb_utility_tap_tap.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/tvpb_utility_tap_tap.csv diff --git a/activitysim/examples/example_sandag/configs_3_zone/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv diff --git a/activitysim/examples/example_sandag/configs_3_zone/tvpb_utility_walk_maz_tap.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/tvpb_utility_walk_maz_tap.csv similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/tvpb_utility_walk_maz_tap.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/tvpb_utility_walk_maz_tap.csv diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/write_trip_matrices.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/write_trip_matrices.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone/write_trip_matrices.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/write_trip_matrices.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/write_trip_matrices_annotate_trips_preprocessor.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone/write_trip_matrices_annotate_trips_preprocessor.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone/write_trip_matrices_annotate_trips_preprocessor.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone/write_trip_matrices_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/example_marin/configs/annotate_households.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/annotate_households.csv similarity index 100% rename from activitysim/examples/example_marin/configs/annotate_households.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/annotate_households.csv diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/annotate_persons.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/annotate_persons.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/annotate_persons.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/annotate_persons.csv diff --git a/activitysim/examples/example_marin/configs/annotate_tours.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/annotate_tours.csv similarity index 100% rename from activitysim/examples/example_marin/configs/annotate_tours.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/annotate_tours.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/constants.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/constants.yaml similarity index 95% rename from activitysim/examples/example_sandag_xborder/configs/constants.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/constants.yaml index 626a0c415e..6199378b42 100755 --- a/activitysim/examples/example_sandag_xborder/configs/constants.yaml +++ b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/constants.yaml @@ -1,64 +1,64 @@ -## ActivitySim -## See full license in LICENSE.txt. - -walk_speed: 3.00 - -#HHT_NONE: 0 -#HHT_FAMILY_MARRIED: 1 -#HHT_FAMILY_MALE: 2 -#HHT_FAMILY_FEMALE: 3 -#HHT_NONFAMILY_MALE_ALONE: 4 -#HHT_NONFAMILY_MALE_NOTALONE: 5 -#HHT_NONFAMILY_FEMALE_ALONE: 6 -#HHT_NONFAMILY_FEMALE_NOTALONE: 7 - -# convenience for expression files -HHT_NONFAMILY: [4, 5, 6, 7] -HHT_FAMILY: [1, 2, 3] - -PSTUDENT_GRADE_OR_HIGH: 1 -PSTUDENT_UNIVERSITY: 2 -PSTUDENT_NOT: 3 - -GRADE_SCHOOL_MAX_AGE: 14 -GRADE_SCHOOL_MIN_AGE: 5 - -SCHOOL_SEGMENT_NONE: 0 -SCHOOL_SEGMENT_GRADE: 1 -SCHOOL_SEGMENT_HIGH: 2 -SCHOOL_SEGMENT_UNIV: 3 - -#INCOME_SEGMENT_LOW: 1 -#INCOME_SEGMENT_MED: 2 -#INCOME_SEGMENT_HIGH: 3 -#INCOME_SEGMENT_VERYHIGH: 4 - -PEMPLOY_FULL: 1 -PEMPLOY_PART: 2 -PEMPLOY_NOT: 3 -PEMPLOY_CHILD: 4 - -PTYPE_FULL: &ptype_full 1 -PTYPE_PART: &ptype_part 2 -PTYPE_UNIVERSITY: &ptype_university 3 -PTYPE_NONWORK: &ptype_nonwork 4 -PTYPE_RETIRED: &ptype_retired 5 -PTYPE_DRIVING: &ptype_driving 6 -PTYPE_SCHOOL: &ptype_school 7 -PTYPE_PRESCHOOL: &ptype_preschool 8 - -# these appear as column headers in non_mandatory_tour_frequency.csv -PTYPE_NAME: - *ptype_full: PTYPE_FULL - *ptype_part: PTYPE_PART - *ptype_university: PTYPE_UNIVERSITY - *ptype_nonwork: PTYPE_NONWORK - *ptype_retired: PTYPE_RETIRED - *ptype_driving: PTYPE_DRIVING - *ptype_school: PTYPE_SCHOOL - *ptype_preschool: PTYPE_PRESCHOOL - - -CDAP_ACTIVITY_MANDATORY: M -CDAP_ACTIVITY_NONMANDATORY: N -CDAP_ACTIVITY_HOME: H +## ActivitySim +## See full license in LICENSE.txt. + +walk_speed: 3.00 + +#HHT_NONE: 0 +#HHT_FAMILY_MARRIED: 1 +#HHT_FAMILY_MALE: 2 +#HHT_FAMILY_FEMALE: 3 +#HHT_NONFAMILY_MALE_ALONE: 4 +#HHT_NONFAMILY_MALE_NOTALONE: 5 +#HHT_NONFAMILY_FEMALE_ALONE: 6 +#HHT_NONFAMILY_FEMALE_NOTALONE: 7 + +# convenience for expression files +HHT_NONFAMILY: [4, 5, 6, 7] +HHT_FAMILY: [1, 2, 3] + +PSTUDENT_GRADE_OR_HIGH: 1 +PSTUDENT_UNIVERSITY: 2 +PSTUDENT_NOT: 3 + +GRADE_SCHOOL_MAX_AGE: 14 +GRADE_SCHOOL_MIN_AGE: 5 + +SCHOOL_SEGMENT_NONE: 0 +SCHOOL_SEGMENT_GRADE: 1 +SCHOOL_SEGMENT_HIGH: 2 +SCHOOL_SEGMENT_UNIV: 3 + +#INCOME_SEGMENT_LOW: 1 +#INCOME_SEGMENT_MED: 2 +#INCOME_SEGMENT_HIGH: 3 +#INCOME_SEGMENT_VERYHIGH: 4 + +PEMPLOY_FULL: 1 +PEMPLOY_PART: 2 +PEMPLOY_NOT: 3 +PEMPLOY_CHILD: 4 + +PTYPE_FULL: &ptype_full 1 +PTYPE_PART: &ptype_part 2 +PTYPE_UNIVERSITY: &ptype_university 3 +PTYPE_NONWORK: &ptype_nonwork 4 +PTYPE_RETIRED: &ptype_retired 5 +PTYPE_DRIVING: &ptype_driving 6 +PTYPE_SCHOOL: &ptype_school 7 +PTYPE_PRESCHOOL: &ptype_preschool 8 + +# these appear as column headers in non_mandatory_tour_frequency.csv +PTYPE_NAME: + *ptype_full: PTYPE_FULL + *ptype_part: PTYPE_PART + *ptype_university: PTYPE_UNIVERSITY + *ptype_nonwork: PTYPE_NONWORK + *ptype_retired: PTYPE_RETIRED + *ptype_driving: PTYPE_DRIVING + *ptype_school: PTYPE_SCHOOL + *ptype_preschool: PTYPE_PRESCHOOL + + +CDAP_ACTIVITY_MANDATORY: M +CDAP_ACTIVITY_NONMANDATORY: N +CDAP_ACTIVITY_HOME: H diff --git a/activitysim/examples/example_marin/configs/initialize_households.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/initialize_households.yaml similarity index 100% rename from activitysim/examples/example_marin/configs/initialize_households.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/initialize_households.yaml diff --git a/activitysim/examples/example_marin/configs/initialize_landuse.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/initialize_landuse.yaml similarity index 100% rename from activitysim/examples/example_marin/configs/initialize_landuse.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/initialize_landuse.yaml diff --git a/activitysim/examples/example_marin/configs/initialize_tours.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/initialize_tours.yaml similarity index 100% rename from activitysim/examples/example_marin/configs/initialize_tours.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/initialize_tours.yaml diff --git a/activitysim/examples/example_marin/configs/logging.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/logging.yaml similarity index 100% rename from activitysim/examples/example_marin/configs/logging.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/logging.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/network_los.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/network_los.yaml similarity index 96% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/network_los.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/network_los.yaml index acb56f6791..1346dce913 100755 --- a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/network_los.yaml +++ b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/network_los.yaml @@ -1,181 +1,181 @@ -inherit_settings: True - -zone_system: 3 - -skim_dict_factory: NumpyArraySkimFactory -#skim_dict_factory: MemMapSkimFactory - -# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) -read_skim_cache: True -# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs -write_skim_cache: True - -# rebuild and overwrite existing tap_tap_utilities cache -rebuild_tvpb_cache: True - - -# when checkpointing cache. also write a csv version of tvpb cache for tracing -# (writes csv file when writing/checkpointing cache (i.e. when cached changed) even if rebuild_tvpb_cache is False) -# (n.b. csv file could be quite large if cache is STATIC!) -trace_tvpb_cache_as_csv: False - -taz_skims: - - HWYSKMAM_taz_rename.omx - - HWYSKMEA_taz_rename.omx - - HWYSKMEV_taz_rename.omx - - HWYSKMMD_taz_rename.omx - - HWYSKMPM_taz_rename.omx - -tap_skims: - # we require that skims for all tap_tap sets have unique names - # and can therefor share a single skim_dict without name collision - # e.g. TRN_XWAIT_FAST__AM, TRN_XWAIT_SHORT__AM, TRN_XWAIT_CHEAP__AM - - transit_skims_AM_SET1_rename.omx - - transit_skims_AM_SET2_rename.omx - - transit_skims_AM_SET3_rename.omx - - transit_skims_EA_SET1_rename.omx - - transit_skims_EA_SET2_rename.omx - - transit_skims_EA_SET3_rename.omx - - transit_skims_EV_SET1_rename.omx - - transit_skims_EV_SET2_rename.omx - - transit_skims_EV_SET3_rename.omx - - transit_skims_MD_SET1_rename.omx - - transit_skims_MD_SET2_rename.omx - - transit_skims_MD_SET3_rename.omx - - transit_skims_PM_SET1_rename.omx - - transit_skims_PM_SET2_rename.omx - - transit_skims_PM_SET3_rename.omx - - -# FIXME why no taz.csv? -# tas: taz.csv - -maz: maz_taz.csv - -tap: tap_data.csv - -tap_lines: tap_lines.csv - -maz_to_maz: - tables: - - maz_maz_walk.csv - - maz_maz_bike.csv - - # maz_to_maz blending distance (missing or 0 means no blending) - max_blend_distance: - # blend distance of 0 means no blending - WALK_DIST: 0 - BIKE_DIST: 0 - - -maz_to_tap: - walk: - table: maz_tap_walk.csv - # if provided, this column will be used (together with tap_lines table) to trim the near tap set - # to only include the nearest tap to origin when more than one tap serves the same line - tap_line_distance_col: WALK_TRANSIT_DIST - #max_dist: 3 - drive: - table: maz_taz_tap_drive.csv - # not trimming because drive_maz_tap utility calculations take into account both drive and walk time and cost - # though some sort of trimming appears to have been done as there are not so many of these in marin data - #tap_line_distance_col: DDIST - - -skim_time_periods: - time_window: 1440 - period_minutes: 30 - periods: [0, 12, 20, 30, 38, 48] - labels: &skim_time_period_labels ['EA', 'AM', 'MD', 'PM', 'EV'] - -demographic_segments: &demographic_segments - - &low_income_segment_id 0 - - &high_income_segment_id 1 - - -# transit virtual path builder settings -TVPB_SETTINGS: - - tour_mode_choice: - units: utility - path_types: - WTW: - access: walk - egress: walk - max_paths_across_tap_sets: 3 - max_paths_per_tap_set: 1 - DTW: - access: drive - egress: walk - max_paths_across_tap_sets: 3 - max_paths_per_tap_set: 1 - WTD: - access: walk - egress: drive - max_paths_across_tap_sets: 3 - max_paths_per_tap_set: 1 - tap_tap_settings: - SPEC: tvpb_utility_tap_tap.csv - PREPROCESSOR: - SPEC: tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv - DF: df - # FIXME this has to be explicitly specified, since e.g. attribute columns are assigned in expression files - attribute_segments: - demographic_segment: *demographic_segments - tod: *skim_time_period_labels - access_mode: ['drive', 'walk'] - attributes_as_columns: - - demographic_segment - - tod - - maz_tap_settings: - walk: - SPEC: tvpb_utility_walk_maz_tap.csv - CHOOSER_COLUMNS: - #- demographic_segment - - WALK_TRANSIT_DIST - drive: - SPEC: tvpb_utility_drive_maz_tap.csv - CHOOSER_COLUMNS: - #- demographic_segment - - DDIST - - DTIME - - WDIST - - CONSTANTS: - C_LOW_INCOME_SEGMENT_ID: *low_income_segment_id - C_HIGH_INCOME_SEGMENT_ID: *high_income_segment_id - TVPB_demographic_segments_by_income_segment: - 1: *low_income_segment_id - 2: *low_income_segment_id - 3: *high_income_segment_id - 4: *high_income_segment_id - c_ivt_high_income: -0.028 - c_ivt_low_income: -0.0175 - c_cost_high_income: -0.00112 - c_cost_low_income: -0.00112 - c_auto_operating_cost_per_mile: 18.29 - # constants used in maz_tap and tap_tap utility expressions - c_drive: 1.5 - c_walk: 1.7 - c_fwt: 1.5 - c_waux: 3.677 - c_xwt: 2 - c_xfers1: 30 - c_xfers2: 45 - c_xfers3: 47.026 - # no Express bus alternative-specific constant - c_lrt_asc: -17 # LRT alternative-specific constant - c_fr_asc: -35 # FR alternative-specific constant - c_hr_asc: -22 # Heavy Rail alternative-specific constant - c_cr_asc: -15 # Commuter Rail alternative-specific constant - c_cr20_40: -20 # Commuter Rail distance 20-40 miles - c_cr40plus: -30 # Commuter Rail distance >40 miles - c_drvExpress: -26 # drive to EB constant - c_drvLRT: 2 # FIXME drive to LRT constant COULD THIS POSSIBLY BE RIGHT? - c_drvFR: -52 # drive to FR constant - c_drvHeavy: -41 # drive to HR constant - c_drvCR: -52 # drive to CR constant - #"max(IVT/Drive time - 0.3,0)",drvRatio,c_ivt* 6 - C_UNAVAILABLE: -999 - +inherit_settings: True + +zone_system: 3 + +skim_dict_factory: NumpyArraySkimFactory +#skim_dict_factory: MemMapSkimFactory + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: True +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: True + +# rebuild and overwrite existing tap_tap_utilities cache +rebuild_tvpb_cache: True + + +# when checkpointing cache. also write a csv version of tvpb cache for tracing +# (writes csv file when writing/checkpointing cache (i.e. when cached changed) even if rebuild_tvpb_cache is False) +# (n.b. csv file could be quite large if cache is STATIC!) +trace_tvpb_cache_as_csv: False + +taz_skims: + - HWYSKMAM_taz_rename.omx + - HWYSKMEA_taz_rename.omx + - HWYSKMEV_taz_rename.omx + - HWYSKMMD_taz_rename.omx + - HWYSKMPM_taz_rename.omx + +tap_skims: + # we require that skims for all tap_tap sets have unique names + # and can therefor share a single skim_dict without name collision + # e.g. TRN_XWAIT_FAST__AM, TRN_XWAIT_SHORT__AM, TRN_XWAIT_CHEAP__AM + - transit_skims_AM_SET1_rename.omx + - transit_skims_AM_SET2_rename.omx + - transit_skims_AM_SET3_rename.omx + - transit_skims_EA_SET1_rename.omx + - transit_skims_EA_SET2_rename.omx + - transit_skims_EA_SET3_rename.omx + - transit_skims_EV_SET1_rename.omx + - transit_skims_EV_SET2_rename.omx + - transit_skims_EV_SET3_rename.omx + - transit_skims_MD_SET1_rename.omx + - transit_skims_MD_SET2_rename.omx + - transit_skims_MD_SET3_rename.omx + - transit_skims_PM_SET1_rename.omx + - transit_skims_PM_SET2_rename.omx + - transit_skims_PM_SET3_rename.omx + + +# FIXME why no taz.csv? +# tas: taz.csv + +maz: maz_taz.csv + +tap: tap_data.csv + +tap_lines: tap_lines.csv + +maz_to_maz: + tables: + - maz_maz_walk.csv + - maz_maz_bike.csv + + # maz_to_maz blending distance (missing or 0 means no blending) + max_blend_distance: + # blend distance of 0 means no blending + WALK_DIST: 0 + BIKE_DIST: 0 + + +maz_to_tap: + walk: + table: maz_tap_walk.csv + # if provided, this column will be used (together with tap_lines table) to trim the near tap set + # to only include the nearest tap to origin when more than one tap serves the same line + tap_line_distance_col: WALK_TRANSIT_DIST + #max_dist: 3 + drive: + table: maz_taz_tap_drive.csv + # not trimming because drive_maz_tap utility calculations take into account both drive and walk time and cost + # though some sort of trimming appears to have been done as there are not so many of these in marin data + #tap_line_distance_col: DDIST + + +skim_time_periods: + time_window: 1440 + period_minutes: 30 + periods: [0, 12, 20, 30, 38, 48] + labels: &skim_time_period_labels ['EA', 'AM', 'MD', 'PM', 'EV'] + +demographic_segments: &demographic_segments + - &low_income_segment_id 0 + - &high_income_segment_id 1 + + +# transit virtual path builder settings +TVPB_SETTINGS: + + tour_mode_choice: + units: utility + path_types: + WTW: + access: walk + egress: walk + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + DTW: + access: drive + egress: walk + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + WTD: + access: walk + egress: drive + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + tap_tap_settings: + SPEC: tvpb_utility_tap_tap.csv + PREPROCESSOR: + SPEC: tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv + DF: df + # FIXME this has to be explicitly specified, since e.g. attribute columns are assigned in expression files + attribute_segments: + demographic_segment: *demographic_segments + tod: *skim_time_period_labels + access_mode: ['drive', 'walk'] + attributes_as_columns: + - demographic_segment + - tod + + maz_tap_settings: + walk: + SPEC: tvpb_utility_walk_maz_tap.csv + CHOOSER_COLUMNS: + #- demographic_segment + - WALK_TRANSIT_DIST + drive: + SPEC: tvpb_utility_drive_maz_tap.csv + CHOOSER_COLUMNS: + #- demographic_segment + - DDIST + - DTIME + - WDIST + + CONSTANTS: + C_LOW_INCOME_SEGMENT_ID: *low_income_segment_id + C_HIGH_INCOME_SEGMENT_ID: *high_income_segment_id + TVPB_demographic_segments_by_income_segment: + 1: *low_income_segment_id + 2: *low_income_segment_id + 3: *high_income_segment_id + 4: *high_income_segment_id + c_ivt_high_income: -0.028 + c_ivt_low_income: -0.0175 + c_cost_high_income: -0.00112 + c_cost_low_income: -0.00112 + c_auto_operating_cost_per_mile: 18.29 + # constants used in maz_tap and tap_tap utility expressions + c_drive: 1.5 + c_walk: 1.7 + c_fwt: 1.5 + c_waux: 3.677 + c_xwt: 2 + c_xfers1: 30 + c_xfers2: 45 + c_xfers3: 47.026 + # no Express bus alternative-specific constant + c_lrt_asc: -17 # LRT alternative-specific constant + c_fr_asc: -35 # FR alternative-specific constant + c_hr_asc: -22 # Heavy Rail alternative-specific constant + c_cr_asc: -15 # Commuter Rail alternative-specific constant + c_cr20_40: -20 # Commuter Rail distance 20-40 miles + c_cr40plus: -30 # Commuter Rail distance >40 miles + c_drvExpress: -26 # drive to EB constant + c_drvLRT: 2 # FIXME drive to LRT constant COULD THIS POSSIBLY BE RIGHT? + c_drvFR: -52 # drive to FR constant + c_drvHeavy: -41 # drive to HR constant + c_drvCR: -52 # drive to CR constant + #"max(IVT/Drive time - 0.3,0)",drvRatio,c_ivt* 6 + C_UNAVAILABLE: -999 + diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/settings.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/settings.yaml similarity index 95% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/settings.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/settings.yaml index 753b5dfbca..c6f3ee2f3e 100755 --- a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/settings.yaml +++ b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/settings.yaml @@ -1,237 +1,237 @@ -inherit_settings: True - -# number of households to simulate -#households_sample_size: 200000 -households_sample_size: 500 - -#chunk_size: 4000000000 -chunk_size: 0 - -#trace_hh_id: 662398 -trace_hh_id: - -# input tables -input_table_list: - - tablename: households - filename: households_asim.csv - index_col: household_id - rename_columns: - HHID: household_id - MAZ: home_zone_id - keep_columns: - - home_zone_id - - HHINCADJ - - NWRKRS_ESR - - VEH - - NP - #- MTCCountyID - #- HHT - #- BLD - #- TYPE - - - tablename: persons - filename: persons_asim.csv - index_col: person_id - rename_columns: - HHID: household_id - PERID: person_id - keep_columns: - - AGEP - - household_id - - type - - value_of_time - - fp_choice - #- SEX - #- SCHL - #- OCCP - #- WKHP - #- WKW - #- EMPLOYED - #- ESR - #- SCHG - - - tablename: land_use - filename: maz_data_asim.csv - index_col: zone_id - rename_columns: - MAZ: zone_id - CountyID: county_id - keep_columns: - - TAZ - - DistID - - ACRES - - POP - - emp_total - - hparkcost - - TERMINALTIME - - county_id -# - level_0 -# - index -# - MAZ_ORIGINAL -# - TAZ_ORIGINAL -# - DistName -# - CountyID -# - CountyName -# - HH -# - ag -# - art_rec -# - constr -# - eat -# - ed_high -# - ed_k12 -# - ed_oth -# - fire -# - gov -# - health -# - hotel -# - info -# - lease -# - logis -# - man_bio -# - man_lgt -# - man_hvy -# - man_tech -# - natres -# - prof -# - ret_loc -# - ret_reg -# - serv_bus -# - serv_pers -# - serv_soc -# - transp -# - util -# - publicEnrollGradeKto8 -# - privateEnrollGradeKto8 -# - publicEnrollGrade9to12 -# - privateEnrollGrade9to12 -# - comm_coll_enroll -# - EnrollGradeKto8 -# - EnrollGrade9to12 -# - collegeEnroll -# - otherCollegeEnroll -# - AdultSchEnrl -# - hstallsoth -# - hstallssam -# - dstallsoth -# - dstallssam -# - mstallsoth -# - mstallssam -# - park_area -# - numfreehrs -# - dparkcost -# - mparkcost -# - ech_dist -# - hch_dist -# - parkarea -# - MAZ_X -# - MAZ_Y -# - TotInt -# - EmpDen -# - RetEmpDen -# - DUDen -# - PopDen -# - IntDenBin -# - EmpDenBin -# - DuDenBin -# - PopEmpDenPerMi -# - mgra -# - mgraParkArea -# - lsWgtAvgCostM -# - lsWgtAvgCostD -# - lsWgtAvgCostH - - - tablename: tours - filename: work_tours.csv - # index_col: - rename_columns: - hh_id: household_id - start_period: start - end_period: end - tour_id: tm2_tour_id - tour_mode: tm2_tour_mode - out_btap: tm2_out_btap - out_atap: tm2_out_atap - in_btap: tm2_in_btap - in_atap: tm2_in_atap - out_set: tm2_out_set - in_set: tm2_in_set - keep_columns: - - person_id - - household_id - - tour_category - - tour_purpose - - orig_mgra - - dest_mgra - - start - - end - # ctramp tm2 fields for validation - - tm2_tour_id # really just ordinal position in ctramp tour file, put probably will be useful for validation - - tm2_tour_mode - - tm2_out_btap - - tm2_out_atap - - tm2_in_btap - - tm2_in_atap - - tm2_out_set - - tm2_in_set -# - person_num -# - person_type -# - tour_distance -# - tour_time -# - atWork_freq -# - num_ob_stops -# - num_ib_stops - - -# set false to disable variability check in simple_simulate and interaction_simulate -check_for_variability: False - -# - shadow pricing global switches - -# turn shadow_pricing on and off for all models (e.g. school and work) -# shadow pricing is deprecated for less than full samples -# see shadow_pricing.yaml for additional settings -use_shadow_pricing: False - -# turn writing of sample_tables on and off for all models -# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) -want_dest_choice_sample_tables: False - -#resume_after: initialize_tvpb - -models: - - initialize_landuse - - initialize_households - - initialize_tours - # --- STATIC cache prebuild steps - # single-process step to create attribute_combination list - - initialize_los - # multi-processable step to build STATIC cache - # (this step is a NOP if cache already exists and network_los.rebuild_tvpb_cache setting is False) - - initialize_tvpb - # --- - - tour_mode_choice_simulate - - write_data_dictionary - - track_skim_usage - - write_tables - - write_summaries - -output_tables: - h5_store: False - action: include - prefix: final_ - # FIXME sort is an undocumented feature - sorts table by best index or ref_col according to traceable_table_indexes - sort: True - tables: - - checkpoints - - accessibility - - land_use - - households - - persons - - tours - - attribute_combinations - -output_summaries: - tours: - - tour_mode - - od_path_set - - do_path_set +inherit_settings: True + +# number of households to simulate +#households_sample_size: 200000 +households_sample_size: 500 + +#chunk_size: 4000000000 +chunk_size: 0 + +#trace_hh_id: 662398 +trace_hh_id: + +# input tables +input_table_list: + - tablename: households + filename: households_asim.csv + index_col: household_id + rename_columns: + HHID: household_id + MAZ: home_zone_id + keep_columns: + - home_zone_id + - HHINCADJ + - NWRKRS_ESR + - VEH + - NP + #- MTCCountyID + #- HHT + #- BLD + #- TYPE + + - tablename: persons + filename: persons_asim.csv + index_col: person_id + rename_columns: + HHID: household_id + PERID: person_id + keep_columns: + - AGEP + - household_id + - type + - value_of_time + - fp_choice + #- SEX + #- SCHL + #- OCCP + #- WKHP + #- WKW + #- EMPLOYED + #- ESR + #- SCHG + + - tablename: land_use + filename: maz_data_asim.csv + index_col: zone_id + rename_columns: + MAZ: zone_id + CountyID: county_id + keep_columns: + - TAZ + - DistID + - ACRES + - POP + - emp_total + - hparkcost + - TERMINALTIME + - county_id +# - level_0 +# - index +# - MAZ_ORIGINAL +# - TAZ_ORIGINAL +# - DistName +# - CountyID +# - CountyName +# - HH +# - ag +# - art_rec +# - constr +# - eat +# - ed_high +# - ed_k12 +# - ed_oth +# - fire +# - gov +# - health +# - hotel +# - info +# - lease +# - logis +# - man_bio +# - man_lgt +# - man_hvy +# - man_tech +# - natres +# - prof +# - ret_loc +# - ret_reg +# - serv_bus +# - serv_pers +# - serv_soc +# - transp +# - util +# - publicEnrollGradeKto8 +# - privateEnrollGradeKto8 +# - publicEnrollGrade9to12 +# - privateEnrollGrade9to12 +# - comm_coll_enroll +# - EnrollGradeKto8 +# - EnrollGrade9to12 +# - collegeEnroll +# - otherCollegeEnroll +# - AdultSchEnrl +# - hstallsoth +# - hstallssam +# - dstallsoth +# - dstallssam +# - mstallsoth +# - mstallssam +# - park_area +# - numfreehrs +# - dparkcost +# - mparkcost +# - ech_dist +# - hch_dist +# - parkarea +# - MAZ_X +# - MAZ_Y +# - TotInt +# - EmpDen +# - RetEmpDen +# - DUDen +# - PopDen +# - IntDenBin +# - EmpDenBin +# - DuDenBin +# - PopEmpDenPerMi +# - mgra +# - mgraParkArea +# - lsWgtAvgCostM +# - lsWgtAvgCostD +# - lsWgtAvgCostH + + - tablename: tours + filename: work_tours.csv + # index_col: + rename_columns: + hh_id: household_id + start_period: start + end_period: end + tour_id: tm2_tour_id + tour_mode: tm2_tour_mode + out_btap: tm2_out_btap + out_atap: tm2_out_atap + in_btap: tm2_in_btap + in_atap: tm2_in_atap + out_set: tm2_out_set + in_set: tm2_in_set + keep_columns: + - person_id + - household_id + - tour_category + - tour_purpose + - orig_mgra + - dest_mgra + - start + - end + # ctramp tm2 fields for validation + - tm2_tour_id # really just ordinal position in ctramp tour file, put probably will be useful for validation + - tm2_tour_mode + - tm2_out_btap + - tm2_out_atap + - tm2_in_btap + - tm2_in_atap + - tm2_out_set + - tm2_in_set +# - person_num +# - person_type +# - tour_distance +# - tour_time +# - atWork_freq +# - num_ob_stops +# - num_ib_stops + + +# set false to disable variability check in simple_simulate and interaction_simulate +check_for_variability: False + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +#resume_after: initialize_tvpb + +models: + - initialize_landuse + - initialize_households + - initialize_tours + # --- STATIC cache prebuild steps + # single-process step to create attribute_combination list + - initialize_los + # multi-processable step to build STATIC cache + # (this step is a NOP if cache already exists and network_los.rebuild_tvpb_cache setting is False) + - initialize_tvpb + # --- + - tour_mode_choice_simulate + - write_data_dictionary + - track_skim_usage + - write_tables + - write_summaries + +output_tables: + h5_store: False + action: include + prefix: final_ + # FIXME sort is an undocumented feature - sorts table by best index or ref_col according to traceable_table_indexes + sort: True + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - attribute_combinations + +output_summaries: + tours: + - tour_mode + - od_path_set + - do_path_set diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/settings_mp.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/settings_mp.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/settings_mp.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/settings_mp.yaml diff --git a/activitysim/examples/example_marin/configs/shadow_pricing.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/shadow_pricing.yaml similarity index 100% rename from activitysim/examples/example_marin/configs/shadow_pricing.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/shadow_pricing.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tour_mode_choice.csv similarity index 99% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tour_mode_choice.csv index 46ba4c72fe..5ac2850bcd 100755 --- a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice.csv +++ b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tour_mode_choice.csv @@ -1,165 +1,165 @@ -Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_TRANSIT,DRIVE_TRANSIT,TAXI,TNC_SINGLE,TNC_SHARED -#,Drive alone no toll,,,,,,,,,,,,,, -#util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,VEH == 0,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,AGEP < 16,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['TIMEDA'] + dot_skims['TIMEDA'],coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_TERMINALTIME,DRIVEALONEFREE - Terminal time,@2 * walktimeshort_multiplier * df.TERMINALTIME,coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['DISTDA'] + dot_skims['DISTDA']),coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['BTOLLDA'] + dot_skims['BTOLLDA']),coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_Person_is_between_16_and_19_years_old,DRIVEALONEFREE - Person is between 16 and 19 years old,@(df.AGEP >= 16) & (df.AGEP <= 19),coef_age1619_da_multiplier,,,,,,,,,,,, -#,Drive alone toll,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,VEH == 0,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,AGEP < 16,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['TOLLTIMEDA'] + dot_skims['TOLLTIMEDA'],,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_TERMINALTIME,DRIVEALONEPAY - Terminal time,@2 * walktimeshort_multiplier * df.TERMINALTIME,,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['TOLLDISTDA'] + dot_skims['TOLLDISTDA']),,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLDA'] + dot_skims['TOLLBTOLLDA']),,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLDA'] + dot_skims['TOLLVTOLLDA']),,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Person_is_between_16_and_19_years_old,DRIVEALONEPAY - Person is between 16 and 19 years old,@(df.AGEP >= 16) & (df.AGEP <= 19),,coef_age1619_da_multiplier,,,,,,,,,,, -#,Shared ride 2,,,,,,,,,,,,,, -util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,, -util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,, -util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@(odt_skims['TIMES2'] + dot_skims['TIMES2']),,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_TERMINALTIME,SHARED2FREE - Terminal time,@2 * walktimeshort_multiplier * df.TERMINALTIME,,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['DISTS2'] + dot_skims['DISTS2']),,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['BTOLLS2'] + dot_skims['BTOLLS2']) / costShareSr2,,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_One_person_household,SHARED2FREE - One person household,@(df.NP == 1),,,coef_hhsize1_sr_multiplier,,,,,,,,,, -util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.NP == 2),,,coef_hhsize2_sr_multiplier,,,,,,,,,, -util_SHARED2FREE_Person_is_16_years_old_or_older,SHARED2FREE - Person is 16 years old or older,@(df.AGEP >= 16),,,coef_age16p_sr_multiplier,,,,,,,,,, -#,Shared ride 2 toll,,,,,,,,,,,,,, -util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,, -util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,, -util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']),,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_TERMINALTIME,SHARED2PAY - Terminal time,@2 * walktimeshort_multiplier * df.TERMINALTIME,,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']),,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2']) / costShareSr2,,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']) / costShareSr2,,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_One_person_household,SHARED2PAY - One person household,@(df.NP == 1),,,,coef_hhsize1_sr_multiplier,,,,,,,,, -util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.NP == 2),,,,coef_hhsize2_sr_multiplier,,,,,,,,, -util_SHARED2PAY_Person_is_16_years_old_or_older,SHARED2PAY - Person is 16 years old or older,@(df.AGEP >= 16),,,,coef_age16p_sr_multiplier,,,,,,,,, -#,Shared ride 3+,,,,,,,,,,,,,, -util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,, -util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@(odt_skims['TIMES3'] + dot_skims['TIMES3']),,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_TERMINALTIME,SHARED3FREE - Terminal time,@2 * walktimeshort_multiplier * df.TERMINALTIME,,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['DISTS3'] + dot_skims['DISTS3']),,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['BTOLLS3'] + dot_skims['BTOLLS3']) / costShareSr3,,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_One_person_household,SHARED3FREE - One person household,@(df.NP == 1),,,,,coef_hhsize1_sr_multiplier,,,,,,,, -util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.NP == 2),,,,,coef_hhsize2_sr_multiplier,,,,,,,, -util_SHARED3FREE_Person_is_16_years_old_or_older,SHARED3FREE - Person is 16 years old or older,@(df.AGEP >= 16),,,,,coef_age16p_sr_multiplier,,,,,,,, -#,Shared ride 3+ toll,,,,,,,,,,,,,, -util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,, -util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@(odt_skims['TOLLTIMES3'] + dot_skims['TOLLTIMES3']),,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_TERMINALTIME,SHARED3PAY - Terminal time,@2 * walktimeshort_multiplier * df.TERMINALTIME,,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['TOLLDISTS3'] + dot_skims['TOLLDISTS3']),,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS3'] + dot_skims['TOLLBTOLLS3']) / costShareSr3,,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS3'] + dot_skims['TOLLVTOLLS3']) / costShareSr3,,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_One_person_household,SHARED3PAY - One person household,@(df.NP == 1),,,,,,coef_hhsize1_sr_multiplier,,,,,,, -util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.NP == 2),,,,,,coef_hhsize2_sr_multiplier,,,,,,, -util_SHARED3PAY_Person_is_16_years_old_or_older,SHARED3PAY - Person is 16 years old or older,@(df.AGEP >= 16),,,,,,coef_age16p_sr_multiplier,,,,,,, -#,Walk,,,,,,,,,,,,,, -util_WALK_Unavailable,WALK - Unavailable,walk_available == False,,,,,,,-999,,,,,, -util_WALK_Time,WALK - walk time,@(od_skims.lookup('WALK_DIST') + od_skims.reverse('WALK_DIST'))*60/walkSpeed,,,,,,,coef_ivt,,,,,, -#,Bike,,,,,,,,,,,,,, -util_BIKE_Unavailable,BIKE - Unavailable,bike_available == False,,,,,,,,-999,,,,, -util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,, -util_BIKE_Time,BIKE - bike time,@(od_skims.lookup('BIKE_DIST') + od_skims.reverse('BIKE_DIST'))*60/bikeSpeed,,,,,,,,coef_ivt,,,,, -#,Walk to Local,,,,,,,,,,,,,, -util_WALK_TRANSIT_Paths_logsums,WALK_TRANSIT - Path logsums,@tvpb_logsum_odt['WTW'] + tvpb_logsum_dot['WTW'],,,,,,,,,coef_one,,,, -util_WALK_TRANSIT_Person_is_less_than_10_years_old,WALK_TRANSIT - Person is less than 10 years old,@(df.AGEP <= 10),,,,,,,,,coef_age010_trn_multiplier,,,, -#,Drive to Local,,,,,,,,,,,,,, -util_DRIVE_TRANSIT_Unavailable_for_zero_auto_households,DRIVE_TRANSIT - Unavailable for zero auto households,VEH == 0,,,,,,,,,,-999,,, -util_DRIVE_TRANSIT_Unavailable_for_persons_less_than_16,DRIVE_TRANSIT - Unavailable for persons less than 16,AGEP < 16,,,,,,,,,,-999,,, -util_DRIVE_TRANSIT_Paths_logsums,DRIVE_TRANSIT - Path logsums,@tvpb_logsum_odt['DTW'] + tvpb_logsum_dot['WTD'],,,,,,,,,,coef_one,,, -util_DRIVE_TRANSIT_Person_is_less_than_10_years_old,DRIVE_TRANSIT - Person is less than 10 years old,@(df.AGEP < 10),,,,,,,,,,coef_age010_trn_multiplier,,, -#,Taxi,,,,,,,,,,,,,, -util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']),,,,,,,,,,,coef_ivt,, -#, FIXME magic constant 1.5,,,,,,,,,,,,,, -util_Taxi_Wait_time,Taxi - Wait time,@1.5 * df.totalWaitTaxi,,,,,,,,,,,coef_ivt,, -util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']),,,,,,,,,,,coef_ivt,, -util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2']),,,,,,,,,,,coef_ivt,, -util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare * 2 + (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']) * Taxi_costPerMile +(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * Taxi_costPerMinute)*100,,,,,,,,,,,coef_ivt,, -#,TNC Single,,,,,,,,,,,,,, -util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']),,,,,,,,,,,,coef_ivt, -util_TNC_Single_Wait_time,TNC Single - Wait time,@1.5 * df.totalWaitSingleTNC,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']),,,,,,,,,,,,coef_ivt, -util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + odr_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2'] + dor_skims['TOLLBTOLLS2']),,,,,,,,,,,,coef_ivt, -util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare * 2 + (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']) * TNC_single_costPerMile + (odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,coef_ivt, -#,TNC Shared,,,,,,,,,,,,,, -util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * TNC_shared_IVTFactor,,,,,,,,,,,,,coef_ivt -#, FIXME magic constant 1.5,,,,,,,,,,,,,, -util_TNC_Shared_Wait_time,TNC Shared - Wait time,@1.5 * df.totalWaitSharedTNC,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']),,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + odr_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2'] + dor_skims['TOLLBTOLLS2']),,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare * 2 + (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']) * TNC_shared_costPerMile + (odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,coef_ivt -#,indiv tour ASCs,,,,,,,,,,,,,, -util_Walk_ASC_Zero_auto,Walk ASC - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,walk_ASC_no_auto,,,,,, -util_Walk_ASC_Auto_deficient,Walk ASC - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,walk_ASC_auto_deficient,,,,,, -util_Walk_ASC_Auto_sufficient,Walk ASC - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,walk_ASC_auto_sufficient,,,,,, -util_Bike_ASC_Zero_auto,Bike ASC - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,bike_ASC_no_auto,,,,, -util_Bike_ASC_Auto_deficient,Bike ASC - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,bike_ASC_auto_deficient,,,,, -util_Bike_ASC_Auto_sufficient,Bike ASC - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,bike_ASC_auto_sufficient,,,,, -util_Shared_ride_2_ASC_Zero_auto,Shared ride 2 ASC - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,sr2_ASC_no_auto,sr2_ASC_no_auto,,,,,,,,, -util_Shared_ride_2_ASC_Auto_deficient,Shared ride 2 ASC - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,sr2_ASC_auto_deficient,sr2_ASC_auto_deficient,,,,,,,,, -util_Shared_ride_2_ASC_Auto_sufficient,Shared ride 2 ASC - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient,,,,,,,,, -util_Shared_ride_3p_Zero_auto,Shared ride 3+ - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,sr3p_ASC_no_auto,sr3p_ASC_no_auto,,,,,,, -util_Shared_ride_3p_Auto_deficient,Shared ride 3+ - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient,,,,,,, -util_Shared_ride_3p_Auto_sufficient,Shared ride 3+ - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient,,,,,,, -util_Walk_to_Transit_Zero_auto,Walk to Transit - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,walk_transit_ASC_no_auto,,,, -util_Walk_to_Transit_Auto_deficient,Walk to Transit - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,walk_transit_ASC_auto_deficient,,,, -util_Walk_to_Transit_Auto_sufficient,Walk to Transit - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,walk_transit_ASC_auto_sufficient,,,, -util_Drive_to_Transit_Zero_auto,Drive to Transit - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,drive_transit_ASC_no_auto,,, -util_Drive_to_Transit_Auto_deficient,Drive to Transit - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,drive_transit_ASC_auto_deficient,,, -util_Drive_to_Transit_Auto_sufficient,Drive to Transit - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,drive_transit_ASC_auto_sufficient,,, -util_Taxi_Zero_auto,Taxi - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,,taxi_ASC_no_auto,, -util_Taxi_Auto_deficient,Taxi - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,taxi_ASC_auto_deficient,, -util_Taxi_Auto_sufficient,Taxi - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,taxi_ASC_auto_sufficient,, -util_TNC_Single_Zero_auto,TNC Single - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,,,tnc_single_ASC_no_auto, -util_TNC_Single_Auto_deficient,TNC Single - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,tnc_single_ASC_auto_deficient, -util_TNC_Single_Auto_sufficient,TNC Single - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,tnc_single_ASC_auto_sufficient, -util_TNC_Shared_Zero_auto,TNC Shared - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,,,,tnc_shared_ASC_no_auto -util_TNC_Shared_Auto_deficient,TNC Shared - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,,tnc_shared_ASC_auto_deficient -util_TNC_Shared_Auto_sufficient,TNC Shared - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,,tnc_shared_ASC_auto_sufficient -#,joint tour ASCs,,,,,,,,,,,,,, -util_Joint_Walk_ASC_Zero_auto,Joint - Walk ASC - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,joint_walk_ASC_no_auto,,,,,, -util_Joint_Walk_ASC_Auto_deficient,Joint - Walk ASC - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,joint_walk_ASC_auto_deficient,,,,,, -util_Joint_Walk_ASC_Auto_sufficient,Joint - Walk ASC - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,joint_walk_ASC_auto_sufficient,,,,,, -util_Joint_Bike_ASC_Zero_auto,Joint - Bike ASC - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,joint_bike_ASC_no_auto,,,,, -util_Joint_Bike_ASC_Auto_deficient,Joint - Bike ASC - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,joint_bike_ASC_auto_deficient,,,,, -util_Joint_Bike_ASC_Auto_sufficient,Joint - Bike ASC - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,joint_bike_ASC_auto_sufficient,,,,, -util_Joint_Shared_ride_2_ASC_Zero_auto,Joint - Shared ride 2 ASC - Zero auto,@(df.is_joint & (df.VEH == 0)),,,joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto,,,,,,,,, -util_Joint_Shared_ride_2_ASC_Auto_deficient,Joint - Shared ride 2 ASC - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient,,,,,,,,, -util_Joint_Shared_ride_2_ASC_Auto_sufficient,Joint - Shared ride 2 ASC - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient,,,,,,,,, -util_Joint_Shared_ride_3p_Zero_auto,Joint - Shared ride 3+ - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto,,,,,,, -util_Joint_Shared_ride_3p_Auto_deficient,Joint - Shared ride 3+ - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient,,,,,,, -util_Joint_Shared_ride_3p_Auto_sufficient,Joint - Shared ride 3+ - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient,,,,,,, -util_Joint_Walk_to_Transit_Zero_auto,Joint - Walk to Transit - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,joint_walk_transit_ASC_no_auto,,,, -util_Joint_Walk_to_Transit_Auto_deficient,Joint - Walk to Transit - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,joint_walk_transit_ASC_auto_deficient,,,, -util_Joint_Walk_to_Transit_Auto_sufficient,Joint - Walk to Transit - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,joint_walk_transit_ASC_auto_sufficient,,,, -util_Joint_Drive_to_Transit_Zero_auto,Joint - Drive to Transit - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,joint_drive_transit_ASC_no_auto,,, -util_Joint_Drive_to_Transit_Auto_deficient,Joint - Drive to Transit - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,joint_drive_transit_ASC_auto_deficient,,, -util_Joint_Drive_to_Transit_Auto_sufficient,Joint - Drive to Transit - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,joint_drive_transit_ASC_auto_sufficient,,, -util_Joint_Taxi_Zero_auto,Joint - Taxi - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,,joint_taxi_ASC_no_auto,, -util_Joint_Taxi_Auto_deficient,Joint - Taxi - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,joint_taxi_ASC_auto_deficient,, -util_Joint_Taxi_Auto_sufficient,Joint - Taxi - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,joint_taxi_ASC_auto_sufficient,, -util_Joint_TNC_Single_Zero_auto,Joint - TNC Single - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,,,joint_tnc_single_ASC_no_auto, -util_Joint_TNC_Single_Auto_deficient,Joint - TNC Single - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,joint_tnc_single_ASC_auto_deficient, -util_Joint_TNC_Single_Auto_sufficient,Joint - TNC Single - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,joint_tnc_single_ASC_auto_sufficient, -util_Joint_TNC_Shared_Zero_auto,Joint - TNC Shared - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,,,,joint_tnc_shared_ASC_no_auto -util_Joint_TNC_Shared_Auto_deficient,Joint - TNC Shared - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,,joint_tnc_shared_ASC_auto_deficient -util_Joint_TNC_Shared_Auto_sufficient,Joint - TNC Shared - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,,joint_tnc_shared_ASC_auto_sufficient -#,calibration,,,,,,,,,,,,,, -util_Walk_to_Transit_dest_CBD,Walk to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,walk_transit_CBD_ASC,,,, -util_Drive_to_Transit_dest_CBD,Drive to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,,drive_transit_CBD_ASC,,, -util_Drive_to_Transit_distance_penalty,Drive to Transit - distance penalty,@drvtrn_distpen_0_multiplier * (1-odt_skims['DISTDA']/drvtrn_distpen_max).clip(lower=0),,,,,,,,,,coef_ivt,,, +Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_TRANSIT,DRIVE_TRANSIT,TAXI,TNC_SINGLE,TNC_SHARED +#,Drive alone no toll,,,,,,,,,,,,,, +#util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,VEH == 0,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,AGEP < 16,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['TIMEDA'] + dot_skims['TIMEDA'],coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_TERMINALTIME,DRIVEALONEFREE - Terminal time,@2 * walktimeshort_multiplier * df.TERMINALTIME,coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['DISTDA'] + dot_skims['DISTDA']),coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['BTOLLDA'] + dot_skims['BTOLLDA']),coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Person_is_between_16_and_19_years_old,DRIVEALONEFREE - Person is between 16 and 19 years old,@(df.AGEP >= 16) & (df.AGEP <= 19),coef_age1619_da_multiplier,,,,,,,,,,,, +#,Drive alone toll,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,VEH == 0,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,AGEP < 16,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['TOLLTIMEDA'] + dot_skims['TOLLTIMEDA'],,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_TERMINALTIME,DRIVEALONEPAY - Terminal time,@2 * walktimeshort_multiplier * df.TERMINALTIME,,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['TOLLDISTDA'] + dot_skims['TOLLDISTDA']),,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLDA'] + dot_skims['TOLLBTOLLDA']),,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLDA'] + dot_skims['TOLLVTOLLDA']),,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Person_is_between_16_and_19_years_old,DRIVEALONEPAY - Person is between 16 and 19 years old,@(df.AGEP >= 16) & (df.AGEP <= 19),,coef_age1619_da_multiplier,,,,,,,,,,, +#,Shared ride 2,,,,,,,,,,,,,, +util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,, +util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,, +util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@(odt_skims['TIMES2'] + dot_skims['TIMES2']),,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_TERMINALTIME,SHARED2FREE - Terminal time,@2 * walktimeshort_multiplier * df.TERMINALTIME,,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['DISTS2'] + dot_skims['DISTS2']),,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['BTOLLS2'] + dot_skims['BTOLLS2']) / costShareSr2,,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_One_person_household,SHARED2FREE - One person household,@(df.NP == 1),,,coef_hhsize1_sr_multiplier,,,,,,,,,, +util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.NP == 2),,,coef_hhsize2_sr_multiplier,,,,,,,,,, +util_SHARED2FREE_Person_is_16_years_old_or_older,SHARED2FREE - Person is 16 years old or older,@(df.AGEP >= 16),,,coef_age16p_sr_multiplier,,,,,,,,,, +#,Shared ride 2 toll,,,,,,,,,,,,,, +util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,, +util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,, +util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']),,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_TERMINALTIME,SHARED2PAY - Terminal time,@2 * walktimeshort_multiplier * df.TERMINALTIME,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']),,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2']) / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']) / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_One_person_household,SHARED2PAY - One person household,@(df.NP == 1),,,,coef_hhsize1_sr_multiplier,,,,,,,,, +util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.NP == 2),,,,coef_hhsize2_sr_multiplier,,,,,,,,, +util_SHARED2PAY_Person_is_16_years_old_or_older,SHARED2PAY - Person is 16 years old or older,@(df.AGEP >= 16),,,,coef_age16p_sr_multiplier,,,,,,,,, +#,Shared ride 3+,,,,,,,,,,,,,, +util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,, +util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@(odt_skims['TIMES3'] + dot_skims['TIMES3']),,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_TERMINALTIME,SHARED3FREE - Terminal time,@2 * walktimeshort_multiplier * df.TERMINALTIME,,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['DISTS3'] + dot_skims['DISTS3']),,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['BTOLLS3'] + dot_skims['BTOLLS3']) / costShareSr3,,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_One_person_household,SHARED3FREE - One person household,@(df.NP == 1),,,,,coef_hhsize1_sr_multiplier,,,,,,,, +util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.NP == 2),,,,,coef_hhsize2_sr_multiplier,,,,,,,, +util_SHARED3FREE_Person_is_16_years_old_or_older,SHARED3FREE - Person is 16 years old or older,@(df.AGEP >= 16),,,,,coef_age16p_sr_multiplier,,,,,,,, +#,Shared ride 3+ toll,,,,,,,,,,,,,, +util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,, +util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@(odt_skims['TOLLTIMES3'] + dot_skims['TOLLTIMES3']),,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_TERMINALTIME,SHARED3PAY - Terminal time,@2 * walktimeshort_multiplier * df.TERMINALTIME,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['TOLLDISTS3'] + dot_skims['TOLLDISTS3']),,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS3'] + dot_skims['TOLLBTOLLS3']) / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS3'] + dot_skims['TOLLVTOLLS3']) / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_One_person_household,SHARED3PAY - One person household,@(df.NP == 1),,,,,,coef_hhsize1_sr_multiplier,,,,,,, +util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.NP == 2),,,,,,coef_hhsize2_sr_multiplier,,,,,,, +util_SHARED3PAY_Person_is_16_years_old_or_older,SHARED3PAY - Person is 16 years old or older,@(df.AGEP >= 16),,,,,,coef_age16p_sr_multiplier,,,,,,, +#,Walk,,,,,,,,,,,,,, +util_WALK_Unavailable,WALK - Unavailable,walk_available == False,,,,,,,-999,,,,,, +util_WALK_Time,WALK - walk time,@(od_skims.lookup('WALK_DIST') + od_skims.reverse('WALK_DIST'))*60/walkSpeed,,,,,,,coef_ivt,,,,,, +#,Bike,,,,,,,,,,,,,, +util_BIKE_Unavailable,BIKE - Unavailable,bike_available == False,,,,,,,,-999,,,,, +util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,, +util_BIKE_Time,BIKE - bike time,@(od_skims.lookup('BIKE_DIST') + od_skims.reverse('BIKE_DIST'))*60/bikeSpeed,,,,,,,,coef_ivt,,,,, +#,Walk to Local,,,,,,,,,,,,,, +util_WALK_TRANSIT_Paths_logsums,WALK_TRANSIT - Path logsums,@tvpb_logsum_odt['WTW'] + tvpb_logsum_dot['WTW'],,,,,,,,,coef_one,,,, +util_WALK_TRANSIT_Person_is_less_than_10_years_old,WALK_TRANSIT - Person is less than 10 years old,@(df.AGEP <= 10),,,,,,,,,coef_age010_trn_multiplier,,,, +#,Drive to Local,,,,,,,,,,,,,, +util_DRIVE_TRANSIT_Unavailable_for_zero_auto_households,DRIVE_TRANSIT - Unavailable for zero auto households,VEH == 0,,,,,,,,,,-999,,, +util_DRIVE_TRANSIT_Unavailable_for_persons_less_than_16,DRIVE_TRANSIT - Unavailable for persons less than 16,AGEP < 16,,,,,,,,,,-999,,, +util_DRIVE_TRANSIT_Paths_logsums,DRIVE_TRANSIT - Path logsums,@tvpb_logsum_odt['DTW'] + tvpb_logsum_dot['WTD'],,,,,,,,,,coef_one,,, +util_DRIVE_TRANSIT_Person_is_less_than_10_years_old,DRIVE_TRANSIT - Person is less than 10 years old,@(df.AGEP < 10),,,,,,,,,,coef_age010_trn_multiplier,,, +#,Taxi,,,,,,,,,,,,,, +util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']),,,,,,,,,,,coef_ivt,, +#, FIXME magic constant 1.5,,,,,,,,,,,,,, +util_Taxi_Wait_time,Taxi - Wait time,@1.5 * df.totalWaitTaxi,,,,,,,,,,,coef_ivt,, +util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']),,,,,,,,,,,coef_ivt,, +util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2']),,,,,,,,,,,coef_ivt,, +util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare * 2 + (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']) * Taxi_costPerMile +(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * Taxi_costPerMinute)*100,,,,,,,,,,,coef_ivt,, +#,TNC Single,,,,,,,,,,,,,, +util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']),,,,,,,,,,,,coef_ivt, +util_TNC_Single_Wait_time,TNC Single - Wait time,@1.5 * df.totalWaitSingleTNC,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']),,,,,,,,,,,,coef_ivt, +util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + odr_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2'] + dor_skims['TOLLBTOLLS2']),,,,,,,,,,,,coef_ivt, +util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare * 2 + (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']) * TNC_single_costPerMile + (odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,coef_ivt, +#,TNC Shared,,,,,,,,,,,,,, +util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * TNC_shared_IVTFactor,,,,,,,,,,,,,coef_ivt +#, FIXME magic constant 1.5,,,,,,,,,,,,,, +util_TNC_Shared_Wait_time,TNC Shared - Wait time,@1.5 * df.totalWaitSharedTNC,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']),,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + odr_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2'] + dor_skims['TOLLBTOLLS2']),,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare * 2 + (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']) * TNC_shared_costPerMile + (odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,coef_ivt +#,indiv tour ASCs,,,,,,,,,,,,,, +util_Walk_ASC_Zero_auto,Walk ASC - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,walk_ASC_no_auto,,,,,, +util_Walk_ASC_Auto_deficient,Walk ASC - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,walk_ASC_auto_deficient,,,,,, +util_Walk_ASC_Auto_sufficient,Walk ASC - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,walk_ASC_auto_sufficient,,,,,, +util_Bike_ASC_Zero_auto,Bike ASC - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,bike_ASC_no_auto,,,,, +util_Bike_ASC_Auto_deficient,Bike ASC - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,bike_ASC_auto_deficient,,,,, +util_Bike_ASC_Auto_sufficient,Bike ASC - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,bike_ASC_auto_sufficient,,,,, +util_Shared_ride_2_ASC_Zero_auto,Shared ride 2 ASC - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,sr2_ASC_no_auto,sr2_ASC_no_auto,,,,,,,,, +util_Shared_ride_2_ASC_Auto_deficient,Shared ride 2 ASC - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,sr2_ASC_auto_deficient,sr2_ASC_auto_deficient,,,,,,,,, +util_Shared_ride_2_ASC_Auto_sufficient,Shared ride 2 ASC - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient,,,,,,,,, +util_Shared_ride_3p_Zero_auto,Shared ride 3+ - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,sr3p_ASC_no_auto,sr3p_ASC_no_auto,,,,,,, +util_Shared_ride_3p_Auto_deficient,Shared ride 3+ - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient,,,,,,, +util_Shared_ride_3p_Auto_sufficient,Shared ride 3+ - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient,,,,,,, +util_Walk_to_Transit_Zero_auto,Walk to Transit - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,walk_transit_ASC_no_auto,,,, +util_Walk_to_Transit_Auto_deficient,Walk to Transit - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,walk_transit_ASC_auto_deficient,,,, +util_Walk_to_Transit_Auto_sufficient,Walk to Transit - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,walk_transit_ASC_auto_sufficient,,,, +util_Drive_to_Transit_Zero_auto,Drive to Transit - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,drive_transit_ASC_no_auto,,, +util_Drive_to_Transit_Auto_deficient,Drive to Transit - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,drive_transit_ASC_auto_deficient,,, +util_Drive_to_Transit_Auto_sufficient,Drive to Transit - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,drive_transit_ASC_auto_sufficient,,, +util_Taxi_Zero_auto,Taxi - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,,taxi_ASC_no_auto,, +util_Taxi_Auto_deficient,Taxi - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,taxi_ASC_auto_deficient,, +util_Taxi_Auto_sufficient,Taxi - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,taxi_ASC_auto_sufficient,, +util_TNC_Single_Zero_auto,TNC Single - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,,,tnc_single_ASC_no_auto, +util_TNC_Single_Auto_deficient,TNC Single - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,tnc_single_ASC_auto_deficient, +util_TNC_Single_Auto_sufficient,TNC Single - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,tnc_single_ASC_auto_sufficient, +util_TNC_Shared_Zero_auto,TNC Shared - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,,,,tnc_shared_ASC_no_auto +util_TNC_Shared_Auto_deficient,TNC Shared - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,,tnc_shared_ASC_auto_deficient +util_TNC_Shared_Auto_sufficient,TNC Shared - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,,tnc_shared_ASC_auto_sufficient +#,joint tour ASCs,,,,,,,,,,,,,, +util_Joint_Walk_ASC_Zero_auto,Joint - Walk ASC - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,joint_walk_ASC_no_auto,,,,,, +util_Joint_Walk_ASC_Auto_deficient,Joint - Walk ASC - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,joint_walk_ASC_auto_deficient,,,,,, +util_Joint_Walk_ASC_Auto_sufficient,Joint - Walk ASC - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,joint_walk_ASC_auto_sufficient,,,,,, +util_Joint_Bike_ASC_Zero_auto,Joint - Bike ASC - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,joint_bike_ASC_no_auto,,,,, +util_Joint_Bike_ASC_Auto_deficient,Joint - Bike ASC - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,joint_bike_ASC_auto_deficient,,,,, +util_Joint_Bike_ASC_Auto_sufficient,Joint - Bike ASC - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,joint_bike_ASC_auto_sufficient,,,,, +util_Joint_Shared_ride_2_ASC_Zero_auto,Joint - Shared ride 2 ASC - Zero auto,@(df.is_joint & (df.VEH == 0)),,,joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Auto_deficient,Joint - Shared ride 2 ASC - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Auto_sufficient,Joint - Shared ride 2 ASC - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient,,,,,,,,, +util_Joint_Shared_ride_3p_Zero_auto,Joint - Shared ride 3+ - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto,,,,,,, +util_Joint_Shared_ride_3p_Auto_deficient,Joint - Shared ride 3+ - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient,,,,,,, +util_Joint_Shared_ride_3p_Auto_sufficient,Joint - Shared ride 3+ - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient,,,,,,, +util_Joint_Walk_to_Transit_Zero_auto,Joint - Walk to Transit - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,joint_walk_transit_ASC_no_auto,,,, +util_Joint_Walk_to_Transit_Auto_deficient,Joint - Walk to Transit - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,joint_walk_transit_ASC_auto_deficient,,,, +util_Joint_Walk_to_Transit_Auto_sufficient,Joint - Walk to Transit - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,joint_walk_transit_ASC_auto_sufficient,,,, +util_Joint_Drive_to_Transit_Zero_auto,Joint - Drive to Transit - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,joint_drive_transit_ASC_no_auto,,, +util_Joint_Drive_to_Transit_Auto_deficient,Joint - Drive to Transit - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,joint_drive_transit_ASC_auto_deficient,,, +util_Joint_Drive_to_Transit_Auto_sufficient,Joint - Drive to Transit - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,joint_drive_transit_ASC_auto_sufficient,,, +util_Joint_Taxi_Zero_auto,Joint - Taxi - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,,joint_taxi_ASC_no_auto,, +util_Joint_Taxi_Auto_deficient,Joint - Taxi - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,joint_taxi_ASC_auto_deficient,, +util_Joint_Taxi_Auto_sufficient,Joint - Taxi - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,joint_taxi_ASC_auto_sufficient,, +util_Joint_TNC_Single_Zero_auto,Joint - TNC Single - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,,,joint_tnc_single_ASC_no_auto, +util_Joint_TNC_Single_Auto_deficient,Joint - TNC Single - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,joint_tnc_single_ASC_auto_deficient, +util_Joint_TNC_Single_Auto_sufficient,Joint - TNC Single - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,joint_tnc_single_ASC_auto_sufficient, +util_Joint_TNC_Shared_Zero_auto,Joint - TNC Shared - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,,,,joint_tnc_shared_ASC_no_auto +util_Joint_TNC_Shared_Auto_deficient,Joint - TNC Shared - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,,joint_tnc_shared_ASC_auto_deficient +util_Joint_TNC_Shared_Auto_sufficient,Joint - TNC Shared - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,,joint_tnc_shared_ASC_auto_sufficient +#,calibration,,,,,,,,,,,,,, +util_Walk_to_Transit_dest_CBD,Walk to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,walk_transit_CBD_ASC,,,, +util_Drive_to_Transit_dest_CBD,Drive to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,,drive_transit_CBD_ASC,,, +util_Drive_to_Transit_distance_penalty,Drive to Transit - distance penalty,@drvtrn_distpen_0_multiplier * (1-odt_skims['DISTDA']/drvtrn_distpen_max).clip(lower=0),,,,,,,,,,coef_ivt,,, diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice.yaml b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tour_mode_choice.yaml similarity index 95% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice.yaml rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tour_mode_choice.yaml index 3e95030d6d..a88096a112 100755 --- a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice.yaml +++ b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tour_mode_choice.yaml @@ -1,188 +1,188 @@ -LOGIT_TYPE: NL -#LOGIT_TYPE: MNL - -tvpb_mode_path_types: - DRIVE_TRANSIT: - od: DTW - do: WTD - WALK_TRANSIT: - od: WTW - do: WTW - -NESTS: - name: root - coefficient: coef_nest_root - alternatives: - - name: AUTO - coefficient: coef_nest_AUTO - alternatives: - - name: DRIVEALONE - coefficient: coef_nest_AUTO_DRIVEALONE - alternatives: - - DRIVEALONEFREE - - DRIVEALONEPAY - - name: SHAREDRIDE2 - coefficient: coef_nest_AUTO_SHAREDRIDE2 - alternatives: - - SHARED2FREE - - SHARED2PAY - - name: SHAREDRIDE3 - coefficient: coef_nest_AUTO_SHAREDRIDE3 - alternatives: - - SHARED3FREE - - SHARED3PAY - - name: NONMOTORIZED - coefficient: coef_nest_NONMOTORIZED - alternatives: - - WALK - - BIKE - - name: TRANSIT - coefficient: coef_nest_TRANSIT - alternatives: - - WALK_TRANSIT - - DRIVE_TRANSIT - - name: RIDEHAIL - coefficient: coef_nest_RIDEHAIL - alternatives: - - TAXI - - TNC_SINGLE - - TNC_SHARED - -SPEC: tour_mode_choice.csv -COEFFICIENTS: tour_mode_choice_coeffs.csv -COEFFICIENT_TEMPLATE: tour_mode_choice_coeffs_template.csv - -CONSTANTS: - #valueOfTime: 8.00 - costPerMile: 18.29 - costShareSr2: 1.75 - costShareSr3: 2.50 -# waitThresh: 10.00 - walkThresh: 3.0 -# shortWalk: 0.333 -# longWalk: 0.667 - walkSpeed: 3.00 - bikeThresh: 12.00 - bikeSpeed: 12.00 -# maxCbdAreaTypeThresh: 2 -# indivTour: 1.00000 -# upperEA: 5 -# upperAM: 10 -# upperMD: 15 -# upperPM: 19 - # RIDEHAIL Settings - Taxi_baseFare: 2.20 - Taxi_costPerMile: 2.30 - Taxi_costPerMinute: 0.10 - Taxi_waitTime_mean: - 1: 5.5 - 2: 9.5 - 3: 13.3 - 4: 17.3 - 5: 26.5 - Taxi_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - TNC_single_baseFare: 2.20 - TNC_single_costPerMile: 1.33 - TNC_single_costPerMinute: 0.24 - TNC_single_costMinimum: 7.20 - TNC_single_waitTime_mean: - 1: 3.0 - 2: 6.3 - 3: 8.4 - 4: 8.5 - 5: 10.3 - TNC_single_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - TNC_shared_baseFare: 2.20 - TNC_shared_costPerMile: 0.53 - TNC_shared_costPerMinute: 0.10 - TNC_shared_costMinimum: 3.00 - TNC_shared_IVTFactor: 1.5 - TNC_shared_waitTime_mean: - 1: 5.0 - 2: 8.0 - 3: 11.0 - 4: 15.0 - 5: 15.0 - TNC_shared_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - min_waitTime: 0 - max_waitTime: 50 -# - ivt_cost_multiplier: 0.6 -# ivt_lrt_multiplier: 0.9 -# ivt_ferry_multiplier: 0.8 -# ivt_exp_multiplier: 1 -# ivt_hvy_multiplier: 0.8 -# ivt_com_multiplier: 0.7 - walktimeshort_multiplier: 2 -# walktimelong_multiplier: 10 -# biketimeshort_multiplier: 4 -# biketimelong_multiplier: 20 -# short_i_wait_multiplier: 2 -# long_i_wait_multiplier: 1 -# wacc_multiplier: 2 -# wegr_multiplier: 2 -# waux_multiplier: 2 -# dtim_multiplier: 2 -# xwait_multiplier: 2 -# dacc_ratio: 0 -# xfers_wlk_multiplier: 10 -# xfers_drv_multiplier: 20 - drvtrn_distpen_0_multiplier: 270 - drvtrn_distpen_max: 15 -# density_index_multiplier: -0.2 - joint_sr2_ASC_no_auto: 0 - joint_sr2_ASC_auto_deficient: 0 - joint_sr2_ASC_auto_sufficient: 0 - joint_drive_transit_ASC_no_auto: 0 - c_auto_operating_cost_per_mile: 18.29 - - -# so far, we can use the same spec as for non-joint tours -preprocessor: - SPEC: tour_mode_choice_annotate_choosers_preprocessor - DF: choosers - TABLES: - - land_use - - tours - -nontour_preprocessor: - SPEC: tour_mode_choice_annotate_choosers_preprocessor - DF: choosers - TABLES: - - land_use - -# to reduce memory needs filter chooser table to these fields -LOGSUM_CHOOSER_COLUMNS: - - tour_type - - hhsize - - density_index - - age - - age_16_p - - age_16_to_19 - - auto_ownership - - number_of_participants - - tour_category - - num_workers - - value_of_time - - free_parking_at_work - - income_segment - - demographic_segment - - c_ivt_for_segment - - c_cost_for_segment - -MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum +LOGIT_TYPE: NL +#LOGIT_TYPE: MNL + +tvpb_mode_path_types: + DRIVE_TRANSIT: + od: DTW + do: WTD + WALK_TRANSIT: + od: WTW + do: WTW + +NESTS: + name: root + coefficient: coef_nest_root + alternatives: + - name: AUTO + coefficient: coef_nest_AUTO + alternatives: + - name: DRIVEALONE + coefficient: coef_nest_AUTO_DRIVEALONE + alternatives: + - DRIVEALONEFREE + - DRIVEALONEPAY + - name: SHAREDRIDE2 + coefficient: coef_nest_AUTO_SHAREDRIDE2 + alternatives: + - SHARED2FREE + - SHARED2PAY + - name: SHAREDRIDE3 + coefficient: coef_nest_AUTO_SHAREDRIDE3 + alternatives: + - SHARED3FREE + - SHARED3PAY + - name: NONMOTORIZED + coefficient: coef_nest_NONMOTORIZED + alternatives: + - WALK + - BIKE + - name: TRANSIT + coefficient: coef_nest_TRANSIT + alternatives: + - WALK_TRANSIT + - DRIVE_TRANSIT + - name: RIDEHAIL + coefficient: coef_nest_RIDEHAIL + alternatives: + - TAXI + - TNC_SINGLE + - TNC_SHARED + +SPEC: tour_mode_choice.csv +COEFFICIENTS: tour_mode_choice_coeffs.csv +COEFFICIENT_TEMPLATE: tour_mode_choice_coeffs_template.csv + +CONSTANTS: + #valueOfTime: 8.00 + costPerMile: 18.29 + costShareSr2: 1.75 + costShareSr3: 2.50 +# waitThresh: 10.00 + walkThresh: 3.0 +# shortWalk: 0.333 +# longWalk: 0.667 + walkSpeed: 3.00 + bikeThresh: 12.00 + bikeSpeed: 12.00 +# maxCbdAreaTypeThresh: 2 +# indivTour: 1.00000 +# upperEA: 5 +# upperAM: 10 +# upperMD: 15 +# upperPM: 19 + # RIDEHAIL Settings + Taxi_baseFare: 2.20 + Taxi_costPerMile: 2.30 + Taxi_costPerMinute: 0.10 + Taxi_waitTime_mean: + 1: 5.5 + 2: 9.5 + 3: 13.3 + 4: 17.3 + 5: 26.5 + Taxi_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_single_baseFare: 2.20 + TNC_single_costPerMile: 1.33 + TNC_single_costPerMinute: 0.24 + TNC_single_costMinimum: 7.20 + TNC_single_waitTime_mean: + 1: 3.0 + 2: 6.3 + 3: 8.4 + 4: 8.5 + 5: 10.3 + TNC_single_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_shared_baseFare: 2.20 + TNC_shared_costPerMile: 0.53 + TNC_shared_costPerMinute: 0.10 + TNC_shared_costMinimum: 3.00 + TNC_shared_IVTFactor: 1.5 + TNC_shared_waitTime_mean: + 1: 5.0 + 2: 8.0 + 3: 11.0 + 4: 15.0 + 5: 15.0 + TNC_shared_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + min_waitTime: 0 + max_waitTime: 50 +# + ivt_cost_multiplier: 0.6 +# ivt_lrt_multiplier: 0.9 +# ivt_ferry_multiplier: 0.8 +# ivt_exp_multiplier: 1 +# ivt_hvy_multiplier: 0.8 +# ivt_com_multiplier: 0.7 + walktimeshort_multiplier: 2 +# walktimelong_multiplier: 10 +# biketimeshort_multiplier: 4 +# biketimelong_multiplier: 20 +# short_i_wait_multiplier: 2 +# long_i_wait_multiplier: 1 +# wacc_multiplier: 2 +# wegr_multiplier: 2 +# waux_multiplier: 2 +# dtim_multiplier: 2 +# xwait_multiplier: 2 +# dacc_ratio: 0 +# xfers_wlk_multiplier: 10 +# xfers_drv_multiplier: 20 + drvtrn_distpen_0_multiplier: 270 + drvtrn_distpen_max: 15 +# density_index_multiplier: -0.2 + joint_sr2_ASC_no_auto: 0 + joint_sr2_ASC_auto_deficient: 0 + joint_sr2_ASC_auto_sufficient: 0 + joint_drive_transit_ASC_no_auto: 0 + c_auto_operating_cost_per_mile: 18.29 + + +# so far, we can use the same spec as for non-joint tours +preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + - tours + +nontour_preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + +# to reduce memory needs filter chooser table to these fields +LOGSUM_CHOOSER_COLUMNS: + - tour_type + - hhsize + - density_index + - age + - age_16_p + - age_16_to_19 + - auto_ownership + - number_of_participants + - tour_category + - num_workers + - value_of_time + - free_parking_at_work + - income_segment + - demographic_segment + - c_ivt_for_segment + - c_cost_for_segment + +MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tour_mode_choice_annotate_choosers_preprocessor.csv similarity index 98% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_annotate_choosers_preprocessor.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tour_mode_choice_annotate_choosers_preprocessor.csv index 5aca337e40..bac931bf4b 100755 --- a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_annotate_choosers_preprocessor.csv +++ b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tour_mode_choice_annotate_choosers_preprocessor.csv @@ -1,75 +1,75 @@ -Description,Target,Expression, -#,,, -,number_of_participants,1, -,is_joint,False, -#,,, -,_HAVE_PARENT_TOURS,False, -,_parent_tour_mode,False, -,work_tour_is_drive,False, -,work_tour_is_bike,False, -,work_tour_is_SOV,False, -#,,, -,is_mandatory,True, -,is_joint,False, -,is_indiv,~is_joint, -,is_atwork_subtour,False, -,is_escort,False, -#,,, -income_in_thousands,income_in_thousands,(df.HHINCADJ / 1000).clip(lower=0), -income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[1, 2, 3, 4]).astype(int)", -,demographic_segment,income_segment.map(TVPB_demographic_segments_by_income_segment), -#,c_ivt_for_segment,"np.where(demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_ivt_low_income, c_ivt_high_income)", -#,c_cost_for_segment,"np.where(demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_cost_low_income, c_cost_high_income)", -#,,, -#,c_cost,(0.60 * c_ivt) / df.value_of_time, -# ivot * (c_ivt_cost_multiplier * c_ivt),,, -,ivot,1.0 / df.value_of_time, -# RIDEHAIL,,, -,origin_density_measure,"(reindex(land_use.POP, df[orig_col_name]) + reindex(land_use.emp_total, df[orig_col_name])) / (reindex(land_use.ACRES, df[orig_col_name]) / 640)", -,dest_density_measure,"(reindex(land_use.POP, df[dest_col_name]) + reindex(land_use.emp_total, df[dest_col_name])) / (reindex(land_use.ACRES, df[dest_col_name]) / 640)", -,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", -,dest_density,"pd.cut(dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", -,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", -,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", -,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", -,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", -# ,, Note that the mean and standard deviation are not the values for the distribution itself, but of the underlying normal distribution it is derived from -,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", -,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", -,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", -,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", -,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", -,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", -,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", -,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", -,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime, -,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime, -,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime, -#,,, -,_free_parking_available,(df.tour_type == 'work') & (df.fp_choice == 1), -,_dest_hourly_peak_parking_cost,"reindex(land_use.hparkcost, df[dest_col_name])", -,_dest_hourly_offpeak_parking_cost,"reindex(land_use.hparkcost, df[dest_col_name])", -,_hourly_peak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_peak_parking_cost)", -,_hourly_offpeak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_offpeak_parking_cost)", -just hourly instead of times duration for now,daily_parking_cost,"np.where(is_mandatory, _hourly_peak_parking_cost, _hourly_offpeak_parking_cost)", -#,,, -,distance,(odt_skims['DISTDA']), -,sov_available,(odt_skims['TIMEDA']>0) & (dot_skims['TIMEDA']>0), -,sovtoll_available,(odt_skims['TOLLVTOLLDA']>0) | (dot_skims['TOLLVTOLLDA']>0), -,hov2_available,(odt_skims['TIMES2'] + dot_skims['TIMES2'])>0, -,hov2toll_available,(odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2'])>0, -,hov3_available,(odt_skims['TIMES3']>0) & (dot_skims['TIMES3']>0), -,hov3toll_available,(odt_skims['TOLLVTOLLS3'] + dot_skims['TOLLVTOLLS3'])>0, -,walk_available,"od_skims.lookup('WALK_DIST').between(0, walkThresh) & od_skims.reverse('WALK_DIST').between(0, walkThresh)", -,bike_available,"od_skims.lookup('BIKE_DIST').between(0, bikeThresh) & od_skims.reverse('BIKE_DIST').between(0, bikeThresh)", -#,,, -#,,,FIXME - this is probably wrong - that is all of Marin -destination district,destination_in_cbd,"reindex(land_use.DistID, df[dest_col_name])==22", -# diagnostic,,, -#,sov_dist_roundtrip,(odt_skims['DISTDA'] + dot_skims['DISTDA']), +Description,Target,Expression, +#,,, +,number_of_participants,1, +,is_joint,False, +#,,, +,_HAVE_PARENT_TOURS,False, +,_parent_tour_mode,False, +,work_tour_is_drive,False, +,work_tour_is_bike,False, +,work_tour_is_SOV,False, +#,,, +,is_mandatory,True, +,is_joint,False, +,is_indiv,~is_joint, +,is_atwork_subtour,False, +,is_escort,False, +#,,, +income_in_thousands,income_in_thousands,(df.HHINCADJ / 1000).clip(lower=0), +income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[1, 2, 3, 4]).astype(int)", +,demographic_segment,income_segment.map(TVPB_demographic_segments_by_income_segment), +#,c_ivt_for_segment,"np.where(demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_ivt_low_income, c_ivt_high_income)", +#,c_cost_for_segment,"np.where(demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_cost_low_income, c_cost_high_income)", +#,,, +#,c_cost,(0.60 * c_ivt) / df.value_of_time, +# ivot * (c_ivt_cost_multiplier * c_ivt),,, +,ivot,1.0 / df.value_of_time, +# RIDEHAIL,,, +,origin_density_measure,"(reindex(land_use.POP, df[orig_col_name]) + reindex(land_use.emp_total, df[orig_col_name])) / (reindex(land_use.ACRES, df[orig_col_name]) / 640)", +,dest_density_measure,"(reindex(land_use.POP, df[dest_col_name]) + reindex(land_use.emp_total, df[dest_col_name])) / (reindex(land_use.ACRES, df[dest_col_name]) / 640)", +,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", +,dest_density,"pd.cut(dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", +,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", +,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", +,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", +,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", +# ,, Note that the mean and standard deviation are not the values for the distribution itself, but of the underlying normal distribution it is derived from +,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", +,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", +,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", +,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", +,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", +,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", +,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", +,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", +,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime, +,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime, +,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime, +#,,, +,_free_parking_available,(df.tour_type == 'work') & (df.fp_choice == 1), +,_dest_hourly_peak_parking_cost,"reindex(land_use.hparkcost, df[dest_col_name])", +,_dest_hourly_offpeak_parking_cost,"reindex(land_use.hparkcost, df[dest_col_name])", +,_hourly_peak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_peak_parking_cost)", +,_hourly_offpeak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_offpeak_parking_cost)", +just hourly instead of times duration for now,daily_parking_cost,"np.where(is_mandatory, _hourly_peak_parking_cost, _hourly_offpeak_parking_cost)", +#,,, +,distance,(odt_skims['DISTDA']), +,sov_available,(odt_skims['TIMEDA']>0) & (dot_skims['TIMEDA']>0), +,sovtoll_available,(odt_skims['TOLLVTOLLDA']>0) | (dot_skims['TOLLVTOLLDA']>0), +,hov2_available,(odt_skims['TIMES2'] + dot_skims['TIMES2'])>0, +,hov2toll_available,(odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2'])>0, +,hov3_available,(odt_skims['TIMES3']>0) & (dot_skims['TIMES3']>0), +,hov3toll_available,(odt_skims['TOLLVTOLLS3'] + dot_skims['TOLLVTOLLS3'])>0, +,walk_available,"od_skims.lookup('WALK_DIST').between(0, walkThresh) & od_skims.reverse('WALK_DIST').between(0, walkThresh)", +,bike_available,"od_skims.lookup('BIKE_DIST').between(0, bikeThresh) & od_skims.reverse('BIKE_DIST').between(0, bikeThresh)", +#,,, +#,,,FIXME - this is probably wrong - that is all of Marin +destination district,destination_in_cbd,"reindex(land_use.DistID, df[dest_col_name])==22", +# diagnostic,,, +#,sov_dist_roundtrip,(odt_skims['DISTDA'] + dot_skims['DISTDA']), diff --git a/activitysim/examples/example_psrc/configs/tour_mode_choice_coeffs.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tour_mode_choice_coeffs.csv similarity index 97% rename from activitysim/examples/example_psrc/configs/tour_mode_choice_coeffs.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tour_mode_choice_coeffs.csv index c5d9a264a2..9693953808 100755 --- a/activitysim/examples/example_psrc/configs/tour_mode_choice_coeffs.csv +++ b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tour_mode_choice_coeffs.csv @@ -1,308 +1,308 @@ -coefficient_name,value,constrain -coef_one,1,T -coef_nest_root,1.00,T -coef_nest_AUTO,0.72,T -coef_nest_AUTO_DRIVEALONE,0.35,T -coef_nest_AUTO_SHAREDRIDE2,0.35,T -coef_nest_AUTO_SHAREDRIDE3,0.35,T -coef_nest_NONMOTORIZED,0.72,T -coef_nest_TRANSIT,0.72,T -coef_nest_TRANSIT_WALKACCESS,0.5,T -coef_nest_TRANSIT_DRIVEACCESS,0.5,T -coef_nest_RIDEHAIL,0.36,T -coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,-0.0175,F -coef_ivt_school_univ,-0.0224,F -coef_ivt_work,-0.0134,F -coef_ivt_atwork,-0.0188,F -coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,15,F -coef_topology_walk_multiplier_atwork,7.5,F -coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,20,F -coef_topology_bike_multiplier_atwork,10,F -coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,2.2,F -coef_topology_trn_multiplier_atwork,2,F -coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F -coef_age1619_da_multiplier_school_univ,-1.3813,F -coef_age1619_da_multiplier_atwork,0.0032336,F -coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F -coef_age010_trn_multiplier_school_univ,-1.5548,F -coef_age010_trn_multiplier_atwork,0.000722,F -coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,-1.366,F -coef_age16p_sr_multiplier_school_univ_work_atwork,0,F -coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,0,F -coef_hhsize1_sr_multiplier_work,-0.734588,F -coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,0,F -coef_hhsize2_sr_multiplier_school_univ,-0.6359,F -walk_ASC_no_auto_eatout,5.1251173,F -walk_ASC_no_auto_escort,2.8012068,F -walk_ASC_no_auto_othdiscr,3.2665946,F -walk_ASC_no_auto_othmaint,1.287299,F -walk_ASC_no_auto_school,18.414557,F -walk_ASC_no_auto_shopping,2.3768773,F -walk_ASC_no_auto_social,1.8680915,F -walk_ASC_no_auto_univ,6.408967,F -walk_ASC_no_auto_work,5.7672157,F -walk_ASC_no_auto_atwork,6.669213,F -walk_ASC_auto_deficient_eatout,3.274605,F -walk_ASC_auto_deficient_escort,-0.90204656,F -walk_ASC_auto_deficient_othdiscr,2.2494075,F -walk_ASC_auto_deficient_othmaint,1.3690404,F -walk_ASC_auto_deficient_school,3.2573624,F -walk_ASC_auto_deficient_shopping,2.2701733,F -walk_ASC_auto_deficient_social,2.870184,F -walk_ASC_auto_deficient_univ,4.50591,F -walk_ASC_auto_deficient_work,2.4010417,F -walk_ASC_auto_deficient_atwork,0.92546093,F -walk_ASC_auto_sufficient_eatout,1.5516903,F -walk_ASC_auto_sufficient_escort,-0.8116066,F -walk_ASC_auto_sufficient_othdiscr,1.2633476,F -walk_ASC_auto_sufficient_othmaint,0.7999634,F -walk_ASC_auto_sufficient_school,0.6476856,F -walk_ASC_auto_sufficient_shopping,0.7312663,F -walk_ASC_auto_sufficient_social,1.7072186,F -walk_ASC_auto_sufficient_univ,1.0607665,F -walk_ASC_auto_sufficient_work,0.053265337,F -walk_ASC_auto_sufficient_atwork,0.677216,F -bike_ASC_no_auto_eatout,0.86807096,F -bike_ASC_no_auto_escort,-0.716212,F -bike_ASC_no_auto_othdiscr,-0.3764232,F -bike_ASC_no_auto_othmaint,1.5394334,F -bike_ASC_no_auto_school,12.098735,F -bike_ASC_no_auto_shopping,0.8341555,F -bike_ASC_no_auto_social,0.02058321,F -bike_ASC_no_auto_univ,4.2945156,F -bike_ASC_no_auto_work,3.1940088,F -bike_ASC_no_auto_atwork,-0.90725845,F -bike_ASC_auto_deficient_eatout,-1.5691106,F -bike_ASC_auto_deficient_escort,-4.527928,F -bike_ASC_auto_deficient_othdiscr,-0.09246834,F -bike_ASC_auto_deficient_othmaint,-1.5184649,F -bike_ASC_auto_deficient_school,-0.5280678,F -bike_ASC_auto_deficient_shopping,-0.87584466,F -bike_ASC_auto_deficient_social,0.6345214,F -bike_ASC_auto_deficient_univ,-0.669235,F -bike_ASC_auto_deficient_work,0.25318968,F -bike_ASC_auto_deficient_atwork,-0.8074083,F -bike_ASC_auto_sufficient_eatout,-1.2003471,F -bike_ASC_auto_sufficient_escort,-5.0631084,F -bike_ASC_auto_sufficient_othdiscr,-1.0714597,F -bike_ASC_auto_sufficient_othmaint,-2.8083024,F -bike_ASC_auto_sufficient_school,-2.1134686,F -bike_ASC_auto_sufficient_shopping,-2.5662103,F -bike_ASC_auto_sufficient_social,-1.368071,F -bike_ASC_auto_sufficient_univ,-1.9397832,F -bike_ASC_auto_sufficient_work,-1.5800232,F -bike_ASC_auto_sufficient_atwork,15.72017,F -sr2_ASC_no_auto_all,0,F -sr2_ASC_auto_deficient_eatout,0.5882345,F -sr2_ASC_auto_deficient_escort,0,F -sr2_ASC_auto_deficient_othdiscr,0.6601513,F -sr2_ASC_auto_deficient_othmaint,0.2621527,F -sr2_ASC_auto_deficient_school,0.12474365,F -sr2_ASC_auto_deficient_shopping,0.24409756,F -sr2_ASC_auto_deficient_social,1.8558528,F -sr2_ASC_auto_deficient_univ,-1.6922346,F -sr2_ASC_auto_deficient_work,-0.33803123,F -sr2_ASC_auto_deficient_atwork,-2.1102421,F -sr2_ASC_auto_sufficient_eatout,0.86280555,F -sr2_ASC_auto_sufficient_escort,0,F -sr2_ASC_auto_sufficient_othdiscr,0.49684617,F -sr2_ASC_auto_sufficient_othmaint,0.25817883,F -sr2_ASC_auto_sufficient_school,-1.6062657,F -sr2_ASC_auto_sufficient_shopping,0.19770707,F -sr2_ASC_auto_sufficient_social,0.5236025,F -sr2_ASC_auto_sufficient_univ,-1.859427,F -sr2_ASC_auto_sufficient_work,-1.0857458,F -sr2_ASC_auto_sufficient_atwork,-1.4450618,F -sr3p_ASC_no_auto_eatout,0.3219998,F -sr3p_ASC_no_auto_escort,-1.8129267,F -sr3p_ASC_no_auto_othdiscr,0.27216902,F -sr3p_ASC_no_auto_othmaint,-0.8031854,F -sr3p_ASC_no_auto_school,-6.0240827,F -sr3p_ASC_no_auto_shopping,-0.27978948,F -sr3p_ASC_no_auto_social,-1.4036902,F -sr3p_ASC_no_auto_univ,-6.056001,F -sr3p_ASC_no_auto_work,-0.5831269,F -sr3p_ASC_no_auto_atwork,0.5826626,F -sr3p_ASC_auto_deficient_eatout,0.04605236,F -sr3p_ASC_auto_deficient_escort,-0.40818766,F -sr3p_ASC_auto_deficient_othdiscr,1.0470966,F -sr3p_ASC_auto_deficient_othmaint,-1.3493925,F -sr3p_ASC_auto_deficient_school,0.7149571,F -sr3p_ASC_auto_deficient_shopping,-0.073370166,F -sr3p_ASC_auto_deficient_social,1.5007243,F -sr3p_ASC_auto_deficient_univ,-1.7277422,F -sr3p_ASC_auto_deficient_work,-0.8527042,F -sr3p_ASC_auto_deficient_atwork,-2.514658,F -sr3p_ASC_auto_sufficient_eatout,0.8468596,F -sr3p_ASC_auto_sufficient_escort,-0.05741253,F -sr3p_ASC_auto_sufficient_othdiscr,0.58850205,F -sr3p_ASC_auto_sufficient_othmaint,-0.07549867,F -sr3p_ASC_auto_sufficient_school,-1.0201935,F -sr3p_ASC_auto_sufficient_shopping,-0.077571295,F -sr3p_ASC_auto_sufficient_social,0.50617886,F -sr3p_ASC_auto_sufficient_univ,-1.9047098,F -sr3p_ASC_auto_sufficient_work,-1.4699702,F -sr3p_ASC_auto_sufficient_atwork,-1.652174,F -walk_transit_ASC_no_auto_eatout,2.5936368,F -walk_transit_ASC_no_auto_escort,-2.2172081,F -walk_transit_ASC_no_auto_othdiscr,2.2437785,F -walk_transit_ASC_no_auto_othmaint,2.5643456,F -walk_transit_ASC_no_auto_school,21.383749,F -walk_transit_ASC_no_auto_shopping,2.1067476,F -walk_transit_ASC_no_auto_social,1.3814651,F -walk_transit_ASC_no_auto_univ,8.786037,F -walk_transit_ASC_no_auto_work,5.0354166,F -walk_transit_ASC_no_auto_atwork,2.7041876,F -walk_transit_ASC_auto_deficient_eatout,-0.03896324,F -walk_transit_ASC_auto_deficient_escort,-4.960704,F -walk_transit_ASC_auto_deficient_othdiscr,0.9530884,F -walk_transit_ASC_auto_deficient_othmaint,-3.0597258,F -walk_transit_ASC_auto_deficient_school,4.120708,F -walk_transit_ASC_auto_deficient_shopping,-0.8476569,F -walk_transit_ASC_auto_deficient_social,0.97444487,F -walk_transit_ASC_auto_deficient_univ,3.1362555,F -walk_transit_ASC_auto_deficient_work,0.65302855,F -walk_transit_ASC_auto_deficient_atwork,-2.9988291,F -walk_transit_ASC_auto_sufficient_eatout,-1.1126906,F -walk_transit_ASC_auto_sufficient_escort,-4.934847,F -walk_transit_ASC_auto_sufficient_othdiscr,-0.80636793,F -walk_transit_ASC_auto_sufficient_othmaint,-1.5471172,F -walk_transit_ASC_auto_sufficient_school,0.74590874,F -walk_transit_ASC_auto_sufficient_shopping,-2.2036798,F -walk_transit_ASC_auto_sufficient_social,-0.3453759,F -walk_transit_ASC_auto_sufficient_univ,0.4731163,F -walk_transit_ASC_auto_sufficient_work,-0.8916507,F -walk_transit_ASC_auto_sufficient_atwork,-3.401027,F -drive_transit_ASC_no_auto_all,0,F -drive_transit_ASC_auto_deficient_eatout,0.5998061,F -drive_transit_ASC_auto_deficient_escort,-1.1537067,F -drive_transit_ASC_auto_deficient_othdiscr,0.3199308,F -drive_transit_ASC_auto_deficient_othmaint,-0.29943228,F -drive_transit_ASC_auto_deficient_school,5.3252654,F -drive_transit_ASC_auto_deficient_shopping,-0.41849178,F -drive_transit_ASC_auto_deficient_social,1.5627195,F -drive_transit_ASC_auto_deficient_univ,1.8501176,F -drive_transit_ASC_auto_deficient_work,0.10081567,F -drive_transit_ASC_auto_deficient_atwork,-998.8196,F -drive_transit_ASC_auto_sufficient_eatout,-0.96951586,F -drive_transit_ASC_auto_sufficient_escort,-4.6014247,F -drive_transit_ASC_auto_sufficient_othdiscr,-0.3785917,F -drive_transit_ASC_auto_sufficient_othmaint,-2.6249478,F -drive_transit_ASC_auto_sufficient_school,1.40135,F -drive_transit_ASC_auto_sufficient_shopping,-2.1718938,F -drive_transit_ASC_auto_sufficient_social,-0.61585575,F -drive_transit_ASC_auto_sufficient_univ,1.3587753,F -drive_transit_ASC_auto_sufficient_work,-1.0045459,F -drive_transit_ASC_auto_sufficient_atwork,-999.21466,F -taxi_ASC_no_auto_eatout_othdiscr_social,0.9923,F -taxi_ASC_no_auto_escort_othmaint_shopping,1.8939,F -taxi_ASC_no_auto_school_univ,-7,T -taxi_ASC_no_auto_work,4.7291,F -taxi_ASC_no_auto_atwork,4.1021,F -taxi_ASC_auto_deficient_eatout_othdiscr_social,-3.1317,F -taxi_ASC_auto_deficient_escort_othmaint_shopping,0.1766,F -taxi_ASC_auto_deficient_school,-0.3338,F -taxi_ASC_auto_deficient_univ,4.2492,F -taxi_ASC_auto_deficient_work,-1.4766,F -taxi_ASC_auto_deficient_atwork,-4.4046,F -taxi_ASC_auto_sufficient_eatout_othdiscr_social,-3.0374,F -taxi_ASC_auto_sufficient_escort_othmaint_shopping,-1.8055,F -taxi_ASC_auto_sufficient_school,-2.4294,F -taxi_ASC_auto_sufficient_univ,-0.3131,F -taxi_ASC_auto_sufficient_work,-4.8509,F -taxi_ASC_auto_sufficient_atwork,-2.8804,F -tnc_single_ASC_no_auto_eatout_othdiscr_social,1.6852,F -tnc_single_ASC_no_auto_escort_othmaint_shopping,1.8605,F -tnc_single_ASC_no_auto_school,-7,T -tnc_single_ASC_no_auto_univ,-2.519,F -tnc_single_ASC_no_auto_work,5.7855,F -tnc_single_ASC_no_auto_atwork,4.4982,F -tnc_single_ASC_auto_deficient_eatout_othdiscr_social,-2.9623,F -tnc_single_ASC_auto_deficient_escort_othmaint_shopping,0.6748,F -tnc_single_ASC_auto_deficient_school,-0.5524,F -tnc_single_ASC_auto_deficient_univ,1.0221,F -tnc_single_ASC_auto_deficient_work,-0.8013,F -tnc_single_ASC_auto_deficient_atwork,-3.7626,F -tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,-2.3239,F -tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,-1.45,F -tnc_single_ASC_auto_sufficient_school,-2.8375,F -tnc_single_ASC_auto_sufficient_univ,0.2088,F -tnc_single_ASC_auto_sufficient_work,-4.1946,F -tnc_single_ASC_auto_sufficient_atwork,-2.7988,F -tnc_shared_ASC_no_auto_eatout_othdiscr_social,0.6464,F -tnc_shared_ASC_no_auto_escort_othmaint_shopping,0.9361,F -tnc_shared_ASC_no_auto_school,-7,T -tnc_shared_ASC_no_auto_univ,-5.8116,F -tnc_shared_ASC_no_auto_work,3.2429,F -tnc_shared_ASC_no_auto_atwork,3.3672,F -tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,-4.3576,F -tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,-0.3863,F -tnc_shared_ASC_auto_deficient_school,-1.4746,F -tnc_shared_ASC_auto_deficient_univ,3.25,F -tnc_shared_ASC_auto_deficient_work,-2.1435,F -tnc_shared_ASC_auto_deficient_atwork,-4.5089,F -tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,-3.6638,F -tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,-2.4365,F -tnc_shared_ASC_auto_sufficient_school,-3.7219,F -tnc_shared_ASC_auto_sufficient_univ,-0.9068,F -tnc_shared_ASC_auto_sufficient_work,-5.3575,F -tnc_shared_ASC_auto_sufficient_atwork,-3.5397,F -joint_walk_ASC_no_auto_all,-0.21274701,F -joint_walk_ASC_auto_deficient_all,-1.9607706,F -joint_walk_ASC_auto_sufficient_all,-3.2352157,F -joint_bike_ASC_no_auto_all,-2.8671598,F -joint_bike_ASC_auto_deficient_all,-6.076415,F -joint_bike_ASC_auto_sufficient_all,-6.3760657,F -joint_sr2_ASC_no_auto_all,0,T -joint_sr2_ASC_auto_deficient_all,0,T -joint_sr2_ASC_auto_sufficient_all,0,T -joint_sr3p_ASC_no_auto_all,0.5630671,F -joint_sr3p_ASC_auto_deficient_all,-1.8841692,F -joint_sr3p_ASC_auto_sufficient_all,-2.234826,F -joint_walk_transit_ASC_no_auto_all,0.62292415,F -joint_walk_transit_ASC_auto_deficient_all,-5.1634483,F -joint_walk_transit_ASC_auto_sufficient_all,-18.264534,F -joint_drive_transit_ASC_no_auto_all,0,T -joint_drive_transit_ASC_auto_deficient_all,-5.9632215,F -joint_drive_transit_ASC_auto_sufficient_all,-8.045285,F -joint_taxi_ASC_no_auto_all,-4.5792,F -joint_taxi_ASC_auto_deficient_all,-9.8157,F -joint_taxi_ASC_auto_sufficient_all,-11.7099,T -joint_tnc_single_ASC_no_auto_all,-4.4917,F -joint_tnc_single_ASC_auto_deficient_all,-9.8961,F -joint_tnc_single_ASC_auto_sufficient_all,-14.0159,T -joint_tnc_shared_ASC_no_auto_all,-4.3002,F -joint_tnc_shared_ASC_auto_deficient_all,-11.1572,F -joint_tnc_shared_ASC_auto_sufficient_all,-13.205,T -local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,-0.090703264,F -local_bus_ASC_school_univ,-0.06508621,F -local_bus_ASC_work,0.06689507,F -walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F -walk_light_rail_ASC_school_univ,1.6814003,F -walk_light_rail_ASC_work,0.8255567,F -drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F -drive_light_rail_ASC_school_univ,1.6814003,F -drive_light_rail_ASC_work,0.8255567,F -walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F -walk_ferry_ASC_school_univ,2.0202317,F -walk_ferry_ASC_work,0.93322605,F -drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F -drive_ferry_ASC_school_univ,2.0202317,F -drive_ferry_ASC_work,0.93322605,F -express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9692316,F -express_bus_ASC_school_univ,0.32496938,F -express_bus_ASC_work,-0.5165474,F -heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7706121,F -heavy_rail_ASC_school_univ,0.96200377,F -heavy_rail_ASC_work,0.64772975,F -commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7270185,F -commuter_rail_ASC_school_univ,1.0336206,F -commuter_rail_ASC_work,0.725503,F -walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F -walk_transit_CBD_ASC_school_univ,0.672,F -walk_transit_CBD_ASC_work,0.804,F -walk_transit_CBD_ASC_atwork,0.564,F -drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F -drive_transit_CBD_ASC_school_univ,0.672,F -drive_transit_CBD_ASC_work,1.1,F -drive_transit_CBD_ASC_atwork,0.564,F +coefficient_name,value,constrain +coef_one,1,T +coef_nest_root,1.00,T +coef_nest_AUTO,0.72,T +coef_nest_AUTO_DRIVEALONE,0.35,T +coef_nest_AUTO_SHAREDRIDE2,0.35,T +coef_nest_AUTO_SHAREDRIDE3,0.35,T +coef_nest_NONMOTORIZED,0.72,T +coef_nest_TRANSIT,0.72,T +coef_nest_TRANSIT_WALKACCESS,0.5,T +coef_nest_TRANSIT_DRIVEACCESS,0.5,T +coef_nest_RIDEHAIL,0.36,T +coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,-0.0175,F +coef_ivt_school_univ,-0.0224,F +coef_ivt_work,-0.0134,F +coef_ivt_atwork,-0.0188,F +coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,15,F +coef_topology_walk_multiplier_atwork,7.5,F +coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,20,F +coef_topology_bike_multiplier_atwork,10,F +coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,2.2,F +coef_topology_trn_multiplier_atwork,2,F +coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F +coef_age1619_da_multiplier_school_univ,-1.3813,F +coef_age1619_da_multiplier_atwork,0.0032336,F +coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F +coef_age010_trn_multiplier_school_univ,-1.5548,F +coef_age010_trn_multiplier_atwork,0.000722,F +coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,-1.366,F +coef_age16p_sr_multiplier_school_univ_work_atwork,0,F +coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,0,F +coef_hhsize1_sr_multiplier_work,-0.734588,F +coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,0,F +coef_hhsize2_sr_multiplier_school_univ,-0.6359,F +walk_ASC_no_auto_eatout,5.1251173,F +walk_ASC_no_auto_escort,2.8012068,F +walk_ASC_no_auto_othdiscr,3.2665946,F +walk_ASC_no_auto_othmaint,1.287299,F +walk_ASC_no_auto_school,18.414557,F +walk_ASC_no_auto_shopping,2.3768773,F +walk_ASC_no_auto_social,1.8680915,F +walk_ASC_no_auto_univ,6.408967,F +walk_ASC_no_auto_work,5.7672157,F +walk_ASC_no_auto_atwork,6.669213,F +walk_ASC_auto_deficient_eatout,3.274605,F +walk_ASC_auto_deficient_escort,-0.90204656,F +walk_ASC_auto_deficient_othdiscr,2.2494075,F +walk_ASC_auto_deficient_othmaint,1.3690404,F +walk_ASC_auto_deficient_school,3.2573624,F +walk_ASC_auto_deficient_shopping,2.2701733,F +walk_ASC_auto_deficient_social,2.870184,F +walk_ASC_auto_deficient_univ,4.50591,F +walk_ASC_auto_deficient_work,2.4010417,F +walk_ASC_auto_deficient_atwork,0.92546093,F +walk_ASC_auto_sufficient_eatout,1.5516903,F +walk_ASC_auto_sufficient_escort,-0.8116066,F +walk_ASC_auto_sufficient_othdiscr,1.2633476,F +walk_ASC_auto_sufficient_othmaint,0.7999634,F +walk_ASC_auto_sufficient_school,0.6476856,F +walk_ASC_auto_sufficient_shopping,0.7312663,F +walk_ASC_auto_sufficient_social,1.7072186,F +walk_ASC_auto_sufficient_univ,1.0607665,F +walk_ASC_auto_sufficient_work,0.053265337,F +walk_ASC_auto_sufficient_atwork,0.677216,F +bike_ASC_no_auto_eatout,0.86807096,F +bike_ASC_no_auto_escort,-0.716212,F +bike_ASC_no_auto_othdiscr,-0.3764232,F +bike_ASC_no_auto_othmaint,1.5394334,F +bike_ASC_no_auto_school,12.098735,F +bike_ASC_no_auto_shopping,0.8341555,F +bike_ASC_no_auto_social,0.02058321,F +bike_ASC_no_auto_univ,4.2945156,F +bike_ASC_no_auto_work,3.1940088,F +bike_ASC_no_auto_atwork,-0.90725845,F +bike_ASC_auto_deficient_eatout,-1.5691106,F +bike_ASC_auto_deficient_escort,-4.527928,F +bike_ASC_auto_deficient_othdiscr,-0.09246834,F +bike_ASC_auto_deficient_othmaint,-1.5184649,F +bike_ASC_auto_deficient_school,-0.5280678,F +bike_ASC_auto_deficient_shopping,-0.87584466,F +bike_ASC_auto_deficient_social,0.6345214,F +bike_ASC_auto_deficient_univ,-0.669235,F +bike_ASC_auto_deficient_work,0.25318968,F +bike_ASC_auto_deficient_atwork,-0.8074083,F +bike_ASC_auto_sufficient_eatout,-1.2003471,F +bike_ASC_auto_sufficient_escort,-5.0631084,F +bike_ASC_auto_sufficient_othdiscr,-1.0714597,F +bike_ASC_auto_sufficient_othmaint,-2.8083024,F +bike_ASC_auto_sufficient_school,-2.1134686,F +bike_ASC_auto_sufficient_shopping,-2.5662103,F +bike_ASC_auto_sufficient_social,-1.368071,F +bike_ASC_auto_sufficient_univ,-1.9397832,F +bike_ASC_auto_sufficient_work,-1.5800232,F +bike_ASC_auto_sufficient_atwork,15.72017,F +sr2_ASC_no_auto_all,0,F +sr2_ASC_auto_deficient_eatout,0.5882345,F +sr2_ASC_auto_deficient_escort,0,F +sr2_ASC_auto_deficient_othdiscr,0.6601513,F +sr2_ASC_auto_deficient_othmaint,0.2621527,F +sr2_ASC_auto_deficient_school,0.12474365,F +sr2_ASC_auto_deficient_shopping,0.24409756,F +sr2_ASC_auto_deficient_social,1.8558528,F +sr2_ASC_auto_deficient_univ,-1.6922346,F +sr2_ASC_auto_deficient_work,-0.33803123,F +sr2_ASC_auto_deficient_atwork,-2.1102421,F +sr2_ASC_auto_sufficient_eatout,0.86280555,F +sr2_ASC_auto_sufficient_escort,0,F +sr2_ASC_auto_sufficient_othdiscr,0.49684617,F +sr2_ASC_auto_sufficient_othmaint,0.25817883,F +sr2_ASC_auto_sufficient_school,-1.6062657,F +sr2_ASC_auto_sufficient_shopping,0.19770707,F +sr2_ASC_auto_sufficient_social,0.5236025,F +sr2_ASC_auto_sufficient_univ,-1.859427,F +sr2_ASC_auto_sufficient_work,-1.0857458,F +sr2_ASC_auto_sufficient_atwork,-1.4450618,F +sr3p_ASC_no_auto_eatout,0.3219998,F +sr3p_ASC_no_auto_escort,-1.8129267,F +sr3p_ASC_no_auto_othdiscr,0.27216902,F +sr3p_ASC_no_auto_othmaint,-0.8031854,F +sr3p_ASC_no_auto_school,-6.0240827,F +sr3p_ASC_no_auto_shopping,-0.27978948,F +sr3p_ASC_no_auto_social,-1.4036902,F +sr3p_ASC_no_auto_univ,-6.056001,F +sr3p_ASC_no_auto_work,-0.5831269,F +sr3p_ASC_no_auto_atwork,0.5826626,F +sr3p_ASC_auto_deficient_eatout,0.04605236,F +sr3p_ASC_auto_deficient_escort,-0.40818766,F +sr3p_ASC_auto_deficient_othdiscr,1.0470966,F +sr3p_ASC_auto_deficient_othmaint,-1.3493925,F +sr3p_ASC_auto_deficient_school,0.7149571,F +sr3p_ASC_auto_deficient_shopping,-0.073370166,F +sr3p_ASC_auto_deficient_social,1.5007243,F +sr3p_ASC_auto_deficient_univ,-1.7277422,F +sr3p_ASC_auto_deficient_work,-0.8527042,F +sr3p_ASC_auto_deficient_atwork,-2.514658,F +sr3p_ASC_auto_sufficient_eatout,0.8468596,F +sr3p_ASC_auto_sufficient_escort,-0.05741253,F +sr3p_ASC_auto_sufficient_othdiscr,0.58850205,F +sr3p_ASC_auto_sufficient_othmaint,-0.07549867,F +sr3p_ASC_auto_sufficient_school,-1.0201935,F +sr3p_ASC_auto_sufficient_shopping,-0.077571295,F +sr3p_ASC_auto_sufficient_social,0.50617886,F +sr3p_ASC_auto_sufficient_univ,-1.9047098,F +sr3p_ASC_auto_sufficient_work,-1.4699702,F +sr3p_ASC_auto_sufficient_atwork,-1.652174,F +walk_transit_ASC_no_auto_eatout,2.5936368,F +walk_transit_ASC_no_auto_escort,-2.2172081,F +walk_transit_ASC_no_auto_othdiscr,2.2437785,F +walk_transit_ASC_no_auto_othmaint,2.5643456,F +walk_transit_ASC_no_auto_school,21.383749,F +walk_transit_ASC_no_auto_shopping,2.1067476,F +walk_transit_ASC_no_auto_social,1.3814651,F +walk_transit_ASC_no_auto_univ,8.786037,F +walk_transit_ASC_no_auto_work,5.0354166,F +walk_transit_ASC_no_auto_atwork,2.7041876,F +walk_transit_ASC_auto_deficient_eatout,-0.03896324,F +walk_transit_ASC_auto_deficient_escort,-4.960704,F +walk_transit_ASC_auto_deficient_othdiscr,0.9530884,F +walk_transit_ASC_auto_deficient_othmaint,-3.0597258,F +walk_transit_ASC_auto_deficient_school,4.120708,F +walk_transit_ASC_auto_deficient_shopping,-0.8476569,F +walk_transit_ASC_auto_deficient_social,0.97444487,F +walk_transit_ASC_auto_deficient_univ,3.1362555,F +walk_transit_ASC_auto_deficient_work,0.65302855,F +walk_transit_ASC_auto_deficient_atwork,-2.9988291,F +walk_transit_ASC_auto_sufficient_eatout,-1.1126906,F +walk_transit_ASC_auto_sufficient_escort,-4.934847,F +walk_transit_ASC_auto_sufficient_othdiscr,-0.80636793,F +walk_transit_ASC_auto_sufficient_othmaint,-1.5471172,F +walk_transit_ASC_auto_sufficient_school,0.74590874,F +walk_transit_ASC_auto_sufficient_shopping,-2.2036798,F +walk_transit_ASC_auto_sufficient_social,-0.3453759,F +walk_transit_ASC_auto_sufficient_univ,0.4731163,F +walk_transit_ASC_auto_sufficient_work,-0.8916507,F +walk_transit_ASC_auto_sufficient_atwork,-3.401027,F +drive_transit_ASC_no_auto_all,0,F +drive_transit_ASC_auto_deficient_eatout,0.5998061,F +drive_transit_ASC_auto_deficient_escort,-1.1537067,F +drive_transit_ASC_auto_deficient_othdiscr,0.3199308,F +drive_transit_ASC_auto_deficient_othmaint,-0.29943228,F +drive_transit_ASC_auto_deficient_school,5.3252654,F +drive_transit_ASC_auto_deficient_shopping,-0.41849178,F +drive_transit_ASC_auto_deficient_social,1.5627195,F +drive_transit_ASC_auto_deficient_univ,1.8501176,F +drive_transit_ASC_auto_deficient_work,0.10081567,F +drive_transit_ASC_auto_deficient_atwork,-998.8196,F +drive_transit_ASC_auto_sufficient_eatout,-0.96951586,F +drive_transit_ASC_auto_sufficient_escort,-4.6014247,F +drive_transit_ASC_auto_sufficient_othdiscr,-0.3785917,F +drive_transit_ASC_auto_sufficient_othmaint,-2.6249478,F +drive_transit_ASC_auto_sufficient_school,1.40135,F +drive_transit_ASC_auto_sufficient_shopping,-2.1718938,F +drive_transit_ASC_auto_sufficient_social,-0.61585575,F +drive_transit_ASC_auto_sufficient_univ,1.3587753,F +drive_transit_ASC_auto_sufficient_work,-1.0045459,F +drive_transit_ASC_auto_sufficient_atwork,-999.21466,F +taxi_ASC_no_auto_eatout_othdiscr_social,0.9923,F +taxi_ASC_no_auto_escort_othmaint_shopping,1.8939,F +taxi_ASC_no_auto_school_univ,-7,T +taxi_ASC_no_auto_work,4.7291,F +taxi_ASC_no_auto_atwork,4.1021,F +taxi_ASC_auto_deficient_eatout_othdiscr_social,-3.1317,F +taxi_ASC_auto_deficient_escort_othmaint_shopping,0.1766,F +taxi_ASC_auto_deficient_school,-0.3338,F +taxi_ASC_auto_deficient_univ,4.2492,F +taxi_ASC_auto_deficient_work,-1.4766,F +taxi_ASC_auto_deficient_atwork,-4.4046,F +taxi_ASC_auto_sufficient_eatout_othdiscr_social,-3.0374,F +taxi_ASC_auto_sufficient_escort_othmaint_shopping,-1.8055,F +taxi_ASC_auto_sufficient_school,-2.4294,F +taxi_ASC_auto_sufficient_univ,-0.3131,F +taxi_ASC_auto_sufficient_work,-4.8509,F +taxi_ASC_auto_sufficient_atwork,-2.8804,F +tnc_single_ASC_no_auto_eatout_othdiscr_social,1.6852,F +tnc_single_ASC_no_auto_escort_othmaint_shopping,1.8605,F +tnc_single_ASC_no_auto_school,-7,T +tnc_single_ASC_no_auto_univ,-2.519,F +tnc_single_ASC_no_auto_work,5.7855,F +tnc_single_ASC_no_auto_atwork,4.4982,F +tnc_single_ASC_auto_deficient_eatout_othdiscr_social,-2.9623,F +tnc_single_ASC_auto_deficient_escort_othmaint_shopping,0.6748,F +tnc_single_ASC_auto_deficient_school,-0.5524,F +tnc_single_ASC_auto_deficient_univ,1.0221,F +tnc_single_ASC_auto_deficient_work,-0.8013,F +tnc_single_ASC_auto_deficient_atwork,-3.7626,F +tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,-2.3239,F +tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,-1.45,F +tnc_single_ASC_auto_sufficient_school,-2.8375,F +tnc_single_ASC_auto_sufficient_univ,0.2088,F +tnc_single_ASC_auto_sufficient_work,-4.1946,F +tnc_single_ASC_auto_sufficient_atwork,-2.7988,F +tnc_shared_ASC_no_auto_eatout_othdiscr_social,0.6464,F +tnc_shared_ASC_no_auto_escort_othmaint_shopping,0.9361,F +tnc_shared_ASC_no_auto_school,-7,T +tnc_shared_ASC_no_auto_univ,-5.8116,F +tnc_shared_ASC_no_auto_work,3.2429,F +tnc_shared_ASC_no_auto_atwork,3.3672,F +tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,-4.3576,F +tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,-0.3863,F +tnc_shared_ASC_auto_deficient_school,-1.4746,F +tnc_shared_ASC_auto_deficient_univ,3.25,F +tnc_shared_ASC_auto_deficient_work,-2.1435,F +tnc_shared_ASC_auto_deficient_atwork,-4.5089,F +tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,-3.6638,F +tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,-2.4365,F +tnc_shared_ASC_auto_sufficient_school,-3.7219,F +tnc_shared_ASC_auto_sufficient_univ,-0.9068,F +tnc_shared_ASC_auto_sufficient_work,-5.3575,F +tnc_shared_ASC_auto_sufficient_atwork,-3.5397,F +joint_walk_ASC_no_auto_all,-0.21274701,F +joint_walk_ASC_auto_deficient_all,-1.9607706,F +joint_walk_ASC_auto_sufficient_all,-3.2352157,F +joint_bike_ASC_no_auto_all,-2.8671598,F +joint_bike_ASC_auto_deficient_all,-6.076415,F +joint_bike_ASC_auto_sufficient_all,-6.3760657,F +joint_sr2_ASC_no_auto_all,0,T +joint_sr2_ASC_auto_deficient_all,0,T +joint_sr2_ASC_auto_sufficient_all,0,T +joint_sr3p_ASC_no_auto_all,0.5630671,F +joint_sr3p_ASC_auto_deficient_all,-1.8841692,F +joint_sr3p_ASC_auto_sufficient_all,-2.234826,F +joint_walk_transit_ASC_no_auto_all,0.62292415,F +joint_walk_transit_ASC_auto_deficient_all,-5.1634483,F +joint_walk_transit_ASC_auto_sufficient_all,-18.264534,F +joint_drive_transit_ASC_no_auto_all,0,T +joint_drive_transit_ASC_auto_deficient_all,-5.9632215,F +joint_drive_transit_ASC_auto_sufficient_all,-8.045285,F +joint_taxi_ASC_no_auto_all,-4.5792,F +joint_taxi_ASC_auto_deficient_all,-9.8157,F +joint_taxi_ASC_auto_sufficient_all,-11.7099,T +joint_tnc_single_ASC_no_auto_all,-4.4917,F +joint_tnc_single_ASC_auto_deficient_all,-9.8961,F +joint_tnc_single_ASC_auto_sufficient_all,-14.0159,T +joint_tnc_shared_ASC_no_auto_all,-4.3002,F +joint_tnc_shared_ASC_auto_deficient_all,-11.1572,F +joint_tnc_shared_ASC_auto_sufficient_all,-13.205,T +local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,-0.090703264,F +local_bus_ASC_school_univ,-0.06508621,F +local_bus_ASC_work,0.06689507,F +walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F +walk_light_rail_ASC_school_univ,1.6814003,F +walk_light_rail_ASC_work,0.8255567,F +drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F +drive_light_rail_ASC_school_univ,1.6814003,F +drive_light_rail_ASC_work,0.8255567,F +walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F +walk_ferry_ASC_school_univ,2.0202317,F +walk_ferry_ASC_work,0.93322605,F +drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F +drive_ferry_ASC_school_univ,2.0202317,F +drive_ferry_ASC_work,0.93322605,F +express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9692316,F +express_bus_ASC_school_univ,0.32496938,F +express_bus_ASC_work,-0.5165474,F +heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7706121,F +heavy_rail_ASC_school_univ,0.96200377,F +heavy_rail_ASC_work,0.64772975,F +commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7270185,F +commuter_rail_ASC_school_univ,1.0336206,F +commuter_rail_ASC_work,0.725503,F +walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F +walk_transit_CBD_ASC_school_univ,0.672,F +walk_transit_CBD_ASC_work,0.804,F +walk_transit_CBD_ASC_atwork,0.564,F +drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F +drive_transit_CBD_ASC_school_univ,0.672,F +drive_transit_CBD_ASC_work,1.1,F +drive_transit_CBD_ASC_atwork,0.564,F diff --git a/activitysim/examples/example_mtc/configs/tour_mode_choice_coefficients_template.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tour_mode_choice_coeffs_template.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/tour_mode_choice_coefficients_template.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tour_mode_choice_coeffs_template.csv diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_drive_maz_tap.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tvpb_utility_drive_maz_tap.csv similarity index 99% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_drive_maz_tap.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tvpb_utility_drive_maz_tap.csv index a5dadfaeb3..9cf267a4ba 100755 --- a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_drive_maz_tap.csv +++ b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tvpb_utility_drive_maz_tap.csv @@ -1,3 +1,3 @@ -Label,Description,Expression,utility -util_drive_time,drive time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_drive * (df.DTIME + (df.WDIST / 5280 / walk_speed * 60))",1 -util_drive_cost,drive cost,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_cost_high_income, c_cost_low_income) * (df.DDIST + (df.WDIST / 5280)) * c_auto_operating_cost_per_mile",1 +Label,Description,Expression,utility +util_drive_time,drive time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_drive * (df.DTIME + (df.WDIST / 5280 / walk_speed * 60))",1 +util_drive_cost,drive cost,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_cost_high_income, c_cost_low_income) * (df.DDIST + (df.WDIST / 5280)) * c_auto_operating_cost_per_mile",1 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap.csv similarity index 99% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap.csv index 68bd560e60..61c03b72b1 100755 --- a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap.csv +++ b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap.csv @@ -1,85 +1,85 @@ -Label,Description,Expression,set1,set2,set3 -# Set 1,,,,, -set1_unavailable,Shut off set if unavailable,@df.not_transit_available_set1,C_UNAVAILABLE,, -set1_ivt,set In-Vehicle Time,"@~df.not_transit_available_set1 * df.c_ivt_for_segment * df.totalIVT_set1",1,, -set1_first_wait_time,First wait time,"@~df.not_transit_available_set1 * c_fwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'IWAIT_SET1')",1,, -set1_xfer_wait_time,set Transfer Wait Time,"@~df.not_transit_available_set1 * c_xwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWAIT_SET1')",1,, -set1_xfer_walk_time,set Walk transfer time,"@~df.not_transit_available_set1 * c_waux * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWTIME_SET1')",1,, -set1_fare,set Fare,"@~df.not_transit_available_set1 * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'FARE_SET1') * 100",1,, -set1_xfers1,0-1 transfers constant,"@~df.not_transit_available_set1 & ~df.bartOnly_set1 & df.xfers1_set1 * c_xfers1 * df.c_ivt_for_segment",1,, -set1_xfers2,1-2 transfers constant,"@~df.not_transit_available_set1 & ~df.bartOnly_set1 & df.xfers2_set1 * c_xfers2 * df.c_ivt_for_segment",1,, -set1_sfers3,>2 transfers constant,"@~df.not_transit_available_set1 & ~df.bartOnly_set1 & df.xfers3_set1 * c_xfers3 * df.c_ivt_for_segment",1,, -set1_xfers1_drive,0-1 transfers penalty for drive access,"@~df.not_transit_available_set1 & ~df.bartOnly_set1 & df.xfers1_set1 & (access_mode=='drive') * (df.c_ivt_for_segment * 15)",1,, -set1_xfers2_drive,1-2 transfers penalty for drive access,"@~df.not_transit_available_set1 & ~df.bartOnly_set1 & df.xfers2_set1 & (access_mode=='drive') * (df.c_ivt_for_segment * 15)",1,, -set1_sfers3_drive,>2 transfers penalty for drive access,"@~df.not_transit_available_set1 & ~df.bartOnly_set1 & df.xfers3_set1 & (access_mode=='drive') * (df.c_ivt_for_segment * 15)",1,, -set1_xfers1_bart,0-1 transfers constant when using only BART,"@~df.not_transit_available_set1 & df.bartOnly_set1 & df.xfers1_set1 * (df.c_ivt_for_segment * 5)",1,, -set1_xfers2_bart,1-2 transfers constant when using only BART,"@~df.not_transit_available_set1 & df.bartOnly_set1 & df.xfers2_set1 * (df.c_ivt_for_segment * 5)",1,, -set1_sfers3_bart,>2 transfers constant when using only BART,"@~df.not_transit_available_set1 & df.bartOnly_set1 & df.xfers3_set1 * (df.c_ivt_for_segment * 5)",1,, -set1_cr_20_40,CR distance 20-40 miles,"@~df.not_transit_available_set1 & (df.crDistance_set1>20) & (df.crDistance_set1<=40) * c_cr20_40 * df.c_ivt_for_segment",1,, -set1_cr_40_plus,CR distance > 40 miles,"@~df.not_transit_available_set1 & (df.crDistance_set1>40) * c_cr40plus * df.c_ivt_for_segment",1,, -set1_CR_drive,drive access to CR,"@~df.not_transit_available_set1 & (access_mode=='drive') * c_drvCR * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1')>0)",1,, -set1_HR_drive,drive access to HR,"@~df.not_transit_available_set1 & (access_mode=='drive') * c_drvHeavy * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')>0)",1,, -set1_FR_drive,drive access to FR,"@~df.not_transit_available_set1 & (access_mode=='drive') * c_drvFR * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET1')>0)",1,, -set1_LRT_drive,drive access to LRT,"@~df.not_transit_available_set1 & (access_mode=='drive') * c_drvLRT * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1')>0)",1,, -set1_EB_drive,drive access to EB,"@~df.not_transit_available_set1 & (access_mode=='drive') * c_drvExpress * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET1')>0)",1,, -set1_ASC_CR,ASC CR,"@~df.not_transit_available_set1 * c_cr_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1')>0)",1,, -set1_ASC_HR,ASC HR,"@~df.not_transit_available_set1 * c_hr_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')>0)",1,, -set1_ASC_FR,ASC FR,"@~df.not_transit_available_set1 * c_fr_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET1')>0)",1,, -set1_ASC_LRT,ASC LRT,"@~df.not_transit_available_set1 * c_lrt_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1')>0)",1,, -#,1/3 ASC for premium with Xfers,"@~df.not_transit_available_set1 * premWithXfer_set1 * asc/3.0",1,, -# Set 2,,,,, -set2_unavailable,Shut off set if unavailable,"@df.not_transit_available_set2",,C_UNAVAILABLE, -set2_ivt,set In-Vehicle Time,"@~df.not_transit_available_set2 * df.c_ivt_for_segment * df.totalIVT_set2",,1, -set2_first_wait_time,First wait time,"@~df.not_transit_available_set2 * c_fwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'IWAIT_SET2')",,1, -set2_xfer_wait_time,set Transfer Wait Time,"@~df.not_transit_available_set2 * c_xwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWAIT_SET2')",,1, -set2_xfer_walk_time,set Walk transfer time,"@~df.not_transit_available_set2 * c_waux * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWTIME_SET2')",,1, -set2_fare,set Fare,"@~df.not_transit_available_set2 * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'FARE_SET2') * 100",,1, -set2_xfers1,0-1 transfers constant,"@~df.not_transit_available_set2 & ~df.bartOnly_set2 & df.xfers1_set2 * c_xfers1 * df.c_ivt_for_segment",,1, -set2_xfers2,1-2 transfers constant,"@~df.not_transit_available_set2 & ~df.bartOnly_set2 & df.xfers2_set2 * c_xfers2 * df.c_ivt_for_segment",,1, -set2_sfers3,>2 transfers constant,"@~df.not_transit_available_set2 & ~df.bartOnly_set2 & df.xfers3_set2 * c_xfers3 * df.c_ivt_for_segment",,1, -set2_xfers1_drive,0-1 transfers penalty for drive access,"@~df.not_transit_available_set2 & ~df.bartOnly_set2 & df.xfers1_set2 & (access_mode=='drive') * (df.c_ivt_for_segment * 15)",,1, -set2_xfers2_drive,1-2 transfers penalty for drive access,"@~df.not_transit_available_set2 & ~df.bartOnly_set2 & df.xfers2_set2 & (access_mode=='drive') * (df.c_ivt_for_segment * 15)",,1, -set2_sfers3_drive,>2 transfers penalty for drive access,"@~df.not_transit_available_set2 & ~df.bartOnly_set2 & (access_mode=='drive') & df.xfers3_set2 * (df.c_ivt_for_segment * 15)",,1, -set2_xfers1_bart,0-1 transfers constant when using only BART,"@~df.not_transit_available_set2 & df.bartOnly_set2 & df.xfers1_set2 * (df.c_ivt_for_segment * 5)",,1, -set2_xfers2_bart,1-2 transfers constant when using only BART,"@~df.not_transit_available_set2 & df.bartOnly_set2 & df.xfers2_set2 * (df.c_ivt_for_segment * 5)",,1, -set2_sfers3_bart,>2 transfers constant when using only BART,"@~df.not_transit_available_set2 & df.bartOnly_set2 & df.xfers3_set2 * (df.c_ivt_for_segment * 5)",,1, -set2_cr_20_40,CR distance 20-40 miles,"@~df.not_transit_available_set2 & (df.crDistance_set2>20) & (df.crDistance_set2<=40) * c_cr20_40 * df.c_ivt_for_segment",,1, -set2_cr_40_plus,CR distance > 40 miles,"@~df.not_transit_available_set2 & (df.crDistance_set2>40) * c_cr40plus * df.c_ivt_for_segment",,1, -set2_CR_drive,drive access to CR,"@~df.not_transit_available_set2 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2')>0) * c_drvCR * df.c_ivt_for_segment",,1, -set2_HR_drive,drive access to HR,"@~df.not_transit_available_set2 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')>0) * c_drvHeavy * df.c_ivt_for_segment",,1, -set2_FR_drive,drive access to FR,"@~df.not_transit_available_set2 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET2')>0) * c_drvFR * df.c_ivt_for_segment",,1, -set2_LRT_drive,drive access to LRT,"@~df.not_transit_available_set2 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2')>0) * c_drvLRT * df.c_ivt_for_segment",,1, -set2_EB_drive,drive access to EB,"@~df.not_transit_available_set2 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET2')>0) * c_drvExpress * df.c_ivt_for_segment",,1, -set2_ASC_CR,ASC CR,"@~df.not_transit_available_set2 & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2')>0) * c_cr_asc * df.c_ivt_for_segment",,1, -set2_ASC_HR,ASC HR,"@~df.not_transit_available_set2 & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')>0) * c_hr_asc * df.c_ivt_for_segment",,1, -set2_ASC_FR,ASC FR,"@~df.not_transit_available_set2 & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET2')>0) * c_fr_asc * df.c_ivt_for_segment",,1, -set2_ASC_LRT,ASC LRT,"@~df.not_transit_available_set2 & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2')>0) * c_lrt_asc * df.c_ivt_for_segment",,1, -#,1/3 ASC for premium with Xfers,"@~df.not_transit_available_set2 * premWithXfer_set2 * asc/3.0",,1, -# Set 3,,,,, -set3_unavailable,Shut off set if unavailable,"@df.not_transit_available_set3",,,C_UNAVAILABLE -set3_ivt,set In-Vehicle Time,"@~df.not_transit_available_set3 * df.c_ivt_for_segment * df.totalIVT_set3",,,1 -set3_first_wait_time,First wait time,"@~df.not_transit_available_set3 * c_fwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'IWAIT_SET3')",,,1 -set3_xfer_wait_time,set Transfer Wait Time,"@~df.not_transit_available_set3 * c_xwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWAIT_SET3')",,,1 -set3_xfer_walk_time,set Walk transfer time,"@~df.not_transit_available_set3 * c_waux * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWTIME_SET3')",,,1 -set3_fare,set Fare,"@~df.not_transit_available_set3 * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'FARE_SET3') * 100",,,1 -set3_xfers1,0-1 transfers constant,"@~df.not_transit_available_set3 & ~df.bartOnly_set3 & df.xfers1_set3 * c_xfers1 * df.c_ivt_for_segment",,,1 -set3_xfers2,1-2 transfers constant,"@~df.not_transit_available_set3 & ~df.bartOnly_set3 & df.xfers2_set3 * c_xfers2 * df.c_ivt_for_segment",,,1 -set3_sfers3,>2 transfers constant,"@~df.not_transit_available_set3 & ~df.bartOnly_set3 & df.xfers3_set3 * c_xfers3 * df.c_ivt_for_segment",,,1 -set3_xfers1_drive,0-1 transfers penalty for drive access,"@~df.not_transit_available_set3 & ~df.bartOnly_set3 & (access_mode=='drive') & df.xfers1_set3 * (df.c_ivt_for_segment * 15)",,,1 -set3_xfers2_drive,1-2 transfers penalty for drive access,"@~df.not_transit_available_set3 & ~df.bartOnly_set3 & (access_mode=='drive') & df.xfers2_set3 * (df.c_ivt_for_segment * 15)",,,1 -set3_sfers3_drive,>2 transfers penalty for drive access,"@~df.not_transit_available_set3 & ~df.bartOnly_set3 & (access_mode=='drive') & df.xfers3_set3 * (df.c_ivt_for_segment * 15)",,,1 -set3_xfers1_bart,0-1 transfers constant when using only BART,"@~df.not_transit_available_set3 & df.bartOnly_set3 & df.xfers1_set3 * (df.c_ivt_for_segment * 5)",,,1 -set3_xfers2_bart,1-2 transfers constant when using only BART,"@~df.not_transit_available_set3 & df.bartOnly_set3 & df.xfers2_set3 * (df.c_ivt_for_segment * 5)",,,1 -set3_sfers3_bart,>2 transfers constant when using only BART,"@~df.not_transit_available_set3 & df.bartOnly_set3 & df.xfers3_set3 * (df.c_ivt_for_segment * 5)",,,1 -set3_cr_20_40,CR distance 20-40 miles,"@~df.not_transit_available_set3 & (df.crDistance_set3>20) & (df.crDistance_set3<=40) * c_cr20_40 * df.c_ivt_for_segment",,,1 -set3_cr_40_plus,CR distance > 40 miles,"@~df.not_transit_available_set3 & (df.crDistance_set3>40) * c_cr40plus * df.c_ivt_for_segment",,,1 -set3_CR_drive,drive access to CR,"@~df.not_transit_available_set3 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3')>0) * c_drvCR * df.c_ivt_for_segment",,,1 -set3_HR_drive,drive access to HR,"@~df.not_transit_available_set3 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')>0) * c_drvHeavy * df.c_ivt_for_segment",,,1 -set3_FR_drive,drive access to FR,"@~df.not_transit_available_set3 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET3')>0) * c_drvFR * df.c_ivt_for_segment",,,1 -set3_LRT_drive,drive access to LRT,"@~df.not_transit_available_set3 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3')>0) * c_drvLRT * df.c_ivt_for_segment",,,1 -set3_EB_drive,drive access to EB,"@~df.not_transit_available_set3 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET3')>0) * c_drvExpress* df.c_ivt_for_segment",,,1 -set3_ASC_CR,ASC CR,"@~df.not_transit_available_set3 & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3')>0) * c_cr_asc * df.c_ivt_for_segment",,,1 -set3_ASC_HR,ASC HR,"@~df.not_transit_available_set3 & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')>0) * c_hr_asc * df.c_ivt_for_segment",,,1 -set3_ASC_FR,ASC FR,"@~df.not_transit_available_set3 & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET3')>0) * c_fr_asc * df.c_ivt_for_segment",,,1 -set3_ASC_LRT,ASC LRT,"@~df.not_transit_available_set3 & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3')>0) * c_lrt_asc * df.c_ivt_for_segment",,,1 -#,1/3 ASC for premium with Xfers,"@~df.not_transit_available_set3 * premWithXfer_set3 * asc/3.0",,,1 +Label,Description,Expression,set1,set2,set3 +# Set 1,,,,, +set1_unavailable,Shut off set if unavailable,@df.not_transit_available_set1,C_UNAVAILABLE,, +set1_ivt,set In-Vehicle Time,"@~df.not_transit_available_set1 * df.c_ivt_for_segment * df.totalIVT_set1",1,, +set1_first_wait_time,First wait time,"@~df.not_transit_available_set1 * c_fwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'IWAIT_SET1')",1,, +set1_xfer_wait_time,set Transfer Wait Time,"@~df.not_transit_available_set1 * c_xwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWAIT_SET1')",1,, +set1_xfer_walk_time,set Walk transfer time,"@~df.not_transit_available_set1 * c_waux * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWTIME_SET1')",1,, +set1_fare,set Fare,"@~df.not_transit_available_set1 * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'FARE_SET1') * 100",1,, +set1_xfers1,0-1 transfers constant,"@~df.not_transit_available_set1 & ~df.bartOnly_set1 & df.xfers1_set1 * c_xfers1 * df.c_ivt_for_segment",1,, +set1_xfers2,1-2 transfers constant,"@~df.not_transit_available_set1 & ~df.bartOnly_set1 & df.xfers2_set1 * c_xfers2 * df.c_ivt_for_segment",1,, +set1_sfers3,>2 transfers constant,"@~df.not_transit_available_set1 & ~df.bartOnly_set1 & df.xfers3_set1 * c_xfers3 * df.c_ivt_for_segment",1,, +set1_xfers1_drive,0-1 transfers penalty for drive access,"@~df.not_transit_available_set1 & ~df.bartOnly_set1 & df.xfers1_set1 & (access_mode=='drive') * (df.c_ivt_for_segment * 15)",1,, +set1_xfers2_drive,1-2 transfers penalty for drive access,"@~df.not_transit_available_set1 & ~df.bartOnly_set1 & df.xfers2_set1 & (access_mode=='drive') * (df.c_ivt_for_segment * 15)",1,, +set1_sfers3_drive,>2 transfers penalty for drive access,"@~df.not_transit_available_set1 & ~df.bartOnly_set1 & df.xfers3_set1 & (access_mode=='drive') * (df.c_ivt_for_segment * 15)",1,, +set1_xfers1_bart,0-1 transfers constant when using only BART,"@~df.not_transit_available_set1 & df.bartOnly_set1 & df.xfers1_set1 * (df.c_ivt_for_segment * 5)",1,, +set1_xfers2_bart,1-2 transfers constant when using only BART,"@~df.not_transit_available_set1 & df.bartOnly_set1 & df.xfers2_set1 * (df.c_ivt_for_segment * 5)",1,, +set1_sfers3_bart,>2 transfers constant when using only BART,"@~df.not_transit_available_set1 & df.bartOnly_set1 & df.xfers3_set1 * (df.c_ivt_for_segment * 5)",1,, +set1_cr_20_40,CR distance 20-40 miles,"@~df.not_transit_available_set1 & (df.crDistance_set1>20) & (df.crDistance_set1<=40) * c_cr20_40 * df.c_ivt_for_segment",1,, +set1_cr_40_plus,CR distance > 40 miles,"@~df.not_transit_available_set1 & (df.crDistance_set1>40) * c_cr40plus * df.c_ivt_for_segment",1,, +set1_CR_drive,drive access to CR,"@~df.not_transit_available_set1 & (access_mode=='drive') * c_drvCR * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1')>0)",1,, +set1_HR_drive,drive access to HR,"@~df.not_transit_available_set1 & (access_mode=='drive') * c_drvHeavy * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')>0)",1,, +set1_FR_drive,drive access to FR,"@~df.not_transit_available_set1 & (access_mode=='drive') * c_drvFR * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET1')>0)",1,, +set1_LRT_drive,drive access to LRT,"@~df.not_transit_available_set1 & (access_mode=='drive') * c_drvLRT * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1')>0)",1,, +set1_EB_drive,drive access to EB,"@~df.not_transit_available_set1 & (access_mode=='drive') * c_drvExpress * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET1')>0)",1,, +set1_ASC_CR,ASC CR,"@~df.not_transit_available_set1 * c_cr_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1')>0)",1,, +set1_ASC_HR,ASC HR,"@~df.not_transit_available_set1 * c_hr_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')>0)",1,, +set1_ASC_FR,ASC FR,"@~df.not_transit_available_set1 * c_fr_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET1')>0)",1,, +set1_ASC_LRT,ASC LRT,"@~df.not_transit_available_set1 * c_lrt_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1')>0)",1,, +#,1/3 ASC for premium with Xfers,"@~df.not_transit_available_set1 * premWithXfer_set1 * asc/3.0",1,, +# Set 2,,,,, +set2_unavailable,Shut off set if unavailable,"@df.not_transit_available_set2",,C_UNAVAILABLE, +set2_ivt,set In-Vehicle Time,"@~df.not_transit_available_set2 * df.c_ivt_for_segment * df.totalIVT_set2",,1, +set2_first_wait_time,First wait time,"@~df.not_transit_available_set2 * c_fwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'IWAIT_SET2')",,1, +set2_xfer_wait_time,set Transfer Wait Time,"@~df.not_transit_available_set2 * c_xwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWAIT_SET2')",,1, +set2_xfer_walk_time,set Walk transfer time,"@~df.not_transit_available_set2 * c_waux * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWTIME_SET2')",,1, +set2_fare,set Fare,"@~df.not_transit_available_set2 * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'FARE_SET2') * 100",,1, +set2_xfers1,0-1 transfers constant,"@~df.not_transit_available_set2 & ~df.bartOnly_set2 & df.xfers1_set2 * c_xfers1 * df.c_ivt_for_segment",,1, +set2_xfers2,1-2 transfers constant,"@~df.not_transit_available_set2 & ~df.bartOnly_set2 & df.xfers2_set2 * c_xfers2 * df.c_ivt_for_segment",,1, +set2_sfers3,>2 transfers constant,"@~df.not_transit_available_set2 & ~df.bartOnly_set2 & df.xfers3_set2 * c_xfers3 * df.c_ivt_for_segment",,1, +set2_xfers1_drive,0-1 transfers penalty for drive access,"@~df.not_transit_available_set2 & ~df.bartOnly_set2 & df.xfers1_set2 & (access_mode=='drive') * (df.c_ivt_for_segment * 15)",,1, +set2_xfers2_drive,1-2 transfers penalty for drive access,"@~df.not_transit_available_set2 & ~df.bartOnly_set2 & df.xfers2_set2 & (access_mode=='drive') * (df.c_ivt_for_segment * 15)",,1, +set2_sfers3_drive,>2 transfers penalty for drive access,"@~df.not_transit_available_set2 & ~df.bartOnly_set2 & (access_mode=='drive') & df.xfers3_set2 * (df.c_ivt_for_segment * 15)",,1, +set2_xfers1_bart,0-1 transfers constant when using only BART,"@~df.not_transit_available_set2 & df.bartOnly_set2 & df.xfers1_set2 * (df.c_ivt_for_segment * 5)",,1, +set2_xfers2_bart,1-2 transfers constant when using only BART,"@~df.not_transit_available_set2 & df.bartOnly_set2 & df.xfers2_set2 * (df.c_ivt_for_segment * 5)",,1, +set2_sfers3_bart,>2 transfers constant when using only BART,"@~df.not_transit_available_set2 & df.bartOnly_set2 & df.xfers3_set2 * (df.c_ivt_for_segment * 5)",,1, +set2_cr_20_40,CR distance 20-40 miles,"@~df.not_transit_available_set2 & (df.crDistance_set2>20) & (df.crDistance_set2<=40) * c_cr20_40 * df.c_ivt_for_segment",,1, +set2_cr_40_plus,CR distance > 40 miles,"@~df.not_transit_available_set2 & (df.crDistance_set2>40) * c_cr40plus * df.c_ivt_for_segment",,1, +set2_CR_drive,drive access to CR,"@~df.not_transit_available_set2 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2')>0) * c_drvCR * df.c_ivt_for_segment",,1, +set2_HR_drive,drive access to HR,"@~df.not_transit_available_set2 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')>0) * c_drvHeavy * df.c_ivt_for_segment",,1, +set2_FR_drive,drive access to FR,"@~df.not_transit_available_set2 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET2')>0) * c_drvFR * df.c_ivt_for_segment",,1, +set2_LRT_drive,drive access to LRT,"@~df.not_transit_available_set2 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2')>0) * c_drvLRT * df.c_ivt_for_segment",,1, +set2_EB_drive,drive access to EB,"@~df.not_transit_available_set2 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET2')>0) * c_drvExpress * df.c_ivt_for_segment",,1, +set2_ASC_CR,ASC CR,"@~df.not_transit_available_set2 & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2')>0) * c_cr_asc * df.c_ivt_for_segment",,1, +set2_ASC_HR,ASC HR,"@~df.not_transit_available_set2 & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')>0) * c_hr_asc * df.c_ivt_for_segment",,1, +set2_ASC_FR,ASC FR,"@~df.not_transit_available_set2 & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET2')>0) * c_fr_asc * df.c_ivt_for_segment",,1, +set2_ASC_LRT,ASC LRT,"@~df.not_transit_available_set2 & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2')>0) * c_lrt_asc * df.c_ivt_for_segment",,1, +#,1/3 ASC for premium with Xfers,"@~df.not_transit_available_set2 * premWithXfer_set2 * asc/3.0",,1, +# Set 3,,,,, +set3_unavailable,Shut off set if unavailable,"@df.not_transit_available_set3",,,C_UNAVAILABLE +set3_ivt,set In-Vehicle Time,"@~df.not_transit_available_set3 * df.c_ivt_for_segment * df.totalIVT_set3",,,1 +set3_first_wait_time,First wait time,"@~df.not_transit_available_set3 * c_fwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'IWAIT_SET3')",,,1 +set3_xfer_wait_time,set Transfer Wait Time,"@~df.not_transit_available_set3 * c_xwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWAIT_SET3')",,,1 +set3_xfer_walk_time,set Walk transfer time,"@~df.not_transit_available_set3 * c_waux * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWTIME_SET3')",,,1 +set3_fare,set Fare,"@~df.not_transit_available_set3 * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'FARE_SET3') * 100",,,1 +set3_xfers1,0-1 transfers constant,"@~df.not_transit_available_set3 & ~df.bartOnly_set3 & df.xfers1_set3 * c_xfers1 * df.c_ivt_for_segment",,,1 +set3_xfers2,1-2 transfers constant,"@~df.not_transit_available_set3 & ~df.bartOnly_set3 & df.xfers2_set3 * c_xfers2 * df.c_ivt_for_segment",,,1 +set3_sfers3,>2 transfers constant,"@~df.not_transit_available_set3 & ~df.bartOnly_set3 & df.xfers3_set3 * c_xfers3 * df.c_ivt_for_segment",,,1 +set3_xfers1_drive,0-1 transfers penalty for drive access,"@~df.not_transit_available_set3 & ~df.bartOnly_set3 & (access_mode=='drive') & df.xfers1_set3 * (df.c_ivt_for_segment * 15)",,,1 +set3_xfers2_drive,1-2 transfers penalty for drive access,"@~df.not_transit_available_set3 & ~df.bartOnly_set3 & (access_mode=='drive') & df.xfers2_set3 * (df.c_ivt_for_segment * 15)",,,1 +set3_sfers3_drive,>2 transfers penalty for drive access,"@~df.not_transit_available_set3 & ~df.bartOnly_set3 & (access_mode=='drive') & df.xfers3_set3 * (df.c_ivt_for_segment * 15)",,,1 +set3_xfers1_bart,0-1 transfers constant when using only BART,"@~df.not_transit_available_set3 & df.bartOnly_set3 & df.xfers1_set3 * (df.c_ivt_for_segment * 5)",,,1 +set3_xfers2_bart,1-2 transfers constant when using only BART,"@~df.not_transit_available_set3 & df.bartOnly_set3 & df.xfers2_set3 * (df.c_ivt_for_segment * 5)",,,1 +set3_sfers3_bart,>2 transfers constant when using only BART,"@~df.not_transit_available_set3 & df.bartOnly_set3 & df.xfers3_set3 * (df.c_ivt_for_segment * 5)",,,1 +set3_cr_20_40,CR distance 20-40 miles,"@~df.not_transit_available_set3 & (df.crDistance_set3>20) & (df.crDistance_set3<=40) * c_cr20_40 * df.c_ivt_for_segment",,,1 +set3_cr_40_plus,CR distance > 40 miles,"@~df.not_transit_available_set3 & (df.crDistance_set3>40) * c_cr40plus * df.c_ivt_for_segment",,,1 +set3_CR_drive,drive access to CR,"@~df.not_transit_available_set3 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3')>0) * c_drvCR * df.c_ivt_for_segment",,,1 +set3_HR_drive,drive access to HR,"@~df.not_transit_available_set3 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')>0) * c_drvHeavy * df.c_ivt_for_segment",,,1 +set3_FR_drive,drive access to FR,"@~df.not_transit_available_set3 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET3')>0) * c_drvFR * df.c_ivt_for_segment",,,1 +set3_LRT_drive,drive access to LRT,"@~df.not_transit_available_set3 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3')>0) * c_drvLRT * df.c_ivt_for_segment",,,1 +set3_EB_drive,drive access to EB,"@~df.not_transit_available_set3 & (access_mode=='drive') & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET3')>0) * c_drvExpress* df.c_ivt_for_segment",,,1 +set3_ASC_CR,ASC CR,"@~df.not_transit_available_set3 & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3')>0) * c_cr_asc * df.c_ivt_for_segment",,,1 +set3_ASC_HR,ASC HR,"@~df.not_transit_available_set3 & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')>0) * c_hr_asc * df.c_ivt_for_segment",,,1 +set3_ASC_FR,ASC FR,"@~df.not_transit_available_set3 & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET3')>0) * c_fr_asc * df.c_ivt_for_segment",,,1 +set3_ASC_LRT,ASC LRT,"@~df.not_transit_available_set3 & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3')>0) * c_lrt_asc * df.c_ivt_for_segment",,,1 +#,1/3 ASC for premium with Xfers,"@~df.not_transit_available_set3 * premWithXfer_set3 * asc/3.0",,,1 diff --git a/activitysim/examples/example_marin/configs/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv similarity index 99% rename from activitysim/examples/example_marin/configs/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv index ba5cbe1d30..f2661a897b 100755 --- a/activitysim/examples/example_marin/configs/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv +++ b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv @@ -1,44 +1,44 @@ -Description,Target,Expression -# time of day,,SHOULD BE PASSED IN -# demographic segment,, -,c_ivt_for_segment,"np.where(df.demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_ivt_low_income, c_ivt_high_income)" -,c_cost_for_segment,"np.where(df.demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_cost_low_income, c_cost_high_income)" -# set1,, -,not_transit_available_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BEST_MODE_SET1')==0" -Total IVT,totalIVT_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1') " -IVT on BART,bartIVT_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')" -premium modes used,premiumMode_set1,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET1')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1')>0)" -only travel by BART,bartOnly_set1,bartIVT_set1 == totalIVT_set1 -Set contains only BART with Xfers,bartWithXfer_set1,"(bartIVT_set1 == totalIVT_set1) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET1')>0)" -Set contains premium mode with transfers to LB,premWithXfer_set1,"(premiumMode_set1>0) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET1')>0)" -Number transfers,transfers_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET1')" -0-1 transfers,xfers1_set1,(transfers_set1>0) & (transfers_set1 <=1) -1-2 transfers,xfers2_set1,(transfers_set1>1) & (transfers_set1 <=2) ->2 transfers,xfers3_set1,(transfers_set1>2) -Commuter Rail Distance in miles [35 mph],crDistance_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1') * (35/60)" -# set2,, -,not_transit_available_set2,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'BEST_MODE_SET2')==0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET2')==0)" -Total IVT,totalIVT_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2') " -IVT on BART,bartIVT_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')" -premium modes used,premiumMode_set2,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET2')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2')>0)" -only travel by BART,bartOnly_set2,bartIVT_set2 == totalIVT_set2 -Set contains only BART with Xfers,bartWithXfer_set2,"(bartIVT_set2 == totalIVT_set2) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET2')>0)" -Set contains premium mode with transfers to LB,premWithXfer_set2,"(premiumMode_set2>0) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET2')>0)" -Number transfers,transfers_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET2')" -0-1 transfers,xfers1_set2,(transfers_set2>0) & (transfers_set2 <=1) -1-2 transfers,xfers2_set2,(transfers_set2>1) & (transfers_set2 <=2) ->2 transfers,xfers3_set2,(transfers_set2>2) -Commuter Rail Distance in miles [35 mph],crDistance_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2') * (35/60)" -# set3,, -,not_transit_available_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BEST_MODE_SET3')==0" -Total IVT,totalIVT_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3') " -IVT on BART,bartIVT_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')" -premium modes used,premiumMode_set3,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET3')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3')>0)" -only travel by BART,bartOnly_set3,bartIVT_set3 == totalIVT_set3 -Set contains only BART with Xfers,bartWithXfer_set3,"(bartIVT_set3 == totalIVT_set3) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET3')>0)" -Set contains premium mode with transfers to LB,premWithXfer_set3,"(premiumMode_set3>0) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET3')>0)" -Number transfers,transfers_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET3')" -0-1 transfers,xfers1_set3,(transfers_set3>0) & (transfers_set3 <=1) -1-2 transfers,xfers2_set3,(transfers_set3>1) & (transfers_set3 <=2) ->2 transfers,xfers3_set3,(transfers_set3>2) -Commuter Rail Distance in miles [35 mph],crDistance_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3') * (35/60)" +Description,Target,Expression +# time of day,,SHOULD BE PASSED IN +# demographic segment,, +,c_ivt_for_segment,"np.where(df.demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_ivt_low_income, c_ivt_high_income)" +,c_cost_for_segment,"np.where(df.demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_cost_low_income, c_cost_high_income)" +# set1,, +,not_transit_available_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BEST_MODE_SET1')==0" +Total IVT,totalIVT_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1') " +IVT on BART,bartIVT_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')" +premium modes used,premiumMode_set1,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET1')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1')>0)" +only travel by BART,bartOnly_set1,bartIVT_set1 == totalIVT_set1 +Set contains only BART with Xfers,bartWithXfer_set1,"(bartIVT_set1 == totalIVT_set1) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET1')>0)" +Set contains premium mode with transfers to LB,premWithXfer_set1,"(premiumMode_set1>0) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET1')>0)" +Number transfers,transfers_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET1')" +0-1 transfers,xfers1_set1,(transfers_set1>0) & (transfers_set1 <=1) +1-2 transfers,xfers2_set1,(transfers_set1>1) & (transfers_set1 <=2) +>2 transfers,xfers3_set1,(transfers_set1>2) +Commuter Rail Distance in miles [35 mph],crDistance_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1') * (35/60)" +# set2,, +,not_transit_available_set2,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'BEST_MODE_SET2')==0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET2')==0)" +Total IVT,totalIVT_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2') " +IVT on BART,bartIVT_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')" +premium modes used,premiumMode_set2,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET2')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2')>0)" +only travel by BART,bartOnly_set2,bartIVT_set2 == totalIVT_set2 +Set contains only BART with Xfers,bartWithXfer_set2,"(bartIVT_set2 == totalIVT_set2) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET2')>0)" +Set contains premium mode with transfers to LB,premWithXfer_set2,"(premiumMode_set2>0) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET2')>0)" +Number transfers,transfers_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET2')" +0-1 transfers,xfers1_set2,(transfers_set2>0) & (transfers_set2 <=1) +1-2 transfers,xfers2_set2,(transfers_set2>1) & (transfers_set2 <=2) +>2 transfers,xfers3_set2,(transfers_set2>2) +Commuter Rail Distance in miles [35 mph],crDistance_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2') * (35/60)" +# set3,, +,not_transit_available_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BEST_MODE_SET3')==0" +Total IVT,totalIVT_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3') " +IVT on BART,bartIVT_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')" +premium modes used,premiumMode_set3,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET3')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3')>0)" +only travel by BART,bartOnly_set3,bartIVT_set3 == totalIVT_set3 +Set contains only BART with Xfers,bartWithXfer_set3,"(bartIVT_set3 == totalIVT_set3) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET3')>0)" +Set contains premium mode with transfers to LB,premWithXfer_set3,"(premiumMode_set3>0) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET3')>0)" +Number transfers,transfers_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET3')" +0-1 transfers,xfers1_set3,(transfers_set3>0) & (transfers_set3 <=1) +1-2 transfers,xfers2_set3,(transfers_set3>1) & (transfers_set3 <=2) +>2 transfers,xfers3_set3,(transfers_set3>2) +Commuter Rail Distance in miles [35 mph],crDistance_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3') * (35/60)" diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_walk_maz_tap.csv b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tvpb_utility_walk_maz_tap.csv similarity index 98% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_walk_maz_tap.csv rename to activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tvpb_utility_walk_maz_tap.csv index 64a0a71559..294cb95ede 100755 --- a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_walk_maz_tap.csv +++ b/activitysim/examples/placeholder_multiple_zone/configs_3_zone_marin/tvpb_utility_walk_maz_tap.csv @@ -1,4 +1,4 @@ -Label,Description,Expression,utility -#,,,FIXME column values shouldn't ever be na if different moides have different tables? -#util_walk_available,walk available,@df.walk_time.isna() * C_UNAVAILABLE,1 -util_walk_time,walk time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_walk * df.WALK_TRANSIT_DIST*(60/walk_speed)",1 +Label,Description,Expression,utility +#,,,FIXME column values shouldn't ever be na if different moides have different tables? +#util_walk_available,walk available,@df.walk_time.isna() * C_UNAVAILABLE,1 +util_walk_time,walk time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_walk * df.WALK_TRANSIT_DIST*(60/walk_speed)",1 diff --git a/activitysim/examples/example_mtc/data/households.csv b/activitysim/examples/placeholder_multiple_zone/data/households.csv similarity index 100% rename from activitysim/examples/example_mtc/data/households.csv rename to activitysim/examples/placeholder_multiple_zone/data/households.csv diff --git a/activitysim/examples/example_multiple_zone/data/land_use.csv b/activitysim/examples/placeholder_multiple_zone/data/land_use.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/data/land_use.csv rename to activitysim/examples/placeholder_multiple_zone/data/land_use.csv diff --git a/activitysim/examples/example_mtc/data/mtc_asim.h5 b/activitysim/examples/placeholder_multiple_zone/data/mtc_asim.h5 similarity index 100% rename from activitysim/examples/example_mtc/data/mtc_asim.h5 rename to activitysim/examples/placeholder_multiple_zone/data/mtc_asim.h5 diff --git a/activitysim/examples/example_mtc/data/override_hh_ids.csv b/activitysim/examples/placeholder_multiple_zone/data/override_hh_ids.csv similarity index 100% rename from activitysim/examples/example_mtc/data/override_hh_ids.csv rename to activitysim/examples/placeholder_multiple_zone/data/override_hh_ids.csv diff --git a/activitysim/examples/example_mtc/data/persons.csv b/activitysim/examples/placeholder_multiple_zone/data/persons.csv similarity index 100% rename from activitysim/examples/example_mtc/data/persons.csv rename to activitysim/examples/placeholder_multiple_zone/data/persons.csv diff --git a/activitysim/examples/example_mtc/data/skims.omx b/activitysim/examples/placeholder_multiple_zone/data/skims.omx similarity index 100% rename from activitysim/examples/example_mtc/data/skims.omx rename to activitysim/examples/placeholder_multiple_zone/data/skims.omx diff --git a/activitysim/examples/example_multiple_zone/data/households.csv b/activitysim/examples/placeholder_multiple_zone/data_1/households.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/data/households.csv rename to activitysim/examples/placeholder_multiple_zone/data_1/households.csv diff --git a/activitysim/examples/example_mtc/data/land_use.csv b/activitysim/examples/placeholder_multiple_zone/data_1/land_use.csv similarity index 100% rename from activitysim/examples/example_mtc/data/land_use.csv rename to activitysim/examples/placeholder_multiple_zone/data_1/land_use.csv diff --git a/activitysim/examples/example_multiple_zone/data/persons.csv b/activitysim/examples/placeholder_multiple_zone/data_1/persons.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/data/persons.csv rename to activitysim/examples/placeholder_multiple_zone/data_1/persons.csv diff --git a/activitysim/examples/example_multiple_zone/data/skims.omx b/activitysim/examples/placeholder_multiple_zone/data_1/skims.omx similarity index 100% rename from activitysim/examples/example_multiple_zone/data/skims.omx rename to activitysim/examples/placeholder_multiple_zone/data_1/skims.omx diff --git a/activitysim/examples/example_multiple_zone/data_2/.gitignore b/activitysim/examples/placeholder_multiple_zone/data_2/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/data_2/.gitignore rename to activitysim/examples/placeholder_multiple_zone/data_2/.gitignore diff --git a/activitysim/examples/example_multiple_zone/data_3/.gitignore b/activitysim/examples/placeholder_multiple_zone/data_3/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/data_3/.gitignore rename to activitysim/examples/placeholder_multiple_zone/data_3/.gitignore diff --git a/activitysim/examples/example_multiple_zone/marin_crop.py b/activitysim/examples/placeholder_multiple_zone/marin_crop.py similarity index 96% rename from activitysim/examples/example_multiple_zone/marin_crop.py rename to activitysim/examples/placeholder_multiple_zone/marin_crop.py index 26d9a53a73..b0f3ca10c9 100644 --- a/activitysim/examples/example_multiple_zone/marin_crop.py +++ b/activitysim/examples/placeholder_multiple_zone/marin_crop.py @@ -1,160 +1,160 @@ - -# crop marin tvpb example data processing to one county -# Ben Stabler, ben.stabler@rsginc.com, 09/17/20 -# jeff doyle added code to introduce MAZ_OFFSET to avoid confusion (and detect associated errors) between zone types - -import os -import pandas as pd -import openmatrix as omx - -# counties = ["Marin" -# counties = ["San Francisco" -counties = ["Marin", "San Francisco"] - -input_dir = './data_3_marin' -output_dir = './data_3_marin/crop' -MAZ_OFFSET = 100000 - - -def input_path(file_name): - return os.path.join(input_dir, file_name) - - -def output_path(file_name): - return os.path.join(output_dir, file_name) - - -def patch_maz(df, maz_offset): - for c in df.columns: - if c in ['MAZ', 'OMAZ', 'DMAZ', 'mgra', 'orig_mgra', 'dest_mgra']: - df[c] += maz_offset - return df - - -def read_csv(file_name): - df = pd.read_csv(input_path(file_name)) - if MAZ_OFFSET: - df = patch_maz(df, MAZ_OFFSET) - print(f"\n\n{file_name}\n{df}") - return df - - -def to_csv(df, file_name): - df.to_csv(output_path(file_name), index=False) - - -######### -# mazs = read_csv("maz_data_asim.csv") -# taps = read_csv("tap_data.csv") -# -# print(f"max maz {mazs.MAZ.max()}") -# print(f"num maz {len(mazs.MAZ.unique())}") -# print(f"num taz {len(mazs.TAZ.unique())}") -# print(f"num tap {len(taps.TAP.unique())}") -# -# num maz 5952 -# num taz 4735 -# num tap 6216 -######### - - -# 0 - get county zones - -mazs = read_csv("maz_data_asim.csv") - -mazs = mazs[mazs["CountyName"].isin(counties)] -to_csv(mazs, "maz_data_asim.csv") - -maz_taz = mazs[['MAZ', 'TAZ']] -to_csv(mazs, "maz_taz.csv") - -tazs = mazs["TAZ"].unique() -tazs.sort() -tazs_indexes = (tazs - 1).tolist() - -taps = read_csv("tap_data.csv") -taps = taps[['TAP', 'TAZ']].sort_values(by='TAP') -taps = taps[taps["TAZ"].isin(tazs)] -to_csv(taps, "tap_data.csv") - -# 1-based tap_ids -taps_indexes = (taps["TAP"] - 1).tolist() - - -# 2 - maz to tap walk, bike - -maz_tap_walk = read_csv("maz_tap_walk.csv") -maz_maz_walk = read_csv("maz_maz_walk.csv") -maz_maz_bike = read_csv("maz_maz_bike.csv") - -maz_tap_walk = maz_tap_walk[maz_tap_walk["MAZ"].isin(mazs["MAZ"]) & maz_tap_walk["TAP"].isin(taps["TAP"])] -maz_maz_walk = maz_maz_walk[maz_maz_walk["OMAZ"].isin(mazs["MAZ"]) & maz_maz_walk["DMAZ"].isin(mazs["MAZ"])] -maz_maz_bike = maz_maz_bike[maz_maz_bike["OMAZ"].isin(mazs["MAZ"]) & maz_maz_bike["DMAZ"].isin(mazs["MAZ"])] - -to_csv(maz_tap_walk, "maz_tap_walk.csv") -to_csv(maz_maz_walk, "maz_maz_walk.csv") -to_csv(maz_maz_bike, "maz_maz_bike.csv") - - -tap_lines = read_csv("tap_lines.csv") -tap_lines = tap_lines[tap_lines['TAP'].isin(taps["TAP"])] -to_csv(tap_lines, "tap_lines.csv") - -# taz to tap drive data - -taz_tap_drive = read_csv("maz_taz_tap_drive.csv") -taz_tap_drive = taz_tap_drive[taz_tap_drive["MAZ"].isin(mazs["MAZ"]) & taz_tap_drive["TAP"].isin(taps["TAP"])] -to_csv(taz_tap_drive, "maz_taz_tap_drive.csv") - - -# 3 - accessibility data - -access = read_csv("access.csv") -access = access[access["mgra"].isin(mazs["MAZ"])] -to_csv(access, "access.csv") - - -# households - -households = read_csv("households_asim.csv") -households = households[households["MAZ"].isin(mazs["MAZ"])] -to_csv(households, "households_asim.csv") - -# persons - -persons = read_csv("persons_asim.csv") -persons = persons[persons["HHID"].isin(households["HHID"])] -to_csv(persons, "persons_asim.csv") - -# tours file - -work_tours = read_csv("work_tours.csv") -work_tours = work_tours[work_tours["hh_id"].isin(households["HHID"])] -work_tours = work_tours[work_tours["orig_mgra"].isin(mazs["MAZ"]) & work_tours["dest_mgra"].isin(mazs["MAZ"])] -to_csv(work_tours, "work_tours.csv") - -# skims - -time_periods = ["AM", "EA", "EV", "MD", "PM"] -for tp in time_periods: - omx_file_name = 'HWYSKM' + tp + '_taz_rename.omx' - taz_file = omx.open_file(input_path(omx_file_name)) - taz_file_rename = omx.open_file(output_path(omx_file_name), 'w') - taz_file_rename.create_mapping('ZONE', tazs.tolist()) - for mat_name in taz_file.list_matrices(): - taz_file_rename[mat_name] = taz_file[mat_name][tazs_indexes, :][:, tazs_indexes] - print(mat_name) - taz_file.close() - taz_file_rename.close() - -for tp in time_periods: - for skim_set in ["SET1", "SET2", "SET3"]: - omx_file_name = 'transit_skims_' + tp + '_' + skim_set + '_rename.omx' - tap_file = omx.open_file(input_path(omx_file_name)) - tap_file_rename = omx.open_file(output_path(omx_file_name), 'w') - tap_file_rename.create_mapping('ZONE', taps["TAP"].tolist()) - for mat_name in tap_file.list_matrices(): - tap_file_rename[mat_name] = tap_file[mat_name][taps_indexes, :][:, taps_indexes] - print(mat_name) - tap_file.close() - tap_file_rename.close() + +# crop marin tvpb example data processing to one county +# Ben Stabler, ben.stabler@rsginc.com, 09/17/20 +# jeff doyle added code to introduce MAZ_OFFSET to avoid confusion (and detect associated errors) between zone types + +import os +import pandas as pd +import openmatrix as omx + +# counties = ["Marin" +# counties = ["San Francisco" +counties = ["Marin", "San Francisco"] + +input_dir = './data_3_marin' +output_dir = './data_3_marin/crop' +MAZ_OFFSET = 100000 + + +def input_path(file_name): + return os.path.join(input_dir, file_name) + + +def output_path(file_name): + return os.path.join(output_dir, file_name) + + +def patch_maz(df, maz_offset): + for c in df.columns: + if c in ['MAZ', 'OMAZ', 'DMAZ', 'mgra', 'orig_mgra', 'dest_mgra']: + df[c] += maz_offset + return df + + +def read_csv(file_name): + df = pd.read_csv(input_path(file_name)) + if MAZ_OFFSET: + df = patch_maz(df, MAZ_OFFSET) + print(f"\n\n{file_name}\n{df}") + return df + + +def to_csv(df, file_name): + df.to_csv(output_path(file_name), index=False) + + +######### +# mazs = read_csv("maz_data_asim.csv") +# taps = read_csv("tap_data.csv") +# +# print(f"max maz {mazs.MAZ.max()}") +# print(f"num maz {len(mazs.MAZ.unique())}") +# print(f"num taz {len(mazs.TAZ.unique())}") +# print(f"num tap {len(taps.TAP.unique())}") +# +# num maz 5952 +# num taz 4735 +# num tap 6216 +######### + + +# 0 - get county zones + +mazs = read_csv("maz_data_asim.csv") + +mazs = mazs[mazs["CountyName"].isin(counties)] +to_csv(mazs, "maz_data_asim.csv") + +maz_taz = mazs[['MAZ', 'TAZ']] +to_csv(mazs, "maz_taz.csv") + +tazs = mazs["TAZ"].unique() +tazs.sort() +tazs_indexes = (tazs - 1).tolist() + +taps = read_csv("tap_data.csv") +taps = taps[['TAP', 'TAZ']].sort_values(by='TAP') +taps = taps[taps["TAZ"].isin(tazs)] +to_csv(taps, "tap_data.csv") + +# 1-based tap_ids +taps_indexes = (taps["TAP"] - 1).tolist() + + +# 2 - maz to tap walk, bike + +maz_tap_walk = read_csv("maz_tap_walk.csv") +maz_maz_walk = read_csv("maz_maz_walk.csv") +maz_maz_bike = read_csv("maz_maz_bike.csv") + +maz_tap_walk = maz_tap_walk[maz_tap_walk["MAZ"].isin(mazs["MAZ"]) & maz_tap_walk["TAP"].isin(taps["TAP"])] +maz_maz_walk = maz_maz_walk[maz_maz_walk["OMAZ"].isin(mazs["MAZ"]) & maz_maz_walk["DMAZ"].isin(mazs["MAZ"])] +maz_maz_bike = maz_maz_bike[maz_maz_bike["OMAZ"].isin(mazs["MAZ"]) & maz_maz_bike["DMAZ"].isin(mazs["MAZ"])] + +to_csv(maz_tap_walk, "maz_tap_walk.csv") +to_csv(maz_maz_walk, "maz_maz_walk.csv") +to_csv(maz_maz_bike, "maz_maz_bike.csv") + + +tap_lines = read_csv("tap_lines.csv") +tap_lines = tap_lines[tap_lines['TAP'].isin(taps["TAP"])] +to_csv(tap_lines, "tap_lines.csv") + +# taz to tap drive data + +taz_tap_drive = read_csv("maz_taz_tap_drive.csv") +taz_tap_drive = taz_tap_drive[taz_tap_drive["MAZ"].isin(mazs["MAZ"]) & taz_tap_drive["TAP"].isin(taps["TAP"])] +to_csv(taz_tap_drive, "maz_taz_tap_drive.csv") + + +# 3 - accessibility data + +access = read_csv("access.csv") +access = access[access["mgra"].isin(mazs["MAZ"])] +to_csv(access, "access.csv") + + +# households + +households = read_csv("households_asim.csv") +households = households[households["MAZ"].isin(mazs["MAZ"])] +to_csv(households, "households_asim.csv") + +# persons + +persons = read_csv("persons_asim.csv") +persons = persons[persons["HHID"].isin(households["HHID"])] +to_csv(persons, "persons_asim.csv") + +# tours file + +work_tours = read_csv("work_tours.csv") +work_tours = work_tours[work_tours["hh_id"].isin(households["HHID"])] +work_tours = work_tours[work_tours["orig_mgra"].isin(mazs["MAZ"]) & work_tours["dest_mgra"].isin(mazs["MAZ"])] +to_csv(work_tours, "work_tours.csv") + +# skims + +time_periods = ["AM", "EA", "EV", "MD", "PM"] +for tp in time_periods: + omx_file_name = 'HWYSKM' + tp + '_taz_rename.omx' + taz_file = omx.open_file(input_path(omx_file_name)) + taz_file_rename = omx.open_file(output_path(omx_file_name), 'w') + taz_file_rename.create_mapping('ZONE', tazs.tolist()) + for mat_name in taz_file.list_matrices(): + taz_file_rename[mat_name] = taz_file[mat_name][tazs_indexes, :][:, tazs_indexes] + print(mat_name) + taz_file.close() + taz_file_rename.close() + +for tp in time_periods: + for skim_set in ["SET1", "SET2", "SET3"]: + omx_file_name = 'transit_skims_' + tp + '_' + skim_set + '_rename.omx' + tap_file = omx.open_file(input_path(omx_file_name)) + tap_file_rename = omx.open_file(output_path(omx_file_name), 'w') + tap_file_rename.create_mapping('ZONE', taps["TAP"].tolist()) + for mat_name in tap_file.list_matrices(): + tap_file_rename[mat_name] = tap_file[mat_name][taps_indexes, :][:, taps_indexes] + print(mat_name) + tap_file.close() + tap_file_rename.close() diff --git a/activitysim/examples/example_marin/scripts/marin_fix.py b/activitysim/examples/placeholder_multiple_zone/marin_fix.py similarity index 100% rename from activitysim/examples/example_marin/scripts/marin_fix.py rename to activitysim/examples/placeholder_multiple_zone/marin_fix.py diff --git a/activitysim/examples/example_multiple_zone/marin_work_tour_mode_choice_data.py b/activitysim/examples/placeholder_multiple_zone/marin_work_tour_mode_choice_data.py similarity index 100% rename from activitysim/examples/example_multiple_zone/marin_work_tour_mode_choice_data.py rename to activitysim/examples/placeholder_multiple_zone/marin_work_tour_mode_choice_data.py diff --git a/activitysim/examples/example_multiple_zone/notes.txt b/activitysim/examples/placeholder_multiple_zone/notes.txt similarity index 89% rename from activitysim/examples/example_multiple_zone/notes.txt rename to activitysim/examples/placeholder_multiple_zone/notes.txt index 1f119fd213..d1d0277463 100644 --- a/activitysim/examples/example_multiple_zone/notes.txt +++ b/activitysim/examples/placeholder_multiple_zone/notes.txt @@ -1,8 +1,8 @@ # for the mtctm1 fudged examples, depending on where you are, run: # from top level activitysim repo: -python activitysim/examples/example_multiple_zone/two_zone_example_data.py -python activitysim/examples/example_multiple_zone/three_zone_example_data.py +python activitysim/examples/placeholder_multiple_zone/two_zone_example_data.py +python activitysim/examples/placeholder_multiple_zone/three_zone_example_data.py # or from this directory: python two_zone_example_data.py @@ -12,7 +12,7 @@ python simulation.py -c configs_local -c configs_1_zone -c configs -o output_1 python simulation.py -c configs_local -c configs_2_zone -c configs -d data_2 -o output_2 python simulation.py -c configs_local -c configs_3_zone -c configs -d data_3 -o output_3 -pytest -x activitysim/examples/example_multiple_zone/test/ +pytest -x activitysim/examples/placeholder_multiple_zone/test/ # for the marin data diff --git a/activitysim/examples/example_multiple_zone/output/mem.csv b/activitysim/examples/placeholder_multiple_zone/output/mem.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/output/mem.csv rename to activitysim/examples/placeholder_multiple_zone/output/mem.csv diff --git a/activitysim/examples/example_multiple_zone/output/pipeline.h5 b/activitysim/examples/placeholder_multiple_zone/output/pipeline.h5 similarity index 100% rename from activitysim/examples/example_multiple_zone/output/pipeline.h5 rename to activitysim/examples/placeholder_multiple_zone/output/pipeline.h5 diff --git a/activitysim/examples/example_arc/test/output/.gitignore b/activitysim/examples/placeholder_multiple_zone/output_1/.gitignore similarity index 100% rename from activitysim/examples/example_arc/test/output/.gitignore rename to activitysim/examples/placeholder_multiple_zone/output_1/.gitignore diff --git a/activitysim/examples/example_multiple_zone/output_1/cache/.gitignore b/activitysim/examples/placeholder_multiple_zone/output_1/cache/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/output_1/cache/.gitignore rename to activitysim/examples/placeholder_multiple_zone/output_1/cache/.gitignore diff --git a/activitysim/examples/example_arc/output/log/.gitignore b/activitysim/examples/placeholder_multiple_zone/output_1/trace/.gitignore similarity index 100% rename from activitysim/examples/example_arc/output/log/.gitignore rename to activitysim/examples/placeholder_multiple_zone/output_1/trace/.gitignore diff --git a/activitysim/examples/example_marin/output/.gitignore b/activitysim/examples/placeholder_multiple_zone/output_2/.gitignore similarity index 100% rename from activitysim/examples/example_marin/output/.gitignore rename to activitysim/examples/placeholder_multiple_zone/output_2/.gitignore diff --git a/activitysim/examples/example_multiple_zone/output_2/cache/.gitignore b/activitysim/examples/placeholder_multiple_zone/output_2/cache/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/output_2/cache/.gitignore rename to activitysim/examples/placeholder_multiple_zone/output_2/cache/.gitignore diff --git a/activitysim/examples/example_arc/output/trace/.gitignore b/activitysim/examples/placeholder_multiple_zone/output_2/trace/.gitignore similarity index 100% rename from activitysim/examples/example_arc/output/trace/.gitignore rename to activitysim/examples/placeholder_multiple_zone/output_2/trace/.gitignore diff --git a/activitysim/examples/example_marin/test/output/.gitignore b/activitysim/examples/placeholder_multiple_zone/output_3/.gitignore similarity index 100% rename from activitysim/examples/example_marin/test/output/.gitignore rename to activitysim/examples/placeholder_multiple_zone/output_3/.gitignore diff --git a/activitysim/examples/example_arc/output/cache/.gitignore b/activitysim/examples/placeholder_multiple_zone/output_3/cache/.gitignore similarity index 100% rename from activitysim/examples/example_arc/output/cache/.gitignore rename to activitysim/examples/placeholder_multiple_zone/output_3/cache/.gitignore diff --git a/activitysim/examples/example_arc/test/output/trace/.gitignore b/activitysim/examples/placeholder_multiple_zone/output_3/trace/.gitignore similarity index 100% rename from activitysim/examples/example_arc/test/output/trace/.gitignore rename to activitysim/examples/placeholder_multiple_zone/output_3/trace/.gitignore diff --git a/activitysim/examples/example_mtc/test/output/.gitignore b/activitysim/examples/placeholder_multiple_zone/output_3_example_marin_mp/.gitignore similarity index 100% rename from activitysim/examples/example_mtc/test/output/.gitignore rename to activitysim/examples/placeholder_multiple_zone/output_3_example_marin_mp/.gitignore diff --git a/activitysim/examples/example_arc/test/output/cache/.gitignore b/activitysim/examples/placeholder_multiple_zone/output_3_example_marin_mp/cache/.gitignore similarity index 100% rename from activitysim/examples/example_arc/test/output/cache/.gitignore rename to activitysim/examples/placeholder_multiple_zone/output_3_example_marin_mp/cache/.gitignore diff --git a/activitysim/examples/example_multiple_zone/output_3_example_marin_mp/log/.gitignore b/activitysim/examples/placeholder_multiple_zone/output_3_example_marin_mp/log/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/output_3_example_marin_mp/log/.gitignore rename to activitysim/examples/placeholder_multiple_zone/output_3_example_marin_mp/log/.gitignore diff --git a/activitysim/examples/example_marin/output/log/.gitignore b/activitysim/examples/placeholder_multiple_zone/output_3_example_marin_mp/trace/.gitignore similarity index 100% rename from activitysim/examples/example_marin/output/log/.gitignore rename to activitysim/examples/placeholder_multiple_zone/output_3_example_marin_mp/trace/.gitignore diff --git a/activitysim/examples/example_mtc_extended/output/.gitignore b/activitysim/examples/placeholder_multiple_zone/output_3_mp/.gitignore similarity index 100% rename from activitysim/examples/example_mtc_extended/output/.gitignore rename to activitysim/examples/placeholder_multiple_zone/output_3_mp/.gitignore diff --git a/activitysim/examples/example_marin/output/cache/.gitignore b/activitysim/examples/placeholder_multiple_zone/output_3_mp/cache/.gitignore similarity index 100% rename from activitysim/examples/example_marin/output/cache/.gitignore rename to activitysim/examples/placeholder_multiple_zone/output_3_mp/cache/.gitignore diff --git a/activitysim/examples/example_multiple_zone/output_3_mp/log/.gitignore b/activitysim/examples/placeholder_multiple_zone/output_3_mp/log/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/output_3_mp/log/.gitignore rename to activitysim/examples/placeholder_multiple_zone/output_3_mp/log/.gitignore diff --git a/activitysim/examples/example_marin/output/trace/.gitignore b/activitysim/examples/placeholder_multiple_zone/output_3_mp/trace/.gitignore similarity index 100% rename from activitysim/examples/example_marin/output/trace/.gitignore rename to activitysim/examples/placeholder_multiple_zone/output_3_mp/trace/.gitignore diff --git a/activitysim/examples/example_multiple_zone/scripts/notes.txt b/activitysim/examples/placeholder_multiple_zone/scripts/notes.txt similarity index 72% rename from activitysim/examples/example_multiple_zone/scripts/notes.txt rename to activitysim/examples/placeholder_multiple_zone/scripts/notes.txt index 3a487586e4..f6eb111363 100644 --- a/activitysim/examples/example_multiple_zone/scripts/notes.txt +++ b/activitysim/examples/placeholder_multiple_zone/scripts/notes.txt @@ -2,8 +2,8 @@ # for the mtctm1 fudged examples, depending on where you are, run: # from top level activitysim repo: -python activitysim/examples/example_multiple_zone/scripts/two_zone_example_data.py -python activitysim/examples/example_multiple_zone/scripts/three_zone_example_data.py +python activitysim/examples/placeholder_multiple_zone/scripts/two_zone_example_data.py +python activitysim/examples/placeholder_multiple_zone/scripts/three_zone_example_data.py # or from this directory: python two_zone_example_data.py diff --git a/activitysim/examples/example_multiple_zone/scripts/three_zone_example_data.py b/activitysim/examples/placeholder_multiple_zone/scripts/three_zone_example_data.py similarity index 100% rename from activitysim/examples/example_multiple_zone/scripts/three_zone_example_data.py rename to activitysim/examples/placeholder_multiple_zone/scripts/three_zone_example_data.py diff --git a/activitysim/examples/example_multiple_zone/scripts/two_zone_example_data.py b/activitysim/examples/placeholder_multiple_zone/scripts/two_zone_example_data.py similarity index 100% rename from activitysim/examples/example_multiple_zone/scripts/two_zone_example_data.py rename to activitysim/examples/placeholder_multiple_zone/scripts/two_zone_example_data.py diff --git a/activitysim/examples/example_mtc_extended/test/simulation.py b/activitysim/examples/placeholder_multiple_zone/simulation.py similarity index 100% rename from activitysim/examples/example_mtc_extended/test/simulation.py rename to activitysim/examples/placeholder_multiple_zone/simulation.py diff --git a/activitysim/examples/example_mtc/test/configs/network_los.yaml b/activitysim/examples/placeholder_multiple_zone/test/configs_2_zone/network_los.yaml similarity index 100% rename from activitysim/examples/example_mtc/test/configs/network_los.yaml rename to activitysim/examples/placeholder_multiple_zone/test/configs_2_zone/network_los.yaml diff --git a/activitysim/examples/example_multiple_zone/test/configs_2_zone/settings.yaml b/activitysim/examples/placeholder_multiple_zone/test/configs_2_zone/settings.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/test/configs_2_zone/settings.yaml rename to activitysim/examples/placeholder_multiple_zone/test/configs_2_zone/settings.yaml diff --git a/activitysim/examples/example_multiple_zone/test/configs_2_zone/settings_mp.yaml b/activitysim/examples/placeholder_multiple_zone/test/configs_2_zone/settings_mp.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/test/configs_2_zone/settings_mp.yaml rename to activitysim/examples/placeholder_multiple_zone/test/configs_2_zone/settings_mp.yaml diff --git a/activitysim/examples/example_multiple_zone/test/configs_3_zone/network_los.yaml b/activitysim/examples/placeholder_multiple_zone/test/configs_3_zone/network_los.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/test/configs_3_zone/network_los.yaml rename to activitysim/examples/placeholder_multiple_zone/test/configs_3_zone/network_los.yaml diff --git a/activitysim/examples/example_multiple_zone/test/configs_3_zone/settings.yaml b/activitysim/examples/placeholder_multiple_zone/test/configs_3_zone/settings.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/test/configs_3_zone/settings.yaml rename to activitysim/examples/placeholder_multiple_zone/test/configs_3_zone/settings.yaml diff --git a/activitysim/examples/example_multiple_zone/test/configs_3_zone/settings_mp.yaml b/activitysim/examples/placeholder_multiple_zone/test/configs_3_zone/settings_mp.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/test/configs_3_zone/settings_mp.yaml rename to activitysim/examples/placeholder_multiple_zone/test/configs_3_zone/settings_mp.yaml diff --git a/activitysim/examples/example_mtc_extended/test/output/.gitignore b/activitysim/examples/placeholder_multiple_zone/test/output/.gitignore similarity index 100% rename from activitysim/examples/example_mtc_extended/test/output/.gitignore rename to activitysim/examples/placeholder_multiple_zone/test/output/.gitignore diff --git a/activitysim/examples/example_marin/test/output/cache/.gitignore b/activitysim/examples/placeholder_multiple_zone/test/output/cache/.gitignore similarity index 100% rename from activitysim/examples/example_marin/test/output/cache/.gitignore rename to activitysim/examples/placeholder_multiple_zone/test/output/cache/.gitignore diff --git a/activitysim/examples/example_marin/test/output/trace/.gitignore b/activitysim/examples/placeholder_multiple_zone/test/output/trace/.gitignore similarity index 100% rename from activitysim/examples/example_marin/test/output/trace/.gitignore rename to activitysim/examples/placeholder_multiple_zone/test/output/trace/.gitignore diff --git a/activitysim/examples/example_multiple_zone/test/regress/final_tours_2_zone.csv b/activitysim/examples/placeholder_multiple_zone/test/regress/final_tours_2_zone.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/test/regress/final_tours_2_zone.csv rename to activitysim/examples/placeholder_multiple_zone/test/regress/final_tours_2_zone.csv diff --git a/activitysim/examples/example_multiple_zone/test/regress/final_tours_3_zone.csv b/activitysim/examples/placeholder_multiple_zone/test/regress/final_tours_3_zone.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/test/regress/final_tours_3_zone.csv rename to activitysim/examples/placeholder_multiple_zone/test/regress/final_tours_3_zone.csv diff --git a/activitysim/examples/example_multiple_zone/test/regress/final_trips_2_zone.csv b/activitysim/examples/placeholder_multiple_zone/test/regress/final_trips_2_zone.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/test/regress/final_trips_2_zone.csv rename to activitysim/examples/placeholder_multiple_zone/test/regress/final_trips_2_zone.csv diff --git a/activitysim/examples/example_multiple_zone/test/regress/final_trips_3_zone.csv b/activitysim/examples/placeholder_multiple_zone/test/regress/final_trips_3_zone.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/test/regress/final_trips_3_zone.csv rename to activitysim/examples/placeholder_multiple_zone/test/regress/final_trips_3_zone.csv diff --git a/activitysim/examples/example_multiple_zone/simulation.py b/activitysim/examples/placeholder_multiple_zone/test/simulation.py old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_multiple_zone/simulation.py rename to activitysim/examples/placeholder_multiple_zone/test/simulation.py diff --git a/activitysim/examples/example_multiple_zone/test/test_multiple_zone.py b/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py similarity index 93% rename from activitysim/examples/example_multiple_zone/test/test_multiple_zone.py rename to activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py index ec400c3fd9..efaa326129 100644 --- a/activitysim/examples/example_multiple_zone/test/test_multiple_zone.py +++ b/activitysim/examples/placeholder_multiple_zone/test/test_multiple_zone.py @@ -17,12 +17,12 @@ def teardown_function(func): def example_path(dirname): - resource = os.path.join('examples', 'example_multiple_zone', dirname) + resource = os.path.join('examples', 'placeholder_multiple_zone', dirname) return pkg_resources.resource_filename('activitysim', resource) def mtc_example_path(dirname): - resource = os.path.join('examples', 'example_mtc', dirname) + resource = os.path.join('examples', 'prototype_mtc', dirname) return pkg_resources.resource_filename('activitysim', resource) @@ -87,7 +87,7 @@ def test_2_zone_mp(data): def test_3_zone(data): # python simulation.py -c configs_3_zone -c ../configs_3_zone -c \ - # ../../example_mtc/configs -d ../data_3 -o output -s settings_mp + # ../../prototype_mtc/configs -d ../data_3 -o output -s settings_mp run_test(zone='3', multiprocess=False) diff --git a/activitysim/examples/example_multiple_zone/three_zone_example_data.py b/activitysim/examples/placeholder_multiple_zone/three_zone_example_data.py similarity index 100% rename from activitysim/examples/example_multiple_zone/three_zone_example_data.py rename to activitysim/examples/placeholder_multiple_zone/three_zone_example_data.py diff --git a/activitysim/examples/example_multiple_zone/two_zone_example_data.py b/activitysim/examples/placeholder_multiple_zone/two_zone_example_data.py similarity index 100% rename from activitysim/examples/example_multiple_zone/two_zone_example_data.py rename to activitysim/examples/placeholder_multiple_zone/two_zone_example_data.py diff --git a/activitysim/examples/example_arc/.gitignore b/activitysim/examples/placeholder_psrc/.gitignore similarity index 100% rename from activitysim/examples/example_arc/.gitignore rename to activitysim/examples/placeholder_psrc/.gitignore diff --git a/activitysim/examples/example_psrc/README.MD b/activitysim/examples/placeholder_psrc/README.MD similarity index 100% rename from activitysim/examples/example_psrc/README.MD rename to activitysim/examples/placeholder_psrc/README.MD diff --git a/activitysim/examples/example_psrc/change_log.txt b/activitysim/examples/placeholder_psrc/change_log.txt similarity index 99% rename from activitysim/examples/example_psrc/change_log.txt rename to activitysim/examples/placeholder_psrc/change_log.txt index 413c65880a..f8f3324f78 100644 --- a/activitysim/examples/example_psrc/change_log.txt +++ b/activitysim/examples/placeholder_psrc/change_log.txt @@ -266,7 +266,7 @@ BLDGSZ,HHID,HHT,MAZ,NOC,PERSONS,PUMA5,SERIALNO,TENURE,UNITTYPE,VEHICL,bucketBin, ##### data/maz_to_maz_walk.csv ### -# these files are wrong - they were simply copied from the example_multiple_zone 2-zone example data for MTC +# these files are wrong - they were simply copied from the placeholder_multiple_zone 2-zone example data for MTC ### ### tour_scheduling_work.csv diff --git a/activitysim/examples/example_arc/configs/_dummy_coefficients.csv b/activitysim/examples/placeholder_psrc/configs/_dummy_coefficients.csv similarity index 100% rename from activitysim/examples/example_arc/configs/_dummy_coefficients.csv rename to activitysim/examples/placeholder_psrc/configs/_dummy_coefficients.csv diff --git a/activitysim/examples/example_multiple_zone/configs_2_zone/accessibility.csv b/activitysim/examples/placeholder_psrc/configs/accessibility.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_2_zone/accessibility.csv rename to activitysim/examples/placeholder_psrc/configs/accessibility.csv diff --git a/activitysim/examples/example_mtc/configs/accessibility.yaml b/activitysim/examples/placeholder_psrc/configs/accessibility.yaml old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/accessibility.yaml rename to activitysim/examples/placeholder_psrc/configs/accessibility.yaml diff --git a/activitysim/examples/example_psrc/configs/annotate_households.csv b/activitysim/examples/placeholder_psrc/configs/annotate_households.csv similarity index 98% rename from activitysim/examples/example_psrc/configs/annotate_households.csv rename to activitysim/examples/placeholder_psrc/configs/annotate_households.csv index ed6f257847..ac2fd94b3d 100755 --- a/activitysim/examples/example_psrc/configs/annotate_households.csv +++ b/activitysim/examples/placeholder_psrc/configs/annotate_households.csv @@ -1,35 +1,35 @@ -Description,Target,Expression -#,, annotate households table after import -,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" -#,,FIXME households.income can be negative - so we clip? -income_in_thousands,income_in_thousands,(households.income / 1000).clip(lower=0) -income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[INCOME_SEGMENT_LOW, INCOME_SEGMENT_MED, INCOME_SEGMENT_HIGH, INCOME_SEGMENT_VERYHIGH]).astype(int)" -#,, -,_MIN_VOT,setting('min_value_of_time') -,_MAX_VOT,setting('max_value_of_time') -,_MU,setting('distributed_vot_mu') -,_SIGMA,setting('distributed_vot_sigma') -median_value_of_time,median_value_of_time,"income_segment.map({k: v for k, v in setting('household_median_value_of_time').items()})" -hh_value_of_time,hh_value_of_time,"rng.lognormal_for_df(df, mu=np.log(median_value_of_time * _MU), sigma=_SIGMA).clip(_MIN_VOT, _MAX_VOT)" -#,, -#num_workers was renamed in import,, -#,num_workers,households.workers -number of non_workers,num_non_workers,households.hhsize - households.num_workers -#,, -#,,we assume that everyone 16 and older is a potential driver -number of drivers,num_drivers,"_PERSON_COUNT('16 <= age', persons, households)" -num_adults,num_adults,"_PERSON_COUNT('adult', persons, households)" -num_children,num_children,"_PERSON_COUNT('~adult', persons, households)" -num_young_children,num_young_children,"_PERSON_COUNT('age <= 5', persons, households)" -num_children_5_to_15,num_children_5_to_15,"_PERSON_COUNT('5 <= age <= 15', persons, households)" -num_children_16_to_17,num_children_16_to_17,"_PERSON_COUNT('16 <= age <= 17', persons, households)" -num_college_age,num_college_age,"_PERSON_COUNT('18 <= age <= 24', persons, households)" -num_young_adults,num_young_adults,"_PERSON_COUNT('25 <= age <= 34', persons, households)" -non_family,non_family,households.HHT.isin(HHT_NONFAMILY) -family,family,households.HHT.isin(HHT_FAMILY) -home_is_urban,home_is_urban,"reindex(land_use.area_type, households.home_zone_id) < setting('urban_threshold')" -home_is_rural,home_is_rural,"reindex(land_use.area_type, households.home_zone_id) > setting('rural_threshold')" - - - - +Description,Target,Expression +#,, annotate households table after import +,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" +#,,FIXME households.income can be negative - so we clip? +income_in_thousands,income_in_thousands,(households.income / 1000).clip(lower=0) +income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[INCOME_SEGMENT_LOW, INCOME_SEGMENT_MED, INCOME_SEGMENT_HIGH, INCOME_SEGMENT_VERYHIGH]).astype(int)" +#,, +,_MIN_VOT,setting('min_value_of_time') +,_MAX_VOT,setting('max_value_of_time') +,_MU,setting('distributed_vot_mu') +,_SIGMA,setting('distributed_vot_sigma') +median_value_of_time,median_value_of_time,"income_segment.map({k: v for k, v in setting('household_median_value_of_time').items()})" +hh_value_of_time,hh_value_of_time,"rng.lognormal_for_df(df, mu=np.log(median_value_of_time * _MU), sigma=_SIGMA).clip(_MIN_VOT, _MAX_VOT)" +#,, +#num_workers was renamed in import,, +#,num_workers,households.workers +number of non_workers,num_non_workers,households.hhsize - households.num_workers +#,, +#,,we assume that everyone 16 and older is a potential driver +number of drivers,num_drivers,"_PERSON_COUNT('16 <= age', persons, households)" +num_adults,num_adults,"_PERSON_COUNT('adult', persons, households)" +num_children,num_children,"_PERSON_COUNT('~adult', persons, households)" +num_young_children,num_young_children,"_PERSON_COUNT('age <= 5', persons, households)" +num_children_5_to_15,num_children_5_to_15,"_PERSON_COUNT('5 <= age <= 15', persons, households)" +num_children_16_to_17,num_children_16_to_17,"_PERSON_COUNT('16 <= age <= 17', persons, households)" +num_college_age,num_college_age,"_PERSON_COUNT('18 <= age <= 24', persons, households)" +num_young_adults,num_young_adults,"_PERSON_COUNT('25 <= age <= 34', persons, households)" +non_family,non_family,households.HHT.isin(HHT_NONFAMILY) +family,family,households.HHT.isin(HHT_FAMILY) +home_is_urban,home_is_urban,"reindex(land_use.area_type, households.home_zone_id) < setting('urban_threshold')" +home_is_rural,home_is_rural,"reindex(land_use.area_type, households.home_zone_id) > setting('rural_threshold')" + + + + diff --git a/activitysim/examples/example_semcog/configs/annotate_households_cdap.csv b/activitysim/examples/placeholder_psrc/configs/annotate_households_cdap.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/annotate_households_cdap.csv rename to activitysim/examples/placeholder_psrc/configs/annotate_households_cdap.csv index a11620f76b..44b4fdcbfd 100755 --- a/activitysim/examples/example_semcog/configs/annotate_households_cdap.csv +++ b/activitysim/examples/placeholder_psrc/configs/annotate_households_cdap.csv @@ -1,9 +1,9 @@ -Description,Target,Expression -#,, annotate households table after cdap model has run -num_under16_not_at_school,num_under16_not_at_school,persons.under16_not_at_school.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) -num_travel_active,num_travel_active,persons.travel_active.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) -num_travel_active_adults,num_travel_active_adults,(persons.adult & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) -num_travel_active_preschoolers,num_travel_active_preschoolers,((persons.ptype == PTYPE_PRESCHOOL) & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) -num_travel_active_children,num_travel_active_children,num_travel_active - num_travel_active_adults -num_travel_active_non_preschoolers,num_travel_active_non_preschoolers,num_travel_active - num_travel_active_preschoolers -participates_in_jtf_model,participates_in_jtf_model,(num_travel_active > 1) & (num_travel_active_non_preschoolers > 0) +Description,Target,Expression +#,, annotate households table after cdap model has run +num_under16_not_at_school,num_under16_not_at_school,persons.under16_not_at_school.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active,num_travel_active,persons.travel_active.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_adults,num_travel_active_adults,(persons.adult & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_preschoolers,num_travel_active_preschoolers,((persons.ptype == PTYPE_PRESCHOOL) & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_children,num_travel_active_children,num_travel_active - num_travel_active_adults +num_travel_active_non_preschoolers,num_travel_active_non_preschoolers,num_travel_active - num_travel_active_preschoolers +participates_in_jtf_model,participates_in_jtf_model,(num_travel_active > 1) & (num_travel_active_non_preschoolers > 0) diff --git a/activitysim/examples/example_mtc/configs/annotate_households_workplace.csv b/activitysim/examples/placeholder_psrc/configs/annotate_households_workplace.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/annotate_households_workplace.csv rename to activitysim/examples/placeholder_psrc/configs/annotate_households_workplace.csv diff --git a/activitysim/examples/example_psrc/configs/annotate_landuse.csv b/activitysim/examples/placeholder_psrc/configs/annotate_landuse.csv similarity index 98% rename from activitysim/examples/example_psrc/configs/annotate_landuse.csv rename to activitysim/examples/placeholder_psrc/configs/annotate_landuse.csv index 1ff6c27843..229833a503 100755 --- a/activitysim/examples/example_psrc/configs/annotate_landuse.csv +++ b/activitysim/examples/placeholder_psrc/configs/annotate_landuse.csv @@ -1,5 +1,5 @@ -Description,Target,Expression -#,, annotate landuse table after import -household_density,household_density,land_use.TOTHH / (land_use.RESACRE + land_use.CIACRE) -employment_density,employment_density,land_use.TOTEMP / (land_use.RESACRE + land_use.CIACRE) -density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1) +Description,Target,Expression +#,, annotate landuse table after import +household_density,household_density,land_use.TOTHH / (land_use.RESACRE + land_use.CIACRE) +employment_density,employment_density,land_use.TOTEMP / (land_use.RESACRE + land_use.CIACRE) +density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1) diff --git a/activitysim/examples/example_mtc/configs/annotate_persons.csv b/activitysim/examples/placeholder_psrc/configs/annotate_persons.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/annotate_persons.csv rename to activitysim/examples/placeholder_psrc/configs/annotate_persons.csv diff --git a/activitysim/examples/example_mtc/configs/annotate_persons_after_hh.csv b/activitysim/examples/placeholder_psrc/configs/annotate_persons_after_hh.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/annotate_persons_after_hh.csv rename to activitysim/examples/placeholder_psrc/configs/annotate_persons_after_hh.csv diff --git a/activitysim/examples/example_mtc/configs/annotate_persons_cdap.csv b/activitysim/examples/placeholder_psrc/configs/annotate_persons_cdap.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/annotate_persons_cdap.csv rename to activitysim/examples/placeholder_psrc/configs/annotate_persons_cdap.csv diff --git a/activitysim/examples/example_mtc/configs/annotate_persons_jtp.csv b/activitysim/examples/placeholder_psrc/configs/annotate_persons_jtp.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/annotate_persons_jtp.csv rename to activitysim/examples/placeholder_psrc/configs/annotate_persons_jtp.csv diff --git a/activitysim/examples/example_mtc/configs/annotate_persons_mtf.csv b/activitysim/examples/placeholder_psrc/configs/annotate_persons_mtf.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/annotate_persons_mtf.csv rename to activitysim/examples/placeholder_psrc/configs/annotate_persons_mtf.csv diff --git a/activitysim/examples/example_mtc/configs/annotate_persons_nmtf.csv b/activitysim/examples/placeholder_psrc/configs/annotate_persons_nmtf.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/annotate_persons_nmtf.csv rename to activitysim/examples/placeholder_psrc/configs/annotate_persons_nmtf.csv diff --git a/activitysim/examples/example_mtc/configs/annotate_persons_school.csv b/activitysim/examples/placeholder_psrc/configs/annotate_persons_school.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/annotate_persons_school.csv rename to activitysim/examples/placeholder_psrc/configs/annotate_persons_school.csv diff --git a/activitysim/examples/example_mtc/configs/annotate_persons_workplace.csv b/activitysim/examples/placeholder_psrc/configs/annotate_persons_workplace.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/annotate_persons_workplace.csv rename to activitysim/examples/placeholder_psrc/configs/annotate_persons_workplace.csv diff --git a/activitysim/examples/example_psrc/configs/atwork_subtour_destination.csv b/activitysim/examples/placeholder_psrc/configs/atwork_subtour_destination.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/atwork_subtour_destination.csv rename to activitysim/examples/placeholder_psrc/configs/atwork_subtour_destination.csv diff --git a/activitysim/examples/example_psrc/configs/atwork_subtour_destination.yaml b/activitysim/examples/placeholder_psrc/configs/atwork_subtour_destination.yaml similarity index 100% rename from activitysim/examples/example_psrc/configs/atwork_subtour_destination.yaml rename to activitysim/examples/placeholder_psrc/configs/atwork_subtour_destination.yaml diff --git a/activitysim/examples/example_mtc/configs/atwork_subtour_destination_coefficients.csv b/activitysim/examples/placeholder_psrc/configs/atwork_subtour_destination_coeffs.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/atwork_subtour_destination_coefficients.csv rename to activitysim/examples/placeholder_psrc/configs/atwork_subtour_destination_coeffs.csv diff --git a/activitysim/examples/example_psrc/configs/atwork_subtour_destination_sample.csv b/activitysim/examples/placeholder_psrc/configs/atwork_subtour_destination_sample.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/atwork_subtour_destination_sample.csv rename to activitysim/examples/placeholder_psrc/configs/atwork_subtour_destination_sample.csv diff --git a/activitysim/examples/example_psrc/configs/atwork_subtour_frequency.csv b/activitysim/examples/placeholder_psrc/configs/atwork_subtour_frequency.csv similarity index 99% rename from activitysim/examples/example_psrc/configs/atwork_subtour_frequency.csv rename to activitysim/examples/placeholder_psrc/configs/atwork_subtour_frequency.csv index 5e7ba625c8..06e9f8878f 100755 --- a/activitysim/examples/example_psrc/configs/atwork_subtour_frequency.csv +++ b/activitysim/examples/placeholder_psrc/configs/atwork_subtour_frequency.csv @@ -1,23 +1,23 @@ -Label,Expression,no_subtours,eat,business1,maint,business2,eat_business -util_dummy_for_full_time_worker,pemploy==1,coefficient_dummy_for_full_time_worker_no_subtours,coefficient_dummy_for_full_time_worker_eat,coefficient_dummy_for_full_time_worker_business1,coefficient_dummy_for_full_time_worker_maint,coefficient_dummy_for_full_time_worker_business2,coefficient_dummy_for_full_time_worker_eat_business -util_dummy_for_non_full_time_worker,pemploy!=1,coefficient_dummy_for_non_full_time_worker_no_subtours,coefficient_dummy_for_non_full_time_worker_eat,coefficient_dummy_for_non_full_time_worker_business1,coefficient_dummy_for_non_full_time_worker_maint,coefficient_dummy_for_non_full_time_worker_business2,coefficient_dummy_for_non_full_time_worker_eat_business -util_dummy_for_non_workers,"ptype in [4, 5]",coefficient_dummy_for_non_workers_no_subtours,coefficient_dummy_for_non_workers_eat,coefficient_dummy_for_non_workers_business1,coefficient_dummy_for_non_workers_maint,coefficient_dummy_for_non_workers_business2,coefficient_dummy_for_non_workers_eat_business -util_medium_hh_income_dummy,income_segment == 2,coefficient_medium_hh_income_dummy_no_subtours,coefficient_medium_hh_income_dummy_eat,coefficient_medium_hh_income_dummy_business1,coefficient_medium_hh_income_dummy_maint,coefficient_medium_hh_income_dummy_business2,coefficient_medium_hh_income_dummy_eat_business -util_high_hh_income_dummy,(income_segment > 2) & (income_segment < 5),coefficient_high_hh_income_dummy_no_subtours,coefficient_high_hh_income_dummy_eat,coefficient_high_hh_income_dummy_business1,coefficient_high_hh_income_dummy_maint,coefficient_high_hh_income_dummy_business2,coefficient_high_hh_income_dummy_eat_business -util_zero_cars_owned_by_hh_dummy, auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business -util_individual_discretionary_tours_made_by_full_time_worker,@(df.pemploy==1)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business -util_individual_discretionary_tours_made_by_part_time_worker,@(df.pemploy==2)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business -util_individual_eating_out_tours_made_by_person,num_eatout_tours,coefficient_individual_eating_out_tours_made_by_person_no_subtours,coefficient_individual_eating_out_tours_made_by_person_eat,coefficient_individual_eating_out_tours_made_by_person_business1,coefficient_individual_eating_out_tours_made_by_person_maint,coefficient_individual_eating_out_tours_made_by_person_business2,coefficient_individual_eating_out_tours_made_by_person_eat_business -util_main_shop_escort_tours_allocated_to_full_time_worker,@(df.pemploy==1)*df.num_maint_shop_escort,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business -util_main_shop_escort_tours_allocated_to_part_time_worker,@(df.pemploy==2)*df.num_maint_shop_escort,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business -util_participation_in_joint_shop_main_eat_tours,num_joint_maint_shop_eat,coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,coefficient_participation_in_joint_shop_main_eat_tours_eat,coefficient_participation_in_joint_shop_main_eat_tours_business1,coefficient_participation_in_joint_shop_main_eat_tours_maint,coefficient_participation_in_joint_shop_main_eat_tours_business2,coefficient_participation_in_joint_shop_main_eat_tours_eat_business -util_participation_in_joint_discretionary_tours,num_joint_discr,coefficient_participation_in_joint_discretionary_tours_no_subtours,coefficient_participation_in_joint_discretionary_tours_eat,coefficient_participation_in_joint_discretionary_tours_business1,coefficient_participation_in_joint_discretionary_tours_maint,coefficient_participation_in_joint_discretionary_tours_business2,coefficient_participation_in_joint_discretionary_tours_eat_business -util_log_of_the_work_tour_duration,@np.log(df.duration+0.5),coefficient_log_of_the_work_tour_duration_no_subtours,coefficient_log_of_the_work_tour_duration_eat,coefficient_log_of_the_work_tour_duration_business1,coefficient_log_of_the_work_tour_duration_maint,coefficient_log_of_the_work_tour_duration_business2,coefficient_log_of_the_work_tour_duration_eat_business -util_dummy_for_drive_alone_mode_for_work_tour,work_tour_is_SOV,coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business -util_two_work_tours_by_person,num_work_tours==2,coefficient_two_work_tours_by_person_no_subtours,coefficient_two_work_tours_by_person_eat,coefficient_two_work_tours_by_person_business1,coefficient_two_work_tours_by_person_maint,coefficient_two_work_tours_by_person_business2,coefficient_two_work_tours_by_person_eat_business -util_workplace_urban_area_dummy,work_zone_area_type<4,coefficient_workplace_urban_area_dummy_no_subtours,coefficient_workplace_urban_area_dummy_eat,coefficient_workplace_urban_area_dummy_business1,coefficient_workplace_urban_area_dummy_maint,coefficient_workplace_urban_area_dummy_business2,coefficient_workplace_urban_area_dummy_eat_business -util_workplace_suburban_area_dummy,(work_zone_area_type>3) & (work_zone_area_type<6),coefficient_workplace_suburban_area_dummy_no_subtours,coefficient_workplace_suburban_area_dummy_eat,coefficient_workplace_suburban_area_dummy_business1,coefficient_workplace_suburban_area_dummy_maint,coefficient_workplace_suburban_area_dummy_business2,coefficient_workplace_suburban_area_dummy_eat_business -util_auto_accessibility_to_retail_for_work_taz,auOpRetail,coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,coefficient_auto_accessibility_to_retail_for_work_taz_eat,coefficient_auto_accessibility_to_retail_for_work_taz_business1,coefficient_auto_accessibility_to_retail_for_work_taz_maint,coefficient_auto_accessibility_to_retail_for_work_taz_business2,coefficient_auto_accessibility_to_retail_for_work_taz_eat_business -util_walk_accessibility_to_retail_for_work_taz,nmRetail,coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,coefficient_walk_accessibility_to_retail_for_work_taz_eat,coefficient_walk_accessibility_to_retail_for_work_taz_business1,coefficient_walk_accessibility_to_retail_for_work_taz_maint,coefficient_walk_accessibility_to_retail_for_work_taz_business2,coefficient_walk_accessibility_to_retail_for_work_taz_eat_business -util_dummy_for_worker_or_student_with_non_mandatory_tour,(is_worker | is_student) * num_non_mand,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business -util_at_work_sub_tour_alternative_specific_constant,1,coefficient_at_work_sub_tour_alternative_specific_constant_no_subtours,coefficient_at_work_sub_tour_alternative_specific_constant_eat,coefficient_at_work_sub_tour_alternative_specific_constant_business1,coefficient_at_work_sub_tour_alternative_specific_constant_maint,coefficient_at_work_sub_tour_alternative_specific_constant_business2,coefficient_at_work_sub_tour_alternative_specific_constant_eat_business +Label,Expression,no_subtours,eat,business1,maint,business2,eat_business +util_dummy_for_full_time_worker,pemploy==1,coefficient_dummy_for_full_time_worker_no_subtours,coefficient_dummy_for_full_time_worker_eat,coefficient_dummy_for_full_time_worker_business1,coefficient_dummy_for_full_time_worker_maint,coefficient_dummy_for_full_time_worker_business2,coefficient_dummy_for_full_time_worker_eat_business +util_dummy_for_non_full_time_worker,pemploy!=1,coefficient_dummy_for_non_full_time_worker_no_subtours,coefficient_dummy_for_non_full_time_worker_eat,coefficient_dummy_for_non_full_time_worker_business1,coefficient_dummy_for_non_full_time_worker_maint,coefficient_dummy_for_non_full_time_worker_business2,coefficient_dummy_for_non_full_time_worker_eat_business +util_dummy_for_non_workers,"ptype in [4, 5]",coefficient_dummy_for_non_workers_no_subtours,coefficient_dummy_for_non_workers_eat,coefficient_dummy_for_non_workers_business1,coefficient_dummy_for_non_workers_maint,coefficient_dummy_for_non_workers_business2,coefficient_dummy_for_non_workers_eat_business +util_medium_hh_income_dummy,income_segment == 2,coefficient_medium_hh_income_dummy_no_subtours,coefficient_medium_hh_income_dummy_eat,coefficient_medium_hh_income_dummy_business1,coefficient_medium_hh_income_dummy_maint,coefficient_medium_hh_income_dummy_business2,coefficient_medium_hh_income_dummy_eat_business +util_high_hh_income_dummy,(income_segment > 2) & (income_segment < 5),coefficient_high_hh_income_dummy_no_subtours,coefficient_high_hh_income_dummy_eat,coefficient_high_hh_income_dummy_business1,coefficient_high_hh_income_dummy_maint,coefficient_high_hh_income_dummy_business2,coefficient_high_hh_income_dummy_eat_business +util_zero_cars_owned_by_hh_dummy, auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business +util_individual_discretionary_tours_made_by_full_time_worker,@(df.pemploy==1)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business +util_individual_discretionary_tours_made_by_part_time_worker,@(df.pemploy==2)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business +util_individual_eating_out_tours_made_by_person,num_eatout_tours,coefficient_individual_eating_out_tours_made_by_person_no_subtours,coefficient_individual_eating_out_tours_made_by_person_eat,coefficient_individual_eating_out_tours_made_by_person_business1,coefficient_individual_eating_out_tours_made_by_person_maint,coefficient_individual_eating_out_tours_made_by_person_business2,coefficient_individual_eating_out_tours_made_by_person_eat_business +util_main_shop_escort_tours_allocated_to_full_time_worker,@(df.pemploy==1)*df.num_maint_shop_escort,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business +util_main_shop_escort_tours_allocated_to_part_time_worker,@(df.pemploy==2)*df.num_maint_shop_escort,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business +util_participation_in_joint_shop_main_eat_tours,num_joint_maint_shop_eat,coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,coefficient_participation_in_joint_shop_main_eat_tours_eat,coefficient_participation_in_joint_shop_main_eat_tours_business1,coefficient_participation_in_joint_shop_main_eat_tours_maint,coefficient_participation_in_joint_shop_main_eat_tours_business2,coefficient_participation_in_joint_shop_main_eat_tours_eat_business +util_participation_in_joint_discretionary_tours,num_joint_discr,coefficient_participation_in_joint_discretionary_tours_no_subtours,coefficient_participation_in_joint_discretionary_tours_eat,coefficient_participation_in_joint_discretionary_tours_business1,coefficient_participation_in_joint_discretionary_tours_maint,coefficient_participation_in_joint_discretionary_tours_business2,coefficient_participation_in_joint_discretionary_tours_eat_business +util_log_of_the_work_tour_duration,@np.log(df.duration+0.5),coefficient_log_of_the_work_tour_duration_no_subtours,coefficient_log_of_the_work_tour_duration_eat,coefficient_log_of_the_work_tour_duration_business1,coefficient_log_of_the_work_tour_duration_maint,coefficient_log_of_the_work_tour_duration_business2,coefficient_log_of_the_work_tour_duration_eat_business +util_dummy_for_drive_alone_mode_for_work_tour,work_tour_is_SOV,coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business +util_two_work_tours_by_person,num_work_tours==2,coefficient_two_work_tours_by_person_no_subtours,coefficient_two_work_tours_by_person_eat,coefficient_two_work_tours_by_person_business1,coefficient_two_work_tours_by_person_maint,coefficient_two_work_tours_by_person_business2,coefficient_two_work_tours_by_person_eat_business +util_workplace_urban_area_dummy,work_zone_area_type<4,coefficient_workplace_urban_area_dummy_no_subtours,coefficient_workplace_urban_area_dummy_eat,coefficient_workplace_urban_area_dummy_business1,coefficient_workplace_urban_area_dummy_maint,coefficient_workplace_urban_area_dummy_business2,coefficient_workplace_urban_area_dummy_eat_business +util_workplace_suburban_area_dummy,(work_zone_area_type>3) & (work_zone_area_type<6),coefficient_workplace_suburban_area_dummy_no_subtours,coefficient_workplace_suburban_area_dummy_eat,coefficient_workplace_suburban_area_dummy_business1,coefficient_workplace_suburban_area_dummy_maint,coefficient_workplace_suburban_area_dummy_business2,coefficient_workplace_suburban_area_dummy_eat_business +util_auto_accessibility_to_retail_for_work_taz,auOpRetail,coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,coefficient_auto_accessibility_to_retail_for_work_taz_eat,coefficient_auto_accessibility_to_retail_for_work_taz_business1,coefficient_auto_accessibility_to_retail_for_work_taz_maint,coefficient_auto_accessibility_to_retail_for_work_taz_business2,coefficient_auto_accessibility_to_retail_for_work_taz_eat_business +util_walk_accessibility_to_retail_for_work_taz,nmRetail,coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,coefficient_walk_accessibility_to_retail_for_work_taz_eat,coefficient_walk_accessibility_to_retail_for_work_taz_business1,coefficient_walk_accessibility_to_retail_for_work_taz_maint,coefficient_walk_accessibility_to_retail_for_work_taz_business2,coefficient_walk_accessibility_to_retail_for_work_taz_eat_business +util_dummy_for_worker_or_student_with_non_mandatory_tour,(is_worker | is_student) * num_non_mand,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business +util_at_work_sub_tour_alternative_specific_constant,1,coefficient_at_work_sub_tour_alternative_specific_constant_no_subtours,coefficient_at_work_sub_tour_alternative_specific_constant_eat,coefficient_at_work_sub_tour_alternative_specific_constant_business1,coefficient_at_work_sub_tour_alternative_specific_constant_maint,coefficient_at_work_sub_tour_alternative_specific_constant_business2,coefficient_at_work_sub_tour_alternative_specific_constant_eat_business diff --git a/activitysim/examples/example_arc/configs/atwork_subtour_frequency.yaml b/activitysim/examples/placeholder_psrc/configs/atwork_subtour_frequency.yaml old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_arc/configs/atwork_subtour_frequency.yaml rename to activitysim/examples/placeholder_psrc/configs/atwork_subtour_frequency.yaml diff --git a/activitysim/examples/example_mtc/configs/atwork_subtour_frequency_alternatives.csv b/activitysim/examples/placeholder_psrc/configs/atwork_subtour_frequency_alternatives.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/atwork_subtour_frequency_alternatives.csv rename to activitysim/examples/placeholder_psrc/configs/atwork_subtour_frequency_alternatives.csv diff --git a/activitysim/examples/example_mtc/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv b/activitysim/examples/placeholder_psrc/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv rename to activitysim/examples/placeholder_psrc/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv diff --git a/activitysim/examples/example_psrc/configs/atwork_subtour_frequency_coeffs.csv b/activitysim/examples/placeholder_psrc/configs/atwork_subtour_frequency_coeffs.csv similarity index 98% rename from activitysim/examples/example_psrc/configs/atwork_subtour_frequency_coeffs.csv rename to activitysim/examples/placeholder_psrc/configs/atwork_subtour_frequency_coeffs.csv index 79640c1d29..c59a4e2ee3 100755 --- a/activitysim/examples/example_psrc/configs/atwork_subtour_frequency_coeffs.csv +++ b/activitysim/examples/placeholder_psrc/configs/atwork_subtour_frequency_coeffs.csv @@ -1,133 +1,133 @@ -coefficient_name,value,constrain -coefficient_dummy_for_full_time_worker_business1,-7.375,F -coefficient_dummy_for_full_time_worker_business2,-14.28,F -coefficient_dummy_for_full_time_worker_eat,-7.28,F -coefficient_dummy_for_full_time_worker_eat_business,-14.79,F -coefficient_dummy_for_full_time_worker_maint,-8.093,F -coefficient_dummy_for_full_time_worker_no_subtours,-0.6,F -coefficient_dummy_for_non_full_time_worker_business1,-8.319,F -coefficient_dummy_for_non_full_time_worker_business2,-14.28,F -coefficient_dummy_for_non_full_time_worker_eat,-8.604,F -coefficient_dummy_for_non_full_time_worker_eat_business,-14.79,F -coefficient_dummy_for_non_full_time_worker_maint,-8.214,F -coefficient_dummy_for_non_full_time_worker_no_subtours,-0.6,F -coefficient_dummy_for_non_workers_business1,-5,T -coefficient_dummy_for_non_workers_business2,-5,T -coefficient_dummy_for_non_workers_eat,0,T -coefficient_dummy_for_non_workers_eat_business,-5,T -coefficient_dummy_for_non_workers_maint,-5,T -coefficient_dummy_for_non_workers_no_subtours,0,T -coefficient_medium_hh_income_dummy_business1,0.5555,F -coefficient_medium_hh_income_dummy_business2,1.111,F -coefficient_medium_hh_income_dummy_eat,0.61,F -coefficient_medium_hh_income_dummy_eat_business,1.1655,F -coefficient_medium_hh_income_dummy_maint,0.1527,F -coefficient_medium_hh_income_dummy_no_subtours,0,T -coefficient_high_hh_income_dummy_business1,1.066,F -coefficient_high_hh_income_dummy_business2,2.132,F -coefficient_high_hh_income_dummy_eat,0.8693,F -coefficient_high_hh_income_dummy_eat_business,1.9353,F -coefficient_high_hh_income_dummy_maint,0.1651,F -coefficient_high_hh_income_dummy_no_subtours,0,T -coefficient_zero_cars_owned_by_hh_dummy_business1,-0.3391,F -coefficient_zero_cars_owned_by_hh_dummy_business2,0,T -coefficient_zero_cars_owned_by_hh_dummy_eat,0,T -coefficient_zero_cars_owned_by_hh_dummy_eat_business,-0.3391,F -coefficient_zero_cars_owned_by_hh_dummy_maint,0.1762,F -coefficient_zero_cars_owned_by_hh_dummy_no_subtours,0,T -coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,0.7045,F -coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,1.409,F -coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,0.2334,F -coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business,0.9379,F -coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,0.5061,F -coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,0,T -coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,0.7045,F -coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,1.409,F -coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,0.6776,F -coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business,1.3821,F -coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,0.5061,F -coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,0,T -coefficient_individual_eating_out_tours_made_by_person_business1,0.5434,F -coefficient_individual_eating_out_tours_made_by_person_business2,1.0868,F -coefficient_individual_eating_out_tours_made_by_person_eat,0.5491,F -coefficient_individual_eating_out_tours_made_by_person_eat_business,1.0925,F -coefficient_individual_eating_out_tours_made_by_person_maint,0.9166,F -coefficient_individual_eating_out_tours_made_by_person_no_subtours,0,T -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,-0.1903,F -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,-0.3806,F -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,0.052,F -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business,-0.2423,F -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,0.1446,F -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,0,T -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,-0.1903,F -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,-0.3806,F -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,-0.3099,F -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business,-0.5002,F -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,-0.2723,F -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,0,T -coefficient_participation_in_joint_shop_main_eat_tours_business1,0.083,F -coefficient_participation_in_joint_shop_main_eat_tours_business2,0.166,F -coefficient_participation_in_joint_shop_main_eat_tours_eat,0.2458,F -coefficient_participation_in_joint_shop_main_eat_tours_eat_business,0.3288,F -coefficient_participation_in_joint_shop_main_eat_tours_maint,0.0803,F -coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,0,T -coefficient_participation_in_joint_discretionary_tours_business1,-0.2637,F -coefficient_participation_in_joint_discretionary_tours_business2,-0.5274,F -coefficient_participation_in_joint_discretionary_tours_eat,0.3588,F -coefficient_participation_in_joint_discretionary_tours_eat_business,0.0951,F -coefficient_participation_in_joint_discretionary_tours_maint,0.5822,F -coefficient_participation_in_joint_discretionary_tours_no_subtours,0,T -coefficient_log_of_the_work_tour_duration_business1,1.142,F -coefficient_log_of_the_work_tour_duration_business2,2.284,F -coefficient_log_of_the_work_tour_duration_eat,1.55,F -coefficient_log_of_the_work_tour_duration_eat_business,2.692,F -coefficient_log_of_the_work_tour_duration_maint,1.659,F -coefficient_log_of_the_work_tour_duration_no_subtours,0,T -coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,0.9901,F -coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,1.9802,F -coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,0.4804,F -coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business,1.4705,F -coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,1.153,F -coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,0,T -coefficient_two_work_tours_by_person_business1,0.3753,F -coefficient_two_work_tours_by_person_business2,0.7506,F -coefficient_two_work_tours_by_person_eat,-0.9862,F -coefficient_two_work_tours_by_person_eat_business,-0.6109,F -coefficient_two_work_tours_by_person_maint,-0.2312,F -coefficient_two_work_tours_by_person_no_subtours,0,T -coefficient_workplace_urban_area_dummy_business1,-0.2235,F -coefficient_workplace_urban_area_dummy_business2,-0.447,F -coefficient_workplace_urban_area_dummy_eat,-0.4182,F -coefficient_workplace_urban_area_dummy_eat_business,-0.6417,F -coefficient_workplace_urban_area_dummy_maint,-0.1479,F -coefficient_workplace_urban_area_dummy_no_subtours,0,T -coefficient_workplace_suburban_area_dummy_business1,-0.1102,F -coefficient_workplace_suburban_area_dummy_business2,-0.2204,F -coefficient_workplace_suburban_area_dummy_eat,-0.2916,F -coefficient_workplace_suburban_area_dummy_eat_business,-0.4018,F -coefficient_workplace_suburban_area_dummy_maint,0,T -coefficient_workplace_suburban_area_dummy_no_subtours,0,T -coefficient_auto_accessibility_to_retail_for_work_taz_business1,0.0534,F -coefficient_auto_accessibility_to_retail_for_work_taz_business2,0.1067,F -coefficient_auto_accessibility_to_retail_for_work_taz_eat,0.015,F -coefficient_auto_accessibility_to_retail_for_work_taz_eat_business,0.0683,F -coefficient_auto_accessibility_to_retail_for_work_taz_maint,0.0265,F -coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,0,T -coefficient_walk_accessibility_to_retail_for_work_taz_business1,0,T -coefficient_walk_accessibility_to_retail_for_work_taz_business2,0,T -coefficient_walk_accessibility_to_retail_for_work_taz_eat,0.06,F -coefficient_walk_accessibility_to_retail_for_work_taz_eat_business,0.06,F -coefficient_walk_accessibility_to_retail_for_work_taz_maint,0.04,F -coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,0,T -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,0,T -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,0,T -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,0,T -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business,0,T -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,-0.3573,F -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,0,T -coefficient_at_work_sub_tour_alternative_specific_constant_business1,-0.5372,F -coefficient_at_work_sub_tour_alternative_specific_constant_business2,-2.1337,F -coefficient_at_work_sub_tour_alternative_specific_constant_eat,0.8576,F -coefficient_at_work_sub_tour_alternative_specific_constant_eat_business,-0.9721,F -coefficient_at_work_sub_tour_alternative_specific_constant_maint,-0.6198,F +coefficient_name,value,constrain +coefficient_dummy_for_full_time_worker_business1,-7.375,F +coefficient_dummy_for_full_time_worker_business2,-14.28,F +coefficient_dummy_for_full_time_worker_eat,-7.28,F +coefficient_dummy_for_full_time_worker_eat_business,-14.79,F +coefficient_dummy_for_full_time_worker_maint,-8.093,F +coefficient_dummy_for_full_time_worker_no_subtours,-0.6,F +coefficient_dummy_for_non_full_time_worker_business1,-8.319,F +coefficient_dummy_for_non_full_time_worker_business2,-14.28,F +coefficient_dummy_for_non_full_time_worker_eat,-8.604,F +coefficient_dummy_for_non_full_time_worker_eat_business,-14.79,F +coefficient_dummy_for_non_full_time_worker_maint,-8.214,F +coefficient_dummy_for_non_full_time_worker_no_subtours,-0.6,F +coefficient_dummy_for_non_workers_business1,-5,T +coefficient_dummy_for_non_workers_business2,-5,T +coefficient_dummy_for_non_workers_eat,0,T +coefficient_dummy_for_non_workers_eat_business,-5,T +coefficient_dummy_for_non_workers_maint,-5,T +coefficient_dummy_for_non_workers_no_subtours,0,T +coefficient_medium_hh_income_dummy_business1,0.5555,F +coefficient_medium_hh_income_dummy_business2,1.111,F +coefficient_medium_hh_income_dummy_eat,0.61,F +coefficient_medium_hh_income_dummy_eat_business,1.1655,F +coefficient_medium_hh_income_dummy_maint,0.1527,F +coefficient_medium_hh_income_dummy_no_subtours,0,T +coefficient_high_hh_income_dummy_business1,1.066,F +coefficient_high_hh_income_dummy_business2,2.132,F +coefficient_high_hh_income_dummy_eat,0.8693,F +coefficient_high_hh_income_dummy_eat_business,1.9353,F +coefficient_high_hh_income_dummy_maint,0.1651,F +coefficient_high_hh_income_dummy_no_subtours,0,T +coefficient_zero_cars_owned_by_hh_dummy_business1,-0.3391,F +coefficient_zero_cars_owned_by_hh_dummy_business2,0,T +coefficient_zero_cars_owned_by_hh_dummy_eat,0,T +coefficient_zero_cars_owned_by_hh_dummy_eat_business,-0.3391,F +coefficient_zero_cars_owned_by_hh_dummy_maint,0.1762,F +coefficient_zero_cars_owned_by_hh_dummy_no_subtours,0,T +coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,0.7045,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,1.409,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,0.2334,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business,0.9379,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,0.5061,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,0,T +coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,0.7045,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,1.409,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,0.6776,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business,1.3821,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,0.5061,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,0,T +coefficient_individual_eating_out_tours_made_by_person_business1,0.5434,F +coefficient_individual_eating_out_tours_made_by_person_business2,1.0868,F +coefficient_individual_eating_out_tours_made_by_person_eat,0.5491,F +coefficient_individual_eating_out_tours_made_by_person_eat_business,1.0925,F +coefficient_individual_eating_out_tours_made_by_person_maint,0.9166,F +coefficient_individual_eating_out_tours_made_by_person_no_subtours,0,T +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,-0.1903,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,-0.3806,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,0.052,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business,-0.2423,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,0.1446,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,0,T +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,-0.1903,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,-0.3806,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,-0.3099,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business,-0.5002,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,-0.2723,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,0,T +coefficient_participation_in_joint_shop_main_eat_tours_business1,0.083,F +coefficient_participation_in_joint_shop_main_eat_tours_business2,0.166,F +coefficient_participation_in_joint_shop_main_eat_tours_eat,0.2458,F +coefficient_participation_in_joint_shop_main_eat_tours_eat_business,0.3288,F +coefficient_participation_in_joint_shop_main_eat_tours_maint,0.0803,F +coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,0,T +coefficient_participation_in_joint_discretionary_tours_business1,-0.2637,F +coefficient_participation_in_joint_discretionary_tours_business2,-0.5274,F +coefficient_participation_in_joint_discretionary_tours_eat,0.3588,F +coefficient_participation_in_joint_discretionary_tours_eat_business,0.0951,F +coefficient_participation_in_joint_discretionary_tours_maint,0.5822,F +coefficient_participation_in_joint_discretionary_tours_no_subtours,0,T +coefficient_log_of_the_work_tour_duration_business1,1.142,F +coefficient_log_of_the_work_tour_duration_business2,2.284,F +coefficient_log_of_the_work_tour_duration_eat,1.55,F +coefficient_log_of_the_work_tour_duration_eat_business,2.692,F +coefficient_log_of_the_work_tour_duration_maint,1.659,F +coefficient_log_of_the_work_tour_duration_no_subtours,0,T +coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,0.9901,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,1.9802,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,0.4804,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business,1.4705,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,1.153,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,0,T +coefficient_two_work_tours_by_person_business1,0.3753,F +coefficient_two_work_tours_by_person_business2,0.7506,F +coefficient_two_work_tours_by_person_eat,-0.9862,F +coefficient_two_work_tours_by_person_eat_business,-0.6109,F +coefficient_two_work_tours_by_person_maint,-0.2312,F +coefficient_two_work_tours_by_person_no_subtours,0,T +coefficient_workplace_urban_area_dummy_business1,-0.2235,F +coefficient_workplace_urban_area_dummy_business2,-0.447,F +coefficient_workplace_urban_area_dummy_eat,-0.4182,F +coefficient_workplace_urban_area_dummy_eat_business,-0.6417,F +coefficient_workplace_urban_area_dummy_maint,-0.1479,F +coefficient_workplace_urban_area_dummy_no_subtours,0,T +coefficient_workplace_suburban_area_dummy_business1,-0.1102,F +coefficient_workplace_suburban_area_dummy_business2,-0.2204,F +coefficient_workplace_suburban_area_dummy_eat,-0.2916,F +coefficient_workplace_suburban_area_dummy_eat_business,-0.4018,F +coefficient_workplace_suburban_area_dummy_maint,0,T +coefficient_workplace_suburban_area_dummy_no_subtours,0,T +coefficient_auto_accessibility_to_retail_for_work_taz_business1,0.0534,F +coefficient_auto_accessibility_to_retail_for_work_taz_business2,0.1067,F +coefficient_auto_accessibility_to_retail_for_work_taz_eat,0.015,F +coefficient_auto_accessibility_to_retail_for_work_taz_eat_business,0.0683,F +coefficient_auto_accessibility_to_retail_for_work_taz_maint,0.0265,F +coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_business1,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_business2,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_eat,0.06,F +coefficient_walk_accessibility_to_retail_for_work_taz_eat_business,0.06,F +coefficient_walk_accessibility_to_retail_for_work_taz_maint,0.04,F +coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,-0.3573,F +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,0,T +coefficient_at_work_sub_tour_alternative_specific_constant_business1,-0.5372,F +coefficient_at_work_sub_tour_alternative_specific_constant_business2,-2.1337,F +coefficient_at_work_sub_tour_alternative_specific_constant_eat,0.8576,F +coefficient_at_work_sub_tour_alternative_specific_constant_eat_business,-0.9721,F +coefficient_at_work_sub_tour_alternative_specific_constant_maint,-0.6198,F coefficient_at_work_sub_tour_alternative_specific_constant_no_subtours,0,T \ No newline at end of file diff --git a/activitysim/examples/example_sandag/configs_3_zone/auto_ownership.csv b/activitysim/examples/placeholder_psrc/configs/auto_ownership.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/auto_ownership.csv rename to activitysim/examples/placeholder_psrc/configs/auto_ownership.csv diff --git a/activitysim/examples/example_psrc/configs/auto_ownership.yaml b/activitysim/examples/placeholder_psrc/configs/auto_ownership.yaml similarity index 93% rename from activitysim/examples/example_psrc/configs/auto_ownership.yaml rename to activitysim/examples/placeholder_psrc/configs/auto_ownership.yaml index 90bff70125..c1af8adb68 100755 --- a/activitysim/examples/example_psrc/configs/auto_ownership.yaml +++ b/activitysim/examples/placeholder_psrc/configs/auto_ownership.yaml @@ -1,17 +1,17 @@ - -SPEC: auto_ownership.csv -COEFFICIENTS: auto_ownership_coeffs.csv - -#LOGIT_TYPE: NL -LOGIT_TYPE: MNL - -CONSTANTS: - ID_SAN_FRANCISCO: 1 - ID_SAN_MATEO: 2 - ID_SANTA_CLARA: 3 - ID_ALAMEDA: 4 - ID_CONTRA_COSTA: 5 - ID_SOLANO: 6 - ID_NAPA: 7 - ID_SONOMA: 8 - ID_MARIN: 9 + +SPEC: auto_ownership.csv +COEFFICIENTS: auto_ownership_coeffs.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +CONSTANTS: + ID_SAN_FRANCISCO: 1 + ID_SAN_MATEO: 2 + ID_SANTA_CLARA: 3 + ID_ALAMEDA: 4 + ID_CONTRA_COSTA: 5 + ID_SOLANO: 6 + ID_NAPA: 7 + ID_SONOMA: 8 + ID_MARIN: 9 diff --git a/activitysim/examples/example_psrc/configs/auto_ownership_coeffs.csv b/activitysim/examples/placeholder_psrc/configs/auto_ownership_coeffs.csv similarity index 97% rename from activitysim/examples/example_psrc/configs/auto_ownership_coeffs.csv rename to activitysim/examples/placeholder_psrc/configs/auto_ownership_coeffs.csv index 9c1470b690..b9d7fd07b0 100755 --- a/activitysim/examples/example_psrc/configs/auto_ownership_coeffs.csv +++ b/activitysim/examples/placeholder_psrc/configs/auto_ownership_coeffs.csv @@ -1,68 +1,68 @@ -coefficient_name,value,constrain -coef_cars1_drivers_2,0,T -coef_cars1_drivers_3,0,T -coef_cars1_persons_16_17,0,T -coef_cars234_asc_marin,0,T -coef_cars1_persons_25_34,0,T -coef_cars1_num_workers_clip_3,0,T -coef_cars1_hh_income_30_up,0,T -coef_cars1_density_0_10_no_workers,0,T -coef_cars1_density_10_up_workers,-0.0152,F -coef_retail_non_motor,-0.03,T -coef_cars4_asc,-5.313,F -coef_cars3_asc,-3.2502,F -coef_cars34_persons_16_17,-1.7313,F -coef_cars2_asc,-1.0846,F -coef_cars34_persons_18_24,-1.0107,F -coef_cars2_persons_18_24,-1.0095,F -coef_cars2_persons_16_17,-0.881,F -coef_cars34_persons_25_34,-0.8596,F -coef_cars1_asc_county,-0.566,F -coef_retail_transit_workers,-0.5117,F -coef_cars2_persons_25_34,-0.4849,F -coef_cars2_asc_county,-0.4429,F -coef_cars1_persons_18_24,-0.4087,F -coef_cars34_density_0_10_no_workers,-0.3654,F -coef_retail_transit_no_workers,-0.3053,F -coef_cars1_asc_marin,-0.2434,F -coef_cars34_asc_county,-0.2372,F -coef_cars2_density_0_10_no_workers,-0.2028,F -coef_cars34_density_10_up_no_workers,-0.1766,F -coef_cars2_density_10_up_no_workers,-0.1106,F -coef_cars2_density_10_up_workers,-0.1106,F -coef_cars1_density_10_up_no_workers,-0.0152,F -coef_cars2_hh_income_30_up,0.0083,F -coef_cars3_hh_income_30_up,0.011,F -coef_cars4_hh_income_30_up,0.0147,F -coef_cars1_presence_children_5_17,0.0158,F -coef_cars1_hh_income_0_30k,0.0383,F -coef_cars2_hh_income_0_30k,0.054,F -coef_cars3_hh_income_0_30k,0.0559,F -coef_cars4_hh_income_0_30k,0.0619,F -coef_retail_auto_no_workers,0.0626,F -coef_cars34_asc_san_francisco,0.1458,F -coef_retail_auto_workers,0.1646,F -coef_cars2_presence_children_5_17,0.2936,F -coef_cars2_num_workers_clip_3,0.2936,F -coef_cars1_presence_children_0_4,0.3669,F -coef_cars1_asc_san_francisco,0.4259,F -coef_cars2_asc_san_francisco,0.4683,F -coef_cars1_auto_time_saving_per_worker,0.4707,F -coef_cars34_presence_children_5_17,0.4769,F -coef_cars3_auto_time_saving_per_worker,0.5705,F -coef_cars2_auto_time_saving_per_worker,0.6142,F -coef_cars3_num_workers_clip_3,0.6389,F -coef_cars234_presence_children_0_4,0.7627,F -coef_cars4_auto_time_saving_per_worker,0.7693,F -coef_cars4_num_workers_clip_3,0.8797,F -coef_cars1_asc,1.1865,F -coef_cars1_drivers_4_up,2.0107,F -coef_cars4_drivers_2,2.6616,F -coef_cars2_drivers_2,3.0773,F -coef_cars3_drivers_2,3.1962,F -coef_cars2_drivers_3,3.5401,F -coef_cars4_drivers_3,5.208,F -coef_cars3_drivers_3,5.5131,F -coef_cars2_drivers_4_up,6.3662,F -coef_cars3_drivers_4_up,8.5148,F -coef_cars4_drivers_4_up,9.5807,F +coefficient_name,value,constrain +coef_cars1_drivers_2,0,T +coef_cars1_drivers_3,0,T +coef_cars1_persons_16_17,0,T +coef_cars234_asc_marin,0,T +coef_cars1_persons_25_34,0,T +coef_cars1_num_workers_clip_3,0,T +coef_cars1_hh_income_30_up,0,T +coef_cars1_density_0_10_no_workers,0,T +coef_cars1_density_10_up_workers,-0.0152,F +coef_retail_non_motor,-0.03,T +coef_cars4_asc,-5.313,F +coef_cars3_asc,-3.2502,F +coef_cars34_persons_16_17,-1.7313,F +coef_cars2_asc,-1.0846,F +coef_cars34_persons_18_24,-1.0107,F +coef_cars2_persons_18_24,-1.0095,F +coef_cars2_persons_16_17,-0.881,F +coef_cars34_persons_25_34,-0.8596,F +coef_cars1_asc_county,-0.566,F +coef_retail_transit_workers,-0.5117,F +coef_cars2_persons_25_34,-0.4849,F +coef_cars2_asc_county,-0.4429,F +coef_cars1_persons_18_24,-0.4087,F +coef_cars34_density_0_10_no_workers,-0.3654,F +coef_retail_transit_no_workers,-0.3053,F +coef_cars1_asc_marin,-0.2434,F +coef_cars34_asc_county,-0.2372,F +coef_cars2_density_0_10_no_workers,-0.2028,F +coef_cars34_density_10_up_no_workers,-0.1766,F +coef_cars2_density_10_up_no_workers,-0.1106,F +coef_cars2_density_10_up_workers,-0.1106,F +coef_cars1_density_10_up_no_workers,-0.0152,F +coef_cars2_hh_income_30_up,0.0083,F +coef_cars3_hh_income_30_up,0.011,F +coef_cars4_hh_income_30_up,0.0147,F +coef_cars1_presence_children_5_17,0.0158,F +coef_cars1_hh_income_0_30k,0.0383,F +coef_cars2_hh_income_0_30k,0.054,F +coef_cars3_hh_income_0_30k,0.0559,F +coef_cars4_hh_income_0_30k,0.0619,F +coef_retail_auto_no_workers,0.0626,F +coef_cars34_asc_san_francisco,0.1458,F +coef_retail_auto_workers,0.1646,F +coef_cars2_presence_children_5_17,0.2936,F +coef_cars2_num_workers_clip_3,0.2936,F +coef_cars1_presence_children_0_4,0.3669,F +coef_cars1_asc_san_francisco,0.4259,F +coef_cars2_asc_san_francisco,0.4683,F +coef_cars1_auto_time_saving_per_worker,0.4707,F +coef_cars34_presence_children_5_17,0.4769,F +coef_cars3_auto_time_saving_per_worker,0.5705,F +coef_cars2_auto_time_saving_per_worker,0.6142,F +coef_cars3_num_workers_clip_3,0.6389,F +coef_cars234_presence_children_0_4,0.7627,F +coef_cars4_auto_time_saving_per_worker,0.7693,F +coef_cars4_num_workers_clip_3,0.8797,F +coef_cars1_asc,1.1865,F +coef_cars1_drivers_4_up,2.0107,F +coef_cars4_drivers_2,2.6616,F +coef_cars2_drivers_2,3.0773,F +coef_cars3_drivers_2,3.1962,F +coef_cars2_drivers_3,3.5401,F +coef_cars4_drivers_3,5.208,F +coef_cars3_drivers_3,5.5131,F +coef_cars2_drivers_4_up,6.3662,F +coef_cars3_drivers_4_up,8.5148,F +coef_cars4_drivers_4_up,9.5807,F diff --git a/activitysim/examples/example_psrc/configs/cdap.yaml b/activitysim/examples/placeholder_psrc/configs/cdap.yaml similarity index 100% rename from activitysim/examples/example_psrc/configs/cdap.yaml rename to activitysim/examples/placeholder_psrc/configs/cdap.yaml diff --git a/activitysim/examples/example_mtc/configs/cdap_coefficients.csv b/activitysim/examples/placeholder_psrc/configs/cdap_coefficients.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/cdap_coefficients.csv rename to activitysim/examples/placeholder_psrc/configs/cdap_coefficients.csv diff --git a/activitysim/examples/example_arc/configs/cdap_fixed_relative_proportions.csv b/activitysim/examples/placeholder_psrc/configs/cdap_fixed_relative_proportions.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_arc/configs/cdap_fixed_relative_proportions.csv rename to activitysim/examples/placeholder_psrc/configs/cdap_fixed_relative_proportions.csv diff --git a/activitysim/examples/example_psrc/configs/cdap_indiv_and_hhsize1.csv b/activitysim/examples/placeholder_psrc/configs/cdap_indiv_and_hhsize1.csv similarity index 99% rename from activitysim/examples/example_psrc/configs/cdap_indiv_and_hhsize1.csv rename to activitysim/examples/placeholder_psrc/configs/cdap_indiv_and_hhsize1.csv index aafe57bf62..0a666e6e73 100755 --- a/activitysim/examples/example_psrc/configs/cdap_indiv_and_hhsize1.csv +++ b/activitysim/examples/placeholder_psrc/configs/cdap_indiv_and_hhsize1.csv @@ -1,51 +1,51 @@ -Description,Expression,M,N,H -Full-time worker alternative-specific constants,ptype == 1,coef_full_time_worker_asc_M,coef_full_time_worker_asc_N, -Part-time worker alternative-specific constants,ptype == 2,coef_part_time_worker_asc_M,coef_part_time_worker_asc_N, -University student alternative-specific constants,ptype == 3,coef_university_student_asc_M,coef_university_student_asc_N, -Non-working adult alternative-specific constants,ptype == 4,coef_UNAVAILABLE,coef_non_working_adult_asc_N, -Retired alternative-specific constants,ptype == 5,coef_UNAVAILABLE,coef_retired_asc_N, -Driving-age child who is in school alternative-specific constants,ptype == 6,coef_driving_age_child_who_is_in_school_asc_M,coef_driving_age_child_who_is_in_school_asc_N, -Pre-driving-age child who is in school alternative-specific constants,ptype == 7,coef_pre_driving_age_child_who_is_in_school_asc_M,coef_pre_driving_age_child_who_is_in_school_asc_N, -Pre-driving-age child who is in school interaction with age 6 to 9,(ptype == 7) & (age >= 6) & (age <= 9),coef_pre_driving_age_child_who_is_in_school_interaction_with_age_6_to_9_M,, -Pre-driving-age child who is in school interaction with age 13 to 15,(ptype == 7) & (age >= 13) & (age <= 15),coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_M,coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_N, -Pre-driving-age child who is too young for school alternative-specific constants,ptype == 8,coef_pre_driving_age_child_who_is_too_young_for_school_asc_M,coef_pre_driving_age_child_who_is_too_young_for_school_asc_N, -# corrected tm1 age bug,,,, -Pre-driving-age child who is too young for school interaction with age 0 to 1,(ptype == 8) & (age >= 0) & (age <= 1),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_0_to_1_M,, -Pre-driving-age child who is too young for school interaction with age 4 to 5,(ptype == 8) & (age >= 4) & (age <= 5),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_4_to_5_M,, -#,,,, -Full-time worker interaction with age less than 40,(ptype == 1) & (age < 40),coef_full_time_worker_interaction_with_age_less_than_40_M,, -Retired interaction with age more than 80,(ptype == 5) & (age > 80),,,coef_retired_interaction_with_age_more_than_80_H -Full-time worker interaction with female gender,(ptype == 1) & (sex == 2),coef_full_time_worker_interaction_with_female_gender_M,, -Non-working adult interaction with female gender,(ptype == 4) & (sex == 2),coef_non_working_adult_interaction_with_female_gender_M,, -Retired interaction with female,(ptype == 5) & (sex == 2),coef_retired_interaction_with_female_M,, -Non-working adult interaction with more cars than workers,(ptype == 4) & (auto_ownership > num_workers),coef_non_working_adult_interaction_with_more_cars_than_workers_M,coef_non_working_adult_interaction_with_more_cars_than_workers_N, -Retired interaction with more cars than workers,(ptype == 5) & (auto_ownership > num_workers),coef_retired_interaction_with_more_cars_than_workers_M,coef_retired_interaction_with_more_cars_than_workers_N, -Pre-driving-age child who is too young for school interaction with more cars than workers,(ptype == 8) & (auto_ownership > num_workers),,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_more_cars_than_workers_N, -Full-time worker interaction with fewer cars than workers,(ptype == 1) & (auto_ownership < num_workers),,,coef_full_time_worker_interaction_with_fewer_cars_than_workers_H -Non-working adult interaction with fewer cars than workers,(ptype == 4) & (auto_ownership < num_workers),,,coef_non_working_adult_interaction_with_fewer_cars_than_workers_H -Retired interaction with fewer cars than workers,(ptype == 5) & (auto_ownership < num_workers),,,coef_retired_interaction_with_fewer_cars_than_workers_H -Driving-age child who is in school interaction with fewer cars than workers,(ptype == 6) & (auto_ownership < num_workers),,,coef_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H -Pre-driving-age child who is in school interaction with fewer cars than workers,(ptype == 7) & (auto_ownership < num_workers),,,coef_pre_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H -Pre-driving-age child who is too young for school interaction with fewer cars than workers,(ptype == 8) & (auto_ownership < num_workers),,,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_fewer_cars_than_workers_H -Full-time worker interaction with income less than $20k,(ptype == 1) & (income_in_thousands < 20),,,coef_full_time_worker_interaction_with_income_less_than_20k_H -Retired interaction with income less than $20k,(ptype == 5) & (income_in_thousands < 20),,,coef_retired_interaction_with_income_less_than_20k_H -Part-time worker interaction with income less than $20k,(ptype == 2) & (income_in_thousands < 20),,,coef_part_time_worker_interaction_with_income_less_than_20k_H -Part-time worker interaction with income between $50k and $100k,(ptype == 2) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,coef_part_time_worker_interaction_with_income_between_50k_and_100k_H -Part-time worker interaction with income more than $100k,(ptype == 2) & (income_in_thousands >= 100),,coef_part_time_worker_interaction_with_income_more_than_100k_N,coef_part_time_worker_interaction_with_income_more_than_100k_H -Non-working adult interaction with income between $50k and $100k,(ptype == 4) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,coef_non_working_adult_interaction_with_income_between_50k_and_100k_H -Non-working adult interaction with income more than $100k,(ptype == 4) & (income_in_thousands >= 100),,,coef_non_working_adult_interaction_with_income_more_than_100k_H -Driving-age child who is in school interaction with less than $20k,(ptype == 6) & (income_in_thousands < 20),,,coef_driving_age_child_who_is_in_school_interaction_with_less_than_20k_H -Driving-age child who is in school interaction income between $50k and $100k,(ptype == 6) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,coef_driving_age_child_who_is_in_school_interaction_income_between_50k_and_100k_H -Driving-age child who is in school interaction with income more than $100k,(ptype == 6) & (income_in_thousands >= 100),,,coef_driving_age_child_who_is_in_school_interaction_with_income_more_than_100k_H -Pre-driving-age child who is too young for school interaction with income between $50k and $100k,(ptype == 8) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_between_50k_and_100k_H -Pre-driving-age child who is too young for school interaction with income more than $100k,(ptype == 8) & (income_in_thousands >= 100),,,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_more_than_100k_H -Full-time worker intraction with peak accessibility to all employment,(ptype == 1) * auPkTotal,coef_full_time_worker_intraction_with_peak_accessibility_to_all_employment_M,, -Part-time worker interaction with peak accessibility to all employment,(ptype == 2) * auPkTotal,coef_part_time_worker_interaction_with_peak_accessibility_to_all_employment_M,, -Non-working adult interaction with peak accessibility to all employment,(ptype == 4) * auPkTotal,coef_non_working_adult_interaction_with_peak_accessibility_to_all_employment_M,, -Retired interaction with peak accessibility to all employment,(ptype == 5) * auPkTotal,coef_retired_interaction_with_peak_accessibility_to_all_employment_M,, -Non-working adult interaction with off-peak accessibility to retail,(ptype == 4) * auOpRetail,,coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N, -Retired interaction with off-peak accessibility to retail,(ptype == 5) * auOpRetail,,coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N, -University student interaction with off-peak accessibility to retail,(ptype == 3) * auOpRetail,,coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N, -Driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 6) * auOpRetail,,coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N, -Pre-driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 7) * auOpRetail,,coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N, -Pre-driving-age child who is too young for school interaction with off-peak accessibility to retail,(ptype == 8) * auOpRetail,,coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N, +Description,Expression,M,N,H +Full-time worker alternative-specific constants,ptype == 1,coef_full_time_worker_asc_M,coef_full_time_worker_asc_N, +Part-time worker alternative-specific constants,ptype == 2,coef_part_time_worker_asc_M,coef_part_time_worker_asc_N, +University student alternative-specific constants,ptype == 3,coef_university_student_asc_M,coef_university_student_asc_N, +Non-working adult alternative-specific constants,ptype == 4,coef_UNAVAILABLE,coef_non_working_adult_asc_N, +Retired alternative-specific constants,ptype == 5,coef_UNAVAILABLE,coef_retired_asc_N, +Driving-age child who is in school alternative-specific constants,ptype == 6,coef_driving_age_child_who_is_in_school_asc_M,coef_driving_age_child_who_is_in_school_asc_N, +Pre-driving-age child who is in school alternative-specific constants,ptype == 7,coef_pre_driving_age_child_who_is_in_school_asc_M,coef_pre_driving_age_child_who_is_in_school_asc_N, +Pre-driving-age child who is in school interaction with age 6 to 9,(ptype == 7) & (age >= 6) & (age <= 9),coef_pre_driving_age_child_who_is_in_school_interaction_with_age_6_to_9_M,, +Pre-driving-age child who is in school interaction with age 13 to 15,(ptype == 7) & (age >= 13) & (age <= 15),coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_M,coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_N, +Pre-driving-age child who is too young for school alternative-specific constants,ptype == 8,coef_pre_driving_age_child_who_is_too_young_for_school_asc_M,coef_pre_driving_age_child_who_is_too_young_for_school_asc_N, +# corrected tm1 age bug,,,, +Pre-driving-age child who is too young for school interaction with age 0 to 1,(ptype == 8) & (age >= 0) & (age <= 1),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_0_to_1_M,, +Pre-driving-age child who is too young for school interaction with age 4 to 5,(ptype == 8) & (age >= 4) & (age <= 5),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_4_to_5_M,, +#,,,, +Full-time worker interaction with age less than 40,(ptype == 1) & (age < 40),coef_full_time_worker_interaction_with_age_less_than_40_M,, +Retired interaction with age more than 80,(ptype == 5) & (age > 80),,,coef_retired_interaction_with_age_more_than_80_H +Full-time worker interaction with female gender,(ptype == 1) & (sex == 2),coef_full_time_worker_interaction_with_female_gender_M,, +Non-working adult interaction with female gender,(ptype == 4) & (sex == 2),coef_non_working_adult_interaction_with_female_gender_M,, +Retired interaction with female,(ptype == 5) & (sex == 2),coef_retired_interaction_with_female_M,, +Non-working adult interaction with more cars than workers,(ptype == 4) & (auto_ownership > num_workers),coef_non_working_adult_interaction_with_more_cars_than_workers_M,coef_non_working_adult_interaction_with_more_cars_than_workers_N, +Retired interaction with more cars than workers,(ptype == 5) & (auto_ownership > num_workers),coef_retired_interaction_with_more_cars_than_workers_M,coef_retired_interaction_with_more_cars_than_workers_N, +Pre-driving-age child who is too young for school interaction with more cars than workers,(ptype == 8) & (auto_ownership > num_workers),,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_more_cars_than_workers_N, +Full-time worker interaction with fewer cars than workers,(ptype == 1) & (auto_ownership < num_workers),,,coef_full_time_worker_interaction_with_fewer_cars_than_workers_H +Non-working adult interaction with fewer cars than workers,(ptype == 4) & (auto_ownership < num_workers),,,coef_non_working_adult_interaction_with_fewer_cars_than_workers_H +Retired interaction with fewer cars than workers,(ptype == 5) & (auto_ownership < num_workers),,,coef_retired_interaction_with_fewer_cars_than_workers_H +Driving-age child who is in school interaction with fewer cars than workers,(ptype == 6) & (auto_ownership < num_workers),,,coef_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H +Pre-driving-age child who is in school interaction with fewer cars than workers,(ptype == 7) & (auto_ownership < num_workers),,,coef_pre_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H +Pre-driving-age child who is too young for school interaction with fewer cars than workers,(ptype == 8) & (auto_ownership < num_workers),,,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_fewer_cars_than_workers_H +Full-time worker interaction with income less than $20k,(ptype == 1) & (income_in_thousands < 20),,,coef_full_time_worker_interaction_with_income_less_than_20k_H +Retired interaction with income less than $20k,(ptype == 5) & (income_in_thousands < 20),,,coef_retired_interaction_with_income_less_than_20k_H +Part-time worker interaction with income less than $20k,(ptype == 2) & (income_in_thousands < 20),,,coef_part_time_worker_interaction_with_income_less_than_20k_H +Part-time worker interaction with income between $50k and $100k,(ptype == 2) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,coef_part_time_worker_interaction_with_income_between_50k_and_100k_H +Part-time worker interaction with income more than $100k,(ptype == 2) & (income_in_thousands >= 100),,coef_part_time_worker_interaction_with_income_more_than_100k_N,coef_part_time_worker_interaction_with_income_more_than_100k_H +Non-working adult interaction with income between $50k and $100k,(ptype == 4) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,coef_non_working_adult_interaction_with_income_between_50k_and_100k_H +Non-working adult interaction with income more than $100k,(ptype == 4) & (income_in_thousands >= 100),,,coef_non_working_adult_interaction_with_income_more_than_100k_H +Driving-age child who is in school interaction with less than $20k,(ptype == 6) & (income_in_thousands < 20),,,coef_driving_age_child_who_is_in_school_interaction_with_less_than_20k_H +Driving-age child who is in school interaction income between $50k and $100k,(ptype == 6) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,coef_driving_age_child_who_is_in_school_interaction_income_between_50k_and_100k_H +Driving-age child who is in school interaction with income more than $100k,(ptype == 6) & (income_in_thousands >= 100),,,coef_driving_age_child_who_is_in_school_interaction_with_income_more_than_100k_H +Pre-driving-age child who is too young for school interaction with income between $50k and $100k,(ptype == 8) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_between_50k_and_100k_H +Pre-driving-age child who is too young for school interaction with income more than $100k,(ptype == 8) & (income_in_thousands >= 100),,,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_more_than_100k_H +Full-time worker intraction with peak accessibility to all employment,(ptype == 1) * auPkTotal,coef_full_time_worker_intraction_with_peak_accessibility_to_all_employment_M,, +Part-time worker interaction with peak accessibility to all employment,(ptype == 2) * auPkTotal,coef_part_time_worker_interaction_with_peak_accessibility_to_all_employment_M,, +Non-working adult interaction with peak accessibility to all employment,(ptype == 4) * auPkTotal,coef_non_working_adult_interaction_with_peak_accessibility_to_all_employment_M,, +Retired interaction with peak accessibility to all employment,(ptype == 5) * auPkTotal,coef_retired_interaction_with_peak_accessibility_to_all_employment_M,, +Non-working adult interaction with off-peak accessibility to retail,(ptype == 4) * auOpRetail,,coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N, +Retired interaction with off-peak accessibility to retail,(ptype == 5) * auOpRetail,,coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N, +University student interaction with off-peak accessibility to retail,(ptype == 3) * auOpRetail,,coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N, +Driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 6) * auOpRetail,,coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N, +Pre-driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 7) * auOpRetail,,coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N, +Pre-driving-age child who is too young for school interaction with off-peak accessibility to retail,(ptype == 8) * auOpRetail,,coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N, diff --git a/activitysim/examples/example_mtc/configs/cdap_interaction_coefficients.csv b/activitysim/examples/placeholder_psrc/configs/cdap_interaction_coefficients.csv old mode 100644 new mode 100755 similarity index 94% rename from activitysim/examples/example_mtc/configs/cdap_interaction_coefficients.csv rename to activitysim/examples/placeholder_psrc/configs/cdap_interaction_coefficients.csv index 854d48ef58..c9d415fe13 --- a/activitysim/examples/example_mtc/configs/cdap_interaction_coefficients.csv +++ b/activitysim/examples/placeholder_psrc/configs/cdap_interaction_coefficients.csv @@ -1,138 +1,138 @@ -activity,interaction_ptypes,coefficient -# 2-way interactions,, -H,11,coef_H_11 -H,12,coef_H_12 -H,13,coef_H_13 -H,14,coef_H_14 -H,15,coef_H_15 -H,16,coef_H_16 -H,17,coef_H_17 -H,18,coef_H_18 -H,22,coef_H_22 -H,23,coef_H_23 -H,24,coef_H_24 -H,25,coef_H_25 -H,26,coef_H_26 -H,27,coef_H_27 -H,28,coef_H_28 -H,33,coef_H_33 -H,34,coef_H_34 -H,35,coef_H_35 -H,36,coef_H_36 -H,37,coef_H_37 -H,38,coef_H_38 -H,44,coef_H_44 -H,45,coef_H_45 -H,46,coef_H_46 -H,47,coef_H_47 -H,48,coef_H_48 -H,55,coef_H_55 -H,56,coef_H_56_57_58 -H,57,coef_H_56_57_58 -H,58,coef_H_56_57_58 -H,66,coef_H_66 -H,67,coef_H_67 -H,68,coef_H_68 -H,77,coef_H_77 -H,78,coef_H_78 -H,88,coef_H_88 -M,11,coef_M_11 -M,12,coef_M_12 -M,13,coef_M_13 -M,16,coef_M_16 -M,17,coef_M_17 -M,18,coef_M_18 -M,22,coef_M_22 -M,23,coef_M_23 -M,26,coef_M_26 -M,27,coef_M_27 -M,28,coef_M_28 -M,33,coef_M_33 -M,36,coef_M_36 -M,37,coef_M_37 -M,38,coef_M_38 -M,66,coef_M_66 -M,67,coef_M_67 -M,68,coef_M_68 -M,77,coef_M_77 -M,78,coef_M_78 -M,88,coef_M_88 -N,11,coef_N_11 -N,12,coef_N_12 -N,13,coef_N_13 -N,14,coef_N_14 -N,15,coef_N_15 -N,16,coef_N_16 -N,17,coef_N_17 -N,18,coef_N_18 -N,22,coef_N_22 -N,23,coef_N_23 -N,24,coef_N_24 -N,25,coef_N_25 -N,26,coef_N_26 -N,27,coef_N_27 -N,28,coef_N_28 -N,33,coef_N_33 -N,34,coef_N_34 -N,35,coef_N_35 -N,36,coef_N_36 -N,37,coef_N_37 -N,38,coef_N_38 -N,44,coef_N_44 -N,45,coef_N_45 -N,46,coef_N_46 -N,47,coef_N_47 -N,48,coef_N_48 -N,55,coef_N_55 -N,56,coef_N_56_57_58 -N,57,coef_N_56_57_58 -N,58,coef_N_56_57_58 -N,66,coef_N_66 -N,67,coef_N_67 -N,68,coef_N_68 -N,77,coef_N_77 -N,78,coef_N_78 -N,88,coef_N_88 -# 3-way interactions,, -H,124,coef_H_124_122_144 -H,122,coef_H_124_122_144 -H,144,coef_H_124_122_144 -H,126,coef_H_126_146 -H,146,coef_H_126_146 -H,222,coef_H_222_224_244 -H,224,coef_H_222_224_244 -H,244,coef_H_222_224_244 -H,226,coef_H_226_246_446 -H,246,coef_H_226_246_446 -H,446,coef_H_226_246_446 -H,266,coef_H_266_466 -H,466,coef_H_266_466 -M,111,coef_M_111 -M,112,coef_M_112_114 -M,114,coef_M_112_114 -M,666,coef_M_666 -N,112,coef_N_112_114 -N,114,coef_N_112_114 -N,124,coef_N_124_122_144 -N,122,coef_N_124_122_144 -N,144,coef_N_124_122_144 -N,166,coef_N_166 -N,222,coef_N_222_224_444 -N,224,coef_N_222_224_444 -N,444,coef_N_222_224_444 -N,246,coef_N_246_226_446 -N,226,coef_N_246_226_446 -N,446,coef_N_246_226_446 -# cdap_final_rules,, -M,5,coef_UNAVAILABLE -M,4,coef_UNAVAILABLE -# cdap_all_people,, -M,***,coef_M_xxx -N,***,coef_N_xxx -H,***,coef_H_xxx -M,****,coef_M_xxxx -N,****,coef_N_xxxx -H,****,coef_H_xxxx -M,*****,coef_M_xxxxx -N,*****,coef_N_xxxxx -H,*****,coef_H_xxxxx +activity,interaction_ptypes,coefficient +# 2-way interactions,, +H,11,coef_H_11 +H,12,coef_H_12 +H,13,coef_H_13 +H,14,coef_H_14 +H,15,coef_H_15 +H,16,coef_H_16 +H,17,coef_H_17 +H,18,coef_H_18 +H,22,coef_H_22 +H,23,coef_H_23 +H,24,coef_H_24 +H,25,coef_H_25 +H,26,coef_H_26 +H,27,coef_H_27 +H,28,coef_H_28 +H,33,coef_H_33 +H,34,coef_H_34 +H,35,coef_H_35 +H,36,coef_H_36 +H,37,coef_H_37 +H,38,coef_H_38 +H,44,coef_H_44 +H,45,coef_H_45 +H,46,coef_H_46 +H,47,coef_H_47 +H,48,coef_H_48 +H,55,coef_H_55 +H,56,coef_H_56_57_58 +H,57,coef_H_56_57_58 +H,58,coef_H_56_57_58 +H,66,coef_H_66 +H,67,coef_H_67 +H,68,coef_H_68 +H,77,coef_H_77 +H,78,coef_H_78 +H,88,coef_H_88 +M,11,coef_M_11 +M,12,coef_M_12 +M,13,coef_M_13 +M,16,coef_M_16 +M,17,coef_M_17 +M,18,coef_M_18 +M,22,coef_M_22 +M,23,coef_M_23 +M,26,coef_M_26 +M,27,coef_M_27 +M,28,coef_M_28 +M,33,coef_M_33 +M,36,coef_M_36 +M,37,coef_M_37 +M,38,coef_M_38 +M,66,coef_M_66 +M,67,coef_M_67 +M,68,coef_M_68 +M,77,coef_M_77 +M,78,coef_M_78 +M,88,coef_M_88 +N,11,coef_N_11 +N,12,coef_N_12 +N,13,coef_N_13 +N,14,coef_N_14 +N,15,coef_N_15 +N,16,coef_N_16 +N,17,coef_N_17 +N,18,coef_N_18 +N,22,coef_N_22 +N,23,coef_N_23 +N,24,coef_N_24 +N,25,coef_N_25 +N,26,coef_N_26 +N,27,coef_N_27 +N,28,coef_N_28 +N,33,coef_N_33 +N,34,coef_N_34 +N,35,coef_N_35 +N,36,coef_N_36 +N,37,coef_N_37 +N,38,coef_N_38 +N,44,coef_N_44 +N,45,coef_N_45 +N,46,coef_N_46 +N,47,coef_N_47 +N,48,coef_N_48 +N,55,coef_N_55 +N,56,coef_N_56_57_58 +N,57,coef_N_56_57_58 +N,58,coef_N_56_57_58 +N,66,coef_N_66 +N,67,coef_N_67 +N,68,coef_N_68 +N,77,coef_N_77 +N,78,coef_N_78 +N,88,coef_N_88 +# 3-way interactions,, +H,124,coef_H_124_122_144 +H,122,coef_H_124_122_144 +H,144,coef_H_124_122_144 +H,126,coef_H_126_146 +H,146,coef_H_126_146 +H,222,coef_H_222_224_244 +H,224,coef_H_222_224_244 +H,244,coef_H_222_224_244 +H,226,coef_H_226_246_446 +H,246,coef_H_226_246_446 +H,446,coef_H_226_246_446 +H,266,coef_H_266_466 +H,466,coef_H_266_466 +M,111,coef_M_111 +M,112,coef_M_112_114 +M,114,coef_M_112_114 +M,666,coef_M_666 +N,112,coef_N_112_114 +N,114,coef_N_112_114 +N,124,coef_N_124_122_144 +N,122,coef_N_124_122_144 +N,144,coef_N_124_122_144 +N,166,coef_N_166 +N,222,coef_N_222_224_444 +N,224,coef_N_222_224_444 +N,444,coef_N_222_224_444 +N,246,coef_N_246_226_446 +N,226,coef_N_246_226_446 +N,446,coef_N_246_226_446 +# cdap_final_rules,, +M,5,coef_UNAVAILABLE +M,4,coef_UNAVAILABLE +# cdap_all_people,, +M,***,coef_M_xxx +N,***,coef_N_xxx +H,***,coef_H_xxx +M,****,coef_M_xxxx +N,****,coef_N_xxxx +H,****,coef_H_xxxx +M,*****,coef_M_xxxxx +N,*****,coef_N_xxxxx +H,*****,coef_H_xxxxx diff --git a/activitysim/examples/example_mtc/configs/constants.yaml b/activitysim/examples/placeholder_psrc/configs/constants.yaml old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/constants.yaml rename to activitysim/examples/placeholder_psrc/configs/constants.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/destination_choice_size_terms.csv b/activitysim/examples/placeholder_psrc/configs/destination_choice_size_terms.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone/destination_choice_size_terms.csv rename to activitysim/examples/placeholder_psrc/configs/destination_choice_size_terms.csv diff --git a/activitysim/examples/example_mtc/configs/free_parking.csv b/activitysim/examples/placeholder_psrc/configs/free_parking.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/free_parking.csv rename to activitysim/examples/placeholder_psrc/configs/free_parking.csv diff --git a/activitysim/examples/example_psrc/configs/free_parking.yaml b/activitysim/examples/placeholder_psrc/configs/free_parking.yaml similarity index 93% rename from activitysim/examples/example_psrc/configs/free_parking.yaml rename to activitysim/examples/placeholder_psrc/configs/free_parking.yaml index 4cfd080400..322e6ddc80 100755 --- a/activitysim/examples/example_psrc/configs/free_parking.yaml +++ b/activitysim/examples/placeholder_psrc/configs/free_parking.yaml @@ -1,25 +1,25 @@ - -SPEC: free_parking.csv -COEFFICIENTS: free_parking_coeffs.csv - -#LOGIT_TYPE: NL -LOGIT_TYPE: MNL - -FREE_PARKING_ALT: 0 - -CONSTANTS: - ID_SAN_FRANCISCO: 1 - ID_SAN_MATEO: 2 - ID_SANTA_CLARA: 3 - ID_ALAMEDA: 4 - ID_CONTRA_COSTA: 5 - ID_SOLANO: 6 - ID_NAPA: 7 - ID_SONOMA: 8 - ID_MARIN: 9 - -preprocessor: - SPEC: free_parking_annotate_persons_preprocessor - DF: persons - TABLES: - - land_use + +SPEC: free_parking.csv +COEFFICIENTS: free_parking_coeffs.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +FREE_PARKING_ALT: 0 + +CONSTANTS: + ID_SAN_FRANCISCO: 1 + ID_SAN_MATEO: 2 + ID_SANTA_CLARA: 3 + ID_ALAMEDA: 4 + ID_CONTRA_COSTA: 5 + ID_SOLANO: 6 + ID_NAPA: 7 + ID_SONOMA: 8 + ID_MARIN: 9 + +preprocessor: + SPEC: free_parking_annotate_persons_preprocessor + DF: persons + TABLES: + - land_use diff --git a/activitysim/examples/example_mtc/configs/free_parking_annotate_persons_preprocessor.csv b/activitysim/examples/placeholder_psrc/configs/free_parking_annotate_persons_preprocessor.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/free_parking_annotate_persons_preprocessor.csv rename to activitysim/examples/placeholder_psrc/configs/free_parking_annotate_persons_preprocessor.csv diff --git a/activitysim/examples/example_semcog/configs/free_parking_coeffs.csv b/activitysim/examples/placeholder_psrc/configs/free_parking_coeffs.csv similarity index 96% rename from activitysim/examples/example_semcog/configs/free_parking_coeffs.csv rename to activitysim/examples/placeholder_psrc/configs/free_parking_coeffs.csv index fab036dfbc..dc15f7d038 100755 --- a/activitysim/examples/example_semcog/configs/free_parking_coeffs.csv +++ b/activitysim/examples/placeholder_psrc/configs/free_parking_coeffs.csv @@ -1,9 +1,9 @@ -coefficient_name,value,constrain -coef_asc_san_francisco,-2.6403,F -coef_asc_santa_clara,0.2118,F -coef_asc_alameda,-0.1092,F -coef_income_very_high,0.23,F -coef_income_high,0.23,F -coef_hh_size_4_up,0.253,F -coef_more_autos_than_workers,0.231,F -coef_fewer_autos_than_workers,-1.479,F +coefficient_name,value,constrain +coef_asc_san_francisco,-2.6403,F +coef_asc_santa_clara,0.2118,F +coef_asc_alameda,-0.1092,F +coef_income_very_high,0.23,F +coef_income_high,0.23,F +coef_hh_size_4_up,0.253,F +coef_more_autos_than_workers,0.231,F +coef_fewer_autos_than_workers,-1.479,F diff --git a/activitysim/examples/example_mtc/configs/initialize_households.yaml b/activitysim/examples/placeholder_psrc/configs/initialize_households.yaml old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/initialize_households.yaml rename to activitysim/examples/placeholder_psrc/configs/initialize_households.yaml diff --git a/activitysim/examples/example_mtc/configs/initialize_landuse.yaml b/activitysim/examples/placeholder_psrc/configs/initialize_landuse.yaml old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/initialize_landuse.yaml rename to activitysim/examples/placeholder_psrc/configs/initialize_landuse.yaml diff --git a/activitysim/examples/example_psrc/configs/joint_tour_composition.csv b/activitysim/examples/placeholder_psrc/configs/joint_tour_composition.csv similarity index 99% rename from activitysim/examples/example_psrc/configs/joint_tour_composition.csv rename to activitysim/examples/placeholder_psrc/configs/joint_tour_composition.csv index f258ea1aa7..de03e3dcc1 100755 --- a/activitysim/examples/example_psrc/configs/joint_tour_composition.csv +++ b/activitysim/examples/placeholder_psrc/configs/joint_tour_composition.csv @@ -1,22 +1,22 @@ -Label,Description,Expression,adults,children,mixed -util_asc,Alternative-specific constant,1,,coef_asc_children,coef_asc_mixed -util_tour_purpose_is_eating_out,Joint tour purpose is eating out (dummy),tour_type=='eat',,coef_tour_purpose_is_eating_out_children,coef_tour_purpose_is_eating_out_mixed -util_tour_purpose_is_discretionary,Joint tour purpose is discretionary (dummy),tour_type=='disc',coef_tour_purpose_is_discretionary_adults,coef_tour_purpose_is_discretionary_children, -util_number_of_full_time_workers,Number of Full-Time Workers in the household,num_full_max3,coef_number_of_full_time_workers_adults,,coef_number_of_full_time_workers_mixed -util_number_of_part_time_workers,Number of Part-Time Workers in the household,num_part_max3,coef_number_of_part_time_workers_adults,,coef_number_of_part_time_workers_mixed -util_number_of_university_students,Number of University students in the household,num_univ_max3,coef_number_of_university_students,, -util_number_of_non_workers,Number of Non-Workers in the household,num_nonwork_max3,coef_number_of_non_workers_adults,,coef_number_of_non_workers_mixed -util_number_of_children_too_young_for_school,Number of Children too Young for School in the household,num_preschool_max3,,coef_number_of_children_too_young_for_school_children,coef_number_of_children_too_young_for_school_mixed -util_number_of_pre_driving_age_children,Number of Pre-driving Age Children in the household,num_school_max3,,coef_number_of_pre_driving_age_children_children,coef_number_of_pre_driving_age_children_mixed -util_number_of_driving_age_children,Number of Driving-age Children in the household,num_driving_max3,,coef_number_of_driving_age_children_children,coef_number_of_driving_age_children_mixed -util_low_income_households,Low income households (dummy),income_in_thousands<30,coef_low_income_households_adults,,coef_low_income_households_mixed -util_medium_income_households,Medium income households (dummy),(income_in_thousands>=30) & (income_in_thousands<60),coef_medium_income_households,, -util_household_has_more_cars_than_workers,Household has more cars than workers (dummy),more_cars_than_workers,coef_household_has_more_cars_than_workers_adults,,coef_household_has_more_cars_than_workers_mixed -util_household_in_urban_area,Household is located in an urban area type (dummy),home_is_urban,coef_household_in_urban_area,, -util_household_in_suburban_area,Household is located in a suburban area type (dummy),~(home_is_urban | home_is_rural),coef_household_in_suburban_area_adults,,coef_household_in_suburban_area_mixed -util_log_max_overlap_of_adults_time_windows,Log of max pair-wise overlap of household adults time windows,log_time_window_overlap_adult,coef_log_max_overlap_of_adults_time_windows,, -util_log_max_overlap_of_childrens_time_windows,Log of max pair-wise overlap of household childrens time windows,log_time_window_overlap_child,,coef_log_max_overlap_of_childrens_time_windows, -util_log_max_overlap_of_time_windows,Log of max pair-wise overlap of household adults and childrens time windows,log_time_window_overlap_adult_child,,,coef_log_max_overlap_of_time_windows -util_two_acive_adults,Two adults must have Mand or Non Mand activity patterns to have adult-only joint travel,num_travel_active_adults<2,coef_unavailable,, -util_two_active_children,Two children must have Mand or Non Mand activity patterns to have children-only joint travel,num_travel_active_children<2,,coef_unavailable, +Label,Description,Expression,adults,children,mixed +util_asc,Alternative-specific constant,1,,coef_asc_children,coef_asc_mixed +util_tour_purpose_is_eating_out,Joint tour purpose is eating out (dummy),tour_type=='eat',,coef_tour_purpose_is_eating_out_children,coef_tour_purpose_is_eating_out_mixed +util_tour_purpose_is_discretionary,Joint tour purpose is discretionary (dummy),tour_type=='disc',coef_tour_purpose_is_discretionary_adults,coef_tour_purpose_is_discretionary_children, +util_number_of_full_time_workers,Number of Full-Time Workers in the household,num_full_max3,coef_number_of_full_time_workers_adults,,coef_number_of_full_time_workers_mixed +util_number_of_part_time_workers,Number of Part-Time Workers in the household,num_part_max3,coef_number_of_part_time_workers_adults,,coef_number_of_part_time_workers_mixed +util_number_of_university_students,Number of University students in the household,num_univ_max3,coef_number_of_university_students,, +util_number_of_non_workers,Number of Non-Workers in the household,num_nonwork_max3,coef_number_of_non_workers_adults,,coef_number_of_non_workers_mixed +util_number_of_children_too_young_for_school,Number of Children too Young for School in the household,num_preschool_max3,,coef_number_of_children_too_young_for_school_children,coef_number_of_children_too_young_for_school_mixed +util_number_of_pre_driving_age_children,Number of Pre-driving Age Children in the household,num_school_max3,,coef_number_of_pre_driving_age_children_children,coef_number_of_pre_driving_age_children_mixed +util_number_of_driving_age_children,Number of Driving-age Children in the household,num_driving_max3,,coef_number_of_driving_age_children_children,coef_number_of_driving_age_children_mixed +util_low_income_households,Low income households (dummy),income_in_thousands<30,coef_low_income_households_adults,,coef_low_income_households_mixed +util_medium_income_households,Medium income households (dummy),(income_in_thousands>=30) & (income_in_thousands<60),coef_medium_income_households,, +util_household_has_more_cars_than_workers,Household has more cars than workers (dummy),more_cars_than_workers,coef_household_has_more_cars_than_workers_adults,,coef_household_has_more_cars_than_workers_mixed +util_household_in_urban_area,Household is located in an urban area type (dummy),home_is_urban,coef_household_in_urban_area,, +util_household_in_suburban_area,Household is located in a suburban area type (dummy),~(home_is_urban | home_is_rural),coef_household_in_suburban_area_adults,,coef_household_in_suburban_area_mixed +util_log_max_overlap_of_adults_time_windows,Log of max pair-wise overlap of household adults time windows,log_time_window_overlap_adult,coef_log_max_overlap_of_adults_time_windows,, +util_log_max_overlap_of_childrens_time_windows,Log of max pair-wise overlap of household childrens time windows,log_time_window_overlap_child,,coef_log_max_overlap_of_childrens_time_windows, +util_log_max_overlap_of_time_windows,Log of max pair-wise overlap of household adults and childrens time windows,log_time_window_overlap_adult_child,,,coef_log_max_overlap_of_time_windows +util_two_acive_adults,Two adults must have Mand or Non Mand activity patterns to have adult-only joint travel,num_travel_active_adults<2,coef_unavailable,, +util_two_active_children,Two children must have Mand or Non Mand activity patterns to have children-only joint travel,num_travel_active_children<2,,coef_unavailable, util_travel_active_adult,At least one adult and at least one child must have Mand or Non Mand activity patterns to have adult/child joint travel,(num_travel_active_adults == 0) | (num_travel_active_children == 0),,,coef_unavailable \ No newline at end of file diff --git a/activitysim/examples/example_arc/configs/joint_tour_composition.yaml b/activitysim/examples/placeholder_psrc/configs/joint_tour_composition.yaml old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_composition.yaml rename to activitysim/examples/placeholder_psrc/configs/joint_tour_composition.yaml diff --git a/activitysim/examples/example_mtc/configs/joint_tour_composition_annotate_households_preprocessor.csv b/activitysim/examples/placeholder_psrc/configs/joint_tour_composition_annotate_households_preprocessor.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/joint_tour_composition_annotate_households_preprocessor.csv rename to activitysim/examples/placeholder_psrc/configs/joint_tour_composition_annotate_households_preprocessor.csv diff --git a/activitysim/examples/example_mtc/configs/joint_tour_composition_coefficients.csv b/activitysim/examples/placeholder_psrc/configs/joint_tour_composition_coeffs.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/joint_tour_composition_coefficients.csv rename to activitysim/examples/placeholder_psrc/configs/joint_tour_composition_coeffs.csv diff --git a/activitysim/examples/example_psrc/configs/joint_tour_destination.yaml b/activitysim/examples/placeholder_psrc/configs/joint_tour_destination.yaml similarity index 96% rename from activitysim/examples/example_psrc/configs/joint_tour_destination.yaml rename to activitysim/examples/placeholder_psrc/configs/joint_tour_destination.yaml index 62ea09b7e1..8d142cb78f 100755 --- a/activitysim/examples/example_psrc/configs/joint_tour_destination.yaml +++ b/activitysim/examples/placeholder_psrc/configs/joint_tour_destination.yaml @@ -1,40 +1,40 @@ -include_settings: non_mandatory_tour_destination.yaml - -#SAMPLE_SPEC: non_mandatory_tour_destination_sample.csv -#SPEC: non_mandatory_tour_destination.csv -#COEFFICIENTS: non_mandatory_tour_destination_coeffs.csv -# -#SAMPLE_SIZE: 30 -# -#SIZE_TERM_SELECTOR: non_mandatory -# -## we can't use use household income_segment as this will also be set for non-workers -#CHOOSER_SEGMENT_COLUMN_NAME: tour_type -# -## optional (comment out if not desired) -#DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum -# -## comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table -#DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample -# -# -#SEGMENTS: -# - shopping -# - othmaint -# - othdiscr -# - eatout -# - social -# - escort -# -#SIMULATE_CHOOSER_COLUMNS: -# - tour_type -# - home_zone_id -# - person_id -# -#LOGSUM_SETTINGS: tour_mode_choice.yaml -# -## model-specific logsum-related settings -#CHOOSER_ORIG_COL_NAME: home_zone_id -#ALT_DEST_COL_NAME: alt_dest -#IN_PERIOD: 14 -#OUT_PERIOD: 14 +include_settings: non_mandatory_tour_destination.yaml + +#SAMPLE_SPEC: non_mandatory_tour_destination_sample.csv +#SPEC: non_mandatory_tour_destination.csv +#COEFFICIENTS: non_mandatory_tour_destination_coeffs.csv +# +#SAMPLE_SIZE: 30 +# +#SIZE_TERM_SELECTOR: non_mandatory +# +## we can't use use household income_segment as this will also be set for non-workers +#CHOOSER_SEGMENT_COLUMN_NAME: tour_type +# +## optional (comment out if not desired) +#DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum +# +## comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +#DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample +# +# +#SEGMENTS: +# - shopping +# - othmaint +# - othdiscr +# - eatout +# - social +# - escort +# +#SIMULATE_CHOOSER_COLUMNS: +# - tour_type +# - home_zone_id +# - person_id +# +#LOGSUM_SETTINGS: tour_mode_choice.yaml +# +## model-specific logsum-related settings +#CHOOSER_ORIG_COL_NAME: home_zone_id +#ALT_DEST_COL_NAME: alt_dest +#IN_PERIOD: 14 +#OUT_PERIOD: 14 diff --git a/activitysim/examples/example_mtc/configs/joint_tour_frequency.csv b/activitysim/examples/placeholder_psrc/configs/joint_tour_frequency.csv old mode 100644 new mode 100755 similarity index 99% rename from activitysim/examples/example_mtc/configs/joint_tour_frequency.csv rename to activitysim/examples/placeholder_psrc/configs/joint_tour_frequency.csv index 8405d30649..ed2da406ee --- a/activitysim/examples/example_mtc/configs/joint_tour_frequency.csv +++ b/activitysim/examples/placeholder_psrc/configs/joint_tour_frequency.csv @@ -1,77 +1,77 @@ -Label,Description,Expression,0_tours,1_Shop,1_Main,1_Eat,1_Visit,1_Disc,2_SS,2_SM,2_SE,2_SV,2_SD,2_MM,2_ME,2_MV,2_MD,2_EE,2_EV,2_ED,2_VV,2_VD,2_DD -util_alternative_specific_constants,alternative_specific_constants,1,coef_asc_0_tours,coef_asc_1_Shop,coef_asc_1_Main,coef_asc_1_Eat,coef_asc_1_Visit,coef_asc_1_Disc,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours -#_zero_tours,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeHomeMaxThree_zero_tours,fullTimeHomeMaxThree_zero_tours,cdap_home_full_max3,coef_fullTimeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, -util_partTimeHomeMaxThree_zero_tours,partTimeHomeMaxThree_zero_tours,cdap_home_part_max3,coef_partTimeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, -util_nonWorkerHomeMaxThree_zero_tours,nonWorkerHomeMaxThree_zero_tours,cdap_home_nonwork_max3,coef_nonWorkerHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, -util_retireeHomeMaxThree_zero_tours,retireeHomeMaxThree_zero_tours,cdap_home_retired_max3,coef_retireeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, -util_universityHomeMaxThree_univ_and_driving_zero_tours,universityHomeMaxThree_univ_and_driving_zero_tours,cdap_home_univ_driving_max3,coef_universityHomeMaxThree_univ_and_driving_zero_tours,,,,,,,,,,,,,,,,,,,, -util_preDrivingHomeMaxThree_preschool_and_school_zero_tours,preDrivingHomeMaxThree_preschool_and_school_zero_tours,cdap_home_nondriving_child_max3,coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours,,,,,,,,,,,,,,,,,,,, -#_shopping,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeNonMandMaxThree_shopping,fullTimeNonMandMaxThree_shopping,cdap_nonmand_full_max3,,coef_fullTimeNonMandMaxThree_shopping,,,,,2 * coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,,,,,,,,,, -util_partTimeNonMandMaxThree_shopping,partTimeNonMandMaxThree_shopping,cdap_nonmand_part_max3,,coef_partTimeNonMandMaxThree_shopping,,,,,2 * coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,,,,,,,,,, -util_nonWorkerNonMandMaxThree_shopping,nonWorkerNonMandMaxThree_shopping,cdap_nonmand_nonwork_max3,,coef_nonWorkerNonMandMaxThree_shopping,,,,,2 * coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,,,,,,,,,, -util_retireeNonMandMaxThree_shopping,retireeNonMandMaxThree_shopping,cdap_nonmand_retired_max3,,coef_retireeNonMandMaxThree_shopping,,,,,2 * coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,,,,,,,,,, -util_universityNonMandMaxThree_shopping,universityNonMandMaxThree_shopping,cdap_nonmand_univ_driving_max3,,coef_universityNonMandMaxThree_shopping,,,,,2 * coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,,,,,,,,,, -util_preDrivingNonMandMaxThree_shopping,preDrivingNonMandMaxThree_shopping,cdap_nonmand_nondriving_child_max3,,coef_preDrivingNonMandMaxThree_shopping,,,,,2 * coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,,,,,,,,,, -util_fullTimeMandMaxThree_shopping,fullTimeMandMaxThree_shopping,cdap_mand_full_max3,,coef_fullTimeMandMaxThree_shopping,,,,,2 * coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,,,,,,,,,, -util_logTimeWindowOverlapAdult_shopping,logTimeWindowOverlapAdult_shopping,log_time_window_overlap_adult,,coef_logTimeWindowOverlapAdult_shopping,,,,,2 * coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,,,,,,,,,, -util_logTimeWindowOverlapChild_shopping,logTimeWindowOverlapChild_shopping,log_time_window_overlap_child,,coef_logTimeWindowOverlapChild_shopping,,,,,2 * coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,,,,,,,,,, -util_logTimeWindowOverlapAdultChild_shopping,logTimeWindowOverlapAdultChild_shopping,log_time_window_overlap_adult_child,,coef_logTimeWindowOverlapAdultChild_shopping,,,,,2 * coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,,,,,,,,,, -util_fewerCarsThanDrivers_shopping,fewerCarsThanDrivers_shopping,(auto_ownership > 0) & (auto_ownership < num_drivers),,coef_fewerCarsThanDrivers_shopping,,,,,2 * coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,,,,,,,,,, -util_moreCarsThanWorkers_shopping,moreCarsThanWorkers_shopping,auto_ownership > num_workers,,coef_moreCarsThanWorkers_shopping,,,,,2 * coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,,,,,,,,,, -#_Maintenance,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeNonMandMaxThree_Maintenance,fullTimeNonMandMaxThree_Maintenance,cdap_nonmand_full_max3,,,coef_fullTimeNonMandMaxThree_maint,,,,,coef_fullTimeNonMandMaxThree_maint,,,,2 * coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,,,,,, -util_partTimeNonMandMaxThree_Maintenance,partTimeNonMandMaxThree_Maintenance,cdap_nonmand_part_max3,,,coef_partTimeNonMandMaxThree_maint,,,,,coef_partTimeNonMandMaxThree_maint,,,,2 * coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,,,,,, -util_nonWorkerNonMandMaxThree_Maintenance,nonWorkerNonMandMaxThree_Maintenance,cdap_nonmand_nonwork_max3,,,coef_nonWorkerNonMandMaxThree_maint,,,,,coef_nonWorkerNonMandMaxThree_maint,,,,2 * coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,,,,,, -util_retireeNonMandMaxThree_Maintenance,retireeNonMandMaxThree_Maintenance,cdap_nonmand_retired_max3,,,coef_retireeNonMandMaxThree_maint,,,,,coef_retireeNonMandMaxThree_maint,,,,2 * coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,,,,,, -util_universityNonMandMaxThree_Maintenance,universityNonMandMaxThree_Maintenance,cdap_nonmand_univ_driving_max3,,,coef_universityNonMandMaxThree_maint,,,,,coef_universityNonMandMaxThree_maint,,,,2 * coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,,,,,, -util_preDrivingNonMandMaxThree_Maintenance,preDrivingNonMandMaxThree_Maintenance,cdap_nonmand_nondriving_child_max3,,,coef_preDrivingNonMandMaxThree_maint,,,,,coef_preDrivingNonMandMaxThree_maint,,,,2 * coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,,,,,, -util_fullTimeMandMaxThree_Maintenance,fullTimeMandMaxThree_Maintenance,cdap_mand_full_max3,,,coef_fullTimeMandMaxThree_maint,,,,,coef_fullTimeMandMaxThree_maint,,,,2 * coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,,,,,, -util_drivingAgeStuMandMaxThree_Maintenance,drivingAgeStuMandMaxThree_Maintenance,cdap_mand_univ_driving_max3,,,coef_drivingAgeStuMandMaxThree_maint,,,,,coef_drivingAgeStuMandMaxThree_maint,,,,2 * coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,,,,,, -util_preDrivingAgeMandMaxThree_Maintenance,preDrivingAgeMandMaxThree_Maintenance,cdap_mand_nondriving_child_max3,,,coef_preDrivingAgeMandMaxThree_maint,,,,,coef_preDrivingAgeMandMaxThree_maint,,,,2 * coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,,,,,, -util_logTimeWindowOverlapAdult_Maintenance,logTimeWindowOverlapAdult_Maintenance,log_time_window_overlap_adult,,,coef_logTimeWindowOverlapAdult_maint,,,,,coef_logTimeWindowOverlapAdult_maint,,,,2 * coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,,,,,, -util_logTimeWindowOverlapChild_Maintenance,logTimeWindowOverlapChild_Maintenance,log_time_window_overlap_child,,,coef_logTimeWindowOverlapChild_maint,,,,,coef_logTimeWindowOverlapChild_maint,,,,2 * coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,,,,,, -util_logTimeWindowOverlapAdultChild_Maintenance,logTimeWindowOverlapAdultChild_Maintenance,log_time_window_overlap_adult_child,,,coef_logTimeWindowOverlapAdultChild_maint,,,,,coef_logTimeWindowOverlapAdultChild_maint,,,,2 * coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,,,,,, -util_fewerCarsThanDrivers_Maintenance,fewerCarsThanDrivers_Maintenance,(auto_ownership > 0) & (auto_ownership < num_drivers),,,coef_fewerCarsThanDrivers_maint,,,,,coef_fewerCarsThanDrivers_maint,,,,2 * coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,,,,,, -#_eatout,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeNonMandMaxThree_eatout,fullTimeNonMandMaxThree_eatout,cdap_nonmand_full_max3,,,,coef_fullTimeNonMandMaxThree_eatout,,,,,coef_fullTimeNonMandMaxThree_eatout,,,,coef_fullTimeNonMandMaxThree_eatout,,,2 * coef_fullTimeNonMandMaxThree_eatout,coef_fullTimeNonMandMaxThree_eatout,coef_fullTimeNonMandMaxThree_eatout,,, -util_partTimeNonMandMaxThree_eatout,partTimeNonMandMaxThree_eatout,cdap_nonmand_part_max3,,,,coef_partTimeNonMandMaxThree_eatout,,,,,coef_partTimeNonMandMaxThree_eatout,,,,coef_partTimeNonMandMaxThree_eatout,,,2 * coef_partTimeNonMandMaxThree_eatout,coef_partTimeNonMandMaxThree_eatout,coef_partTimeNonMandMaxThree_eatout,,, -util_nonWorkerNonMandMaxThree_eatout,nonWorkerNonMandMaxThree_eatout,cdap_nonmand_nonwork_max3,,,,coef_nonWorkerNonMandMaxThree_eatout,,,,,coef_nonWorkerNonMandMaxThree_eatout,,,,coef_nonWorkerNonMandMaxThree_eatout,,,2 * coef_nonWorkerNonMandMaxThree_eatout,coef_nonWorkerNonMandMaxThree_eatout,coef_nonWorkerNonMandMaxThree_eatout,,, -util_retireeNonMandMaxThree_eatout,retireeNonMandMaxThree_eatout,cdap_nonmand_retired_max3,,,,coef_retireeNonMandMaxThree_eatout,,,,,coef_retireeNonMandMaxThree_eatout,,,,coef_retireeNonMandMaxThree_eatout,,,2 * coef_retireeNonMandMaxThree_eatout,coef_retireeNonMandMaxThree_eatout,coef_retireeNonMandMaxThree_eatout,,, -util_universityNonMandMaxThree_eatout,universityNonMandMaxThree_eatout,cdap_nonmand_univ_driving_max3,,,,coef_universityNonMandMaxThree_eatout,,,,,coef_universityNonMandMaxThree_eatout,,,,coef_universityNonMandMaxThree_eatout,,,2 * coef_universityNonMandMaxThree_eatout,coef_universityNonMandMaxThree_eatout,coef_universityNonMandMaxThree_eatout,,, -util_preDrivingNonMandMaxThree_eatout,preDrivingNonMandMaxThree_eatout,cdap_nonmand_nondriving_child_max3,,,,coef_preDrivingNonMandMaxThree_eatout,,,,,coef_preDrivingNonMandMaxThree_eatout,,,,coef_preDrivingNonMandMaxThree_eatout,,,2 * coef_preDrivingNonMandMaxThree_eatout,coef_preDrivingNonMandMaxThree_eatout,coef_preDrivingNonMandMaxThree_eatout,,, -util_logTimeWindowOverlapAdult_eatout,logTimeWindowOverlapAdult_eatout,log_time_window_overlap_adult,,,,coef_logTimeWindowOverlapAdult_eatout,,,,,coef_logTimeWindowOverlapAdult_eatout,,,,coef_logTimeWindowOverlapAdult_eatout,,,2 * coef_logTimeWindowOverlapAdult_eatout,coef_logTimeWindowOverlapAdult_eatout,coef_logTimeWindowOverlapAdult_eatout,,, -util_logTimeWindowOverlapAdultChild_eatout,logTimeWindowOverlapAdultChild_eatout,log_time_window_overlap_adult_child,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,2 * coef_logTimeWindowOverlapAdultChild_eatout,coef_logTimeWindowOverlapAdultChild_eatout,coef_logTimeWindowOverlapAdultChild_eatout,,, -util_incomeBetween50And100_eatout,incomeBetween50And100_eatout,income_between_50_and_100,,,,coef_incomeBetween50And100_eatout,,,,,coef_incomeBetween50And100_eatout,,,,coef_incomeBetween50And100_eatout,,,2 * coef_incomeBetween50And100_eatout,coef_incomeBetween50And100_eatout,coef_incomeBetween50And100_eatout,,, -util_incomeGreaterThan100_eatout,incomeGreaterThan100_eatout,income_greater_than_100,,,,coef_incomeGreaterThan100_eatout,,,,,coef_incomeGreaterThan100_eatout,,,,coef_incomeGreaterThan100_eatout,,,2 * coef_incomeGreaterThan100_eatout,coef_incomeGreaterThan100_eatout,coef_incomeGreaterThan100_eatout,,, -util_incomeMissing_dummy_always_zero_eatout,incomeMissing_dummy_always_zero_eatout,income_missing,,,,coef_incomeMissing_dummy_always_zero_eatout,,,,,coef_incomeMissing_dummy_always_zero_eatout,,,,coef_incomeMissing_dummy_always_zero_eatout,,,2 * coef_incomeMissing_dummy_always_zero_eatout,coef_incomeMissing_dummy_always_zero_eatout,coef_incomeMissing_dummy_always_zero_eatout,,, -util_moreCarsThanWorkers_eatout,moreCarsThanWorkers_eatout,auto_ownership > num_workers,,,,coef_moreCarsThanWorkers_eatout,,,,,coef_moreCarsThanWorkers_eatout,,,,coef_moreCarsThanWorkers_eatout,,,2 * coef_moreCarsThanWorkers_eatout,coef_moreCarsThanWorkers_eatout,coef_moreCarsThanWorkers_eatout,,, -util_walkRetailAccessibility_eatout,walkRetailAccessibility_eatout,non_motorized_retail_accessibility,,,,coef_walkRetailAccessibility_eatout,,,,,coef_walkRetailAccessibility_eatout,,,,coef_walkRetailAccessibility_eatout,,,2 * coef_walkRetailAccessibility_eatout,coef_walkRetailAccessibility_eatout,coef_walkRetailAccessibility_eatout,,, -#_visiting,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeNonMandMaxThree_visiting,fullTimeNonMandMaxThree_visiting,cdap_nonmand_full_max3,,,,,coef_fullTimeNonMandMaxThree_visiting,,,,,coef_fullTimeNonMandMaxThree_visiting,,,,coef_fullTimeNonMandMaxThree_visiting,,,coef_fullTimeNonMandMaxThree_visiting,,2 * coef_fullTimeNonMandMaxThree_visiting,coef_fullTimeNonMandMaxThree_visiting, -util_partTimeNonMandMaxThree_visiting,partTimeNonMandMaxThree_visiting,cdap_nonmand_part_max3,,,,,coef_partTimeNonMandMaxThree_visiting,,,,,coef_partTimeNonMandMaxThree_visiting,,,,coef_partTimeNonMandMaxThree_visiting,,,coef_partTimeNonMandMaxThree_visiting,,2 * coef_partTimeNonMandMaxThree_visiting,coef_partTimeNonMandMaxThree_visiting, -util_nonWorkerNonMandMaxThree_visiting,nonWorkerNonMandMaxThree_visiting,cdap_nonmand_nonwork_max3,,,,,coef_nonWorkerNonMandMaxThree_visiting,,,,,coef_nonWorkerNonMandMaxThree_visiting,,,,coef_nonWorkerNonMandMaxThree_visiting,,,coef_nonWorkerNonMandMaxThree_visiting,,2 * coef_nonWorkerNonMandMaxThree_visiting,coef_nonWorkerNonMandMaxThree_visiting, -util_retireeNonMandMaxThree_visiting,retireeNonMandMaxThree_visiting,cdap_nonmand_retired_max3,,,,,coef_retireeNonMandMaxThree_visiting,,,,,coef_retireeNonMandMaxThree_visiting,,,,coef_retireeNonMandMaxThree_visiting,,,coef_retireeNonMandMaxThree_visiting,,2 * coef_retireeNonMandMaxThree_visiting,coef_retireeNonMandMaxThree_visiting, -util_universityNonMandMaxThree_visiting,universityNonMandMaxThree_visiting,cdap_nonmand_univ_driving_max3,,,,,coef_universityNonMandMaxThree_visiting,,,,,coef_universityNonMandMaxThree_visiting,,,,coef_universityNonMandMaxThree_visiting,,,coef_universityNonMandMaxThree_visiting,,2 * coef_universityNonMandMaxThree_visiting,coef_universityNonMandMaxThree_visiting, -util_preDrivingNonMandMaxThree_visiting,preDrivingNonMandMaxThree_visiting,cdap_nonmand_nondriving_child_max3,,,,,coef_preDrivingNonMandMaxThree_visiting,,,,,coef_preDrivingNonMandMaxThree_visiting,,,,coef_preDrivingNonMandMaxThree_visiting,,,coef_preDrivingNonMandMaxThree_visiting,,2 * coef_preDrivingNonMandMaxThree_visiting,coef_preDrivingNonMandMaxThree_visiting, -util_timeWindowOverlapAdult_visiting,timeWindowOverlapAdult_visiting,time_window_overlap_adult,,,,,coef_timeWindowOverlapAdult_visiting,,,,,coef_timeWindowOverlapAdult_visiting,,,,coef_timeWindowOverlapAdult_visiting,,,coef_timeWindowOverlapAdult_visiting,,2 * coef_timeWindowOverlapAdult_visiting,coef_timeWindowOverlapAdult_visiting, -util_timeWindowOverlapChild_visiting,timeWindowOverlapChild_visiting,time_window_overlap_child,,,,,coef_timeWindowOverlapChild_visiting,,,,,coef_timeWindowOverlapChild_visiting,,,,coef_timeWindowOverlapChild_visiting,,,coef_timeWindowOverlapChild_visiting,,2 * coef_timeWindowOverlapChild_visiting,coef_timeWindowOverlapChild_visiting, -util_timeWindowOverlapAdultChild_visiting,timeWindowOverlapAdultChild_visiting,time_window_overlap_adult_child,,,,,coef_timeWindowOverlapAdultChild_visiting,,,,,coef_timeWindowOverlapAdultChild_visiting,,,,coef_timeWindowOverlapAdultChild_visiting,,,coef_timeWindowOverlapAdultChild_visiting,,2 * coef_timeWindowOverlapAdultChild_visiting,coef_timeWindowOverlapAdultChild_visiting, -util_zeroAutomobiles_visiting,zeroAutomobiles_visiting,auto_ownership == 0,,,,,coef_zeroAutomobiles_visiting,,,,,coef_zeroAutomobiles_visiting,,,,coef_zeroAutomobiles_visiting,,,coef_zeroAutomobiles_visiting,,2 * coef_zeroAutomobiles_visiting,coef_zeroAutomobiles_visiting, -#_discretionary,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeNonMandMaxThree_disc,fullTimeNonMandMaxThree_disc,cdap_nonmand_full_max3,,,,,,coef_fullTimeNonMandMaxThree_disc,,,,,coef_fullTimeNonMandMaxThree_disc,,,,coef_fullTimeNonMandMaxThree_disc,,,coef_fullTimeNonMandMaxThree_disc,,coef_fullTimeNonMandMaxThree_disc,2 * coef_fullTimeNonMandMaxThree_disc -util_partTimeNonMandMaxThree_disc,partTimeNonMandMaxThree_disc,cdap_nonmand_part_max3,,,,,,coef_partTimeNonMandMaxThree_disc,,,,,coef_partTimeNonMandMaxThree_disc,,,,coef_partTimeNonMandMaxThree_disc,,,coef_partTimeNonMandMaxThree_disc,,coef_partTimeNonMandMaxThree_disc,2 * coef_partTimeNonMandMaxThree_disc -util_nonWorkerNonMandMaxThree_disc,nonWorkerNonMandMaxThree_disc,cdap_nonmand_nonwork_max3,,,,,,coef_nonWorkerNonMandMaxThree_disc,,,,,coef_nonWorkerNonMandMaxThree_disc,,,,coef_nonWorkerNonMandMaxThree_disc,,,coef_nonWorkerNonMandMaxThree_disc,,coef_nonWorkerNonMandMaxThree_disc,2 * coef_nonWorkerNonMandMaxThree_disc -util_retireeNonMandMaxThree_disc,retireeNonMandMaxThree_disc,cdap_nonmand_retired_max3,,,,,,coef_retireeNonMandMaxThree_disc,,,,,coef_retireeNonMandMaxThree_disc,,,,coef_retireeNonMandMaxThree_disc,,,coef_retireeNonMandMaxThree_disc,,coef_retireeNonMandMaxThree_disc,2 * coef_retireeNonMandMaxThree_disc -util_universityNonMandMaxThree_disc,universityNonMandMaxThree_disc,cdap_nonmand_univ_driving_max3,,,,,,coef_universityNonMandMaxThree_disc,,,,,coef_universityNonMandMaxThree_disc,,,,coef_universityNonMandMaxThree_disc,,,coef_universityNonMandMaxThree_disc,,coef_universityNonMandMaxThree_disc,2 * coef_universityNonMandMaxThree_disc -util_preDrivingNonMandMaxThree_disc,preDrivingNonMandMaxThree_disc,cdap_nonmand_nondriving_child_max3,,,,,,coef_preDrivingNonMandMaxThree_disc,,,,,coef_preDrivingNonMandMaxThree_disc,,,,coef_preDrivingNonMandMaxThree_disc,,,coef_preDrivingNonMandMaxThree_disc,,coef_preDrivingNonMandMaxThree_disc,2 * coef_preDrivingNonMandMaxThree_disc -util_drivingAgeStuMandMaxThree_disc,drivingAgeStuMandMaxThree_disc,cdap_mand_univ_driving_max3,,,,,,coef_drivingAgeStuMandMaxThree_disc,,,,,coef_drivingAgeStuMandMaxThree_disc,,,,coef_drivingAgeStuMandMaxThree_disc,,,coef_drivingAgeStuMandMaxThree_disc,,coef_drivingAgeStuMandMaxThree_disc,2 * coef_drivingAgeStuMandMaxThree_disc -util_preDrivingAgeMandMaxThree_disc,preDrivingAgeMandMaxThree_disc,cdap_mand_nondriving_child_max3,,,,,,coef_preDrivingAgeMandMaxThree_disc,,,,,coef_preDrivingAgeMandMaxThree_disc,,,,coef_preDrivingAgeMandMaxThree_disc,,,coef_preDrivingAgeMandMaxThree_disc,,coef_preDrivingAgeMandMaxThree_disc,2 * coef_preDrivingAgeMandMaxThree_disc -util_logTimeWindowOverlapAdult_disc,logTimeWindowOverlapAdult_disc,log_time_window_overlap_adult,,,,,,coef_logTimeWindowOverlapAdult_disc,,,,,coef_logTimeWindowOverlapAdult_disc,,,,coef_logTimeWindowOverlapAdult_disc,,,coef_logTimeWindowOverlapAdult_disc,,coef_logTimeWindowOverlapAdult_disc,2 * coef_logTimeWindowOverlapAdult_disc -util_logTimeWindowOverlapChild_disc,logTimeWindowOverlapChild_disc,log_time_window_overlap_child,,,,,,coef_logTimeWindowOverlapChild_disc,,,,,coef_logTimeWindowOverlapChild_disc,,,,coef_logTimeWindowOverlapChild_disc,,,coef_logTimeWindowOverlapChild_disc,,coef_logTimeWindowOverlapChild_disc,2 * coef_logTimeWindowOverlapChild_disc -util_logTimeWindowOverlapAdultChild_disc,logTimeWindowOverlapAdultChild_disc,log_time_window_overlap_adult_child,,,,,,coef_logTimeWindowOverlapAdultChild_disc,,,,,coef_logTimeWindowOverlapAdultChild_disc,,,,coef_logTimeWindowOverlapAdultChild_disc,,,coef_logTimeWindowOverlapAdultChild_disc,,coef_logTimeWindowOverlapAdultChild_disc,2 * coef_logTimeWindowOverlapAdultChild_disc -util_incomeBetween50And100_disc,incomeBetween50And100_disc,income_between_50_and_100,,,,,,coef_incomeBetween50And100_disc,,,,,coef_incomeBetween50And100_disc,,,,coef_incomeBetween50And100_disc,,,coef_incomeBetween50And100_disc,,coef_incomeBetween50And100_disc,2 * coef_incomeBetween50And100_disc -util_incomeGreaterThan100_disc,incomeGreaterThan100_disc,income_greater_than_100,,,,,,coef_incomeGreaterThan100_disc,,,,,coef_incomeGreaterThan100_disc,,,,coef_incomeGreaterThan100_disc,,,coef_incomeGreaterThan100_disc,,coef_incomeGreaterThan100_disc,2 * coef_incomeGreaterThan100_disc -util_incomeMissing_dummy_always_zero_disc,incomeMissing_dummy_always_zero_disc,income_missing,,,,,,coef_incomeMissing_dummy_always_zero_disc,,,,,coef_incomeMissing_dummy_always_zero_disc,,,,coef_incomeMissing_dummy_always_zero_disc,,,coef_incomeMissing_dummy_always_zero_disc,,coef_incomeMissing_dummy_always_zero_disc,2 * coef_incomeMissing_dummy_always_zero_disc +Label,Description,Expression,0_tours,1_Shop,1_Main,1_Eat,1_Visit,1_Disc,2_SS,2_SM,2_SE,2_SV,2_SD,2_MM,2_ME,2_MV,2_MD,2_EE,2_EV,2_ED,2_VV,2_VD,2_DD +util_alternative_specific_constants,alternative_specific_constants,1,coef_asc_0_tours,coef_asc_1_Shop,coef_asc_1_Main,coef_asc_1_Eat,coef_asc_1_Visit,coef_asc_1_Disc,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours +#_zero_tours,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeHomeMaxThree_zero_tours,fullTimeHomeMaxThree_zero_tours,cdap_home_full_max3,coef_fullTimeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, +util_partTimeHomeMaxThree_zero_tours,partTimeHomeMaxThree_zero_tours,cdap_home_part_max3,coef_partTimeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, +util_nonWorkerHomeMaxThree_zero_tours,nonWorkerHomeMaxThree_zero_tours,cdap_home_nonwork_max3,coef_nonWorkerHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, +util_retireeHomeMaxThree_zero_tours,retireeHomeMaxThree_zero_tours,cdap_home_retired_max3,coef_retireeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, +util_universityHomeMaxThree_univ_and_driving_zero_tours,universityHomeMaxThree_univ_and_driving_zero_tours,cdap_home_univ_driving_max3,coef_universityHomeMaxThree_univ_and_driving_zero_tours,,,,,,,,,,,,,,,,,,,, +util_preDrivingHomeMaxThree_preschool_and_school_zero_tours,preDrivingHomeMaxThree_preschool_and_school_zero_tours,cdap_home_nondriving_child_max3,coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours,,,,,,,,,,,,,,,,,,,, +#_shopping,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeNonMandMaxThree_shopping,fullTimeNonMandMaxThree_shopping,cdap_nonmand_full_max3,,coef_fullTimeNonMandMaxThree_shopping,,,,,2 * coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,,,,,,,,,, +util_partTimeNonMandMaxThree_shopping,partTimeNonMandMaxThree_shopping,cdap_nonmand_part_max3,,coef_partTimeNonMandMaxThree_shopping,,,,,2 * coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,,,,,,,,,, +util_nonWorkerNonMandMaxThree_shopping,nonWorkerNonMandMaxThree_shopping,cdap_nonmand_nonwork_max3,,coef_nonWorkerNonMandMaxThree_shopping,,,,,2 * coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,,,,,,,,,, +util_retireeNonMandMaxThree_shopping,retireeNonMandMaxThree_shopping,cdap_nonmand_retired_max3,,coef_retireeNonMandMaxThree_shopping,,,,,2 * coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,,,,,,,,,, +util_universityNonMandMaxThree_shopping,universityNonMandMaxThree_shopping,cdap_nonmand_univ_driving_max3,,coef_universityNonMandMaxThree_shopping,,,,,2 * coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,,,,,,,,,, +util_preDrivingNonMandMaxThree_shopping,preDrivingNonMandMaxThree_shopping,cdap_nonmand_nondriving_child_max3,,coef_preDrivingNonMandMaxThree_shopping,,,,,2 * coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,,,,,,,,,, +util_fullTimeMandMaxThree_shopping,fullTimeMandMaxThree_shopping,cdap_mand_full_max3,,coef_fullTimeMandMaxThree_shopping,,,,,2 * coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,,,,,,,,,, +util_logTimeWindowOverlapAdult_shopping,logTimeWindowOverlapAdult_shopping,log_time_window_overlap_adult,,coef_logTimeWindowOverlapAdult_shopping,,,,,2 * coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,,,,,,,,,, +util_logTimeWindowOverlapChild_shopping,logTimeWindowOverlapChild_shopping,log_time_window_overlap_child,,coef_logTimeWindowOverlapChild_shopping,,,,,2 * coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,,,,,,,,,, +util_logTimeWindowOverlapAdultChild_shopping,logTimeWindowOverlapAdultChild_shopping,log_time_window_overlap_adult_child,,coef_logTimeWindowOverlapAdultChild_shopping,,,,,2 * coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,,,,,,,,,, +util_fewerCarsThanDrivers_shopping,fewerCarsThanDrivers_shopping,(auto_ownership > 0) & (auto_ownership < num_drivers),,coef_fewerCarsThanDrivers_shopping,,,,,2 * coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,,,,,,,,,, +util_moreCarsThanWorkers_shopping,moreCarsThanWorkers_shopping,auto_ownership > num_workers,,coef_moreCarsThanWorkers_shopping,,,,,2 * coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,,,,,,,,,, +#_Maintenance,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeNonMandMaxThree_Maintenance,fullTimeNonMandMaxThree_Maintenance,cdap_nonmand_full_max3,,,coef_fullTimeNonMandMaxThree_maint,,,,,coef_fullTimeNonMandMaxThree_maint,,,,2 * coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,,,,,, +util_partTimeNonMandMaxThree_Maintenance,partTimeNonMandMaxThree_Maintenance,cdap_nonmand_part_max3,,,coef_partTimeNonMandMaxThree_maint,,,,,coef_partTimeNonMandMaxThree_maint,,,,2 * coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,,,,,, +util_nonWorkerNonMandMaxThree_Maintenance,nonWorkerNonMandMaxThree_Maintenance,cdap_nonmand_nonwork_max3,,,coef_nonWorkerNonMandMaxThree_maint,,,,,coef_nonWorkerNonMandMaxThree_maint,,,,2 * coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,,,,,, +util_retireeNonMandMaxThree_Maintenance,retireeNonMandMaxThree_Maintenance,cdap_nonmand_retired_max3,,,coef_retireeNonMandMaxThree_maint,,,,,coef_retireeNonMandMaxThree_maint,,,,2 * coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,,,,,, +util_universityNonMandMaxThree_Maintenance,universityNonMandMaxThree_Maintenance,cdap_nonmand_univ_driving_max3,,,coef_universityNonMandMaxThree_maint,,,,,coef_universityNonMandMaxThree_maint,,,,2 * coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,,,,,, +util_preDrivingNonMandMaxThree_Maintenance,preDrivingNonMandMaxThree_Maintenance,cdap_nonmand_nondriving_child_max3,,,coef_preDrivingNonMandMaxThree_maint,,,,,coef_preDrivingNonMandMaxThree_maint,,,,2 * coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,,,,,, +util_fullTimeMandMaxThree_Maintenance,fullTimeMandMaxThree_Maintenance,cdap_mand_full_max3,,,coef_fullTimeMandMaxThree_maint,,,,,coef_fullTimeMandMaxThree_maint,,,,2 * coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,,,,,, +util_drivingAgeStuMandMaxThree_Maintenance,drivingAgeStuMandMaxThree_Maintenance,cdap_mand_univ_driving_max3,,,coef_drivingAgeStuMandMaxThree_maint,,,,,coef_drivingAgeStuMandMaxThree_maint,,,,2 * coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,,,,,, +util_preDrivingAgeMandMaxThree_Maintenance,preDrivingAgeMandMaxThree_Maintenance,cdap_mand_nondriving_child_max3,,,coef_preDrivingAgeMandMaxThree_maint,,,,,coef_preDrivingAgeMandMaxThree_maint,,,,2 * coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,,,,,, +util_logTimeWindowOverlapAdult_Maintenance,logTimeWindowOverlapAdult_Maintenance,log_time_window_overlap_adult,,,coef_logTimeWindowOverlapAdult_maint,,,,,coef_logTimeWindowOverlapAdult_maint,,,,2 * coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,,,,,, +util_logTimeWindowOverlapChild_Maintenance,logTimeWindowOverlapChild_Maintenance,log_time_window_overlap_child,,,coef_logTimeWindowOverlapChild_maint,,,,,coef_logTimeWindowOverlapChild_maint,,,,2 * coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,,,,,, +util_logTimeWindowOverlapAdultChild_Maintenance,logTimeWindowOverlapAdultChild_Maintenance,log_time_window_overlap_adult_child,,,coef_logTimeWindowOverlapAdultChild_maint,,,,,coef_logTimeWindowOverlapAdultChild_maint,,,,2 * coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,,,,,, +util_fewerCarsThanDrivers_Maintenance,fewerCarsThanDrivers_Maintenance,(auto_ownership > 0) & (auto_ownership < num_drivers),,,coef_fewerCarsThanDrivers_maint,,,,,coef_fewerCarsThanDrivers_maint,,,,2 * coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,,,,,, +#_eatout,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeNonMandMaxThree_eatout,fullTimeNonMandMaxThree_eatout,cdap_nonmand_full_max3,,,,coef_fullTimeNonMandMaxThree_eatout,,,,,coef_fullTimeNonMandMaxThree_eatout,,,,coef_fullTimeNonMandMaxThree_eatout,,,2 * coef_fullTimeNonMandMaxThree_eatout,coef_fullTimeNonMandMaxThree_eatout,coef_fullTimeNonMandMaxThree_eatout,,, +util_partTimeNonMandMaxThree_eatout,partTimeNonMandMaxThree_eatout,cdap_nonmand_part_max3,,,,coef_partTimeNonMandMaxThree_eatout,,,,,coef_partTimeNonMandMaxThree_eatout,,,,coef_partTimeNonMandMaxThree_eatout,,,2 * coef_partTimeNonMandMaxThree_eatout,coef_partTimeNonMandMaxThree_eatout,coef_partTimeNonMandMaxThree_eatout,,, +util_nonWorkerNonMandMaxThree_eatout,nonWorkerNonMandMaxThree_eatout,cdap_nonmand_nonwork_max3,,,,coef_nonWorkerNonMandMaxThree_eatout,,,,,coef_nonWorkerNonMandMaxThree_eatout,,,,coef_nonWorkerNonMandMaxThree_eatout,,,2 * coef_nonWorkerNonMandMaxThree_eatout,coef_nonWorkerNonMandMaxThree_eatout,coef_nonWorkerNonMandMaxThree_eatout,,, +util_retireeNonMandMaxThree_eatout,retireeNonMandMaxThree_eatout,cdap_nonmand_retired_max3,,,,coef_retireeNonMandMaxThree_eatout,,,,,coef_retireeNonMandMaxThree_eatout,,,,coef_retireeNonMandMaxThree_eatout,,,2 * coef_retireeNonMandMaxThree_eatout,coef_retireeNonMandMaxThree_eatout,coef_retireeNonMandMaxThree_eatout,,, +util_universityNonMandMaxThree_eatout,universityNonMandMaxThree_eatout,cdap_nonmand_univ_driving_max3,,,,coef_universityNonMandMaxThree_eatout,,,,,coef_universityNonMandMaxThree_eatout,,,,coef_universityNonMandMaxThree_eatout,,,2 * coef_universityNonMandMaxThree_eatout,coef_universityNonMandMaxThree_eatout,coef_universityNonMandMaxThree_eatout,,, +util_preDrivingNonMandMaxThree_eatout,preDrivingNonMandMaxThree_eatout,cdap_nonmand_nondriving_child_max3,,,,coef_preDrivingNonMandMaxThree_eatout,,,,,coef_preDrivingNonMandMaxThree_eatout,,,,coef_preDrivingNonMandMaxThree_eatout,,,2 * coef_preDrivingNonMandMaxThree_eatout,coef_preDrivingNonMandMaxThree_eatout,coef_preDrivingNonMandMaxThree_eatout,,, +util_logTimeWindowOverlapAdult_eatout,logTimeWindowOverlapAdult_eatout,log_time_window_overlap_adult,,,,coef_logTimeWindowOverlapAdult_eatout,,,,,coef_logTimeWindowOverlapAdult_eatout,,,,coef_logTimeWindowOverlapAdult_eatout,,,2 * coef_logTimeWindowOverlapAdult_eatout,coef_logTimeWindowOverlapAdult_eatout,coef_logTimeWindowOverlapAdult_eatout,,, +util_logTimeWindowOverlapAdultChild_eatout,logTimeWindowOverlapAdultChild_eatout,log_time_window_overlap_adult_child,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,2 * coef_logTimeWindowOverlapAdultChild_eatout,coef_logTimeWindowOverlapAdultChild_eatout,coef_logTimeWindowOverlapAdultChild_eatout,,, +util_incomeBetween50And100_eatout,incomeBetween50And100_eatout,income_between_50_and_100,,,,coef_incomeBetween50And100_eatout,,,,,coef_incomeBetween50And100_eatout,,,,coef_incomeBetween50And100_eatout,,,2 * coef_incomeBetween50And100_eatout,coef_incomeBetween50And100_eatout,coef_incomeBetween50And100_eatout,,, +util_incomeGreaterThan100_eatout,incomeGreaterThan100_eatout,income_greater_than_100,,,,coef_incomeGreaterThan100_eatout,,,,,coef_incomeGreaterThan100_eatout,,,,coef_incomeGreaterThan100_eatout,,,2 * coef_incomeGreaterThan100_eatout,coef_incomeGreaterThan100_eatout,coef_incomeGreaterThan100_eatout,,, +util_incomeMissing_dummy_always_zero_eatout,incomeMissing_dummy_always_zero_eatout,income_missing,,,,coef_incomeMissing_dummy_always_zero_eatout,,,,,coef_incomeMissing_dummy_always_zero_eatout,,,,coef_incomeMissing_dummy_always_zero_eatout,,,2 * coef_incomeMissing_dummy_always_zero_eatout,coef_incomeMissing_dummy_always_zero_eatout,coef_incomeMissing_dummy_always_zero_eatout,,, +util_moreCarsThanWorkers_eatout,moreCarsThanWorkers_eatout,auto_ownership > num_workers,,,,coef_moreCarsThanWorkers_eatout,,,,,coef_moreCarsThanWorkers_eatout,,,,coef_moreCarsThanWorkers_eatout,,,2 * coef_moreCarsThanWorkers_eatout,coef_moreCarsThanWorkers_eatout,coef_moreCarsThanWorkers_eatout,,, +util_walkRetailAccessibility_eatout,walkRetailAccessibility_eatout,non_motorized_retail_accessibility,,,,coef_walkRetailAccessibility_eatout,,,,,coef_walkRetailAccessibility_eatout,,,,coef_walkRetailAccessibility_eatout,,,2 * coef_walkRetailAccessibility_eatout,coef_walkRetailAccessibility_eatout,coef_walkRetailAccessibility_eatout,,, +#_visiting,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeNonMandMaxThree_visiting,fullTimeNonMandMaxThree_visiting,cdap_nonmand_full_max3,,,,,coef_fullTimeNonMandMaxThree_visiting,,,,,coef_fullTimeNonMandMaxThree_visiting,,,,coef_fullTimeNonMandMaxThree_visiting,,,coef_fullTimeNonMandMaxThree_visiting,,2 * coef_fullTimeNonMandMaxThree_visiting,coef_fullTimeNonMandMaxThree_visiting, +util_partTimeNonMandMaxThree_visiting,partTimeNonMandMaxThree_visiting,cdap_nonmand_part_max3,,,,,coef_partTimeNonMandMaxThree_visiting,,,,,coef_partTimeNonMandMaxThree_visiting,,,,coef_partTimeNonMandMaxThree_visiting,,,coef_partTimeNonMandMaxThree_visiting,,2 * coef_partTimeNonMandMaxThree_visiting,coef_partTimeNonMandMaxThree_visiting, +util_nonWorkerNonMandMaxThree_visiting,nonWorkerNonMandMaxThree_visiting,cdap_nonmand_nonwork_max3,,,,,coef_nonWorkerNonMandMaxThree_visiting,,,,,coef_nonWorkerNonMandMaxThree_visiting,,,,coef_nonWorkerNonMandMaxThree_visiting,,,coef_nonWorkerNonMandMaxThree_visiting,,2 * coef_nonWorkerNonMandMaxThree_visiting,coef_nonWorkerNonMandMaxThree_visiting, +util_retireeNonMandMaxThree_visiting,retireeNonMandMaxThree_visiting,cdap_nonmand_retired_max3,,,,,coef_retireeNonMandMaxThree_visiting,,,,,coef_retireeNonMandMaxThree_visiting,,,,coef_retireeNonMandMaxThree_visiting,,,coef_retireeNonMandMaxThree_visiting,,2 * coef_retireeNonMandMaxThree_visiting,coef_retireeNonMandMaxThree_visiting, +util_universityNonMandMaxThree_visiting,universityNonMandMaxThree_visiting,cdap_nonmand_univ_driving_max3,,,,,coef_universityNonMandMaxThree_visiting,,,,,coef_universityNonMandMaxThree_visiting,,,,coef_universityNonMandMaxThree_visiting,,,coef_universityNonMandMaxThree_visiting,,2 * coef_universityNonMandMaxThree_visiting,coef_universityNonMandMaxThree_visiting, +util_preDrivingNonMandMaxThree_visiting,preDrivingNonMandMaxThree_visiting,cdap_nonmand_nondriving_child_max3,,,,,coef_preDrivingNonMandMaxThree_visiting,,,,,coef_preDrivingNonMandMaxThree_visiting,,,,coef_preDrivingNonMandMaxThree_visiting,,,coef_preDrivingNonMandMaxThree_visiting,,2 * coef_preDrivingNonMandMaxThree_visiting,coef_preDrivingNonMandMaxThree_visiting, +util_timeWindowOverlapAdult_visiting,timeWindowOverlapAdult_visiting,time_window_overlap_adult,,,,,coef_timeWindowOverlapAdult_visiting,,,,,coef_timeWindowOverlapAdult_visiting,,,,coef_timeWindowOverlapAdult_visiting,,,coef_timeWindowOverlapAdult_visiting,,2 * coef_timeWindowOverlapAdult_visiting,coef_timeWindowOverlapAdult_visiting, +util_timeWindowOverlapChild_visiting,timeWindowOverlapChild_visiting,time_window_overlap_child,,,,,coef_timeWindowOverlapChild_visiting,,,,,coef_timeWindowOverlapChild_visiting,,,,coef_timeWindowOverlapChild_visiting,,,coef_timeWindowOverlapChild_visiting,,2 * coef_timeWindowOverlapChild_visiting,coef_timeWindowOverlapChild_visiting, +util_timeWindowOverlapAdultChild_visiting,timeWindowOverlapAdultChild_visiting,time_window_overlap_adult_child,,,,,coef_timeWindowOverlapAdultChild_visiting,,,,,coef_timeWindowOverlapAdultChild_visiting,,,,coef_timeWindowOverlapAdultChild_visiting,,,coef_timeWindowOverlapAdultChild_visiting,,2 * coef_timeWindowOverlapAdultChild_visiting,coef_timeWindowOverlapAdultChild_visiting, +util_zeroAutomobiles_visiting,zeroAutomobiles_visiting,auto_ownership == 0,,,,,coef_zeroAutomobiles_visiting,,,,,coef_zeroAutomobiles_visiting,,,,coef_zeroAutomobiles_visiting,,,coef_zeroAutomobiles_visiting,,2 * coef_zeroAutomobiles_visiting,coef_zeroAutomobiles_visiting, +#_discretionary,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeNonMandMaxThree_disc,fullTimeNonMandMaxThree_disc,cdap_nonmand_full_max3,,,,,,coef_fullTimeNonMandMaxThree_disc,,,,,coef_fullTimeNonMandMaxThree_disc,,,,coef_fullTimeNonMandMaxThree_disc,,,coef_fullTimeNonMandMaxThree_disc,,coef_fullTimeNonMandMaxThree_disc,2 * coef_fullTimeNonMandMaxThree_disc +util_partTimeNonMandMaxThree_disc,partTimeNonMandMaxThree_disc,cdap_nonmand_part_max3,,,,,,coef_partTimeNonMandMaxThree_disc,,,,,coef_partTimeNonMandMaxThree_disc,,,,coef_partTimeNonMandMaxThree_disc,,,coef_partTimeNonMandMaxThree_disc,,coef_partTimeNonMandMaxThree_disc,2 * coef_partTimeNonMandMaxThree_disc +util_nonWorkerNonMandMaxThree_disc,nonWorkerNonMandMaxThree_disc,cdap_nonmand_nonwork_max3,,,,,,coef_nonWorkerNonMandMaxThree_disc,,,,,coef_nonWorkerNonMandMaxThree_disc,,,,coef_nonWorkerNonMandMaxThree_disc,,,coef_nonWorkerNonMandMaxThree_disc,,coef_nonWorkerNonMandMaxThree_disc,2 * coef_nonWorkerNonMandMaxThree_disc +util_retireeNonMandMaxThree_disc,retireeNonMandMaxThree_disc,cdap_nonmand_retired_max3,,,,,,coef_retireeNonMandMaxThree_disc,,,,,coef_retireeNonMandMaxThree_disc,,,,coef_retireeNonMandMaxThree_disc,,,coef_retireeNonMandMaxThree_disc,,coef_retireeNonMandMaxThree_disc,2 * coef_retireeNonMandMaxThree_disc +util_universityNonMandMaxThree_disc,universityNonMandMaxThree_disc,cdap_nonmand_univ_driving_max3,,,,,,coef_universityNonMandMaxThree_disc,,,,,coef_universityNonMandMaxThree_disc,,,,coef_universityNonMandMaxThree_disc,,,coef_universityNonMandMaxThree_disc,,coef_universityNonMandMaxThree_disc,2 * coef_universityNonMandMaxThree_disc +util_preDrivingNonMandMaxThree_disc,preDrivingNonMandMaxThree_disc,cdap_nonmand_nondriving_child_max3,,,,,,coef_preDrivingNonMandMaxThree_disc,,,,,coef_preDrivingNonMandMaxThree_disc,,,,coef_preDrivingNonMandMaxThree_disc,,,coef_preDrivingNonMandMaxThree_disc,,coef_preDrivingNonMandMaxThree_disc,2 * coef_preDrivingNonMandMaxThree_disc +util_drivingAgeStuMandMaxThree_disc,drivingAgeStuMandMaxThree_disc,cdap_mand_univ_driving_max3,,,,,,coef_drivingAgeStuMandMaxThree_disc,,,,,coef_drivingAgeStuMandMaxThree_disc,,,,coef_drivingAgeStuMandMaxThree_disc,,,coef_drivingAgeStuMandMaxThree_disc,,coef_drivingAgeStuMandMaxThree_disc,2 * coef_drivingAgeStuMandMaxThree_disc +util_preDrivingAgeMandMaxThree_disc,preDrivingAgeMandMaxThree_disc,cdap_mand_nondriving_child_max3,,,,,,coef_preDrivingAgeMandMaxThree_disc,,,,,coef_preDrivingAgeMandMaxThree_disc,,,,coef_preDrivingAgeMandMaxThree_disc,,,coef_preDrivingAgeMandMaxThree_disc,,coef_preDrivingAgeMandMaxThree_disc,2 * coef_preDrivingAgeMandMaxThree_disc +util_logTimeWindowOverlapAdult_disc,logTimeWindowOverlapAdult_disc,log_time_window_overlap_adult,,,,,,coef_logTimeWindowOverlapAdult_disc,,,,,coef_logTimeWindowOverlapAdult_disc,,,,coef_logTimeWindowOverlapAdult_disc,,,coef_logTimeWindowOverlapAdult_disc,,coef_logTimeWindowOverlapAdult_disc,2 * coef_logTimeWindowOverlapAdult_disc +util_logTimeWindowOverlapChild_disc,logTimeWindowOverlapChild_disc,log_time_window_overlap_child,,,,,,coef_logTimeWindowOverlapChild_disc,,,,,coef_logTimeWindowOverlapChild_disc,,,,coef_logTimeWindowOverlapChild_disc,,,coef_logTimeWindowOverlapChild_disc,,coef_logTimeWindowOverlapChild_disc,2 * coef_logTimeWindowOverlapChild_disc +util_logTimeWindowOverlapAdultChild_disc,logTimeWindowOverlapAdultChild_disc,log_time_window_overlap_adult_child,,,,,,coef_logTimeWindowOverlapAdultChild_disc,,,,,coef_logTimeWindowOverlapAdultChild_disc,,,,coef_logTimeWindowOverlapAdultChild_disc,,,coef_logTimeWindowOverlapAdultChild_disc,,coef_logTimeWindowOverlapAdultChild_disc,2 * coef_logTimeWindowOverlapAdultChild_disc +util_incomeBetween50And100_disc,incomeBetween50And100_disc,income_between_50_and_100,,,,,,coef_incomeBetween50And100_disc,,,,,coef_incomeBetween50And100_disc,,,,coef_incomeBetween50And100_disc,,,coef_incomeBetween50And100_disc,,coef_incomeBetween50And100_disc,2 * coef_incomeBetween50And100_disc +util_incomeGreaterThan100_disc,incomeGreaterThan100_disc,income_greater_than_100,,,,,,coef_incomeGreaterThan100_disc,,,,,coef_incomeGreaterThan100_disc,,,,coef_incomeGreaterThan100_disc,,,coef_incomeGreaterThan100_disc,,coef_incomeGreaterThan100_disc,2 * coef_incomeGreaterThan100_disc +util_incomeMissing_dummy_always_zero_disc,incomeMissing_dummy_always_zero_disc,income_missing,,,,,,coef_incomeMissing_dummy_always_zero_disc,,,,,coef_incomeMissing_dummy_always_zero_disc,,,,coef_incomeMissing_dummy_always_zero_disc,,,coef_incomeMissing_dummy_always_zero_disc,,coef_incomeMissing_dummy_always_zero_disc,2 * coef_incomeMissing_dummy_always_zero_disc util_zeroAutomobiles_dis,zeroAutomobiles_disc,auto_ownership == 0,,,,,,coef_zeroAutomobiles_disc,,,,,coef_zeroAutomobiles_disc,,,,coef_zeroAutomobiles_disc,,,coef_zeroAutomobiles_disc,,coef_zeroAutomobiles_disc,2 * coef_zeroAutomobiles_disc \ No newline at end of file diff --git a/activitysim/examples/example_arc/configs/joint_tour_frequency.yaml b/activitysim/examples/placeholder_psrc/configs/joint_tour_frequency.yaml old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_frequency.yaml rename to activitysim/examples/placeholder_psrc/configs/joint_tour_frequency.yaml diff --git a/activitysim/examples/example_mtc/configs/joint_tour_frequency_alternatives.csv b/activitysim/examples/placeholder_psrc/configs/joint_tour_frequency_alternatives.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/joint_tour_frequency_alternatives.csv rename to activitysim/examples/placeholder_psrc/configs/joint_tour_frequency_alternatives.csv diff --git a/activitysim/examples/example_mtc/configs/joint_tour_frequency_annotate_households_preprocessor.csv b/activitysim/examples/placeholder_psrc/configs/joint_tour_frequency_annotate_households_preprocessor.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/joint_tour_frequency_annotate_households_preprocessor.csv rename to activitysim/examples/placeholder_psrc/configs/joint_tour_frequency_annotate_households_preprocessor.csv diff --git a/activitysim/examples/example_mtc/configs/joint_tour_frequency_coefficients.csv b/activitysim/examples/placeholder_psrc/configs/joint_tour_frequency_coeffs.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/joint_tour_frequency_coefficients.csv rename to activitysim/examples/placeholder_psrc/configs/joint_tour_frequency_coeffs.csv diff --git a/activitysim/examples/example_psrc/configs/joint_tour_participation.csv b/activitysim/examples/placeholder_psrc/configs/joint_tour_participation.csv similarity index 99% rename from activitysim/examples/example_psrc/configs/joint_tour_participation.csv rename to activitysim/examples/placeholder_psrc/configs/joint_tour_participation.csv index da2e65d1ea..cd692d8d27 100755 --- a/activitysim/examples/example_psrc/configs/joint_tour_participation.csv +++ b/activitysim/examples/placeholder_psrc/configs/joint_tour_participation.csv @@ -1,67 +1,67 @@ -Label,Description,Expression,participate,not_participate -util_full_time_worker_mixed_party,"Full-Time Worker, mixed party",person_is_full & tour_composition_is_mixed,coef_full_time_worker_mixed_party,coef_full_time_worker_mixed_party_not -util_part_time_worker_adults_only_party,"Part-Time Worker, adults-only party",person_is_part & tour_composition_is_adults,coef_part_time_worker_adults_only_party,coef_part_time_worker_adults_only_party_not -util_part_time_worker_mixed_party,"Part-Time Worker, mixed party",person_is_part & tour_composition_is_mixed,coef_part_time_worker_mixed_party, -util_university_student_mixed_party,"University Student, mixed party",person_is_univ & tour_composition_is_mixed,coef_university_student_mixed_party, -util_non_worker_adults_only_party,"Non-Worker, adults-only party",person_is_nonwork & tour_composition_is_adults,coef_non_worker_adults_only_party, -util_non_worker_mixed_party,"Non-Worker, mixed party",person_is_nonwork & tour_composition_is_mixed,coef_non_worker_mixed_party, -util_child_too_young_for_school_children_only_party,"Child too Young for School, children-only party",person_is_preschool & tour_composition_is_children,coef_child_too_young_for_school_children_only_party, -util_child_too_young_for_school_mixed_party,"Child too Young for School, mixed party",person_is_preschool & tour_composition_is_mixed,coef_child_too_young_for_school_mixed_party, -util_pre_driving_age_student_children_only_party,"Pre-driving age Student, children-only party",person_is_school & tour_composition_is_children,coef_pre_driving_age_student_children_only_party, -util_pre_driving_age_student_mixed_party,"Pre-driving age Student, mixed party",person_is_school & tour_composition_is_mixed,coef_pre_driving_age_student_mixed_party, -util_driving_age_student_children_only_party,"Driving-age Student, children-only party",person_is_driving & tour_composition_is_children,coef_driving_age_student_children_only_party, -util_driving_age_student_mixed_party,"Driving-age Student, mixed party",person_is_driving & tour_composition_is_mixed,coef_driving_age_student_mixed_party, -#,,,, -util_full_time_worker_specific_to_eating_out_joint_tours,"Full-Time Worker, specific to eating out joint tours",person_is_full & tour_type_is_eat,coef_full_time_worker_specific_to_eating_out_joint_tours,coef_full_time_worker_specific_to_eating_out_joint_tours_not -util_full_time_worker_specific_to_discretionary_joint_tours,"Full-Time Worker, specific to discretionary joint tours",person_is_full & tour_type_is_disc,coef_full_time_worker_specific_to_discretionary_joint_tours,coef_full_time_worker_specific_to_discretionary_joint_tours_not -util_part_time_worker_specific_to_eating_out_joint_tours,"Part-Time Worker, specific to eating out joint tours",person_is_part & tour_type_is_eat,coef_part_time_worker_specific_to_eating_out_joint_tours, -util_part_time_worker_specific_to_discretionary_joint_tours,"Part-Time Worker, specific to discretionary joint tours",person_is_part & tour_type_is_disc,coef_part_time_worker_specific_to_discretionary_joint_tours, -util_university_student_specific_to_eating_out_joint_tours,"University Student, specific to eating out joint tours",person_is_univ & tour_type_is_eat,coef_university_student_specific_to_eating_out_joint_tours, -util_university_student_specific_to_discretionary_joint_tours,"University Student, specific to discretionary joint tours",person_is_univ & tour_type_is_disc,coef_university_student_specific_to_discretionary_joint_tours, -util_non_worker_specific_to_eating_out_joint_tours,"Non-worker, specific to eating out joint tours",person_is_nonwork & tour_type_is_eat,coef_non_worker_specific_to_eating_out_joint_tours, -util_non_worker_specific_to_discretionary_joint_tours,"Non-worker, specific to discretionary joint tours",person_is_nonwork & tour_type_is_disc,coef_non_worker_specific_to_discretionary_joint_tours, -util_child_too_young_for_school_specific_to_eating_out_joint_tours,"Child too Young for School, specific to eating out joint tours",person_is_preschool & tour_type_is_eat,coef_child_too_young_for_school_specific_to_eating_out_joint_tours, -util_child_too_young_for_school_specific_to_discretionary_joint_tours,"Child too Young for School, specific to discretionary joint tours",person_is_preschool & tour_type_is_disc,coef_child_too_young_for_school_specific_to_discretionary_joint_tours, -util_pre_driving_age_student_specific_to_eating_out_joint_tours,"Pre-driving Age Student, specific to eating out joint tours",person_is_school & tour_type_is_eat,coef_pre_driving_age_student_specific_to_eating_out_joint_tours, -util_pre_driving_age_student_specific_to_discretionary_joint_tours,"Pre-driving age Student, specific to discretionary joint tours",person_is_school & tour_type_is_disc,coef_pre_driving_age_student_specific_to_discretionary_joint_tours, -util_driving_age_student_specific_to_eating_out_joint_tours,"Driving-age Student, specific to eating out joint tours",person_is_driving & tour_type_is_eat,coef_driving_age_student_specific_to_eating_out_joint_tours, -util_driving_age_student_specific_to_discretionary_joint_tours,"Driving-age Student, specific to discretionary joint tours",person_is_driving & tour_type_is_disc,coef_driving_age_student_specific_to_discretionary_joint_tours, -#,,,, -util_household_in_urban_area_adult_adult_only_party,"Household in urban area, adult, adult-only party",home_is_urban & adult & tour_composition_is_adults,coef_household_in_urban_area_adult_adult_only_party, -util_household_in_urban_area_adult_mixed_party,"Household in urban area, adult, mixed party",home_is_urban & adult & tour_composition_is_mixed,coef_household_in_urban_area_adult_mixed_party, -util_household_in_urban_area_child_child_only_party,"Household in urban area, child, child-only party",home_is_urban & ~adult & tour_composition_is_children,coef_household_in_urban_area_child_child_only_party, -util_household_in_urban_area_child_mixed_party,"Household in urban area, child, mixed party",home_is_urban & ~adult & tour_composition_is_mixed,coef_household_in_urban_area_child_mixed_party, -util_household_in_suburban_area_adult_adult_only_party,"Household in suburban area, adult, adult-only party",home_is_suburban & adult & tour_composition_is_adults,coef_household_in_suburban_area_adult_adult_only_party, -util_household_in_suburban_area_adult_mixed_party,"Household in suburban area, adult, mixed party",home_is_suburban & adult & tour_composition_is_mixed,coef_household_in_suburban_area_adult_mixed_party, -util_household_in_suburban_area_child_child_only_party,"Household in suburban area, child, child-only party",home_is_suburban & ~adult & tour_composition_is_children,coef_household_in_suburban_area_child_child_only_party, -util_household_in_suburban_area_child_mixed_party,"Household in suburban area, child, mixed party",home_is_suburban & ~adult & tour_composition_is_mixed,coef_household_in_suburban_area_child_mixed_party, -util_adult_more_automobiles_than_workers_adult_only_party,"Adult, more automobiles than workers, adult-only party",adult & more_cars_than_workers & tour_composition_is_adults,coef_adult_more_automobiles_than_workers_adult_only_party, -util_adult_more_automobiles_than_workers_mixed_party,"Adult, more automobiles than workers, mixed party",adult & more_cars_than_workers & tour_composition_is_mixed,coef_adult_more_automobiles_than_workers_mixed_party, -util_child_more_automobiles_than_workers_child_only_party,"Child, more automobiles than workers, child-only party",adult & more_cars_than_workers & tour_composition_is_children,coef_child_more_automobiles_than_workers_child_only_party, -util_child_more_automobiles_than_workers_mixed_party,"Child, more automobiles than workers, mixed party",adult & more_cars_than_workers & tour_composition_is_mixed,coef_child_more_automobiles_than_workers_mixed_party, -#,,,, -util_dummy_for_high_income_for_adult_in_adult_party,Dummy for high income for adult in adult party,high_income & tour_composition_is_adults,coef_dummy_for_high_income_for_adult_in_adult_party, -util_dummy_for_high_income_for_adult_in_mixed_party,Dummy for high income for adult in mixed party,high_income & tour_composition_is_mixed,coef_dummy_for_high_income_for_adult_in_mixed_party, -util_dummy_for_high_income_for_child_in_children_party,Dummy for high income for child in children party,high_income & tour_composition_is_children,coef_dummy_for_high_income_for_child_in_children_party, -util_dummy_for_high_income_for_child_in_mixed_party,Dummy for high income for child in mixed party,high_income & tour_composition_is_mixed,coef_dummy_for_high_income_for_child_in_mixed_party, -util_adult_number_of_joint_tours_adult_only,"Adult, number of joint tours, adult-only",(adult & tour_composition_is_adults) * num_hh_joint_tours,coef_adult_number_of_joint_tours_adult_only, -util_adult_number_of_joint_tours_mixed,"Adult, number of joint tours, mixed",(adult & tour_composition_is_mixed) * num_hh_joint_tours,coef_adult_number_of_joint_tours_mixed, -util_child_number_of_joint_tours_child_only,"Child, number of joint tours, child only",(~adult & tour_composition_is_children) * num_hh_joint_tours,coef_child_number_of_joint_tours_child_only, -util_child_number_of_joint_tours_mixed,"Child, number of joint tours, mixed",(~adult & tour_composition_is_mixed) * num_hh_joint_tours,coef_child_number_of_joint_tours_mixed, -util_adult_number_of_other_adults_in_the_household_adults_only_party,"Adult, number of other adults in the household, adults-only party",(adult & tour_composition_is_adults) * (num_adults - 1),coef_adult_number_of_other_adults_in_the_household_adults_only_party, -util_adult_number_of_other_adults_in_the_household_mixed_party,"Adult, number of other adults in the household, mixed party",(adult & tour_composition_is_mixed) * (num_adults - 1),coef_adult_number_of_other_adults_in_the_household_mixed_party, -util_child_number_of_other_children_in_the_household_child_only_party,"Child, number of other children in the household, child-only party",(~adult & tour_composition_is_children) * (num_children - 1),coef_child_number_of_other_children_in_the_household_child_only_party, -util_child_number_of_other_children_in_the_household_mixed,"Child, number of other children in the household, mixed",(~adult & tour_composition_is_mixed) * (num_children - 1),coef_child_number_of_other_children_in_the_household_mixed, -#,,,, -util_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,"Adult, log of max window overlap with an adult, adult-only party",(adult & tour_composition_is_adults) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party, -util_adult_log_of_max_window_overlap_with_a_child_mixed,"Adult, log of max window overlap with a child, mixed",(adult & tour_composition_is_mixed) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_a_child_mixed, -util_child_log_of_max_window_overlap_with_an_adult_mixed,"Child, log of max window overlap with an adult, mixed",(~adult & tour_composition_is_mixed) * log_time_window_overlap_adult,coef_child_log_of_max_window_overlap_with_an_adult_mixed, -util_child_log_of_max_window_overlap_with_a_child_child,"Child, log of max window overlap with a child, child",(~adult & tour_composition_is_children) * log_time_window_overlap_adult,coef_child_log_of_max_window_overlap_with_a_child_child, -#,,,, -util_adults_are_prohibited_in_participating_in_child_only_tours,Adults are prohibited in participating in child-only tours,adult & tour_composition_is_children,coef_unavailable, -util_children_are_prohibited_in_participating_in_adult_only_tours,Children are prohibited in participating in adult-only tours,~adult & tour_composition_is_adults,coef_unavailable, -util_persons_with_home_activity_patterns_are_prohibilted_from_participating,Persons with Home activity patterns are prohibilted from participating,~travel_active,coef_unavailable, -util_if_only_two_available_adults_both_must_participate_in_adult_only_tour,"If only two available adults, both must participate in adult-only tour",adult & travel_active & tour_composition_is_adults & (num_travel_active_adults<3),,coef_unavailable -util_if_only_one_available_adult_traveler_must_participate_in_mixed_tour,"If only one available adult, traveler must participate in mixed tour",adult & travel_active & tour_composition_is_mixed & (num_travel_active_adults<2),,coef_unavailable -util_if_only_two_available_children_both_must_participate_in_child_only_tour,"If only two available children, both must participate in child-only tour",~adult & travel_active & tour_composition_is_children & (num_travel_active_children<3),,coef_unavailable -util_if_only_one_available_child_traveler_must_participate_in_mixed_tour,"If only one available child, traveler must participate in mixed tour",~adult & travel_active & tour_composition_is_mixed & (num_travel_active_children<2),,coef_unavailable +Label,Description,Expression,participate,not_participate +util_full_time_worker_mixed_party,"Full-Time Worker, mixed party",person_is_full & tour_composition_is_mixed,coef_full_time_worker_mixed_party,coef_full_time_worker_mixed_party_not +util_part_time_worker_adults_only_party,"Part-Time Worker, adults-only party",person_is_part & tour_composition_is_adults,coef_part_time_worker_adults_only_party,coef_part_time_worker_adults_only_party_not +util_part_time_worker_mixed_party,"Part-Time Worker, mixed party",person_is_part & tour_composition_is_mixed,coef_part_time_worker_mixed_party, +util_university_student_mixed_party,"University Student, mixed party",person_is_univ & tour_composition_is_mixed,coef_university_student_mixed_party, +util_non_worker_adults_only_party,"Non-Worker, adults-only party",person_is_nonwork & tour_composition_is_adults,coef_non_worker_adults_only_party, +util_non_worker_mixed_party,"Non-Worker, mixed party",person_is_nonwork & tour_composition_is_mixed,coef_non_worker_mixed_party, +util_child_too_young_for_school_children_only_party,"Child too Young for School, children-only party",person_is_preschool & tour_composition_is_children,coef_child_too_young_for_school_children_only_party, +util_child_too_young_for_school_mixed_party,"Child too Young for School, mixed party",person_is_preschool & tour_composition_is_mixed,coef_child_too_young_for_school_mixed_party, +util_pre_driving_age_student_children_only_party,"Pre-driving age Student, children-only party",person_is_school & tour_composition_is_children,coef_pre_driving_age_student_children_only_party, +util_pre_driving_age_student_mixed_party,"Pre-driving age Student, mixed party",person_is_school & tour_composition_is_mixed,coef_pre_driving_age_student_mixed_party, +util_driving_age_student_children_only_party,"Driving-age Student, children-only party",person_is_driving & tour_composition_is_children,coef_driving_age_student_children_only_party, +util_driving_age_student_mixed_party,"Driving-age Student, mixed party",person_is_driving & tour_composition_is_mixed,coef_driving_age_student_mixed_party, +#,,,, +util_full_time_worker_specific_to_eating_out_joint_tours,"Full-Time Worker, specific to eating out joint tours",person_is_full & tour_type_is_eat,coef_full_time_worker_specific_to_eating_out_joint_tours,coef_full_time_worker_specific_to_eating_out_joint_tours_not +util_full_time_worker_specific_to_discretionary_joint_tours,"Full-Time Worker, specific to discretionary joint tours",person_is_full & tour_type_is_disc,coef_full_time_worker_specific_to_discretionary_joint_tours,coef_full_time_worker_specific_to_discretionary_joint_tours_not +util_part_time_worker_specific_to_eating_out_joint_tours,"Part-Time Worker, specific to eating out joint tours",person_is_part & tour_type_is_eat,coef_part_time_worker_specific_to_eating_out_joint_tours, +util_part_time_worker_specific_to_discretionary_joint_tours,"Part-Time Worker, specific to discretionary joint tours",person_is_part & tour_type_is_disc,coef_part_time_worker_specific_to_discretionary_joint_tours, +util_university_student_specific_to_eating_out_joint_tours,"University Student, specific to eating out joint tours",person_is_univ & tour_type_is_eat,coef_university_student_specific_to_eating_out_joint_tours, +util_university_student_specific_to_discretionary_joint_tours,"University Student, specific to discretionary joint tours",person_is_univ & tour_type_is_disc,coef_university_student_specific_to_discretionary_joint_tours, +util_non_worker_specific_to_eating_out_joint_tours,"Non-worker, specific to eating out joint tours",person_is_nonwork & tour_type_is_eat,coef_non_worker_specific_to_eating_out_joint_tours, +util_non_worker_specific_to_discretionary_joint_tours,"Non-worker, specific to discretionary joint tours",person_is_nonwork & tour_type_is_disc,coef_non_worker_specific_to_discretionary_joint_tours, +util_child_too_young_for_school_specific_to_eating_out_joint_tours,"Child too Young for School, specific to eating out joint tours",person_is_preschool & tour_type_is_eat,coef_child_too_young_for_school_specific_to_eating_out_joint_tours, +util_child_too_young_for_school_specific_to_discretionary_joint_tours,"Child too Young for School, specific to discretionary joint tours",person_is_preschool & tour_type_is_disc,coef_child_too_young_for_school_specific_to_discretionary_joint_tours, +util_pre_driving_age_student_specific_to_eating_out_joint_tours,"Pre-driving Age Student, specific to eating out joint tours",person_is_school & tour_type_is_eat,coef_pre_driving_age_student_specific_to_eating_out_joint_tours, +util_pre_driving_age_student_specific_to_discretionary_joint_tours,"Pre-driving age Student, specific to discretionary joint tours",person_is_school & tour_type_is_disc,coef_pre_driving_age_student_specific_to_discretionary_joint_tours, +util_driving_age_student_specific_to_eating_out_joint_tours,"Driving-age Student, specific to eating out joint tours",person_is_driving & tour_type_is_eat,coef_driving_age_student_specific_to_eating_out_joint_tours, +util_driving_age_student_specific_to_discretionary_joint_tours,"Driving-age Student, specific to discretionary joint tours",person_is_driving & tour_type_is_disc,coef_driving_age_student_specific_to_discretionary_joint_tours, +#,,,, +util_household_in_urban_area_adult_adult_only_party,"Household in urban area, adult, adult-only party",home_is_urban & adult & tour_composition_is_adults,coef_household_in_urban_area_adult_adult_only_party, +util_household_in_urban_area_adult_mixed_party,"Household in urban area, adult, mixed party",home_is_urban & adult & tour_composition_is_mixed,coef_household_in_urban_area_adult_mixed_party, +util_household_in_urban_area_child_child_only_party,"Household in urban area, child, child-only party",home_is_urban & ~adult & tour_composition_is_children,coef_household_in_urban_area_child_child_only_party, +util_household_in_urban_area_child_mixed_party,"Household in urban area, child, mixed party",home_is_urban & ~adult & tour_composition_is_mixed,coef_household_in_urban_area_child_mixed_party, +util_household_in_suburban_area_adult_adult_only_party,"Household in suburban area, adult, adult-only party",home_is_suburban & adult & tour_composition_is_adults,coef_household_in_suburban_area_adult_adult_only_party, +util_household_in_suburban_area_adult_mixed_party,"Household in suburban area, adult, mixed party",home_is_suburban & adult & tour_composition_is_mixed,coef_household_in_suburban_area_adult_mixed_party, +util_household_in_suburban_area_child_child_only_party,"Household in suburban area, child, child-only party",home_is_suburban & ~adult & tour_composition_is_children,coef_household_in_suburban_area_child_child_only_party, +util_household_in_suburban_area_child_mixed_party,"Household in suburban area, child, mixed party",home_is_suburban & ~adult & tour_composition_is_mixed,coef_household_in_suburban_area_child_mixed_party, +util_adult_more_automobiles_than_workers_adult_only_party,"Adult, more automobiles than workers, adult-only party",adult & more_cars_than_workers & tour_composition_is_adults,coef_adult_more_automobiles_than_workers_adult_only_party, +util_adult_more_automobiles_than_workers_mixed_party,"Adult, more automobiles than workers, mixed party",adult & more_cars_than_workers & tour_composition_is_mixed,coef_adult_more_automobiles_than_workers_mixed_party, +util_child_more_automobiles_than_workers_child_only_party,"Child, more automobiles than workers, child-only party",adult & more_cars_than_workers & tour_composition_is_children,coef_child_more_automobiles_than_workers_child_only_party, +util_child_more_automobiles_than_workers_mixed_party,"Child, more automobiles than workers, mixed party",adult & more_cars_than_workers & tour_composition_is_mixed,coef_child_more_automobiles_than_workers_mixed_party, +#,,,, +util_dummy_for_high_income_for_adult_in_adult_party,Dummy for high income for adult in adult party,high_income & tour_composition_is_adults,coef_dummy_for_high_income_for_adult_in_adult_party, +util_dummy_for_high_income_for_adult_in_mixed_party,Dummy for high income for adult in mixed party,high_income & tour_composition_is_mixed,coef_dummy_for_high_income_for_adult_in_mixed_party, +util_dummy_for_high_income_for_child_in_children_party,Dummy for high income for child in children party,high_income & tour_composition_is_children,coef_dummy_for_high_income_for_child_in_children_party, +util_dummy_for_high_income_for_child_in_mixed_party,Dummy for high income for child in mixed party,high_income & tour_composition_is_mixed,coef_dummy_for_high_income_for_child_in_mixed_party, +util_adult_number_of_joint_tours_adult_only,"Adult, number of joint tours, adult-only",(adult & tour_composition_is_adults) * num_hh_joint_tours,coef_adult_number_of_joint_tours_adult_only, +util_adult_number_of_joint_tours_mixed,"Adult, number of joint tours, mixed",(adult & tour_composition_is_mixed) * num_hh_joint_tours,coef_adult_number_of_joint_tours_mixed, +util_child_number_of_joint_tours_child_only,"Child, number of joint tours, child only",(~adult & tour_composition_is_children) * num_hh_joint_tours,coef_child_number_of_joint_tours_child_only, +util_child_number_of_joint_tours_mixed,"Child, number of joint tours, mixed",(~adult & tour_composition_is_mixed) * num_hh_joint_tours,coef_child_number_of_joint_tours_mixed, +util_adult_number_of_other_adults_in_the_household_adults_only_party,"Adult, number of other adults in the household, adults-only party",(adult & tour_composition_is_adults) * (num_adults - 1),coef_adult_number_of_other_adults_in_the_household_adults_only_party, +util_adult_number_of_other_adults_in_the_household_mixed_party,"Adult, number of other adults in the household, mixed party",(adult & tour_composition_is_mixed) * (num_adults - 1),coef_adult_number_of_other_adults_in_the_household_mixed_party, +util_child_number_of_other_children_in_the_household_child_only_party,"Child, number of other children in the household, child-only party",(~adult & tour_composition_is_children) * (num_children - 1),coef_child_number_of_other_children_in_the_household_child_only_party, +util_child_number_of_other_children_in_the_household_mixed,"Child, number of other children in the household, mixed",(~adult & tour_composition_is_mixed) * (num_children - 1),coef_child_number_of_other_children_in_the_household_mixed, +#,,,, +util_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,"Adult, log of max window overlap with an adult, adult-only party",(adult & tour_composition_is_adults) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party, +util_adult_log_of_max_window_overlap_with_a_child_mixed,"Adult, log of max window overlap with a child, mixed",(adult & tour_composition_is_mixed) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_a_child_mixed, +util_child_log_of_max_window_overlap_with_an_adult_mixed,"Child, log of max window overlap with an adult, mixed",(~adult & tour_composition_is_mixed) * log_time_window_overlap_adult,coef_child_log_of_max_window_overlap_with_an_adult_mixed, +util_child_log_of_max_window_overlap_with_a_child_child,"Child, log of max window overlap with a child, child",(~adult & tour_composition_is_children) * log_time_window_overlap_adult,coef_child_log_of_max_window_overlap_with_a_child_child, +#,,,, +util_adults_are_prohibited_in_participating_in_child_only_tours,Adults are prohibited in participating in child-only tours,adult & tour_composition_is_children,coef_unavailable, +util_children_are_prohibited_in_participating_in_adult_only_tours,Children are prohibited in participating in adult-only tours,~adult & tour_composition_is_adults,coef_unavailable, +util_persons_with_home_activity_patterns_are_prohibilted_from_participating,Persons with Home activity patterns are prohibilted from participating,~travel_active,coef_unavailable, +util_if_only_two_available_adults_both_must_participate_in_adult_only_tour,"If only two available adults, both must participate in adult-only tour",adult & travel_active & tour_composition_is_adults & (num_travel_active_adults<3),,coef_unavailable +util_if_only_one_available_adult_traveler_must_participate_in_mixed_tour,"If only one available adult, traveler must participate in mixed tour",adult & travel_active & tour_composition_is_mixed & (num_travel_active_adults<2),,coef_unavailable +util_if_only_two_available_children_both_must_participate_in_child_only_tour,"If only two available children, both must participate in child-only tour",~adult & travel_active & tour_composition_is_children & (num_travel_active_children<3),,coef_unavailable +util_if_only_one_available_child_traveler_must_participate_in_mixed_tour,"If only one available child, traveler must participate in mixed tour",~adult & travel_active & tour_composition_is_mixed & (num_travel_active_children<2),,coef_unavailable diff --git a/activitysim/examples/example_semcog/configs/joint_tour_participation.yaml b/activitysim/examples/placeholder_psrc/configs/joint_tour_participation.yaml similarity index 95% rename from activitysim/examples/example_semcog/configs/joint_tour_participation.yaml rename to activitysim/examples/placeholder_psrc/configs/joint_tour_participation.yaml index aee45349f9..59941e8324 100755 --- a/activitysim/examples/example_semcog/configs/joint_tour_participation.yaml +++ b/activitysim/examples/placeholder_psrc/configs/joint_tour_participation.yaml @@ -1,20 +1,20 @@ - -SPEC: joint_tour_participation.csv -COEFFICIENTS: joint_tour_participation_coeffs.csv - -LOGIT_TYPE: MNL - -#max_participation_choice_iterations: 5000 - -preprocessor: - SPEC: joint_tour_participation_annotate_participants_preprocessor - DF: participants -# TABLES: -# - persons -# - accessibility - -annotate_persons: - SPEC: annotate_persons_jtp - DF: persons - TABLES: - - joint_tour_participants + +SPEC: joint_tour_participation.csv +COEFFICIENTS: joint_tour_participation_coeffs.csv + +LOGIT_TYPE: MNL + +#max_participation_choice_iterations: 5000 + +preprocessor: + SPEC: joint_tour_participation_annotate_participants_preprocessor + DF: participants +# TABLES: +# - persons +# - accessibility + +annotate_persons: + SPEC: annotate_persons_jtp + DF: persons + TABLES: + - joint_tour_participants diff --git a/activitysim/examples/example_mtc/configs/joint_tour_participation_annotate_participants_preprocessor.csv b/activitysim/examples/placeholder_psrc/configs/joint_tour_participation_annotate_participants_preprocessor.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/joint_tour_participation_annotate_participants_preprocessor.csv rename to activitysim/examples/placeholder_psrc/configs/joint_tour_participation_annotate_participants_preprocessor.csv diff --git a/activitysim/examples/example_mtc/configs/joint_tour_participation_coefficients.csv b/activitysim/examples/placeholder_psrc/configs/joint_tour_participation_coeffs.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/joint_tour_participation_coefficients.csv rename to activitysim/examples/placeholder_psrc/configs/joint_tour_participation_coeffs.csv diff --git a/activitysim/examples/example_semcog/configs/joint_tour_scheduling.yaml b/activitysim/examples/placeholder_psrc/configs/joint_tour_scheduling.yaml similarity index 95% rename from activitysim/examples/example_semcog/configs/joint_tour_scheduling.yaml rename to activitysim/examples/placeholder_psrc/configs/joint_tour_scheduling.yaml index 0eebc991b9..1eba33e752 100755 --- a/activitysim/examples/example_semcog/configs/joint_tour_scheduling.yaml +++ b/activitysim/examples/placeholder_psrc/configs/joint_tour_scheduling.yaml @@ -1,12 +1,13 @@ -LOGIT_TYPE: MNL - -SPEC: tour_scheduling_joint.csv -COEFFICIENTS: tour_scheduling_joint_coeffs.csv - -preprocessor: - SPEC: joint_tour_scheduling_annotate_tours_preprocessor - DF: joint_tours - TABLES: - - land_use - - households - - joint_tour_participants + +SPEC: tour_scheduling_joint.csv +COEFFICIENTS: tour_scheduling_joint_coeffs.csv + +LOGIT_TYPE: MNL + +preprocessor: + SPEC: joint_tour_scheduling_annotate_tours_preprocessor + DF: joint_tours + TABLES: + - land_use + - households + - joint_tour_participants diff --git a/activitysim/examples/example_mtc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv b/activitysim/examples/placeholder_psrc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv rename to activitysim/examples/placeholder_psrc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv diff --git a/activitysim/examples/example_psrc/configs/logging.yaml b/activitysim/examples/placeholder_psrc/configs/logging.yaml similarity index 95% rename from activitysim/examples/example_psrc/configs/logging.yaml rename to activitysim/examples/placeholder_psrc/configs/logging.yaml index 815f31f44d..71ac15cc1f 100755 --- a/activitysim/examples/example_psrc/configs/logging.yaml +++ b/activitysim/examples/placeholder_psrc/configs/logging.yaml @@ -1,54 +1,54 @@ -# Config for logging -# ------------------ -# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema - -logging: - version: 1 - disable_existing_loggers: true - - - # Configuring the default (root) logger is highly recommended - root: - level: NOTSET - handlers: [console, logfile] - - loggers: - - activitysim: - level: INFO - handlers: [console, logfile] - propagate: false - - orca: - level: WARN - handlers: [console, logfile] - propagate: false - - handlers: - - logfile: - class: logging.FileHandler - filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] - mode: w - formatter: fileFormatter - level: NOTSET - - console: - class: logging.StreamHandler - stream: ext://sys.stdout - formatter: simpleFormatter - level: NOTSET - - formatters: - - simpleFormatter: - class: logging.Formatter - # format: '%(levelname)s - %(name)s - %(message)s' - format: '%(levelname)s - %(message)s' - datefmt: '%d/%m/%Y %H:%M:%S' - - fileFormatter: - class: logging.Formatter - format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' - datefmt: '%d/%m/%Y %H:%M:%S' - +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console, logfile] + + loggers: + + activitysim: + level: INFO + handlers: [console, logfile] + propagate: false + + orca: + level: WARN + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + level: NOTSET + + formatters: + + simpleFormatter: + class: logging.Formatter + # format: '%(levelname)s - %(name)s - %(message)s' + format: '%(levelname)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + diff --git a/activitysim/examples/example_mtc/configs/mandatory_tour_frequency.csv b/activitysim/examples/placeholder_psrc/configs/mandatory_tour_frequency.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/mandatory_tour_frequency.csv rename to activitysim/examples/placeholder_psrc/configs/mandatory_tour_frequency.csv diff --git a/activitysim/examples/example_semcog/configs/mandatory_tour_frequency.yaml b/activitysim/examples/placeholder_psrc/configs/mandatory_tour_frequency.yaml similarity index 94% rename from activitysim/examples/example_semcog/configs/mandatory_tour_frequency.yaml rename to activitysim/examples/placeholder_psrc/configs/mandatory_tour_frequency.yaml index ce768bbaa7..de8e115fd5 100755 --- a/activitysim/examples/example_semcog/configs/mandatory_tour_frequency.yaml +++ b/activitysim/examples/placeholder_psrc/configs/mandatory_tour_frequency.yaml @@ -1,10 +1,10 @@ - -SPEC: mandatory_tour_frequency.csv -COEFFICIENTS: mandatory_tour_frequency_coeffs.csv - -annotate_persons: - SPEC: annotate_persons_mtf - DF: persons - TABLES: - - tours - + +SPEC: mandatory_tour_frequency.csv +COEFFICIENTS: mandatory_tour_frequency_coeffs.csv + +annotate_persons: + SPEC: annotate_persons_mtf + DF: persons + TABLES: + - tours + diff --git a/activitysim/examples/example_mtc/configs/mandatory_tour_frequency_alternatives.csv b/activitysim/examples/placeholder_psrc/configs/mandatory_tour_frequency_alternatives.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/mandatory_tour_frequency_alternatives.csv rename to activitysim/examples/placeholder_psrc/configs/mandatory_tour_frequency_alternatives.csv diff --git a/activitysim/examples/example_psrc/configs/mandatory_tour_frequency_coeffs.csv b/activitysim/examples/placeholder_psrc/configs/mandatory_tour_frequency_coeffs.csv similarity index 97% rename from activitysim/examples/example_psrc/configs/mandatory_tour_frequency_coeffs.csv rename to activitysim/examples/placeholder_psrc/configs/mandatory_tour_frequency_coeffs.csv index 8a4137324f..9bf04b2878 100755 --- a/activitysim/examples/example_psrc/configs/mandatory_tour_frequency_coeffs.csv +++ b/activitysim/examples/placeholder_psrc/configs/mandatory_tour_frequency_coeffs.csv @@ -1,54 +1,54 @@ -coefficient_name,value,constrain -coef_unavailable,-999,T -coef_ft_worker_work2_asc,-3.3781,F -coef_pt_worker_work2_asc,-3.0476,F -coef_univ_work1_asc,2.166,F -coef_univ_work2_asc,-1.3965,F -coef_univ_school2_asc,-3.7429,F -coef_univ_work_and_school_asc,0.1073,F -coef_driving_age_child_school2_asc,-3.136,F -coef_driving_age_child_work_and_school_asc,-4.4362,F -coef_pre_driving_age_child_school2_asc,-3.9703,F -coef_female_work1,0.1737,F -coef_female_work2,-0.2255,F -coef_female_school1,0.1592,F -coef_female_school2,0.114,F -coef_female_work_and_school,-0.3442,F -coef_female_univ_work1,0.1737,F -coef_under_35_work1,-0.4629,F -coef_under_35_work2,-0.1375,F -coef_under_35_school1,0.7218,F -coef_under_35_school2,1.275,F -coef_under_35_work_and_school,0.9761,F -coef_can_walk_to_work_work2,0.5268,F -coef_can_walk_to_work_school2,0.7114,F -coef_can_walk_to_work_and_school,0.1391,F -coef_round_trip_auto_time_to_work_work2,-0.0035,F -coef_round_trip_auto_time_to_work_school2,-0.0034,F -coef_round_trip_auto_time_to_work_work_and_school,-0.0031,F -coef_student_employed,3.014,F -coef_non_student_goes_to_school,3.883,F -coef_no_cars_in_hh_work2,-1.306,F -coef_no_cars_in_hh_school2,-1.413,F -coef_no_cars_in_hh_work_and_school,-1.302,F -coef_few_cars_than_drivers_school2,-0.5759,F -coef_num_preschool_in_hh_work1,0.2191,F -coef_num_preschool_in_hh_work2,-0.1478,F -coef_num_preschool_in_hh_school1,-0.1335,F -coef_num_preschool_in_hh_school2,-0.5577,F -coef_num_preschool_in_hh_work_and_school,-0.1251,F -coef_num_non_workers_in_hh_school1,0.2574,F -coef_hh_income_gt_50k_work,-0.0528,F -coef_hh_income_gt_50k_school1,0.0347,F -coef_hh_income_gt_50k_worker_work_and_school,0.0347,F -coef_hh_income_gt_50k_student_work_and_school,-0.0528,F -coef_non_family_hh_category1,-0.25,F -coef_non_family_hh_category2,-0.1792,F -coef_num_under_16_not_at_school_work2,0.1804 -coef_num_under_16_not_at_school_school2,0.0866 -coef_num_under_16_not_at_school_work_and_school,-0.1955 -coef_home_urban_work1,-0.2831 -coef_home_urban_work2,0.2308 -coef_home_urban_school1,-0.1361 -coef_home_urban_school2,0.317 -coef_home_urban_work_and_school,-0.3509 +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_ft_worker_work2_asc,-3.3781,F +coef_pt_worker_work2_asc,-3.0476,F +coef_univ_work1_asc,2.166,F +coef_univ_work2_asc,-1.3965,F +coef_univ_school2_asc,-3.7429,F +coef_univ_work_and_school_asc,0.1073,F +coef_driving_age_child_school2_asc,-3.136,F +coef_driving_age_child_work_and_school_asc,-4.4362,F +coef_pre_driving_age_child_school2_asc,-3.9703,F +coef_female_work1,0.1737,F +coef_female_work2,-0.2255,F +coef_female_school1,0.1592,F +coef_female_school2,0.114,F +coef_female_work_and_school,-0.3442,F +coef_female_univ_work1,0.1737,F +coef_under_35_work1,-0.4629,F +coef_under_35_work2,-0.1375,F +coef_under_35_school1,0.7218,F +coef_under_35_school2,1.275,F +coef_under_35_work_and_school,0.9761,F +coef_can_walk_to_work_work2,0.5268,F +coef_can_walk_to_work_school2,0.7114,F +coef_can_walk_to_work_and_school,0.1391,F +coef_round_trip_auto_time_to_work_work2,-0.0035,F +coef_round_trip_auto_time_to_work_school2,-0.0034,F +coef_round_trip_auto_time_to_work_work_and_school,-0.0031,F +coef_student_employed,3.014,F +coef_non_student_goes_to_school,3.883,F +coef_no_cars_in_hh_work2,-1.306,F +coef_no_cars_in_hh_school2,-1.413,F +coef_no_cars_in_hh_work_and_school,-1.302,F +coef_few_cars_than_drivers_school2,-0.5759,F +coef_num_preschool_in_hh_work1,0.2191,F +coef_num_preschool_in_hh_work2,-0.1478,F +coef_num_preschool_in_hh_school1,-0.1335,F +coef_num_preschool_in_hh_school2,-0.5577,F +coef_num_preschool_in_hh_work_and_school,-0.1251,F +coef_num_non_workers_in_hh_school1,0.2574,F +coef_hh_income_gt_50k_work,-0.0528,F +coef_hh_income_gt_50k_school1,0.0347,F +coef_hh_income_gt_50k_worker_work_and_school,0.0347,F +coef_hh_income_gt_50k_student_work_and_school,-0.0528,F +coef_non_family_hh_category1,-0.25,F +coef_non_family_hh_category2,-0.1792,F +coef_num_under_16_not_at_school_work2,0.1804 +coef_num_under_16_not_at_school_school2,0.0866 +coef_num_under_16_not_at_school_work_and_school,-0.1955 +coef_home_urban_work1,-0.2831 +coef_home_urban_work2,0.2308 +coef_home_urban_school1,-0.1361 +coef_home_urban_school2,0.317 +coef_home_urban_work_and_school,-0.3509 diff --git a/activitysim/examples/example_psrc/configs/mandatory_tour_scheduling.yaml b/activitysim/examples/placeholder_psrc/configs/mandatory_tour_scheduling.yaml similarity index 95% rename from activitysim/examples/example_psrc/configs/mandatory_tour_scheduling.yaml rename to activitysim/examples/placeholder_psrc/configs/mandatory_tour_scheduling.yaml index 511e708b13..871f159ca7 100755 --- a/activitysim/examples/example_psrc/configs/mandatory_tour_scheduling.yaml +++ b/activitysim/examples/placeholder_psrc/configs/mandatory_tour_scheduling.yaml @@ -1,42 +1,42 @@ - -SIMULATE_CHOOSER_COLUMNS: - - ptype - - hhsize - - roundtrip_auto_time_to_work - - num_workers - - income_in_thousands - - work_and_school_and_worker - - work_and_school_and_student - - workplace_in_cbd - - home_is_rural - - mandatory_tour_frequency - - is_worker - - is_student - - is_university - - workplace_zone_id - - school_zone_id - - home_zone_id - -LOGSUM_SETTINGS: tour_mode_choice.yaml - -# school and univ have the same spec file and coefficients but are handled seperately -# because mode_choice_logsums has distinct specs and ceofficients for univ and school -TOUR_SPEC_SEGMENTS: - work: work - school: school - univ: school - -SPEC_SEGMENTS: - work: - 'SPEC': tour_scheduling_work.csv - 'COEFFICIENTS': tour_scheduling_work_coeffs.csv - school: - 'SPEC': tour_scheduling_school.csv - 'COEFFICIENTS': tour_scheduling_school_coeffs.csv - -#CHOOSER_ORIG_COL_NAME: home_zone_id - -DESTINATION_FOR_TOUR_PURPOSE: - work: workplace_zone_id - school: school_zone_id - univ: school_zone_id + +SIMULATE_CHOOSER_COLUMNS: + - ptype + - hhsize + - roundtrip_auto_time_to_work + - num_workers + - income_in_thousands + - work_and_school_and_worker + - work_and_school_and_student + - workplace_in_cbd + - home_is_rural + - mandatory_tour_frequency + - is_worker + - is_student + - is_university + - workplace_zone_id + - school_zone_id + - home_zone_id + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# school and univ have the same spec file and coefficients but are handled seperately +# because mode_choice_logsums has distinct specs and ceofficients for univ and school +TOUR_SPEC_SEGMENTS: + work: work + school: school + univ: school + +SPEC_SEGMENTS: + work: + 'SPEC': tour_scheduling_work.csv + 'COEFFICIENTS': tour_scheduling_work_coeffs.csv + school: + 'SPEC': tour_scheduling_school.csv + 'COEFFICIENTS': tour_scheduling_school_coeffs.csv + +#CHOOSER_ORIG_COL_NAME: home_zone_id + +DESTINATION_FOR_TOUR_PURPOSE: + work: workplace_zone_id + school: school_zone_id + univ: school_zone_id diff --git a/activitysim/examples/example_psrc/configs/network_los.yaml b/activitysim/examples/placeholder_psrc/configs/network_los.yaml similarity index 96% rename from activitysim/examples/example_psrc/configs/network_los.yaml rename to activitysim/examples/placeholder_psrc/configs/network_los.yaml index afe6a260e0..0debd458b6 100755 --- a/activitysim/examples/example_psrc/configs/network_los.yaml +++ b/activitysim/examples/placeholder_psrc/configs/network_los.yaml @@ -1,35 +1,35 @@ -#inherit_settings: True - -# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) -read_skim_cache: True -# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs -write_skim_cache: True - -zone_system: 2 - -# glob 'skims*.omx' will match one or more files: skims.omx, skims1.omx, skims2.omx... -taz_skims: skims*.omx - - -maz: maz.csv - -maz_to_maz: - tables: - - maz_to_maz_walk.csv - - maz_to_maz_bike.csv - - # maz_to_maz blending distance (missing or 0 means no blending) - max_blend_distance: - DIST: 0 - # blend distance of 0 means no blending - DISTBIKE: 0 - DISTWALK: 0 - - # missing means use the skim value itself rather than DIST skim (e.g. DISTBIKE) - #blend_distance_skim_name: DIST - -skim_time_periods: - time_window: 1440 - period_minutes: 60 - periods: [0, 6, 11, 16, 20, 24] - labels: ['EA', 'AM', 'MD', 'PM', 'EV'] +#inherit_settings: True + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: True +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: True + +zone_system: 2 + +# glob 'skims*.omx' will match one or more files: skims.omx, skims1.omx, skims2.omx... +taz_skims: skims*.omx + + +maz: maz.csv + +maz_to_maz: + tables: + - maz_to_maz_walk.csv + - maz_to_maz_bike.csv + + # maz_to_maz blending distance (missing or 0 means no blending) + max_blend_distance: + DIST: 0 + # blend distance of 0 means no blending + DISTBIKE: 0 + DISTWALK: 0 + + # missing means use the skim value itself rather than DIST skim (e.g. DISTBIKE) + #blend_distance_skim_name: DIST + +skim_time_periods: + time_window: 1440 + period_minutes: 60 + periods: [0, 6, 11, 16, 20, 24] + labels: ['EA', 'AM', 'MD', 'PM', 'EV'] diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_destination.csv b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_destination.csv old mode 100644 new mode 100755 similarity index 99% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_destination.csv rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_destination.csv index 169becf1e3..18c6234ac4 --- a/activitysim/examples/example_mtc/configs/non_mandatory_tour_destination.csv +++ b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_destination.csv @@ -1,10 +1,10 @@ -Description,Expression,escort,escortkids,escortnokids,shopping,eatout,othmaint,social,othdiscr -"Distance, piecewise linear from 0 to 1 miles","@skims['DIST'].clip(0,1)",coef_escort_dist_0_2,coef_escort_dist_0_2,coef_escort_dist_0_2,0,coef_eatout_dist_0_2,0,coef_eatout_dist_0_2,coef_othdiscr_dist_0_2 -"Distance, piecewise linear from 1 to 2 miles","@(skims['DIST']-1).clip(0,1)",coef_escort_dist_0_2,coef_escort_dist_0_2,coef_escort_dist_0_2,0,coef_eatout_dist_0_2,0,coef_eatout_dist_0_2,coef_othdiscr_dist_0_2 -"Distance, piecewise linear from 2 to 5 miles","@(skims['DIST']-2).clip(0,3)",coef_escort_dist_2_5,coef_escort_dist_2_5,coef_escort_dist_2_5,coef_shopping_dist_2_5,coef_eatout_dist_2_5,coef_othmaint_dist_2_5,coef_social_dist_2_5,coef_othdiscr_dist_2_5 -"Distance, piecewise linear from 5 to 15 miles","@(skims['DIST']-5).clip(0,10)",coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_shopping_dist_5_plus,coef_eatout_dist_5_plus,coef_othmaint_dist_5_plus,coef_social_dist_5_plus,coef_othdiscr_dist_5_plus -"Distance, piecewise linear for 15+ miles",@(skims['DIST']-15.0).clip(0),coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_shopping_dist_5_plus,coef_eatout_dist_5_plus,coef_othmaint_dist_5_plus,coef_social_dist_5_plus,coef_othdiscr_dist_5_plus -Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1,1,1 -No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999,-999,-999 -Mode choice logsum,mode_choice_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum -Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1,1,1 +Description,Expression,escort,escortkids,escortnokids,shopping,eatout,othmaint,social,othdiscr +"Distance, piecewise linear from 0 to 1 miles","@skims['DIST'].clip(0,1)",coef_escort_dist_0_2,coef_escort_dist_0_2,coef_escort_dist_0_2,0,coef_eatout_dist_0_2,0,coef_eatout_dist_0_2,coef_othdiscr_dist_0_2 +"Distance, piecewise linear from 1 to 2 miles","@(skims['DIST']-1).clip(0,1)",coef_escort_dist_0_2,coef_escort_dist_0_2,coef_escort_dist_0_2,0,coef_eatout_dist_0_2,0,coef_eatout_dist_0_2,coef_othdiscr_dist_0_2 +"Distance, piecewise linear from 2 to 5 miles","@(skims['DIST']-2).clip(0,3)",coef_escort_dist_2_5,coef_escort_dist_2_5,coef_escort_dist_2_5,coef_shopping_dist_2_5,coef_eatout_dist_2_5,coef_othmaint_dist_2_5,coef_social_dist_2_5,coef_othdiscr_dist_2_5 +"Distance, piecewise linear from 5 to 15 miles","@(skims['DIST']-5).clip(0,10)",coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_shopping_dist_5_plus,coef_eatout_dist_5_plus,coef_othmaint_dist_5_plus,coef_social_dist_5_plus,coef_othdiscr_dist_5_plus +"Distance, piecewise linear for 15+ miles",@(skims['DIST']-15.0).clip(0),coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_shopping_dist_5_plus,coef_eatout_dist_5_plus,coef_othmaint_dist_5_plus,coef_social_dist_5_plus,coef_othdiscr_dist_5_plus +Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1,1,1 +No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999,-999,-999 +Mode choice logsum,mode_choice_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum +Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1,1,1 diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_destination.yaml b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_destination.yaml similarity index 95% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_destination.yaml rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_destination.yaml index d790aea2f7..3bc2483c88 100755 --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_destination.yaml +++ b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_destination.yaml @@ -1,38 +1,38 @@ -SAMPLE_SPEC: non_mandatory_tour_destination_sample.csv -SPEC: non_mandatory_tour_destination.csv -COEFFICIENTS: non_mandatory_tour_destination_coeffs.csv - -SAMPLE_SIZE: 30 - -SIZE_TERM_SELECTOR: non_mandatory - -# we can't use use household income_segment as this will also be set for non-workers -CHOOSER_SEGMENT_COLUMN_NAME: tour_type - -# optional (comment out if not desired) -DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum - -# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if no saved alt logsum table -DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample - - -SEGMENTS: - - shopping - - othmaint - - othdiscr - - eatout - - social - - escort - -SIMULATE_CHOOSER_COLUMNS: - - tour_type - - home_zone_id - - person_id - -LOGSUM_SETTINGS: tour_mode_choice.yaml - -# model-specific logsum-related settings -CHOOSER_ORIG_COL_NAME: home_zone_id -ALT_DEST_COL_NAME: alt_dest -IN_PERIOD: 14 -OUT_PERIOD: 14 +SAMPLE_SPEC: non_mandatory_tour_destination_sample.csv +SPEC: non_mandatory_tour_destination.csv +COEFFICIENTS: non_mandatory_tour_destination_coeffs.csv + +SAMPLE_SIZE: 30 + +SIZE_TERM_SELECTOR: non_mandatory + +# we can't use use household income_segment as this will also be set for non-workers +CHOOSER_SEGMENT_COLUMN_NAME: tour_type + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if no saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample + + +SEGMENTS: + - shopping + - othmaint + - othdiscr + - eatout + - social + - escort + +SIMULATE_CHOOSER_COLUMNS: + - tour_type + - home_zone_id + - person_id + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 14 diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_destination_coeffs.csv b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_destination_coeffs.csv similarity index 96% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_destination_coeffs.csv rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_destination_coeffs.csv index 50fd905b41..6e3d75d717 100755 --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_destination_coeffs.csv +++ b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_destination_coeffs.csv @@ -1,21 +1,21 @@ -coefficient_name,value,constrain -coef_mode_logsum,0.6755,F -coef_escort_dist_0_2,-0.1499,F -coef_eatout_dist_0_2,-0.5609,F -coef_eatout_social_0_2,-0.5609,F -# coef_eatout_dist_0_2,-0.7841,F -coef_othdiscr_dist_0_2,-0.1677,F -coef_escort_dist_2_5,-0.8671,F -coef_shopping_dist_2_5,-0.5655,F -coef_eatout_dist_2_5,-0.3192,F -coef_othmaint_dist_2_5,-0.6055,F -coef_social_dist_2_5,-0.3485,F -coef_othdiscr_dist_2_5,-0.4955,F -coef_escort_dist_5_plus,-0.2137,F -coef_shopping_dist_5_plus,-0.1832,F -coef_eatout_dist_5_plus,-0.1238,F -coef_othmaint_dist_5_plus,-0.1093,F -coef_social_dist_5_plus,-0.1306,F -coef_othdiscr_dist_5_plus,-0.1193,F - - +coefficient_name,value,constrain +coef_mode_logsum,0.6755,F +coef_escort_dist_0_2,-0.1499,F +coef_eatout_dist_0_2,-0.5609,F +coef_eatout_social_0_2,-0.5609,F +# coef_eatout_dist_0_2,-0.7841,F +coef_othdiscr_dist_0_2,-0.1677,F +coef_escort_dist_2_5,-0.8671,F +coef_shopping_dist_2_5,-0.5655,F +coef_eatout_dist_2_5,-0.3192,F +coef_othmaint_dist_2_5,-0.6055,F +coef_social_dist_2_5,-0.3485,F +coef_othdiscr_dist_2_5,-0.4955,F +coef_escort_dist_5_plus,-0.2137,F +coef_shopping_dist_5_plus,-0.1832,F +coef_eatout_dist_5_plus,-0.1238,F +coef_othmaint_dist_5_plus,-0.1093,F +coef_social_dist_5_plus,-0.1306,F +coef_othdiscr_dist_5_plus,-0.1193,F + + diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_destination_sample.csv b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_destination_sample.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_destination_sample.csv rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_destination_sample.csv diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency.csv b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency.csv old mode 100644 new mode 100755 similarity index 99% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency.csv rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency.csv index c295ea5b07..76c60822e2 --- a/activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency.csv +++ b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency.csv @@ -1,211 +1,211 @@ -Label,Description,Expression,PTYPE_FULL,PTYPE_PART,PTYPE_UNIVERSITY,PTYPE_NONWORK,PTYPE_RETIRED,PTYPE_DRIVING,PTYPE_SCHOOL,PTYPE_PRESCHOOL -util_escorting_tour,Escorting Tour,escort,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour -util_discretionary_tour,Discretionary Tour,othdiscr,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour -util_shopping_tour,Shopping Tour,shopping,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour -util_maintenance_tour,Maintenance Tour,othmaint,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour -util_visiting_or_social_tour,Visiting/Social Tour,social,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour -util_eating_out_tour,Eating Out Tour,eatout,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour -util_total_number_of_tours_is_0_no_prior_tours,Total Number of Tours = 0 (No Prior Tours),(tot_tours == 0) & (num_mand == 0) & (num_hh_joint_tours == 0),coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours -util_total_number_of_tours_is_0_prior_tours,Total Number of Tours = 0 (1 or more Prior Tours),(tot_tours == 0) & ((num_mand > 0) | (num_hh_joint_tours > 0)),coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours -util_total_number_of_tours_is_1,Total Number of Tours = 1,tot_tours == 1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1 -util_total_number_of_tours_is_2,Total Number of Tours = 2,tot_tours == 2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2 -util_total_number_of_tours_is_3,Total Number of Tours = 3,tot_tours == 3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3 -util_total_number_of_tours_is_4,Total Number of Tours = 4,tot_tours == 4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4 -util_total_number_of_tours_is_5,Total Number of Tours = 5,tot_tours == 5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5 -util_total_number_of_tours_is_6_plus,Total Number of Tours = 6+,tot_tours > 5,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus -util_number_of_mandatory_tours_and_tour_frequency_is_0,Number of Mandatory tours & tour frequency =0,num_mand*(tot_tours == 0),coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0 -util_number_of_mandatory_tours_and_tour_frequency_is_1,Number of Mandatory tours & tour frequency =1,num_mand*(tot_tours == 1),coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1 -util_number_of_mandatory_tours_and_tour_frequency_is_2,Number of Mandatory tours & tour frequency =2,num_mand*(tot_tours == 2),coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2 -util_number_of_mandatory_tours_and_tour_frequency_is_3,Number of Mandatory tours & tour frequency =3,num_mand*(tot_tours == 3),coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3 -util_number_of_mandatory_tours_and_tour_frequency_is_4,Number of Mandatory tours & tour frequency =4,num_mand*(tot_tours == 4),coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4 -util_number_of_mandatory_tours_and_tour_frequency_is_5_plus,Number of Mandatory tours & tour frequency = 5+,num_mand*(tot_tours > 4),coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus -util_number_of_joint_tours_and_tour_frequency_is_0,Number of Joint tours & tour frequency =0,num_hh_joint_tours*(tot_tours == 0),coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0 -util_number_of_joint_tours_and_tour_frequency_is_1,Number of Joint tours & tour frequency =1,num_hh_joint_tours*(tot_tours == 1),coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1 -util_number_of_joint_tours_and_tour_frequency_is_2,Number of Joint tours & tour frequency =2,num_hh_joint_tours*(tot_tours == 2),coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2 -util_number_of_joint_tours_and_tour_frequency_is_3,Number of Joint tours & tour frequency =3,num_hh_joint_tours*(tot_tours == 3),coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3 -util_number_of_joint_tours_and_tour_frequency_is_4,Number of Joint tours & tour frequency =4,num_hh_joint_tours*(tot_tours == 4),coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4 -util_number_of_joint_tours_and_tour_frequency_is_5_plus,Number of Joint tours & tour frequency = 5+,num_hh_joint_tours*(tot_tours > 4),coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus -util_number_of_joint_shopping_tours,Number of Joint Shopping tours,shopping * num_hh_joint_shop_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours -util_number_of_joint_maintenance_tours,Number of Joint Maintenance tours,othmaint * num_hh_joint_maint_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours -util_number_of_joint_eating_out_tours,Number of Joint Eating Out tours,eatout * num_hh_joint_eatout_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours -util_number_of_joint_visit_tours,Number of Joint Visit tours,social * num_hh_joint_social_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours -util_number_of_joint_discretionary_tours,Number of Joint Discretionary tours,othdiscr * num_hh_joint_othdiscr_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours -util_logged_maximum_residual_window_tour_frequency_is_0,"Logged Maximum Residual Window, tour frequency =0",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 0),coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0 -util_logged_maximum_residual_window_tour_frequency_is_1,"Logged Maximum Residual Window, tour frequency =1",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 1),coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1 -util_logged_maximum_residual_window_tour_frequency_is_2,"Logged Maximum Residual Window, tour frequency =2",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 2),coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2 -util_logged_maximum_residual_window_tour_frequency_is_3,"Logged Maximum Residual Window, tour frequency =3",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 3),coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3 -util_logged_maximum_residual_window_tour_frequency_is_4,"Logged Maximum Residual Window, tour frequency =4",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 4),coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4 -util_logged_maximum_residual_window_tour_frequency_is_5_plus,"Logged Maximum Residual Window, tour frequency =5+",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours > 4),coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus -util_mediumlow_income_group_and_tour_frequency_is_1,Dummy for Mediumlow Income group (20K-50K) & tour frequency=1,medium_low_income & (tot_tours == 1),coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1 -util_mediumlow_income_group_and_tour_frequency_is_2,Dummy for Mediumlow Income group (20K-50K) & tour frequency=2,medium_low_income & (tot_tours == 2),coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2 -util_mediumlow_income_group_and_tour_frequency_is_3,Dummy for Mediumlow Income group (20K-50K) & tour frequency=3,medium_low_income & (tot_tours == 3),coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3 -util_mediumlow_income_group_and_tour_frequency_is_4,Dummy for Mediumlow Income group (20K-50K) & tour frequency=4,medium_low_income & (tot_tours == 4),coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4 -util_mediumlow_income_group_and_tour_frequency_is_5_plus,Dummy for Mediumlow Income group (20K-50K) & tour frequency=5+,medium_low_income & (tot_tours > 4),coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus -util_mediumhigh_income_group_and_tour_frequency_is_1,Dummy for MediumHigh Income group (50K-100K) & tour frequency=1,medium_high_income & (tot_tours == 1),coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1 -util_mediumhigh_income_group_and_tour_frequency_is_2,Dummy for MediumHigh Income group (50K-100K) & tour frequency=2,medium_high_income & (tot_tours == 2),coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2 -util_mediumhigh_income_group_and_tour_frequency_is_3,Dummy for MediumHigh Income group (50K-100K) & tour frequency=3,medium_high_income & (tot_tours == 3),coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3 -util_mediumhigh_income_group_and_tour_frequency_is_4,Dummy for MediumHigh Income group (50K-100K) & tour frequency=4,medium_high_income & (tot_tours == 4),coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4 -util_mediumhigh_income_group_and_tour_frequency_is_5_plus,Dummy for MediumHigh Income group (50K-100K) & tour frequency=5+,medium_high_income & (tot_tours > 4),coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus -util_high_income_group_and_tour_frequency_is_1,Dummy for High Income group (>100K) & tour frequency=1,high_income & (tot_tours == 1),coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1 -util_high_income_group_and_tour_frequency_is_2,Dummy for High Income group (>100K) & tour frequency=2,high_income & (tot_tours == 2),coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2 -util_high_income_group_and_tour_frequency_is_3,Dummy for High Income group (>100K) & tour frequency=3,high_income & (tot_tours == 3),coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3 -util_high_income_group_and_tour_frequency_is_4,Dummy for High Income group (>100K) & tour frequency=4,high_income & (tot_tours == 4),coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4 -util_high_income_group_and_tour_frequency_is_5_plus,Dummy for High Income group (>100K) & tour frequency=5+,high_income & (tot_tours > 4),coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus -util_mediumlow_income_group_and_shopping_tour,Dummy for Mediumlow Income group (20K-50K) & shopping tour,medium_low_income * shopping,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour -util_mediumhigh_income_group_and_shopping_tour,Dummy for Mediumhigh Income group (50K-100K) & shopping tour,medium_high_income * shopping,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour -util_high_income_group_and_shopping_tour,Dummy for High Income group (>100K) & shopping tour,high_income * shopping,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour -util_mediumlow_income_group_and_maintenance_tour,Dummy for Mediumlow Income group (20K-50K) & maintenance tour,medium_low_income * othmaint,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour -util_mediumhigh_income_group_and_maintenance_tour,Dummy for Mediumhigh Income group (50K-100K) & maintenance tour,medium_high_income * othmaint,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour -util_high_income_group_and_maintenance_tour,Dummy for High Income group (>100K) & maintenance tour,high_income * othmaint,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour -util_mediumlow_income_group_and_eating_out_tour,Dummy for Mediumlow Income group (20K-50K) & Eating out tour,medium_low_income * eatout,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour -util_mediumhigh_income_group_and_eating_out_tour,Dummy for Mediumhigh Income group (50K-100K) & Eating out tour,medium_high_income * eatout,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour -util_high_income_group_and_eating_out_tour,Dummy for High Income group (>100K) & Eating out tour,high_income * eatout,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour -util_mediumlow_income_group_and_discretionary_tour,Dummy for Mediumlow Income group (20K-50K) & Discretionary tour,medium_low_income * othdiscr,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour -util_mediumhigh_income_group_and_discretionary_tour,Dummy for Mediumhigh Income group (50K-100K) & Discretionary tour,medium_high_income * othdiscr,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour -util_high_income_group_and_discretionary_tour,Dummy for High Income group (>100K) & Discretionary tour,high_income * othdiscr,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour -util_mediumlow_income_group_and_visiting_tour,Dummy for Mediumlow Income group (20K-50K) & Visiting tour,medium_low_income * social,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour -util_mediumhigh_income_group_and_visiting_tour,Dummy for Mediumhigh Income group (50K-100K) & Visiting tour,medium_high_income * social,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour -util_high_income_group_and_visiting_tour,Dummy for High Income group (>100K) & Visiting tour,high_income * social,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour -util_female_and_tour_frequency_is_1,Dummy for Female & tour frequency =1,female & (tot_tours == 1),coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1 -util_female_and_tour_frequency_is_2,Dummy for Female & tour frequency =2,female & (tot_tours == 2),coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2 -util_female_and_tour_frequency_is_3,Dummy for Female & tour frequency =3,female & (tot_tours == 3),coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3 -util_female_and_tour_frequency_is_4,Dummy for Female & tour frequency =4,female & (tot_tours == 4),coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4 -util_female_and_tour_frequency_is_5,Dummy for Female & tour frequency =5,female & (tot_tours == 5),coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5 -util_female_and_escorting_tour,Dummy for Female & Escorting Tour,female * escort,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour -util_female_and_shopping_tour,Dummy for Female & Shopping Tour,female * shopping,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour -util_female_and_maintenance_tour,Dummy for Female & Maintenance Tour,female * othmaint,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour -util_female_and_eatingout_tour,Dummy for Female & EatingOut Tour,female * eatout,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour -util_female_and_discretionary_tour,Dummy for Female & Discretionary Tour,female * othdiscr,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour -util_zero_car_ownership_and_tour_frequency_is_1,Dummy for zero car ownership & tour frequency =1,no_cars & (tot_tours == 1),coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1 -util_zero_car_ownership_and_tour_frequency_is_2,Dummy for zero car ownership & tour frequency =2,no_cars & (tot_tours == 2),coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2 -util_zero_car_ownership_and_tour_frequency_is_3,Dummy for zero car ownership & tour frequency =3,no_cars & (tot_tours == 3),coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3 -util_zero_car_ownership_and_tour_frequency_is_4,Dummy for zero car ownership & tour frequency =4,no_cars & (tot_tours == 4),coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4 -util_zero_car_ownership_and_tour_frequency_is_5_plus,Dummy for zero car ownership & tour frequency =5+,no_cars & (tot_tours > 4),coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus -util_car_shortage_vs_workers_and_tour_frequency_is_1,Dummy for Car Shortage vs Workers & tour frequency =1,~no_cars & (car_sufficiency < 0) & (tot_tours == 1),coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1 -util_car_shortage_vs_workers_and_tour_frequency_is_2,Dummy for Car Shortage vs Workers & tour frequency =2,~no_cars & (car_sufficiency < 0) & (tot_tours == 2),coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2 -util_car_shortage_vs_workers_and_tour_frequency_is_3,Dummy for Car Shortage vs Workers & tour frequency =3,~no_cars & (car_sufficiency < 0) & (tot_tours == 3),coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3 -util_car_shortage_vs_workers_and_tour_frequency_is_4,Dummy for Car Shortage vs Workers & tour frequency =4,~no_cars & (car_sufficiency < 0) & (tot_tours == 4),coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4 -util_car_shortage_vs_workers_and_tour_frequency_is_5_plus,Dummy for Car Shortage vs Workers & tour frequency =5+,~no_cars & (car_sufficiency < 0) & (tot_tours > 4),coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus -util_car_surplus_vs_workers_and_tour_frequency_is_1,Dummy for Car Surplus vs Workers & tour frequency =1,~no_cars & (car_sufficiency > 0) & (tot_tours == 1),coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1 -util_car_surplus_vs_workers_and_tour_frequency_is_2,Dummy for Car Surplus vs Workers & tour frequency =2,~no_cars & (car_sufficiency > 0) & (tot_tours == 2),coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2 -util_car_surplus_vs_workers_and_tour_frequency_is_3,Dummy for Car Surplus vs Workers & tour frequency =3,~no_cars & (car_sufficiency > 0) & (tot_tours == 3),coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3 -util_car_surplus_vs_workers_and_tour_frequency_is_4,Dummy for Car Surplus vs Workers & tour frequency =4,~no_cars & (car_sufficiency > 0) & (tot_tours == 4),coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4 -util_car_surplus_vs_workers_and_tour_frequency_is_5_plus,Dummy for Car Surplus vs Workers & tour frequency =5+,~no_cars & (car_sufficiency > 0) & (tot_tours > 4),coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus -util_presence_of_non_worker_and_tour_frequency_is_1,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =1,has_non_worker & (tot_tours == 1),coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1 -util_presence_of_non_worker_and_tour_frequency_is_2,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =2,has_non_worker & (tot_tours == 2),coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2 -util_presence_of_non_worker_and_tour_frequency_is_3,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =3,has_non_worker & (tot_tours == 3),coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3 -util_presence_of_non_worker_and_tour_frequency_is_4,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =4,has_non_worker & (tot_tours == 4),coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4 -util_presence_of_non_worker_and_tour_frequency_is_5,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =5,has_non_worker & (tot_tours == 5),coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5 -util_presence_of_retiree_and_tour_frequency_is_1,Dummy for Presence of Retiree(other than modeled person) & tour frequency =1,has_retiree & (tot_tours == 1),coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1 -util_presence_of_retiree_and_tour_frequency_is_2,Dummy for Presence of Retiree(other than modeled person) & tour frequency =2,has_retiree & (tot_tours == 2),coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2 -util_presence_of_retiree_and_tour_frequency_is_3,Dummy for Presence of Retiree(other than modeled person) & tour frequency =3,has_retiree & (tot_tours == 3),coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3 -util_presence_of_retiree_and_tour_frequency_is_4,Dummy for Presence of Retiree(other than modeled person) & tour frequency =4,has_retiree & (tot_tours == 4),coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4 -util_presence_of_retiree_and_tour_frequency_is_5,Dummy for Presence of Retiree(other than modeled person) & tour frequency =5,has_retiree & (tot_tours == 5),coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5 -util_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =1,has_preschool_kid & (tot_tours == 1),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1 -util_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =2,has_preschool_kid & (tot_tours == 2),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2 -util_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =3,has_preschool_kid & (tot_tours == 3),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3 -util_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =4,has_preschool_kid & (tot_tours == 4),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4 -util_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =5,has_preschool_kid & (tot_tours == 5),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5 -util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =1,has_school_kid & (tot_tours == 1),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1 -util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =2,has_school_kid & (tot_tours == 2),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2 -util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =3,has_school_kid & (tot_tours == 3),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3 -util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =4,has_school_kid & (tot_tours == 4),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4 -util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =5,has_school_kid & (tot_tours == 5),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5 -util_presence_of_full_time_worker_and_escorting_tour,Dummy for Presence of Full time Worker (other than modeled person) & Escorting tour ,has_full_time * escort,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour -util_presence_of_part_time_worker_and_escorting_tour,Dummy for Presence of Part time Worker (other than modeled person) & Escorting tour ,has_part_time * escort,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour -util_presence_of_non_worker_and_escorting_tour,Dummy for Presence of Non-Worker (other than modeled person) & Escorting tour ,has_non_worker * escort,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour -util_presence_of_retiree_and_escorting_tour,Dummy for Presence of Retiree (other than modeled person) & Escorting tour ,has_retiree * escort,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour -util_presence_of_university_student_and_escorting_tour,Dummy for Presence of University Student (other than modeled person) & Escorting tour ,has_university * escort,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour -util_presence_of_driving_school_kid_and_escorting_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Escorting tour ,has_driving_kid * escort,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour -util_presence_of_pre_driving_school_kid_and_escorting_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Escorting tour ,has_school_kid * escort,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour -util_presence_of_pre_school_kid_and_escorting_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Escorting tour ,has_preschool_kid * escort,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour -util_at_home_pre_driving_school_kid_and_escorting_tour,Dummy for At home Pre-Driving School Kid & Escorting tour ,has_school_kid_at_home * escort,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour -util_at_home_pre_school_kid_and_escorting_tour,Dummy for At homef Pre-School Kid & Escorting tour ,has_preschool_kid_at_home * escort,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour -util_presence_of_full_time_worker_and_shopping_tour,Dummy for Presence of Full time Worker (other than modeled person) & Shopping tour ,has_full_time * shopping,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour -util_presence_of_part_time_worker_and_shopping_tour,Dummy for Presence of Part time Worker (other than modeled person) & Shopping tour ,has_part_time * shopping,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour -util_presence_of_non_worker_and_shopping_tour,Dummy for Presence of Non-Worker (other than modeled person) & Shopping tour ,has_non_worker * shopping,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour -util_presence_of_retiree_and_shopping_tour,Dummy for Presence of Retiree (other than modeled person) & Shopping tour ,has_retiree * shopping,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour -util_presence_of_university_student_and_shopping_tour,Dummy for Presence of University Student (other than modeled person) & Shopping tour ,has_university * shopping,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour -util_presence_of_driving_school_kid_and_shopping_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Shopping tour ,has_driving_kid * shopping,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour -util_presence_of_pre_driving_school_kid_and_shopping_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Shopping tour ,has_school_kid * shopping,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour -util_presence_of_pre_school_kid_and_shopping_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Shopping tour ,has_preschool_kid * shopping,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour -util_at_home_pre_driving_school_kid_and_shopping_tour,Dummy for At home Pre-Driving School Kid & Shopping tour ,has_school_kid_at_home * shopping,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour -util_at_home_pre_school_kid_and_shopping_tour,Dummy for At homef Pre-School Kid & Shopping tour ,has_preschool_kid_at_home * shopping,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour -util_presence_of_full_time_worker_and_maintenance_tour,Dummy for Presence of Full time Worker (other than modeled person) & Maintenance tour ,has_full_time * othmaint,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour -util_presence_of_part_time_worker_and_maintenance_tour,Dummy for Presence of Part time Worker (other than modeled person) & Maintenance tour ,has_part_time * othmaint,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour -util_presence_of_non_worker_and_maintenance_tour,Dummy for Presence of Non-Worker(other than modeled person) & Maintenance tour ,has_non_worker * othmaint,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour -util_presence_of_retiree_and_maintenance_tour,Dummy for Presence of Retiree (other than modeled person) & Maintenance tour ,has_retiree * othmaint,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour -util_presence_of_university_student_and_maintenance_tour,Dummy for Presence of University Student (other than modeled person) & Maintenance tour ,has_university * othmaint,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour -util_presence_of_driving_school_kid_and_maintenance_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Maintenance tour ,has_driving_kid * othmaint,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour -util_presence_of_pre_driving_school_kid_and_maintenance_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Maintenance tour ,has_school_kid * othmaint,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour -util_presence_of_pre_school_kid_and_maintenance_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Maintenance tour ,has_preschool_kid * othmaint,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour -util_at_home_pre_driving_school_kid_and_maintenance_tour,Dummy for At home Pre-Driving School Kid & Maintenance tour ,has_school_kid_at_home * othmaint,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour -util_at_home_pre_school_kid_and_maintenance_tour,Dummy for At homef Pre-School Kid & Maintenance tour ,has_preschool_kid_at_home * othmaint,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour -util_presence_of_full_time_worker_and_eating_out_tour,Dummy for Presence of Full time Worker (other than modeled person) & Eating Out tour ,has_full_time * eatout,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour -util_presence_of_part_time_worker_and_eating_out_tour,Dummy for Presence of Part time Worker (other than modeled person) & Eating Out tour ,has_part_time * eatout,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour -util_presence_of_non_worker_and_eating_out_tour,Dummy for Presence of Non-Worker (other than modeled person) & Eating Out tour ,has_non_worker * eatout,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour -util_presence_of_retiree_and_eating_out_tour,Dummy for Presence of Retiree (other than modeled person) & Eating Out tour ,has_retiree * eatout,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour -util_presence_of_university_student_and_eating_out_tour,Dummy for Presence of University Student (other than modeled person) & Eating Out tour ,has_university * eatout,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour -util_presence_of_driving_school_kid_and_eating_out_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Eating Out tour ,has_driving_kid * eatout,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour -util_presence_of_pre_driving_school_kid_and_eating_out_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Eating Out tour ,has_school_kid * eatout,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour -util_presence_of_pre_school_kid_and_eating_out_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Eating Out tour ,has_preschool_kid * eatout,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour -util_at_home_pre_driving_school_kid_and_eating_out_tour,Dummy for At home Pre-Driving School Kid & Eating Out tour ,has_school_kid_at_home * eatout,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour -util_at_home_pre_school_kid_and_eating_out_tour,Dummy for At homef Pre-School Kid & Eating Out tour ,has_preschool_kid_at_home * eatout,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour -util_presence_of_full_time_worker_and_discretionary_tour,Dummy for Presence of Full time Worker (other than modeled person) & Discretionary tour ,has_full_time * othdiscr,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour -util_presence_of_part_time_worker_and_discretionary_tour,Dummy for Presence of Part time Worker (other than modeled person) & Discretionary tour ,has_part_time * othdiscr,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour -util_presence_of_non_worker_and_discretionary_tour,Dummy for Presence of Non-Worker (other than modeled person) & Discretionary tour ,has_non_worker * othdiscr,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour -util_presence_of_retiree_and_discretionary_tour,Dummy for Presence of Retiree (other than modeled person) & Discretionary tour ,has_retiree * othdiscr,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour -util_presence_of_university_student_and_discretionary_tour,Dummy for Presence of University Student (other than modeled person) & Discretionary tour ,has_university * othdiscr,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour -util_presence_of_driving_school_kid_and_discretionary_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Discretionary tour ,has_driving_kid * othdiscr,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour -util_presence_of_pre_driving_school_kid_and_discretionary_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Discretionary tour ,has_school_kid * othdiscr,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour -util_presence_of_pre_school_kid_and_discretionary_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Discretionary tour ,has_preschool_kid * othdiscr,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour -util_at_home_pre_driving_school_kid_and_discretionary_tour,Dummy for At home Pre-Driving School Kid & Discretionary tour ,has_school_kid_at_home * othdiscr,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour -util_at_home_pre_school_kid_and_discretionary_tour,Dummy for At homef Pre-School Kid & Discretionary tour ,has_preschool_kid_at_home * othdiscr,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour -util_walk_access_to_retail_and_tour_frequency_is_1,Walk Access to Retail & Tour Frequency =1,nmRetail * (tot_tours == 1),coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1 -util_walk_access_to_retail_and_tour_frequency_is_2,Walk Access to Retail & Tour Frequency =2,nmRetail * (tot_tours == 2),coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2 -util_walk_access_to_retail_and_tour_frequency_is_3,Walk Access to Retail & Tour Frequency =3,nmRetail * (tot_tours == 3),coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3 -util_walk_access_to_retail_and_tour_frequency_is_4,Walk Access to Retail & Tour Frequency =4,nmRetail * (tot_tours == 4),coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4 -util_walk_access_to_retail_and_tour_frequency_is_5_plus,Walk Access to Retail & Tour Frequency =5+,nmRetail * (tot_tours > 4),coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus -util_transit_access_to_retail_and_tour_frequency_is_1,Transit Access to Retail & Tour Frequency =1,trOpRetail * (tot_tours == 1),coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1 -util_transit_access_to_retail_and_tour_frequency_is_2,Transit Access to Retail & Tour Frequency =2,trOpRetail * (tot_tours == 2),coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2 -util_transit_access_to_retail_and_tour_frequency_is_3,Transit Access to Retail & Tour Frequency =3,trOpRetail * (tot_tours == 3),coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3 -util_transit_access_to_retail_and_tour_frequency_is_4,Transit Access to Retail & Tour Frequency =4,trOpRetail * (tot_tours == 4),coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4 -util_transit_access_to_retail_and_tour_frequency_is_5_plus,Transit Access to Retail & Tour Frequency =5+,trOpRetail * (tot_tours > 4),coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus -util_auto_access_to_retail_and_tour_frequency_is_1,Auto Access to Retail & Tour Frequency =1,auOpRetail * (tot_tours == 1),coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1 -util_auto_access_to_retail_and_tour_frequency_is_2,Auto Access to Retail & Tour Frequency =2,auOpRetail * (tot_tours == 2),coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2 -util_auto_access_to_retail_and_tour_frequency_is_3,Auto Access to Retail & Tour Frequency =3,auOpRetail * (tot_tours == 3),coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3 -util_auto_access_to_retail_and_tour_frequency_is_4,Auto Access to Retail & Tour Frequency =4,auOpRetail * (tot_tours == 4),coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4 -util_auto_access_to_retail_and_tour_frequency_is_5_plus,Auto Access to Retail & Tour Frequency =5+,auOpRetail * (tot_tours > 4),coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus -util_walk_access_to_retail_and_escorting,Walk Access to Retail & Escorting ,nmRetail * escort,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting -util_transit_access_to_retail_and_escorting,Transit Access to Retail & Escorting ,trOpRetail * escort,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting -util_auto_access_to_retail_and_escorting,Auto Access to Retail & Escorting ,auOpRetail * escort,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting -util_walk_access_to_retail_and_shopping,Walk Access to Retail & Shopping ,nmRetail * shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping -util_transit_access_to_retail_and_shopping,Transit Access to Retail & Shopping ,trOpRetail * shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping -util_auto_access_to_retail_and_shopping,Auto Access to Retail & Shopping ,auOpRetail * shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping -util_walk_access_to_retail_and_maintenance,Walk Access to Retail & Maintenance ,nmRetail * othmaint,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance -util_transit_access_to_retail_and_maintenance,Transit Access to Retail & Maintenance ,trOpRetail * othmaint,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance -util_auto_access_to_retail_and_maintenance,Auto Access to Retail & Maintenance ,auOpRetail * othmaint,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance -util_walk_access_to_retail_and_eating_out,Walk Access to Retail & Eating Out ,nmRetail * eatout,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out -util_transit_access_to_retail_and_eating_out,Transit Access to Retail & Eating Out ,trOpRetail * eatout,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out -util_auto_access_to_retail_and_eating_out,Auto Access to Retail & Eating Out ,auOpRetail * eatout,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out -util_walk_access_to_retail_and_discretionary,Walk Access to Retail & Discretionary ,nmRetail * othdiscr,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary -util_transit_access_to_retail_and_discretionary,Transit Access to Retail & Discretionary ,trOpRetail * othdiscr,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary -util_auto_access_to_retail_and_discretionary,Auto Access to Retail & Discretionary ,auOpRetail * othdiscr,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary -util_urban_and_tour_frequency_is_1,Urban Areatype & Tour Frequency =1,home_is_urban & (tot_tours == 1),coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1 -util_urban_and_tour_frequency_is_2,Urban Areatype & Tour Frequency =2,home_is_urban & (tot_tours == 2),coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2 -util_urban_and_tour_frequency_is_3,Urban Areatype & Tour Frequency =3,home_is_urban & (tot_tours == 3),coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3 -util_urban_and_tour_frequency_is_4,Urban Areatype & Tour Frequency =4,home_is_urban & (tot_tours == 4),coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4 -util_urban_and_tour_frequency_is_5_plus,Urban Areatype & Tour Frequency =5+,home_is_urban & (tot_tours > 4),coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus -util_urban_and_escorting_tour,Urban Areatype & Escorting tour,home_is_urban * escort,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour -util_urban_and_shopping_tour,Urban Areatype &Shopping tour,home_is_urban * shopping,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour -util_urban_and_maintenance_tour,Urban Areatype & Maintenance tour,home_is_urban * othmaint,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour -util_urban_and_eatingout_tour,Urban Areatype & EatingOut tour,home_is_urban * eatout,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour -util_urban_and_discretionary_tour,Urban Areatype & Discretionary tour,home_is_urban * othdiscr,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour -util_1_escort_tour_constant,1 Escort Tour Constant,escort == 1,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant -util_2_plus_escort_tours_constant,2+ Escort Tours Constant,escort >= 2,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant -util_1_plus_shopping_tours_constant,1+ Shopping Tours Constant,shopping >= 1,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant -util_1_plus_maintenance_tours_constant,1+ Maintenance Tours Constant,othmaint >= 1,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant -util_1_plus_eating_out_tours_constant,1+ Eating Out Tours Constant,eatout >= 1,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant -util_1_plus_visting_tours_constant,1+ Visting Tours Constant,social >= 1,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant -util_1_plus_other_discretionary_tours_constant,1+ Other Discretionary Tours Constant,othdiscr >= 1,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant +Label,Description,Expression,PTYPE_FULL,PTYPE_PART,PTYPE_UNIVERSITY,PTYPE_NONWORK,PTYPE_RETIRED,PTYPE_DRIVING,PTYPE_SCHOOL,PTYPE_PRESCHOOL +util_escorting_tour,Escorting Tour,escort,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour +util_discretionary_tour,Discretionary Tour,othdiscr,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour +util_shopping_tour,Shopping Tour,shopping,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour +util_maintenance_tour,Maintenance Tour,othmaint,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour +util_visiting_or_social_tour,Visiting/Social Tour,social,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour +util_eating_out_tour,Eating Out Tour,eatout,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour +util_total_number_of_tours_is_0_no_prior_tours,Total Number of Tours = 0 (No Prior Tours),(tot_tours == 0) & (num_mand == 0) & (num_hh_joint_tours == 0),coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours +util_total_number_of_tours_is_0_prior_tours,Total Number of Tours = 0 (1 or more Prior Tours),(tot_tours == 0) & ((num_mand > 0) | (num_hh_joint_tours > 0)),coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours +util_total_number_of_tours_is_1,Total Number of Tours = 1,tot_tours == 1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1 +util_total_number_of_tours_is_2,Total Number of Tours = 2,tot_tours == 2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2 +util_total_number_of_tours_is_3,Total Number of Tours = 3,tot_tours == 3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3 +util_total_number_of_tours_is_4,Total Number of Tours = 4,tot_tours == 4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4 +util_total_number_of_tours_is_5,Total Number of Tours = 5,tot_tours == 5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5 +util_total_number_of_tours_is_6_plus,Total Number of Tours = 6+,tot_tours > 5,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus +util_number_of_mandatory_tours_and_tour_frequency_is_0,Number of Mandatory tours & tour frequency =0,num_mand*(tot_tours == 0),coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0 +util_number_of_mandatory_tours_and_tour_frequency_is_1,Number of Mandatory tours & tour frequency =1,num_mand*(tot_tours == 1),coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1 +util_number_of_mandatory_tours_and_tour_frequency_is_2,Number of Mandatory tours & tour frequency =2,num_mand*(tot_tours == 2),coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2 +util_number_of_mandatory_tours_and_tour_frequency_is_3,Number of Mandatory tours & tour frequency =3,num_mand*(tot_tours == 3),coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3 +util_number_of_mandatory_tours_and_tour_frequency_is_4,Number of Mandatory tours & tour frequency =4,num_mand*(tot_tours == 4),coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4 +util_number_of_mandatory_tours_and_tour_frequency_is_5_plus,Number of Mandatory tours & tour frequency = 5+,num_mand*(tot_tours > 4),coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus +util_number_of_joint_tours_and_tour_frequency_is_0,Number of Joint tours & tour frequency =0,num_hh_joint_tours*(tot_tours == 0),coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0 +util_number_of_joint_tours_and_tour_frequency_is_1,Number of Joint tours & tour frequency =1,num_hh_joint_tours*(tot_tours == 1),coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1 +util_number_of_joint_tours_and_tour_frequency_is_2,Number of Joint tours & tour frequency =2,num_hh_joint_tours*(tot_tours == 2),coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2 +util_number_of_joint_tours_and_tour_frequency_is_3,Number of Joint tours & tour frequency =3,num_hh_joint_tours*(tot_tours == 3),coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3 +util_number_of_joint_tours_and_tour_frequency_is_4,Number of Joint tours & tour frequency =4,num_hh_joint_tours*(tot_tours == 4),coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4 +util_number_of_joint_tours_and_tour_frequency_is_5_plus,Number of Joint tours & tour frequency = 5+,num_hh_joint_tours*(tot_tours > 4),coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus +util_number_of_joint_shopping_tours,Number of Joint Shopping tours,shopping * num_hh_joint_shop_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours +util_number_of_joint_maintenance_tours,Number of Joint Maintenance tours,othmaint * num_hh_joint_maint_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours +util_number_of_joint_eating_out_tours,Number of Joint Eating Out tours,eatout * num_hh_joint_eatout_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours +util_number_of_joint_visit_tours,Number of Joint Visit tours,social * num_hh_joint_social_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours +util_number_of_joint_discretionary_tours,Number of Joint Discretionary tours,othdiscr * num_hh_joint_othdiscr_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours +util_logged_maximum_residual_window_tour_frequency_is_0,"Logged Maximum Residual Window, tour frequency =0",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 0),coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0 +util_logged_maximum_residual_window_tour_frequency_is_1,"Logged Maximum Residual Window, tour frequency =1",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 1),coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1 +util_logged_maximum_residual_window_tour_frequency_is_2,"Logged Maximum Residual Window, tour frequency =2",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 2),coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2 +util_logged_maximum_residual_window_tour_frequency_is_3,"Logged Maximum Residual Window, tour frequency =3",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 3),coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3 +util_logged_maximum_residual_window_tour_frequency_is_4,"Logged Maximum Residual Window, tour frequency =4",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 4),coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4 +util_logged_maximum_residual_window_tour_frequency_is_5_plus,"Logged Maximum Residual Window, tour frequency =5+",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours > 4),coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus +util_mediumlow_income_group_and_tour_frequency_is_1,Dummy for Mediumlow Income group (20K-50K) & tour frequency=1,medium_low_income & (tot_tours == 1),coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1 +util_mediumlow_income_group_and_tour_frequency_is_2,Dummy for Mediumlow Income group (20K-50K) & tour frequency=2,medium_low_income & (tot_tours == 2),coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2 +util_mediumlow_income_group_and_tour_frequency_is_3,Dummy for Mediumlow Income group (20K-50K) & tour frequency=3,medium_low_income & (tot_tours == 3),coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3 +util_mediumlow_income_group_and_tour_frequency_is_4,Dummy for Mediumlow Income group (20K-50K) & tour frequency=4,medium_low_income & (tot_tours == 4),coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4 +util_mediumlow_income_group_and_tour_frequency_is_5_plus,Dummy for Mediumlow Income group (20K-50K) & tour frequency=5+,medium_low_income & (tot_tours > 4),coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus +util_mediumhigh_income_group_and_tour_frequency_is_1,Dummy for MediumHigh Income group (50K-100K) & tour frequency=1,medium_high_income & (tot_tours == 1),coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1 +util_mediumhigh_income_group_and_tour_frequency_is_2,Dummy for MediumHigh Income group (50K-100K) & tour frequency=2,medium_high_income & (tot_tours == 2),coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2 +util_mediumhigh_income_group_and_tour_frequency_is_3,Dummy for MediumHigh Income group (50K-100K) & tour frequency=3,medium_high_income & (tot_tours == 3),coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3 +util_mediumhigh_income_group_and_tour_frequency_is_4,Dummy for MediumHigh Income group (50K-100K) & tour frequency=4,medium_high_income & (tot_tours == 4),coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4 +util_mediumhigh_income_group_and_tour_frequency_is_5_plus,Dummy for MediumHigh Income group (50K-100K) & tour frequency=5+,medium_high_income & (tot_tours > 4),coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus +util_high_income_group_and_tour_frequency_is_1,Dummy for High Income group (>100K) & tour frequency=1,high_income & (tot_tours == 1),coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1 +util_high_income_group_and_tour_frequency_is_2,Dummy for High Income group (>100K) & tour frequency=2,high_income & (tot_tours == 2),coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2 +util_high_income_group_and_tour_frequency_is_3,Dummy for High Income group (>100K) & tour frequency=3,high_income & (tot_tours == 3),coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3 +util_high_income_group_and_tour_frequency_is_4,Dummy for High Income group (>100K) & tour frequency=4,high_income & (tot_tours == 4),coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4 +util_high_income_group_and_tour_frequency_is_5_plus,Dummy for High Income group (>100K) & tour frequency=5+,high_income & (tot_tours > 4),coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus +util_mediumlow_income_group_and_shopping_tour,Dummy for Mediumlow Income group (20K-50K) & shopping tour,medium_low_income * shopping,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour +util_mediumhigh_income_group_and_shopping_tour,Dummy for Mediumhigh Income group (50K-100K) & shopping tour,medium_high_income * shopping,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour +util_high_income_group_and_shopping_tour,Dummy for High Income group (>100K) & shopping tour,high_income * shopping,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour +util_mediumlow_income_group_and_maintenance_tour,Dummy for Mediumlow Income group (20K-50K) & maintenance tour,medium_low_income * othmaint,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour +util_mediumhigh_income_group_and_maintenance_tour,Dummy for Mediumhigh Income group (50K-100K) & maintenance tour,medium_high_income * othmaint,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour +util_high_income_group_and_maintenance_tour,Dummy for High Income group (>100K) & maintenance tour,high_income * othmaint,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour +util_mediumlow_income_group_and_eating_out_tour,Dummy for Mediumlow Income group (20K-50K) & Eating out tour,medium_low_income * eatout,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour +util_mediumhigh_income_group_and_eating_out_tour,Dummy for Mediumhigh Income group (50K-100K) & Eating out tour,medium_high_income * eatout,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour +util_high_income_group_and_eating_out_tour,Dummy for High Income group (>100K) & Eating out tour,high_income * eatout,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour +util_mediumlow_income_group_and_discretionary_tour,Dummy for Mediumlow Income group (20K-50K) & Discretionary tour,medium_low_income * othdiscr,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour +util_mediumhigh_income_group_and_discretionary_tour,Dummy for Mediumhigh Income group (50K-100K) & Discretionary tour,medium_high_income * othdiscr,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour +util_high_income_group_and_discretionary_tour,Dummy for High Income group (>100K) & Discretionary tour,high_income * othdiscr,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour +util_mediumlow_income_group_and_visiting_tour,Dummy for Mediumlow Income group (20K-50K) & Visiting tour,medium_low_income * social,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour +util_mediumhigh_income_group_and_visiting_tour,Dummy for Mediumhigh Income group (50K-100K) & Visiting tour,medium_high_income * social,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour +util_high_income_group_and_visiting_tour,Dummy for High Income group (>100K) & Visiting tour,high_income * social,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour +util_female_and_tour_frequency_is_1,Dummy for Female & tour frequency =1,female & (tot_tours == 1),coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1 +util_female_and_tour_frequency_is_2,Dummy for Female & tour frequency =2,female & (tot_tours == 2),coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2 +util_female_and_tour_frequency_is_3,Dummy for Female & tour frequency =3,female & (tot_tours == 3),coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3 +util_female_and_tour_frequency_is_4,Dummy for Female & tour frequency =4,female & (tot_tours == 4),coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4 +util_female_and_tour_frequency_is_5,Dummy for Female & tour frequency =5,female & (tot_tours == 5),coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5 +util_female_and_escorting_tour,Dummy for Female & Escorting Tour,female * escort,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour +util_female_and_shopping_tour,Dummy for Female & Shopping Tour,female * shopping,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour +util_female_and_maintenance_tour,Dummy for Female & Maintenance Tour,female * othmaint,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour +util_female_and_eatingout_tour,Dummy for Female & EatingOut Tour,female * eatout,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour +util_female_and_discretionary_tour,Dummy for Female & Discretionary Tour,female * othdiscr,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour +util_zero_car_ownership_and_tour_frequency_is_1,Dummy for zero car ownership & tour frequency =1,no_cars & (tot_tours == 1),coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1 +util_zero_car_ownership_and_tour_frequency_is_2,Dummy for zero car ownership & tour frequency =2,no_cars & (tot_tours == 2),coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2 +util_zero_car_ownership_and_tour_frequency_is_3,Dummy for zero car ownership & tour frequency =3,no_cars & (tot_tours == 3),coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3 +util_zero_car_ownership_and_tour_frequency_is_4,Dummy for zero car ownership & tour frequency =4,no_cars & (tot_tours == 4),coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4 +util_zero_car_ownership_and_tour_frequency_is_5_plus,Dummy for zero car ownership & tour frequency =5+,no_cars & (tot_tours > 4),coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus +util_car_shortage_vs_workers_and_tour_frequency_is_1,Dummy for Car Shortage vs Workers & tour frequency =1,~no_cars & (car_sufficiency < 0) & (tot_tours == 1),coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1 +util_car_shortage_vs_workers_and_tour_frequency_is_2,Dummy for Car Shortage vs Workers & tour frequency =2,~no_cars & (car_sufficiency < 0) & (tot_tours == 2),coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2 +util_car_shortage_vs_workers_and_tour_frequency_is_3,Dummy for Car Shortage vs Workers & tour frequency =3,~no_cars & (car_sufficiency < 0) & (tot_tours == 3),coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3 +util_car_shortage_vs_workers_and_tour_frequency_is_4,Dummy for Car Shortage vs Workers & tour frequency =4,~no_cars & (car_sufficiency < 0) & (tot_tours == 4),coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4 +util_car_shortage_vs_workers_and_tour_frequency_is_5_plus,Dummy for Car Shortage vs Workers & tour frequency =5+,~no_cars & (car_sufficiency < 0) & (tot_tours > 4),coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus +util_car_surplus_vs_workers_and_tour_frequency_is_1,Dummy for Car Surplus vs Workers & tour frequency =1,~no_cars & (car_sufficiency > 0) & (tot_tours == 1),coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1 +util_car_surplus_vs_workers_and_tour_frequency_is_2,Dummy for Car Surplus vs Workers & tour frequency =2,~no_cars & (car_sufficiency > 0) & (tot_tours == 2),coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2 +util_car_surplus_vs_workers_and_tour_frequency_is_3,Dummy for Car Surplus vs Workers & tour frequency =3,~no_cars & (car_sufficiency > 0) & (tot_tours == 3),coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3 +util_car_surplus_vs_workers_and_tour_frequency_is_4,Dummy for Car Surplus vs Workers & tour frequency =4,~no_cars & (car_sufficiency > 0) & (tot_tours == 4),coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4 +util_car_surplus_vs_workers_and_tour_frequency_is_5_plus,Dummy for Car Surplus vs Workers & tour frequency =5+,~no_cars & (car_sufficiency > 0) & (tot_tours > 4),coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus +util_presence_of_non_worker_and_tour_frequency_is_1,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =1,has_non_worker & (tot_tours == 1),coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1 +util_presence_of_non_worker_and_tour_frequency_is_2,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =2,has_non_worker & (tot_tours == 2),coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2 +util_presence_of_non_worker_and_tour_frequency_is_3,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =3,has_non_worker & (tot_tours == 3),coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3 +util_presence_of_non_worker_and_tour_frequency_is_4,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =4,has_non_worker & (tot_tours == 4),coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4 +util_presence_of_non_worker_and_tour_frequency_is_5,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =5,has_non_worker & (tot_tours == 5),coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5 +util_presence_of_retiree_and_tour_frequency_is_1,Dummy for Presence of Retiree(other than modeled person) & tour frequency =1,has_retiree & (tot_tours == 1),coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1 +util_presence_of_retiree_and_tour_frequency_is_2,Dummy for Presence of Retiree(other than modeled person) & tour frequency =2,has_retiree & (tot_tours == 2),coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2 +util_presence_of_retiree_and_tour_frequency_is_3,Dummy for Presence of Retiree(other than modeled person) & tour frequency =3,has_retiree & (tot_tours == 3),coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3 +util_presence_of_retiree_and_tour_frequency_is_4,Dummy for Presence of Retiree(other than modeled person) & tour frequency =4,has_retiree & (tot_tours == 4),coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4 +util_presence_of_retiree_and_tour_frequency_is_5,Dummy for Presence of Retiree(other than modeled person) & tour frequency =5,has_retiree & (tot_tours == 5),coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5 +util_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =1,has_preschool_kid & (tot_tours == 1),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1 +util_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =2,has_preschool_kid & (tot_tours == 2),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2 +util_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =3,has_preschool_kid & (tot_tours == 3),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3 +util_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =4,has_preschool_kid & (tot_tours == 4),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4 +util_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =5,has_preschool_kid & (tot_tours == 5),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5 +util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =1,has_school_kid & (tot_tours == 1),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1 +util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =2,has_school_kid & (tot_tours == 2),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2 +util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =3,has_school_kid & (tot_tours == 3),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3 +util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =4,has_school_kid & (tot_tours == 4),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4 +util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =5,has_school_kid & (tot_tours == 5),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5 +util_presence_of_full_time_worker_and_escorting_tour,Dummy for Presence of Full time Worker (other than modeled person) & Escorting tour ,has_full_time * escort,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour +util_presence_of_part_time_worker_and_escorting_tour,Dummy for Presence of Part time Worker (other than modeled person) & Escorting tour ,has_part_time * escort,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour +util_presence_of_non_worker_and_escorting_tour,Dummy for Presence of Non-Worker (other than modeled person) & Escorting tour ,has_non_worker * escort,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour +util_presence_of_retiree_and_escorting_tour,Dummy for Presence of Retiree (other than modeled person) & Escorting tour ,has_retiree * escort,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour +util_presence_of_university_student_and_escorting_tour,Dummy for Presence of University Student (other than modeled person) & Escorting tour ,has_university * escort,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour +util_presence_of_driving_school_kid_and_escorting_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Escorting tour ,has_driving_kid * escort,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour +util_presence_of_pre_driving_school_kid_and_escorting_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Escorting tour ,has_school_kid * escort,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour +util_presence_of_pre_school_kid_and_escorting_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Escorting tour ,has_preschool_kid * escort,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour +util_at_home_pre_driving_school_kid_and_escorting_tour,Dummy for At home Pre-Driving School Kid & Escorting tour ,has_school_kid_at_home * escort,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour +util_at_home_pre_school_kid_and_escorting_tour,Dummy for At homef Pre-School Kid & Escorting tour ,has_preschool_kid_at_home * escort,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour +util_presence_of_full_time_worker_and_shopping_tour,Dummy for Presence of Full time Worker (other than modeled person) & Shopping tour ,has_full_time * shopping,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour +util_presence_of_part_time_worker_and_shopping_tour,Dummy for Presence of Part time Worker (other than modeled person) & Shopping tour ,has_part_time * shopping,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour +util_presence_of_non_worker_and_shopping_tour,Dummy for Presence of Non-Worker (other than modeled person) & Shopping tour ,has_non_worker * shopping,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour +util_presence_of_retiree_and_shopping_tour,Dummy for Presence of Retiree (other than modeled person) & Shopping tour ,has_retiree * shopping,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour +util_presence_of_university_student_and_shopping_tour,Dummy for Presence of University Student (other than modeled person) & Shopping tour ,has_university * shopping,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour +util_presence_of_driving_school_kid_and_shopping_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Shopping tour ,has_driving_kid * shopping,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour +util_presence_of_pre_driving_school_kid_and_shopping_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Shopping tour ,has_school_kid * shopping,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour +util_presence_of_pre_school_kid_and_shopping_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Shopping tour ,has_preschool_kid * shopping,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour +util_at_home_pre_driving_school_kid_and_shopping_tour,Dummy for At home Pre-Driving School Kid & Shopping tour ,has_school_kid_at_home * shopping,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour +util_at_home_pre_school_kid_and_shopping_tour,Dummy for At homef Pre-School Kid & Shopping tour ,has_preschool_kid_at_home * shopping,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour +util_presence_of_full_time_worker_and_maintenance_tour,Dummy for Presence of Full time Worker (other than modeled person) & Maintenance tour ,has_full_time * othmaint,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour +util_presence_of_part_time_worker_and_maintenance_tour,Dummy for Presence of Part time Worker (other than modeled person) & Maintenance tour ,has_part_time * othmaint,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour +util_presence_of_non_worker_and_maintenance_tour,Dummy for Presence of Non-Worker(other than modeled person) & Maintenance tour ,has_non_worker * othmaint,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour +util_presence_of_retiree_and_maintenance_tour,Dummy for Presence of Retiree (other than modeled person) & Maintenance tour ,has_retiree * othmaint,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour +util_presence_of_university_student_and_maintenance_tour,Dummy for Presence of University Student (other than modeled person) & Maintenance tour ,has_university * othmaint,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour +util_presence_of_driving_school_kid_and_maintenance_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Maintenance tour ,has_driving_kid * othmaint,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour +util_presence_of_pre_driving_school_kid_and_maintenance_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Maintenance tour ,has_school_kid * othmaint,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour +util_presence_of_pre_school_kid_and_maintenance_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Maintenance tour ,has_preschool_kid * othmaint,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour +util_at_home_pre_driving_school_kid_and_maintenance_tour,Dummy for At home Pre-Driving School Kid & Maintenance tour ,has_school_kid_at_home * othmaint,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour +util_at_home_pre_school_kid_and_maintenance_tour,Dummy for At homef Pre-School Kid & Maintenance tour ,has_preschool_kid_at_home * othmaint,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour +util_presence_of_full_time_worker_and_eating_out_tour,Dummy for Presence of Full time Worker (other than modeled person) & Eating Out tour ,has_full_time * eatout,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour +util_presence_of_part_time_worker_and_eating_out_tour,Dummy for Presence of Part time Worker (other than modeled person) & Eating Out tour ,has_part_time * eatout,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour +util_presence_of_non_worker_and_eating_out_tour,Dummy for Presence of Non-Worker (other than modeled person) & Eating Out tour ,has_non_worker * eatout,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour +util_presence_of_retiree_and_eating_out_tour,Dummy for Presence of Retiree (other than modeled person) & Eating Out tour ,has_retiree * eatout,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour +util_presence_of_university_student_and_eating_out_tour,Dummy for Presence of University Student (other than modeled person) & Eating Out tour ,has_university * eatout,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour +util_presence_of_driving_school_kid_and_eating_out_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Eating Out tour ,has_driving_kid * eatout,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour +util_presence_of_pre_driving_school_kid_and_eating_out_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Eating Out tour ,has_school_kid * eatout,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour +util_presence_of_pre_school_kid_and_eating_out_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Eating Out tour ,has_preschool_kid * eatout,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour +util_at_home_pre_driving_school_kid_and_eating_out_tour,Dummy for At home Pre-Driving School Kid & Eating Out tour ,has_school_kid_at_home * eatout,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour +util_at_home_pre_school_kid_and_eating_out_tour,Dummy for At homef Pre-School Kid & Eating Out tour ,has_preschool_kid_at_home * eatout,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour +util_presence_of_full_time_worker_and_discretionary_tour,Dummy for Presence of Full time Worker (other than modeled person) & Discretionary tour ,has_full_time * othdiscr,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour +util_presence_of_part_time_worker_and_discretionary_tour,Dummy for Presence of Part time Worker (other than modeled person) & Discretionary tour ,has_part_time * othdiscr,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour +util_presence_of_non_worker_and_discretionary_tour,Dummy for Presence of Non-Worker (other than modeled person) & Discretionary tour ,has_non_worker * othdiscr,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour +util_presence_of_retiree_and_discretionary_tour,Dummy for Presence of Retiree (other than modeled person) & Discretionary tour ,has_retiree * othdiscr,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour +util_presence_of_university_student_and_discretionary_tour,Dummy for Presence of University Student (other than modeled person) & Discretionary tour ,has_university * othdiscr,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour +util_presence_of_driving_school_kid_and_discretionary_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Discretionary tour ,has_driving_kid * othdiscr,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour +util_presence_of_pre_driving_school_kid_and_discretionary_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Discretionary tour ,has_school_kid * othdiscr,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour +util_presence_of_pre_school_kid_and_discretionary_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Discretionary tour ,has_preschool_kid * othdiscr,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour +util_at_home_pre_driving_school_kid_and_discretionary_tour,Dummy for At home Pre-Driving School Kid & Discretionary tour ,has_school_kid_at_home * othdiscr,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour +util_at_home_pre_school_kid_and_discretionary_tour,Dummy for At homef Pre-School Kid & Discretionary tour ,has_preschool_kid_at_home * othdiscr,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour +util_walk_access_to_retail_and_tour_frequency_is_1,Walk Access to Retail & Tour Frequency =1,nmRetail * (tot_tours == 1),coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1 +util_walk_access_to_retail_and_tour_frequency_is_2,Walk Access to Retail & Tour Frequency =2,nmRetail * (tot_tours == 2),coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2 +util_walk_access_to_retail_and_tour_frequency_is_3,Walk Access to Retail & Tour Frequency =3,nmRetail * (tot_tours == 3),coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3 +util_walk_access_to_retail_and_tour_frequency_is_4,Walk Access to Retail & Tour Frequency =4,nmRetail * (tot_tours == 4),coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4 +util_walk_access_to_retail_and_tour_frequency_is_5_plus,Walk Access to Retail & Tour Frequency =5+,nmRetail * (tot_tours > 4),coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus +util_transit_access_to_retail_and_tour_frequency_is_1,Transit Access to Retail & Tour Frequency =1,trOpRetail * (tot_tours == 1),coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1 +util_transit_access_to_retail_and_tour_frequency_is_2,Transit Access to Retail & Tour Frequency =2,trOpRetail * (tot_tours == 2),coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2 +util_transit_access_to_retail_and_tour_frequency_is_3,Transit Access to Retail & Tour Frequency =3,trOpRetail * (tot_tours == 3),coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3 +util_transit_access_to_retail_and_tour_frequency_is_4,Transit Access to Retail & Tour Frequency =4,trOpRetail * (tot_tours == 4),coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4 +util_transit_access_to_retail_and_tour_frequency_is_5_plus,Transit Access to Retail & Tour Frequency =5+,trOpRetail * (tot_tours > 4),coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus +util_auto_access_to_retail_and_tour_frequency_is_1,Auto Access to Retail & Tour Frequency =1,auOpRetail * (tot_tours == 1),coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1 +util_auto_access_to_retail_and_tour_frequency_is_2,Auto Access to Retail & Tour Frequency =2,auOpRetail * (tot_tours == 2),coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2 +util_auto_access_to_retail_and_tour_frequency_is_3,Auto Access to Retail & Tour Frequency =3,auOpRetail * (tot_tours == 3),coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3 +util_auto_access_to_retail_and_tour_frequency_is_4,Auto Access to Retail & Tour Frequency =4,auOpRetail * (tot_tours == 4),coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4 +util_auto_access_to_retail_and_tour_frequency_is_5_plus,Auto Access to Retail & Tour Frequency =5+,auOpRetail * (tot_tours > 4),coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus +util_walk_access_to_retail_and_escorting,Walk Access to Retail & Escorting ,nmRetail * escort,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting +util_transit_access_to_retail_and_escorting,Transit Access to Retail & Escorting ,trOpRetail * escort,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting +util_auto_access_to_retail_and_escorting,Auto Access to Retail & Escorting ,auOpRetail * escort,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting +util_walk_access_to_retail_and_shopping,Walk Access to Retail & Shopping ,nmRetail * shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping +util_transit_access_to_retail_and_shopping,Transit Access to Retail & Shopping ,trOpRetail * shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping +util_auto_access_to_retail_and_shopping,Auto Access to Retail & Shopping ,auOpRetail * shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping +util_walk_access_to_retail_and_maintenance,Walk Access to Retail & Maintenance ,nmRetail * othmaint,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance +util_transit_access_to_retail_and_maintenance,Transit Access to Retail & Maintenance ,trOpRetail * othmaint,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance +util_auto_access_to_retail_and_maintenance,Auto Access to Retail & Maintenance ,auOpRetail * othmaint,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance +util_walk_access_to_retail_and_eating_out,Walk Access to Retail & Eating Out ,nmRetail * eatout,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out +util_transit_access_to_retail_and_eating_out,Transit Access to Retail & Eating Out ,trOpRetail * eatout,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out +util_auto_access_to_retail_and_eating_out,Auto Access to Retail & Eating Out ,auOpRetail * eatout,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out +util_walk_access_to_retail_and_discretionary,Walk Access to Retail & Discretionary ,nmRetail * othdiscr,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary +util_transit_access_to_retail_and_discretionary,Transit Access to Retail & Discretionary ,trOpRetail * othdiscr,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary +util_auto_access_to_retail_and_discretionary,Auto Access to Retail & Discretionary ,auOpRetail * othdiscr,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary +util_urban_and_tour_frequency_is_1,Urban Areatype & Tour Frequency =1,home_is_urban & (tot_tours == 1),coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1 +util_urban_and_tour_frequency_is_2,Urban Areatype & Tour Frequency =2,home_is_urban & (tot_tours == 2),coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2 +util_urban_and_tour_frequency_is_3,Urban Areatype & Tour Frequency =3,home_is_urban & (tot_tours == 3),coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3 +util_urban_and_tour_frequency_is_4,Urban Areatype & Tour Frequency =4,home_is_urban & (tot_tours == 4),coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4 +util_urban_and_tour_frequency_is_5_plus,Urban Areatype & Tour Frequency =5+,home_is_urban & (tot_tours > 4),coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus +util_urban_and_escorting_tour,Urban Areatype & Escorting tour,home_is_urban * escort,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour +util_urban_and_shopping_tour,Urban Areatype &Shopping tour,home_is_urban * shopping,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour +util_urban_and_maintenance_tour,Urban Areatype & Maintenance tour,home_is_urban * othmaint,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour +util_urban_and_eatingout_tour,Urban Areatype & EatingOut tour,home_is_urban * eatout,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour +util_urban_and_discretionary_tour,Urban Areatype & Discretionary tour,home_is_urban * othdiscr,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour +util_1_escort_tour_constant,1 Escort Tour Constant,escort == 1,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant +util_2_plus_escort_tours_constant,2+ Escort Tours Constant,escort >= 2,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant +util_1_plus_shopping_tours_constant,1+ Shopping Tours Constant,shopping >= 1,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant +util_1_plus_maintenance_tours_constant,1+ Maintenance Tours Constant,othmaint >= 1,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant +util_1_plus_eating_out_tours_constant,1+ Eating Out Tours Constant,eatout >= 1,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant +util_1_plus_visting_tours_constant,1+ Visting Tours Constant,social >= 1,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant +util_1_plus_other_discretionary_tours_constant,1+ Other Discretionary Tours Constant,othdiscr >= 1,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant util_0_auto_household_and_escorting_tour,Dummy for 0-auto household & Escorting Tour,escort * no_cars,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour \ No newline at end of file diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_frequency.yaml b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency.yaml old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_frequency.yaml rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency.yaml diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_alternatives.csv b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_alternatives.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_alternatives.csv rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_alternatives.csv diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_extension_probs.csv b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_extension_probs.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_extension_probs.csv rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_frequency_extension_probs.csv diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_scheduling.yaml b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_scheduling.yaml similarity index 95% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_scheduling.yaml rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_scheduling.yaml index 33177e41a2..d02c60c0ec 100755 --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_scheduling.yaml +++ b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_scheduling.yaml @@ -1,21 +1,21 @@ - -SPEC: tour_scheduling_nonmandatory.csv -COEFFICIENTS: tour_scheduling_nonmandatory_coeffs.csv - -LOGIT_TYPE: MNL - -preprocessor: - SPEC: non_mandatory_tour_scheduling_annotate_tours_preprocessor - DF: non_mandatory_tours - TABLES: - - land_use - - joint_tour_participants - -SIMULATE_CHOOSER_COLUMNS: - - ptype - - num_children - - roundtrip_auto_time_to_work - - num_mand - - num_escort_tours - - num_non_escort_tours - - adult + +SPEC: tour_scheduling_nonmandatory.csv +COEFFICIENTS: tour_scheduling_nonmandatory_coeffs.csv + +LOGIT_TYPE: MNL + +preprocessor: + SPEC: non_mandatory_tour_scheduling_annotate_tours_preprocessor + DF: non_mandatory_tours + TABLES: + - land_use + - joint_tour_participants + +SIMULATE_CHOOSER_COLUMNS: + - ptype + - num_children + - roundtrip_auto_time_to_work + - num_mand + - num_escort_tours + - num_non_escort_tours + - adult diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv b/activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv rename to activitysim/examples/placeholder_psrc/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv diff --git a/activitysim/examples/example_mtc/configs/school_location.csv b/activitysim/examples/placeholder_psrc/configs/school_location.csv old mode 100644 new mode 100755 similarity index 99% rename from activitysim/examples/example_mtc/configs/school_location.csv rename to activitysim/examples/placeholder_psrc/configs/school_location.csv index 04d4647262..9448d12af2 --- a/activitysim/examples/example_mtc/configs/school_location.csv +++ b/activitysim/examples/placeholder_psrc/configs/school_location.csv @@ -1,12 +1,12 @@ -Label,Description,Expression,university,highschool,gradeschool -local_dist,,_DIST@skims['DIST'],1,1,1 -util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_univ_dist_0_1,coef_high_dist_0_1,coef_grade_dist_0_1 -util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_univ_dist_1_2,coef_high_grade_dist_1_2,coef_high_grade_dist_1_2 -util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_univ_dist_2_5,coef_high_grade_dist_2_5,coef_high_grade_dist_2_5 -util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_univ_dist_5_15,coef_high_dist_5_15,coef_grade_dist_5_15 -util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_univ_dist_15_up,coef_high_dist_15_up,coef_grade_dist_15_up -util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1,1,1 -util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1 -util_no_attractions,No attractions,@df['size_term']==0,-999,-999,-999 -util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum -util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1 +Label,Description,Expression,university,highschool,gradeschool +local_dist,,_DIST@skims['DIST'],1,1,1 +util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_univ_dist_0_1,coef_high_dist_0_1,coef_grade_dist_0_1 +util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_univ_dist_1_2,coef_high_grade_dist_1_2,coef_high_grade_dist_1_2 +util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_univ_dist_2_5,coef_high_grade_dist_2_5,coef_high_grade_dist_2_5 +util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_univ_dist_5_15,coef_high_dist_5_15,coef_grade_dist_5_15 +util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_univ_dist_15_up,coef_high_dist_15_up,coef_grade_dist_15_up +util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1,1,1 +util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1 +util_no_attractions,No attractions,@df['size_term']==0,-999,-999,-999 +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum +util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1 diff --git a/activitysim/examples/example_psrc/configs/school_location.yaml b/activitysim/examples/placeholder_psrc/configs/school_location.yaml similarity index 96% rename from activitysim/examples/example_psrc/configs/school_location.yaml rename to activitysim/examples/placeholder_psrc/configs/school_location.yaml index 16f2378507..07114fd0ea 100755 --- a/activitysim/examples/example_psrc/configs/school_location.yaml +++ b/activitysim/examples/placeholder_psrc/configs/school_location.yaml @@ -1,65 +1,65 @@ -SAMPLE_SIZE: 30 - -SIMULATE_CHOOSER_COLUMNS: - - home_zone_id - - school_segment - - household_id - -# model-specific logsum-related settings -CHOOSER_ORIG_COL_NAME: home_zone_id -ALT_DEST_COL_NAME: alt_dest -IN_PERIOD: 14 -OUT_PERIOD: 8 - -DEST_CHOICE_COLUMN_NAME: school_zone_id -# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table -DEST_CHOICE_LOGSUM_COLUMN_NAME: school_location_logsum - -# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table -DEST_CHOICE_SAMPLE_TABLE_NAME: school_location_sample - - -SAMPLE_SPEC: school_location_sample.csv -SPEC: school_location.csv -COEFFICIENTS: school_location_coeffs.csv - -LOGSUM_SETTINGS: tour_mode_choice.yaml -LOGSUM_PREPROCESSOR: nontour_preprocessor - -LOGSUM_TOUR_PURPOSE: - university: univ - highschool: school - gradeschool: school - -annotate_persons: - SPEC: annotate_persons_school - DF: persons - -# - shadow pricing - -# required by initialize_households when creating school_destination_size table -CHOOSER_TABLE_NAME: persons - -# size_terms model_selector -MODEL_SELECTOR: school - -# chooser column with segment_id for this segment type -CHOOSER_SEGMENT_COLUMN_NAME: school_segment - -# boolean column to filter choosers (True means keep) -CHOOSER_FILTER_COLUMN_NAME: is_student - - -# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this -SEGMENT_IDS: - university: 3 - highschool: 2 - gradeschool: 1 - - -# model adds these tables (informational - not added if commented out) -SHADOW_PRICE_TABLE: school_shadow_prices -MODELED_SIZE_TABLE: school_modeled_size - -# not loaded if commented out -SAVED_SHADOW_PRICE_TABLE_NAME: school_shadow_prices.csv +SAMPLE_SIZE: 30 + +SIMULATE_CHOOSER_COLUMNS: + - home_zone_id + - school_segment + - household_id + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 8 + +DEST_CHOICE_COLUMN_NAME: school_zone_id +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +DEST_CHOICE_LOGSUM_COLUMN_NAME: school_location_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: school_location_sample + + +SAMPLE_SPEC: school_location_sample.csv +SPEC: school_location.csv +COEFFICIENTS: school_location_coeffs.csv + +LOGSUM_SETTINGS: tour_mode_choice.yaml +LOGSUM_PREPROCESSOR: nontour_preprocessor + +LOGSUM_TOUR_PURPOSE: + university: univ + highschool: school + gradeschool: school + +annotate_persons: + SPEC: annotate_persons_school + DF: persons + +# - shadow pricing + +# required by initialize_households when creating school_destination_size table +CHOOSER_TABLE_NAME: persons + +# size_terms model_selector +MODEL_SELECTOR: school + +# chooser column with segment_id for this segment type +CHOOSER_SEGMENT_COLUMN_NAME: school_segment + +# boolean column to filter choosers (True means keep) +CHOOSER_FILTER_COLUMN_NAME: is_student + + +# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this +SEGMENT_IDS: + university: 3 + highschool: 2 + gradeschool: 1 + + +# model adds these tables (informational - not added if commented out) +SHADOW_PRICE_TABLE: school_shadow_prices +MODELED_SIZE_TABLE: school_modeled_size + +# not loaded if commented out +SAVED_SHADOW_PRICE_TABLE_NAME: school_shadow_prices.csv diff --git a/activitysim/examples/example_psrc/configs/school_location_coeffs.csv b/activitysim/examples/placeholder_psrc/configs/school_location_coeffs.csv similarity index 96% rename from activitysim/examples/example_psrc/configs/school_location_coeffs.csv rename to activitysim/examples/placeholder_psrc/configs/school_location_coeffs.csv index 4e4d638772..b9ef59c83f 100755 --- a/activitysim/examples/example_psrc/configs/school_location_coeffs.csv +++ b/activitysim/examples/placeholder_psrc/configs/school_location_coeffs.csv @@ -1,17 +1,17 @@ -coefficient_name,value,constrain -coef_univ_dist_0_1,-3.2451,F -coef_univ_dist_1_2,-2.7011,F -coef_univ_dist_2_5,-0.5707,F -coef_univ_dist_5_15,-0.5002,F -coef_univ_dist_15_up,-0.073,F -coef_high_dist_0_1,-0.9523,F -coef_high_grade_dist_1_2,-0.57,F -coef_high_grade_dist_2_5,-0.57,F -coef_high_dist_5_15,-0.193,F -coef_high_dist_15_up,-0.1882,F -coef_grade_dist_0_1,-1.6419,F -#coef_high_grade_dist_1_2,-0.57,F -#coef_high_grade_dist_2_5,-0.57,F -coef_grade_dist_5_15,-0.2031,F -coef_grade_dist_15_up,-0.046,F -coef_mode_logsum,0.5358,F +coefficient_name,value,constrain +coef_univ_dist_0_1,-3.2451,F +coef_univ_dist_1_2,-2.7011,F +coef_univ_dist_2_5,-0.5707,F +coef_univ_dist_5_15,-0.5002,F +coef_univ_dist_15_up,-0.073,F +coef_high_dist_0_1,-0.9523,F +coef_high_grade_dist_1_2,-0.57,F +coef_high_grade_dist_2_5,-0.57,F +coef_high_dist_5_15,-0.193,F +coef_high_dist_15_up,-0.1882,F +coef_grade_dist_0_1,-1.6419,F +#coef_high_grade_dist_1_2,-0.57,F +#coef_high_grade_dist_2_5,-0.57,F +coef_grade_dist_5_15,-0.2031,F +coef_grade_dist_15_up,-0.046,F +coef_mode_logsum,0.5358,F diff --git a/activitysim/examples/example_mtc/configs/school_location_sample.csv b/activitysim/examples/placeholder_psrc/configs/school_location_sample.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/school_location_sample.csv rename to activitysim/examples/placeholder_psrc/configs/school_location_sample.csv diff --git a/activitysim/examples/example_psrc/configs/settings.yaml b/activitysim/examples/placeholder_psrc/configs/settings.yaml similarity index 95% rename from activitysim/examples/example_psrc/configs/settings.yaml rename to activitysim/examples/placeholder_psrc/configs/settings.yaml index 3760af7049..779d6a1822 100755 --- a/activitysim/examples/example_psrc/configs/settings.yaml +++ b/activitysim/examples/placeholder_psrc/configs/settings.yaml @@ -1,176 +1,176 @@ -#inherit_settings: True - -# activitysim run -c configs -d data -o output - - -# number of households to simulate -households_sample_size: 100 -# simulate all households -# households_sample_size: 0 - -chunk_size: 0 - -# assume enough RAM to not chunk -chunk_training_mode: disabled - -# set false to disable variability check in simple_simulate and interaction_simulate -check_for_variability: False - -# - shadow pricing global switches - -# turn shadow_pricing on and off for all models (e.g. school and work) -# shadow pricing is deprecated for less than full samples -# see shadow_pricing.yaml for additional settings -use_shadow_pricing: False - -# turn writing of sample_tables on and off for all models -# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) -want_dest_choice_sample_tables: False - -# - tracing - -# trace household id; comment out or leave empty for no trace -# households with all tour types -trace_hh_id: - -# trace origin, destination in accessibility calculation; comment out or leave empty for no trace -trace_od: - - -# input tables -input_table_list: - - tablename: households - filename: households.csv - index_col: household_id - rename_columns: - HHID: household_id - PERSONS: hhsize - workers: num_workers - VEHICL: auto_ownership - MAZ: home_zone_id - keep_columns: - - home_zone_id - - income - - hhsize - - HHT - - auto_ownership - - num_workers - - tablename: persons - filename: persons.csv - index_col: person_id - rename_columns: - PERID: person_id - keep_columns: - - household_id - - age - - PNUM - - sex - - pemploy - - pstudent - - ptype - - tablename: land_use - filename: land_use.csv - index_col: zone_id - rename_columns: - MAZ: zone_id - COUNTY: county_id - keep_columns: - - TAZ - - DISTRICT - - SD - - county_id - - TOTHH - - TOTPOP - - TOTACRE - - RESACRE - - CIACRE - - TOTEMP - - AGE0519 - - RETEMPN - - FPSEMPN - - HEREMPN - - OTHEMPN - - AGREMPN - - MWTEMPN - - PRKCST - - OPRKCST - - area_type - - HSENROLL - - COLLFTE - - COLLPTE - - TOPOLOGY - - TERMINAL -# - access_dist_transit - -# to resume after last successful checkpoint, specify resume_after: _ -#resume_after: trip_scheduling - -models: - - initialize_landuse - - initialize_households - - compute_accessibility - - school_location - - workplace_location - - auto_ownership_simulate - - free_parking - - cdap_simulate - - mandatory_tour_frequency - - mandatory_tour_scheduling - - joint_tour_frequency - - joint_tour_composition - - joint_tour_participation - - joint_tour_destination - - joint_tour_scheduling - - non_mandatory_tour_frequency - - non_mandatory_tour_destination - - non_mandatory_tour_scheduling - - tour_mode_choice_simulate - - atwork_subtour_frequency - - atwork_subtour_destination - - atwork_subtour_scheduling - - atwork_subtour_mode_choice - - stop_frequency - - trip_purpose - - trip_destination - - trip_purpose_and_destination - - trip_scheduling - - trip_mode_choice - - write_data_dictionary - - track_skim_usage - - write_trip_matrices - - write_tables - - -output_tables: - h5_store: False - action: include - prefix: final_ - sort: True - tables: - - checkpoints - - accessibility - - land_use - - households - - persons - - tours - - trips - - joint_tour_participants - -# area_types less than this are considered urban -urban_threshold: 4 -cbd_threshold: 2 -rural_threshold: 6 - - -# value_of_time = lognormal(np.log(median_value_of_time * mu), sigma).clip(min_vot, max_vot) - -min_value_of_time: 1 -max_value_of_time: 50 -distributed_vot_mu: 0.684 -distributed_vot_sigma: 0.85 - -household_median_value_of_time: - 1: 6.01 - 2: 8.81 - 3: 10.44 - 4: 12.86 +#inherit_settings: True + +# activitysim run -c configs -d data -o output + + +# number of households to simulate +households_sample_size: 100 +# simulate all households +# households_sample_size: 0 + +chunk_size: 0 + +# assume enough RAM to not chunk +chunk_training_mode: disabled + +# set false to disable variability check in simple_simulate and interaction_simulate +check_for_variability: False + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +# - tracing + +# trace household id; comment out or leave empty for no trace +# households with all tour types +trace_hh_id: + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +trace_od: + + +# input tables +input_table_list: + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + HHID: household_id + PERSONS: hhsize + workers: num_workers + VEHICL: auto_ownership + MAZ: home_zone_id + keep_columns: + - home_zone_id + - income + - hhsize + - HHT + - auto_ownership + - num_workers + - tablename: persons + filename: persons.csv + index_col: person_id + rename_columns: + PERID: person_id + keep_columns: + - household_id + - age + - PNUM + - sex + - pemploy + - pstudent + - ptype + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + MAZ: zone_id + COUNTY: county_id + keep_columns: + - TAZ + - DISTRICT + - SD + - county_id + - TOTHH + - TOTPOP + - TOTACRE + - RESACRE + - CIACRE + - TOTEMP + - AGE0519 + - RETEMPN + - FPSEMPN + - HEREMPN + - OTHEMPN + - AGREMPN + - MWTEMPN + - PRKCST + - OPRKCST + - area_type + - HSENROLL + - COLLFTE + - COLLPTE + - TOPOLOGY + - TERMINAL +# - access_dist_transit + +# to resume after last successful checkpoint, specify resume_after: _ +#resume_after: trip_scheduling + +models: + - initialize_landuse + - initialize_households + - compute_accessibility + - school_location + - workplace_location + - auto_ownership_simulate + - free_parking + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + - write_data_dictionary + - track_skim_usage + - write_trip_matrices + - write_tables + + +output_tables: + h5_store: False + action: include + prefix: final_ + sort: True + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + - joint_tour_participants + +# area_types less than this are considered urban +urban_threshold: 4 +cbd_threshold: 2 +rural_threshold: 6 + + +# value_of_time = lognormal(np.log(median_value_of_time * mu), sigma).clip(min_vot, max_vot) + +min_value_of_time: 1 +max_value_of_time: 50 +distributed_vot_mu: 0.684 +distributed_vot_sigma: 0.85 + +household_median_value_of_time: + 1: 6.01 + 2: 8.81 + 3: 10.44 + 4: 12.86 diff --git a/activitysim/examples/example_psrc/configs/settings_mp.yaml b/activitysim/examples/placeholder_psrc/configs/settings_mp.yaml similarity index 100% rename from activitysim/examples/example_psrc/configs/settings_mp.yaml rename to activitysim/examples/placeholder_psrc/configs/settings_mp.yaml diff --git a/activitysim/examples/example_mtc/configs/shadow_pricing.yaml b/activitysim/examples/placeholder_psrc/configs/shadow_pricing.yaml old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/shadow_pricing.yaml rename to activitysim/examples/placeholder_psrc/configs/shadow_pricing.yaml diff --git a/activitysim/examples/example_mtc/configs/stop_frequency.yaml b/activitysim/examples/placeholder_psrc/configs/stop_frequency.yaml old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency.yaml rename to activitysim/examples/placeholder_psrc/configs/stop_frequency.yaml diff --git a/activitysim/examples/example_arc/configs/stop_frequency_alternatives.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_alternatives.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_arc/configs/stop_frequency_alternatives.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_alternatives.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_annotate_tours_preprocessor.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_annotate_tours_preprocessor.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_annotate_tours_preprocessor.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_annotate_tours_preprocessor.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_atwork.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_atwork.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_atwork.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_atwork.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_coefficients_atwork.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_atwork.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_coefficients_atwork.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_atwork.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_coefficients_eatout.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_eatout.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_coefficients_eatout.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_eatout.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_coefficients_escort.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_escort.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_coefficients_escort.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_escort.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_coefficients_othdiscr.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_othdiscr.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_coefficients_othdiscr.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_othdiscr.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_coefficients_othmaint.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_othmaint.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_coefficients_othmaint.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_othmaint.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_coefficients_school.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_school.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_coefficients_school.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_school.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_coefficients_shopping.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_shopping.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_coefficients_shopping.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_shopping.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_coefficients_social.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_social.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_coefficients_social.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_social.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_coefficients_univ.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_univ.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_coefficients_univ.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_univ.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_coefficients_work.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_work.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_coefficients_work.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_coefficients_work.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_eatout.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_eatout.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_eatout.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_eatout.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_escort.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_escort.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_escort.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_escort.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_othdiscr.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_othdiscr.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_othdiscr.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_othdiscr.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_othmaint.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_othmaint.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_othmaint.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_othmaint.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_school.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_school.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_school.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_school.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_shopping.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_shopping.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_shopping.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_shopping.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_social.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_social.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_social.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_social.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_univ.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_univ.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_univ.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_univ.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_work.csv b/activitysim/examples/placeholder_psrc/configs/stop_frequency_work.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_work.csv rename to activitysim/examples/placeholder_psrc/configs/stop_frequency_work.csv diff --git a/activitysim/examples/example_marin/configs/tour_departure_and_duration_alternatives.csv b/activitysim/examples/placeholder_psrc/configs/tour_departure_and_duration_alternatives.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_marin/configs/tour_departure_and_duration_alternatives.csv rename to activitysim/examples/placeholder_psrc/configs/tour_departure_and_duration_alternatives.csv diff --git a/activitysim/examples/example_psrc/configs/tour_mode_choice.csv b/activitysim/examples/placeholder_psrc/configs/tour_mode_choice.csv similarity index 99% rename from activitysim/examples/example_psrc/configs/tour_mode_choice.csv rename to activitysim/examples/placeholder_psrc/configs/tour_mode_choice.csv index e1b04fbcd9..798b6ded20 100755 --- a/activitysim/examples/example_psrc/configs/tour_mode_choice.csv +++ b/activitysim/examples/placeholder_psrc/configs/tour_mode_choice.csv @@ -1,346 +1,346 @@ -Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_LOC,WALK_LRF,WALK_EXP,WALK_HVY,WALK_COM,DRIVE_LOC,DRIVE_LRF,DRIVE_EXP,DRIVE_HVY,DRIVE_COM,TAXI,TNC_SINGLE,TNC_SHARED -#,Drive alone no toll,,,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,auto_ownership == 0,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['SOV_TIME'] + dot_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Terminal_time,DRIVEALONEFREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']),coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOV_BTOLL'] + dot_skims['SOV_BTOLL']),coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Person_is_between_16_and_19_years_old,DRIVEALONEFREE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da_multiplier,,,,,,,,,,,,,,,,,,,, -#,Drive alone toll,,,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,auto_ownership == 0,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,age < 16,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['SOVTOLL_TIME'] + dot_skims['SOVTOLL_TIME'],,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Terminal_time,DRIVEALONEPAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOVTOLL_DIST'] + dot_skims['SOVTOLL_DIST']),,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOVTOLL_BTOLL'] + dot_skims['SOVTOLL_BTOLL']),,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOVTOLL_VTOLL'] + dot_skims['SOVTOLL_VTOLL']),,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Person_is_between_16_and_19_years_old,DRIVEALONEPAY - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),,coef_age1619_da_multiplier,,,,,,,,,,,,,,,,,,, -#,Shared ride 2,,,,,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Terminal_time,SHARED2FREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']),,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']) / costShareSr2,,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_One_person_household,SHARED2FREE - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Person_is_16_years_old_or_older,SHARED2FREE - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,,,,, -#,Shared ride 2 toll,,,,,,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,,,,,,,,,, -util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Terminal_time,SHARED2PAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']),,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']) / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']) / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_One_person_household,SHARED2PAY - One person household,@(df.hhsize == 1),,,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.hhsize == 2),,,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Person_is_16_years_old_or_older,SHARED2PAY - Person is 16 years old or older,@(df.age >= 16),,,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,,,, -#,Shared ride 3+,,,,,,,,,,,,,,,,,,,,,, -util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,,,,,,,,,, -util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@(odt_skims['HOV3_TIME'] + dot_skims['HOV3_TIME']),,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_Terminal_time,SHARED3FREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3_DIST'] + dot_skims['HOV3_DIST']),,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3_BTOLL'] + dot_skims['HOV3_BTOLL']) / costShareSr3,,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_One_person_household,SHARED3FREE - One person household,@(df.hhsize == 1),,,,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,,, -util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.hhsize == 2),,,,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,,, -util_SHARED3FREE_Person_is_16_years_old_or_older,SHARED3FREE - Person is 16 years old or older,@(df.age >= 16),,,,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,,, -#,Shared ride 3+ toll,,,,,,,,,,,,,,,,,,,,,, -util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,,,,,,,,,, -util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@(odt_skims['HOV3TOLL_TIME'] + dot_skims['HOV3TOLL_TIME']),,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Terminal_time,SHARED3PAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3TOLL_DIST'] + dot_skims['HOV3TOLL_DIST']),,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3TOLL_BTOLL'] + dot_skims['HOV3TOLL_BTOLL']) / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3TOLL_VTOLL'] + dot_skims['HOV3TOLL_VTOLL']) / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_One_person_household,SHARED3PAY - One person household,@(df.hhsize == 1),,,,,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,, -util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.hhsize == 2),,,,,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,, -util_SHARED3PAY_Person_is_16_years_old_or_older,SHARED3PAY - Person is 16 years old or older,@(df.age >= 16),,,,,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,, -#,Walk,,,,,,,,,,,,,,,,,,,,,, -#,FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,,,,,,,,,, -util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@walktimeshort_multiplier * (od_skims['DISTWALK'].clip(upper=walkThresh) + od_skims.reverse('DISTWALK').clip(upper=walkThresh))*60/walkSpeed,,,,,,,coef_ivt,,,,,,,,,,,,,, -util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * ((od_skims['DISTWALK'] - walkThresh).clip(lower=0) + (od_skims.reverse('DISTWALK') - walkThresh).clip(lower=0))*60/walkSpeed,,,,,,,coef_ivt,,,,,,,,,,,,,, -util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,coef_ivt,,,,,,,,,,,,,, -util_WALK_Topology,WALK - Topology,@coef_topology_walk_multiplier * df.dest_topology,,,,,,,coef_ivt,,,,,,,,,,,,,, -#,Bike,,,,,,,,,,,,,,,,,,,,,, -#,FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,,,,,,,,,, -util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,,,,,,,,,, -util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@biketimeshort_multiplier * (od_skims['DISTBIKE'].clip(upper=bikeThresh) + od_skims.reverse('DISTBIKE').clip(upper=bikeThresh))*60/bikeSpeed,,,,,,,,coef_ivt,,,,,,,,,,,,, -util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@biketimelong_multiplier * ((od_skims['DISTBIKE']-bikeThresh).clip(lower=0) + (od_skims.reverse('DISTBIKE')-bikeThresh).clip(lower=0))*60/bikeSpeed,,,,,,,,coef_ivt,,,,,,,,,,,,, -util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,coef_ivt,,,,,,,,,,,,, -util_BIKE_Topology,BIKE - Topology,@coef_topology_bike_multiplier * df.dest_topology,,,,,,,,coef_ivt,,,,,,,,,,,,, -#,Walk to Local,,,,,,,,,,,,,,,,,,,,,, -util_WALK_LOC_Unavailable,WALK_LOC - Unavailable,walk_local_available == False,,,,,,,,,-999,,,,,,,,,,,, -util_WALK_LOC_In_vehicle_time,WALK_LOC - In-vehicle time,@(odt_skims['WLK_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Short_iwait_time,WALK_LOC - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Long_iwait_time,WALK_LOC - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_transfer_wait_time,WALK_LOC - transfer wait time,@xwait_multiplier * (odt_skims['WLK_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_number_of_transfers,WALK_LOC - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_LOC_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_LOC_WLK_BOARDS']-1).clip(0)),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Walk_egress_time,WALK_LOC - Walk egress time,@2 * wegr_multiplier * df.destination_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Walk_other_time,WALK_LOC - Walk other time,@waux_multiplier * (odt_skims['WLK_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Fare,WALK_LOC - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_LOC_WLK_FAR'] + dot_skims['WLK_LOC_WLK_FAR']),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Destination_zone_densityIndex,WALK_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Topology,WALK_LOC - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Person_is_less_than_10_years_old,WALK_LOC - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,coef_age010_trn_multiplier,,,,,,,,,,,, -#,Walk to Light rail/Ferry,,,,,,,,,,,,,,,,,,,,,, -util_WALK_LRF_Unavailable,WALK_LRF - Unavailable,walk_lrf_available == False,,,,,,,,,,-999,,,,,,,,,,, -util_WALK_LRF_In_vehicle_time,WALK_LRF - In-vehicle time,@(odt_skims['WLK_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, -#, FIXME coefficients below are wrong or needlessly complex? could be re-expressed to avoid subtract?,,,,,,,,,,,,,,,,,,,,,, -util_WALK_LRF_In_vehicle_time_on_Light_Rail,WALK_LRF - In-vehicle time on Light Rail (incremental w/ ivt),@(ivt_lrt_multiplier-1)*(odt_skims['WLK_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_In_vehicle_time_on_Ferry,WALK_LRF - In-vehicle time on Ferry (incremental w/keyivt),@(ivt_ferry_multiplier-ivt_lrt_multiplier)*(odt_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Short_iwait_time,WALK_LRF - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Long_iwait_time,WALK_LRF - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_transfer_wait_time,WALK_LRF - transfer wait time,@xwait_multiplier * (odt_skims['WLK_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_number_of_transfers,WALK_LRF - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_LRF_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_LRF_WLK_BOARDS']-1).clip(0)),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Walk_access_time,WALK_LRF - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Walk_egress_time,WALK_LRF - Walk egress time,@2 * wegr_multiplier * df.destination_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Walk_other_time,WALK_LRF - Walk other time,@waux_multiplier * (odt_skims['WLK_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Fare,WALK_LRF - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_LRF_WLK_FAR'] + dot_skims['WLK_LRF_WLK_FAR']),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Destination_zone_densityIndex,WALK_LRF - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Topology,WALK_LRF - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Person_is_less_than_10_years_old,WALK_LRF - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,coef_age010_trn_multiplier,,,,,,,,,,, -#,Walk to Express bus,,,,,,,,,,,,,,,,,,,,,, -util_WALK_EXP_Unavailable,WALK_EXP - Unavailable,walk_express_available == False,,,,,,,,,,,-999,,,,,,,,,, -util_WALK_EXP_In_vehicle_time,WALK_EXP - In-vehicle time,@(odt_skims['WLK_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_In_vehicle_time_on_Express_bus,WALK_EXP - In-vehicle time on Express bus (incremental w/ ivt),@(ivt_exp_multiplier - 1)*(odt_skims['WLK_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Short_iwait_time,WALK_EXP - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Long_iwait_time,WALK_EXP - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_transfer_wait_time,WALK_EXP - transfer wait time,@xwait_multiplier * (odt_skims['WLK_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_number_of_transfers,WALK_EXP - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_EXP_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_EXP_WLK_BOARDS']-1).clip(0)),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Walk_access_time,WALK_EXP - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Walk_egress_time,WALK_EXP - Walk egress time,@2 * wegr_multiplier * df.destination_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Walk_other_time,WALK_EXP - Walk other time,@waux_multiplier * (odt_skims['WLK_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Fare,WALK_EXP - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_EXP_WLK_FAR'] + dot_skims['WLK_EXP_WLK_FAR']),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Destination_zone_densityIndex,WALK_EXP - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Topology,WALK_EXP - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Person_is_less_than_10_years_old,WALK_EXP - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,,coef_age010_trn_multiplier,,,,,,,,,, -#,Walk to Heavy Rail,,,,,,,,,,,,,,,,,,,,,, -util_WALK_HVY_Unavailable,WALK_HVY - Unavailable,walk_heavyrail_available == False,,,,,,,,,,,,-999,,,,,,,,, -util_WALK_HVY_In_vehicle_time,WALK_HVY - In-vehicle time,@(odt_skims['WLK_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_In_vehicle_time_on_heavy_rail,WALK_HVY - In-vehicle time on heavy rail (incremental w/ ivt),@(ivt_hvy_multiplier-1) * (odt_skims['WLK_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Short_iwait_time,WALK_HVY - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Long_iwait_time,WALK_HVY - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_transfer_wait_time,WALK_HVY - transfer wait time,@xwait_multiplier * (odt_skims['WLK_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_number_of_transfers,WALK_HVY - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_HVY_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_HVY_WLK_BOARDS']-1).clip(0)),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Walk_access_time,WALK_HVY - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Walk_egress_time,WALK_HVY - Walk egress time,@wegr_multiplier * 2 *df.destination_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Walk_other_time,WALK_HVY - Walk other time,@waux_multiplier * (odt_skims['WLK_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Fare,WALK_HVY - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_HVY_WLK_FAR'] + dot_skims['WLK_HVY_WLK_FAR']),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Destination_zone_densityIndex,WALK_HVY - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Topology,WALK_HVY - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Person_is_less_than_10_years_old,WALK_HVY - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,,,coef_age010_trn_multiplier,,,,,,,,, -#,Walk to Commuter rail,,,,,,,,,,,,,,,,,,,,,, -util_WALK_COM_Unavailable,WALK_COM - Unavailable,walk_commuter_available == False,,,,,,,,,,,,,-999,,,,,,,, -util_WALK_COM_In_vehicle_time,WALK_COM - In-vehicle time,@(odt_skims['WLK_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_In_vehicle_time_on_commuter_rail,WALK_COM - In-vehicle time on commuter rail (incremental w/ ivt),@(ivt_com_multiplier - 1) * (odt_skims['WLK_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Short_iwait_time,WALK_COM - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Long_iwait_time,WALK_COM - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_transfer_wait_time,WALK_COM - transfer wait time,@xwait_multiplier * (odt_skims['WLK_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_number_of_transfers,WALK_COM - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_COM_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_COM_WLK_BOARDS']-1).clip(0)),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Walk_access_time,WALK_COM - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Walk_egress_time,WALK_COM - Walk egress time,@2 * wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Walk_other_time,WALK_COM - Walk other time,@waux_multiplier * (odt_skims['WLK_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Fare,WALK_COM - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_COM_WLK_FAR'] + dot_skims['WLK_COM_WLK_FAR']),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Destination_zone_densityIndex,WALK_COM - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Topology,WALK_COM - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Person_is_less_than_10_years_old,WALK_COM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,coef_age010_trn_multiplier,,,,,,,, -#,Drive to Local,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_LOC_Unavailable,DRIVE_LOC - Unavailable,drive_local_available == False,,,,,,,,,,,,,,-999,,,,,,, -util_DRIVE_LOC_Unavailable_for_zero_auto_households,DRIVE_LOC - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,-999,,,,,,, -util_DRIVE_LOC_Unavailable_for_persons_less_than_16,DRIVE_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,-999,,,,,,, -util_DRIVE_LOC_In_vehicle_time,DRIVE_LOC - In-vehicle time,@(odt_skims['DRV_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Short_iwait_time,DRIVE_LOC - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_LOC_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Long_iwait_time,DRIVE_LOC - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_LOC_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_transfer_wait_time,DRIVE_LOC - transfer wait time,@xwait_multiplier * (odt_skims['DRV_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_number_of_transfers,DRIVE_LOC - number of transfers,@xfers_wlk_multiplier * ((odt_skims['DRV_LOC_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_LOC_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Drive_time,DRIVE_LOC - Drive time,@dtim_multiplier * (odt_skims['DRV_LOC_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Walk_access_time,DRIVE_LOC - Walk access time,@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Walk_egress_time,DRIVE_LOC - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Walk_other_time,DRIVE_LOC - Walk other time,@waux_multiplier * (odt_skims['DRV_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Fare_and_operating_cost,DRIVE_LOC - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_LOC_WLK_FAR'] + dot_skims['WLK_LOC_DRV_FAR']) + ((odt_skims['DRV_LOC_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_LOC_DRV_DDIST']/TRANSIT_SCALE_FACTOR) * costPerMile)),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LOC - Ratio of drive access distance to OD distance,@dacc_ratio * ((odt_skims['DRV_LOC_WLK_DDIST']/TRANSIT_SCALE_FACTOR+ dot_skims['WLK_LOC_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']*2)),,,,,,,,,,,,,,1,,,,,,, -util_DRIVE_LOC_Destination_zone_densityIndex,DRIVE_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Topology,DRIVE_LOC - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Person_is_less_than_10_years_old,DRIVE_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,coef_age010_trn_multiplier,,,,,,, -#,Drive to Light Rail/Ferry,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_LRF_Unavailable,DRIVE_LRF - Unavailable,drive_lrf_available == False,,,,,,,,,,,,,,,-999,,,,,, -util_DRIVE_LRF_Unavailable_for_zero_auto_households,DRIVE_LRF - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,-999,,,,,, -util_DRIVE_LRF_Unavailable_for_persons_less_than_16,DRIVE_LRF - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,-999,,,,,, -util_DRIVE_LRF_In_vehicle_time,DRIVE_LRF - In-vehicle time,@(odt_skims['DRV_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_In_vehicle_time_on_Light_Rail,DRIVE_LRF - In-vehicle time on Light Rail (incremental w/ ivt),@(ivt_lrt_multiplier-1) * (odt_skims['DRV_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_In_vehicle_time_on_Ferry,DRIVE_LRF - In-vehicle time on Ferry (incremental w/ keyivt),@(ivt_ferry_multiplier-ivt_lrt_multiplier)*(odt_skims['DRV_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_FERRYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Short_iwait_time,DRIVE_LRF - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_LRF_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Long_iwait_time,DRIVE_LRF - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_LRF_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_transfer_wait_time,DRIVE_LRF - transfer wait time,@xwait_multiplier * (odt_skims['DRV_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_number_of_transfers,DRIVE_LRF - number of transfers,@xfers_drv_multiplier * ((odt_skims['DRV_LRF_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_LRF_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Drive_time,DRIVE_LRF - Drive time,@dtim_multiplier * (odt_skims['DRV_LRF_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Walk_access_time,DRIVE_LRF - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Walk_egress_time,DRIVE_LRF - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Walk_other_time,DRIVE_LRF - Walk other time,@waux_multiplier * (odt_skims['DRV_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Fare_and_operating_cost,DRIVE_LRF - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_LRF_WLK_FAR']+dot_skims['WLK_LRF_DRV_FAR']) + ((odt_skims['DRV_LRF_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_LRF_DRV_DDIST']/TRANSIT_SCALE_FACTOR) *costPerMile)),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LRF - Ratio of drive access distance to OD distance,@dacc_ratio * ((odt_skims['DRV_LRF_WLK_DDIST']/TRANSIT_SCALE_FACTOR+ dot_skims['WLK_LRF_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']*2)),,,,,,,,,,,,,,,1,,,,,, -util_DRIVE_LRF_Destination_zone_densityIndex,DRIVE_LRF - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Topology,DRIVE_LRF - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Person_is_less_than_10_years_old,DRIVE_LRF - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,coef_age010_trn_multiplier,,,,,, -#,Drive to Express bus,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_EXP_Unavailable,DRIVE_EXP - Unavailable,drive_express_available == False,,,,,,,,,,,,,,,,-999,,,,, -util_DRIVE_EXP_Unavailable_for_zero_auto_households,DRIVE_EXP - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,-999,,,,, -util_DRIVE_EXP_Unavailable_for_persons_less_than_16,DRIVE_EXP - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,-999,,,,, -util_DRIVE_EXP_In_vehicle_time,DRIVE_EXP - In-vehicle time,@(odt_skims['DRV_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_In_vehicle_time_on_Express_bus,DRIVE_EXP - In-vehicle time on Express bus (incremental w/ ivt),@(ivt_exp_multiplier-1) * (odt_skims['DRV_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Short_iwait_time,DRIVE_EXP - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_EXP_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Long_iwait_time,DRIVE_EXP - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_EXP_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_transfer_wait_time,DRIVE_EXP - transfer wait time,@xwait_multiplier * (odt_skims['DRV_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_number_of_transfers,DRIVE_EXP - number of transfers,@xfers_drv_multiplier * ((odt_skims['DRV_EXP_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_EXP_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Drive_time,DRIVE_EXP - Drive time,@dtim_multiplier * (odt_skims['DRV_EXP_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Walk_access_time,DRIVE_EXP - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Walk_egress_ime,DRIVE_EXP - Walk egress ime (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Walk_other_time,DRIVE_EXP - Walk other time,@waux_multiplier * (odt_skims['DRV_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Fare_and_operating_cost,DRIVE_EXP - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_EXP_WLK_FAR']+dot_skims['WLK_EXP_DRV_FAR']) + ((odt_skims['DRV_EXP_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_EXP_DRV_DDIST']/TRANSIT_SCALE_FACTOR) *costPerMile)),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_EXP - Ratio of drive access distance to OD distance,@dacc_ratio * ((odt_skims['DRV_EXP_WLK_DDIST']/TRANSIT_SCALE_FACTOR+ dot_skims['WLK_EXP_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']*2)),,,,,,,,,,,,,,,,1,,,,, -util_DRIVE_EXP_Destination_zone_densityIndex,DRIVE_EXP - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Topology,DRIVE_EXP - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Person_is_less_than_10_years_old,DRIVE_EXP - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,coef_age010_trn_multiplier,,,,, -#,Drive to Heavy Rail,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_HVY_Unavailable,DRIVE_HVY - Unavailable,drive_heavyrail_available == False,,,,,,,,,,,,,,,,,-999,,,, -util_DRIVE_HVY_Unavailable_for_zero_auto_households,DRIVE_HVY - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,,-999,,,, -util_DRIVE_HVY_Unavailable_for_persons_less_than_16,DRIVE_HVY - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,-999,,,, -util_DRIVE_HVY_In_vehicle_time,DRIVE_HVY - In-vehicle time,@(odt_skims['DRV_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_In_vehicle_time_on_heavy_rail,DRIVE_HVY - In-vehicle time on heavy rail (incremental w/ ivt),@(ivt_hvy_multiplier-1) * (odt_skims['DRV_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Short_iwait_time,DRIVE_HVY - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_HVY_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Long_iwait_time,DRIVE_HVY - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_HVY_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_transfer_wait_time,DRIVE_HVY - transfer wait time,@xwait_multiplier * (odt_skims['DRV_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_number_of_transfers,DRIVE_HVY - number of transfers,@xfers_drv_multiplier * ((odt_skims['DRV_HVY_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_HVY_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Drive_time,DRIVE_HVY - Drive time,@dtim_multiplier * (odt_skims['DRV_HVY_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Walk_access_time,DRIVE_HVY - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Walk_egress_time,DRIVE_HVY - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Walk_other_time,DRIVE_HVY - Walk other time,@waux_multiplier * (odt_skims['DRV_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Fare_and_operating_cost,DRIVE_HVY - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_HVY_WLK_FAR']+dot_skims['WLK_HVY_DRV_FAR']) + ((odt_skims['DRV_HVY_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_HVY_DRV_DDIST']/TRANSIT_SCALE_FACTOR) *costPerMile)),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_HVY - Ratio of drive access distance to OD distance,@dacc_ratio * (odt_skims['DRV_HVY_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,,1,,,, -util_DRIVE_HVY_Destination_zone_densityIndex,DRIVE_HVY - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Topology,DRIVE_HVY - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Person_is_less_than_10_years_old,DRIVE_HVY - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,,coef_age010_trn_multiplier,,,, -#,Drive to Commuter Rail,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_COM_Unavailable,DRIVE_COM - Unavailable,drive_commuter_available == False,,,,,,,,,,,,,,,,,,-999,,, -util_DRIVE_COM_Unavailable_for_zero_auto_households,DRIVE_COM - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,,,-999,,, -util_DRIVE_COM_Unavailable_for_persons_less_than_16,DRIVE_COM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,,-999,,, -util_DRIVE_COM_In_vehicle_time,DRIVE_COM - In-vehicle time,@(odt_skims['DRV_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_In_vehicle_time_on_commuter_rail,DRIVE_COM - In-vehicle time on commuter rail (incremental w/ ivt),@(ivt_com_multiplier - 1) * (odt_skims['DRV_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Short_iwait_time,DRIVE_COM - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_COM_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Long_iwait_time,DRIVE_COM - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_COM_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_transfer_wait_time,DRIVE_COM - transfer wait time,@xwait_multiplier * (odt_skims['DRV_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_number_of_transfers,DRIVE_COM - number of transfers,@xfers_drv_multiplier * ((odt_skims['DRV_COM_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_COM_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Drive_time,DRIVE_COM - Drive time,@dtim_multiplier * (odt_skims['DRV_COM_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Walk_access_time,DRIVE_COM - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Walk_egress_time,DRIVE_COM - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Walk_other_time,DRIVE_COM - Walk other time,@waux_multiplier * (odt_skims['DRV_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Fare_and_operating_cost,DRIVE_COM - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_COM_WLK_FAR']+dot_skims['WLK_COM_DRV_FAR']) + ((odt_skims['DRV_COM_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_COM_DRV_DDIST']/TRANSIT_SCALE_FACTOR) *costPerMile)),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_COM - Ratio of drive access distance to OD distance,@dacc_ratio * ((odt_skims['DRV_COM_WLK_DDIST']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']*2)),,,,,,,,,,,,,,,,,,1,,, -util_DRIVE_COM_Destination_zone_densityIndex,DRIVE_COM - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Topology,DRIVE_COM - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Person_is_less_than_10_years_old,DRIVE_COM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,,,coef_age010_trn_multiplier,,, -#,Taxi,,,,,,,,,,,,,,,,,,,,,, -util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,,,,,,,,,,,,,,,,coef_ivt,, -#, FIXME magic constant 1.5,,,,,,,,,,,,,,,,,,,,,, -util_Taxi_Wait_time,Taxi - Wait time,@1.5 * df.totalWaitTaxi,,,,,,,,,,,,,,,,,,,coef_ivt,, -util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,,,,coef_ivt,, -util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,coef_ivt,, -util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * Taxi_costPerMile +(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * Taxi_costPerMinute)*100,,,,,,,,,,,,,,,,,,,coef_ivt,, -#,TNC Single,,,,,,,,,,,,,,,,,,,,,, -util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Wait_time,TNC Single - Wait time,@1.5 * df.totalWaitSingleTNC,,,,,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * TNC_single_costPerMile + (odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,coef_ivt, -#,TNC Shared,,,,,,,,,,,,,,,,,,,,,, -util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_shared_IVTFactor,,,,,,,,,,,,,,,,,,,,,coef_ivt -#, FIXME magic constant 1.5,,,,,,,,,,,,,,,,,,,,,, -util_TNC_Shared_Wait_time,TNC Shared - Wait time,@1.5 * df.totalWaitSharedTNC,,,,,,,,,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * TNC_shared_costPerMile + (odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,,coef_ivt -#,indiv tour ASCs,,,,,,,,,,,,,,,,,,,,,, -util_Walk_ASC_Zero_auto,Walk ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,walk_ASC_no_auto,,,,,,,,,,,,,, -util_Walk_ASC_Auto_deficient,Walk ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,walk_ASC_auto_deficient,,,,,,,,,,,,,, -util_Walk_ASC_Auto_sufficient,Walk ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,walk_ASC_auto_sufficient,,,,,,,,,,,,,, -util_Bike_ASC_Zero_auto,Bike ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,bike_ASC_no_auto,,,,,,,,,,,,, -util_Bike_ASC_Auto_deficient,Bike ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,bike_ASC_auto_deficient,,,,,,,,,,,,, -util_Bike_ASC_Auto_sufficient,Bike ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,bike_ASC_auto_sufficient,,,,,,,,,,,,, -util_Shared_ride_2_ASC_Zero_auto,Shared ride 2 ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,sr2_ASC_no_auto,sr2_ASC_no_auto,,,,,,,,,,,,,,,,, -util_Shared_ride_2_ASC_Auto_deficient,Shared ride 2 ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,sr2_ASC_auto_deficient,sr2_ASC_auto_deficient,,,,,,,,,,,,,,,,, -util_Shared_ride_2_ASC_Auto_sufficient,Shared ride 2 ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient,,,,,,,,,,,,,,,,, -util_Shared_ride_3p_Zero_auto,Shared ride 3+ - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,sr3p_ASC_no_auto,sr3p_ASC_no_auto,,,,,,,,,,,,,,, -util_Shared_ride_3p_Auto_deficient,Shared ride 3+ - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient,,,,,,,,,,,,,,, -util_Shared_ride_3p_Auto_sufficient,Shared ride 3+ - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient,,,,,,,,,,,,,,, -util_Walk_to_Transit_Zero_auto,Walk to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,,,,,,,, -util_Walk_to_Transit_Auto_deficient,Walk to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,,,,,,,, -util_Walk_to_Transit_Auto_sufficient,Walk to Transit - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,,,,,,,, -util_Drive_to_Transit_Zero_auto,Drive to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,drive_transit_ASC_no_auto,drive_transit_ASC_no_auto,drive_transit_ASC_no_auto,drive_transit_ASC_no_auto,drive_transit_ASC_no_auto,,, -util_Drive_to_Transit_Auto_deficient,Drive to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient,,, -util_Drive_to_Transit_Auto_sufficient,Drive to Transit - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,,drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient,,, -util_Taxi_Zero_auto,Taxi - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,taxi_ASC_no_auto,, -util_Taxi_Auto_deficient,Taxi - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,taxi_ASC_auto_deficient,, -util_Taxi_Auto_sufficient,Taxi - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,,,,,,,taxi_ASC_auto_sufficient,, -util_TNC_Single_Zero_auto,TNC Single - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,tnc_single_ASC_no_auto, -util_TNC_Single_Auto_deficient,TNC Single - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,tnc_single_ASC_auto_deficient, -util_TNC_Single_Auto_sufficient,TNC Single - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,,,,,,,,tnc_single_ASC_auto_sufficient, -util_TNC_Shared_Zero_auto,TNC Shared - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,,tnc_shared_ASC_no_auto -util_TNC_Shared_Auto_deficient,TNC Shared - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,,tnc_shared_ASC_auto_deficient -util_TNC_Shared_Auto_sufficient,TNC Shared - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,,,,,,,,,tnc_shared_ASC_auto_sufficient -#,joint tour ASCs,,,,,,,,,,,,,,,,,,,,,, -util_Joint_Walk_ASC_Zero_auto,Joint - Walk ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,joint_walk_ASC_no_auto,,,,,,,,,,,,,, -util_Joint_Walk_ASC_Auto_deficient,Joint - Walk ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,joint_walk_ASC_auto_deficient,,,,,,,,,,,,,, -util_Joint_Walk_ASC_Auto_sufficient,Joint - Walk ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,joint_walk_ASC_auto_sufficient,,,,,,,,,,,,,, -util_Joint_Bike_ASC_Zero_auto,Joint - Bike ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,joint_bike_ASC_no_auto,,,,,,,,,,,,, -util_Joint_Bike_ASC_Auto_deficient,Joint - Bike ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,joint_bike_ASC_auto_deficient,,,,,,,,,,,,, -util_Joint_Bike_ASC_Auto_sufficient,Joint - Bike ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,joint_bike_ASC_auto_sufficient,,,,,,,,,,,,, -util_Joint_Shared_ride_2_ASC_Zero_auto,Joint - Shared ride 2 ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto,,,,,,,,,,,,,,,,, -util_Joint_Shared_ride_2_ASC_Auto_deficient,Joint - Shared ride 2 ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient,,,,,,,,,,,,,,,,, -util_Joint_Shared_ride_2_ASC_Auto_sufficient,Joint - Shared ride 2 ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient,,,,,,,,,,,,,,,,, -util_Joint_Shared_ride_3p_Zero_auto,Joint - Shared ride 3+ - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto,,,,,,,,,,,,,,, -util_Joint_Shared_ride_3p_Auto_deficient,Joint - Shared ride 3+ - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient,,,,,,,,,,,,,,, -util_Joint_Shared_ride_3p_Auto_sufficient,Joint - Shared ride 3+ - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient,,,,,,,,,,,,,,, -util_Joint_Walk_to_Transit_Zero_auto,Joint - Walk to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,,,,,,,, -util_Joint_Walk_to_Transit_Auto_deficient,Joint - Walk to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,,,,,,,, -util_Joint_Walk_to_Transit_Auto_sufficient,Joint - Walk to Transit - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,,,,,,,, -util_Joint_Drive_to_Transit_Zero_auto,Joint - Drive to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto,,, -util_Joint_Drive_to_Transit_Auto_deficient,Joint - Drive to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient,,, -util_Joint_Drive_to_Transit_Auto_sufficient,Joint - Drive to Transit - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,,joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient,,, -util_Joint_Taxi_Zero_auto,Joint - Taxi - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,joint_taxi_ASC_no_auto,, -util_Joint_Taxi_Auto_deficient,Joint - Taxi - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,joint_taxi_ASC_auto_deficient,, -util_Joint_Taxi_Auto_sufficient,Joint - Taxi - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,,,,,,,joint_taxi_ASC_auto_sufficient,, -util_Joint_TNC_Single_Zero_auto,Joint - TNC Single - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,joint_tnc_single_ASC_no_auto, -util_Joint_TNC_Single_Auto_deficient,Joint - TNC Single - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,joint_tnc_single_ASC_auto_deficient, -util_Joint_TNC_Single_Auto_sufficient,Joint - TNC Single - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,,,,,,,,joint_tnc_single_ASC_auto_sufficient, -util_Joint_TNC_Shared_Zero_auto,Joint - TNC Shared - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_no_auto -util_Joint_TNC_Shared_Auto_deficient,Joint - TNC Shared - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_auto_deficient -util_Joint_TNC_Shared_Auto_sufficient,Joint - TNC Shared - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_auto_sufficient -util_Local_bus_ASC,Local bus ASC,1,,,,,,,,,local_bus_ASC,,,,,local_bus_ASC,,,,,,, -util_Walk_to_Light_Rail_ASC,Walk to Light Rail ASC,@(df.walk_ferry_available == False),,,,,,,,,,walk_light_rail_ASC,,,,,,,,,,, -util_Drive_to_Light_Rail_ASC,Drive to Light Rail ASC,@(df.drive_ferry_available == False),,,,,,,,,,,,,,,drive_light_rail_ASC,,,,,, -util_Walk_to_Ferry_ASC,Walk to Ferry ASC,@df.walk_ferry_available,,,,,,,,,,walk_ferry_ASC,,,,,,,,,,, -util_Drive_to_Ferry_ASC,Drive to Ferry ASC,@df.drive_ferry_available,,,,,,,,,,,,,,,drive_ferry_ASC,,,,,, -util_Express_Bus_ASC,Express Bus ASC,1,,,,,,,,,,,express_bus_ASC,,,,,express_bus_ASC,,,,, -util_Heavy_Rail_ASC,Heavy Rail ASC,1,,,,,,,,,,,,heavy_rail_ASC,,,,,heavy_rail_ASC,,,, -util_Commuter_Rail,Commuter Rail,1,,,,,,,,,,,,,commuter_rail_ASC,,,,,commuter_rail_ASC,,, -util_Walk_to_Transit_dest_CBD,Walk to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,walk_transit_CBD_ASC,walk_transit_CBD_ASC,walk_transit_CBD_ASC,walk_transit_CBD_ASC,walk_transit_CBD_ASC,,,,,,,, -util_Drive_to_Transit_dest_CBD,Drive to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,,,,,,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,,, -util_Drive_to_Transit_distance_penalty,Drive to Transit - distance penalty,@drvtrn_distpen_0_multiplier * (1-od_skims['DIST']/drvtrn_distpen_max).clip(lower=0),,,,,,,,,,,,,,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,,, -#, FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,,,,,,,,,, -util_Walk_not_available_for_long_distances,Walk not available for long distances,@od_skims.max('DISTWALK') > 3,,,,,,,-999,,,,,,,,,,,,,, -util_Bike_not_available_for_long_distances,Bike not available for long distances,@od_skims.max('DISTBIKE') > 8,,,,,,,,-999,,,,,,,,,,,,, -util_Drive_alone_not_available_for_escort_tours,Drive alone not available for escort tours,is_escort,-999,-999,,,,,,,,,,,,,,,,,,, -#, max(c_densityIndexOrigin*originDensityIndex,originDensityIndexMax),,,,,,,,,1,1,1,1,1,1,1,,,,,, +Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_LOC,WALK_LRF,WALK_EXP,WALK_HVY,WALK_COM,DRIVE_LOC,DRIVE_LRF,DRIVE_EXP,DRIVE_HVY,DRIVE_COM,TAXI,TNC_SINGLE,TNC_SHARED +#,Drive alone no toll,,,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,auto_ownership == 0,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['SOV_TIME'] + dot_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Terminal_time,DRIVEALONEFREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']),coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOV_BTOLL'] + dot_skims['SOV_BTOLL']),coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Person_is_between_16_and_19_years_old,DRIVEALONEFREE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da_multiplier,,,,,,,,,,,,,,,,,,,, +#,Drive alone toll,,,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,auto_ownership == 0,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,age < 16,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['SOVTOLL_TIME'] + dot_skims['SOVTOLL_TIME'],,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Terminal_time,DRIVEALONEPAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOVTOLL_DIST'] + dot_skims['SOVTOLL_DIST']),,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOVTOLL_BTOLL'] + dot_skims['SOVTOLL_BTOLL']),,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOVTOLL_VTOLL'] + dot_skims['SOVTOLL_VTOLL']),,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Person_is_between_16_and_19_years_old,DRIVEALONEPAY - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),,coef_age1619_da_multiplier,,,,,,,,,,,,,,,,,,, +#,Shared ride 2,,,,,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Terminal_time,SHARED2FREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']),,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']) / costShareSr2,,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_One_person_household,SHARED2FREE - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Person_is_16_years_old_or_older,SHARED2FREE - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,,,,, +#,Shared ride 2 toll,,,,,,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,,,,,,,,,, +util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Terminal_time,SHARED2PAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']),,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']) / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']) / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_One_person_household,SHARED2PAY - One person household,@(df.hhsize == 1),,,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.hhsize == 2),,,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Person_is_16_years_old_or_older,SHARED2PAY - Person is 16 years old or older,@(df.age >= 16),,,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,,,, +#,Shared ride 3+,,,,,,,,,,,,,,,,,,,,,, +util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,,,,,,,,,, +util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@(odt_skims['HOV3_TIME'] + dot_skims['HOV3_TIME']),,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_Terminal_time,SHARED3FREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3_DIST'] + dot_skims['HOV3_DIST']),,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3_BTOLL'] + dot_skims['HOV3_BTOLL']) / costShareSr3,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_One_person_household,SHARED3FREE - One person household,@(df.hhsize == 1),,,,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,,, +util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.hhsize == 2),,,,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,,, +util_SHARED3FREE_Person_is_16_years_old_or_older,SHARED3FREE - Person is 16 years old or older,@(df.age >= 16),,,,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,,, +#,Shared ride 3+ toll,,,,,,,,,,,,,,,,,,,,,, +util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,,,,,,,,,, +util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@(odt_skims['HOV3TOLL_TIME'] + dot_skims['HOV3TOLL_TIME']),,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Terminal_time,SHARED3PAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3TOLL_DIST'] + dot_skims['HOV3TOLL_DIST']),,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3TOLL_BTOLL'] + dot_skims['HOV3TOLL_BTOLL']) / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3TOLL_VTOLL'] + dot_skims['HOV3TOLL_VTOLL']) / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_One_person_household,SHARED3PAY - One person household,@(df.hhsize == 1),,,,,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,, +util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.hhsize == 2),,,,,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,, +util_SHARED3PAY_Person_is_16_years_old_or_older,SHARED3PAY - Person is 16 years old or older,@(df.age >= 16),,,,,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,, +#,Walk,,,,,,,,,,,,,,,,,,,,,, +#,FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,,,,,,,,,, +util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@walktimeshort_multiplier * (od_skims['DISTWALK'].clip(upper=walkThresh) + od_skims.reverse('DISTWALK').clip(upper=walkThresh))*60/walkSpeed,,,,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * ((od_skims['DISTWALK'] - walkThresh).clip(lower=0) + (od_skims.reverse('DISTWALK') - walkThresh).clip(lower=0))*60/walkSpeed,,,,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Topology,WALK - Topology,@coef_topology_walk_multiplier * df.dest_topology,,,,,,,coef_ivt,,,,,,,,,,,,,, +#,Bike,,,,,,,,,,,,,,,,,,,,,, +#,FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,,,,,,,,,, +util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,,,,,,,,,, +util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@biketimeshort_multiplier * (od_skims['DISTBIKE'].clip(upper=bikeThresh) + od_skims.reverse('DISTBIKE').clip(upper=bikeThresh))*60/bikeSpeed,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@biketimelong_multiplier * ((od_skims['DISTBIKE']-bikeThresh).clip(lower=0) + (od_skims.reverse('DISTBIKE')-bikeThresh).clip(lower=0))*60/bikeSpeed,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Topology,BIKE - Topology,@coef_topology_bike_multiplier * df.dest_topology,,,,,,,,coef_ivt,,,,,,,,,,,,, +#,Walk to Local,,,,,,,,,,,,,,,,,,,,,, +util_WALK_LOC_Unavailable,WALK_LOC - Unavailable,walk_local_available == False,,,,,,,,,-999,,,,,,,,,,,, +util_WALK_LOC_In_vehicle_time,WALK_LOC - In-vehicle time,@(odt_skims['WLK_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Short_iwait_time,WALK_LOC - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Long_iwait_time,WALK_LOC - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_transfer_wait_time,WALK_LOC - transfer wait time,@xwait_multiplier * (odt_skims['WLK_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_number_of_transfers,WALK_LOC - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_LOC_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_LOC_WLK_BOARDS']-1).clip(0)),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_egress_time,WALK_LOC - Walk egress time,@2 * wegr_multiplier * df.destination_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_other_time,WALK_LOC - Walk other time,@waux_multiplier * (odt_skims['WLK_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Fare,WALK_LOC - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_LOC_WLK_FAR'] + dot_skims['WLK_LOC_WLK_FAR']),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Destination_zone_densityIndex,WALK_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Topology,WALK_LOC - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Person_is_less_than_10_years_old,WALK_LOC - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,coef_age010_trn_multiplier,,,,,,,,,,,, +#,Walk to Light rail/Ferry,,,,,,,,,,,,,,,,,,,,,, +util_WALK_LRF_Unavailable,WALK_LRF - Unavailable,walk_lrf_available == False,,,,,,,,,,-999,,,,,,,,,,, +util_WALK_LRF_In_vehicle_time,WALK_LRF - In-vehicle time,@(odt_skims['WLK_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, +#, FIXME coefficients below are wrong or needlessly complex? could be re-expressed to avoid subtract?,,,,,,,,,,,,,,,,,,,,,, +util_WALK_LRF_In_vehicle_time_on_Light_Rail,WALK_LRF - In-vehicle time on Light Rail (incremental w/ ivt),@(ivt_lrt_multiplier-1)*(odt_skims['WLK_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_In_vehicle_time_on_Ferry,WALK_LRF - In-vehicle time on Ferry (incremental w/keyivt),@(ivt_ferry_multiplier-ivt_lrt_multiplier)*(odt_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Short_iwait_time,WALK_LRF - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Long_iwait_time,WALK_LRF - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_transfer_wait_time,WALK_LRF - transfer wait time,@xwait_multiplier * (odt_skims['WLK_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_number_of_transfers,WALK_LRF - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_LRF_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_LRF_WLK_BOARDS']-1).clip(0)),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Walk_access_time,WALK_LRF - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Walk_egress_time,WALK_LRF - Walk egress time,@2 * wegr_multiplier * df.destination_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Walk_other_time,WALK_LRF - Walk other time,@waux_multiplier * (odt_skims['WLK_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Fare,WALK_LRF - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_LRF_WLK_FAR'] + dot_skims['WLK_LRF_WLK_FAR']),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Destination_zone_densityIndex,WALK_LRF - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Topology,WALK_LRF - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Person_is_less_than_10_years_old,WALK_LRF - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,coef_age010_trn_multiplier,,,,,,,,,,, +#,Walk to Express bus,,,,,,,,,,,,,,,,,,,,,, +util_WALK_EXP_Unavailable,WALK_EXP - Unavailable,walk_express_available == False,,,,,,,,,,,-999,,,,,,,,,, +util_WALK_EXP_In_vehicle_time,WALK_EXP - In-vehicle time,@(odt_skims['WLK_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_In_vehicle_time_on_Express_bus,WALK_EXP - In-vehicle time on Express bus (incremental w/ ivt),@(ivt_exp_multiplier - 1)*(odt_skims['WLK_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Short_iwait_time,WALK_EXP - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Long_iwait_time,WALK_EXP - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_transfer_wait_time,WALK_EXP - transfer wait time,@xwait_multiplier * (odt_skims['WLK_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_number_of_transfers,WALK_EXP - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_EXP_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_EXP_WLK_BOARDS']-1).clip(0)),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Walk_access_time,WALK_EXP - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Walk_egress_time,WALK_EXP - Walk egress time,@2 * wegr_multiplier * df.destination_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Walk_other_time,WALK_EXP - Walk other time,@waux_multiplier * (odt_skims['WLK_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Fare,WALK_EXP - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_EXP_WLK_FAR'] + dot_skims['WLK_EXP_WLK_FAR']),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Destination_zone_densityIndex,WALK_EXP - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Topology,WALK_EXP - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Person_is_less_than_10_years_old,WALK_EXP - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,,coef_age010_trn_multiplier,,,,,,,,,, +#,Walk to Heavy Rail,,,,,,,,,,,,,,,,,,,,,, +util_WALK_HVY_Unavailable,WALK_HVY - Unavailable,walk_heavyrail_available == False,,,,,,,,,,,,-999,,,,,,,,, +util_WALK_HVY_In_vehicle_time,WALK_HVY - In-vehicle time,@(odt_skims['WLK_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_In_vehicle_time_on_heavy_rail,WALK_HVY - In-vehicle time on heavy rail (incremental w/ ivt),@(ivt_hvy_multiplier-1) * (odt_skims['WLK_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Short_iwait_time,WALK_HVY - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Long_iwait_time,WALK_HVY - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_transfer_wait_time,WALK_HVY - transfer wait time,@xwait_multiplier * (odt_skims['WLK_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_number_of_transfers,WALK_HVY - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_HVY_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_HVY_WLK_BOARDS']-1).clip(0)),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Walk_access_time,WALK_HVY - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Walk_egress_time,WALK_HVY - Walk egress time,@wegr_multiplier * 2 *df.destination_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Walk_other_time,WALK_HVY - Walk other time,@waux_multiplier * (odt_skims['WLK_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Fare,WALK_HVY - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_HVY_WLK_FAR'] + dot_skims['WLK_HVY_WLK_FAR']),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Destination_zone_densityIndex,WALK_HVY - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Topology,WALK_HVY - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Person_is_less_than_10_years_old,WALK_HVY - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,,,coef_age010_trn_multiplier,,,,,,,,, +#,Walk to Commuter rail,,,,,,,,,,,,,,,,,,,,,, +util_WALK_COM_Unavailable,WALK_COM - Unavailable,walk_commuter_available == False,,,,,,,,,,,,,-999,,,,,,,, +util_WALK_COM_In_vehicle_time,WALK_COM - In-vehicle time,@(odt_skims['WLK_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_In_vehicle_time_on_commuter_rail,WALK_COM - In-vehicle time on commuter rail (incremental w/ ivt),@(ivt_com_multiplier - 1) * (odt_skims['WLK_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Short_iwait_time,WALK_COM - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Long_iwait_time,WALK_COM - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_transfer_wait_time,WALK_COM - transfer wait time,@xwait_multiplier * (odt_skims['WLK_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_number_of_transfers,WALK_COM - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_COM_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_COM_WLK_BOARDS']-1).clip(0)),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Walk_access_time,WALK_COM - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Walk_egress_time,WALK_COM - Walk egress time,@2 * wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Walk_other_time,WALK_COM - Walk other time,@waux_multiplier * (odt_skims['WLK_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Fare,WALK_COM - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_COM_WLK_FAR'] + dot_skims['WLK_COM_WLK_FAR']),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Destination_zone_densityIndex,WALK_COM - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Topology,WALK_COM - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Person_is_less_than_10_years_old,WALK_COM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,coef_age010_trn_multiplier,,,,,,,, +#,Drive to Local,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_LOC_Unavailable,DRIVE_LOC - Unavailable,drive_local_available == False,,,,,,,,,,,,,,-999,,,,,,, +util_DRIVE_LOC_Unavailable_for_zero_auto_households,DRIVE_LOC - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,-999,,,,,,, +util_DRIVE_LOC_Unavailable_for_persons_less_than_16,DRIVE_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,-999,,,,,,, +util_DRIVE_LOC_In_vehicle_time,DRIVE_LOC - In-vehicle time,@(odt_skims['DRV_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Short_iwait_time,DRIVE_LOC - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_LOC_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Long_iwait_time,DRIVE_LOC - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_LOC_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_transfer_wait_time,DRIVE_LOC - transfer wait time,@xwait_multiplier * (odt_skims['DRV_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_number_of_transfers,DRIVE_LOC - number of transfers,@xfers_wlk_multiplier * ((odt_skims['DRV_LOC_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_LOC_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Drive_time,DRIVE_LOC - Drive time,@dtim_multiplier * (odt_skims['DRV_LOC_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Walk_access_time,DRIVE_LOC - Walk access time,@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Walk_egress_time,DRIVE_LOC - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Walk_other_time,DRIVE_LOC - Walk other time,@waux_multiplier * (odt_skims['DRV_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Fare_and_operating_cost,DRIVE_LOC - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_LOC_WLK_FAR'] + dot_skims['WLK_LOC_DRV_FAR']) + ((odt_skims['DRV_LOC_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_LOC_DRV_DDIST']/TRANSIT_SCALE_FACTOR) * costPerMile)),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LOC - Ratio of drive access distance to OD distance,@dacc_ratio * ((odt_skims['DRV_LOC_WLK_DDIST']/TRANSIT_SCALE_FACTOR+ dot_skims['WLK_LOC_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']*2)),,,,,,,,,,,,,,1,,,,,,, +util_DRIVE_LOC_Destination_zone_densityIndex,DRIVE_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Topology,DRIVE_LOC - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Person_is_less_than_10_years_old,DRIVE_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,coef_age010_trn_multiplier,,,,,,, +#,Drive to Light Rail/Ferry,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_LRF_Unavailable,DRIVE_LRF - Unavailable,drive_lrf_available == False,,,,,,,,,,,,,,,-999,,,,,, +util_DRIVE_LRF_Unavailable_for_zero_auto_households,DRIVE_LRF - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,-999,,,,,, +util_DRIVE_LRF_Unavailable_for_persons_less_than_16,DRIVE_LRF - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,-999,,,,,, +util_DRIVE_LRF_In_vehicle_time,DRIVE_LRF - In-vehicle time,@(odt_skims['DRV_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_In_vehicle_time_on_Light_Rail,DRIVE_LRF - In-vehicle time on Light Rail (incremental w/ ivt),@(ivt_lrt_multiplier-1) * (odt_skims['DRV_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_In_vehicle_time_on_Ferry,DRIVE_LRF - In-vehicle time on Ferry (incremental w/ keyivt),@(ivt_ferry_multiplier-ivt_lrt_multiplier)*(odt_skims['DRV_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_FERRYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Short_iwait_time,DRIVE_LRF - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_LRF_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Long_iwait_time,DRIVE_LRF - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_LRF_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_transfer_wait_time,DRIVE_LRF - transfer wait time,@xwait_multiplier * (odt_skims['DRV_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_number_of_transfers,DRIVE_LRF - number of transfers,@xfers_drv_multiplier * ((odt_skims['DRV_LRF_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_LRF_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Drive_time,DRIVE_LRF - Drive time,@dtim_multiplier * (odt_skims['DRV_LRF_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Walk_access_time,DRIVE_LRF - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Walk_egress_time,DRIVE_LRF - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Walk_other_time,DRIVE_LRF - Walk other time,@waux_multiplier * (odt_skims['DRV_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Fare_and_operating_cost,DRIVE_LRF - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_LRF_WLK_FAR']+dot_skims['WLK_LRF_DRV_FAR']) + ((odt_skims['DRV_LRF_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_LRF_DRV_DDIST']/TRANSIT_SCALE_FACTOR) *costPerMile)),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LRF - Ratio of drive access distance to OD distance,@dacc_ratio * ((odt_skims['DRV_LRF_WLK_DDIST']/TRANSIT_SCALE_FACTOR+ dot_skims['WLK_LRF_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']*2)),,,,,,,,,,,,,,,1,,,,,, +util_DRIVE_LRF_Destination_zone_densityIndex,DRIVE_LRF - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Topology,DRIVE_LRF - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Person_is_less_than_10_years_old,DRIVE_LRF - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,coef_age010_trn_multiplier,,,,,, +#,Drive to Express bus,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_EXP_Unavailable,DRIVE_EXP - Unavailable,drive_express_available == False,,,,,,,,,,,,,,,,-999,,,,, +util_DRIVE_EXP_Unavailable_for_zero_auto_households,DRIVE_EXP - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,-999,,,,, +util_DRIVE_EXP_Unavailable_for_persons_less_than_16,DRIVE_EXP - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,-999,,,,, +util_DRIVE_EXP_In_vehicle_time,DRIVE_EXP - In-vehicle time,@(odt_skims['DRV_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_In_vehicle_time_on_Express_bus,DRIVE_EXP - In-vehicle time on Express bus (incremental w/ ivt),@(ivt_exp_multiplier-1) * (odt_skims['DRV_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Short_iwait_time,DRIVE_EXP - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_EXP_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Long_iwait_time,DRIVE_EXP - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_EXP_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_transfer_wait_time,DRIVE_EXP - transfer wait time,@xwait_multiplier * (odt_skims['DRV_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_number_of_transfers,DRIVE_EXP - number of transfers,@xfers_drv_multiplier * ((odt_skims['DRV_EXP_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_EXP_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Drive_time,DRIVE_EXP - Drive time,@dtim_multiplier * (odt_skims['DRV_EXP_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Walk_access_time,DRIVE_EXP - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Walk_egress_ime,DRIVE_EXP - Walk egress ime (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Walk_other_time,DRIVE_EXP - Walk other time,@waux_multiplier * (odt_skims['DRV_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Fare_and_operating_cost,DRIVE_EXP - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_EXP_WLK_FAR']+dot_skims['WLK_EXP_DRV_FAR']) + ((odt_skims['DRV_EXP_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_EXP_DRV_DDIST']/TRANSIT_SCALE_FACTOR) *costPerMile)),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_EXP - Ratio of drive access distance to OD distance,@dacc_ratio * ((odt_skims['DRV_EXP_WLK_DDIST']/TRANSIT_SCALE_FACTOR+ dot_skims['WLK_EXP_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']*2)),,,,,,,,,,,,,,,,1,,,,, +util_DRIVE_EXP_Destination_zone_densityIndex,DRIVE_EXP - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Topology,DRIVE_EXP - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Person_is_less_than_10_years_old,DRIVE_EXP - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,coef_age010_trn_multiplier,,,,, +#,Drive to Heavy Rail,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_HVY_Unavailable,DRIVE_HVY - Unavailable,drive_heavyrail_available == False,,,,,,,,,,,,,,,,,-999,,,, +util_DRIVE_HVY_Unavailable_for_zero_auto_households,DRIVE_HVY - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,,-999,,,, +util_DRIVE_HVY_Unavailable_for_persons_less_than_16,DRIVE_HVY - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,-999,,,, +util_DRIVE_HVY_In_vehicle_time,DRIVE_HVY - In-vehicle time,@(odt_skims['DRV_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_In_vehicle_time_on_heavy_rail,DRIVE_HVY - In-vehicle time on heavy rail (incremental w/ ivt),@(ivt_hvy_multiplier-1) * (odt_skims['DRV_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Short_iwait_time,DRIVE_HVY - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_HVY_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Long_iwait_time,DRIVE_HVY - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_HVY_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_transfer_wait_time,DRIVE_HVY - transfer wait time,@xwait_multiplier * (odt_skims['DRV_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_number_of_transfers,DRIVE_HVY - number of transfers,@xfers_drv_multiplier * ((odt_skims['DRV_HVY_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_HVY_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Drive_time,DRIVE_HVY - Drive time,@dtim_multiplier * (odt_skims['DRV_HVY_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Walk_access_time,DRIVE_HVY - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Walk_egress_time,DRIVE_HVY - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Walk_other_time,DRIVE_HVY - Walk other time,@waux_multiplier * (odt_skims['DRV_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Fare_and_operating_cost,DRIVE_HVY - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_HVY_WLK_FAR']+dot_skims['WLK_HVY_DRV_FAR']) + ((odt_skims['DRV_HVY_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_HVY_DRV_DDIST']/TRANSIT_SCALE_FACTOR) *costPerMile)),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_HVY - Ratio of drive access distance to OD distance,@dacc_ratio * (odt_skims['DRV_HVY_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,,1,,,, +util_DRIVE_HVY_Destination_zone_densityIndex,DRIVE_HVY - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Topology,DRIVE_HVY - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Person_is_less_than_10_years_old,DRIVE_HVY - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,,coef_age010_trn_multiplier,,,, +#,Drive to Commuter Rail,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_COM_Unavailable,DRIVE_COM - Unavailable,drive_commuter_available == False,,,,,,,,,,,,,,,,,,-999,,, +util_DRIVE_COM_Unavailable_for_zero_auto_households,DRIVE_COM - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,,,-999,,, +util_DRIVE_COM_Unavailable_for_persons_less_than_16,DRIVE_COM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,,-999,,, +util_DRIVE_COM_In_vehicle_time,DRIVE_COM - In-vehicle time,@(odt_skims['DRV_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_In_vehicle_time_on_commuter_rail,DRIVE_COM - In-vehicle time on commuter rail (incremental w/ ivt),@(ivt_com_multiplier - 1) * (odt_skims['DRV_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Short_iwait_time,DRIVE_COM - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_COM_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Long_iwait_time,DRIVE_COM - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_COM_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_transfer_wait_time,DRIVE_COM - transfer wait time,@xwait_multiplier * (odt_skims['DRV_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_number_of_transfers,DRIVE_COM - number of transfers,@xfers_drv_multiplier * ((odt_skims['DRV_COM_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_COM_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Drive_time,DRIVE_COM - Drive time,@dtim_multiplier * (odt_skims['DRV_COM_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Walk_access_time,DRIVE_COM - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Walk_egress_time,DRIVE_COM - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Walk_other_time,DRIVE_COM - Walk other time,@waux_multiplier * (odt_skims['DRV_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Fare_and_operating_cost,DRIVE_COM - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_COM_WLK_FAR']+dot_skims['WLK_COM_DRV_FAR']) + ((odt_skims['DRV_COM_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_COM_DRV_DDIST']/TRANSIT_SCALE_FACTOR) *costPerMile)),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_COM - Ratio of drive access distance to OD distance,@dacc_ratio * ((odt_skims['DRV_COM_WLK_DDIST']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']*2)),,,,,,,,,,,,,,,,,,1,,, +util_DRIVE_COM_Destination_zone_densityIndex,DRIVE_COM - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Topology,DRIVE_COM - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Person_is_less_than_10_years_old,DRIVE_COM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,,,coef_age010_trn_multiplier,,, +#,Taxi,,,,,,,,,,,,,,,,,,,,,, +util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,,,,,,,,,,,,,,,,coef_ivt,, +#, FIXME magic constant 1.5,,,,,,,,,,,,,,,,,,,,,, +util_Taxi_Wait_time,Taxi - Wait time,@1.5 * df.totalWaitTaxi,,,,,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * Taxi_costPerMile +(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * Taxi_costPerMinute)*100,,,,,,,,,,,,,,,,,,,coef_ivt,, +#,TNC Single,,,,,,,,,,,,,,,,,,,,,, +util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Wait_time,TNC Single - Wait time,@1.5 * df.totalWaitSingleTNC,,,,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * TNC_single_costPerMile + (odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,coef_ivt, +#,TNC Shared,,,,,,,,,,,,,,,,,,,,,, +util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_shared_IVTFactor,,,,,,,,,,,,,,,,,,,,,coef_ivt +#, FIXME magic constant 1.5,,,,,,,,,,,,,,,,,,,,,, +util_TNC_Shared_Wait_time,TNC Shared - Wait time,@1.5 * df.totalWaitSharedTNC,,,,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * TNC_shared_costPerMile + (odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,,coef_ivt +#,indiv tour ASCs,,,,,,,,,,,,,,,,,,,,,, +util_Walk_ASC_Zero_auto,Walk ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,walk_ASC_no_auto,,,,,,,,,,,,,, +util_Walk_ASC_Auto_deficient,Walk ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,walk_ASC_auto_deficient,,,,,,,,,,,,,, +util_Walk_ASC_Auto_sufficient,Walk ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,walk_ASC_auto_sufficient,,,,,,,,,,,,,, +util_Bike_ASC_Zero_auto,Bike ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,bike_ASC_no_auto,,,,,,,,,,,,, +util_Bike_ASC_Auto_deficient,Bike ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,bike_ASC_auto_deficient,,,,,,,,,,,,, +util_Bike_ASC_Auto_sufficient,Bike ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,bike_ASC_auto_sufficient,,,,,,,,,,,,, +util_Shared_ride_2_ASC_Zero_auto,Shared ride 2 ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,sr2_ASC_no_auto,sr2_ASC_no_auto,,,,,,,,,,,,,,,,, +util_Shared_ride_2_ASC_Auto_deficient,Shared ride 2 ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,sr2_ASC_auto_deficient,sr2_ASC_auto_deficient,,,,,,,,,,,,,,,,, +util_Shared_ride_2_ASC_Auto_sufficient,Shared ride 2 ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient,,,,,,,,,,,,,,,,, +util_Shared_ride_3p_Zero_auto,Shared ride 3+ - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,sr3p_ASC_no_auto,sr3p_ASC_no_auto,,,,,,,,,,,,,,, +util_Shared_ride_3p_Auto_deficient,Shared ride 3+ - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient,,,,,,,,,,,,,,, +util_Shared_ride_3p_Auto_sufficient,Shared ride 3+ - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient,,,,,,,,,,,,,,, +util_Walk_to_Transit_Zero_auto,Walk to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,,,,,,,, +util_Walk_to_Transit_Auto_deficient,Walk to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,,,,,,,, +util_Walk_to_Transit_Auto_sufficient,Walk to Transit - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,,,,,,,, +util_Drive_to_Transit_Zero_auto,Drive to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,drive_transit_ASC_no_auto,drive_transit_ASC_no_auto,drive_transit_ASC_no_auto,drive_transit_ASC_no_auto,drive_transit_ASC_no_auto,,, +util_Drive_to_Transit_Auto_deficient,Drive to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient,,, +util_Drive_to_Transit_Auto_sufficient,Drive to Transit - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,,drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient,,, +util_Taxi_Zero_auto,Taxi - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,taxi_ASC_no_auto,, +util_Taxi_Auto_deficient,Taxi - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,taxi_ASC_auto_deficient,, +util_Taxi_Auto_sufficient,Taxi - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,,,,,,,taxi_ASC_auto_sufficient,, +util_TNC_Single_Zero_auto,TNC Single - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,tnc_single_ASC_no_auto, +util_TNC_Single_Auto_deficient,TNC Single - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,tnc_single_ASC_auto_deficient, +util_TNC_Single_Auto_sufficient,TNC Single - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,,,,,,,,tnc_single_ASC_auto_sufficient, +util_TNC_Shared_Zero_auto,TNC Shared - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,,tnc_shared_ASC_no_auto +util_TNC_Shared_Auto_deficient,TNC Shared - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,,tnc_shared_ASC_auto_deficient +util_TNC_Shared_Auto_sufficient,TNC Shared - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,,,,,,,,,tnc_shared_ASC_auto_sufficient +#,joint tour ASCs,,,,,,,,,,,,,,,,,,,,,, +util_Joint_Walk_ASC_Zero_auto,Joint - Walk ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,joint_walk_ASC_no_auto,,,,,,,,,,,,,, +util_Joint_Walk_ASC_Auto_deficient,Joint - Walk ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,joint_walk_ASC_auto_deficient,,,,,,,,,,,,,, +util_Joint_Walk_ASC_Auto_sufficient,Joint - Walk ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,joint_walk_ASC_auto_sufficient,,,,,,,,,,,,,, +util_Joint_Bike_ASC_Zero_auto,Joint - Bike ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,joint_bike_ASC_no_auto,,,,,,,,,,,,, +util_Joint_Bike_ASC_Auto_deficient,Joint - Bike ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,joint_bike_ASC_auto_deficient,,,,,,,,,,,,, +util_Joint_Bike_ASC_Auto_sufficient,Joint - Bike ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,joint_bike_ASC_auto_sufficient,,,,,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Zero_auto,Joint - Shared ride 2 ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto,,,,,,,,,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Auto_deficient,Joint - Shared ride 2 ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient,,,,,,,,,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Auto_sufficient,Joint - Shared ride 2 ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient,,,,,,,,,,,,,,,,, +util_Joint_Shared_ride_3p_Zero_auto,Joint - Shared ride 3+ - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto,,,,,,,,,,,,,,, +util_Joint_Shared_ride_3p_Auto_deficient,Joint - Shared ride 3+ - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient,,,,,,,,,,,,,,, +util_Joint_Shared_ride_3p_Auto_sufficient,Joint - Shared ride 3+ - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient,,,,,,,,,,,,,,, +util_Joint_Walk_to_Transit_Zero_auto,Joint - Walk to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,,,,,,,, +util_Joint_Walk_to_Transit_Auto_deficient,Joint - Walk to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,,,,,,,, +util_Joint_Walk_to_Transit_Auto_sufficient,Joint - Walk to Transit - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,,,,,,,, +util_Joint_Drive_to_Transit_Zero_auto,Joint - Drive to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto,,, +util_Joint_Drive_to_Transit_Auto_deficient,Joint - Drive to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient,,, +util_Joint_Drive_to_Transit_Auto_sufficient,Joint - Drive to Transit - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,,joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient,,, +util_Joint_Taxi_Zero_auto,Joint - Taxi - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,joint_taxi_ASC_no_auto,, +util_Joint_Taxi_Auto_deficient,Joint - Taxi - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,joint_taxi_ASC_auto_deficient,, +util_Joint_Taxi_Auto_sufficient,Joint - Taxi - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,,,,,,,joint_taxi_ASC_auto_sufficient,, +util_Joint_TNC_Single_Zero_auto,Joint - TNC Single - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,joint_tnc_single_ASC_no_auto, +util_Joint_TNC_Single_Auto_deficient,Joint - TNC Single - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,joint_tnc_single_ASC_auto_deficient, +util_Joint_TNC_Single_Auto_sufficient,Joint - TNC Single - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,,,,,,,,joint_tnc_single_ASC_auto_sufficient, +util_Joint_TNC_Shared_Zero_auto,Joint - TNC Shared - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_no_auto +util_Joint_TNC_Shared_Auto_deficient,Joint - TNC Shared - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_auto_deficient +util_Joint_TNC_Shared_Auto_sufficient,Joint - TNC Shared - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers)),,,,,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_auto_sufficient +util_Local_bus_ASC,Local bus ASC,1,,,,,,,,,local_bus_ASC,,,,,local_bus_ASC,,,,,,, +util_Walk_to_Light_Rail_ASC,Walk to Light Rail ASC,@(df.walk_ferry_available == False),,,,,,,,,,walk_light_rail_ASC,,,,,,,,,,, +util_Drive_to_Light_Rail_ASC,Drive to Light Rail ASC,@(df.drive_ferry_available == False),,,,,,,,,,,,,,,drive_light_rail_ASC,,,,,, +util_Walk_to_Ferry_ASC,Walk to Ferry ASC,@df.walk_ferry_available,,,,,,,,,,walk_ferry_ASC,,,,,,,,,,, +util_Drive_to_Ferry_ASC,Drive to Ferry ASC,@df.drive_ferry_available,,,,,,,,,,,,,,,drive_ferry_ASC,,,,,, +util_Express_Bus_ASC,Express Bus ASC,1,,,,,,,,,,,express_bus_ASC,,,,,express_bus_ASC,,,,, +util_Heavy_Rail_ASC,Heavy Rail ASC,1,,,,,,,,,,,,heavy_rail_ASC,,,,,heavy_rail_ASC,,,, +util_Commuter_Rail,Commuter Rail,1,,,,,,,,,,,,,commuter_rail_ASC,,,,,commuter_rail_ASC,,, +util_Walk_to_Transit_dest_CBD,Walk to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,walk_transit_CBD_ASC,walk_transit_CBD_ASC,walk_transit_CBD_ASC,walk_transit_CBD_ASC,walk_transit_CBD_ASC,,,,,,,, +util_Drive_to_Transit_dest_CBD,Drive to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,,,,,,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,,, +util_Drive_to_Transit_distance_penalty,Drive to Transit - distance penalty,@drvtrn_distpen_0_multiplier * (1-od_skims['DIST']/drvtrn_distpen_max).clip(lower=0),,,,,,,,,,,,,,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,,, +#, FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,,,,,,,,,, +util_Walk_not_available_for_long_distances,Walk not available for long distances,@od_skims.max('DISTWALK') > 3,,,,,,,-999,,,,,,,,,,,,,, +util_Bike_not_available_for_long_distances,Bike not available for long distances,@od_skims.max('DISTBIKE') > 8,,,,,,,,-999,,,,,,,,,,,,, +util_Drive_alone_not_available_for_escort_tours,Drive alone not available for escort tours,is_escort,-999,-999,,,,,,,,,,,,,,,,,,, +#, max(c_densityIndexOrigin*originDensityIndex,originDensityIndexMax),,,,,,,,,1,1,1,1,1,1,1,,,,,, diff --git a/activitysim/examples/example_psrc/configs/tour_mode_choice.yaml b/activitysim/examples/placeholder_psrc/configs/tour_mode_choice.yaml similarity index 95% rename from activitysim/examples/example_psrc/configs/tour_mode_choice.yaml rename to activitysim/examples/placeholder_psrc/configs/tour_mode_choice.yaml index 2ce70b9cae..5caa0d586d 100755 --- a/activitysim/examples/example_psrc/configs/tour_mode_choice.yaml +++ b/activitysim/examples/placeholder_psrc/configs/tour_mode_choice.yaml @@ -1,189 +1,189 @@ -LOGIT_TYPE: NL -#LOGIT_TYPE: MNL - -NESTS: - name: root - coefficient: coef_nest_root - alternatives: - - name: AUTO - coefficient: coef_nest_AUTO - alternatives: - - name: DRIVEALONE - coefficient: coef_nest_AUTO_DRIVEALONE - alternatives: - - DRIVEALONEFREE - - DRIVEALONEPAY - - name: SHAREDRIDE2 - coefficient: coef_nest_AUTO_SHAREDRIDE2 - alternatives: - - SHARED2FREE - - SHARED2PAY - - name: SHAREDRIDE3 - coefficient: coef_nest_AUTO_SHAREDRIDE3 - alternatives: - - SHARED3FREE - - SHARED3PAY - - name: NONMOTORIZED - coefficient: coef_nest_NONMOTORIZED - alternatives: - - WALK - - BIKE - - name: TRANSIT - coefficient: coef_nest_TRANSIT - alternatives: - - name: WALKACCESS - coefficient: coef_nest_TRANSIT_WALKACCESS - alternatives: - - WALK_LOC - - WALK_LRF - - WALK_EXP - - WALK_HVY - - WALK_COM - - name: DRIVEACCESS - coefficient: coef_nest_TRANSIT_DRIVEACCESS - alternatives: - - DRIVE_LOC - - DRIVE_LRF - - DRIVE_EXP - - DRIVE_HVY - - DRIVE_COM - - name: RIDEHAIL - coefficient: coef_nest_RIDEHAIL - alternatives: - - TAXI - - TNC_SINGLE - - TNC_SHARED - -SPEC: tour_mode_choice.csv -COEFFICIENTS: tour_mode_choice_coeffs.csv -COEFFICIENT_TEMPLATE: tour_mode_choice_coeffs_template.csv - -CONSTANTS: - #valueOfTime: 8.00 - costPerMile: 18.29 - costShareSr2: 1.75 - costShareSr3: 2.50 - waitThresh: 10.00 - walkThresh: 1.50 - shortWalk: 0.333 - longWalk: 0.667 - walkSpeed: 3.00 - bikeThresh: 6.00 - bikeSpeed: 12.00 - maxCbdAreaTypeThresh: 2 - indivTour: 1.00000 - upperEA: 5 - upperAM: 10 - upperMD: 15 - upperPM: 19 - # RIDEHAIL Settings - Taxi_baseFare: 2.20 - Taxi_costPerMile: 2.30 - Taxi_costPerMinute: 0.10 - Taxi_waitTime_mean: - 1: 5.5 - 2: 9.5 - 3: 13.3 - 4: 17.3 - 5: 26.5 - Taxi_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - TNC_single_baseFare: 2.20 - TNC_single_costPerMile: 1.33 - TNC_single_costPerMinute: 0.24 - TNC_single_costMinimum: 7.20 - TNC_single_waitTime_mean: - 1: 3.0 - 2: 6.3 - 3: 8.4 - 4: 8.5 - 5: 10.3 - TNC_single_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - TNC_shared_baseFare: 2.20 - TNC_shared_costPerMile: 0.53 - TNC_shared_costPerMinute: 0.10 - TNC_shared_costMinimum: 3.00 - TNC_shared_IVTFactor: 1.5 - TNC_shared_waitTime_mean: - 1: 5.0 - 2: 8.0 - 3: 11.0 - 4: 15.0 - 5: 15.0 - TNC_shared_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - min_waitTime: 0 - max_waitTime: 50 - - ivt_cost_multiplier: 0.6 - ivt_lrt_multiplier: 0.9 - ivt_ferry_multiplier: 0.8 - ivt_exp_multiplier: 1 - ivt_hvy_multiplier: 0.8 - ivt_com_multiplier: 0.7 - walktimeshort_multiplier: 2 - walktimelong_multiplier: 10 - biketimeshort_multiplier: 4 - biketimelong_multiplier: 20 - short_i_wait_multiplier: 2 - long_i_wait_multiplier: 1 - wacc_multiplier: 2 - wegr_multiplier: 2 - waux_multiplier: 2 - dtim_multiplier: 2 - xwait_multiplier: 2 - dacc_ratio: 0 - xfers_wlk_multiplier: 10 - xfers_drv_multiplier: 20 - drvtrn_distpen_0_multiplier: 270 - drvtrn_distpen_max: 15 - density_index_multiplier: -0.2 -# joint_sr2_ASC_no_auto: 0 -# joint_sr2_ASC_auto_deficient: 0 -# joint_sr2_ASC_auto_sufficient: 0 -# joint_drive_transit_ASC_no_auto: 0 - -# so far, we can use the same spec as for non-joint tours -preprocessor: - SPEC: tour_mode_choice_annotate_choosers_preprocessor - DF: choosers - TABLES: - - land_use - - tours - -nontour_preprocessor: - SPEC: tour_mode_choice_annotate_choosers_preprocessor - DF: choosers - TABLES: - - land_use - -# to reduce memory needs filter chooser table to these fields -LOGSUM_CHOOSER_COLUMNS: - - tour_type - - hhsize - - density_index - - age - - age_16_p - - age_16_to_19 - - auto_ownership - - number_of_participants - - tour_category - - num_workers - - value_of_time - - free_parking_at_work - - -MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum +LOGIT_TYPE: NL +#LOGIT_TYPE: MNL + +NESTS: + name: root + coefficient: coef_nest_root + alternatives: + - name: AUTO + coefficient: coef_nest_AUTO + alternatives: + - name: DRIVEALONE + coefficient: coef_nest_AUTO_DRIVEALONE + alternatives: + - DRIVEALONEFREE + - DRIVEALONEPAY + - name: SHAREDRIDE2 + coefficient: coef_nest_AUTO_SHAREDRIDE2 + alternatives: + - SHARED2FREE + - SHARED2PAY + - name: SHAREDRIDE3 + coefficient: coef_nest_AUTO_SHAREDRIDE3 + alternatives: + - SHARED3FREE + - SHARED3PAY + - name: NONMOTORIZED + coefficient: coef_nest_NONMOTORIZED + alternatives: + - WALK + - BIKE + - name: TRANSIT + coefficient: coef_nest_TRANSIT + alternatives: + - name: WALKACCESS + coefficient: coef_nest_TRANSIT_WALKACCESS + alternatives: + - WALK_LOC + - WALK_LRF + - WALK_EXP + - WALK_HVY + - WALK_COM + - name: DRIVEACCESS + coefficient: coef_nest_TRANSIT_DRIVEACCESS + alternatives: + - DRIVE_LOC + - DRIVE_LRF + - DRIVE_EXP + - DRIVE_HVY + - DRIVE_COM + - name: RIDEHAIL + coefficient: coef_nest_RIDEHAIL + alternatives: + - TAXI + - TNC_SINGLE + - TNC_SHARED + +SPEC: tour_mode_choice.csv +COEFFICIENTS: tour_mode_choice_coeffs.csv +COEFFICIENT_TEMPLATE: tour_mode_choice_coeffs_template.csv + +CONSTANTS: + #valueOfTime: 8.00 + costPerMile: 18.29 + costShareSr2: 1.75 + costShareSr3: 2.50 + waitThresh: 10.00 + walkThresh: 1.50 + shortWalk: 0.333 + longWalk: 0.667 + walkSpeed: 3.00 + bikeThresh: 6.00 + bikeSpeed: 12.00 + maxCbdAreaTypeThresh: 2 + indivTour: 1.00000 + upperEA: 5 + upperAM: 10 + upperMD: 15 + upperPM: 19 + # RIDEHAIL Settings + Taxi_baseFare: 2.20 + Taxi_costPerMile: 2.30 + Taxi_costPerMinute: 0.10 + Taxi_waitTime_mean: + 1: 5.5 + 2: 9.5 + 3: 13.3 + 4: 17.3 + 5: 26.5 + Taxi_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_single_baseFare: 2.20 + TNC_single_costPerMile: 1.33 + TNC_single_costPerMinute: 0.24 + TNC_single_costMinimum: 7.20 + TNC_single_waitTime_mean: + 1: 3.0 + 2: 6.3 + 3: 8.4 + 4: 8.5 + 5: 10.3 + TNC_single_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_shared_baseFare: 2.20 + TNC_shared_costPerMile: 0.53 + TNC_shared_costPerMinute: 0.10 + TNC_shared_costMinimum: 3.00 + TNC_shared_IVTFactor: 1.5 + TNC_shared_waitTime_mean: + 1: 5.0 + 2: 8.0 + 3: 11.0 + 4: 15.0 + 5: 15.0 + TNC_shared_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + min_waitTime: 0 + max_waitTime: 50 + + ivt_cost_multiplier: 0.6 + ivt_lrt_multiplier: 0.9 + ivt_ferry_multiplier: 0.8 + ivt_exp_multiplier: 1 + ivt_hvy_multiplier: 0.8 + ivt_com_multiplier: 0.7 + walktimeshort_multiplier: 2 + walktimelong_multiplier: 10 + biketimeshort_multiplier: 4 + biketimelong_multiplier: 20 + short_i_wait_multiplier: 2 + long_i_wait_multiplier: 1 + wacc_multiplier: 2 + wegr_multiplier: 2 + waux_multiplier: 2 + dtim_multiplier: 2 + xwait_multiplier: 2 + dacc_ratio: 0 + xfers_wlk_multiplier: 10 + xfers_drv_multiplier: 20 + drvtrn_distpen_0_multiplier: 270 + drvtrn_distpen_max: 15 + density_index_multiplier: -0.2 +# joint_sr2_ASC_no_auto: 0 +# joint_sr2_ASC_auto_deficient: 0 +# joint_sr2_ASC_auto_sufficient: 0 +# joint_drive_transit_ASC_no_auto: 0 + +# so far, we can use the same spec as for non-joint tours +preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + - tours + +nontour_preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + +# to reduce memory needs filter chooser table to these fields +LOGSUM_CHOOSER_COLUMNS: + - tour_type + - hhsize + - density_index + - age + - age_16_p + - age_16_to_19 + - auto_ownership + - number_of_participants + - tour_category + - num_workers + - value_of_time + - free_parking_at_work + + +MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum diff --git a/activitysim/examples/example_psrc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/placeholder_psrc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv similarity index 99% rename from activitysim/examples/example_psrc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv rename to activitysim/examples/placeholder_psrc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv index 121a908882..d35d2d1de1 100755 --- a/activitysim/examples/example_psrc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv +++ b/activitysim/examples/placeholder_psrc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv @@ -1,93 +1,93 @@ -Description,Target,Expression -#,, -local,_DF_IS_TOUR,'tour_type' in df.columns -,number_of_participants,df.number_of_participants if _DF_IS_TOUR else 1 -,is_joint,(df.tour_category=='joint') if _DF_IS_TOUR else False -#,, - local,_HAVE_PARENT_TOURS,'parent_tour_id' in df.columns -,_parent_tour_mode,"reindex(tours.tour_mode, df.parent_tour_id) if _HAVE_PARENT_TOURS else ''" -,work_tour_is_drive,"_parent_tour_mode.isin(['DRIVEALONEFREE','DRIVEALONEPAY'])" -,work_tour_is_bike,_parent_tour_mode=='BIKE' -,work_tour_is_SOV,"_parent_tour_mode.isin(['DRIVEALONEFREE','DRIVEALONEPAY'])" -#,, -,is_mandatory,(df.tour_category=='mandatory') if 'tour_category' in df.columns else False -,is_joint,(df.tour_category=='joint') if 'tour_category' in df.columns else False -,is_indiv,~is_joint -,is_atwork_subtour,(df.tour_category=='atwork') if 'tour_category' in df.columns else False -,is_escort,(df.tour_type == 'escort') if _DF_IS_TOUR else False -# FIXME why inverse of value of time? need better name?,, -#,c_cost,(0.60 * c_ivt) / df.value_of_time -# ivot * (c_ivt_cost_multiplier * c_ivt) -,ivot,1.0 / df.value_of_time -#,, -,dest_topology,"reindex(land_use.TOPOLOGY, df[dest_col_name])" -,terminal_time,"reindex(land_use.TERMINAL, df[dest_col_name])" -,dest_density_index,"reindex(land_use.density_index, df[dest_col_name])" -# FIXME no transit subzones for ONE_ZONE version, so all zones short walk to transit,, -,_origin_distance_to_transit,"reindex(land_use.access_dist_transit, df[orig_col_name]) if 'access_dist_transit' in land_use else shortWalk" -,_destination_distance_to_transit,"reindex(land_use.access_dist_transit, df[dest_col_name]) if 'access_dist_transit' in land_use else shortWalk" -,walk_transit_available,(_origin_distance_to_transit > 0) & (_destination_distance_to_transit > 0) -,drive_transit_available,(_destination_distance_to_transit > 0) & (df.auto_ownership > 0) -,origin_walk_time,_origin_distance_to_transit*60/walkSpeed -,destination_walk_time,_destination_distance_to_transit*60/walkSpeed -# RIDEHAIL,, -,origin_density_measure,"(reindex(land_use.TOTPOP, df[orig_col_name]) + reindex(land_use.TOTEMP, df[orig_col_name])) / (reindex(land_use.TOTACRE, df[orig_col_name]) / 640)" -,dest_density_measure,"(reindex(land_use.TOTPOP, df[dest_col_name]) + reindex(land_use.TOTEMP, df[dest_col_name])) / (reindex(land_use.TOTACRE, df[dest_col_name]) / 640)" -,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" -,dest_density,"pd.cut(dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" -,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" -,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" -,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" -,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" -# ,, Note that the mean and standard deviation are not the values for the distribution itself, but of the underlying normal distribution it is derived from -,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" -,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" -,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" -,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" -,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" -,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" -,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" -,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" -,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime -,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime -,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime -#,, -,_free_parking_available,(df.tour_type == 'work') & df.free_parking_at_work if _DF_IS_TOUR else False -,_dest_hourly_peak_parking_cost,"reindex(land_use.PRKCST, df[dest_col_name])" -,_dest_hourly_offpeak_parking_cost,"reindex(land_use.OPRKCST, df[dest_col_name])" -,_hourly_peak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_peak_parking_cost)" -,_hourly_offpeak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_offpeak_parking_cost)" -,daily_parking_cost,"np.where(is_mandatory, _hourly_peak_parking_cost * df.duration, _hourly_offpeak_parking_cost * df.duration)" -#,, -,distance,od_skims['DIST'] -,distance_walk_od,od_skims['DISTWALK'] -,distance_bike_od,od_skims['DISTBIKE'] -#,, -,sov_available,(odt_skims['SOV_TIME']>0) & (dot_skims['SOV_TIME']>0) -,sovtoll_available,(odt_skims['SOVTOLL_VTOLL']>0) | (dot_skims['SOVTOLL_VTOLL']>0) -,hov2_available,(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME'])>0 -,hov2toll_available,(odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL'])>0 -,hov3_available,(odt_skims['HOV3_TIME']>0) & (dot_skims['HOV3_TIME']>0) -,hov3toll_available,(odt_skims['HOV3TOLL_VTOLL'] + dot_skims['HOV3TOLL_VTOLL'])>0 -,walk_local_available,walk_transit_available & (odt_skims['WLK_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) -,walk_commuter_available,walk_transit_available & (odt_skims['WLK_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & ((odt_skims['WLK_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR)>0) -,walk_express_available,walk_transit_available & (odt_skims['WLK_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & ((odt_skims['WLK_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR)>0) -,walk_heavyrail_available,walk_transit_available & (odt_skims['WLK_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & ((odt_skims['WLK_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR)>0) -,walk_lrf_available,walk_transit_available & (odt_skims['WLK_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & ((odt_skims['WLK_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR)>0) -,walk_ferry_available,walk_lrf_available & ((odt_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR)>0) -,drive_local_available,drive_transit_available & (odt_skims['DRV_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_LOC_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR>0) -,drive_commuter_available,drive_transit_available & (odt_skims['DRV_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_COM_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & ((odt_skims['DRV_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR)>0) -,drive_express_available,drive_transit_available & (odt_skims['DRV_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_EXP_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & ((odt_skims['DRV_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR)>0) -,drive_heavyrail_available,drive_transit_available & (odt_skims['DRV_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_HVY_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & ((odt_skims['DRV_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR)>0) -,drive_lrf_available,drive_transit_available & (odt_skims['DRV_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_LRF_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & ((odt_skims['DRV_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR)>0) -,drive_ferry_available,drive_lrf_available & ((odt_skims['DRV_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR)>0) -#,, -destination in central business district,destination_in_cbd,"(reindex(land_use.area_type, df[dest_col_name]) < setting('cbd_threshold')) * 1" -#,,FIXME diagnostic -#,sov_dist_rt,(odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']) +Description,Target,Expression +#,, +local,_DF_IS_TOUR,'tour_type' in df.columns +,number_of_participants,df.number_of_participants if _DF_IS_TOUR else 1 +,is_joint,(df.tour_category=='joint') if _DF_IS_TOUR else False +#,, + local,_HAVE_PARENT_TOURS,'parent_tour_id' in df.columns +,_parent_tour_mode,"reindex(tours.tour_mode, df.parent_tour_id) if _HAVE_PARENT_TOURS else ''" +,work_tour_is_drive,"_parent_tour_mode.isin(['DRIVEALONEFREE','DRIVEALONEPAY'])" +,work_tour_is_bike,_parent_tour_mode=='BIKE' +,work_tour_is_SOV,"_parent_tour_mode.isin(['DRIVEALONEFREE','DRIVEALONEPAY'])" +#,, +,is_mandatory,(df.tour_category=='mandatory') if 'tour_category' in df.columns else False +,is_joint,(df.tour_category=='joint') if 'tour_category' in df.columns else False +,is_indiv,~is_joint +,is_atwork_subtour,(df.tour_category=='atwork') if 'tour_category' in df.columns else False +,is_escort,(df.tour_type == 'escort') if _DF_IS_TOUR else False +# FIXME why inverse of value of time? need better name?,, +#,c_cost,(0.60 * c_ivt) / df.value_of_time +# ivot * (c_ivt_cost_multiplier * c_ivt) +,ivot,1.0 / df.value_of_time +#,, +,dest_topology,"reindex(land_use.TOPOLOGY, df[dest_col_name])" +,terminal_time,"reindex(land_use.TERMINAL, df[dest_col_name])" +,dest_density_index,"reindex(land_use.density_index, df[dest_col_name])" +# FIXME no transit subzones for ONE_ZONE version, so all zones short walk to transit,, +,_origin_distance_to_transit,"reindex(land_use.access_dist_transit, df[orig_col_name]) if 'access_dist_transit' in land_use else shortWalk" +,_destination_distance_to_transit,"reindex(land_use.access_dist_transit, df[dest_col_name]) if 'access_dist_transit' in land_use else shortWalk" +,walk_transit_available,(_origin_distance_to_transit > 0) & (_destination_distance_to_transit > 0) +,drive_transit_available,(_destination_distance_to_transit > 0) & (df.auto_ownership > 0) +,origin_walk_time,_origin_distance_to_transit*60/walkSpeed +,destination_walk_time,_destination_distance_to_transit*60/walkSpeed +# RIDEHAIL,, +,origin_density_measure,"(reindex(land_use.TOTPOP, df[orig_col_name]) + reindex(land_use.TOTEMP, df[orig_col_name])) / (reindex(land_use.TOTACRE, df[orig_col_name]) / 640)" +,dest_density_measure,"(reindex(land_use.TOTPOP, df[dest_col_name]) + reindex(land_use.TOTEMP, df[dest_col_name])) / (reindex(land_use.TOTACRE, df[dest_col_name]) / 640)" +,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" +,dest_density,"pd.cut(dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" +,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" +,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" +,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" +,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" +# ,, Note that the mean and standard deviation are not the values for the distribution itself, but of the underlying normal distribution it is derived from +,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" +,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" +,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" +,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" +,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" +,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" +,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" +,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" +,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime +,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime +,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime +#,, +,_free_parking_available,(df.tour_type == 'work') & df.free_parking_at_work if _DF_IS_TOUR else False +,_dest_hourly_peak_parking_cost,"reindex(land_use.PRKCST, df[dest_col_name])" +,_dest_hourly_offpeak_parking_cost,"reindex(land_use.OPRKCST, df[dest_col_name])" +,_hourly_peak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_peak_parking_cost)" +,_hourly_offpeak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_offpeak_parking_cost)" +,daily_parking_cost,"np.where(is_mandatory, _hourly_peak_parking_cost * df.duration, _hourly_offpeak_parking_cost * df.duration)" +#,, +,distance,od_skims['DIST'] +,distance_walk_od,od_skims['DISTWALK'] +,distance_bike_od,od_skims['DISTBIKE'] +#,, +,sov_available,(odt_skims['SOV_TIME']>0) & (dot_skims['SOV_TIME']>0) +,sovtoll_available,(odt_skims['SOVTOLL_VTOLL']>0) | (dot_skims['SOVTOLL_VTOLL']>0) +,hov2_available,(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME'])>0 +,hov2toll_available,(odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL'])>0 +,hov3_available,(odt_skims['HOV3_TIME']>0) & (dot_skims['HOV3_TIME']>0) +,hov3toll_available,(odt_skims['HOV3TOLL_VTOLL'] + dot_skims['HOV3TOLL_VTOLL'])>0 +,walk_local_available,walk_transit_available & (odt_skims['WLK_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) +,walk_commuter_available,walk_transit_available & (odt_skims['WLK_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & ((odt_skims['WLK_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR)>0) +,walk_express_available,walk_transit_available & (odt_skims['WLK_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & ((odt_skims['WLK_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR)>0) +,walk_heavyrail_available,walk_transit_available & (odt_skims['WLK_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & ((odt_skims['WLK_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR)>0) +,walk_lrf_available,walk_transit_available & (odt_skims['WLK_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & ((odt_skims['WLK_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR)>0) +,walk_ferry_available,walk_lrf_available & ((odt_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR)>0) +,drive_local_available,drive_transit_available & (odt_skims['DRV_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_LOC_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR>0) +,drive_commuter_available,drive_transit_available & (odt_skims['DRV_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_COM_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & ((odt_skims['DRV_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR)>0) +,drive_express_available,drive_transit_available & (odt_skims['DRV_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_EXP_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & ((odt_skims['DRV_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR)>0) +,drive_heavyrail_available,drive_transit_available & (odt_skims['DRV_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_HVY_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & ((odt_skims['DRV_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR)>0) +,drive_lrf_available,drive_transit_available & (odt_skims['DRV_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & (dot_skims['WLK_LRF_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR>0) & ((odt_skims['DRV_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR)>0) +,drive_ferry_available,drive_lrf_available & ((odt_skims['DRV_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR)>0) +#,, +destination in central business district,destination_in_cbd,"(reindex(land_use.area_type, df[dest_col_name]) < setting('cbd_threshold')) * 1" +#,,FIXME diagnostic +#,sov_dist_rt,(odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']) diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_coeffs.csv b/activitysim/examples/placeholder_psrc/configs/tour_mode_choice_coeffs.csv similarity index 97% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_coeffs.csv rename to activitysim/examples/placeholder_psrc/configs/tour_mode_choice_coeffs.csv index c5d9a264a2..9693953808 100755 --- a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_coeffs.csv +++ b/activitysim/examples/placeholder_psrc/configs/tour_mode_choice_coeffs.csv @@ -1,308 +1,308 @@ -coefficient_name,value,constrain -coef_one,1,T -coef_nest_root,1.00,T -coef_nest_AUTO,0.72,T -coef_nest_AUTO_DRIVEALONE,0.35,T -coef_nest_AUTO_SHAREDRIDE2,0.35,T -coef_nest_AUTO_SHAREDRIDE3,0.35,T -coef_nest_NONMOTORIZED,0.72,T -coef_nest_TRANSIT,0.72,T -coef_nest_TRANSIT_WALKACCESS,0.5,T -coef_nest_TRANSIT_DRIVEACCESS,0.5,T -coef_nest_RIDEHAIL,0.36,T -coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,-0.0175,F -coef_ivt_school_univ,-0.0224,F -coef_ivt_work,-0.0134,F -coef_ivt_atwork,-0.0188,F -coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,15,F -coef_topology_walk_multiplier_atwork,7.5,F -coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,20,F -coef_topology_bike_multiplier_atwork,10,F -coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,2.2,F -coef_topology_trn_multiplier_atwork,2,F -coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F -coef_age1619_da_multiplier_school_univ,-1.3813,F -coef_age1619_da_multiplier_atwork,0.0032336,F -coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F -coef_age010_trn_multiplier_school_univ,-1.5548,F -coef_age010_trn_multiplier_atwork,0.000722,F -coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,-1.366,F -coef_age16p_sr_multiplier_school_univ_work_atwork,0,F -coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,0,F -coef_hhsize1_sr_multiplier_work,-0.734588,F -coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,0,F -coef_hhsize2_sr_multiplier_school_univ,-0.6359,F -walk_ASC_no_auto_eatout,5.1251173,F -walk_ASC_no_auto_escort,2.8012068,F -walk_ASC_no_auto_othdiscr,3.2665946,F -walk_ASC_no_auto_othmaint,1.287299,F -walk_ASC_no_auto_school,18.414557,F -walk_ASC_no_auto_shopping,2.3768773,F -walk_ASC_no_auto_social,1.8680915,F -walk_ASC_no_auto_univ,6.408967,F -walk_ASC_no_auto_work,5.7672157,F -walk_ASC_no_auto_atwork,6.669213,F -walk_ASC_auto_deficient_eatout,3.274605,F -walk_ASC_auto_deficient_escort,-0.90204656,F -walk_ASC_auto_deficient_othdiscr,2.2494075,F -walk_ASC_auto_deficient_othmaint,1.3690404,F -walk_ASC_auto_deficient_school,3.2573624,F -walk_ASC_auto_deficient_shopping,2.2701733,F -walk_ASC_auto_deficient_social,2.870184,F -walk_ASC_auto_deficient_univ,4.50591,F -walk_ASC_auto_deficient_work,2.4010417,F -walk_ASC_auto_deficient_atwork,0.92546093,F -walk_ASC_auto_sufficient_eatout,1.5516903,F -walk_ASC_auto_sufficient_escort,-0.8116066,F -walk_ASC_auto_sufficient_othdiscr,1.2633476,F -walk_ASC_auto_sufficient_othmaint,0.7999634,F -walk_ASC_auto_sufficient_school,0.6476856,F -walk_ASC_auto_sufficient_shopping,0.7312663,F -walk_ASC_auto_sufficient_social,1.7072186,F -walk_ASC_auto_sufficient_univ,1.0607665,F -walk_ASC_auto_sufficient_work,0.053265337,F -walk_ASC_auto_sufficient_atwork,0.677216,F -bike_ASC_no_auto_eatout,0.86807096,F -bike_ASC_no_auto_escort,-0.716212,F -bike_ASC_no_auto_othdiscr,-0.3764232,F -bike_ASC_no_auto_othmaint,1.5394334,F -bike_ASC_no_auto_school,12.098735,F -bike_ASC_no_auto_shopping,0.8341555,F -bike_ASC_no_auto_social,0.02058321,F -bike_ASC_no_auto_univ,4.2945156,F -bike_ASC_no_auto_work,3.1940088,F -bike_ASC_no_auto_atwork,-0.90725845,F -bike_ASC_auto_deficient_eatout,-1.5691106,F -bike_ASC_auto_deficient_escort,-4.527928,F -bike_ASC_auto_deficient_othdiscr,-0.09246834,F -bike_ASC_auto_deficient_othmaint,-1.5184649,F -bike_ASC_auto_deficient_school,-0.5280678,F -bike_ASC_auto_deficient_shopping,-0.87584466,F -bike_ASC_auto_deficient_social,0.6345214,F -bike_ASC_auto_deficient_univ,-0.669235,F -bike_ASC_auto_deficient_work,0.25318968,F -bike_ASC_auto_deficient_atwork,-0.8074083,F -bike_ASC_auto_sufficient_eatout,-1.2003471,F -bike_ASC_auto_sufficient_escort,-5.0631084,F -bike_ASC_auto_sufficient_othdiscr,-1.0714597,F -bike_ASC_auto_sufficient_othmaint,-2.8083024,F -bike_ASC_auto_sufficient_school,-2.1134686,F -bike_ASC_auto_sufficient_shopping,-2.5662103,F -bike_ASC_auto_sufficient_social,-1.368071,F -bike_ASC_auto_sufficient_univ,-1.9397832,F -bike_ASC_auto_sufficient_work,-1.5800232,F -bike_ASC_auto_sufficient_atwork,15.72017,F -sr2_ASC_no_auto_all,0,F -sr2_ASC_auto_deficient_eatout,0.5882345,F -sr2_ASC_auto_deficient_escort,0,F -sr2_ASC_auto_deficient_othdiscr,0.6601513,F -sr2_ASC_auto_deficient_othmaint,0.2621527,F -sr2_ASC_auto_deficient_school,0.12474365,F -sr2_ASC_auto_deficient_shopping,0.24409756,F -sr2_ASC_auto_deficient_social,1.8558528,F -sr2_ASC_auto_deficient_univ,-1.6922346,F -sr2_ASC_auto_deficient_work,-0.33803123,F -sr2_ASC_auto_deficient_atwork,-2.1102421,F -sr2_ASC_auto_sufficient_eatout,0.86280555,F -sr2_ASC_auto_sufficient_escort,0,F -sr2_ASC_auto_sufficient_othdiscr,0.49684617,F -sr2_ASC_auto_sufficient_othmaint,0.25817883,F -sr2_ASC_auto_sufficient_school,-1.6062657,F -sr2_ASC_auto_sufficient_shopping,0.19770707,F -sr2_ASC_auto_sufficient_social,0.5236025,F -sr2_ASC_auto_sufficient_univ,-1.859427,F -sr2_ASC_auto_sufficient_work,-1.0857458,F -sr2_ASC_auto_sufficient_atwork,-1.4450618,F -sr3p_ASC_no_auto_eatout,0.3219998,F -sr3p_ASC_no_auto_escort,-1.8129267,F -sr3p_ASC_no_auto_othdiscr,0.27216902,F -sr3p_ASC_no_auto_othmaint,-0.8031854,F -sr3p_ASC_no_auto_school,-6.0240827,F -sr3p_ASC_no_auto_shopping,-0.27978948,F -sr3p_ASC_no_auto_social,-1.4036902,F -sr3p_ASC_no_auto_univ,-6.056001,F -sr3p_ASC_no_auto_work,-0.5831269,F -sr3p_ASC_no_auto_atwork,0.5826626,F -sr3p_ASC_auto_deficient_eatout,0.04605236,F -sr3p_ASC_auto_deficient_escort,-0.40818766,F -sr3p_ASC_auto_deficient_othdiscr,1.0470966,F -sr3p_ASC_auto_deficient_othmaint,-1.3493925,F -sr3p_ASC_auto_deficient_school,0.7149571,F -sr3p_ASC_auto_deficient_shopping,-0.073370166,F -sr3p_ASC_auto_deficient_social,1.5007243,F -sr3p_ASC_auto_deficient_univ,-1.7277422,F -sr3p_ASC_auto_deficient_work,-0.8527042,F -sr3p_ASC_auto_deficient_atwork,-2.514658,F -sr3p_ASC_auto_sufficient_eatout,0.8468596,F -sr3p_ASC_auto_sufficient_escort,-0.05741253,F -sr3p_ASC_auto_sufficient_othdiscr,0.58850205,F -sr3p_ASC_auto_sufficient_othmaint,-0.07549867,F -sr3p_ASC_auto_sufficient_school,-1.0201935,F -sr3p_ASC_auto_sufficient_shopping,-0.077571295,F -sr3p_ASC_auto_sufficient_social,0.50617886,F -sr3p_ASC_auto_sufficient_univ,-1.9047098,F -sr3p_ASC_auto_sufficient_work,-1.4699702,F -sr3p_ASC_auto_sufficient_atwork,-1.652174,F -walk_transit_ASC_no_auto_eatout,2.5936368,F -walk_transit_ASC_no_auto_escort,-2.2172081,F -walk_transit_ASC_no_auto_othdiscr,2.2437785,F -walk_transit_ASC_no_auto_othmaint,2.5643456,F -walk_transit_ASC_no_auto_school,21.383749,F -walk_transit_ASC_no_auto_shopping,2.1067476,F -walk_transit_ASC_no_auto_social,1.3814651,F -walk_transit_ASC_no_auto_univ,8.786037,F -walk_transit_ASC_no_auto_work,5.0354166,F -walk_transit_ASC_no_auto_atwork,2.7041876,F -walk_transit_ASC_auto_deficient_eatout,-0.03896324,F -walk_transit_ASC_auto_deficient_escort,-4.960704,F -walk_transit_ASC_auto_deficient_othdiscr,0.9530884,F -walk_transit_ASC_auto_deficient_othmaint,-3.0597258,F -walk_transit_ASC_auto_deficient_school,4.120708,F -walk_transit_ASC_auto_deficient_shopping,-0.8476569,F -walk_transit_ASC_auto_deficient_social,0.97444487,F -walk_transit_ASC_auto_deficient_univ,3.1362555,F -walk_transit_ASC_auto_deficient_work,0.65302855,F -walk_transit_ASC_auto_deficient_atwork,-2.9988291,F -walk_transit_ASC_auto_sufficient_eatout,-1.1126906,F -walk_transit_ASC_auto_sufficient_escort,-4.934847,F -walk_transit_ASC_auto_sufficient_othdiscr,-0.80636793,F -walk_transit_ASC_auto_sufficient_othmaint,-1.5471172,F -walk_transit_ASC_auto_sufficient_school,0.74590874,F -walk_transit_ASC_auto_sufficient_shopping,-2.2036798,F -walk_transit_ASC_auto_sufficient_social,-0.3453759,F -walk_transit_ASC_auto_sufficient_univ,0.4731163,F -walk_transit_ASC_auto_sufficient_work,-0.8916507,F -walk_transit_ASC_auto_sufficient_atwork,-3.401027,F -drive_transit_ASC_no_auto_all,0,F -drive_transit_ASC_auto_deficient_eatout,0.5998061,F -drive_transit_ASC_auto_deficient_escort,-1.1537067,F -drive_transit_ASC_auto_deficient_othdiscr,0.3199308,F -drive_transit_ASC_auto_deficient_othmaint,-0.29943228,F -drive_transit_ASC_auto_deficient_school,5.3252654,F -drive_transit_ASC_auto_deficient_shopping,-0.41849178,F -drive_transit_ASC_auto_deficient_social,1.5627195,F -drive_transit_ASC_auto_deficient_univ,1.8501176,F -drive_transit_ASC_auto_deficient_work,0.10081567,F -drive_transit_ASC_auto_deficient_atwork,-998.8196,F -drive_transit_ASC_auto_sufficient_eatout,-0.96951586,F -drive_transit_ASC_auto_sufficient_escort,-4.6014247,F -drive_transit_ASC_auto_sufficient_othdiscr,-0.3785917,F -drive_transit_ASC_auto_sufficient_othmaint,-2.6249478,F -drive_transit_ASC_auto_sufficient_school,1.40135,F -drive_transit_ASC_auto_sufficient_shopping,-2.1718938,F -drive_transit_ASC_auto_sufficient_social,-0.61585575,F -drive_transit_ASC_auto_sufficient_univ,1.3587753,F -drive_transit_ASC_auto_sufficient_work,-1.0045459,F -drive_transit_ASC_auto_sufficient_atwork,-999.21466,F -taxi_ASC_no_auto_eatout_othdiscr_social,0.9923,F -taxi_ASC_no_auto_escort_othmaint_shopping,1.8939,F -taxi_ASC_no_auto_school_univ,-7,T -taxi_ASC_no_auto_work,4.7291,F -taxi_ASC_no_auto_atwork,4.1021,F -taxi_ASC_auto_deficient_eatout_othdiscr_social,-3.1317,F -taxi_ASC_auto_deficient_escort_othmaint_shopping,0.1766,F -taxi_ASC_auto_deficient_school,-0.3338,F -taxi_ASC_auto_deficient_univ,4.2492,F -taxi_ASC_auto_deficient_work,-1.4766,F -taxi_ASC_auto_deficient_atwork,-4.4046,F -taxi_ASC_auto_sufficient_eatout_othdiscr_social,-3.0374,F -taxi_ASC_auto_sufficient_escort_othmaint_shopping,-1.8055,F -taxi_ASC_auto_sufficient_school,-2.4294,F -taxi_ASC_auto_sufficient_univ,-0.3131,F -taxi_ASC_auto_sufficient_work,-4.8509,F -taxi_ASC_auto_sufficient_atwork,-2.8804,F -tnc_single_ASC_no_auto_eatout_othdiscr_social,1.6852,F -tnc_single_ASC_no_auto_escort_othmaint_shopping,1.8605,F -tnc_single_ASC_no_auto_school,-7,T -tnc_single_ASC_no_auto_univ,-2.519,F -tnc_single_ASC_no_auto_work,5.7855,F -tnc_single_ASC_no_auto_atwork,4.4982,F -tnc_single_ASC_auto_deficient_eatout_othdiscr_social,-2.9623,F -tnc_single_ASC_auto_deficient_escort_othmaint_shopping,0.6748,F -tnc_single_ASC_auto_deficient_school,-0.5524,F -tnc_single_ASC_auto_deficient_univ,1.0221,F -tnc_single_ASC_auto_deficient_work,-0.8013,F -tnc_single_ASC_auto_deficient_atwork,-3.7626,F -tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,-2.3239,F -tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,-1.45,F -tnc_single_ASC_auto_sufficient_school,-2.8375,F -tnc_single_ASC_auto_sufficient_univ,0.2088,F -tnc_single_ASC_auto_sufficient_work,-4.1946,F -tnc_single_ASC_auto_sufficient_atwork,-2.7988,F -tnc_shared_ASC_no_auto_eatout_othdiscr_social,0.6464,F -tnc_shared_ASC_no_auto_escort_othmaint_shopping,0.9361,F -tnc_shared_ASC_no_auto_school,-7,T -tnc_shared_ASC_no_auto_univ,-5.8116,F -tnc_shared_ASC_no_auto_work,3.2429,F -tnc_shared_ASC_no_auto_atwork,3.3672,F -tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,-4.3576,F -tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,-0.3863,F -tnc_shared_ASC_auto_deficient_school,-1.4746,F -tnc_shared_ASC_auto_deficient_univ,3.25,F -tnc_shared_ASC_auto_deficient_work,-2.1435,F -tnc_shared_ASC_auto_deficient_atwork,-4.5089,F -tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,-3.6638,F -tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,-2.4365,F -tnc_shared_ASC_auto_sufficient_school,-3.7219,F -tnc_shared_ASC_auto_sufficient_univ,-0.9068,F -tnc_shared_ASC_auto_sufficient_work,-5.3575,F -tnc_shared_ASC_auto_sufficient_atwork,-3.5397,F -joint_walk_ASC_no_auto_all,-0.21274701,F -joint_walk_ASC_auto_deficient_all,-1.9607706,F -joint_walk_ASC_auto_sufficient_all,-3.2352157,F -joint_bike_ASC_no_auto_all,-2.8671598,F -joint_bike_ASC_auto_deficient_all,-6.076415,F -joint_bike_ASC_auto_sufficient_all,-6.3760657,F -joint_sr2_ASC_no_auto_all,0,T -joint_sr2_ASC_auto_deficient_all,0,T -joint_sr2_ASC_auto_sufficient_all,0,T -joint_sr3p_ASC_no_auto_all,0.5630671,F -joint_sr3p_ASC_auto_deficient_all,-1.8841692,F -joint_sr3p_ASC_auto_sufficient_all,-2.234826,F -joint_walk_transit_ASC_no_auto_all,0.62292415,F -joint_walk_transit_ASC_auto_deficient_all,-5.1634483,F -joint_walk_transit_ASC_auto_sufficient_all,-18.264534,F -joint_drive_transit_ASC_no_auto_all,0,T -joint_drive_transit_ASC_auto_deficient_all,-5.9632215,F -joint_drive_transit_ASC_auto_sufficient_all,-8.045285,F -joint_taxi_ASC_no_auto_all,-4.5792,F -joint_taxi_ASC_auto_deficient_all,-9.8157,F -joint_taxi_ASC_auto_sufficient_all,-11.7099,T -joint_tnc_single_ASC_no_auto_all,-4.4917,F -joint_tnc_single_ASC_auto_deficient_all,-9.8961,F -joint_tnc_single_ASC_auto_sufficient_all,-14.0159,T -joint_tnc_shared_ASC_no_auto_all,-4.3002,F -joint_tnc_shared_ASC_auto_deficient_all,-11.1572,F -joint_tnc_shared_ASC_auto_sufficient_all,-13.205,T -local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,-0.090703264,F -local_bus_ASC_school_univ,-0.06508621,F -local_bus_ASC_work,0.06689507,F -walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F -walk_light_rail_ASC_school_univ,1.6814003,F -walk_light_rail_ASC_work,0.8255567,F -drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F -drive_light_rail_ASC_school_univ,1.6814003,F -drive_light_rail_ASC_work,0.8255567,F -walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F -walk_ferry_ASC_school_univ,2.0202317,F -walk_ferry_ASC_work,0.93322605,F -drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F -drive_ferry_ASC_school_univ,2.0202317,F -drive_ferry_ASC_work,0.93322605,F -express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9692316,F -express_bus_ASC_school_univ,0.32496938,F -express_bus_ASC_work,-0.5165474,F -heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7706121,F -heavy_rail_ASC_school_univ,0.96200377,F -heavy_rail_ASC_work,0.64772975,F -commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7270185,F -commuter_rail_ASC_school_univ,1.0336206,F -commuter_rail_ASC_work,0.725503,F -walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F -walk_transit_CBD_ASC_school_univ,0.672,F -walk_transit_CBD_ASC_work,0.804,F -walk_transit_CBD_ASC_atwork,0.564,F -drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F -drive_transit_CBD_ASC_school_univ,0.672,F -drive_transit_CBD_ASC_work,1.1,F -drive_transit_CBD_ASC_atwork,0.564,F +coefficient_name,value,constrain +coef_one,1,T +coef_nest_root,1.00,T +coef_nest_AUTO,0.72,T +coef_nest_AUTO_DRIVEALONE,0.35,T +coef_nest_AUTO_SHAREDRIDE2,0.35,T +coef_nest_AUTO_SHAREDRIDE3,0.35,T +coef_nest_NONMOTORIZED,0.72,T +coef_nest_TRANSIT,0.72,T +coef_nest_TRANSIT_WALKACCESS,0.5,T +coef_nest_TRANSIT_DRIVEACCESS,0.5,T +coef_nest_RIDEHAIL,0.36,T +coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,-0.0175,F +coef_ivt_school_univ,-0.0224,F +coef_ivt_work,-0.0134,F +coef_ivt_atwork,-0.0188,F +coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,15,F +coef_topology_walk_multiplier_atwork,7.5,F +coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,20,F +coef_topology_bike_multiplier_atwork,10,F +coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,2.2,F +coef_topology_trn_multiplier_atwork,2,F +coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F +coef_age1619_da_multiplier_school_univ,-1.3813,F +coef_age1619_da_multiplier_atwork,0.0032336,F +coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F +coef_age010_trn_multiplier_school_univ,-1.5548,F +coef_age010_trn_multiplier_atwork,0.000722,F +coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,-1.366,F +coef_age16p_sr_multiplier_school_univ_work_atwork,0,F +coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,0,F +coef_hhsize1_sr_multiplier_work,-0.734588,F +coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,0,F +coef_hhsize2_sr_multiplier_school_univ,-0.6359,F +walk_ASC_no_auto_eatout,5.1251173,F +walk_ASC_no_auto_escort,2.8012068,F +walk_ASC_no_auto_othdiscr,3.2665946,F +walk_ASC_no_auto_othmaint,1.287299,F +walk_ASC_no_auto_school,18.414557,F +walk_ASC_no_auto_shopping,2.3768773,F +walk_ASC_no_auto_social,1.8680915,F +walk_ASC_no_auto_univ,6.408967,F +walk_ASC_no_auto_work,5.7672157,F +walk_ASC_no_auto_atwork,6.669213,F +walk_ASC_auto_deficient_eatout,3.274605,F +walk_ASC_auto_deficient_escort,-0.90204656,F +walk_ASC_auto_deficient_othdiscr,2.2494075,F +walk_ASC_auto_deficient_othmaint,1.3690404,F +walk_ASC_auto_deficient_school,3.2573624,F +walk_ASC_auto_deficient_shopping,2.2701733,F +walk_ASC_auto_deficient_social,2.870184,F +walk_ASC_auto_deficient_univ,4.50591,F +walk_ASC_auto_deficient_work,2.4010417,F +walk_ASC_auto_deficient_atwork,0.92546093,F +walk_ASC_auto_sufficient_eatout,1.5516903,F +walk_ASC_auto_sufficient_escort,-0.8116066,F +walk_ASC_auto_sufficient_othdiscr,1.2633476,F +walk_ASC_auto_sufficient_othmaint,0.7999634,F +walk_ASC_auto_sufficient_school,0.6476856,F +walk_ASC_auto_sufficient_shopping,0.7312663,F +walk_ASC_auto_sufficient_social,1.7072186,F +walk_ASC_auto_sufficient_univ,1.0607665,F +walk_ASC_auto_sufficient_work,0.053265337,F +walk_ASC_auto_sufficient_atwork,0.677216,F +bike_ASC_no_auto_eatout,0.86807096,F +bike_ASC_no_auto_escort,-0.716212,F +bike_ASC_no_auto_othdiscr,-0.3764232,F +bike_ASC_no_auto_othmaint,1.5394334,F +bike_ASC_no_auto_school,12.098735,F +bike_ASC_no_auto_shopping,0.8341555,F +bike_ASC_no_auto_social,0.02058321,F +bike_ASC_no_auto_univ,4.2945156,F +bike_ASC_no_auto_work,3.1940088,F +bike_ASC_no_auto_atwork,-0.90725845,F +bike_ASC_auto_deficient_eatout,-1.5691106,F +bike_ASC_auto_deficient_escort,-4.527928,F +bike_ASC_auto_deficient_othdiscr,-0.09246834,F +bike_ASC_auto_deficient_othmaint,-1.5184649,F +bike_ASC_auto_deficient_school,-0.5280678,F +bike_ASC_auto_deficient_shopping,-0.87584466,F +bike_ASC_auto_deficient_social,0.6345214,F +bike_ASC_auto_deficient_univ,-0.669235,F +bike_ASC_auto_deficient_work,0.25318968,F +bike_ASC_auto_deficient_atwork,-0.8074083,F +bike_ASC_auto_sufficient_eatout,-1.2003471,F +bike_ASC_auto_sufficient_escort,-5.0631084,F +bike_ASC_auto_sufficient_othdiscr,-1.0714597,F +bike_ASC_auto_sufficient_othmaint,-2.8083024,F +bike_ASC_auto_sufficient_school,-2.1134686,F +bike_ASC_auto_sufficient_shopping,-2.5662103,F +bike_ASC_auto_sufficient_social,-1.368071,F +bike_ASC_auto_sufficient_univ,-1.9397832,F +bike_ASC_auto_sufficient_work,-1.5800232,F +bike_ASC_auto_sufficient_atwork,15.72017,F +sr2_ASC_no_auto_all,0,F +sr2_ASC_auto_deficient_eatout,0.5882345,F +sr2_ASC_auto_deficient_escort,0,F +sr2_ASC_auto_deficient_othdiscr,0.6601513,F +sr2_ASC_auto_deficient_othmaint,0.2621527,F +sr2_ASC_auto_deficient_school,0.12474365,F +sr2_ASC_auto_deficient_shopping,0.24409756,F +sr2_ASC_auto_deficient_social,1.8558528,F +sr2_ASC_auto_deficient_univ,-1.6922346,F +sr2_ASC_auto_deficient_work,-0.33803123,F +sr2_ASC_auto_deficient_atwork,-2.1102421,F +sr2_ASC_auto_sufficient_eatout,0.86280555,F +sr2_ASC_auto_sufficient_escort,0,F +sr2_ASC_auto_sufficient_othdiscr,0.49684617,F +sr2_ASC_auto_sufficient_othmaint,0.25817883,F +sr2_ASC_auto_sufficient_school,-1.6062657,F +sr2_ASC_auto_sufficient_shopping,0.19770707,F +sr2_ASC_auto_sufficient_social,0.5236025,F +sr2_ASC_auto_sufficient_univ,-1.859427,F +sr2_ASC_auto_sufficient_work,-1.0857458,F +sr2_ASC_auto_sufficient_atwork,-1.4450618,F +sr3p_ASC_no_auto_eatout,0.3219998,F +sr3p_ASC_no_auto_escort,-1.8129267,F +sr3p_ASC_no_auto_othdiscr,0.27216902,F +sr3p_ASC_no_auto_othmaint,-0.8031854,F +sr3p_ASC_no_auto_school,-6.0240827,F +sr3p_ASC_no_auto_shopping,-0.27978948,F +sr3p_ASC_no_auto_social,-1.4036902,F +sr3p_ASC_no_auto_univ,-6.056001,F +sr3p_ASC_no_auto_work,-0.5831269,F +sr3p_ASC_no_auto_atwork,0.5826626,F +sr3p_ASC_auto_deficient_eatout,0.04605236,F +sr3p_ASC_auto_deficient_escort,-0.40818766,F +sr3p_ASC_auto_deficient_othdiscr,1.0470966,F +sr3p_ASC_auto_deficient_othmaint,-1.3493925,F +sr3p_ASC_auto_deficient_school,0.7149571,F +sr3p_ASC_auto_deficient_shopping,-0.073370166,F +sr3p_ASC_auto_deficient_social,1.5007243,F +sr3p_ASC_auto_deficient_univ,-1.7277422,F +sr3p_ASC_auto_deficient_work,-0.8527042,F +sr3p_ASC_auto_deficient_atwork,-2.514658,F +sr3p_ASC_auto_sufficient_eatout,0.8468596,F +sr3p_ASC_auto_sufficient_escort,-0.05741253,F +sr3p_ASC_auto_sufficient_othdiscr,0.58850205,F +sr3p_ASC_auto_sufficient_othmaint,-0.07549867,F +sr3p_ASC_auto_sufficient_school,-1.0201935,F +sr3p_ASC_auto_sufficient_shopping,-0.077571295,F +sr3p_ASC_auto_sufficient_social,0.50617886,F +sr3p_ASC_auto_sufficient_univ,-1.9047098,F +sr3p_ASC_auto_sufficient_work,-1.4699702,F +sr3p_ASC_auto_sufficient_atwork,-1.652174,F +walk_transit_ASC_no_auto_eatout,2.5936368,F +walk_transit_ASC_no_auto_escort,-2.2172081,F +walk_transit_ASC_no_auto_othdiscr,2.2437785,F +walk_transit_ASC_no_auto_othmaint,2.5643456,F +walk_transit_ASC_no_auto_school,21.383749,F +walk_transit_ASC_no_auto_shopping,2.1067476,F +walk_transit_ASC_no_auto_social,1.3814651,F +walk_transit_ASC_no_auto_univ,8.786037,F +walk_transit_ASC_no_auto_work,5.0354166,F +walk_transit_ASC_no_auto_atwork,2.7041876,F +walk_transit_ASC_auto_deficient_eatout,-0.03896324,F +walk_transit_ASC_auto_deficient_escort,-4.960704,F +walk_transit_ASC_auto_deficient_othdiscr,0.9530884,F +walk_transit_ASC_auto_deficient_othmaint,-3.0597258,F +walk_transit_ASC_auto_deficient_school,4.120708,F +walk_transit_ASC_auto_deficient_shopping,-0.8476569,F +walk_transit_ASC_auto_deficient_social,0.97444487,F +walk_transit_ASC_auto_deficient_univ,3.1362555,F +walk_transit_ASC_auto_deficient_work,0.65302855,F +walk_transit_ASC_auto_deficient_atwork,-2.9988291,F +walk_transit_ASC_auto_sufficient_eatout,-1.1126906,F +walk_transit_ASC_auto_sufficient_escort,-4.934847,F +walk_transit_ASC_auto_sufficient_othdiscr,-0.80636793,F +walk_transit_ASC_auto_sufficient_othmaint,-1.5471172,F +walk_transit_ASC_auto_sufficient_school,0.74590874,F +walk_transit_ASC_auto_sufficient_shopping,-2.2036798,F +walk_transit_ASC_auto_sufficient_social,-0.3453759,F +walk_transit_ASC_auto_sufficient_univ,0.4731163,F +walk_transit_ASC_auto_sufficient_work,-0.8916507,F +walk_transit_ASC_auto_sufficient_atwork,-3.401027,F +drive_transit_ASC_no_auto_all,0,F +drive_transit_ASC_auto_deficient_eatout,0.5998061,F +drive_transit_ASC_auto_deficient_escort,-1.1537067,F +drive_transit_ASC_auto_deficient_othdiscr,0.3199308,F +drive_transit_ASC_auto_deficient_othmaint,-0.29943228,F +drive_transit_ASC_auto_deficient_school,5.3252654,F +drive_transit_ASC_auto_deficient_shopping,-0.41849178,F +drive_transit_ASC_auto_deficient_social,1.5627195,F +drive_transit_ASC_auto_deficient_univ,1.8501176,F +drive_transit_ASC_auto_deficient_work,0.10081567,F +drive_transit_ASC_auto_deficient_atwork,-998.8196,F +drive_transit_ASC_auto_sufficient_eatout,-0.96951586,F +drive_transit_ASC_auto_sufficient_escort,-4.6014247,F +drive_transit_ASC_auto_sufficient_othdiscr,-0.3785917,F +drive_transit_ASC_auto_sufficient_othmaint,-2.6249478,F +drive_transit_ASC_auto_sufficient_school,1.40135,F +drive_transit_ASC_auto_sufficient_shopping,-2.1718938,F +drive_transit_ASC_auto_sufficient_social,-0.61585575,F +drive_transit_ASC_auto_sufficient_univ,1.3587753,F +drive_transit_ASC_auto_sufficient_work,-1.0045459,F +drive_transit_ASC_auto_sufficient_atwork,-999.21466,F +taxi_ASC_no_auto_eatout_othdiscr_social,0.9923,F +taxi_ASC_no_auto_escort_othmaint_shopping,1.8939,F +taxi_ASC_no_auto_school_univ,-7,T +taxi_ASC_no_auto_work,4.7291,F +taxi_ASC_no_auto_atwork,4.1021,F +taxi_ASC_auto_deficient_eatout_othdiscr_social,-3.1317,F +taxi_ASC_auto_deficient_escort_othmaint_shopping,0.1766,F +taxi_ASC_auto_deficient_school,-0.3338,F +taxi_ASC_auto_deficient_univ,4.2492,F +taxi_ASC_auto_deficient_work,-1.4766,F +taxi_ASC_auto_deficient_atwork,-4.4046,F +taxi_ASC_auto_sufficient_eatout_othdiscr_social,-3.0374,F +taxi_ASC_auto_sufficient_escort_othmaint_shopping,-1.8055,F +taxi_ASC_auto_sufficient_school,-2.4294,F +taxi_ASC_auto_sufficient_univ,-0.3131,F +taxi_ASC_auto_sufficient_work,-4.8509,F +taxi_ASC_auto_sufficient_atwork,-2.8804,F +tnc_single_ASC_no_auto_eatout_othdiscr_social,1.6852,F +tnc_single_ASC_no_auto_escort_othmaint_shopping,1.8605,F +tnc_single_ASC_no_auto_school,-7,T +tnc_single_ASC_no_auto_univ,-2.519,F +tnc_single_ASC_no_auto_work,5.7855,F +tnc_single_ASC_no_auto_atwork,4.4982,F +tnc_single_ASC_auto_deficient_eatout_othdiscr_social,-2.9623,F +tnc_single_ASC_auto_deficient_escort_othmaint_shopping,0.6748,F +tnc_single_ASC_auto_deficient_school,-0.5524,F +tnc_single_ASC_auto_deficient_univ,1.0221,F +tnc_single_ASC_auto_deficient_work,-0.8013,F +tnc_single_ASC_auto_deficient_atwork,-3.7626,F +tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,-2.3239,F +tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,-1.45,F +tnc_single_ASC_auto_sufficient_school,-2.8375,F +tnc_single_ASC_auto_sufficient_univ,0.2088,F +tnc_single_ASC_auto_sufficient_work,-4.1946,F +tnc_single_ASC_auto_sufficient_atwork,-2.7988,F +tnc_shared_ASC_no_auto_eatout_othdiscr_social,0.6464,F +tnc_shared_ASC_no_auto_escort_othmaint_shopping,0.9361,F +tnc_shared_ASC_no_auto_school,-7,T +tnc_shared_ASC_no_auto_univ,-5.8116,F +tnc_shared_ASC_no_auto_work,3.2429,F +tnc_shared_ASC_no_auto_atwork,3.3672,F +tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,-4.3576,F +tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,-0.3863,F +tnc_shared_ASC_auto_deficient_school,-1.4746,F +tnc_shared_ASC_auto_deficient_univ,3.25,F +tnc_shared_ASC_auto_deficient_work,-2.1435,F +tnc_shared_ASC_auto_deficient_atwork,-4.5089,F +tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,-3.6638,F +tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,-2.4365,F +tnc_shared_ASC_auto_sufficient_school,-3.7219,F +tnc_shared_ASC_auto_sufficient_univ,-0.9068,F +tnc_shared_ASC_auto_sufficient_work,-5.3575,F +tnc_shared_ASC_auto_sufficient_atwork,-3.5397,F +joint_walk_ASC_no_auto_all,-0.21274701,F +joint_walk_ASC_auto_deficient_all,-1.9607706,F +joint_walk_ASC_auto_sufficient_all,-3.2352157,F +joint_bike_ASC_no_auto_all,-2.8671598,F +joint_bike_ASC_auto_deficient_all,-6.076415,F +joint_bike_ASC_auto_sufficient_all,-6.3760657,F +joint_sr2_ASC_no_auto_all,0,T +joint_sr2_ASC_auto_deficient_all,0,T +joint_sr2_ASC_auto_sufficient_all,0,T +joint_sr3p_ASC_no_auto_all,0.5630671,F +joint_sr3p_ASC_auto_deficient_all,-1.8841692,F +joint_sr3p_ASC_auto_sufficient_all,-2.234826,F +joint_walk_transit_ASC_no_auto_all,0.62292415,F +joint_walk_transit_ASC_auto_deficient_all,-5.1634483,F +joint_walk_transit_ASC_auto_sufficient_all,-18.264534,F +joint_drive_transit_ASC_no_auto_all,0,T +joint_drive_transit_ASC_auto_deficient_all,-5.9632215,F +joint_drive_transit_ASC_auto_sufficient_all,-8.045285,F +joint_taxi_ASC_no_auto_all,-4.5792,F +joint_taxi_ASC_auto_deficient_all,-9.8157,F +joint_taxi_ASC_auto_sufficient_all,-11.7099,T +joint_tnc_single_ASC_no_auto_all,-4.4917,F +joint_tnc_single_ASC_auto_deficient_all,-9.8961,F +joint_tnc_single_ASC_auto_sufficient_all,-14.0159,T +joint_tnc_shared_ASC_no_auto_all,-4.3002,F +joint_tnc_shared_ASC_auto_deficient_all,-11.1572,F +joint_tnc_shared_ASC_auto_sufficient_all,-13.205,T +local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,-0.090703264,F +local_bus_ASC_school_univ,-0.06508621,F +local_bus_ASC_work,0.06689507,F +walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F +walk_light_rail_ASC_school_univ,1.6814003,F +walk_light_rail_ASC_work,0.8255567,F +drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F +drive_light_rail_ASC_school_univ,1.6814003,F +drive_light_rail_ASC_work,0.8255567,F +walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F +walk_ferry_ASC_school_univ,2.0202317,F +walk_ferry_ASC_work,0.93322605,F +drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F +drive_ferry_ASC_school_univ,2.0202317,F +drive_ferry_ASC_work,0.93322605,F +express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9692316,F +express_bus_ASC_school_univ,0.32496938,F +express_bus_ASC_work,-0.5165474,F +heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7706121,F +heavy_rail_ASC_school_univ,0.96200377,F +heavy_rail_ASC_work,0.64772975,F +commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7270185,F +commuter_rail_ASC_school_univ,1.0336206,F +commuter_rail_ASC_work,0.725503,F +walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F +walk_transit_CBD_ASC_school_univ,0.672,F +walk_transit_CBD_ASC_work,0.804,F +walk_transit_CBD_ASC_atwork,0.564,F +drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F +drive_transit_CBD_ASC_school_univ,0.672,F +drive_transit_CBD_ASC_work,1.1,F +drive_transit_CBD_ASC_atwork,0.564,F diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_coeffs_template.csv b/activitysim/examples/placeholder_psrc/configs/tour_mode_choice_coeffs_template.csv similarity index 99% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_coeffs_template.csv rename to activitysim/examples/placeholder_psrc/configs/tour_mode_choice_coeffs_template.csv index 2e97238f2c..b1b009a3f0 100755 --- a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tour_mode_choice_coeffs_template.csv +++ b/activitysim/examples/placeholder_psrc/configs/tour_mode_choice_coeffs_template.csv @@ -1,87 +1,87 @@ -coefficient_name,eatout,escort,othdiscr,othmaint,school,shopping,social,univ,work,atwork -#same for all segments,,,,,,,,,, -coef_one,,,,,,,,,, -coef_nest_root,,,,,,,,,, -coef_nest_AUTO,,,,,,,,,, -coef_nest_AUTO_DRIVEALONE,,,,,,,,,, -coef_nest_AUTO_SHAREDRIDE2,,,,,,,,,, -coef_nest_AUTO_SHAREDRIDE3,,,,,,,,,, -coef_nest_NONMOTORIZED,,,,,,,,,, -coef_nest_TRANSIT,,,,,,,,,, -coef_nest_TRANSIT_WALKACCESS,,,,,,,,,, -coef_nest_TRANSIT_DRIVEACCESS,,,,,,,,,, -coef_nest_RIDEHAIL,,,,,,,,,, -#,,,,,,,,,, -coef_ivt,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_work,coef_ivt_atwork -coef_topology_walk_multiplier,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_atwork -coef_topology_bike_multiplier,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_atwork -coef_topology_trn_multiplier,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_atwork -coef_age1619_da_multiplier,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_atwork -coef_age010_trn_multiplier,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_atwork -coef_age16p_sr_multiplier,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork -coef_hhsize1_sr_multiplier,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_work,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork -coef_hhsize2_sr_multiplier,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork -walk_ASC_no_auto,walk_ASC_no_auto_eatout,walk_ASC_no_auto_escort,walk_ASC_no_auto_othdiscr,walk_ASC_no_auto_othmaint,walk_ASC_no_auto_school,walk_ASC_no_auto_shopping,walk_ASC_no_auto_social,walk_ASC_no_auto_univ,walk_ASC_no_auto_work,walk_ASC_no_auto_atwork -walk_ASC_auto_deficient,walk_ASC_auto_deficient_eatout,walk_ASC_auto_deficient_escort,walk_ASC_auto_deficient_othdiscr,walk_ASC_auto_deficient_othmaint,walk_ASC_auto_deficient_school,walk_ASC_auto_deficient_shopping,walk_ASC_auto_deficient_social,walk_ASC_auto_deficient_univ,walk_ASC_auto_deficient_work,walk_ASC_auto_deficient_atwork -walk_ASC_auto_sufficient,walk_ASC_auto_sufficient_eatout,walk_ASC_auto_sufficient_escort,walk_ASC_auto_sufficient_othdiscr,walk_ASC_auto_sufficient_othmaint,walk_ASC_auto_sufficient_school,walk_ASC_auto_sufficient_shopping,walk_ASC_auto_sufficient_social,walk_ASC_auto_sufficient_univ,walk_ASC_auto_sufficient_work,walk_ASC_auto_sufficient_atwork -bike_ASC_no_auto,bike_ASC_no_auto_eatout,bike_ASC_no_auto_escort,bike_ASC_no_auto_othdiscr,bike_ASC_no_auto_othmaint,bike_ASC_no_auto_school,bike_ASC_no_auto_shopping,bike_ASC_no_auto_social,bike_ASC_no_auto_univ,bike_ASC_no_auto_work,bike_ASC_no_auto_atwork -bike_ASC_auto_deficient,bike_ASC_auto_deficient_eatout,bike_ASC_auto_deficient_escort,bike_ASC_auto_deficient_othdiscr,bike_ASC_auto_deficient_othmaint,bike_ASC_auto_deficient_school,bike_ASC_auto_deficient_shopping,bike_ASC_auto_deficient_social,bike_ASC_auto_deficient_univ,bike_ASC_auto_deficient_work,bike_ASC_auto_deficient_atwork -bike_ASC_auto_sufficient,bike_ASC_auto_sufficient_eatout,bike_ASC_auto_sufficient_escort,bike_ASC_auto_sufficient_othdiscr,bike_ASC_auto_sufficient_othmaint,bike_ASC_auto_sufficient_school,bike_ASC_auto_sufficient_shopping,bike_ASC_auto_sufficient_social,bike_ASC_auto_sufficient_univ,bike_ASC_auto_sufficient_work,bike_ASC_auto_sufficient_atwork -sr2_ASC_no_auto,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all -sr2_ASC_auto_deficient,sr2_ASC_auto_deficient_eatout,sr2_ASC_auto_deficient_escort,sr2_ASC_auto_deficient_othdiscr,sr2_ASC_auto_deficient_othmaint,sr2_ASC_auto_deficient_school,sr2_ASC_auto_deficient_shopping,sr2_ASC_auto_deficient_social,sr2_ASC_auto_deficient_univ,sr2_ASC_auto_deficient_work,sr2_ASC_auto_deficient_atwork -sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient_eatout,sr2_ASC_auto_sufficient_escort,sr2_ASC_auto_sufficient_othdiscr,sr2_ASC_auto_sufficient_othmaint,sr2_ASC_auto_sufficient_school,sr2_ASC_auto_sufficient_shopping,sr2_ASC_auto_sufficient_social,sr2_ASC_auto_sufficient_univ,sr2_ASC_auto_sufficient_work,sr2_ASC_auto_sufficient_atwork -sr3p_ASC_no_auto,sr3p_ASC_no_auto_eatout,sr3p_ASC_no_auto_escort,sr3p_ASC_no_auto_othdiscr,sr3p_ASC_no_auto_othmaint,sr3p_ASC_no_auto_school,sr3p_ASC_no_auto_shopping,sr3p_ASC_no_auto_social,sr3p_ASC_no_auto_univ,sr3p_ASC_no_auto_work,sr3p_ASC_no_auto_atwork -sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient_eatout,sr3p_ASC_auto_deficient_escort,sr3p_ASC_auto_deficient_othdiscr,sr3p_ASC_auto_deficient_othmaint,sr3p_ASC_auto_deficient_school,sr3p_ASC_auto_deficient_shopping,sr3p_ASC_auto_deficient_social,sr3p_ASC_auto_deficient_univ,sr3p_ASC_auto_deficient_work,sr3p_ASC_auto_deficient_atwork -sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient_eatout,sr3p_ASC_auto_sufficient_escort,sr3p_ASC_auto_sufficient_othdiscr,sr3p_ASC_auto_sufficient_othmaint,sr3p_ASC_auto_sufficient_school,sr3p_ASC_auto_sufficient_shopping,sr3p_ASC_auto_sufficient_social,sr3p_ASC_auto_sufficient_univ,sr3p_ASC_auto_sufficient_work,sr3p_ASC_auto_sufficient_atwork -walk_transit_ASC_no_auto,walk_transit_ASC_no_auto_eatout,walk_transit_ASC_no_auto_escort,walk_transit_ASC_no_auto_othdiscr,walk_transit_ASC_no_auto_othmaint,walk_transit_ASC_no_auto_school,walk_transit_ASC_no_auto_shopping,walk_transit_ASC_no_auto_social,walk_transit_ASC_no_auto_univ,walk_transit_ASC_no_auto_work,walk_transit_ASC_no_auto_atwork -walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient_eatout,walk_transit_ASC_auto_deficient_escort,walk_transit_ASC_auto_deficient_othdiscr,walk_transit_ASC_auto_deficient_othmaint,walk_transit_ASC_auto_deficient_school,walk_transit_ASC_auto_deficient_shopping,walk_transit_ASC_auto_deficient_social,walk_transit_ASC_auto_deficient_univ,walk_transit_ASC_auto_deficient_work,walk_transit_ASC_auto_deficient_atwork -walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient_eatout,walk_transit_ASC_auto_sufficient_escort,walk_transit_ASC_auto_sufficient_othdiscr,walk_transit_ASC_auto_sufficient_othmaint,walk_transit_ASC_auto_sufficient_school,walk_transit_ASC_auto_sufficient_shopping,walk_transit_ASC_auto_sufficient_social,walk_transit_ASC_auto_sufficient_univ,walk_transit_ASC_auto_sufficient_work,walk_transit_ASC_auto_sufficient_atwork -drive_transit_ASC_no_auto,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all -drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient_eatout,drive_transit_ASC_auto_deficient_escort,drive_transit_ASC_auto_deficient_othdiscr,drive_transit_ASC_auto_deficient_othmaint,drive_transit_ASC_auto_deficient_school,drive_transit_ASC_auto_deficient_shopping,drive_transit_ASC_auto_deficient_social,drive_transit_ASC_auto_deficient_univ,drive_transit_ASC_auto_deficient_work,drive_transit_ASC_auto_deficient_atwork -drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient_eatout,drive_transit_ASC_auto_sufficient_escort,drive_transit_ASC_auto_sufficient_othdiscr,drive_transit_ASC_auto_sufficient_othmaint,drive_transit_ASC_auto_sufficient_school,drive_transit_ASC_auto_sufficient_shopping,drive_transit_ASC_auto_sufficient_social,drive_transit_ASC_auto_sufficient_univ,drive_transit_ASC_auto_sufficient_work,drive_transit_ASC_auto_sufficient_atwork -taxi_ASC_no_auto,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_work,taxi_ASC_no_auto_atwork -taxi_ASC_auto_deficient,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_school,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_univ,taxi_ASC_auto_deficient_work,taxi_ASC_auto_deficient_atwork -taxi_ASC_auto_sufficient,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_school,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_univ,taxi_ASC_auto_sufficient_work,taxi_ASC_auto_sufficient_atwork -tnc_single_ASC_no_auto,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_school,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_univ,tnc_single_ASC_no_auto_work,tnc_single_ASC_no_auto_atwork -tnc_single_ASC_auto_deficient,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_school,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_univ,tnc_single_ASC_auto_deficient_work,tnc_single_ASC_auto_deficient_atwork -tnc_single_ASC_auto_sufficient,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_school,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_univ,tnc_single_ASC_auto_sufficient_work,tnc_single_ASC_auto_sufficient_atwork -tnc_shared_ASC_no_auto,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_school,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_univ,tnc_shared_ASC_no_auto_work,tnc_shared_ASC_no_auto_atwork -tnc_shared_ASC_auto_deficient,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_school,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_univ,tnc_shared_ASC_auto_deficient_work,tnc_shared_ASC_auto_deficient_atwork -tnc_shared_ASC_auto_sufficient,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_school,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_univ,tnc_shared_ASC_auto_sufficient_work,tnc_shared_ASC_auto_sufficient_atwork -joint_walk_ASC_no_auto,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all -joint_walk_ASC_auto_deficient,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all -joint_walk_ASC_auto_sufficient,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all -joint_bike_ASC_no_auto,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all -joint_bike_ASC_auto_deficient,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all -joint_bike_ASC_auto_sufficient,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all -joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all -joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all -joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all -joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all -joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all -joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all -joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all -joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all -joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all -joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all -joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all -joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all -joint_taxi_ASC_no_auto,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all -joint_taxi_ASC_auto_deficient,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all -joint_taxi_ASC_auto_sufficient,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all -joint_tnc_single_ASC_no_auto,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all -joint_tnc_single_ASC_auto_deficient,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all -joint_tnc_single_ASC_auto_sufficient,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all -joint_tnc_shared_ASC_no_auto,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all -joint_tnc_shared_ASC_auto_deficient,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all -joint_tnc_shared_ASC_auto_sufficient,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all -local_bus_ASC,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_school_univ,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_school_univ,local_bus_ASC_work,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -walk_light_rail_ASC,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_work,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -drive_light_rail_ASC,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_work,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -walk_ferry_ASC,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_work,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -drive_ferry_ASC,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_work,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -express_bus_ASC,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_work,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -heavy_rail_ASC,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_work,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -commuter_rail_ASC,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_work,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -walk_transit_CBD_ASC,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_work,walk_transit_CBD_ASC_atwork -drive_transit_CBD_ASC,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_work,drive_transit_CBD_ASC_atwork +coefficient_name,eatout,escort,othdiscr,othmaint,school,shopping,social,univ,work,atwork +#same for all segments,,,,,,,,,, +coef_one,,,,,,,,,, +coef_nest_root,,,,,,,,,, +coef_nest_AUTO,,,,,,,,,, +coef_nest_AUTO_DRIVEALONE,,,,,,,,,, +coef_nest_AUTO_SHAREDRIDE2,,,,,,,,,, +coef_nest_AUTO_SHAREDRIDE3,,,,,,,,,, +coef_nest_NONMOTORIZED,,,,,,,,,, +coef_nest_TRANSIT,,,,,,,,,, +coef_nest_TRANSIT_WALKACCESS,,,,,,,,,, +coef_nest_TRANSIT_DRIVEACCESS,,,,,,,,,, +coef_nest_RIDEHAIL,,,,,,,,,, +#,,,,,,,,,, +coef_ivt,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_work,coef_ivt_atwork +coef_topology_walk_multiplier,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_atwork +coef_topology_bike_multiplier,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_atwork +coef_topology_trn_multiplier,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_atwork +coef_age1619_da_multiplier,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_atwork +coef_age010_trn_multiplier,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_atwork +coef_age16p_sr_multiplier,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork +coef_hhsize1_sr_multiplier,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_work,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork +coef_hhsize2_sr_multiplier,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork +walk_ASC_no_auto,walk_ASC_no_auto_eatout,walk_ASC_no_auto_escort,walk_ASC_no_auto_othdiscr,walk_ASC_no_auto_othmaint,walk_ASC_no_auto_school,walk_ASC_no_auto_shopping,walk_ASC_no_auto_social,walk_ASC_no_auto_univ,walk_ASC_no_auto_work,walk_ASC_no_auto_atwork +walk_ASC_auto_deficient,walk_ASC_auto_deficient_eatout,walk_ASC_auto_deficient_escort,walk_ASC_auto_deficient_othdiscr,walk_ASC_auto_deficient_othmaint,walk_ASC_auto_deficient_school,walk_ASC_auto_deficient_shopping,walk_ASC_auto_deficient_social,walk_ASC_auto_deficient_univ,walk_ASC_auto_deficient_work,walk_ASC_auto_deficient_atwork +walk_ASC_auto_sufficient,walk_ASC_auto_sufficient_eatout,walk_ASC_auto_sufficient_escort,walk_ASC_auto_sufficient_othdiscr,walk_ASC_auto_sufficient_othmaint,walk_ASC_auto_sufficient_school,walk_ASC_auto_sufficient_shopping,walk_ASC_auto_sufficient_social,walk_ASC_auto_sufficient_univ,walk_ASC_auto_sufficient_work,walk_ASC_auto_sufficient_atwork +bike_ASC_no_auto,bike_ASC_no_auto_eatout,bike_ASC_no_auto_escort,bike_ASC_no_auto_othdiscr,bike_ASC_no_auto_othmaint,bike_ASC_no_auto_school,bike_ASC_no_auto_shopping,bike_ASC_no_auto_social,bike_ASC_no_auto_univ,bike_ASC_no_auto_work,bike_ASC_no_auto_atwork +bike_ASC_auto_deficient,bike_ASC_auto_deficient_eatout,bike_ASC_auto_deficient_escort,bike_ASC_auto_deficient_othdiscr,bike_ASC_auto_deficient_othmaint,bike_ASC_auto_deficient_school,bike_ASC_auto_deficient_shopping,bike_ASC_auto_deficient_social,bike_ASC_auto_deficient_univ,bike_ASC_auto_deficient_work,bike_ASC_auto_deficient_atwork +bike_ASC_auto_sufficient,bike_ASC_auto_sufficient_eatout,bike_ASC_auto_sufficient_escort,bike_ASC_auto_sufficient_othdiscr,bike_ASC_auto_sufficient_othmaint,bike_ASC_auto_sufficient_school,bike_ASC_auto_sufficient_shopping,bike_ASC_auto_sufficient_social,bike_ASC_auto_sufficient_univ,bike_ASC_auto_sufficient_work,bike_ASC_auto_sufficient_atwork +sr2_ASC_no_auto,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all +sr2_ASC_auto_deficient,sr2_ASC_auto_deficient_eatout,sr2_ASC_auto_deficient_escort,sr2_ASC_auto_deficient_othdiscr,sr2_ASC_auto_deficient_othmaint,sr2_ASC_auto_deficient_school,sr2_ASC_auto_deficient_shopping,sr2_ASC_auto_deficient_social,sr2_ASC_auto_deficient_univ,sr2_ASC_auto_deficient_work,sr2_ASC_auto_deficient_atwork +sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient_eatout,sr2_ASC_auto_sufficient_escort,sr2_ASC_auto_sufficient_othdiscr,sr2_ASC_auto_sufficient_othmaint,sr2_ASC_auto_sufficient_school,sr2_ASC_auto_sufficient_shopping,sr2_ASC_auto_sufficient_social,sr2_ASC_auto_sufficient_univ,sr2_ASC_auto_sufficient_work,sr2_ASC_auto_sufficient_atwork +sr3p_ASC_no_auto,sr3p_ASC_no_auto_eatout,sr3p_ASC_no_auto_escort,sr3p_ASC_no_auto_othdiscr,sr3p_ASC_no_auto_othmaint,sr3p_ASC_no_auto_school,sr3p_ASC_no_auto_shopping,sr3p_ASC_no_auto_social,sr3p_ASC_no_auto_univ,sr3p_ASC_no_auto_work,sr3p_ASC_no_auto_atwork +sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient_eatout,sr3p_ASC_auto_deficient_escort,sr3p_ASC_auto_deficient_othdiscr,sr3p_ASC_auto_deficient_othmaint,sr3p_ASC_auto_deficient_school,sr3p_ASC_auto_deficient_shopping,sr3p_ASC_auto_deficient_social,sr3p_ASC_auto_deficient_univ,sr3p_ASC_auto_deficient_work,sr3p_ASC_auto_deficient_atwork +sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient_eatout,sr3p_ASC_auto_sufficient_escort,sr3p_ASC_auto_sufficient_othdiscr,sr3p_ASC_auto_sufficient_othmaint,sr3p_ASC_auto_sufficient_school,sr3p_ASC_auto_sufficient_shopping,sr3p_ASC_auto_sufficient_social,sr3p_ASC_auto_sufficient_univ,sr3p_ASC_auto_sufficient_work,sr3p_ASC_auto_sufficient_atwork +walk_transit_ASC_no_auto,walk_transit_ASC_no_auto_eatout,walk_transit_ASC_no_auto_escort,walk_transit_ASC_no_auto_othdiscr,walk_transit_ASC_no_auto_othmaint,walk_transit_ASC_no_auto_school,walk_transit_ASC_no_auto_shopping,walk_transit_ASC_no_auto_social,walk_transit_ASC_no_auto_univ,walk_transit_ASC_no_auto_work,walk_transit_ASC_no_auto_atwork +walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient_eatout,walk_transit_ASC_auto_deficient_escort,walk_transit_ASC_auto_deficient_othdiscr,walk_transit_ASC_auto_deficient_othmaint,walk_transit_ASC_auto_deficient_school,walk_transit_ASC_auto_deficient_shopping,walk_transit_ASC_auto_deficient_social,walk_transit_ASC_auto_deficient_univ,walk_transit_ASC_auto_deficient_work,walk_transit_ASC_auto_deficient_atwork +walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient_eatout,walk_transit_ASC_auto_sufficient_escort,walk_transit_ASC_auto_sufficient_othdiscr,walk_transit_ASC_auto_sufficient_othmaint,walk_transit_ASC_auto_sufficient_school,walk_transit_ASC_auto_sufficient_shopping,walk_transit_ASC_auto_sufficient_social,walk_transit_ASC_auto_sufficient_univ,walk_transit_ASC_auto_sufficient_work,walk_transit_ASC_auto_sufficient_atwork +drive_transit_ASC_no_auto,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all +drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient_eatout,drive_transit_ASC_auto_deficient_escort,drive_transit_ASC_auto_deficient_othdiscr,drive_transit_ASC_auto_deficient_othmaint,drive_transit_ASC_auto_deficient_school,drive_transit_ASC_auto_deficient_shopping,drive_transit_ASC_auto_deficient_social,drive_transit_ASC_auto_deficient_univ,drive_transit_ASC_auto_deficient_work,drive_transit_ASC_auto_deficient_atwork +drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient_eatout,drive_transit_ASC_auto_sufficient_escort,drive_transit_ASC_auto_sufficient_othdiscr,drive_transit_ASC_auto_sufficient_othmaint,drive_transit_ASC_auto_sufficient_school,drive_transit_ASC_auto_sufficient_shopping,drive_transit_ASC_auto_sufficient_social,drive_transit_ASC_auto_sufficient_univ,drive_transit_ASC_auto_sufficient_work,drive_transit_ASC_auto_sufficient_atwork +taxi_ASC_no_auto,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_work,taxi_ASC_no_auto_atwork +taxi_ASC_auto_deficient,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_school,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_univ,taxi_ASC_auto_deficient_work,taxi_ASC_auto_deficient_atwork +taxi_ASC_auto_sufficient,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_school,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_univ,taxi_ASC_auto_sufficient_work,taxi_ASC_auto_sufficient_atwork +tnc_single_ASC_no_auto,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_school,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_univ,tnc_single_ASC_no_auto_work,tnc_single_ASC_no_auto_atwork +tnc_single_ASC_auto_deficient,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_school,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_univ,tnc_single_ASC_auto_deficient_work,tnc_single_ASC_auto_deficient_atwork +tnc_single_ASC_auto_sufficient,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_school,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_univ,tnc_single_ASC_auto_sufficient_work,tnc_single_ASC_auto_sufficient_atwork +tnc_shared_ASC_no_auto,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_school,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_univ,tnc_shared_ASC_no_auto_work,tnc_shared_ASC_no_auto_atwork +tnc_shared_ASC_auto_deficient,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_school,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_univ,tnc_shared_ASC_auto_deficient_work,tnc_shared_ASC_auto_deficient_atwork +tnc_shared_ASC_auto_sufficient,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_school,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_univ,tnc_shared_ASC_auto_sufficient_work,tnc_shared_ASC_auto_sufficient_atwork +joint_walk_ASC_no_auto,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all +joint_walk_ASC_auto_deficient,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all +joint_walk_ASC_auto_sufficient,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all +joint_bike_ASC_no_auto,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all +joint_bike_ASC_auto_deficient,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all +joint_bike_ASC_auto_sufficient,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all +joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all +joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all +joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all +joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all +joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all +joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all +joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all +joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all +joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all +joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all +joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all +joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all +joint_taxi_ASC_no_auto,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all +joint_taxi_ASC_auto_deficient,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all +joint_taxi_ASC_auto_sufficient,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all +joint_tnc_single_ASC_no_auto,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all +joint_tnc_single_ASC_auto_deficient,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all +joint_tnc_single_ASC_auto_sufficient,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all +joint_tnc_shared_ASC_no_auto,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all +joint_tnc_shared_ASC_auto_deficient,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all +joint_tnc_shared_ASC_auto_sufficient,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all +local_bus_ASC,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_school_univ,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_school_univ,local_bus_ASC_work,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +walk_light_rail_ASC,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_work,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +drive_light_rail_ASC,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_work,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +walk_ferry_ASC,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_work,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +drive_ferry_ASC,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_work,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +express_bus_ASC,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_work,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +heavy_rail_ASC,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_work,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +commuter_rail_ASC,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_work,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +walk_transit_CBD_ASC,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_work,walk_transit_CBD_ASC_atwork +drive_transit_CBD_ASC,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_work,drive_transit_CBD_ASC_atwork diff --git a/activitysim/examples/example_psrc/configs/tour_scheduling_atwork.csv b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_atwork.csv similarity index 99% rename from activitysim/examples/example_psrc/configs/tour_scheduling_atwork.csv rename to activitysim/examples/placeholder_psrc/configs/tour_scheduling_atwork.csv index c99dc7d3c4..9e64b6e016 100755 --- a/activitysim/examples/example_psrc/configs/tour_scheduling_atwork.csv +++ b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_atwork.csv @@ -1,59 +1,59 @@ -Label,Description,Expression,Coefficient -#,Departure Constants,, -util_early_start_at_5,Early start at 5,start < 6,coef_early_start_at_5 -util_am_peak_start_at_6,AM peak start at 6,start == 6,coef_am_peak_start_at_6 -util_am_peak_start_at_7,AM peak start at 7,start == 7,coef_am_peak_start_at_7 -util_am_peak_start_at_8,AM peak start at 8,start == 8,coef_am_peak_start_at_8 -util_am_peak_start_at_9,AM peak start at 9,start == 9,coef_am_peak_start_at_9 -util_midday_start_at_10_11_12,Midday start at 10/11/12,(start > 9) & (start < 13),coef_midday_start_at_10_11_12 -util_midday_start_at_13_14_15,Midday start at 13/14/15,(start > 12) & (start < 16),coef_midday_start_at_13_14_15 -util_pm_peak_start_at_16_17_18,PM peak start at 16/17/18,(start > 15) & (start < 19),coef_pm_peak_start_at_16_17_18 -util_evening_start_at_19_20_21,Evening start at 19/20/21,(start > 18) & (start < 22),coef_evening_start_at_19_20_21 -util_late_start_at_22_23,Late start at 22/23,start > 21,coef_late_start_at_22_23 -#,Arrival Constants,, -util_early_end_at_5_6,Early end at 5/6 ,end < 7,coef_early_end_at_5_6 -util_am_peak_end,AM peak end,(end > 6) & (end < 10),coef_am_peak_end -util_midday_end_at_10_11_12,Midday end at 10/11/12,(end > 9) & (end < 13),coef_midday_end_at_10_11_12 -util_midday_end_at_13_14,Midday end at 13/14,(end > 12) & (end < 15),coef_midday_end_at_13_14 -util_pm_peak_end_at_15,PM peak end at 15,end == 15,coef_pm_peak_end_at_15 -util_pm_peak_end_at_16,PM peak end at 16,end == 16,coef_pm_peak_end_at_16 -util_pm_peak_end_at_17,PM peak end at 17,end == 17,coef_pm_peak_end_at_17 -util_pm_peak_end_at_18,PM peak end at 18,end == 18,coef_pm_peak_end_at_18 -util_evening_end_at_19_20_21,Evening end at 19/20/21,(end > 18) & (end < 22),coef_evening_end_at_19_20_21 -util_late_end_at_22_23,Late end at 22/23,end > 21,coef_late_end_at_22_23 -#,,, -util_duration_of_0_hours,Duration of 0 hours,duration==0,coef_duration_of_0_hours -util_duration_of_1_hour,Duration of 1 hour,duration==1,coef_duration_of_1_hour -util_duration_of_2_to_3_hours,Duration of 2 to 3 hours,(duration >=1) and (duration <= 4),coef_duration_of_2_to_3_hours -util_duration_of_4_to_5_hours,Duration of 4 to 5 hours,(duration >=4) and (duration <=5),coef_duration_of_4_to_5_hours -util_duration_of_6_to_7_hours,Duration of 6 to 7 hours,(duration >=6) and (duration <=7),coef_duration_of_6_to_7_hours -util_duration_of_8_to_10_hours,Duration of 8 to 10 hours,(duration >=8) and (duration <=10),coef_duration_of_8_to_10_hours -util_duration_of_11_to_13_hours,Duration of 11 to 13 hours,(duration >=11) and (duration <=13),coef_duration_of_11_to_13_hours -util_duration_of_14_to_18_hours,Duration of 14 to 18 hours,(duration >=14) and (duration <=18),coef_duration_of_14_to_18_hours -util_#,,, -util_start_shift_for_outbound_auto_travel_time_off_peak,Start shift for outbound auto travel time for off-peak,"@df.start * np.minimum(df.sovtimemd, time_cap)",coef_start_shift_for_outbound_auto_travel_time_off_peak -util_start_shift_for_inbound_auto_travel_time_off_peak,Start shift for inbound auto travel time for off-peak,"@df.start * np.minimum(df.sovtimemd_t, time_cap)",coef_start_shift_for_inbound_auto_travel_time_off_peak -util_duration_shift_for_outbound_auto_travel_time_off_peak,Duration shift for outbound auto travel time for off-peak,"@df.duration * np.minimum(df.sovtimemd, time_cap)",coef_duration_shift_for_outbound_auto_travel_time_off_peak -util_duration_shift_for_inbound_auto_travel_time_off_peak,Duration shift for inbound auto travel time for off-peak,"@df.duration * np.minimum(df.sovtimemd_t, time_cap)",coef_duration_shift_for_inbound_auto_travel_time_off_peak -#,,, -util_start_shift_for_business_related_,Start shift for business-related sub-tour purpose,(tour_type == 'business') * start,coef_start_shift_for_business_related_ -util_duration_shift_for_business_related_,Duration shift for business-related sub-tour purpose,(tour_type == 'business') * duration,coef_duration_shift_for_business_related_ -util_start_shift_for_first_sub_tour_of_same_work_tour,Start shift for first sub-tour of the same work tour,(tour_type_num == 1) * start,coef_start_shift_for_first_sub_tour_of_same_work_tour -util_duration_shift_for_first_sub_tour_of_same_work_tour,Duration shift for first sub-tour of the same work tour,(tour_type_num == 1) * duration,coef_duration_shift_for_first_sub_tour_of_same_work_tour -util_start_shift_for_subsequent_sub_tour_of_same_work_tour,Start shift for subsequent sub-tour of the same work tour,(tour_type_num == 2) * start,coef_start_shift_for_subsequent_sub_tour_of_same_work_tour -util_duration_shift_for_subsequent_sub_tour_of_same_work_tour,Duration shift for subsequent sub-tour of the same work tour,(tour_type_num == 2) * duration,coef_duration_shift_for_subsequent_sub_tour_of_same_work_tour -util_start_shift_for_number_of_mandatory_tours,Start shift for number of mandatory tours made by the person,start * num_mand,coef_start_shift_for_number_of_mandatory_tours -util_duration_shift_for_number_of_mandatory_tours,Duration shift for number of mandatory tours made by the person,duration * num_mand,coef_duration_shift_for_number_of_mandatory_tours -util_start_shift_for_number_of_joint_tours,Start shift for number of joint tours in which the person participated,start * num_joint_tours,coef_start_shift_for_number_of_joint_tours -util_duration_shift_for_number_of_joint_tours,Duration shift for number of joint tours in which the person participated,duration * num_joint_tours,coef_duration_shift_for_number_of_joint_tours -util_start_shift_for_number_of_individual_nonmandatory_tours,Start shift for number of individual nonm tours (including escort) made by the person,start * num_non_mand,coef_start_shift_for_number_of_individual_nonmandatory_tours -util_duration_shift_for_number_of_individual_nonmandatory_tours,Duration shift for number of individual nonm tours (including escort) made by the person,duration * num_non_mand,coef_duration_shift_for_number_of_individual_nonmandatory_tours -#,,, -util_dummy_for_business_related_purpose_and_duration_from_0_to_1,Dummy for business-related purpose and duration from 0 to 1,(tour_type == 'business') & (duration <=1),coef_dummy_for_business_related_purpose_and_duration_from_0_to_1 -util_dummy_for_eating_out_purpose_and_duration_of_1_hour,Dummy for eating-out purpose and duration of 1 hour,(tour_type == 'business') & (duration ==1),coef_dummy_for_eating_out_purpose_and_duration_of_1_hour -util_dummy_for_eating_out_purpose_and_departure_at_11,Dummy for eating-out purpose and departure at 11,(tour_type == 'business') & (start == 11),coef_dummy_for_eating_out_purpose_and_departure_at_11 -util_dummy_for_eating_out_purpose_and_departure_at_12,Dummy for eating-out purpose and departure at 12,(tour_type == 'business') & (start == 12),coef_dummy_for_eating_out_purpose_and_departure_at_12 -util_dummy_for_eating_out_purpose_and_departure_at_13,Dummy for eating-out purpose and departure at 13,(tour_type == 'business') & (start == 13),coef_dummy_for_eating_out_purpose_and_departure_at_13 -#,,, -#,Mode Choice Logsum,mode_choice_logsum, +Label,Description,Expression,Coefficient +#,Departure Constants,, +util_early_start_at_5,Early start at 5,start < 6,coef_early_start_at_5 +util_am_peak_start_at_6,AM peak start at 6,start == 6,coef_am_peak_start_at_6 +util_am_peak_start_at_7,AM peak start at 7,start == 7,coef_am_peak_start_at_7 +util_am_peak_start_at_8,AM peak start at 8,start == 8,coef_am_peak_start_at_8 +util_am_peak_start_at_9,AM peak start at 9,start == 9,coef_am_peak_start_at_9 +util_midday_start_at_10_11_12,Midday start at 10/11/12,(start > 9) & (start < 13),coef_midday_start_at_10_11_12 +util_midday_start_at_13_14_15,Midday start at 13/14/15,(start > 12) & (start < 16),coef_midday_start_at_13_14_15 +util_pm_peak_start_at_16_17_18,PM peak start at 16/17/18,(start > 15) & (start < 19),coef_pm_peak_start_at_16_17_18 +util_evening_start_at_19_20_21,Evening start at 19/20/21,(start > 18) & (start < 22),coef_evening_start_at_19_20_21 +util_late_start_at_22_23,Late start at 22/23,start > 21,coef_late_start_at_22_23 +#,Arrival Constants,, +util_early_end_at_5_6,Early end at 5/6 ,end < 7,coef_early_end_at_5_6 +util_am_peak_end,AM peak end,(end > 6) & (end < 10),coef_am_peak_end +util_midday_end_at_10_11_12,Midday end at 10/11/12,(end > 9) & (end < 13),coef_midday_end_at_10_11_12 +util_midday_end_at_13_14,Midday end at 13/14,(end > 12) & (end < 15),coef_midday_end_at_13_14 +util_pm_peak_end_at_15,PM peak end at 15,end == 15,coef_pm_peak_end_at_15 +util_pm_peak_end_at_16,PM peak end at 16,end == 16,coef_pm_peak_end_at_16 +util_pm_peak_end_at_17,PM peak end at 17,end == 17,coef_pm_peak_end_at_17 +util_pm_peak_end_at_18,PM peak end at 18,end == 18,coef_pm_peak_end_at_18 +util_evening_end_at_19_20_21,Evening end at 19/20/21,(end > 18) & (end < 22),coef_evening_end_at_19_20_21 +util_late_end_at_22_23,Late end at 22/23,end > 21,coef_late_end_at_22_23 +#,,, +util_duration_of_0_hours,Duration of 0 hours,duration==0,coef_duration_of_0_hours +util_duration_of_1_hour,Duration of 1 hour,duration==1,coef_duration_of_1_hour +util_duration_of_2_to_3_hours,Duration of 2 to 3 hours,(duration >=1) and (duration <= 4),coef_duration_of_2_to_3_hours +util_duration_of_4_to_5_hours,Duration of 4 to 5 hours,(duration >=4) and (duration <=5),coef_duration_of_4_to_5_hours +util_duration_of_6_to_7_hours,Duration of 6 to 7 hours,(duration >=6) and (duration <=7),coef_duration_of_6_to_7_hours +util_duration_of_8_to_10_hours,Duration of 8 to 10 hours,(duration >=8) and (duration <=10),coef_duration_of_8_to_10_hours +util_duration_of_11_to_13_hours,Duration of 11 to 13 hours,(duration >=11) and (duration <=13),coef_duration_of_11_to_13_hours +util_duration_of_14_to_18_hours,Duration of 14 to 18 hours,(duration >=14) and (duration <=18),coef_duration_of_14_to_18_hours +util_#,,, +util_start_shift_for_outbound_auto_travel_time_off_peak,Start shift for outbound auto travel time for off-peak,"@df.start * np.minimum(df.sovtimemd, time_cap)",coef_start_shift_for_outbound_auto_travel_time_off_peak +util_start_shift_for_inbound_auto_travel_time_off_peak,Start shift for inbound auto travel time for off-peak,"@df.start * np.minimum(df.sovtimemd_t, time_cap)",coef_start_shift_for_inbound_auto_travel_time_off_peak +util_duration_shift_for_outbound_auto_travel_time_off_peak,Duration shift for outbound auto travel time for off-peak,"@df.duration * np.minimum(df.sovtimemd, time_cap)",coef_duration_shift_for_outbound_auto_travel_time_off_peak +util_duration_shift_for_inbound_auto_travel_time_off_peak,Duration shift for inbound auto travel time for off-peak,"@df.duration * np.minimum(df.sovtimemd_t, time_cap)",coef_duration_shift_for_inbound_auto_travel_time_off_peak +#,,, +util_start_shift_for_business_related_,Start shift for business-related sub-tour purpose,(tour_type == 'business') * start,coef_start_shift_for_business_related_ +util_duration_shift_for_business_related_,Duration shift for business-related sub-tour purpose,(tour_type == 'business') * duration,coef_duration_shift_for_business_related_ +util_start_shift_for_first_sub_tour_of_same_work_tour,Start shift for first sub-tour of the same work tour,(tour_type_num == 1) * start,coef_start_shift_for_first_sub_tour_of_same_work_tour +util_duration_shift_for_first_sub_tour_of_same_work_tour,Duration shift for first sub-tour of the same work tour,(tour_type_num == 1) * duration,coef_duration_shift_for_first_sub_tour_of_same_work_tour +util_start_shift_for_subsequent_sub_tour_of_same_work_tour,Start shift for subsequent sub-tour of the same work tour,(tour_type_num == 2) * start,coef_start_shift_for_subsequent_sub_tour_of_same_work_tour +util_duration_shift_for_subsequent_sub_tour_of_same_work_tour,Duration shift for subsequent sub-tour of the same work tour,(tour_type_num == 2) * duration,coef_duration_shift_for_subsequent_sub_tour_of_same_work_tour +util_start_shift_for_number_of_mandatory_tours,Start shift for number of mandatory tours made by the person,start * num_mand,coef_start_shift_for_number_of_mandatory_tours +util_duration_shift_for_number_of_mandatory_tours,Duration shift for number of mandatory tours made by the person,duration * num_mand,coef_duration_shift_for_number_of_mandatory_tours +util_start_shift_for_number_of_joint_tours,Start shift for number of joint tours in which the person participated,start * num_joint_tours,coef_start_shift_for_number_of_joint_tours +util_duration_shift_for_number_of_joint_tours,Duration shift for number of joint tours in which the person participated,duration * num_joint_tours,coef_duration_shift_for_number_of_joint_tours +util_start_shift_for_number_of_individual_nonmandatory_tours,Start shift for number of individual nonm tours (including escort) made by the person,start * num_non_mand,coef_start_shift_for_number_of_individual_nonmandatory_tours +util_duration_shift_for_number_of_individual_nonmandatory_tours,Duration shift for number of individual nonm tours (including escort) made by the person,duration * num_non_mand,coef_duration_shift_for_number_of_individual_nonmandatory_tours +#,,, +util_dummy_for_business_related_purpose_and_duration_from_0_to_1,Dummy for business-related purpose and duration from 0 to 1,(tour_type == 'business') & (duration <=1),coef_dummy_for_business_related_purpose_and_duration_from_0_to_1 +util_dummy_for_eating_out_purpose_and_duration_of_1_hour,Dummy for eating-out purpose and duration of 1 hour,(tour_type == 'business') & (duration ==1),coef_dummy_for_eating_out_purpose_and_duration_of_1_hour +util_dummy_for_eating_out_purpose_and_departure_at_11,Dummy for eating-out purpose and departure at 11,(tour_type == 'business') & (start == 11),coef_dummy_for_eating_out_purpose_and_departure_at_11 +util_dummy_for_eating_out_purpose_and_departure_at_12,Dummy for eating-out purpose and departure at 12,(tour_type == 'business') & (start == 12),coef_dummy_for_eating_out_purpose_and_departure_at_12 +util_dummy_for_eating_out_purpose_and_departure_at_13,Dummy for eating-out purpose and departure at 13,(tour_type == 'business') & (start == 13),coef_dummy_for_eating_out_purpose_and_departure_at_13 +#,,, +#,Mode Choice Logsum,mode_choice_logsum, #,,, \ No newline at end of file diff --git a/activitysim/examples/example_psrc/configs/tour_scheduling_atwork.yaml b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_atwork.yaml similarity index 94% rename from activitysim/examples/example_psrc/configs/tour_scheduling_atwork.yaml rename to activitysim/examples/placeholder_psrc/configs/tour_scheduling_atwork.yaml index 9209ada983..a4509ce984 100755 --- a/activitysim/examples/example_psrc/configs/tour_scheduling_atwork.yaml +++ b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_atwork.yaml @@ -1,13 +1,13 @@ - -SPEC: tour_scheduling_atwork.csv -COEFFICIENTS: tour_scheduling_atwork_coeffs.csv - -preprocessor: - SPEC: tour_scheduling_atwork_preprocessor - DF: df -# TABLES: -# - land_use -# - tours - -CONSTANTS: - time_cap: 30 + +SPEC: tour_scheduling_atwork.csv +COEFFICIENTS: tour_scheduling_atwork_coeffs.csv + +preprocessor: + SPEC: tour_scheduling_atwork_preprocessor + DF: df +# TABLES: +# - land_use +# - tours + +CONSTANTS: + time_cap: 30 diff --git a/activitysim/examples/example_mtc/configs/tour_scheduling_atwork_coefficients.csv b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_atwork_coeffs.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/tour_scheduling_atwork_coefficients.csv rename to activitysim/examples/placeholder_psrc/configs/tour_scheduling_atwork_coeffs.csv diff --git a/activitysim/examples/example_mtc/configs/tour_scheduling_atwork_preprocessor.csv b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_atwork_preprocessor.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/tour_scheduling_atwork_preprocessor.csv rename to activitysim/examples/placeholder_psrc/configs/tour_scheduling_atwork_preprocessor.csv diff --git a/activitysim/examples/example_psrc/configs/tour_scheduling_joint.csv b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_joint.csv similarity index 99% rename from activitysim/examples/example_psrc/configs/tour_scheduling_joint.csv rename to activitysim/examples/placeholder_psrc/configs/tour_scheduling_joint.csv index e0266f3171..36fa7940ee 100755 --- a/activitysim/examples/example_psrc/configs/tour_scheduling_joint.csv +++ b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_joint.csv @@ -1,66 +1,66 @@ -Label,Description,Expression,Coefficient -util_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends,Subsequent tour must start after previous tour for this purpose ends,(start < end_previous) & (tour_type_num > 1),coef_unavailable -util_free_flow_round_trip_auto_time_shift_effects_duration,Free-flow round trip auto time shift effects - duration,roundtrip_auto_time * duration,coef_free_flow_round_trip_auto_time_shift_effects_duration -util_shopping_tour_departure_shift_effects,Shopping tour - departure shift effects,"(tour_type == 'shopping') * start",coef_shopping_tour_departure_shift_effects -util_shopping_tour_duration_shift_effects,Shopping tour - duration shift effects,"(tour_type == 'shopping') * duration",coef_shopping_tour_duration_shift_effects -util_maintenance_tour_departure_shift_effects,Maintenance tour - departure shift effects,"(tour_type == 'othmaint') * start",coef_maintenance_tour_departure_shift_effects -util_maintenance_tour_duration_shift_effects,Maintenance tour - departure shift effects,"(tour_type == 'othmaint') * duration",coef_maintenance_tour_duration_shift_effects -util_visit_tour_departure_shift_effects,Visit tour - departure shift effects,"(tour_type == 'social') * start",coef_visit_tour_departure_shift_effects -util_visit_tour_duration_shift_effects,Visit tour - departure shift effects,"(tour_type == 'social') * duration",coef_visit_tour_duration_shift_effects -util_eat_out_tour_departure_shift_effects,Eat Out tour - departure shift effects,"(tour_type == 'eatout') * start",coef_eat_out_tour_departure_shift_effects -util_school_child_age_16_plus_departure_shift_effects,School child age 16+ - departure shift effects,(ptype == 6) * start,coef_school_child_age_16_plus_departure_shift_effects -util_school_child_age_16_plus_duration_shift_effects,School child age 16+ - duration shift effects,(ptype == 6) * duration,coef_school_child_age_16_plus_duration_shift_effects -util_school_child_age_under_16_departure_shift_effects,School child age under 16 - departure shift effects,(ptype == 7) * start,coef_school_child_age_under_16_departure_shift_effects -util_school_child_age_under_16_duration_shift_effects,School child age under 16 - duration shift effects,(ptype == 7) * duration,coef_school_child_age_under_16_duration_shift_effects -util_destination_in_cbd_duration_shift_effects,Destination in CBD - duration shift effects,destination_in_cbd * duration,coef_destination_in_cbd_duration_shift_effects -util_number_of_mandatory_tours_departure_shift_effects,Number of mandatory tours - departure shift effects,num_mand * start,coef_number_of_mandatory_tours_departure_shift_effects -util_number_of_joint_tours_departure_shift_effects,Number of joint tours - departure shift effects,num_person_joint_tours * start,coef_number_of_joint_tours_departure_shift_effects -util_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,First of 2+ tours for same purpose - departure shift effect,((tour_type_count>1) & (tour_type_num == 1)) * start,coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect -util_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,subsequent of 2+ tours for same purpose - duration shift effect,(tour_type_num > 1) * duration,coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect -util_maintenance_tour_depart_before_7,Maintenance tour - depart before 7,"(tour_type == 'othmaint') & (start < 7)",coef_maintenance_tour_depart_before_7 -util_shopping_tour_depart_before_8,Shopping tour - depart before 8,"(tour_type == 'shopping') & (start < 8)",coef_shopping_tour_depart_before_8 -util_shopping_tour_arrive_after_22,Shopping tour - arrive after 22,"(tour_type == 'shopping') & (end > 22)",coef_shopping_tour_arrive_after_22 -util_school_child_under_16_arrive_after_22,School child under 16 - arrive after 22,(ptype == 7) & (end > 22),coef_school_child_under_16_arrive_after_22 -util_university_student_arrive_after_22,University student - arrive after 22,(ptype == 3) & (end > 22),coef_university_student_arrive_after_22 -util_shopping_tour_duration_lt_2_hours,Shopping tour - duration < 2 hours,"(tour_type == 'shopping') & (duration < 2)",coef_shopping_tour_duration_lt_2_hours -util_discretionary_tour_duration_lt_2_hours,Discretionary tour - duration < 2 hours,"(tour_type == 'othdiscr') & (duration < 2)",coef_discretionary_tour_duration_lt_2_hours -util_adult_with_children_in_hh_arrive_19_21,Adult with children in HH - arrive 19 - 21,adult & (num_children > 0) & ( end > 18 ) & ( end < 22 ),coef_adult_with_children_in_hh_arrive_19_21 -#,, -#,Mode Choice Logsum,mode_choice_logsum, -#,, -#,, -util_some_previously_scheduled_tour_ends_in_this_departure_hour,Some previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.tour_id, df.start)",coef_some_previously_scheduled_tour_ends_in_this_departure_hour -util_some_previously_scheduled_tour_begins_in_this_arrival_hour,Some previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.tour_id, df.end)",coef_some_previously_scheduled_tour_begins_in_this_arrival_hour -util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & tt.adjacent_window_before(df.tour_id, df.start)",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & tt.adjacent_window_after(df.tour_id, df.end)",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -util_adjacent_window_exists_before_this_departure_hour_second_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_type_num > 1) & tt.adjacent_window_before(df.tour_id, df.start)",coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_type_num > 1) & tt.adjacent_window_after(df.tour_id, df.end)",coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction -#,, -#,, -util_departure_constants_early,Departure Constants -- Early (up to 5),"(tour_type != 'escort') & (start < 6)",coef_departure_constants_early -util_departure_constants_am_peak_1,Departure Constants -- AM peak 1 (6),"(tour_type != 'escort') & (start == 6)",coef_departure_constants_am_peak_1 -util_departure_constants_am_peak_2,Departure Constants -- AM peak 2 (7),"(tour_type != 'escort') & (start == 7)",coef_departure_constants_am_peak_2 -util_departure_constants_am_peak_3,Departure Constants -- AM peak 3 (8),"(tour_type != 'escort') & (start == 8)",coef_departure_constants_am_peak_3 -util_departure_constants_am_peak_4,Departure Constants -- AM peak 4 (9),"(tour_type != 'escort') & (start == 9)",coef_departure_constants_am_peak_4 -util_departure_constants_midday_1,Departure Constants -- Midday 1 (10 to 12),"(tour_type != 'escort') & (start > 9) & (start < 13)",coef_departure_constants_midday_1 -util_departure_constants_midday_2,Departure Constants -- Midday 2 (13 to 15),"(tour_type != 'escort') & (start > 12) & (start < 16)",coef_departure_constants_midday_2 -util_departure_constants_pm_peak,Departure Constants -- PM peak (16 to 18),"(tour_type != 'escort') & (start > 15) & (start < 19)",coef_departure_constants_pm_peak -util_departure_constants_evening,Departure Constants -- Evening (19 to 21),"(tour_type != 'escort') & (start > 18) & (start < 22)",coef_departure_constants_evening -util_departure_constants_late,Departure Constants -- Late (22 and later),"(tour_type != 'escort') & (start > 21)",coef_departure_constants_late -util_arrival_constants_early,Arrival Constants -- Early (up to 6),"(tour_type != 'escort') & (end < 7)",coef_arrival_constants_early -util_arrival_constants_am_peak,Arrival Constants -- AM peak (7 to 9),"(tour_type != 'escort') & (end > 6) & (end < 10)",coef_arrival_constants_am_peak -util_arrival_constants_midday_1,Arrival Constants -- Midday 1 (10 to 12),"(tour_type != 'escort') & (end > 9) & (end < 13)",coef_arrival_constants_midday_1 -util_arrival_constants_midday_2,Arrival Constants -- Midday 2 (13 to 14),"(tour_type != 'escort') & (end > 12) & (end < 15)",coef_arrival_constants_midday_2 -util_arrival_constants_pm_peak_1,Arrival Constants -- PM peak 1 (15),"(tour_type != 'escort') & (end == 15)",coef_arrival_constants_pm_peak_1 -util_arrival_constants_pm_peak_2,Arrival Constants -- PM peak 2 (16),"(tour_type != 'escort') & (end == 16)",coef_arrival_constants_pm_peak_2 -util_arrival_constants_pm_peak_3,Arrival Constants -- PM peak 3 (17),"(tour_type != 'escort') & (end == 17)",coef_arrival_constants_pm_peak_3 -util_arrival_constants_pm_peak_4,Arrival Constants -- PM peak 4 (18),"(tour_type != 'escort') & (end == 18)",coef_arrival_constants_pm_peak_4 -util_arrival_constants_evening,Arrival Constants -- Evening (19 to 21),"(tour_type != 'escort') & (end > 18) & (end < 22)",coef_arrival_constants_evening -util_arrival_constants_late,Arrival Constants -- Late (22 and later),"(tour_type != 'escort') & (end > 21)",coef_arrival_constants_late -util_duration_constants_0_to_1_hours,Duration Constants -- 0 to 1 hours,"(tour_type != 'escort') & (duration < 2)",coef_duration_constants_0_to_1_hours -util_duration_constants_2_to_3_hours,Duration Constants -- 2 to 3 hours,"(tour_type != 'escort') & (duration > 1) & (duration < 4)",coef_duration_constants_2_to_3_hours -util_duration_constants_4_to_5_hours,Duration Constants -- 4 to 5 hours,"(tour_type != 'escort') & (duration > 3) & (duration < 6)",coef_duration_constants_4_to_5_hours -util_duration_constants_6_to_7_hours,Duration Constants -- 6 to 7 hours,"(tour_type != 'escort') & (duration > 5) & (duration < 8)",coef_duration_constants_6_to_7_hours -util_duration_constants_8_to_10_hours,Duration Constants -- 8 to 10 hours,"(tour_type != 'escort') & (duration > 7) & (duration < 11)",coef_duration_constants_8_to_10_hours -util_duration_constants_11_to_13_hours,Duration Constants -- 11 to 13 hours,"(tour_type != 'escort') & (duration > 10) & (duration < 14)",coef_duration_constants_11_to_13_hours +Label,Description,Expression,Coefficient +util_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends,Subsequent tour must start after previous tour for this purpose ends,(start < end_previous) & (tour_type_num > 1),coef_unavailable +util_free_flow_round_trip_auto_time_shift_effects_duration,Free-flow round trip auto time shift effects - duration,roundtrip_auto_time * duration,coef_free_flow_round_trip_auto_time_shift_effects_duration +util_shopping_tour_departure_shift_effects,Shopping tour - departure shift effects,"(tour_type == 'shopping') * start",coef_shopping_tour_departure_shift_effects +util_shopping_tour_duration_shift_effects,Shopping tour - duration shift effects,"(tour_type == 'shopping') * duration",coef_shopping_tour_duration_shift_effects +util_maintenance_tour_departure_shift_effects,Maintenance tour - departure shift effects,"(tour_type == 'othmaint') * start",coef_maintenance_tour_departure_shift_effects +util_maintenance_tour_duration_shift_effects,Maintenance tour - departure shift effects,"(tour_type == 'othmaint') * duration",coef_maintenance_tour_duration_shift_effects +util_visit_tour_departure_shift_effects,Visit tour - departure shift effects,"(tour_type == 'social') * start",coef_visit_tour_departure_shift_effects +util_visit_tour_duration_shift_effects,Visit tour - departure shift effects,"(tour_type == 'social') * duration",coef_visit_tour_duration_shift_effects +util_eat_out_tour_departure_shift_effects,Eat Out tour - departure shift effects,"(tour_type == 'eatout') * start",coef_eat_out_tour_departure_shift_effects +util_school_child_age_16_plus_departure_shift_effects,School child age 16+ - departure shift effects,(ptype == 6) * start,coef_school_child_age_16_plus_departure_shift_effects +util_school_child_age_16_plus_duration_shift_effects,School child age 16+ - duration shift effects,(ptype == 6) * duration,coef_school_child_age_16_plus_duration_shift_effects +util_school_child_age_under_16_departure_shift_effects,School child age under 16 - departure shift effects,(ptype == 7) * start,coef_school_child_age_under_16_departure_shift_effects +util_school_child_age_under_16_duration_shift_effects,School child age under 16 - duration shift effects,(ptype == 7) * duration,coef_school_child_age_under_16_duration_shift_effects +util_destination_in_cbd_duration_shift_effects,Destination in CBD - duration shift effects,destination_in_cbd * duration,coef_destination_in_cbd_duration_shift_effects +util_number_of_mandatory_tours_departure_shift_effects,Number of mandatory tours - departure shift effects,num_mand * start,coef_number_of_mandatory_tours_departure_shift_effects +util_number_of_joint_tours_departure_shift_effects,Number of joint tours - departure shift effects,num_person_joint_tours * start,coef_number_of_joint_tours_departure_shift_effects +util_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,First of 2+ tours for same purpose - departure shift effect,((tour_type_count>1) & (tour_type_num == 1)) * start,coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect +util_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,subsequent of 2+ tours for same purpose - duration shift effect,(tour_type_num > 1) * duration,coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect +util_maintenance_tour_depart_before_7,Maintenance tour - depart before 7,"(tour_type == 'othmaint') & (start < 7)",coef_maintenance_tour_depart_before_7 +util_shopping_tour_depart_before_8,Shopping tour - depart before 8,"(tour_type == 'shopping') & (start < 8)",coef_shopping_tour_depart_before_8 +util_shopping_tour_arrive_after_22,Shopping tour - arrive after 22,"(tour_type == 'shopping') & (end > 22)",coef_shopping_tour_arrive_after_22 +util_school_child_under_16_arrive_after_22,School child under 16 - arrive after 22,(ptype == 7) & (end > 22),coef_school_child_under_16_arrive_after_22 +util_university_student_arrive_after_22,University student - arrive after 22,(ptype == 3) & (end > 22),coef_university_student_arrive_after_22 +util_shopping_tour_duration_lt_2_hours,Shopping tour - duration < 2 hours,"(tour_type == 'shopping') & (duration < 2)",coef_shopping_tour_duration_lt_2_hours +util_discretionary_tour_duration_lt_2_hours,Discretionary tour - duration < 2 hours,"(tour_type == 'othdiscr') & (duration < 2)",coef_discretionary_tour_duration_lt_2_hours +util_adult_with_children_in_hh_arrive_19_21,Adult with children in HH - arrive 19 - 21,adult & (num_children > 0) & ( end > 18 ) & ( end < 22 ),coef_adult_with_children_in_hh_arrive_19_21 +#,, +#,Mode Choice Logsum,mode_choice_logsum, +#,, +#,, +util_some_previously_scheduled_tour_ends_in_this_departure_hour,Some previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.tour_id, df.start)",coef_some_previously_scheduled_tour_ends_in_this_departure_hour +util_some_previously_scheduled_tour_begins_in_this_arrival_hour,Some previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.tour_id, df.end)",coef_some_previously_scheduled_tour_begins_in_this_arrival_hour +util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & tt.adjacent_window_before(df.tour_id, df.start)",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & tt.adjacent_window_after(df.tour_id, df.end)",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_second_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_type_num > 1) & tt.adjacent_window_before(df.tour_id, df.start)",coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_type_num > 1) & tt.adjacent_window_after(df.tour_id, df.end)",coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction +#,, +#,, +util_departure_constants_early,Departure Constants -- Early (up to 5),"(tour_type != 'escort') & (start < 6)",coef_departure_constants_early +util_departure_constants_am_peak_1,Departure Constants -- AM peak 1 (6),"(tour_type != 'escort') & (start == 6)",coef_departure_constants_am_peak_1 +util_departure_constants_am_peak_2,Departure Constants -- AM peak 2 (7),"(tour_type != 'escort') & (start == 7)",coef_departure_constants_am_peak_2 +util_departure_constants_am_peak_3,Departure Constants -- AM peak 3 (8),"(tour_type != 'escort') & (start == 8)",coef_departure_constants_am_peak_3 +util_departure_constants_am_peak_4,Departure Constants -- AM peak 4 (9),"(tour_type != 'escort') & (start == 9)",coef_departure_constants_am_peak_4 +util_departure_constants_midday_1,Departure Constants -- Midday 1 (10 to 12),"(tour_type != 'escort') & (start > 9) & (start < 13)",coef_departure_constants_midday_1 +util_departure_constants_midday_2,Departure Constants -- Midday 2 (13 to 15),"(tour_type != 'escort') & (start > 12) & (start < 16)",coef_departure_constants_midday_2 +util_departure_constants_pm_peak,Departure Constants -- PM peak (16 to 18),"(tour_type != 'escort') & (start > 15) & (start < 19)",coef_departure_constants_pm_peak +util_departure_constants_evening,Departure Constants -- Evening (19 to 21),"(tour_type != 'escort') & (start > 18) & (start < 22)",coef_departure_constants_evening +util_departure_constants_late,Departure Constants -- Late (22 and later),"(tour_type != 'escort') & (start > 21)",coef_departure_constants_late +util_arrival_constants_early,Arrival Constants -- Early (up to 6),"(tour_type != 'escort') & (end < 7)",coef_arrival_constants_early +util_arrival_constants_am_peak,Arrival Constants -- AM peak (7 to 9),"(tour_type != 'escort') & (end > 6) & (end < 10)",coef_arrival_constants_am_peak +util_arrival_constants_midday_1,Arrival Constants -- Midday 1 (10 to 12),"(tour_type != 'escort') & (end > 9) & (end < 13)",coef_arrival_constants_midday_1 +util_arrival_constants_midday_2,Arrival Constants -- Midday 2 (13 to 14),"(tour_type != 'escort') & (end > 12) & (end < 15)",coef_arrival_constants_midday_2 +util_arrival_constants_pm_peak_1,Arrival Constants -- PM peak 1 (15),"(tour_type != 'escort') & (end == 15)",coef_arrival_constants_pm_peak_1 +util_arrival_constants_pm_peak_2,Arrival Constants -- PM peak 2 (16),"(tour_type != 'escort') & (end == 16)",coef_arrival_constants_pm_peak_2 +util_arrival_constants_pm_peak_3,Arrival Constants -- PM peak 3 (17),"(tour_type != 'escort') & (end == 17)",coef_arrival_constants_pm_peak_3 +util_arrival_constants_pm_peak_4,Arrival Constants -- PM peak 4 (18),"(tour_type != 'escort') & (end == 18)",coef_arrival_constants_pm_peak_4 +util_arrival_constants_evening,Arrival Constants -- Evening (19 to 21),"(tour_type != 'escort') & (end > 18) & (end < 22)",coef_arrival_constants_evening +util_arrival_constants_late,Arrival Constants -- Late (22 and later),"(tour_type != 'escort') & (end > 21)",coef_arrival_constants_late +util_duration_constants_0_to_1_hours,Duration Constants -- 0 to 1 hours,"(tour_type != 'escort') & (duration < 2)",coef_duration_constants_0_to_1_hours +util_duration_constants_2_to_3_hours,Duration Constants -- 2 to 3 hours,"(tour_type != 'escort') & (duration > 1) & (duration < 4)",coef_duration_constants_2_to_3_hours +util_duration_constants_4_to_5_hours,Duration Constants -- 4 to 5 hours,"(tour_type != 'escort') & (duration > 3) & (duration < 6)",coef_duration_constants_4_to_5_hours +util_duration_constants_6_to_7_hours,Duration Constants -- 6 to 7 hours,"(tour_type != 'escort') & (duration > 5) & (duration < 8)",coef_duration_constants_6_to_7_hours +util_duration_constants_8_to_10_hours,Duration Constants -- 8 to 10 hours,"(tour_type != 'escort') & (duration > 7) & (duration < 11)",coef_duration_constants_8_to_10_hours +util_duration_constants_11_to_13_hours,Duration Constants -- 11 to 13 hours,"(tour_type != 'escort') & (duration > 10) & (duration < 14)",coef_duration_constants_11_to_13_hours util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,"(tour_type != 'escort') & (duration > 13) & (duration < 19)",coef_duration_constants_14_to_18_hours \ No newline at end of file diff --git a/activitysim/examples/example_psrc/configs/tour_scheduling_joint_coeffs.csv b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_joint_coeffs.csv similarity index 98% rename from activitysim/examples/example_psrc/configs/tour_scheduling_joint_coeffs.csv rename to activitysim/examples/placeholder_psrc/configs/tour_scheduling_joint_coeffs.csv index 8d0a940eac..6d382b52de 100755 --- a/activitysim/examples/example_psrc/configs/tour_scheduling_joint_coeffs.csv +++ b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_joint_coeffs.csv @@ -1,60 +1,60 @@ -coefficient_name,value,constrain -coef_unavailable,-999,T -coef_free_flow_round_trip_auto_time_shift_effects_duration,0.003195,F -coef_shopping_tour_departure_shift_effects,-0.06015,F -coef_shopping_tour_duration_shift_effects,-0.1208,F -coef_maintenance_tour_departure_shift_effects,-0.1489,F -coef_maintenance_tour_duration_shift_effects,-0.08372,F -coef_visit_tour_departure_shift_effects,0.09688,F -coef_visit_tour_duration_shift_effects,0.1638,F -coef_eat_out_tour_departure_shift_effects,0.07549,F -coef_school_child_age_16_plus_departure_shift_effects,0.07266,F -coef_school_child_age_16_plus_duration_shift_effects,0.2095,F -coef_school_child_age_under_16_departure_shift_effects,0.04657,F -coef_school_child_age_under_16_duration_shift_effects,0.3272,F -coef_destination_in_cbd_duration_shift_effects,0.1067,F -coef_number_of_mandatory_tours_departure_shift_effects,0.04673,F -coef_number_of_joint_tours_departure_shift_effects,0.05208,F -coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,-0.2364,F -coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,-0.1731,F -coef_maintenance_tour_depart_before_7,-0.8826,F -coef_shopping_tour_depart_before_8,-1.037,F -coef_shopping_tour_arrive_after_22,-0.6027,F -coef_school_child_under_16_arrive_after_22,-1.18,F -coef_university_student_arrive_after_22,0.5466,F -coef_shopping_tour_duration_lt_2_hours,0.5168,F -coef_discretionary_tour_duration_lt_2_hours,-0.6974,F -coef_adult_with_children_in_hh_arrive_19_21,0.336,F -coef_some_previously_scheduled_tour_ends_in_this_departure_hour,-0.4562,F -coef_some_previously_scheduled_tour_begins_in_this_arrival_hour,-0.3992,F -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.008442,F -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,-0.0257,F -coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction,-0.0593,F -coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction,-0.02734,F -coef_departure_constants_early,-14.47708,F -coef_departure_constants_am_peak_1,-11.59505,F -coef_departure_constants_am_peak_2,-9.00519,F -coef_departure_constants_am_peak_3,-2.73315,F -coef_departure_constants_am_peak_4,0.26654,F -coef_departure_constants_midday_1,0,T -coef_departure_constants_midday_2,-1.6026,F -coef_departure_constants_pm_peak,-17.69598,F -coef_departure_constants_evening,-18.98737,F -coef_departure_constants_late,-20.27807,F -coef_arrival_constants_early,-8.72888,F -coef_arrival_constants_am_peak,-8.72888,F -coef_arrival_constants_midday_1,0,T -coef_arrival_constants_midday_2,1.40804,F -coef_arrival_constants_pm_peak_1,1.02036,F -coef_arrival_constants_pm_peak_2,1.06863,F -coef_arrival_constants_pm_peak_3,0,T -coef_arrival_constants_pm_peak_4,-0.59626,F -coef_arrival_constants_evening,-2.74894,F -coef_arrival_constants_late,-4.24253,F -coef_duration_constants_0_to_1_hours,-2.22826,F -coef_duration_constants_2_to_3_hours,0,T -coef_duration_constants_4_to_5_hours,-0.56174,F -coef_duration_constants_6_to_7_hours,-0.65547,F -coef_duration_constants_8_to_10_hours,-0.74062,F -coef_duration_constants_11_to_13_hours,-0.81519,F +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_free_flow_round_trip_auto_time_shift_effects_duration,0.003195,F +coef_shopping_tour_departure_shift_effects,-0.06015,F +coef_shopping_tour_duration_shift_effects,-0.1208,F +coef_maintenance_tour_departure_shift_effects,-0.1489,F +coef_maintenance_tour_duration_shift_effects,-0.08372,F +coef_visit_tour_departure_shift_effects,0.09688,F +coef_visit_tour_duration_shift_effects,0.1638,F +coef_eat_out_tour_departure_shift_effects,0.07549,F +coef_school_child_age_16_plus_departure_shift_effects,0.07266,F +coef_school_child_age_16_plus_duration_shift_effects,0.2095,F +coef_school_child_age_under_16_departure_shift_effects,0.04657,F +coef_school_child_age_under_16_duration_shift_effects,0.3272,F +coef_destination_in_cbd_duration_shift_effects,0.1067,F +coef_number_of_mandatory_tours_departure_shift_effects,0.04673,F +coef_number_of_joint_tours_departure_shift_effects,0.05208,F +coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,-0.2364,F +coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,-0.1731,F +coef_maintenance_tour_depart_before_7,-0.8826,F +coef_shopping_tour_depart_before_8,-1.037,F +coef_shopping_tour_arrive_after_22,-0.6027,F +coef_school_child_under_16_arrive_after_22,-1.18,F +coef_university_student_arrive_after_22,0.5466,F +coef_shopping_tour_duration_lt_2_hours,0.5168,F +coef_discretionary_tour_duration_lt_2_hours,-0.6974,F +coef_adult_with_children_in_hh_arrive_19_21,0.336,F +coef_some_previously_scheduled_tour_ends_in_this_departure_hour,-0.4562,F +coef_some_previously_scheduled_tour_begins_in_this_arrival_hour,-0.3992,F +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.008442,F +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,-0.0257,F +coef_adjacent_window_exists_before_this_departure_hour_second_tour_interaction,-0.0593,F +coef_adjacent_window_exists_after_this_arrival_hour_second_tour_interaction,-0.02734,F +coef_departure_constants_early,-14.47708,F +coef_departure_constants_am_peak_1,-11.59505,F +coef_departure_constants_am_peak_2,-9.00519,F +coef_departure_constants_am_peak_3,-2.73315,F +coef_departure_constants_am_peak_4,0.26654,F +coef_departure_constants_midday_1,0,T +coef_departure_constants_midday_2,-1.6026,F +coef_departure_constants_pm_peak,-17.69598,F +coef_departure_constants_evening,-18.98737,F +coef_departure_constants_late,-20.27807,F +coef_arrival_constants_early,-8.72888,F +coef_arrival_constants_am_peak,-8.72888,F +coef_arrival_constants_midday_1,0,T +coef_arrival_constants_midday_2,1.40804,F +coef_arrival_constants_pm_peak_1,1.02036,F +coef_arrival_constants_pm_peak_2,1.06863,F +coef_arrival_constants_pm_peak_3,0,T +coef_arrival_constants_pm_peak_4,-0.59626,F +coef_arrival_constants_evening,-2.74894,F +coef_arrival_constants_late,-4.24253,F +coef_duration_constants_0_to_1_hours,-2.22826,F +coef_duration_constants_2_to_3_hours,0,T +coef_duration_constants_4_to_5_hours,-0.56174,F +coef_duration_constants_6_to_7_hours,-0.65547,F +coef_duration_constants_8_to_10_hours,-0.74062,F +coef_duration_constants_11_to_13_hours,-0.81519,F coef_duration_constants_14_to_18_hours,-2.73844,F \ No newline at end of file diff --git a/activitysim/examples/example_psrc/configs/tour_scheduling_nonmandatory.csv b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_nonmandatory.csv similarity index 99% rename from activitysim/examples/example_psrc/configs/tour_scheduling_nonmandatory.csv rename to activitysim/examples/placeholder_psrc/configs/tour_scheduling_nonmandatory.csv index 048aafd3cf..0c5c1fab7b 100755 --- a/activitysim/examples/example_psrc/configs/tour_scheduling_nonmandatory.csv +++ b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_nonmandatory.csv @@ -1,94 +1,94 @@ -Label,Description,Expression,Coefficient -util_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends,Subsequent tour must start after previous tour for this purpose ends,(start < end_previous) & (tour_type_num > 1),coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends -util_free_flow_round_trip_auto_time_shift_effects_duration,Free-flow round trip auto time shift effects - duration,roundtrip_auto_time_to_work * duration,coef_free_flow_round_trip_auto_time_shift_effects_duration -util_shopping_tour_departure_shift_effects,Shopping tour - departure shift effects,(tour_type == 'shopping') * start,coef_shopping_tour_departure_shift_effects -util_shopping_tour_duration_shift_effects,Shopping tour - duration shift effects,(tour_type == 'shopping') * duration,coef_shopping_tour_duration_shift_effects -util_maintenance_tour_departure_shift_effects,Maintenance tour - departure shift effects,(tour_type == 'othmaint') * start,coef_maintenance_tour_departure_shift_effects -util_maintenance_tour_duration_shift_effects,Maintenance tour - departure shift effects,(tour_type == 'othmaint') * duration,coef_maintenance_tour_duration_shift_effects -util_visit_tour_departure_shift_effects_start,Visit tour - departure shift effects,(tour_type == 'social') * start,coef_visit_tour_departure_shift_effects -util_visit_tour_duration_shift_effects_duration,Visit tour - departure shift effects,(tour_type == 'social') * duration,coef_visit_tour_duration_shift_effects -util_eat_out_tour_departure_shift_effects,Eat Out tour - departure shift effects,(tour_type == 'eatout') * start,coef_eat_out_tour_departure_shift_effects -util_school_child_age_16_plus_departure_shift_effects,School child age 16+ - departure shift effects,(ptype == 6) * start,coef_school_child_age_16_plus_departure_shift_effects -util_school_child_age_16_plus_duration_shift_effects,School child age 16+ - duration shift effects,(ptype == 6) * duration,coef_school_child_age_16_plus_duration_shift_effects -util_school_child_age_under_16_departure_shift_effects,School child age under 16 - departure shift effects,(ptype == 7) * start,coef_school_child_age_under_16_departure_shift_effects -util_school_child_age_under_16_duration_shift_effects,School child age under 16 - duration shift effects,(ptype == 7) * duration,coef_school_child_age_under_16_duration_shift_effects -util_destination_in_cbd_duration_shift_effects,Destination in CBD - duration shift effects,destination_in_cbd * duration,coef_destination_in_cbd_duration_shift_effects -util_number_of_mandatory_tours_departure_shift_effects,Number of mandatory tours - departure shift effects,num_mand * start,coef_number_of_mandatory_tours_departure_shift_effects -util_number_of_joint_tours_departure_shift_effects,Number of joint tours - departure shift effects,num_person_joint_tours * start,coef_number_of_joint_tours_departure_shift_effects -util_number_of_escort_tours_departure_shift_effects,Number of escort tours - departure shift effects,num_escort_tours * start,coef_number_of_escort_tours_departure_shift_effects -util_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects,Number of idividual non-mandatory tours (excluding escort) - departure shift effects,num_non_escort_tours * start,coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects -util_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,First of 2+ tours for same purpose - departure shift effect,((tour_type_count>1) & (tour_type_num == 1)) * start,coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect -util_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,subsequent of 2+ tours for same purpose - duration shift effect,(tour_type_num > 1) * duration,coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect -util_maintenance_tour_depart_before_7,Maintenance tour - depart before 7,(tour_type == 'othmaint') & (start < 7),coef_maintenance_tour_depart_before_7 -util_shopping_tour_depart_before_8,Shopping tour - depart before 8,(tour_type == 'shopping') & (start < 8),coef_shopping_tour_depart_before_8 -util_shopping_tour_arrive_after_22,Shopping tour - arrive after 22,(tour_type == 'shopping') & (end > 22),coef_shopping_tour_arrive_after_22 -util_school_child_under_16_arrive_after_22,School child under 16 - arrive after 22,(ptype == 7) & (end > 22),coef_school_child_under_16_arrive_after_22 -util_university_student_arrive_after_22,University student - arrive after 22,(ptype == 3) & (end > 22),coef_university_student_arrive_after_22 -util_shopping_tour_duration_lt_2_hours,Shopping tour - duration < 2 hours,(tour_type == 'shopping') & (duration < 2),coef_shopping_tour_duration_lt_2_hours -util_discretionary_tour_duration_lt_2_hours,Discretionary tour - duration < 2 hours,(tour_type == 'othdiscr') & (duration < 2),coef_discretionary_tour_duration_lt_2_hours -util_adult_with_children_in_hh_arrive_19_21,Adult with children in HH - arrive 19 - 21,adult & (num_children > 0) & ( end > 18 ) & ( end < 22 ),coef_adult_with_children_in_hh_arrive_19_21 -#,,, -#,Mode Choice Logsum,mode_choice_logsum,#mode_choice_logsum -#,,,# -util_some_previously_scheduled_tour_ends_in_this_departure_hour,Some previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.person_id, df.start)",coef_some_previously_scheduled_tour_ends_in_this_departure_hour -util_some_previously_scheduled_tour_begins_in_this_arrival_hour,Some previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.person_id, df.end)",coef_some_previously_scheduled_tour_begins_in_this_arrival_hour -util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & tt.adjacent_window_before(df.person_id, df.start)",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & tt.adjacent_window_after(df.person_id, df.end)",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_type_num > 1) & tt.adjacent_window_before(df.person_id, df.start)",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_type_num > 1) & tt.adjacent_window_after(df.person_id, df.end)",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction -util_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours,Remaining individual non-mandatory tours to be scheduled / number of unscheduled hours,"@((1.0 + df.tour_count - df.tour_num)) / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours -#,#,,# -util_departure_constants_early,Departure Constants -- Early (up to 5),(tour_type != 'escort') & (start < 6),coef_departure_constants_early -util_departure_constants_am_peak_1,Departure Constants -- AM peak 1 (6),(tour_type != 'escort') & (start == 6),coef_departure_constants_am_peak_1 -util_departure_constants_am_peak_2,Departure Constants -- AM peak 2 (7),(tour_type != 'escort') & (start == 7),coef_departure_constants_am_peak_2 -util_departure_constants_am_peak_3,Departure Constants -- AM peak 3 (8),(tour_type != 'escort') & (start == 8),coef_departure_constants_am_peak_3 -util_departure_constants_am_peak_4,Departure Constants -- AM peak 4 (9),(tour_type != 'escort') & (start == 9),coef_departure_constants_am_peak_4 -util_departure_constants_midday_1,Departure Constants -- Midday 1 (10 to 12),(tour_type != 'escort') & (start > 9) & (start < 13),coef_departure_constants_midday_1 -util_departure_constants_midday_2,Departure Constants -- Midday 2 (13 to 15),(tour_type != 'escort') & (start > 12) & (start < 16),coef_departure_constants_midday_2 -util_departure_constants_pm_peak,Departure Constants -- PM peak (16 to 18),(tour_type != 'escort') & (start > 15) & (start < 19),coef_departure_constants_pm_peak -util_departure_constants_evening,Departure Constants -- Evening (19 to 21),(tour_type != 'escort') & (start > 18) & (start < 22),coef_departure_constants_evening -util_departure_constants_late,Departure Constants -- Late (22 and later),(tour_type != 'escort') & (start > 21),coef_departure_constants_late -util_arrival_constants_early,Arrival Constants -- Early (up to 6),(tour_type != 'escort') & (end < 7),coef_arrival_constants_early -util_arrival_constants_am_peak,Arrival Constants -- AM peak (7 to 9),(tour_type != 'escort') & (end > 6) & (end < 10),coef_arrival_constants_am_peak -util_arrival_constants_midday_1,Arrival Constants -- Midday 1 (10 to 12),(tour_type != 'escort') & (end > 9) & (end < 13),coef_arrival_constants_midday_1 -util_arrival_constants_midday_2,Arrival Constants -- Midday 2 (13 to 14),(tour_type != 'escort') & (end > 12) & (end < 15),coef_arrival_constants_midday_2 -util_arrival_constants_pm_peak_1,Arrival Constants -- PM peak 1 (15),(tour_type != 'escort') & (end == 15),coef_arrival_constants_pm_peak_1 -util_arrival_constants_pm_peak_2,Arrival Constants -- PM peak 2 (16),(tour_type != 'escort') & (end == 16),coef_arrival_constants_pm_peak_2 -util_arrival_constants_pm_peak_3,Arrival Constants -- PM peak 3 (17),(tour_type != 'escort') & (end == 17),coef_arrival_constants_pm_peak_3 -util_arrival_constants_pm_peak_4,Arrival Constants -- PM peak 4 (18),(tour_type != 'escort') & (end == 18),coef_arrival_constants_pm_peak_4 -util_arrival_constants_evening,Arrival Constants -- Evening (19 to 21),(tour_type != 'escort') & (end > 18) & (end < 22),coef_arrival_constants_evening -util_arrival_constants_late,Arrival Constants -- Late (22 and later),(tour_type != 'escort') & (end > 21),coef_arrival_constants_late -util_duration_constants_0_to_1_hours,Duration Constants -- 0 to 1 hours,(tour_type != 'escort') & (duration < 2),coef_duration_constants_0_to_1_hours -util_duration_constants_2_to_3_hours,Duration Constants -- 2 to 3 hours,(tour_type != 'escort') & (duration > 1) & (duration < 4),coef_duration_constants_2_to_3_hours -util_duration_constants_4_to_5_hours,Duration Constants -- 4 to 5 hours,(tour_type != 'escort') & (duration > 3) & (duration < 6),coef_duration_constants_4_to_5_hours -util_duration_constants_6_to_7_hours,Duration Constants -- 6 to 7 hours,(tour_type != 'escort') & (duration > 5) & (duration < 8),coef_duration_constants_6_to_7_hours -util_duration_constants_8_to_10_hours,Duration Constants -- 8 to 10 hours,(tour_type != 'escort') & (duration > 7) & (duration < 11),coef_duration_constants_8_to_10_hours -util_duration_constants_11_to_13_hours,Duration Constants -- 11 to 13 hours,(tour_type != 'escort') & (duration > 10) & (duration < 14),coef_duration_constants_11_to_13_hours -util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,(tour_type != 'escort') & (duration > 13) & (duration < 19),coef_duration_constants_14_to_18_hours -util_escort_tour_departure_constants_early,Escort Tour Departure Constants -- Early (up to 5),(tour_type == 'escort') & (start < 6),coef_escort_tour_departure_constants_early -util_escort_tour_departure_constants_am_peak_1,Escort Tour Departure Constants -- AM peak 1 (6),(tour_type == 'escort') & (start == 6),coef_escort_tour_departure_constants_am_peak_1 -util_escort_tour_departure_constants_am_peak_2,Escort Tour Departure Constants -- AM peak 2 (7),(tour_type == 'escort') & (start == 7),coef_escort_tour_departure_constants_am_peak_2 -util_escort_tour_departure_constants_am_peak_3,Escort Tour Departure Constants -- AM peak 3 (8),(tour_type == 'escort') & (start == 8),coef_escort_tour_departure_constants_am_peak_3 -util_escort_tour_departure_constants_am_peak_4,Escort Tour Departure Constants -- AM peak 4 (9),(tour_type == 'escort') & (start == 9),coef_escort_tour_departure_constants_am_peak_4 -util_escort_tour_departure_constants_midday_1,Escort Tour Departure Constants -- Midday 1 (10 to 12),(tour_type == 'escort') & (start > 9) & (start < 13),coef_escort_tour_departure_constants_midday_1 -util_escort_tour_departure_constants_midday_2,Escort Tour Departure Constants -- Midday 2 (13 to 15),(tour_type == 'escort') & (start > 12) & (start < 16),coef_escort_tour_departure_constants_midday_2 -util_escort_tour_departure_constants_pm_peak,Escort Tour Departure Constants -- PM peak (16 to 18),(tour_type == 'escort') & (start > 15) & (start < 19),coef_escort_tour_departure_constants_pm_peak -util_escort_tour_departure_constants_evening,Escort Tour Departure Constants -- Evening (19 to 21),(tour_type == 'escort') & (start > 18) & (start < 22),coef_escort_tour_departure_constants_evening -util_escort_tour_departure_constants_late,Escort Tour Departure Constants -- Late (22 and later),(tour_type == 'escort') & (start > 21),coef_escort_tour_departure_constants_late -util_escort_tour_arrival_constants_early,Escort Tour Arrival Constants -- Early (up to 6),(tour_type == 'escort') & (end < 7),coef_escort_tour_arrival_constants_early -util_escort_tour_arrival_constants_am_peak,Escort Tour Arrival Constants -- AM peak (7 to 9),(tour_type == 'escort') & (end > 6) & (end < 10),coef_escort_tour_arrival_constants_am_peak -util_escort_tour_arrival_constants_midday_1,Escort Tour Arrival Constants -- Midday 1 (10 to 12),(tour_type == 'escort') & (end > 9) & (end < 13),coef_escort_tour_arrival_constants_midday_1 -util_escort_tour_arrival_constants_midday_2,Escort Tour Arrival Constants -- Midday 2 (13 to 14),(tour_type == 'escort') & (end > 12) & (end < 15),coef_escort_tour_arrival_constants_midday_2 -util_escort_tour_arrival_constants_pm_peak_1,Escort Tour Arrival Constants -- PM peak 1 (15),(tour_type == 'escort') & (end == 15),coef_escort_tour_arrival_constants_pm_peak_1 -util_escort_tour_arrival_constants_pm_peak_2,Escort Tour Arrival Constants -- PM peak 2 (16),(tour_type == 'escort') & (end == 16),coef_escort_tour_arrival_constants_pm_peak_2 -util_escort_tour_arrival_constants_pm_peak_3,Escort Tour Arrival Constants -- PM peak 3 (17),(tour_type == 'escort') & (end == 17),coef_escort_tour_arrival_constants_pm_peak_3 -util_escort_tour_arrival_constants_pm_peak_4,Escort Tour Arrival Constants -- PM peak 4 (18),(tour_type == 'escort') & (end == 18),coef_escort_tour_arrival_constants_pm_peak_4 -util_escort_tour_arrival_constants_evening,Escort Tour Arrival Constants -- Evening (19 to 21),(tour_type == 'escort') & (end > 18) & (end < 22),coef_escort_tour_arrival_constants_evening -util_escort_tour_arrival_constants_late,Escort Tour Arrival Constants -- Late (22 and later),(tour_type == 'escort') & (end > 21),coef_escort_tour_arrival_constants_late -util_escort_tour_duration_constants_0_to_1_hours,Escort Tour Duration Constants -- 0 to 1 hours,(tour_type == 'escort') & (duration < 2),coef_escort_tour_duration_constants_0_to_1_hours -util_escort_tour_duration_constants_2_to_3_hours,Escort Tour Duration Constants -- 2 to 3 hours,(tour_type == 'escort') & (duration > 1) & (duration < 4),coef_escort_tour_duration_constants_2_to_3_hours -util_escort_tour_duration_constants_4_to_5_hours,Escort Tour Duration Constants -- 4 to 5 hours,(tour_type == 'escort') & (duration > 3) & (duration < 6),coef_escort_tour_duration_constants_4_to_5_hours -util_escort_tour_duration_constants_6_to_7_hours,Escort Tour Duration Constants -- 6 to 7 hours,(tour_type == 'escort') & (duration > 5) & (duration < 8),coef_escort_tour_duration_constants_6_to_7_hours -util_escort_tour_duration_constants_8_to_10_hours,Escort Tour Duration Constants -- 8 to 10 hours,(tour_type == 'escort') & (duration > 7) & (duration < 11),coef_escort_tour_duration_constants_8_to_10_hours -util_escort_tour_duration_constants_11_to_13_hours,Escort Tour Duration Constants -- 11 to 13 hours,(tour_type == 'escort') & (duration > 10) & (duration < 14),coef_escort_tour_duration_constants_11_to_13_hours +Label,Description,Expression,Coefficient +util_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends,Subsequent tour must start after previous tour for this purpose ends,(start < end_previous) & (tour_type_num > 1),coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends +util_free_flow_round_trip_auto_time_shift_effects_duration,Free-flow round trip auto time shift effects - duration,roundtrip_auto_time_to_work * duration,coef_free_flow_round_trip_auto_time_shift_effects_duration +util_shopping_tour_departure_shift_effects,Shopping tour - departure shift effects,(tour_type == 'shopping') * start,coef_shopping_tour_departure_shift_effects +util_shopping_tour_duration_shift_effects,Shopping tour - duration shift effects,(tour_type == 'shopping') * duration,coef_shopping_tour_duration_shift_effects +util_maintenance_tour_departure_shift_effects,Maintenance tour - departure shift effects,(tour_type == 'othmaint') * start,coef_maintenance_tour_departure_shift_effects +util_maintenance_tour_duration_shift_effects,Maintenance tour - departure shift effects,(tour_type == 'othmaint') * duration,coef_maintenance_tour_duration_shift_effects +util_visit_tour_departure_shift_effects_start,Visit tour - departure shift effects,(tour_type == 'social') * start,coef_visit_tour_departure_shift_effects +util_visit_tour_duration_shift_effects_duration,Visit tour - departure shift effects,(tour_type == 'social') * duration,coef_visit_tour_duration_shift_effects +util_eat_out_tour_departure_shift_effects,Eat Out tour - departure shift effects,(tour_type == 'eatout') * start,coef_eat_out_tour_departure_shift_effects +util_school_child_age_16_plus_departure_shift_effects,School child age 16+ - departure shift effects,(ptype == 6) * start,coef_school_child_age_16_plus_departure_shift_effects +util_school_child_age_16_plus_duration_shift_effects,School child age 16+ - duration shift effects,(ptype == 6) * duration,coef_school_child_age_16_plus_duration_shift_effects +util_school_child_age_under_16_departure_shift_effects,School child age under 16 - departure shift effects,(ptype == 7) * start,coef_school_child_age_under_16_departure_shift_effects +util_school_child_age_under_16_duration_shift_effects,School child age under 16 - duration shift effects,(ptype == 7) * duration,coef_school_child_age_under_16_duration_shift_effects +util_destination_in_cbd_duration_shift_effects,Destination in CBD - duration shift effects,destination_in_cbd * duration,coef_destination_in_cbd_duration_shift_effects +util_number_of_mandatory_tours_departure_shift_effects,Number of mandatory tours - departure shift effects,num_mand * start,coef_number_of_mandatory_tours_departure_shift_effects +util_number_of_joint_tours_departure_shift_effects,Number of joint tours - departure shift effects,num_person_joint_tours * start,coef_number_of_joint_tours_departure_shift_effects +util_number_of_escort_tours_departure_shift_effects,Number of escort tours - departure shift effects,num_escort_tours * start,coef_number_of_escort_tours_departure_shift_effects +util_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects,Number of idividual non-mandatory tours (excluding escort) - departure shift effects,num_non_escort_tours * start,coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects +util_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,First of 2+ tours for same purpose - departure shift effect,((tour_type_count>1) & (tour_type_num == 1)) * start,coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect +util_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,subsequent of 2+ tours for same purpose - duration shift effect,(tour_type_num > 1) * duration,coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect +util_maintenance_tour_depart_before_7,Maintenance tour - depart before 7,(tour_type == 'othmaint') & (start < 7),coef_maintenance_tour_depart_before_7 +util_shopping_tour_depart_before_8,Shopping tour - depart before 8,(tour_type == 'shopping') & (start < 8),coef_shopping_tour_depart_before_8 +util_shopping_tour_arrive_after_22,Shopping tour - arrive after 22,(tour_type == 'shopping') & (end > 22),coef_shopping_tour_arrive_after_22 +util_school_child_under_16_arrive_after_22,School child under 16 - arrive after 22,(ptype == 7) & (end > 22),coef_school_child_under_16_arrive_after_22 +util_university_student_arrive_after_22,University student - arrive after 22,(ptype == 3) & (end > 22),coef_university_student_arrive_after_22 +util_shopping_tour_duration_lt_2_hours,Shopping tour - duration < 2 hours,(tour_type == 'shopping') & (duration < 2),coef_shopping_tour_duration_lt_2_hours +util_discretionary_tour_duration_lt_2_hours,Discretionary tour - duration < 2 hours,(tour_type == 'othdiscr') & (duration < 2),coef_discretionary_tour_duration_lt_2_hours +util_adult_with_children_in_hh_arrive_19_21,Adult with children in HH - arrive 19 - 21,adult & (num_children > 0) & ( end > 18 ) & ( end < 22 ),coef_adult_with_children_in_hh_arrive_19_21 +#,,, +#,Mode Choice Logsum,mode_choice_logsum,#mode_choice_logsum +#,,,# +util_some_previously_scheduled_tour_ends_in_this_departure_hour,Some previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.person_id, df.start)",coef_some_previously_scheduled_tour_ends_in_this_departure_hour +util_some_previously_scheduled_tour_begins_in_this_arrival_hour,Some previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.person_id, df.end)",coef_some_previously_scheduled_tour_begins_in_this_arrival_hour +util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & tt.adjacent_window_before(df.person_id, df.start)",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & tt.adjacent_window_after(df.person_id, df.end)",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_type_num > 1) & tt.adjacent_window_before(df.person_id, df.start)",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_type_num > 1) & tt.adjacent_window_after(df.person_id, df.end)",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction +util_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours,Remaining individual non-mandatory tours to be scheduled / number of unscheduled hours,"@((1.0 + df.tour_count - df.tour_num)) / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours +#,#,,# +util_departure_constants_early,Departure Constants -- Early (up to 5),(tour_type != 'escort') & (start < 6),coef_departure_constants_early +util_departure_constants_am_peak_1,Departure Constants -- AM peak 1 (6),(tour_type != 'escort') & (start == 6),coef_departure_constants_am_peak_1 +util_departure_constants_am_peak_2,Departure Constants -- AM peak 2 (7),(tour_type != 'escort') & (start == 7),coef_departure_constants_am_peak_2 +util_departure_constants_am_peak_3,Departure Constants -- AM peak 3 (8),(tour_type != 'escort') & (start == 8),coef_departure_constants_am_peak_3 +util_departure_constants_am_peak_4,Departure Constants -- AM peak 4 (9),(tour_type != 'escort') & (start == 9),coef_departure_constants_am_peak_4 +util_departure_constants_midday_1,Departure Constants -- Midday 1 (10 to 12),(tour_type != 'escort') & (start > 9) & (start < 13),coef_departure_constants_midday_1 +util_departure_constants_midday_2,Departure Constants -- Midday 2 (13 to 15),(tour_type != 'escort') & (start > 12) & (start < 16),coef_departure_constants_midday_2 +util_departure_constants_pm_peak,Departure Constants -- PM peak (16 to 18),(tour_type != 'escort') & (start > 15) & (start < 19),coef_departure_constants_pm_peak +util_departure_constants_evening,Departure Constants -- Evening (19 to 21),(tour_type != 'escort') & (start > 18) & (start < 22),coef_departure_constants_evening +util_departure_constants_late,Departure Constants -- Late (22 and later),(tour_type != 'escort') & (start > 21),coef_departure_constants_late +util_arrival_constants_early,Arrival Constants -- Early (up to 6),(tour_type != 'escort') & (end < 7),coef_arrival_constants_early +util_arrival_constants_am_peak,Arrival Constants -- AM peak (7 to 9),(tour_type != 'escort') & (end > 6) & (end < 10),coef_arrival_constants_am_peak +util_arrival_constants_midday_1,Arrival Constants -- Midday 1 (10 to 12),(tour_type != 'escort') & (end > 9) & (end < 13),coef_arrival_constants_midday_1 +util_arrival_constants_midday_2,Arrival Constants -- Midday 2 (13 to 14),(tour_type != 'escort') & (end > 12) & (end < 15),coef_arrival_constants_midday_2 +util_arrival_constants_pm_peak_1,Arrival Constants -- PM peak 1 (15),(tour_type != 'escort') & (end == 15),coef_arrival_constants_pm_peak_1 +util_arrival_constants_pm_peak_2,Arrival Constants -- PM peak 2 (16),(tour_type != 'escort') & (end == 16),coef_arrival_constants_pm_peak_2 +util_arrival_constants_pm_peak_3,Arrival Constants -- PM peak 3 (17),(tour_type != 'escort') & (end == 17),coef_arrival_constants_pm_peak_3 +util_arrival_constants_pm_peak_4,Arrival Constants -- PM peak 4 (18),(tour_type != 'escort') & (end == 18),coef_arrival_constants_pm_peak_4 +util_arrival_constants_evening,Arrival Constants -- Evening (19 to 21),(tour_type != 'escort') & (end > 18) & (end < 22),coef_arrival_constants_evening +util_arrival_constants_late,Arrival Constants -- Late (22 and later),(tour_type != 'escort') & (end > 21),coef_arrival_constants_late +util_duration_constants_0_to_1_hours,Duration Constants -- 0 to 1 hours,(tour_type != 'escort') & (duration < 2),coef_duration_constants_0_to_1_hours +util_duration_constants_2_to_3_hours,Duration Constants -- 2 to 3 hours,(tour_type != 'escort') & (duration > 1) & (duration < 4),coef_duration_constants_2_to_3_hours +util_duration_constants_4_to_5_hours,Duration Constants -- 4 to 5 hours,(tour_type != 'escort') & (duration > 3) & (duration < 6),coef_duration_constants_4_to_5_hours +util_duration_constants_6_to_7_hours,Duration Constants -- 6 to 7 hours,(tour_type != 'escort') & (duration > 5) & (duration < 8),coef_duration_constants_6_to_7_hours +util_duration_constants_8_to_10_hours,Duration Constants -- 8 to 10 hours,(tour_type != 'escort') & (duration > 7) & (duration < 11),coef_duration_constants_8_to_10_hours +util_duration_constants_11_to_13_hours,Duration Constants -- 11 to 13 hours,(tour_type != 'escort') & (duration > 10) & (duration < 14),coef_duration_constants_11_to_13_hours +util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,(tour_type != 'escort') & (duration > 13) & (duration < 19),coef_duration_constants_14_to_18_hours +util_escort_tour_departure_constants_early,Escort Tour Departure Constants -- Early (up to 5),(tour_type == 'escort') & (start < 6),coef_escort_tour_departure_constants_early +util_escort_tour_departure_constants_am_peak_1,Escort Tour Departure Constants -- AM peak 1 (6),(tour_type == 'escort') & (start == 6),coef_escort_tour_departure_constants_am_peak_1 +util_escort_tour_departure_constants_am_peak_2,Escort Tour Departure Constants -- AM peak 2 (7),(tour_type == 'escort') & (start == 7),coef_escort_tour_departure_constants_am_peak_2 +util_escort_tour_departure_constants_am_peak_3,Escort Tour Departure Constants -- AM peak 3 (8),(tour_type == 'escort') & (start == 8),coef_escort_tour_departure_constants_am_peak_3 +util_escort_tour_departure_constants_am_peak_4,Escort Tour Departure Constants -- AM peak 4 (9),(tour_type == 'escort') & (start == 9),coef_escort_tour_departure_constants_am_peak_4 +util_escort_tour_departure_constants_midday_1,Escort Tour Departure Constants -- Midday 1 (10 to 12),(tour_type == 'escort') & (start > 9) & (start < 13),coef_escort_tour_departure_constants_midday_1 +util_escort_tour_departure_constants_midday_2,Escort Tour Departure Constants -- Midday 2 (13 to 15),(tour_type == 'escort') & (start > 12) & (start < 16),coef_escort_tour_departure_constants_midday_2 +util_escort_tour_departure_constants_pm_peak,Escort Tour Departure Constants -- PM peak (16 to 18),(tour_type == 'escort') & (start > 15) & (start < 19),coef_escort_tour_departure_constants_pm_peak +util_escort_tour_departure_constants_evening,Escort Tour Departure Constants -- Evening (19 to 21),(tour_type == 'escort') & (start > 18) & (start < 22),coef_escort_tour_departure_constants_evening +util_escort_tour_departure_constants_late,Escort Tour Departure Constants -- Late (22 and later),(tour_type == 'escort') & (start > 21),coef_escort_tour_departure_constants_late +util_escort_tour_arrival_constants_early,Escort Tour Arrival Constants -- Early (up to 6),(tour_type == 'escort') & (end < 7),coef_escort_tour_arrival_constants_early +util_escort_tour_arrival_constants_am_peak,Escort Tour Arrival Constants -- AM peak (7 to 9),(tour_type == 'escort') & (end > 6) & (end < 10),coef_escort_tour_arrival_constants_am_peak +util_escort_tour_arrival_constants_midday_1,Escort Tour Arrival Constants -- Midday 1 (10 to 12),(tour_type == 'escort') & (end > 9) & (end < 13),coef_escort_tour_arrival_constants_midday_1 +util_escort_tour_arrival_constants_midday_2,Escort Tour Arrival Constants -- Midday 2 (13 to 14),(tour_type == 'escort') & (end > 12) & (end < 15),coef_escort_tour_arrival_constants_midday_2 +util_escort_tour_arrival_constants_pm_peak_1,Escort Tour Arrival Constants -- PM peak 1 (15),(tour_type == 'escort') & (end == 15),coef_escort_tour_arrival_constants_pm_peak_1 +util_escort_tour_arrival_constants_pm_peak_2,Escort Tour Arrival Constants -- PM peak 2 (16),(tour_type == 'escort') & (end == 16),coef_escort_tour_arrival_constants_pm_peak_2 +util_escort_tour_arrival_constants_pm_peak_3,Escort Tour Arrival Constants -- PM peak 3 (17),(tour_type == 'escort') & (end == 17),coef_escort_tour_arrival_constants_pm_peak_3 +util_escort_tour_arrival_constants_pm_peak_4,Escort Tour Arrival Constants -- PM peak 4 (18),(tour_type == 'escort') & (end == 18),coef_escort_tour_arrival_constants_pm_peak_4 +util_escort_tour_arrival_constants_evening,Escort Tour Arrival Constants -- Evening (19 to 21),(tour_type == 'escort') & (end > 18) & (end < 22),coef_escort_tour_arrival_constants_evening +util_escort_tour_arrival_constants_late,Escort Tour Arrival Constants -- Late (22 and later),(tour_type == 'escort') & (end > 21),coef_escort_tour_arrival_constants_late +util_escort_tour_duration_constants_0_to_1_hours,Escort Tour Duration Constants -- 0 to 1 hours,(tour_type == 'escort') & (duration < 2),coef_escort_tour_duration_constants_0_to_1_hours +util_escort_tour_duration_constants_2_to_3_hours,Escort Tour Duration Constants -- 2 to 3 hours,(tour_type == 'escort') & (duration > 1) & (duration < 4),coef_escort_tour_duration_constants_2_to_3_hours +util_escort_tour_duration_constants_4_to_5_hours,Escort Tour Duration Constants -- 4 to 5 hours,(tour_type == 'escort') & (duration > 3) & (duration < 6),coef_escort_tour_duration_constants_4_to_5_hours +util_escort_tour_duration_constants_6_to_7_hours,Escort Tour Duration Constants -- 6 to 7 hours,(tour_type == 'escort') & (duration > 5) & (duration < 8),coef_escort_tour_duration_constants_6_to_7_hours +util_escort_tour_duration_constants_8_to_10_hours,Escort Tour Duration Constants -- 8 to 10 hours,(tour_type == 'escort') & (duration > 7) & (duration < 11),coef_escort_tour_duration_constants_8_to_10_hours +util_escort_tour_duration_constants_11_to_13_hours,Escort Tour Duration Constants -- 11 to 13 hours,(tour_type == 'escort') & (duration > 10) & (duration < 14),coef_escort_tour_duration_constants_11_to_13_hours util_escort_tour_duration_constants_14_to_18_hours,Escort Tour Duration Constants -- 14 to 18 hours,(tour_type == 'escort') & (duration > 13) & (duration < 19),coef_escort_tour_duration_constants_14_to_18_hours \ No newline at end of file diff --git a/activitysim/examples/example_psrc/configs/tour_scheduling_nonmandatory_coeffs.csv b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_nonmandatory_coeffs.csv similarity index 98% rename from activitysim/examples/example_psrc/configs/tour_scheduling_nonmandatory_coeffs.csv rename to activitysim/examples/placeholder_psrc/configs/tour_scheduling_nonmandatory_coeffs.csv index 5cc43331ad..11120c6592 100755 --- a/activitysim/examples/example_psrc/configs/tour_scheduling_nonmandatory_coeffs.csv +++ b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_nonmandatory_coeffs.csv @@ -1,94 +1,94 @@ -coefficient_name,value,constrain -coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends,-999,T -coef_free_flow_round_trip_auto_time_shift_effects_duration,0.004741,F -coef_shopping_tour_departure_shift_effects,-0.06015,F -coef_shopping_tour_duration_shift_effects,-0.1208,F -coef_maintenance_tour_departure_shift_effects,-0.1489,F -coef_maintenance_tour_duration_shift_effects,-0.08372,F -coef_visit_tour_departure_shift_effects,0.09688,F -coef_visit_tour_duration_shift_effects,0.1638,F -coef_eat_out_tour_departure_shift_effects,0.07549,F -coef_school_child_age_16_plus_departure_shift_effects,0.07266,F -coef_school_child_age_16_plus_duration_shift_effects,0.2095,F -coef_school_child_age_under_16_departure_shift_effects,0.04657,F -coef_school_child_age_under_16_duration_shift_effects,0.3272,F -coef_destination_in_cbd_duration_shift_effects,0.1067,F -coef_number_of_mandatory_tours_departure_shift_effects,0.04673,F -coef_number_of_joint_tours_departure_shift_effects,0.05208,F -coef_number_of_escort_tours_departure_shift_effects,0.02013,F -coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects,0.03896,F -coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,-0.2364,F -coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,-0.1731,F -coef_maintenance_tour_depart_before_7,-0.8826,F -coef_shopping_tour_depart_before_8,-1.037,F -coef_shopping_tour_arrive_after_22,-0.6027,F -coef_school_child_under_16_arrive_after_22,-1.18,F -coef_university_student_arrive_after_22,0.5466,F -coef_shopping_tour_duration_lt_2_hours,0.5168,F -coef_discretionary_tour_duration_lt_2_hours,-0.6974,F -coef_adult_with_children_in_hh_arrive_19_21,0.336,F -#,, -#mode_choice_logsum,, -#,, -coef_some_previously_scheduled_tour_ends_in_this_departure_hour,-0.4562,F -coef_some_previously_scheduled_tour_begins_in_this_arrival_hour,-0.3992,F -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.008442,F -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,-0.0257,F -coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-0.0593,F -coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.02734,F -coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours,-13.63,F -#,,F -coef_departure_constants_early,-1.740135661,F -coef_departure_constants_am_peak_1,-0.654163573,F -coef_departure_constants_am_peak_2,0.554282571,F -coef_departure_constants_am_peak_3,1.050561087,F -coef_departure_constants_am_peak_4,0.971568228,F -coef_departure_constants_midday_1,0.881991986,F -coef_departure_constants_midday_2,0.411103634,F -coef_departure_constants_pm_peak,0,T -coef_departure_constants_evening,-1.856475096,F -coef_departure_constants_late,-8.228880141,F -coef_arrival_constants_early,-0.051990748,F -coef_arrival_constants_am_peak,-1.814822602,F -coef_arrival_constants_midday_1,0.000371501,F -coef_arrival_constants_midday_2,0.532116031,F -coef_arrival_constants_pm_peak_1,0.628481567,F -coef_arrival_constants_pm_peak_2,0.650521416,F -coef_arrival_constants_pm_peak_3,0.402894406,F -coef_arrival_constants_pm_peak_4,0.154213293,F -coef_arrival_constants_evening,0,T -coef_arrival_constants_late,-0.866671315,F -coef_duration_constants_0_to_1_hours,0,T -coef_duration_constants_2_to_3_hours,0.051385565,F -coef_duration_constants_4_to_5_hours,-0.593951321,F -coef_duration_constants_6_to_7_hours,-0.951155328,F -coef_duration_constants_8_to_10_hours,-0.828108399,F -coef_duration_constants_11_to_13_hours,-0.955635554,F -coef_duration_constants_14_to_18_hours,-1.042580879,F -coef_escort_tour_departure_constants_early,-1.740135661,F -coef_escort_tour_departure_constants_am_peak_1,-1.112357753,F -coef_escort_tour_departure_constants_am_peak_2,0.698788185,F -coef_escort_tour_departure_constants_am_peak_3,1.196268813,F -coef_escort_tour_departure_constants_am_peak_4,-0.225258221,F -coef_escort_tour_departure_constants_midday_1,0.028662017,F -coef_escort_tour_departure_constants_midday_2,0,T -coef_escort_tour_departure_constants_pm_peak,-1.180140161,F -coef_escort_tour_departure_constants_evening,-3.948732811,F -coef_escort_tour_departure_constants_late,-8.228880141,F -coef_escort_tour_arrival_constants_early,0,T -coef_escort_tour_arrival_constants_am_peak,0,T -coef_escort_tour_arrival_constants_midday_1,0,T -coef_escort_tour_arrival_constants_midday_2,0,T -coef_escort_tour_arrival_constants_pm_peak_1,0,T -coef_escort_tour_arrival_constants_pm_peak_2,0,T -coef_escort_tour_arrival_constants_pm_peak_3,0,T -coef_escort_tour_arrival_constants_pm_peak_4,0,T -coef_escort_tour_arrival_constants_evening,-0.536918728,F -coef_escort_tour_arrival_constants_late,-1.008290213,F -coef_escort_tour_duration_constants_0_to_1_hours,0,T -coef_escort_tour_duration_constants_2_to_3_hours,-2.042013897,F -coef_escort_tour_duration_constants_4_to_5_hours,-2.880293896,F -coef_escort_tour_duration_constants_6_to_7_hours,-2.973533731,F -coef_escort_tour_duration_constants_8_to_10_hours,-3.020213758,F -coef_escort_tour_duration_constants_11_to_13_hours,-2.974364976,F +coefficient_name,value,constrain +coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends,-999,T +coef_free_flow_round_trip_auto_time_shift_effects_duration,0.004741,F +coef_shopping_tour_departure_shift_effects,-0.06015,F +coef_shopping_tour_duration_shift_effects,-0.1208,F +coef_maintenance_tour_departure_shift_effects,-0.1489,F +coef_maintenance_tour_duration_shift_effects,-0.08372,F +coef_visit_tour_departure_shift_effects,0.09688,F +coef_visit_tour_duration_shift_effects,0.1638,F +coef_eat_out_tour_departure_shift_effects,0.07549,F +coef_school_child_age_16_plus_departure_shift_effects,0.07266,F +coef_school_child_age_16_plus_duration_shift_effects,0.2095,F +coef_school_child_age_under_16_departure_shift_effects,0.04657,F +coef_school_child_age_under_16_duration_shift_effects,0.3272,F +coef_destination_in_cbd_duration_shift_effects,0.1067,F +coef_number_of_mandatory_tours_departure_shift_effects,0.04673,F +coef_number_of_joint_tours_departure_shift_effects,0.05208,F +coef_number_of_escort_tours_departure_shift_effects,0.02013,F +coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects,0.03896,F +coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,-0.2364,F +coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,-0.1731,F +coef_maintenance_tour_depart_before_7,-0.8826,F +coef_shopping_tour_depart_before_8,-1.037,F +coef_shopping_tour_arrive_after_22,-0.6027,F +coef_school_child_under_16_arrive_after_22,-1.18,F +coef_university_student_arrive_after_22,0.5466,F +coef_shopping_tour_duration_lt_2_hours,0.5168,F +coef_discretionary_tour_duration_lt_2_hours,-0.6974,F +coef_adult_with_children_in_hh_arrive_19_21,0.336,F +#,, +#mode_choice_logsum,, +#,, +coef_some_previously_scheduled_tour_ends_in_this_departure_hour,-0.4562,F +coef_some_previously_scheduled_tour_begins_in_this_arrival_hour,-0.3992,F +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.008442,F +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,-0.0257,F +coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-0.0593,F +coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.02734,F +coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours,-13.63,F +#,,F +coef_departure_constants_early,-1.740135661,F +coef_departure_constants_am_peak_1,-0.654163573,F +coef_departure_constants_am_peak_2,0.554282571,F +coef_departure_constants_am_peak_3,1.050561087,F +coef_departure_constants_am_peak_4,0.971568228,F +coef_departure_constants_midday_1,0.881991986,F +coef_departure_constants_midday_2,0.411103634,F +coef_departure_constants_pm_peak,0,T +coef_departure_constants_evening,-1.856475096,F +coef_departure_constants_late,-8.228880141,F +coef_arrival_constants_early,-0.051990748,F +coef_arrival_constants_am_peak,-1.814822602,F +coef_arrival_constants_midday_1,0.000371501,F +coef_arrival_constants_midday_2,0.532116031,F +coef_arrival_constants_pm_peak_1,0.628481567,F +coef_arrival_constants_pm_peak_2,0.650521416,F +coef_arrival_constants_pm_peak_3,0.402894406,F +coef_arrival_constants_pm_peak_4,0.154213293,F +coef_arrival_constants_evening,0,T +coef_arrival_constants_late,-0.866671315,F +coef_duration_constants_0_to_1_hours,0,T +coef_duration_constants_2_to_3_hours,0.051385565,F +coef_duration_constants_4_to_5_hours,-0.593951321,F +coef_duration_constants_6_to_7_hours,-0.951155328,F +coef_duration_constants_8_to_10_hours,-0.828108399,F +coef_duration_constants_11_to_13_hours,-0.955635554,F +coef_duration_constants_14_to_18_hours,-1.042580879,F +coef_escort_tour_departure_constants_early,-1.740135661,F +coef_escort_tour_departure_constants_am_peak_1,-1.112357753,F +coef_escort_tour_departure_constants_am_peak_2,0.698788185,F +coef_escort_tour_departure_constants_am_peak_3,1.196268813,F +coef_escort_tour_departure_constants_am_peak_4,-0.225258221,F +coef_escort_tour_departure_constants_midday_1,0.028662017,F +coef_escort_tour_departure_constants_midday_2,0,T +coef_escort_tour_departure_constants_pm_peak,-1.180140161,F +coef_escort_tour_departure_constants_evening,-3.948732811,F +coef_escort_tour_departure_constants_late,-8.228880141,F +coef_escort_tour_arrival_constants_early,0,T +coef_escort_tour_arrival_constants_am_peak,0,T +coef_escort_tour_arrival_constants_midday_1,0,T +coef_escort_tour_arrival_constants_midday_2,0,T +coef_escort_tour_arrival_constants_pm_peak_1,0,T +coef_escort_tour_arrival_constants_pm_peak_2,0,T +coef_escort_tour_arrival_constants_pm_peak_3,0,T +coef_escort_tour_arrival_constants_pm_peak_4,0,T +coef_escort_tour_arrival_constants_evening,-0.536918728,F +coef_escort_tour_arrival_constants_late,-1.008290213,F +coef_escort_tour_duration_constants_0_to_1_hours,0,T +coef_escort_tour_duration_constants_2_to_3_hours,-2.042013897,F +coef_escort_tour_duration_constants_4_to_5_hours,-2.880293896,F +coef_escort_tour_duration_constants_6_to_7_hours,-2.973533731,F +coef_escort_tour_duration_constants_8_to_10_hours,-3.020213758,F +coef_escort_tour_duration_constants_11_to_13_hours,-2.974364976,F coef_escort_tour_duration_constants_14_to_18_hours,-2.507447146,F \ No newline at end of file diff --git a/activitysim/examples/example_psrc/configs/tour_scheduling_school.csv b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_school.csv similarity index 99% rename from activitysim/examples/example_psrc/configs/tour_scheduling_school.csv rename to activitysim/examples/placeholder_psrc/configs/tour_scheduling_school.csv index 8ade903066..845e55a0b1 100755 --- a/activitysim/examples/example_psrc/configs/tour_scheduling_school.csv +++ b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_school.csv @@ -1,63 +1,63 @@ -Label,Description,Expression,Coefficient -util_roundtrip_auto_time_to_work,Free-flow round trip auto time shift effects - duration,roundtrip_auto_time_to_work * duration,coef_roundtrip_auto_time_to_work -util_ft_worker_departure,Full-time worker departure shift effects,(ptype == 1) * start,coef_ft_worker_departure -util_ft_worker_duration,Full-time worker duration shift effects,(ptype == 1) * duration,coef_ft_worker_duration -util_non_worker_departure,Non-working adult departure shift effects,(ptype == 4) * start,coef_non_worker_departure -util_univ_departure,University student departure shift effects,(ptype == 3) * start,coef_univ_departure -util_univ_duration,University student duration shift effects,(ptype == 3) * duration,coef_univ_duration -util_student_driver_duration,Student driving age duration shift effects,(ptype == 7) * duration,coef_student_driver_duration -util_all_adults_ft_worker_duration,All adults work full time- duration,(num_workers == hhsize) * duration,coef_all_adults_ft_worker_duration -util_subsequent_tour_must_start_after_previous_tour_ends,Subsequent tour must start after previous tour ends,(tour_num > 1) & (start < end_previous),coef_subsequent_tour_must_start_after_previous_tour_ends -util_first_of_2plus_school_tours_departure,First of 2+ school/univ. tours- departure,((tour_count>1) & (tour_num == 1)) * start,coef_first_of_2plus_school_tours_departure -util_first_of_2plus_school_tours_duration,First of 2+ school/univ. tours- duration,((tour_count>1) & (tour_num == 1)) * duration,coef_first_of_2plus_school_tours_duration -util_subsequent_2plus_school_tours_duration,Subsequent of 2+ school/univ. tours- duration,(tour_num > 1) * duration,coef_subsequent_2plus_school_tours_duration -util_hh_income_early_departure,Household income -- Early departure interaction,(income_in_thousands >= 100) & (start < 6),coef_hh_income_early_departure -util_hh_income_late_arrival,Household income -- Late arrival interaction,(income_in_thousands >= 100) & (end > 22),coef_hh_income_late_arrival -util_first_of_2plus_school_lt_6_hours,First of 2+ school/univ tours- duration<6 hrs,(tour_count>1) & (tour_num == 1) & (duration < 6),coef_first_of_2plus_school_lt_6_hours -util_subsequent_of_2plus_school_lt_6_hours,Subsequent of 2+ school/univ tours- duration<6 hrs,(tour_num > 1) & (duration < 6),coef_subsequent_of_2plus_school_lt_6_hours -util_school_plus_work_tours_by_student_lt_6_hours,School+work tours by student- duration<6 hrs,work_and_school_and_worker & (duration < 6),coef_school_plus_work_tours_by_student_lt_6_hours -util_school_plus_work_tours_by_worker_lt_6_hours,School+work tours by worker- duration<6 hrs,work_and_school_and_student & (duration < 6),coef_school_plus_work_tours_by_worker_lt_6_hours -util_mode_choice_logsum,Mode Choice Logsum,mode_choice_logsum,coef_mode_choice_logsum -util_previous_tour_ends_this_departure_hour,Previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.person_id, df.start)",coef_previous_tour_ends_this_departure_hour -util_previous_tour_begins_this_arrival_hour,Previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.person_id, df.end)",coef_previous_tour_begins_this_arrival_hour -#,,, FIXME - use temps as timetable ops can be very time-consuming -#,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & tt.adjacent_window_before(df.person_id, df.start)",0.08975 -#,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & tt.adjacent_window_after(df.person_id, df.end)",-0.003049 -#,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) & tt.adjacent_window_before(df.person_id, df.start)",-0.44 -#,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) & tt.adjacent_window_after(df.person_id, df.end)",-0.5271 -util_dummy_adjacent_before,,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)",coef_dummy -util_dummy_adjacent_after,,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)",coef_dummy -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction -util_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours,Remaining work/school tours to be scheduled / number of unscheduled hours,"@((df.tour_count>1) & (df.tour_num == 1)) * 1.0 / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours -util_departure_constants_early_up_to_5,Departure Constants -- Early (up to 5),start < 6,coef_departure_constants_early -util_departure_constants_am_peak_1 _6,Departure Constants -- AM peak 1 (6),start == 6,coef_departure_constants_am_peak_1 -util_departure_constants_am_peak_2_7,Departure Constants -- AM peak 2 (7),start == 7,coef_departure_constants_am_peak_2 -util_departure_constants_am_peak_3_8,Departure Constants -- AM peak 3 (8),start == 8,coef_departure_constants_am_peak_3 -util_departure_constants_am_peak_4_9,Departure Constants -- AM peak 4 (9),start == 9,coef_departure_constants_am_peak_4 -util_departure_constants_midday_1_10_to_12,Departure Constants -- Midday 1 (10 to 12),(start > 9) & (start < 13),coef_departure_constants_midday_1 -util_departure_constants_midday_2_13_to_15,Departure Constants -- Midday 2 (13 to 15),(start > 12) & (start < 16),coef_departure_constants_midday_2 -util_departure_constants_pm_peak_16_to_18,Departure Constants -- PM peak (16 to 18),(start > 15) & (start < 19),coef_departure_constants_pm_peak -util_departure_constants_evening_19_to_21,Departure Constants -- Evening (19 to 21),(start > 18) & (start < 22),coef_departure_constants_evening -util_departure_constants_late_22_and_later,Departure Constants -- Late (22 and later),start > 21,coef_departure_constants_late -util_arrival_constants_early_up_to_6,Arrival Constants -- Early (up to 6),end < 7,coef_arrival_constants_early -util_arrival_constants_am_peak_7_to_9,Arrival Constants -- AM peak (7 to 9),(end > 6) & (end < 10),coef_arrival_constants_am_peak -util_arrival_constants_midday_1_10_to_12,Arrival Constants -- Midday 1 (10 to 12),(end > 9) & (end < 13),coef_arrival_constants_midday_1 -util_arrival_constants_midday_2_13_to_14,Arrival Constants -- Midday 2 (13 to 14),(end > 12) & (end < 15),coef_arrival_constants_midday_2 -util_arrival_constants_pm_peak_1_15,Arrival Constants -- PM peak 1 (15),end == 15,coef_arrival_constants_pm_peak_1 -util_arrival_constants_pm_peak_2_16,Arrival Constants -- PM peak 2 (16),end == 16,coef_arrival_constants_pm_peak_2 -util_arrival_constants_pm_peak_3_17,Arrival Constants -- PM peak 3 (17),end == 17,coef_arrival_constants_pm_peak_3 -util_arrival_constants_pm_peak_4_18,Arrival Constants -- PM peak 4 (18),end == 18,coef_arrival_constants_pm_peak_4 -util_arrival_constants_evening_19_to_21,Arrival Constants -- Evening (19 to 21),(end > 18) & (end < 22),coef_arrival_constants_evening -util_arrival_constants_late_22_and_later,Arrival Constants -- Late (22 and later),end > 21,coef_arrival_constants_late -util_duration_constants_0_to_2_hours,Duration Constants -- 0 to 2 hours,duration < 3,coef_duration_constants_0_to_2_hours -util_duration_constants_3_to_4_hours,Duration Constants -- 3 to 4 hours,(duration > 2) & (duration < 5),coef_duration_constants_3_to_4_hours -util_duration_constants_5_to_6_hours,Duration Constants -- 5 to 6 hours,(duration > 4) & (duration < 7),coef_duration_constants_5_to_6_hours -util_duration_constants_7_to_8_hours,Duration Constants -- 7 to 8 hours,(duration > 6) & (duration < 9),coef_duration_constants_7_to_8_hours -util_duration_constants_9_hours,Duration Constants -- 9 hours,duration == 9,coef_duration_constants_9_hours -util_duration_constants_10_hours,Duration Constants -- 10 hours,duration == 10,coef_duration_constants_10_hours -util_duration_constants_11_hours,Duration Constants -- 11 hours,duration == 11,coef_duration_constants_11_hours -util_duration_constants_12_to_13_hours,Duration Constants -- 12 to 13 hours,(duration > 11) & (duration < 14),coef_duration_constants_12_to_13_hours -util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,(duration > 13) & (duration < 19),coef_duration_constants_14_to_18_hours +Label,Description,Expression,Coefficient +util_roundtrip_auto_time_to_work,Free-flow round trip auto time shift effects - duration,roundtrip_auto_time_to_work * duration,coef_roundtrip_auto_time_to_work +util_ft_worker_departure,Full-time worker departure shift effects,(ptype == 1) * start,coef_ft_worker_departure +util_ft_worker_duration,Full-time worker duration shift effects,(ptype == 1) * duration,coef_ft_worker_duration +util_non_worker_departure,Non-working adult departure shift effects,(ptype == 4) * start,coef_non_worker_departure +util_univ_departure,University student departure shift effects,(ptype == 3) * start,coef_univ_departure +util_univ_duration,University student duration shift effects,(ptype == 3) * duration,coef_univ_duration +util_student_driver_duration,Student driving age duration shift effects,(ptype == 7) * duration,coef_student_driver_duration +util_all_adults_ft_worker_duration,All adults work full time- duration,(num_workers == hhsize) * duration,coef_all_adults_ft_worker_duration +util_subsequent_tour_must_start_after_previous_tour_ends,Subsequent tour must start after previous tour ends,(tour_num > 1) & (start < end_previous),coef_subsequent_tour_must_start_after_previous_tour_ends +util_first_of_2plus_school_tours_departure,First of 2+ school/univ. tours- departure,((tour_count>1) & (tour_num == 1)) * start,coef_first_of_2plus_school_tours_departure +util_first_of_2plus_school_tours_duration,First of 2+ school/univ. tours- duration,((tour_count>1) & (tour_num == 1)) * duration,coef_first_of_2plus_school_tours_duration +util_subsequent_2plus_school_tours_duration,Subsequent of 2+ school/univ. tours- duration,(tour_num > 1) * duration,coef_subsequent_2plus_school_tours_duration +util_hh_income_early_departure,Household income -- Early departure interaction,(income_in_thousands >= 100) & (start < 6),coef_hh_income_early_departure +util_hh_income_late_arrival,Household income -- Late arrival interaction,(income_in_thousands >= 100) & (end > 22),coef_hh_income_late_arrival +util_first_of_2plus_school_lt_6_hours,First of 2+ school/univ tours- duration<6 hrs,(tour_count>1) & (tour_num == 1) & (duration < 6),coef_first_of_2plus_school_lt_6_hours +util_subsequent_of_2plus_school_lt_6_hours,Subsequent of 2+ school/univ tours- duration<6 hrs,(tour_num > 1) & (duration < 6),coef_subsequent_of_2plus_school_lt_6_hours +util_school_plus_work_tours_by_student_lt_6_hours,School+work tours by student- duration<6 hrs,work_and_school_and_worker & (duration < 6),coef_school_plus_work_tours_by_student_lt_6_hours +util_school_plus_work_tours_by_worker_lt_6_hours,School+work tours by worker- duration<6 hrs,work_and_school_and_student & (duration < 6),coef_school_plus_work_tours_by_worker_lt_6_hours +util_mode_choice_logsum,Mode Choice Logsum,mode_choice_logsum,coef_mode_choice_logsum +util_previous_tour_ends_this_departure_hour,Previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.person_id, df.start)",coef_previous_tour_ends_this_departure_hour +util_previous_tour_begins_this_arrival_hour,Previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.person_id, df.end)",coef_previous_tour_begins_this_arrival_hour +#,,, FIXME - use temps as timetable ops can be very time-consuming +#,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & tt.adjacent_window_before(df.person_id, df.start)",0.08975 +#,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & tt.adjacent_window_after(df.person_id, df.end)",-0.003049 +#,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) & tt.adjacent_window_before(df.person_id, df.start)",-0.44 +#,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) & tt.adjacent_window_after(df.person_id, df.end)",-0.5271 +util_dummy_adjacent_before,,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)",coef_dummy +util_dummy_adjacent_after,,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)",coef_dummy +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction +util_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours,Remaining work/school tours to be scheduled / number of unscheduled hours,"@((df.tour_count>1) & (df.tour_num == 1)) * 1.0 / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours +util_departure_constants_early_up_to_5,Departure Constants -- Early (up to 5),start < 6,coef_departure_constants_early +util_departure_constants_am_peak_1 _6,Departure Constants -- AM peak 1 (6),start == 6,coef_departure_constants_am_peak_1 +util_departure_constants_am_peak_2_7,Departure Constants -- AM peak 2 (7),start == 7,coef_departure_constants_am_peak_2 +util_departure_constants_am_peak_3_8,Departure Constants -- AM peak 3 (8),start == 8,coef_departure_constants_am_peak_3 +util_departure_constants_am_peak_4_9,Departure Constants -- AM peak 4 (9),start == 9,coef_departure_constants_am_peak_4 +util_departure_constants_midday_1_10_to_12,Departure Constants -- Midday 1 (10 to 12),(start > 9) & (start < 13),coef_departure_constants_midday_1 +util_departure_constants_midday_2_13_to_15,Departure Constants -- Midday 2 (13 to 15),(start > 12) & (start < 16),coef_departure_constants_midday_2 +util_departure_constants_pm_peak_16_to_18,Departure Constants -- PM peak (16 to 18),(start > 15) & (start < 19),coef_departure_constants_pm_peak +util_departure_constants_evening_19_to_21,Departure Constants -- Evening (19 to 21),(start > 18) & (start < 22),coef_departure_constants_evening +util_departure_constants_late_22_and_later,Departure Constants -- Late (22 and later),start > 21,coef_departure_constants_late +util_arrival_constants_early_up_to_6,Arrival Constants -- Early (up to 6),end < 7,coef_arrival_constants_early +util_arrival_constants_am_peak_7_to_9,Arrival Constants -- AM peak (7 to 9),(end > 6) & (end < 10),coef_arrival_constants_am_peak +util_arrival_constants_midday_1_10_to_12,Arrival Constants -- Midday 1 (10 to 12),(end > 9) & (end < 13),coef_arrival_constants_midday_1 +util_arrival_constants_midday_2_13_to_14,Arrival Constants -- Midday 2 (13 to 14),(end > 12) & (end < 15),coef_arrival_constants_midday_2 +util_arrival_constants_pm_peak_1_15,Arrival Constants -- PM peak 1 (15),end == 15,coef_arrival_constants_pm_peak_1 +util_arrival_constants_pm_peak_2_16,Arrival Constants -- PM peak 2 (16),end == 16,coef_arrival_constants_pm_peak_2 +util_arrival_constants_pm_peak_3_17,Arrival Constants -- PM peak 3 (17),end == 17,coef_arrival_constants_pm_peak_3 +util_arrival_constants_pm_peak_4_18,Arrival Constants -- PM peak 4 (18),end == 18,coef_arrival_constants_pm_peak_4 +util_arrival_constants_evening_19_to_21,Arrival Constants -- Evening (19 to 21),(end > 18) & (end < 22),coef_arrival_constants_evening +util_arrival_constants_late_22_and_later,Arrival Constants -- Late (22 and later),end > 21,coef_arrival_constants_late +util_duration_constants_0_to_2_hours,Duration Constants -- 0 to 2 hours,duration < 3,coef_duration_constants_0_to_2_hours +util_duration_constants_3_to_4_hours,Duration Constants -- 3 to 4 hours,(duration > 2) & (duration < 5),coef_duration_constants_3_to_4_hours +util_duration_constants_5_to_6_hours,Duration Constants -- 5 to 6 hours,(duration > 4) & (duration < 7),coef_duration_constants_5_to_6_hours +util_duration_constants_7_to_8_hours,Duration Constants -- 7 to 8 hours,(duration > 6) & (duration < 9),coef_duration_constants_7_to_8_hours +util_duration_constants_9_hours,Duration Constants -- 9 hours,duration == 9,coef_duration_constants_9_hours +util_duration_constants_10_hours,Duration Constants -- 10 hours,duration == 10,coef_duration_constants_10_hours +util_duration_constants_11_hours,Duration Constants -- 11 hours,duration == 11,coef_duration_constants_11_hours +util_duration_constants_12_to_13_hours,Duration Constants -- 12 to 13 hours,(duration > 11) & (duration < 14),coef_duration_constants_12_to_13_hours +util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,(duration > 13) & (duration < 19),coef_duration_constants_14_to_18_hours diff --git a/activitysim/examples/example_psrc/configs/tour_scheduling_school_coeffs.csv b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_school_coeffs.csv similarity index 98% rename from activitysim/examples/example_psrc/configs/tour_scheduling_school_coeffs.csv rename to activitysim/examples/placeholder_psrc/configs/tour_scheduling_school_coeffs.csv index b5d8c8050b..6fd040134d 100755 --- a/activitysim/examples/example_psrc/configs/tour_scheduling_school_coeffs.csv +++ b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_school_coeffs.csv @@ -1,57 +1,57 @@ -coefficient_name,value,constrain -coef_dummy,1,T -coef_roundtrip_auto_time_to_work,0.003195,F -coef_ft_worker_departure,0.3971,F -coef_ft_worker_duration,-0.1908,F -coef_non_worker_departure,0.5539,F -coef_univ_departure,0.28,F -coef_univ_duration,-0.2907,F -coef_student_driver_duration,0.03464,F -coef_all_adults_ft_worker_duration,0.1093,F -coef_subsequent_tour_must_start_after_previous_tour_ends,-100,T -coef_first_of_2plus_school_tours_departure,-0.3002,F -coef_first_of_2plus_school_tours_duration,-0.1593,F -coef_subsequent_2plus_school_tours_duration,-0.2338,F -coef_hh_income_early_departure,-0.8837,F -coef_hh_income_late_arrival,-0.3533,F -coef_first_of_2plus_school_lt_6_hours,1.487,F -coef_subsequent_of_2plus_school_lt_6_hours,2.142,F -coef_school_plus_work_tours_by_student_lt_6_hours,1.73,F -coef_school_plus_work_tours_by_worker_lt_6_hours,2.142,F -coef_mode_choice_logsum,2.127,F -coef_previous_tour_ends_this_departure_hour,-0.5995,F -coef_previous_tour_begins_this_arrival_hour,-1.102,F -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.08975,F -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,-0.003049,F -coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-0.44,F -coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.5271,F -coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours,-16.67,F -coef_departure_constants_early,-3.820662404,F -coef_departure_constants_am_peak_1,-1.617644056,F -coef_departure_constants_am_peak_2,0,T -coef_departure_constants_am_peak_3,-0.073826841,F -coef_departure_constants_am_peak_4,-2.080570769,F -coef_departure_constants_midday_1,-2.985739457,F -coef_departure_constants_midday_2,-3.628434646,F -coef_departure_constants_pm_peak,-3.10250515,F -coef_departure_constants_evening,-5.230287836,F -coef_departure_constants_late,-11.88604728,F -coef_arrival_constants_early,-2.428718399,F -coef_arrival_constants_am_peak,-2.428718399,F -coef_arrival_constants_midday_1,-1.237908768,F -coef_arrival_constants_midday_2,-0.539768931,F -coef_arrival_constants_pm_peak_1,0,T -coef_arrival_constants_pm_peak_2,-0.389169248,F -coef_arrival_constants_pm_peak_3,-0.198120349,F -coef_arrival_constants_pm_peak_4,-0.253624684,F -coef_arrival_constants_evening,-0.870146904,F -coef_arrival_constants_late,-1.75200049,F -coef_duration_constants_0_to_2_hours,-1.409955689,F -coef_duration_constants_3_to_4_hours,-0.745893252,F -coef_duration_constants_5_to_6_hours,-0.567636622,F -coef_duration_constants_7_to_8_hours,0,T -coef_duration_constants_9_hours,-0.650806684,F -coef_duration_constants_10_hours,-0.904788983,F -coef_duration_constants_11_hours,-1.521162604,F -coef_duration_constants_12_to_13_hours,-2.418488917,F +coefficient_name,value,constrain +coef_dummy,1,T +coef_roundtrip_auto_time_to_work,0.003195,F +coef_ft_worker_departure,0.3971,F +coef_ft_worker_duration,-0.1908,F +coef_non_worker_departure,0.5539,F +coef_univ_departure,0.28,F +coef_univ_duration,-0.2907,F +coef_student_driver_duration,0.03464,F +coef_all_adults_ft_worker_duration,0.1093,F +coef_subsequent_tour_must_start_after_previous_tour_ends,-100,T +coef_first_of_2plus_school_tours_departure,-0.3002,F +coef_first_of_2plus_school_tours_duration,-0.1593,F +coef_subsequent_2plus_school_tours_duration,-0.2338,F +coef_hh_income_early_departure,-0.8837,F +coef_hh_income_late_arrival,-0.3533,F +coef_first_of_2plus_school_lt_6_hours,1.487,F +coef_subsequent_of_2plus_school_lt_6_hours,2.142,F +coef_school_plus_work_tours_by_student_lt_6_hours,1.73,F +coef_school_plus_work_tours_by_worker_lt_6_hours,2.142,F +coef_mode_choice_logsum,2.127,F +coef_previous_tour_ends_this_departure_hour,-0.5995,F +coef_previous_tour_begins_this_arrival_hour,-1.102,F +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.08975,F +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,-0.003049,F +coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-0.44,F +coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.5271,F +coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours,-16.67,F +coef_departure_constants_early,-3.820662404,F +coef_departure_constants_am_peak_1,-1.617644056,F +coef_departure_constants_am_peak_2,0,T +coef_departure_constants_am_peak_3,-0.073826841,F +coef_departure_constants_am_peak_4,-2.080570769,F +coef_departure_constants_midday_1,-2.985739457,F +coef_departure_constants_midday_2,-3.628434646,F +coef_departure_constants_pm_peak,-3.10250515,F +coef_departure_constants_evening,-5.230287836,F +coef_departure_constants_late,-11.88604728,F +coef_arrival_constants_early,-2.428718399,F +coef_arrival_constants_am_peak,-2.428718399,F +coef_arrival_constants_midday_1,-1.237908768,F +coef_arrival_constants_midday_2,-0.539768931,F +coef_arrival_constants_pm_peak_1,0,T +coef_arrival_constants_pm_peak_2,-0.389169248,F +coef_arrival_constants_pm_peak_3,-0.198120349,F +coef_arrival_constants_pm_peak_4,-0.253624684,F +coef_arrival_constants_evening,-0.870146904,F +coef_arrival_constants_late,-1.75200049,F +coef_duration_constants_0_to_2_hours,-1.409955689,F +coef_duration_constants_3_to_4_hours,-0.745893252,F +coef_duration_constants_5_to_6_hours,-0.567636622,F +coef_duration_constants_7_to_8_hours,0,T +coef_duration_constants_9_hours,-0.650806684,F +coef_duration_constants_10_hours,-0.904788983,F +coef_duration_constants_11_hours,-1.521162604,F +coef_duration_constants_12_to_13_hours,-2.418488917,F coef_duration_constants_14_to_18_hours,-2.503137295,F \ No newline at end of file diff --git a/activitysim/examples/example_psrc/configs/tour_scheduling_work.csv b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_work.csv similarity index 98% rename from activitysim/examples/example_psrc/configs/tour_scheduling_work.csv rename to activitysim/examples/placeholder_psrc/configs/tour_scheduling_work.csv index 9c6474f9c3..0d8b8aeff2 100755 --- a/activitysim/examples/example_psrc/configs/tour_scheduling_work.csv +++ b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_work.csv @@ -1,136 +1,136 @@ -Label,Description,Expression,Coefficient -util_free_flow_round_trip_auto_time_shift_effects_departure,Free-flow round trip auto time shift effects - departure,roundtrip_auto_time_to_work * start,coef_free_flow_round_trip_auto_time_shift_effects_departure -util_free_flow_round_trip_auto_time_shift_effects_duration,Free-flow round trip auto time shift effects - duration,roundtrip_auto_time_to_work * duration,coef_free_flow_round_trip_auto_time_shift_effects_duration -util_part_time_worker_departure_shift_effects,Part-time worker departure shift effects,(ptype == 2) * start,coef_part_time_worker_departure_shift_effects -util_non_working_adult_duration_shift_effects,Non-working adult duration shift effects,(ptype == 4) * duration,coef_non_working_adult_duration_shift_effects -util_university_student_departure_shift_effects,University student departure shift effects,(ptype == 3) * start,coef_university_student_departure_shift_effects -util_household_income_departure_shift_effects,Household income departure shift effects,income_in_thousands * start,coef_household_income_departure_shift_effects -util_destination_in_cbd_departure_shift_effects,Destination in CBD departure shift effects,workplace_in_cbd * start,coef_destination_in_cbd_departure_shift_effects -util_destination_in_cbd_duration_shift_effects,Destination in CBD duration shift effects,workplace_in_cbd * duration,coef_destination_in_cbd_duration_shift_effects -util_subsequent_tour_must_start_after_previous_tour_ends,Subsequent tour must start after previous tour ends,(tour_num > 1) & (start < end_previous),coef_subsequent_tour_must_start_after_previous_tour_ends -util_first_of_2plus_work_tours_departure_shift_effects,First of 2+ work tours departure shift effects,((tour_count>1) & (tour_num == 1)) * start,coef_first_of_2plus_work_tours_departure_shift_effects -util_first_of_2plus_work_tours_duration_shift_effects,First of 2+ work tours duration shift effects,((tour_count>1) & (tour_num == 1)) * duration,coef_first_of_2plus_work_tours_duration_shift_effects -util_subsequent_2plus_work_departure_tours_shift_effects,Subsequent 2+ work departure tours shift effects,(tour_num == 2) * start,coef_subsequent_2plus_work_departure_tours_shift_effects -util_subsequent_2plus_work_duration_tours_shift_effects,Subsequent 2+ work duration tours shift effects,(tour_num == 2) * duration,coef_subsequent_2plus_work_duration_tours_shift_effects -util_household_income_early_departure_interaction,Household income -- Early departure interaction,(income_in_thousands > 100) & (start < 6),coef_household_income_early_departure_interaction -util_household_income_late_arrival_interaction,Household income -- Late arrival interaction,(income_in_thousands > 100) & (end > 22),coef_household_income_late_arrival_interaction -util_destination_in_cbd_early_departure_interaction,Destination in CBD -- Early departure interaction,workplace_in_cbd & (start < 6),coef_destination_in_cbd_early_departure_interaction -util_destination_in_cbd_late_arrival_interaction,Destination in CBD -- Late arrival interaction,workplace_in_cbd & (end > 22),coef_destination_in_cbd_late_arrival_interaction -util_rural_household_early_departure_interaction,Rural household -- Early departure interaction,home_is_rural & (start < 6),coef_rural_household_early_departure_interaction -util_rural_household_late_arrival_interaction,Rural household -- Late arrival interaction,home_is_rural & (end > 22),coef_rural_household_late_arrival_interaction -util_full_time_worker_duration_lt_9_hours_interaction,Full-time worker -- duration < 9 hours interaction,(ptype == 1) & (duration < 9),coef_full_time_worker_duration_lt_9_hours_interaction -util_full_time_worker_10_to_12_departure_interaction,Full-time worker -- 10 to 12 departure interaction,(ptype == 1) & (start > 9) & (start < 13),coef_full_time_worker_10_to_12_departure_interaction -util_worker_13_to_15_arrival_interaction,Part-time worker -- 13 to 15 arrival interaction,(ptype == 2) & (end > 12) & (end < 16),coef_part_time_worker_13_to_15_arrival_interaction -util_first_of_2plus_work_tours_duration_lt_8_hrs,First of 2+ work tours- duration<8 hrs,((tour_count>1) & (tour_num == 1)) & (duration < 8),coef_first_of_2plus_work_tours_duration_lt_8_hrs -util_subsequent_of_2plus_work_tours_duration_lt_8_hrs,Subsequent of 2+ work tours- duration<8 hrs,(tour_num == 2) & (duration < 8),coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs -util_tours_by_worker_duration_lt_8_hrs,Work+school tours by worker- duration<8 hrs,(mandatory_tour_frequency == 'work_and_school') & is_worker & (duration < 8),coef_tours_by_worker_duration_lt_8_hrs -util_tours_by_student_duration_lt_8_hrs,School+work tours by student- duration<8 hrs,(mandatory_tour_frequency == 'work_and_school') & is_student & (duration < 8),coef_tours_by_student_duration_lt_8_hrs -util_mode_choice_logsum,Mode Choice Logsum,mode_choice_logsum,coef_mode_choice_logsum -util_uled_tour_ends_in_this_departure_hour,Previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.person_id, df.start)",coef_previously_scheduled_tour_ends_in_this_departure_hour -util_previously_scheduled_tour_begins_in_this_arrival_hour,Previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.person_id, df.end)",coef_previously_scheduled_tour_begins_in_this_arrival_hour -#,FIXME - use temps as timetable ops can be very time-consuming,, -util_dummy_adjacent_before,local temp variable,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)",coef_dummy -util_dummy_adjacent_after,local temp variable,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)",coef_dummy -util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction -util_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours,Remaining work/school tours to be scheduled / number of unscheduled hours,"@((df.tour_count>1) & (df.tour_num == 1)) * 1.0 / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours -util_departure_constants_early,Departure Constants -- Early (up to 5),start < 6,coef_departure_constants_early -util_departure_constants_am_peak_1,Departure Constants -- AM peak 1 (6),start == 6,coef_departure_constants_am_peak_1 -util_departure_constants_am_peak_2,Departure Constants -- AM peak 2 (7),start == 7,coef_departure_constants_am_peak_2 -util_departure_constants_am_peak_3,Departure Constants -- AM peak 3 (8),start == 8,coef_departure_constants_am_peak_3 -util_departure_constants_am_peak_4,Departure Constants -- AM peak 4 (9),start == 9,coef_departure_constants_am_peak_4 -util_departure_constants_midday_1,Departure Constants -- Midday 1 (10 to 12),(start > 9) & (start < 13),coef_departure_constants_midday_1 -util_departure_constants_midday_2,Departure Constants -- Midday 2 (13 to 15),(start > 12) & (start < 16),coef_departure_constants_midday_2 -util_departure_constants_pm_peak,Departure Constants -- PM peak (16 to 18),(start > 15) & (start < 19),coef_departure_constants_pm_peak -util_departure_constants_evening,Departure Constants -- Evening (19 to 21),(start > 18) & (start < 22),coef_departure_constants_evening -util_departure_constants_late,Departure Constants -- Late (22 and later),start > 21,coef_departure_constants_late -util_arrival_constants_early,Arrival Constants -- Early (up to 6),end < 7,coef_arrival_constants_early -util_arrival_constants_am_peak,Arrival Constants -- AM peak (7 to 9),(end > 6) & (end < 10),coef_arrival_constants_am_peak -util_arrival_constants_midday_1,Arrival Constants -- Midday 1 (10 to 12),(end > 9) & (end < 13),coef_arrival_constants_midday_1 -util_arrival_constants_midday_2,Arrival Constants -- Midday 2 (13 to 14),(end > 12) & (end < 15),coef_arrival_constants_midday_2 -util_arrival_constants_pm_peak_1,Arrival Constants -- PM peak 1 (15),end == 15,coef_arrival_constants_pm_peak_1 -util_arrival_constants_pm_peak_2,Arrival Constants -- PM peak 2 (16),end == 16,coef_arrival_constants_pm_peak_2 -util_arrival_constants_pm_peak_3,Arrival Constants -- PM peak 3 (17),end == 17,coef_arrival_constants_pm_peak_3 -util_arrival_constants_pm_peak_4,Arrival Constants -- PM peak 4 (18),end == 18,coef_arrival_constants_pm_peak_4 -util_arrival_constants_evening,Arrival Constants -- Evening (19 to 21),(end > 18) & (end < 22),coef_arrival_constants_evening -util_arrival_constants_late,Arrival Constants -- Late (22 and later),end > 21,coef_arrival_constants_late -util_duration_constants_0_to_2_hours,Duration Constants -- 0 to 2 hours,duration < 3,coef_duration_constants_0_to_2_hours -util_duration_constants_3_to_4_hours,Duration Constants -- 3 to 4 hours,(duration > 2) & (duration < 5),coef_duration_constants_3_to_4_hours -util_duration_constants_5_to_6_hours,Duration Constants -- 5 to 6 hours,(duration > 4) & (duration < 7),coef_duration_constants_5_to_6_hours -util_duration_constants_7_to_8_hours,Duration Constants -- 7 to 8 hours,(duration > 6) & (duration < 9),coef_duration_constants_7_to_8_hours -util_duration_constants_9_hours,Duration Constants -- 9 hours,duration == 9,coef_duration_constants_9_hours -util_duration_constants_10_hours,Duration Constants -- 10 hours,duration == 10,coef_duration_constants_10_hours -util_duration_constants_11_hours,Duration Constants -- 11 hours,duration == 11,coef_duration_constants_11_hours -util_duration_constants_12_to_13_hours,Duration Constants -- 12 to 13 hours,(duration > 11) & (duration < 14),coef_duration_constants_12_to_13_hours -util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,(duration > 13) & (duration < 19),coef_duration_constants_14_to_18_hours - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +Label,Description,Expression,Coefficient +util_free_flow_round_trip_auto_time_shift_effects_departure,Free-flow round trip auto time shift effects - departure,roundtrip_auto_time_to_work * start,coef_free_flow_round_trip_auto_time_shift_effects_departure +util_free_flow_round_trip_auto_time_shift_effects_duration,Free-flow round trip auto time shift effects - duration,roundtrip_auto_time_to_work * duration,coef_free_flow_round_trip_auto_time_shift_effects_duration +util_part_time_worker_departure_shift_effects,Part-time worker departure shift effects,(ptype == 2) * start,coef_part_time_worker_departure_shift_effects +util_non_working_adult_duration_shift_effects,Non-working adult duration shift effects,(ptype == 4) * duration,coef_non_working_adult_duration_shift_effects +util_university_student_departure_shift_effects,University student departure shift effects,(ptype == 3) * start,coef_university_student_departure_shift_effects +util_household_income_departure_shift_effects,Household income departure shift effects,income_in_thousands * start,coef_household_income_departure_shift_effects +util_destination_in_cbd_departure_shift_effects,Destination in CBD departure shift effects,workplace_in_cbd * start,coef_destination_in_cbd_departure_shift_effects +util_destination_in_cbd_duration_shift_effects,Destination in CBD duration shift effects,workplace_in_cbd * duration,coef_destination_in_cbd_duration_shift_effects +util_subsequent_tour_must_start_after_previous_tour_ends,Subsequent tour must start after previous tour ends,(tour_num > 1) & (start < end_previous),coef_subsequent_tour_must_start_after_previous_tour_ends +util_first_of_2plus_work_tours_departure_shift_effects,First of 2+ work tours departure shift effects,((tour_count>1) & (tour_num == 1)) * start,coef_first_of_2plus_work_tours_departure_shift_effects +util_first_of_2plus_work_tours_duration_shift_effects,First of 2+ work tours duration shift effects,((tour_count>1) & (tour_num == 1)) * duration,coef_first_of_2plus_work_tours_duration_shift_effects +util_subsequent_2plus_work_departure_tours_shift_effects,Subsequent 2+ work departure tours shift effects,(tour_num == 2) * start,coef_subsequent_2plus_work_departure_tours_shift_effects +util_subsequent_2plus_work_duration_tours_shift_effects,Subsequent 2+ work duration tours shift effects,(tour_num == 2) * duration,coef_subsequent_2plus_work_duration_tours_shift_effects +util_household_income_early_departure_interaction,Household income -- Early departure interaction,(income_in_thousands > 100) & (start < 6),coef_household_income_early_departure_interaction +util_household_income_late_arrival_interaction,Household income -- Late arrival interaction,(income_in_thousands > 100) & (end > 22),coef_household_income_late_arrival_interaction +util_destination_in_cbd_early_departure_interaction,Destination in CBD -- Early departure interaction,workplace_in_cbd & (start < 6),coef_destination_in_cbd_early_departure_interaction +util_destination_in_cbd_late_arrival_interaction,Destination in CBD -- Late arrival interaction,workplace_in_cbd & (end > 22),coef_destination_in_cbd_late_arrival_interaction +util_rural_household_early_departure_interaction,Rural household -- Early departure interaction,home_is_rural & (start < 6),coef_rural_household_early_departure_interaction +util_rural_household_late_arrival_interaction,Rural household -- Late arrival interaction,home_is_rural & (end > 22),coef_rural_household_late_arrival_interaction +util_full_time_worker_duration_lt_9_hours_interaction,Full-time worker -- duration < 9 hours interaction,(ptype == 1) & (duration < 9),coef_full_time_worker_duration_lt_9_hours_interaction +util_full_time_worker_10_to_12_departure_interaction,Full-time worker -- 10 to 12 departure interaction,(ptype == 1) & (start > 9) & (start < 13),coef_full_time_worker_10_to_12_departure_interaction +util_worker_13_to_15_arrival_interaction,Part-time worker -- 13 to 15 arrival interaction,(ptype == 2) & (end > 12) & (end < 16),coef_part_time_worker_13_to_15_arrival_interaction +util_first_of_2plus_work_tours_duration_lt_8_hrs,First of 2+ work tours- duration<8 hrs,((tour_count>1) & (tour_num == 1)) & (duration < 8),coef_first_of_2plus_work_tours_duration_lt_8_hrs +util_subsequent_of_2plus_work_tours_duration_lt_8_hrs,Subsequent of 2+ work tours- duration<8 hrs,(tour_num == 2) & (duration < 8),coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs +util_tours_by_worker_duration_lt_8_hrs,Work+school tours by worker- duration<8 hrs,(mandatory_tour_frequency == 'work_and_school') & is_worker & (duration < 8),coef_tours_by_worker_duration_lt_8_hrs +util_tours_by_student_duration_lt_8_hrs,School+work tours by student- duration<8 hrs,(mandatory_tour_frequency == 'work_and_school') & is_student & (duration < 8),coef_tours_by_student_duration_lt_8_hrs +util_mode_choice_logsum,Mode Choice Logsum,mode_choice_logsum,coef_mode_choice_logsum +util_uled_tour_ends_in_this_departure_hour,Previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.person_id, df.start)",coef_previously_scheduled_tour_ends_in_this_departure_hour +util_previously_scheduled_tour_begins_in_this_arrival_hour,Previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.person_id, df.end)",coef_previously_scheduled_tour_begins_in_this_arrival_hour +#,FIXME - use temps as timetable ops can be very time-consuming,, +util_dummy_adjacent_before,local temp variable,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)",coef_dummy +util_dummy_adjacent_after,local temp variable,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)",coef_dummy +util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction +util_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours,Remaining work/school tours to be scheduled / number of unscheduled hours,"@((df.tour_count>1) & (df.tour_num == 1)) * 1.0 / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours +util_departure_constants_early,Departure Constants -- Early (up to 5),start < 6,coef_departure_constants_early +util_departure_constants_am_peak_1,Departure Constants -- AM peak 1 (6),start == 6,coef_departure_constants_am_peak_1 +util_departure_constants_am_peak_2,Departure Constants -- AM peak 2 (7),start == 7,coef_departure_constants_am_peak_2 +util_departure_constants_am_peak_3,Departure Constants -- AM peak 3 (8),start == 8,coef_departure_constants_am_peak_3 +util_departure_constants_am_peak_4,Departure Constants -- AM peak 4 (9),start == 9,coef_departure_constants_am_peak_4 +util_departure_constants_midday_1,Departure Constants -- Midday 1 (10 to 12),(start > 9) & (start < 13),coef_departure_constants_midday_1 +util_departure_constants_midday_2,Departure Constants -- Midday 2 (13 to 15),(start > 12) & (start < 16),coef_departure_constants_midday_2 +util_departure_constants_pm_peak,Departure Constants -- PM peak (16 to 18),(start > 15) & (start < 19),coef_departure_constants_pm_peak +util_departure_constants_evening,Departure Constants -- Evening (19 to 21),(start > 18) & (start < 22),coef_departure_constants_evening +util_departure_constants_late,Departure Constants -- Late (22 and later),start > 21,coef_departure_constants_late +util_arrival_constants_early,Arrival Constants -- Early (up to 6),end < 7,coef_arrival_constants_early +util_arrival_constants_am_peak,Arrival Constants -- AM peak (7 to 9),(end > 6) & (end < 10),coef_arrival_constants_am_peak +util_arrival_constants_midday_1,Arrival Constants -- Midday 1 (10 to 12),(end > 9) & (end < 13),coef_arrival_constants_midday_1 +util_arrival_constants_midday_2,Arrival Constants -- Midday 2 (13 to 14),(end > 12) & (end < 15),coef_arrival_constants_midday_2 +util_arrival_constants_pm_peak_1,Arrival Constants -- PM peak 1 (15),end == 15,coef_arrival_constants_pm_peak_1 +util_arrival_constants_pm_peak_2,Arrival Constants -- PM peak 2 (16),end == 16,coef_arrival_constants_pm_peak_2 +util_arrival_constants_pm_peak_3,Arrival Constants -- PM peak 3 (17),end == 17,coef_arrival_constants_pm_peak_3 +util_arrival_constants_pm_peak_4,Arrival Constants -- PM peak 4 (18),end == 18,coef_arrival_constants_pm_peak_4 +util_arrival_constants_evening,Arrival Constants -- Evening (19 to 21),(end > 18) & (end < 22),coef_arrival_constants_evening +util_arrival_constants_late,Arrival Constants -- Late (22 and later),end > 21,coef_arrival_constants_late +util_duration_constants_0_to_2_hours,Duration Constants -- 0 to 2 hours,duration < 3,coef_duration_constants_0_to_2_hours +util_duration_constants_3_to_4_hours,Duration Constants -- 3 to 4 hours,(duration > 2) & (duration < 5),coef_duration_constants_3_to_4_hours +util_duration_constants_5_to_6_hours,Duration Constants -- 5 to 6 hours,(duration > 4) & (duration < 7),coef_duration_constants_5_to_6_hours +util_duration_constants_7_to_8_hours,Duration Constants -- 7 to 8 hours,(duration > 6) & (duration < 9),coef_duration_constants_7_to_8_hours +util_duration_constants_9_hours,Duration Constants -- 9 hours,duration == 9,coef_duration_constants_9_hours +util_duration_constants_10_hours,Duration Constants -- 10 hours,duration == 10,coef_duration_constants_10_hours +util_duration_constants_11_hours,Duration Constants -- 11 hours,duration == 11,coef_duration_constants_11_hours +util_duration_constants_12_to_13_hours,Duration Constants -- 12 to 13 hours,(duration > 11) & (duration < 14),coef_duration_constants_12_to_13_hours +util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,(duration > 13) & (duration < 19),coef_duration_constants_14_to_18_hours + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/activitysim/examples/example_psrc/configs/tour_scheduling_work_coeffs.csv b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_work_coeffs.csv similarity index 98% rename from activitysim/examples/example_psrc/configs/tour_scheduling_work_coeffs.csv rename to activitysim/examples/placeholder_psrc/configs/tour_scheduling_work_coeffs.csv index 7404512d1f..3b842e5351 100755 --- a/activitysim/examples/example_psrc/configs/tour_scheduling_work_coeffs.csv +++ b/activitysim/examples/placeholder_psrc/configs/tour_scheduling_work_coeffs.csv @@ -1,65 +1,65 @@ -coefficient_name,value,constrain -coef_dummy,1,T -coef_free_flow_round_trip_auto_time_shift_effects_departure,-0.00114,F -coef_free_flow_round_trip_auto_time_shift_effects_duration,0.00221,F -coef_part_time_worker_departure_shift_effects,0.06736,F -coef_non_working_adult_duration_shift_effects,-0.1207,F -coef_university_student_departure_shift_effects,0.05747,F -coef_household_income_departure_shift_effects,0.000208,F -coef_destination_in_cbd_departure_shift_effects,0.04717,F -coef_destination_in_cbd_duration_shift_effects,0.08679,F -coef_subsequent_tour_must_start_after_previous_tour_ends,-100,T -coef_first_of_2plus_work_tours_departure_shift_effects,-0.3033,F -coef_first_of_2plus_work_tours_duration_shift_effects,-0.1861,F -coef_subsequent_2plus_work_departure_tours_shift_effects,-0.5381,F -coef_subsequent_2plus_work_duration_tours_shift_effects,-0.3174,F -coef_household_income_early_departure_interaction,-0.4854,F -coef_household_income_late_arrival_interaction,-0.3839,F -coef_destination_in_cbd_early_departure_interaction,-0.4566,F -coef_destination_in_cbd_late_arrival_interaction,-0.2334,F -coef_rural_household_early_departure_interaction,0.4039,F -coef_rural_household_late_arrival_interaction,-0.3451,F -coef_full_time_worker_duration_lt_9_hours_interaction,-1.257,F -coef_full_time_worker_10_to_12_departure_interaction,-0.5182,F -coef_part_time_worker_13_to_15_arrival_interaction,0.5433,F -coef_first_of_2plus_work_tours_duration_lt_8_hrs,1.98,F -coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs,2.582,F -coef_tours_by_worker_duration_lt_8_hrs,0.9126,F -coef_tours_by_student_duration_lt_8_hrs,2.582,F -coef_mode_choice_logsum,1.027,F -coef_previously_scheduled_tour_ends_in_this_departure_hour,-0.8935,F -coef_previously_scheduled_tour_begins_in_this_arrival_hour,-1.334,F -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.1771,F -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,0.3627,F -coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-0.2123,F -coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.1012,F -coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours,-18.68,F -coef_departure_constants_early,-0.95272527,F -coef_departure_constants_am_peak_1,-0.616180906,F -coef_departure_constants_am_peak_2,0,T -coef_departure_constants_am_peak_3,-0.254714726, -coef_departure_constants_am_peak_4,-1.251346024,F -coef_departure_constants_midday_1,-1.705868992,F -coef_departure_constants_midday_2,-1.693570583,F -coef_departure_constants_pm_peak,-1.439991962,F -coef_departure_constants_evening,-1.610513243,F -coef_departure_constants_late,-2.883415223,F -coef_arrival_constants_early,0,T -coef_arrival_constants_am_peak,-1.854520626,F -coef_arrival_constants_midday_1,-0.495972037,F -coef_arrival_constants_midday_2,-0.378554081,F -coef_arrival_constants_pm_peak_1,0,T -coef_arrival_constants_pm_peak_2,0.2760839,F -coef_arrival_constants_pm_peak_3,0.699587132,F -coef_arrival_constants_pm_peak_4,0.799289377,F -coef_arrival_constants_evening,0.103566251,F -coef_arrival_constants_late,-0.965957339,F -coef_duration_constants_0_to_2_hours,-2.52826639,F -coef_duration_constants_3_to_4_hours,-0.918974457,F -coef_duration_constants_5_to_6_hours,-0.718550288,F -coef_duration_constants_7_to_8_hours,-0.139623566,F -coef_duration_constants_9_hours,0.055706243,F -coef_duration_constants_10_hours,0,T -coef_duration_constants_11_hours,-0.347795391,F -coef_duration_constants_12_to_13_hours,-1.008222346,F +coefficient_name,value,constrain +coef_dummy,1,T +coef_free_flow_round_trip_auto_time_shift_effects_departure,-0.00114,F +coef_free_flow_round_trip_auto_time_shift_effects_duration,0.00221,F +coef_part_time_worker_departure_shift_effects,0.06736,F +coef_non_working_adult_duration_shift_effects,-0.1207,F +coef_university_student_departure_shift_effects,0.05747,F +coef_household_income_departure_shift_effects,0.000208,F +coef_destination_in_cbd_departure_shift_effects,0.04717,F +coef_destination_in_cbd_duration_shift_effects,0.08679,F +coef_subsequent_tour_must_start_after_previous_tour_ends,-100,T +coef_first_of_2plus_work_tours_departure_shift_effects,-0.3033,F +coef_first_of_2plus_work_tours_duration_shift_effects,-0.1861,F +coef_subsequent_2plus_work_departure_tours_shift_effects,-0.5381,F +coef_subsequent_2plus_work_duration_tours_shift_effects,-0.3174,F +coef_household_income_early_departure_interaction,-0.4854,F +coef_household_income_late_arrival_interaction,-0.3839,F +coef_destination_in_cbd_early_departure_interaction,-0.4566,F +coef_destination_in_cbd_late_arrival_interaction,-0.2334,F +coef_rural_household_early_departure_interaction,0.4039,F +coef_rural_household_late_arrival_interaction,-0.3451,F +coef_full_time_worker_duration_lt_9_hours_interaction,-1.257,F +coef_full_time_worker_10_to_12_departure_interaction,-0.5182,F +coef_part_time_worker_13_to_15_arrival_interaction,0.5433,F +coef_first_of_2plus_work_tours_duration_lt_8_hrs,1.98,F +coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs,2.582,F +coef_tours_by_worker_duration_lt_8_hrs,0.9126,F +coef_tours_by_student_duration_lt_8_hrs,2.582,F +coef_mode_choice_logsum,1.027,F +coef_previously_scheduled_tour_ends_in_this_departure_hour,-0.8935,F +coef_previously_scheduled_tour_begins_in_this_arrival_hour,-1.334,F +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.1771,F +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,0.3627,F +coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-0.2123,F +coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.1012,F +coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours,-18.68,F +coef_departure_constants_early,-0.95272527,F +coef_departure_constants_am_peak_1,-0.616180906,F +coef_departure_constants_am_peak_2,0,T +coef_departure_constants_am_peak_3,-0.254714726, +coef_departure_constants_am_peak_4,-1.251346024,F +coef_departure_constants_midday_1,-1.705868992,F +coef_departure_constants_midday_2,-1.693570583,F +coef_departure_constants_pm_peak,-1.439991962,F +coef_departure_constants_evening,-1.610513243,F +coef_departure_constants_late,-2.883415223,F +coef_arrival_constants_early,0,T +coef_arrival_constants_am_peak,-1.854520626,F +coef_arrival_constants_midday_1,-0.495972037,F +coef_arrival_constants_midday_2,-0.378554081,F +coef_arrival_constants_pm_peak_1,0,T +coef_arrival_constants_pm_peak_2,0.2760839,F +coef_arrival_constants_pm_peak_3,0.699587132,F +coef_arrival_constants_pm_peak_4,0.799289377,F +coef_arrival_constants_evening,0.103566251,F +coef_arrival_constants_late,-0.965957339,F +coef_duration_constants_0_to_2_hours,-2.52826639,F +coef_duration_constants_3_to_4_hours,-0.918974457,F +coef_duration_constants_5_to_6_hours,-0.718550288,F +coef_duration_constants_7_to_8_hours,-0.139623566,F +coef_duration_constants_9_hours,0.055706243,F +coef_duration_constants_10_hours,0,T +coef_duration_constants_11_hours,-0.347795391,F +coef_duration_constants_12_to_13_hours,-1.008222346,F coef_duration_constants_14_to_18_hours,-1.701858847,F \ No newline at end of file diff --git a/activitysim/examples/example_psrc/configs/trip_destination.csv b/activitysim/examples/placeholder_psrc/configs/trip_destination.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/trip_destination.csv rename to activitysim/examples/placeholder_psrc/configs/trip_destination.csv diff --git a/activitysim/examples/example_psrc/configs/trip_destination.yaml b/activitysim/examples/placeholder_psrc/configs/trip_destination.yaml similarity index 100% rename from activitysim/examples/example_psrc/configs/trip_destination.yaml rename to activitysim/examples/placeholder_psrc/configs/trip_destination.yaml diff --git a/activitysim/examples/example_mtc/configs/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/placeholder_psrc/configs/trip_destination_annotate_trips_preprocessor.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/trip_destination_annotate_trips_preprocessor.csv rename to activitysim/examples/placeholder_psrc/configs/trip_destination_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/example_psrc/configs/trip_destination_sample.csv b/activitysim/examples/placeholder_psrc/configs/trip_destination_sample.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/trip_destination_sample.csv rename to activitysim/examples/placeholder_psrc/configs/trip_destination_sample.csv diff --git a/activitysim/examples/example_psrc/configs/trip_mode_choice.csv b/activitysim/examples/placeholder_psrc/configs/trip_mode_choice.csv similarity index 99% rename from activitysim/examples/example_psrc/configs/trip_mode_choice.csv rename to activitysim/examples/placeholder_psrc/configs/trip_mode_choice.csv index 2da242e956..3ac15254ba 100644 --- a/activitysim/examples/example_psrc/configs/trip_mode_choice.csv +++ b/activitysim/examples/placeholder_psrc/configs/trip_mode_choice.csv @@ -1,405 +1,405 @@ -Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_LOC,WALK_LRF,WALK_EXP,WALK_HVY,WALK_COM,DRIVE_LOC,DRIVE_LRF,DRIVE_EXP,DRIVE_HVY,DRIVE_COM,TAXI,TNC_SINGLE,TNC_SHARED -#,Drive alone no toll,,,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,auto_ownership == 0,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Terminal_time,DRIVEALONEFREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['SOV_DIST'],coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost,coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOV_BTOLL'],coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Person_is_between_16_and_19_years_old,DRIVEALONEFREE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da,,,,,,,,,,,,,,,,,,,, -#,Drive alone toll,,,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,auto_ownership == 0,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,age < 16,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['SOVTOLL_TIME'],,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Terminal_time,DRIVEALONEPAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['SOVTOLL_DIST'],,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost,,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOVTOLL_BTOLL'],,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOVTOLL_VTOLL'],,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Person_is_between_16_and_19_years_old,DRIVEALONEPAY - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),,coef_age1619_da,,,,,,,,,,,,,,,,,,, -#,Shared ride 2,,,,,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@odt_skims['HOV2_TIME'],,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Terminal_time,SHARED2FREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV2_DIST'],,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2_BTOLL'] / costShareSr2,,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_One_person_household,SHARED2FREE - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Person_is_16_years_old_or_older,SHARED2FREE - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr,,,,,,,,,,,,,,,,,, -#,Shared ride 2 toll,,,,,,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,,,,,,,,,, -util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@odt_skims['HOV2TOLL_TIME'],,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Terminal_time,SHARED2PAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV2TOLL_DIST'],,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_BTOLL'] / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'] / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_One_person_household,SHARED2PAY - One person household,@(df.hhsize == 1),,,,coef_hhsize1_sr,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.hhsize == 2),,,,coef_hhsize2_sr,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Person_is_16_years_old_or_older,SHARED2PAY - Person is 16 years old or older,@(df.age >= 16),,,,coef_age16p_sr,,,,,,,,,,,,,,,,, -#,Shared ride 3+,,,,,,,,,,,,,,,,,,,,,, -util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,,,,,,,,,, -util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@odt_skims['HOV3_TIME'],,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_Terminal_time,SHARED3FREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV3_DIST'],,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3_BTOLL'] / costShareSr3,,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_One_person_household,SHARED3FREE - One person household,@(df.hhsize == 1),,,,,coef_hhsize1_sr,,,,,,,,,,,,,,,, -util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.hhsize == 2),,,,,coef_hhsize2_sr,,,,,,,,,,,,,,,, -util_SHARED3FREE_Person_is_16_years_old_or_older,SHARED3FREE - Person is 16 years old or older,@(df.age >= 16),,,,,coef_age16p_sr,,,,,,,,,,,,,,,, -#,Shared ride 3+ toll,,,,,,,,,,,,,,,,,,,,,, -util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,,,,,,,,,, -util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@odt_skims['HOV3TOLL_TIME'],,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Terminal_time,SHARED3PAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV3TOLL_DIST'],,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3TOLL_BTOLL'] / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3TOLL_VTOLL'] / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_One_person_household,SHARED3PAY - One person household,@(df.hhsize == 1),,,,,,coef_hhsize1_sr,,,,,,,,,,,,,,, -util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.hhsize == 2),,,,,,coef_hhsize2_sr,,,,,,,,,,,,,,, -util_SHARED3PAY_Person_is_16_years_old_or_older,SHARED3PAY - Person is 16 years old or older,@(df.age >= 16),,,,,,coef_age16p_sr,,,,,,,,,,,,,,, -#,Walk,,,,,,,,,,,,,,,,,,,,,, -util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@coef_walktimeshort_multiplier * od_skims['DISTWALK'].clip(upper=walkThresh) * 60/walkSpeed,,,,,,,coef_ivt,,,,,,,,,,,,,, -util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * (od_skims['DISTWALK'] - walkThresh).clip(lower=0) * 60/walkSpeed,,,,,,,coef_ivt,,,,,,,,,,,,,, -util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,coef_ivt,,,,,,,,,,,,,, -util_WALK_Topology,WALK - Topology,@topology_walk_multiplier * df.trip_topology,,,,,,,coef_ivt,,,,,,,,,,,,,, -#,Bike,,,,,,,,,,,,,,,,,,,,,, -util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,,,,,,,,,, -util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@coef_biketimeshort_multiplier * od_skims['DISTBIKE'].clip(upper=bikeThresh)*60/bikeSpeed,,,,,,,,coef_ivt,,,,,,,,,,,,, -util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@coef_biketimeshort_multiplier * biketimelong_multiplier * (od_skims['DISTBIKE']-bikeThresh).clip(lower=0)*60/bikeSpeed,,,,,,,,coef_ivt,,,,,,,,,,,,, -util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier*df.density_index,,,,,,,,coef_ivt,,,,,,,,,,,,, -util_BIKE_Topology,BIKE - Topology,@topology_bike_multiplier * df.trip_topology,,,,,,,,coef_ivt,,,,,,,,,,,,, -#,Walk to Local,,,,,,,,,,,,,,,,,,,,,, -util_WALK_LOC_Unavailable,WALK_LOC - Unavailable,walk_local_available == False,,,,,,,,,-999,,,,,,,,,,,, -util_WALK_LOC_In_vehicle_time,WALK_LOC - In-vehicle time,@odt_skims['WLK_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Short_iwait_time,WALK_LOC - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Long_iwait_time,WALK_LOC - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_transfer_wait_time,WALK_LOC - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_number_of_transfers,WALK_LOC - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_LOC_WLK_BOARDS']-1).clip(0),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Walk_egress_time,WALK_LOC - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Walk_other_time,WALK_LOC - Walk other time,@coef_waux_multiplier * odt_skims['WLK_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Fare,WALK_LOC - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_LOC_WLK_FAR'],,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Destination_zone_densityIndex,WALK_LOC - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Topology,WALK_LOC - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Person_is_less_than_10_years_old,WALK_LOC - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,coef_age010_trn,,,,,,,,,,,, -#,Walk to Light rail/Ferry,,,,,,,,,,,,,,,,,,,,,, -util_WALK_LRF_Unavailable,WALK_LRF - Unavailable,walk_lrf_available == False,,,,,,,,,,-999,,,,,,,,,,, -util_WALK_LRF_In_vehicle_time,WALK_LRF - In-vehicle time,@odt_skims['WLK_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_In_vehicle_time_on_Light_Rail,WALK_LRF - In-vehicle time on Light Rail (incremental w/ ivt),@(coef_ivt_lrt_multiplier-1) * odt_skims['WLK_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_In_vehicle_time_on_Ferry,WALK_LRF - In-vehicle time on Ferry (incremental w/keyivt),@(coef_ivt_ferry_multiplier-coef_ivt_lrt_multiplier) * odt_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Short_iwait_time,WALK_LRF - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Long_iwait_time,WALK_LRF - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_transfer_wait_time,WALK_LRF - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_number_of_transfers,WALK_LRF - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_LRF_WLK_BOARDS']-1).clip(0),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Walk_access_time,WALK_LRF - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Walk_egress_time,WALK_LRF - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Walk_other_time,WALK_LRF - Walk otherLight rail/Ferry time,@coef_waux_multiplier * odt_skims['WLK_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Fare,WALK_LRF - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_LRF_WLK_FAR'],,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Destination_zone_densityIndex,WALK_LRF - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Topology,WALK_LRF - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Person_is_less_than_10_years_old,WALK_LRF - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,coef_age010_trn,,,,,,,,,,, -#,Walk to Express bus,,,,,,,,,,,,,,,,,,,,,, -util_WALK_EXP_Unavailable,WALK_EXP - Unavailable,walk_express_available == False,,,,,,,,,,,-999,,,,,,,,,, -util_WALK_EXP_In_vehicle_time,WALK_EXP - In-vehicle time,@odt_skims['WLK_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_In_vehicle_time_on_Express_bus,WALK_EXP - In-vehicle time on Express bus (incremental w/ ivt),@(ivt_exp_multiplier - 1) * odt_skims['WLK_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Short_iwait_time,WALK_EXP - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Long_iwait_time,WALK_EXP - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_transfer_wait_time,WALK_EXP - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_number_of_transfers,WALK_EXP - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_EXP_WLK_BOARDS']-1).clip(0),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Walk_access_time,WALK_EXP - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Walk_egress_time,WALK_EXP - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Walk_other_time,WALK_EXP - Walk other time,@coef_waux_multiplier * odt_skims['WLK_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Fare,WALK_EXP - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_EXP_WLK_FAR'],,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Destination_zone_densityIndex,WALK_EXP - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Topology,WALK_EXP - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Person_is_less_than_10_years_old,WALK_EXP - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,,coef_age010_trn,,,,,,,,,, -#,Walk to Heavy Rail,,,,,,,,,,,,,,,,,,,,,, -util_WALK_HVY_Unavailable,WALK_HVY - Unavailable,walk_heavyrail_available == False,,,,,,,,,,,,-999,,,,,,,,, -util_WALK_HVY_In_vehicle_time,WALK_HVY - In-vehicle time,@odt_skims['WLK_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_In_vehicle_time_on_heavy_rail,WALK_HVY - In-vehicle time on heavy rail (incremental w/ ivt),@(ivt_hvy_multiplier-1) * odt_skims['WLK_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Short_iwait_time,WALK_HVY - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Long_iwait_time,WALK_HVY - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_transfer_wait_time,WALK_HVY - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_number_of_transfers,WALK_HVY - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_HVY_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Walk_access_time,WALK_HVY - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Walk_egress_time,WALK_HVY - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Walk_other_time,WALK_HVY - Walk other time,@coef_waux_multiplier * odt_skims['WLK_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Fare,WALK_HVY - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_HVY_WLK_FAR'],,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Destination_zone_densityIndex,WALK_HVY - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Topology,WALK_HVY - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Person_is_less_than_10_years_old,WALK_HVY - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,,,coef_age010_trn,,,,,,,,, -#,Walk to Commuter rail,,,,,,,,,,,,,,,,,,,,,, -util_WALK_COM_Unavailable,WALK_COM - Unavailable,walk_commuter_available == False,,,,,,,,,,,,,-999,,,,,,,, -util_WALK_COM_In_vehicle_time,WALK_COM - In-vehicle time,@odt_skims['WLK_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_In_vehicle_time_on_commuter_rail,WALK_COM - In-vehicle time on commuter rail (incremental w/ ivt),@(ivt_com_multiplier - 1) * odt_skims['WLK_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Short_iwait_time,WALK_COM - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Long_iwait_time,WALK_COM - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_transfer_wait_time,WALK_COM - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_number_of_transfers,WALK_COM - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_COM_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Walk_access_time,WALK_COM - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Walk_egress_time,WALK_COM - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Walk_other_time,WALK_COM - Walk other time,@coef_waux_multiplier * odt_skims['WLK_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Fare,WALK_COM - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_COM_WLK_FAR'],,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Destination_zone_densityIndex,WALK_COM - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Topology,WALK_COM - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Person_is_less_than_10_years_old,WALK_COM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,coef_age010_trn,,,,,,,, -#,Drive to Local,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_LOC_Unavailable_for_zero_auto_households,DRIVE_LOC - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,-999,,,,,,, -util_DRIVE_LOC_Unavailable_for_persons_less_than_16,DRIVE_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,-999,,,,,,, -util_DRIVE_LOC_Destination_zone_densityIndex,DRIVE_LOC - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Topology,DRIVE_LOC - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Person_is_less_than_10_years_old,DRIVE_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,coef_age010_trn,,,,,,, -util_DRIVE_LOC_outbound_Unavailable,DRIVE_LOC outbound - Unavailable,outbound & ~drive_local_available_outbound,,,,,,,,,,,,,,-999,,,,,,, -util_DRIVE_LOC_outbound_In_vehicle_time,DRIVE_LOC outbound - In-vehicle time,@df.outbound * odt_skims['DRV_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_Short_iwait_time,DRIVE_LOC outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_Long_iwait_time,DRIVE_LOC outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_transfer_wait_time,DRIVE_LOC outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_number_of_transfers,DRIVE_LOC outbound - number of transfers,@df.outbound * xfers_wlk_multiplier * (odt_skims['DRV_LOC_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_Drive_time,DRIVE_LOC outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_LOC_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_Walk_egress_time,DRIVE_LOC outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_Walk_other_time,DRIVE_LOC outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_Fare_and_operating_cost,DRIVE_LOC outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_LOC_WLK_FAR'] + costPerMile*odt_skims['DRV_LOC_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LOC outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_LOC_WLK_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']),,,,,,,,,,,,,,1,,,,,,, -util_DRIVE_LOC_inbound_Unavailable,DRIVE_LOC inbound - Unavailable,inbound & ~drive_local_available_inbound,,,,,,,,,,,,,,-999,,,,,,, -util_DRIVE_LOC_inbound_In_vehicle_time,DRIVE_LOC inbound - In-vehicle time,@df.inbound * odt_skims['WLK_LOC_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_Short_iwait_time,DRIVE_LOC inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_LOC_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_Long_iwait_time,DRIVE_LOC inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_LOC_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_transfer_wait_time,DRIVE_LOC inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_LOC_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_number_of_transfers,DRIVE_LOC inbound - number of transfers,@df.inbound * xfers_wlk_multiplier * (odt_skims['WLK_LOC_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_Drive_time,DRIVE_LOC inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WLK_LOC_DRV_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_Walk_access_time,DRIVE_LOC inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_Walk_other_time,DRIVE_LOC inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WLK_LOC_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_Fare_and_operating_cost,DRIVE_LOC inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_LOC_DRV_FAR'] + costPerMile*odt_skims['WLK_LOC_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LOC inbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['WLK_LOC_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']),,,,,,,,,,,,,,1,,,,,,, -#,Drive to Light Rail/Ferry,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_LRF_Unavailable_for_zero_auto_households,DRIVE_LRF - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,-999,,,,,, -util_DRIVE_LRF_Unavailable_for_persons_less_than_16,DRIVE_LRF - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,-999,,,,,, -util_DRIVE_LRF_Destination_zone_densityIndex,DRIVE_LRF - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Topology,DRIVE_LRF - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Person_is_less_than_10_years_old,DRIVE_LRF - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,coef_age010_trn,,,,,, -util_DRIVE_LRF_outbound_Unavailable,DRIVE_LRF outbound - Unavailable,outbound & ~drive_lrf_available_outbound,,,,,,,,,,,,,,,-999,,,,,, -util_DRIVE_LRF_outbound_In_vehicle_time,DRIVE_LRF outbound - In-vehicle time,@df.outbound * odt_skims['DRV_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_In_vehicle_time_on_LR,DRIVE_LRF outbound - In-vehicle time on Light Rail (incremental w/ ivt),@df.outbound * (coef_ivt_lrt_multiplier - 1)*odt_skims['DRV_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_In_vehicle_time_on_Ferry,DRIVE_LRF outbound - In-vehicle time on Ferry (incremental w/ keyivt),@df.outbound * (coef_ivt_ferry_multiplier-coef_ivt_lrt_multiplier)*odt_skims['DRV_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_Short_iwait_time,DRIVE_LRF outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_Long_iwait_time,DRIVE_LRF outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_transfer_wait_time,DRIVE_LRF outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_number_of_transfers,DRIVE_LRF outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DRV_LRF_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_Drive_time,DRIVE_LRF outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_LRF_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_Walk_egress_time,DRIVE_LRF outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_Walk_other_time,DRIVE_LRF outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_Fare_and_operating_cost,DRIVE_LRF outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_LRF_WLK_FAR'] + costPerMile * odt_skims['DRV_LRF_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LRF outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_LRF_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,1,,,,,, -util_DRIVE_LRF_inbound_Unavailable,DRIVE_LRF inbound - Unavailable,inbound & ~drive_lrf_available_inbound,,,,,,,,,,,,,,,-999,,,,,, -util_DRIVE_LRF_inbound_In_vehicle_time,DRIVE_LRF inbound - In-vehicle time,@df.inbound * odt_skims['WLK_LRF_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_In_vehicle_time_on_LR,DRIVE_LRF inbound - In-vehicle time on Light Rail (incremental w/ ivt),@df.inbound * (coef_ivt_lrt_multiplier - 1)*odt_skims['WLK_LRF_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_In_vehicle_time_on_Ferry,DRIVE_LRF inbound - In-vehicle time on Ferry (incremental w/ keyivt),@df.inbound * (coef_ivt_ferry_multiplier-coef_ivt_lrt_multiplier)*odt_skims['WLK_LRF_DRV_FERRYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_Short_iwait_time,DRIVE_LRF inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_LRF_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_Long_iwait_time,DRIVE_LRF inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_LRF_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_transfer_wait_time,DRIVE_LRF inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_LRF_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_number_of_transfers,DRIVE_LRF inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WLK_LRF_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_Drive_time,DRIVE_LRF inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WLK_LRF_DRV_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_Walk_access_time,DRIVE_LRF inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_Walk_other_time,DRIVE_LRF inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WLK_LRF_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_Fare_and_operating_cost,DRIVE_LRF inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_LRF_DRV_FAR'] + costPerMile * odt_skims['WLK_LRF_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LRF inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WLK_LRF_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ od_skims['DIST'],,,,,,,,,,,,,,,1,,,,,, -#,Drive to Express bus,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_EXP_Unavailable_for_zero_auto_households,DRIVE_EXP - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,-999,,,,, -util_DRIVE_EXP_Unavailable_for_persons_less_than_16,DRIVE_EXP - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,-999,,,,, -util_DRIVE_EXP_Destination_zone_densityIndex,DRIVE_EXP - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Topology,DRIVE_EXP - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Person_is_less_than_10_years_old,DRIVE_EXP - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,coef_age010_trn,,,,, -util_DRIVE_EXP_outbound_Unavailable,DRIVE_EXP outbound - Unavailable,outbound & ~drive_express_available_outbound,,,,,,,,,,,,,,,,-999,,,,, -util_DRIVE_EXP_outbound_In_vehicle_time,DRIVE_EXP outbound - In-vehicle time,@df.outbound * odt_skims['DRV_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_In_vehicle_time_on_EXP,DRIVE_EXP outbound - In-vehicle time on Express bus (incremental w/ ivt),@df.outbound * (ivt_exp_multiplier - 1) * odt_skims['DRV_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_Short_iwait_time,DRIVE_EXP outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_Long_iwait_time,DRIVE_EXP outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_transfer_wait_time,DRIVE_EXP outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_number_of_transfers,DRIVE_EXP outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DRV_EXP_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_Drive_time,DRIVE_EXP outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_EXP_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_Walk_egress_time,DRIVE_EXP outbound - Walk egress ime,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_Walk_other_time,DRIVE_EXP outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_Fare_and_operating_cost,DRIVE_EXP outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_EXP_WLK_FAR'] + costPerMile * odt_skims['DRV_EXP_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_EXP outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_EXP_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_Unavailable,DRIVE_EXP inbound - Unavailable,inbound & ~drive_express_available_inbound,,,,,,,,,,,,,,,,-999,,,,, -util_DRIVE_EXP_inbound_In_vehicle_time,DRIVE_EXP inbound - In-vehicle time,@df.inbound * odt_skims['WLK_EXP_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_In_vehicle_time_on_EXP,DRIVE_EXP inbound - In-vehicle time on Express bus (incremental w/ ivt),@df.inbound * (ivt_exp_multiplier - 1) * odt_skims['WLK_EXP_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_Short_iwait_time,DRIVE_EXP inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_EXP_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_Long_iwait_time,DRIVE_EXP inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_EXP_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_transfer_wait_time,DRIVE_EXP inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_EXP_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_number_of_transfers,DRIVE_EXP inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WLK_EXP_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_Drive_time,DRIVE_EXP inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WLK_EXP_DRV_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_Walk_access_time,DRIVE_EXP inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_Walk_other_time,DRIVE_EXP inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WLK_EXP_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_Fare_and_operating_cost,DRIVE_EXP inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_EXP_DRV_FAR'] + costPerMile * odt_skims['WLK_EXP_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_EXP inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WLK_EXP_DRV_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,1,,,,, -#,Drive to Heavy Rail,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_HVY_Unavailable_for_zero_auto_households,DRIVE_HVY - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,,-999,,,, -util_DRIVE_HVY_Unavailable_for_persons_less_than_16,DRIVE_HVY - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,-999,,,, -util_DRIVE_HVY_Destination_zone_densityIndex,DRIVE_HVY - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Topology,DRIVE_HVY - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Person_is_less_than_10_years_old,DRIVE_HVY - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,,coef_age010_trn,,,, -util_DRIVE_HVY_outbound_Unavailable,DRIVE_HVY outbound - Unavailable,outbound & ~drive_heavyrail_available_outbound,,,,,,,,,,,,,,,,,-999,,,, -util_DRIVE_HVY_outbound_In_vehicle_time,DRIVE_HVY outbound - In-vehicle time,@df.outbound * odt_skims['DRV_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_In_vehicle_time_on_HVY,DRIVE_HVY outbound - In-vehicle time on heavy rail (incremental w/ ivt),@df.outbound * (ivt_hvy_multiplier - 1) * odt_skims['DRV_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_Short_iwait_time,DRIVE_HVY outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_Long_iwait_time,DRIVE_HVY outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_transfer_wait_time,DRIVE_HVY outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_number_of_transfers,DRIVE_HVY outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DRV_HVY_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_Drive_time,DRIVE_HVY outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_HVY_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_Walk_egress_time,DRIVE_HVY outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_Walk_other_time,DRIVE_HVY outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_Fare_and_operating_cost,DRIVE_HVY outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_HVY_WLK_FAR'] + costPerMile * odt_skims['DRV_HVY_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_HVY outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_HVY_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_Unavailable,DRIVE_HVY inbound - Unavailable,inbound & ~drive_heavyrail_available_inbound,,,,,,,,,,,,,,,,,-999,,,, -util_DRIVE_HVY_inbound_In_vehicle_time,DRIVE_HVY inbound - In-vehicle time,@df.inbound * odt_skims['WLK_HVY_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_In_vehicle_time_on_HVY,DRIVE_HVY inbound - In-vehicle time on heavy rail (incremental w/ ivt),@df.inbound * (ivt_hvy_multiplier - 1) * odt_skims['WLK_HVY_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_Short_iwait_time,DRIVE_HVY inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_HVY_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_Long_iwait_time,DRIVE_HVY inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_HVY_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_transfer_wait_time,DRIVE_HVY inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_HVY_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_number_of_transfers,DRIVE_HVY inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WLK_HVY_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_Drive_time,DRIVE_HVY inbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_HVY_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_Walk_access_time,DRIVE_HVY inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_Walk_other_time,DRIVE_HVY inbound - Walk other time,@coef_waux_multiplier * odt_skims['WLK_HVY_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_Fare_and_operating_cost,DRIVE_HVY inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_HVY_DRV_FAR'] + costPerMile * odt_skims['WLK_HVY_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_HVY inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WLK_HVY_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ od_skims['DIST'],,,,,,,,,,,,,,,,,1,,,, -#,#Drive to Commuter Rail,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_COM_Unavailable_for_zero_auto_households,DRIVE_COM - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,,,-999,,, -util_DRIVE_COM_Unavailable_for_persons_less_than_16,DRIVE_COM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,,-999,,, -util_DRIVE_COM_Destination_zone_densityIndex,DRIVE_COM - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Topology,DRIVE_COM - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Person_is_less_than_10_years_old,DRIVE_COM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,,,coef_age010_trn,,, -util_DRIVE_COM_outbound_Unavailable,DRIVE_COM outbound - Unavailable,outbound & ~drive_commuter_available_outbound,,,,,,,,,,,,,,,,,,-999,,, -util_DRIVE_COM_outbound_In_vehicle_time,DRIVE_COM outbound - In-vehicle time,@df.outbound * odt_skims['DRV_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_In_vehicle_time_on_COM,DRIVE_COM outbound - In-vehicle time on commuter rail (incremental w/ ivt),@df.outbound * (ivt_com_multiplier - 1) * odt_skims['DRV_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_Short_iwait_time,DRIVE_COM outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_Long_iwait_time,DRIVE_COM outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_transfer_wait_time,DRIVE_COM outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_number_of_transfers,DRIVE_COM outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DRV_COM_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_Drive_time,DRIVE_COM outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_COM_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_Walk_egress_time,DRIVE_COM outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_Walk_other_time,DRIVE_COM outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_Fare_and_operating_cost,DRIVE_COM outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_COM_WLK_FAR'] + costPerMile * odt_skims['DRV_COM_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_COM outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_COM_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,,,1,,, -util_DRIVE_COM_inbound_Unavailable,DRIVE_COM inbound - Unavailable,inbound & ~drive_commuter_available_inbound,,,,,,,,,,,,,,,,,,-999,,, -util_DRIVE_COM_inbound_In_vehicle_time,DRIVE_COM inbound - In-vehicle time,@df.inbound * odt_skims['WLK_COM_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_In_vehicle_time_on_COM,DRIVE_COM inbound - In-vehicle time on commuter rail (incremental w/ ivt),@df.inbound * (ivt_com_multiplier - 1) * odt_skims['WLK_COM_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_Short_iwait_time,DRIVE_COM inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_COM_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_Long_iwait_time,DRIVE_COM inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_COM_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_transfer_wait_time,DRIVE_COM inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_COM_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_number_of_transfers,DRIVE_COM inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WLK_COM_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_Drive_time,DRIVE_COM inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WLK_COM_DRV_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_Walk_access_time,DRIVE_COM inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_Walk_other_time,DRIVE_COM inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WLK_COM_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_Fare_and_operating_cost,DRIVE_COM inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_COM_DRV_FAR'] + costPerMile * odt_skims['WLK_COM_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_COM inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WLK_COM_DRV_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,,,1,,, -#,Taxi,,,,,,,,,,,,,,,,,,,,,, -util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@odt_skims['HOV2TOLL_TIME'],,,,,,,,,,,,,,,,,,,coef_ivt,, -util_Taxi_Wait_time,Taxi - Wait time,@ridehail_wait_time_multiplier * df.origTaxiWaitTime,,,,,,,,,,,,,,,,,,,coef_ivt,, -util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,,,,,,,,coef_ivt,, -util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_BTOLL'],,,,,,,,,,,,,,,,,,,coef_ivt,, -util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare + odt_skims['HOV2TOLL_DIST'] * Taxi_costPerMile + odt_skims['HOV2TOLL_TIME'] * Taxi_costPerMinute)*100,,,,,,,,,,,,,,,,,,,coef_ivt,, -#,TNC Single,,,,,,,,,,,,,,,,,,,,,, -util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@odt_skims['HOV2TOLL_TIME'] ,,,,,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Wait_time,TNC Single - Wait time,@ridehail_wait_time_multiplier * df.origSingleTNCWaitTime,,,,,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare + odt_skims['HOV2TOLL_DIST'] * TNC_single_costPerMile + odt_skims['HOV2TOLL_TIME'] * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,coef_ivt, -#,#TNC Shared,,,,,,,,,,,,,,,,,,,,,, -util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@odt_skims['HOV2TOLL_TIME'] * TNC_shared_IVTFactor,,,,,,,,,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Wait_time,TNC Shared - Wait time,@ridehail_wait_time_multiplier * df.origSharedTNCWaitTime,,,,,,,,,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare + odt_skims['HOV2TOLL_DIST'] * TNC_shared_costPerMile + odt_skims['HOV2TOLL_TIME']* TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,,coef_ivt -#,,,,,,,,,,,,,,,,,,,,,,, -util_tour_mode_is_auto,Auto tour mode availability,tour_mode_is_auto,,,,,,,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, -util_tour_mode_is_walk,Walk tour mode availability,tour_mode_is_walk,-999,-999,-999,-999,-999,-999,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, -util_tour_mode_is_bike,Bike tour mode availability,tour_mode_is_bike,-999,-999,-999,-999,-999,-999,,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, -util_tour_mode_is_walk_transit,Walk to Transit tour mode availability,tour_mode_is_walk_transit,-999,-999,,,,,,-999,,,,,,-999,-999,-999,-999,-999,,, -util_tour_mode_is_drive_transit,Drive to Transit tour modes availability,tour_mode_is_drive_transit,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,,,,,,, -util_tour_mode_is_ride_hail,Ride hail tour modes availability,tour_mode_is_ride_hail,-999,-999,,,,,,-999,,,,,,-999,-999,-999,-999,-999,,, -,#indiv tour ASCs,,,,,,,,,,,,,,,,,,,,,, -util_Drive_Alone_tour_mode_ASC_shared_ride_2_df_is_indiv,Drive Alone tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,sov_ASC_sr2,sov_ASC_sr2,,,,,,,,,,,,,,,,, -util_Drive_Alone_tour_mode_ASC_shared_ride_3_plus,Drive Alone tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,sov_ASC_sr3p,sov_ASC_sr3p,,,,,,,,,,,,,,, -util_Drive_Alone_tour_mode_ASC_walk,Drive Alone tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,sov_ASC_walk,,,,,,,,,,,,,, -util_Drive_Alone_tour_mode_ASC_ride_hail,Drive Alone tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,,,,,,,,,,,,,sov_ASC_rh,sov_ASC_rh,sov_ASC_rh -util_Shared_Ride_2_tour_mode_ASC_shared_ride_2,Shared Ride 2 tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,sr2_ASC_sr2,sr2_ASC_sr2,,,,,,,,,,,,,,,,, -util_Shared_Ride_2_tour_mode_ASC_shared_ride_3_plus,Shared Ride 2 tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,sr2_ASC_sr3p,sr2_ASC_sr3p,,,,,,,,,,,,,,, -util_Shared_Ride_2_tour_mode_ASC_walk,Shared Ride 2 tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,sr2_ASC_walk,,,,,,,,,,,,,, -util_Shared_Ride_2_tour_mode_ASC_ride_hail,Shared Ride 2 tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,,,,,,,,,,,,,sr2_ASC_rh,sr2_ASC_rh,sr2_ASC_rh -util_Shared_Ride_3_tour_mode_ASC_shared_ride_2,Shared Ride 3+ tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,sr3p_ASC_sr2,sr3p_ASC_sr2,,,,,,,,,,,,,,,,, -util_Shared_Ride_3_tour_mode_ASC_shared_ride_3_plus,Shared Ride 3+ tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,sr3p_ASC_sr3p,sr3p_ASC_sr3p,,,,,,,,,,,,,,, -util_Shared_Ride_3_tour_mode_ASC_walk,Shared Ride 3+ tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,sr3p_ASC_walk,,,,,,,,,,,,,, -util_Shared_Ride_3_tour_mode_ASC_ride_hail,Shared Ride 3+ tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,,,,,,,,,,,,,sr3p_ASC_rh,sr3p_ASC_rh,sr3p_ASC_rh -util_Walk_tour_mode_ASC_ride_hail,Walk tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_WALK_MODE),,,,,,,,,,,,,,,,,,,walk_ASC_rh,walk_ASC_rh,walk_ASC_rh -util_Bike_tour_mode_ASC_walk,Bike tour mode ASC -- walk,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,,,,bike_ASC_walk,,,,,,,,,,,,,, -util_Bike_tour_mode_ASC_ride_hail,Bike tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,,,,bike_ASC_rh,bike_ASC_rh,bike_ASC_rh -util_Walk_to_Transit_tour_mode_ASC_light_rail,Walk to Transit tour mode ASC -- light rail,@(df.is_indiv & df.tour_mode_is_walk_transit & ~df.walk_ferry_available),,,,,,,,,,walk_transit_ASC_lightrail,,,,,,,,,,, -util_Walk_to_Transit_tour_mode_ASC_ferry,Walk to Transit tour mode ASC -- ferry,@(df.is_indiv & df.tour_mode_is_walk_transit & df.walk_ferry_available),,,,,,,,,,walk_transit_ASC_ferry,,,,,,,,,,, -util_Walk_to_Transit_tour_mode_ASC_express_bus,Walk to Transit tour mode ASC -- express bus,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,walk_transit_ASC_express,,,,,,,,,, -util_Walk_to_Transit_tour_mode_ASC_heavy_rail,Walk to Transit tour mode ASC -- heavy rail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,walk_transit_ASC_heavyrail,,,,,,,,, -util_Walk_to_Transit_tour_mode_ASC_commuter_rail,Walk to Transit tour mode ASC -- commuter rail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,,walk_transit_ASC_commuter,,,,,,,, -util_Walk_to_Transit_tour_mode_ASC_shared_ride_2,Walk to Transit tour mode ASC -- shared ride 2,@(df.is_indiv & df.tour_mode_is_walk_transit),,,walk_transit_ASC_sr2,walk_transit_ASC_sr2,,,,,,,,,,,,,,,,, -util_Walk_to_Transit_tour_mode_ASC_shared_ride_3_plus,Walk to Transit tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,walk_transit_ASC_sr3p,walk_transit_ASC_sr3p,,,,,,,,,,,,,,, -util_Walk_to_Transit_tour_mode_ASC_walk,Walk to Transit tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,walk_transit_ASC_walk,,,,,,,,,,,,,, -util_Walk_to_Transit_tour_mode_ASC_ride_hail,Walk to Transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,,,,,,,,walk_transit_ASC_rh,walk_transit_ASC_rh,walk_transit_ASC_rh -util_Drive_to_Transit_tour_mode_ASC_light_rail_skims_differ,Drive to Transit tour mode ASC -- light rail (higher b/c loc d-trn skims differ),@(df.is_indiv & df.tour_mode_is_drive_transit & ~df.drive_ferry_available),,,,,,,,,,,,,,,drive_transit_ASC_lightrail,,,,,, -util_Drive_to_Transit_tour_mode_ASC_ferry,Drive to Transit tour mode ASC -- ferry,@(df.is_indiv & df.tour_mode_is_drive_transit & df.drive_ferry_available),,,,,,,,,,,,,,,drive_transit_ASC_ferry,,,,,, -util_Drive_to_Transit_tour_mode_ASC_express_bus,Drive to Transit tour mode ASC -- express bus,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,drive_transit_ASC_express,,,,, -util_Drive_to_Transit_tour_mode_ASC_heavy_rail,Drive to Transit tour mode ASC -- heavy rail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,drive_transit_ASC_heavyrail,,,, -util_Drive_to_Transit_tour_mode_ASC_commuter_rail,Drive to Transit tour mode ASC -- commuter rail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,drive_transit_ASC_commuter,,, -util_Drive_to_Transit_tour_mode_ASC_ride_hail,Drive to Transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,,drive_transit_ASC_rh,drive_transit_ASC_rh,drive_transit_ASC_rh -util_Ride_Hail_tour_mode_ASC_shared_ride_2,Ride Hail tour mode ASC -- shared ride 2,@(df.is_indiv & df.tour_mode_is_ride_hail),,,ride_hail_ASC_sr2,ride_hail_ASC_sr2,,,,,,,,,,,,,,,,, -util_Ride_Hail_tour_mode_ASC_shared_ride_3_plus,Ride Hail tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,ride_hail_ASC_sr3p,ride_hail_ASC_sr3p,,,,,,,,,,,,,,, -util_Ride_Hail_tour_mode_ASC_walk,Ride Hail tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,ride_hail_ASC_walk,,,,,,,,,,,,,, -util_Ride_Hail_tour_mode_ASC_walk_to_transit,Ride Hail tour mode ASC -- walk to transit,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,,,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,,,,,,,, -util_Ride_Hail_tour_mode_ASC_ride_hail_taxi,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,ride_hail_ASC_taxi,, -util_Ride_Hail_tour_mode_ASC_ride_hail_single,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,ride_hail_ASC_tnc_single, -util_Ride_Hail_tour_mode_ASC_ride_hail_shared,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,,ride_hail_ASC_tnc_shared -#,joint tour ASCs,,,,,,,,,,,,,,,,,,,,,, -util_joint_auto_tour_mode_ASC_shared_ride_2,joint - auto tour mode ASC -- shared ride 2,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,joint_auto_ASC_sr2,joint_auto_ASC_sr2,,,,,,,,,,,,,,,,, -util_joint_auto_tour_mode_ASC_shared_ride_3_,joint - auto tour mode ASC -- shared ride 3+,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,,joint_auto_ASC_sr3p,joint_auto_ASC_sr3p,,,,,,,,,,,,,,, -util_joint_auto_tour_mode_ASC_walk,joint - auto tour mode ASC -- walk,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,,,,joint_auto_ASC_walk,,,,,,,,,,,,,, -util_joint_auto_tour_mode_ASC_ride_hail,joint - auto tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,joint_auto_ASC_rh,joint_auto_ASC_rh,joint_auto_ASC_rh -util_joint_Walk_tour_mode_ASC_ride_hail,joint - Walk tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,joint_walk_ASC_rh,,,,,,,,,,,,,, -util_joint_Bike_tour_mode_ASC_walk,joint - Bike tour mode ASC -- walk,@df.is_joint & (df.i_tour_mode == I_BIKE_MODE),,,,,,,joint_bike_ASC_walk,,,,,,,,,,,,,, -util_joint_Bike_tour_mode_ASC_ride_hail,joint - Bike tour mode ASC -- ride hail,@df.is_joint & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,,,,joint_bike_ASC_rh,joint_bike_ASC_rh,joint_bike_ASC_rh -util_joint_Walk_to_Transit_tour_mode_ASC_light_rail,joint - Walk to Transit tour mode ASC -- light rail,@(df.is_joint & df.tour_mode_is_walk_transit & ~df.walk_ferry_available),,,,,,,,,,joint_walk_transit_ASC_lightrail,,,,,,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_ferry,joint - Walk to Transit tour mode ASC -- ferry,@(df.is_joint & df.tour_mode_is_walk_transit & df.walk_ferry_available),,,,,,,,,,joint_walk_transit_ASC_ferry,,,,,,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_express_bus,joint - Walk to Transit tour mode ASC -- express bus,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,joint_walk_transit_ASC_express,,,,,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_heavy_rail,joint - Walk to Transit tour mode ASC -- heavy rail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,joint_walk_transit_ASC_heavyrail,,,,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_commuter_rail,joint - Walk to Transit tour mode ASC -- commuter rail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,,joint_walk_transit_ASC_commuter,,,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_shared_ride_2,joint - Walk to Transit tour mode ASC -- shared ride 2,@(df.is_joint & df.tour_mode_is_walk_transit),,,joint_walk_transit_ASC_sr2,joint_walk_transit_ASC_sr2,,,,,,,,,,,,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_shared_ride_3_plus,joint - Walk to Transit tour mode ASC -- shared ride 3+,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,joint_walk_transit_ASC_sr3p,joint_walk_transit_ASC_sr3p,,,,,,,,,,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_walk,joint - Walk to Transit tour mode ASC -- walk,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,joint_walk_transit_ASC_walk,,,,,,,,,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_ride_hail,joint - Walk to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,,,,,,,,joint_walk_transit_ASC_rh,joint_walk_transit_ASC_rh,joint_walk_transit_ASC_rh -util_joint_Drive_to_Transit_tour_mode_ASC_light_rail_skims_differ,joint - Drive to Transit tour mode ASC -- light rail (higher b/c loc d-trn skims differ),@(df.is_joint & df.tour_mode_is_drive_transit & ~df.drive_ferry_available),,,,,,,,,,,,,,,joint_drive_transit_ASC_lightrail,,,,,, -util_joint_Drive_to_Transit_tour_mode_ASC_ferry,joint - Drive to Transit tour mode ASC -- ferry,@(df.is_joint & df.tour_mode_is_drive_transit & df.drive_ferry_available),,,,,,,,,,,,,,,joint_drive_transit_ASC_ferry,,,,,, -util_joint_Drive_to_Transit_tour_mode_ASC_express_bus,joint - Drive to Transit tour mode ASC -- express bus,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,joint_drive_transit_ASC_express,,,,, -util_joint_Drive_to_Transit_tour_mode_ASC_heavy_rail,joint - Drive to Transit tour mode ASC -- heavy rail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,joint_drive_transit_ASC_heavyrail,,,, -util_joint_Drive_to_Transit_tour_mode_ASC_commuter_rail,joint - Drive to Transit tour mode ASC -- commuter rail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,joint_drive_transit_ASC_commuter,,, -util_joint_Drive_to_Transit_tour_mode_ASC_ride_hail,joint - Drive to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,,joint_drive_transit_ASC_rh,joint_drive_transit_ASC_rh,joint_drive_transit_ASC_rh -util_joint_Ride_Hail_tour_mode_ASC_shared_ride_2,joint - Ride Hail tour mode ASC -- shared ride 2,@(df.is_joint & df.tour_mode_is_ride_hail),,,joint_ride_hail_ASC_sr2,joint_ride_hail_ASC_sr2,,,,,,,,,,,,,,,,, -util_joint_Ride_Hail_tour_mode_ASC_shared_ride_3_plus,joint - Ride Hail tour mode ASC -- shared ride 3+,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,joint_ride_hail_ASC_sr3p,joint_ride_hail_ASC_sr3p,,,,,,,,,,,,,,, -util_joint_Ride_Hail_tour_mode_ASC_walk,joint - Ride Hail tour mode ASC -- walk,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,joint_ride_hail_ASC_walk,,,,,,,,,,,,,, -util_joint_Ride_Hail_tour_mode_ASC_walk_to_transit,joint - Ride Hail tour mode ASC -- walk to transit,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,,,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,,,,,,,, -util_joint_Ride_Hail_tour_mode_ASC_ride_hail_taxi,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_taxi,, -util_joint_Ride_Hail_tour_mode_ASC_ride_hail_single,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_tnc_single, -util_joint_Ride_Hail_tour_mode_ASC_ride_hail_shared,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_tnc_shared -#,#,,,,,,,,,,,,,,,,,,,,,, -util_Walk_not_available_for_long_distances,Walk not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTWALK'] > 3),,,,,,,-999,,,,,,,,,,,,,, -util_Bike_not_available_for_long_distances,Bike not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTBIKE'] > 8),,,,,,,,-999,,,,,,,,,,,,, -util_origin_density_index,Origin density index,@origin_density_applied*(origin_density_index_multiplier*df.origin_density_index).clip(origin_density_index_max),,,,,,,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,,,,,,,coef_ivt,coef_ivt -util_walk_express_penalty,Walk-express penalty for intermediate stops,@walk_express_penalty * ~(df.first_trip | df.first_trip),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_adjust_tnc_shared,TNC shared adjustment,@adjust_tnc_shared,,,,,,,,,,,,,,,,,,,,,coef_ivt +Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_LOC,WALK_LRF,WALK_EXP,WALK_HVY,WALK_COM,DRIVE_LOC,DRIVE_LRF,DRIVE_EXP,DRIVE_HVY,DRIVE_COM,TAXI,TNC_SINGLE,TNC_SHARED +#,Drive alone no toll,,,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,auto_ownership == 0,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Terminal_time,DRIVEALONEFREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['SOV_DIST'],coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost,coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOV_BTOLL'],coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Person_is_between_16_and_19_years_old,DRIVEALONEFREE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da,,,,,,,,,,,,,,,,,,,, +#,Drive alone toll,,,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,auto_ownership == 0,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,age < 16,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['SOVTOLL_TIME'],,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Terminal_time,DRIVEALONEPAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['SOVTOLL_DIST'],,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost,,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOVTOLL_BTOLL'],,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOVTOLL_VTOLL'],,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Person_is_between_16_and_19_years_old,DRIVEALONEPAY - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),,coef_age1619_da,,,,,,,,,,,,,,,,,,, +#,Shared ride 2,,,,,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@odt_skims['HOV2_TIME'],,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Terminal_time,SHARED2FREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV2_DIST'],,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2_BTOLL'] / costShareSr2,,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_One_person_household,SHARED2FREE - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Person_is_16_years_old_or_older,SHARED2FREE - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr,,,,,,,,,,,,,,,,,, +#,Shared ride 2 toll,,,,,,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,,,,,,,,,, +util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@odt_skims['HOV2TOLL_TIME'],,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Terminal_time,SHARED2PAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV2TOLL_DIST'],,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_BTOLL'] / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'] / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_One_person_household,SHARED2PAY - One person household,@(df.hhsize == 1),,,,coef_hhsize1_sr,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.hhsize == 2),,,,coef_hhsize2_sr,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Person_is_16_years_old_or_older,SHARED2PAY - Person is 16 years old or older,@(df.age >= 16),,,,coef_age16p_sr,,,,,,,,,,,,,,,,, +#,Shared ride 3+,,,,,,,,,,,,,,,,,,,,,, +util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,,,,,,,,,, +util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@odt_skims['HOV3_TIME'],,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_Terminal_time,SHARED3FREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV3_DIST'],,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3_BTOLL'] / costShareSr3,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_One_person_household,SHARED3FREE - One person household,@(df.hhsize == 1),,,,,coef_hhsize1_sr,,,,,,,,,,,,,,,, +util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.hhsize == 2),,,,,coef_hhsize2_sr,,,,,,,,,,,,,,,, +util_SHARED3FREE_Person_is_16_years_old_or_older,SHARED3FREE - Person is 16 years old or older,@(df.age >= 16),,,,,coef_age16p_sr,,,,,,,,,,,,,,,, +#,Shared ride 3+ toll,,,,,,,,,,,,,,,,,,,,,, +util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,,,,,,,,,, +util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@odt_skims['HOV3TOLL_TIME'],,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Terminal_time,SHARED3PAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV3TOLL_DIST'],,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3TOLL_BTOLL'] / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3TOLL_VTOLL'] / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_One_person_household,SHARED3PAY - One person household,@(df.hhsize == 1),,,,,,coef_hhsize1_sr,,,,,,,,,,,,,,, +util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.hhsize == 2),,,,,,coef_hhsize2_sr,,,,,,,,,,,,,,, +util_SHARED3PAY_Person_is_16_years_old_or_older,SHARED3PAY - Person is 16 years old or older,@(df.age >= 16),,,,,,coef_age16p_sr,,,,,,,,,,,,,,, +#,Walk,,,,,,,,,,,,,,,,,,,,,, +util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@coef_walktimeshort_multiplier * od_skims['DISTWALK'].clip(upper=walkThresh) * 60/walkSpeed,,,,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * (od_skims['DISTWALK'] - walkThresh).clip(lower=0) * 60/walkSpeed,,,,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Topology,WALK - Topology,@topology_walk_multiplier * df.trip_topology,,,,,,,coef_ivt,,,,,,,,,,,,,, +#,Bike,,,,,,,,,,,,,,,,,,,,,, +util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,,,,,,,,,, +util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@coef_biketimeshort_multiplier * od_skims['DISTBIKE'].clip(upper=bikeThresh)*60/bikeSpeed,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@coef_biketimeshort_multiplier * biketimelong_multiplier * (od_skims['DISTBIKE']-bikeThresh).clip(lower=0)*60/bikeSpeed,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier*df.density_index,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Topology,BIKE - Topology,@topology_bike_multiplier * df.trip_topology,,,,,,,,coef_ivt,,,,,,,,,,,,, +#,Walk to Local,,,,,,,,,,,,,,,,,,,,,, +util_WALK_LOC_Unavailable,WALK_LOC - Unavailable,walk_local_available == False,,,,,,,,,-999,,,,,,,,,,,, +util_WALK_LOC_In_vehicle_time,WALK_LOC - In-vehicle time,@odt_skims['WLK_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Short_iwait_time,WALK_LOC - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Long_iwait_time,WALK_LOC - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_transfer_wait_time,WALK_LOC - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_number_of_transfers,WALK_LOC - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_LOC_WLK_BOARDS']-1).clip(0),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_egress_time,WALK_LOC - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_other_time,WALK_LOC - Walk other time,@coef_waux_multiplier * odt_skims['WLK_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Fare,WALK_LOC - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_LOC_WLK_FAR'],,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Destination_zone_densityIndex,WALK_LOC - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Topology,WALK_LOC - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Person_is_less_than_10_years_old,WALK_LOC - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,coef_age010_trn,,,,,,,,,,,, +#,Walk to Light rail/Ferry,,,,,,,,,,,,,,,,,,,,,, +util_WALK_LRF_Unavailable,WALK_LRF - Unavailable,walk_lrf_available == False,,,,,,,,,,-999,,,,,,,,,,, +util_WALK_LRF_In_vehicle_time,WALK_LRF - In-vehicle time,@odt_skims['WLK_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_In_vehicle_time_on_Light_Rail,WALK_LRF - In-vehicle time on Light Rail (incremental w/ ivt),@(coef_ivt_lrt_multiplier-1) * odt_skims['WLK_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_In_vehicle_time_on_Ferry,WALK_LRF - In-vehicle time on Ferry (incremental w/keyivt),@(coef_ivt_ferry_multiplier-coef_ivt_lrt_multiplier) * odt_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Short_iwait_time,WALK_LRF - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Long_iwait_time,WALK_LRF - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_transfer_wait_time,WALK_LRF - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_number_of_transfers,WALK_LRF - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_LRF_WLK_BOARDS']-1).clip(0),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Walk_access_time,WALK_LRF - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Walk_egress_time,WALK_LRF - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Walk_other_time,WALK_LRF - Walk otherLight rail/Ferry time,@coef_waux_multiplier * odt_skims['WLK_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Fare,WALK_LRF - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_LRF_WLK_FAR'],,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Destination_zone_densityIndex,WALK_LRF - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Topology,WALK_LRF - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Person_is_less_than_10_years_old,WALK_LRF - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,coef_age010_trn,,,,,,,,,,, +#,Walk to Express bus,,,,,,,,,,,,,,,,,,,,,, +util_WALK_EXP_Unavailable,WALK_EXP - Unavailable,walk_express_available == False,,,,,,,,,,,-999,,,,,,,,,, +util_WALK_EXP_In_vehicle_time,WALK_EXP - In-vehicle time,@odt_skims['WLK_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_In_vehicle_time_on_Express_bus,WALK_EXP - In-vehicle time on Express bus (incremental w/ ivt),@(ivt_exp_multiplier - 1) * odt_skims['WLK_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Short_iwait_time,WALK_EXP - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Long_iwait_time,WALK_EXP - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_transfer_wait_time,WALK_EXP - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_number_of_transfers,WALK_EXP - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_EXP_WLK_BOARDS']-1).clip(0),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Walk_access_time,WALK_EXP - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Walk_egress_time,WALK_EXP - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Walk_other_time,WALK_EXP - Walk other time,@coef_waux_multiplier * odt_skims['WLK_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Fare,WALK_EXP - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_EXP_WLK_FAR'],,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Destination_zone_densityIndex,WALK_EXP - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Topology,WALK_EXP - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Person_is_less_than_10_years_old,WALK_EXP - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,,coef_age010_trn,,,,,,,,,, +#,Walk to Heavy Rail,,,,,,,,,,,,,,,,,,,,,, +util_WALK_HVY_Unavailable,WALK_HVY - Unavailable,walk_heavyrail_available == False,,,,,,,,,,,,-999,,,,,,,,, +util_WALK_HVY_In_vehicle_time,WALK_HVY - In-vehicle time,@odt_skims['WLK_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_In_vehicle_time_on_heavy_rail,WALK_HVY - In-vehicle time on heavy rail (incremental w/ ivt),@(ivt_hvy_multiplier-1) * odt_skims['WLK_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Short_iwait_time,WALK_HVY - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Long_iwait_time,WALK_HVY - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_transfer_wait_time,WALK_HVY - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_number_of_transfers,WALK_HVY - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_HVY_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Walk_access_time,WALK_HVY - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Walk_egress_time,WALK_HVY - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Walk_other_time,WALK_HVY - Walk other time,@coef_waux_multiplier * odt_skims['WLK_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Fare,WALK_HVY - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_HVY_WLK_FAR'],,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Destination_zone_densityIndex,WALK_HVY - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Topology,WALK_HVY - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Person_is_less_than_10_years_old,WALK_HVY - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,,,coef_age010_trn,,,,,,,,, +#,Walk to Commuter rail,,,,,,,,,,,,,,,,,,,,,, +util_WALK_COM_Unavailable,WALK_COM - Unavailable,walk_commuter_available == False,,,,,,,,,,,,,-999,,,,,,,, +util_WALK_COM_In_vehicle_time,WALK_COM - In-vehicle time,@odt_skims['WLK_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_In_vehicle_time_on_commuter_rail,WALK_COM - In-vehicle time on commuter rail (incremental w/ ivt),@(ivt_com_multiplier - 1) * odt_skims['WLK_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Short_iwait_time,WALK_COM - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Long_iwait_time,WALK_COM - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_transfer_wait_time,WALK_COM - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_number_of_transfers,WALK_COM - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_COM_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Walk_access_time,WALK_COM - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Walk_egress_time,WALK_COM - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Walk_other_time,WALK_COM - Walk other time,@coef_waux_multiplier * odt_skims['WLK_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Fare,WALK_COM - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_COM_WLK_FAR'],,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Destination_zone_densityIndex,WALK_COM - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Topology,WALK_COM - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Person_is_less_than_10_years_old,WALK_COM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,coef_age010_trn,,,,,,,, +#,Drive to Local,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_LOC_Unavailable_for_zero_auto_households,DRIVE_LOC - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,-999,,,,,,, +util_DRIVE_LOC_Unavailable_for_persons_less_than_16,DRIVE_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,-999,,,,,,, +util_DRIVE_LOC_Destination_zone_densityIndex,DRIVE_LOC - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Topology,DRIVE_LOC - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Person_is_less_than_10_years_old,DRIVE_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,coef_age010_trn,,,,,,, +util_DRIVE_LOC_outbound_Unavailable,DRIVE_LOC outbound - Unavailable,outbound & ~drive_local_available_outbound,,,,,,,,,,,,,,-999,,,,,,, +util_DRIVE_LOC_outbound_In_vehicle_time,DRIVE_LOC outbound - In-vehicle time,@df.outbound * odt_skims['DRV_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_Short_iwait_time,DRIVE_LOC outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_Long_iwait_time,DRIVE_LOC outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_transfer_wait_time,DRIVE_LOC outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_number_of_transfers,DRIVE_LOC outbound - number of transfers,@df.outbound * xfers_wlk_multiplier * (odt_skims['DRV_LOC_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_Drive_time,DRIVE_LOC outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_LOC_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_Walk_egress_time,DRIVE_LOC outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_Walk_other_time,DRIVE_LOC outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_Fare_and_operating_cost,DRIVE_LOC outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_LOC_WLK_FAR'] + costPerMile*odt_skims['DRV_LOC_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LOC outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_LOC_WLK_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']),,,,,,,,,,,,,,1,,,,,,, +util_DRIVE_LOC_inbound_Unavailable,DRIVE_LOC inbound - Unavailable,inbound & ~drive_local_available_inbound,,,,,,,,,,,,,,-999,,,,,,, +util_DRIVE_LOC_inbound_In_vehicle_time,DRIVE_LOC inbound - In-vehicle time,@df.inbound * odt_skims['WLK_LOC_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_Short_iwait_time,DRIVE_LOC inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_LOC_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_Long_iwait_time,DRIVE_LOC inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_LOC_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_transfer_wait_time,DRIVE_LOC inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_LOC_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_number_of_transfers,DRIVE_LOC inbound - number of transfers,@df.inbound * xfers_wlk_multiplier * (odt_skims['WLK_LOC_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_Drive_time,DRIVE_LOC inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WLK_LOC_DRV_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_Walk_access_time,DRIVE_LOC inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_Walk_other_time,DRIVE_LOC inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WLK_LOC_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_Fare_and_operating_cost,DRIVE_LOC inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_LOC_DRV_FAR'] + costPerMile*odt_skims['WLK_LOC_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LOC inbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['WLK_LOC_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']),,,,,,,,,,,,,,1,,,,,,, +#,Drive to Light Rail/Ferry,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_LRF_Unavailable_for_zero_auto_households,DRIVE_LRF - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,-999,,,,,, +util_DRIVE_LRF_Unavailable_for_persons_less_than_16,DRIVE_LRF - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,-999,,,,,, +util_DRIVE_LRF_Destination_zone_densityIndex,DRIVE_LRF - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Topology,DRIVE_LRF - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Person_is_less_than_10_years_old,DRIVE_LRF - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,coef_age010_trn,,,,,, +util_DRIVE_LRF_outbound_Unavailable,DRIVE_LRF outbound - Unavailable,outbound & ~drive_lrf_available_outbound,,,,,,,,,,,,,,,-999,,,,,, +util_DRIVE_LRF_outbound_In_vehicle_time,DRIVE_LRF outbound - In-vehicle time,@df.outbound * odt_skims['DRV_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_In_vehicle_time_on_LR,DRIVE_LRF outbound - In-vehicle time on Light Rail (incremental w/ ivt),@df.outbound * (coef_ivt_lrt_multiplier - 1)*odt_skims['DRV_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_In_vehicle_time_on_Ferry,DRIVE_LRF outbound - In-vehicle time on Ferry (incremental w/ keyivt),@df.outbound * (coef_ivt_ferry_multiplier-coef_ivt_lrt_multiplier)*odt_skims['DRV_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_Short_iwait_time,DRIVE_LRF outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_Long_iwait_time,DRIVE_LRF outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_transfer_wait_time,DRIVE_LRF outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_number_of_transfers,DRIVE_LRF outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DRV_LRF_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_Drive_time,DRIVE_LRF outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_LRF_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_Walk_egress_time,DRIVE_LRF outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_Walk_other_time,DRIVE_LRF outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_Fare_and_operating_cost,DRIVE_LRF outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_LRF_WLK_FAR'] + costPerMile * odt_skims['DRV_LRF_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LRF outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_LRF_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,1,,,,,, +util_DRIVE_LRF_inbound_Unavailable,DRIVE_LRF inbound - Unavailable,inbound & ~drive_lrf_available_inbound,,,,,,,,,,,,,,,-999,,,,,, +util_DRIVE_LRF_inbound_In_vehicle_time,DRIVE_LRF inbound - In-vehicle time,@df.inbound * odt_skims['WLK_LRF_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_In_vehicle_time_on_LR,DRIVE_LRF inbound - In-vehicle time on Light Rail (incremental w/ ivt),@df.inbound * (coef_ivt_lrt_multiplier - 1)*odt_skims['WLK_LRF_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_In_vehicle_time_on_Ferry,DRIVE_LRF inbound - In-vehicle time on Ferry (incremental w/ keyivt),@df.inbound * (coef_ivt_ferry_multiplier-coef_ivt_lrt_multiplier)*odt_skims['WLK_LRF_DRV_FERRYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_Short_iwait_time,DRIVE_LRF inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_LRF_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_Long_iwait_time,DRIVE_LRF inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_LRF_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_transfer_wait_time,DRIVE_LRF inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_LRF_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_number_of_transfers,DRIVE_LRF inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WLK_LRF_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_Drive_time,DRIVE_LRF inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WLK_LRF_DRV_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_Walk_access_time,DRIVE_LRF inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_Walk_other_time,DRIVE_LRF inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WLK_LRF_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_Fare_and_operating_cost,DRIVE_LRF inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_LRF_DRV_FAR'] + costPerMile * odt_skims['WLK_LRF_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LRF inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WLK_LRF_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ od_skims['DIST'],,,,,,,,,,,,,,,1,,,,,, +#,Drive to Express bus,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_EXP_Unavailable_for_zero_auto_households,DRIVE_EXP - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,-999,,,,, +util_DRIVE_EXP_Unavailable_for_persons_less_than_16,DRIVE_EXP - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,-999,,,,, +util_DRIVE_EXP_Destination_zone_densityIndex,DRIVE_EXP - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Topology,DRIVE_EXP - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Person_is_less_than_10_years_old,DRIVE_EXP - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,coef_age010_trn,,,,, +util_DRIVE_EXP_outbound_Unavailable,DRIVE_EXP outbound - Unavailable,outbound & ~drive_express_available_outbound,,,,,,,,,,,,,,,,-999,,,,, +util_DRIVE_EXP_outbound_In_vehicle_time,DRIVE_EXP outbound - In-vehicle time,@df.outbound * odt_skims['DRV_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_In_vehicle_time_on_EXP,DRIVE_EXP outbound - In-vehicle time on Express bus (incremental w/ ivt),@df.outbound * (ivt_exp_multiplier - 1) * odt_skims['DRV_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_Short_iwait_time,DRIVE_EXP outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_Long_iwait_time,DRIVE_EXP outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_transfer_wait_time,DRIVE_EXP outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_number_of_transfers,DRIVE_EXP outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DRV_EXP_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_Drive_time,DRIVE_EXP outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_EXP_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_Walk_egress_time,DRIVE_EXP outbound - Walk egress ime,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_Walk_other_time,DRIVE_EXP outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_Fare_and_operating_cost,DRIVE_EXP outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_EXP_WLK_FAR'] + costPerMile * odt_skims['DRV_EXP_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_EXP outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_EXP_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_Unavailable,DRIVE_EXP inbound - Unavailable,inbound & ~drive_express_available_inbound,,,,,,,,,,,,,,,,-999,,,,, +util_DRIVE_EXP_inbound_In_vehicle_time,DRIVE_EXP inbound - In-vehicle time,@df.inbound * odt_skims['WLK_EXP_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_In_vehicle_time_on_EXP,DRIVE_EXP inbound - In-vehicle time on Express bus (incremental w/ ivt),@df.inbound * (ivt_exp_multiplier - 1) * odt_skims['WLK_EXP_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_Short_iwait_time,DRIVE_EXP inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_EXP_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_Long_iwait_time,DRIVE_EXP inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_EXP_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_transfer_wait_time,DRIVE_EXP inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_EXP_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_number_of_transfers,DRIVE_EXP inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WLK_EXP_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_Drive_time,DRIVE_EXP inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WLK_EXP_DRV_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_Walk_access_time,DRIVE_EXP inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_Walk_other_time,DRIVE_EXP inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WLK_EXP_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_Fare_and_operating_cost,DRIVE_EXP inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_EXP_DRV_FAR'] + costPerMile * odt_skims['WLK_EXP_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_EXP inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WLK_EXP_DRV_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,1,,,,, +#,Drive to Heavy Rail,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_HVY_Unavailable_for_zero_auto_households,DRIVE_HVY - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,,-999,,,, +util_DRIVE_HVY_Unavailable_for_persons_less_than_16,DRIVE_HVY - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,-999,,,, +util_DRIVE_HVY_Destination_zone_densityIndex,DRIVE_HVY - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Topology,DRIVE_HVY - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Person_is_less_than_10_years_old,DRIVE_HVY - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,,coef_age010_trn,,,, +util_DRIVE_HVY_outbound_Unavailable,DRIVE_HVY outbound - Unavailable,outbound & ~drive_heavyrail_available_outbound,,,,,,,,,,,,,,,,,-999,,,, +util_DRIVE_HVY_outbound_In_vehicle_time,DRIVE_HVY outbound - In-vehicle time,@df.outbound * odt_skims['DRV_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_In_vehicle_time_on_HVY,DRIVE_HVY outbound - In-vehicle time on heavy rail (incremental w/ ivt),@df.outbound * (ivt_hvy_multiplier - 1) * odt_skims['DRV_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_Short_iwait_time,DRIVE_HVY outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_Long_iwait_time,DRIVE_HVY outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_transfer_wait_time,DRIVE_HVY outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_number_of_transfers,DRIVE_HVY outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DRV_HVY_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_Drive_time,DRIVE_HVY outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_HVY_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_Walk_egress_time,DRIVE_HVY outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_Walk_other_time,DRIVE_HVY outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_Fare_and_operating_cost,DRIVE_HVY outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_HVY_WLK_FAR'] + costPerMile * odt_skims['DRV_HVY_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_HVY outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_HVY_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_Unavailable,DRIVE_HVY inbound - Unavailable,inbound & ~drive_heavyrail_available_inbound,,,,,,,,,,,,,,,,,-999,,,, +util_DRIVE_HVY_inbound_In_vehicle_time,DRIVE_HVY inbound - In-vehicle time,@df.inbound * odt_skims['WLK_HVY_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_In_vehicle_time_on_HVY,DRIVE_HVY inbound - In-vehicle time on heavy rail (incremental w/ ivt),@df.inbound * (ivt_hvy_multiplier - 1) * odt_skims['WLK_HVY_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_Short_iwait_time,DRIVE_HVY inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_HVY_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_Long_iwait_time,DRIVE_HVY inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_HVY_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_transfer_wait_time,DRIVE_HVY inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_HVY_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_number_of_transfers,DRIVE_HVY inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WLK_HVY_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_Drive_time,DRIVE_HVY inbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_HVY_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_Walk_access_time,DRIVE_HVY inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_Walk_other_time,DRIVE_HVY inbound - Walk other time,@coef_waux_multiplier * odt_skims['WLK_HVY_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_Fare_and_operating_cost,DRIVE_HVY inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_HVY_DRV_FAR'] + costPerMile * odt_skims['WLK_HVY_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_HVY inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WLK_HVY_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ od_skims['DIST'],,,,,,,,,,,,,,,,,1,,,, +#,#Drive to Commuter Rail,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_COM_Unavailable_for_zero_auto_households,DRIVE_COM - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,,,-999,,, +util_DRIVE_COM_Unavailable_for_persons_less_than_16,DRIVE_COM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,,-999,,, +util_DRIVE_COM_Destination_zone_densityIndex,DRIVE_COM - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Topology,DRIVE_COM - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Person_is_less_than_10_years_old,DRIVE_COM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,,,coef_age010_trn,,, +util_DRIVE_COM_outbound_Unavailable,DRIVE_COM outbound - Unavailable,outbound & ~drive_commuter_available_outbound,,,,,,,,,,,,,,,,,,-999,,, +util_DRIVE_COM_outbound_In_vehicle_time,DRIVE_COM outbound - In-vehicle time,@df.outbound * odt_skims['DRV_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_In_vehicle_time_on_COM,DRIVE_COM outbound - In-vehicle time on commuter rail (incremental w/ ivt),@df.outbound * (ivt_com_multiplier - 1) * odt_skims['DRV_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_Short_iwait_time,DRIVE_COM outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_Long_iwait_time,DRIVE_COM outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_transfer_wait_time,DRIVE_COM outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_number_of_transfers,DRIVE_COM outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DRV_COM_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_Drive_time,DRIVE_COM outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_COM_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_Walk_egress_time,DRIVE_COM outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_Walk_other_time,DRIVE_COM outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_Fare_and_operating_cost,DRIVE_COM outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_COM_WLK_FAR'] + costPerMile * odt_skims['DRV_COM_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_COM outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_COM_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,,,1,,, +util_DRIVE_COM_inbound_Unavailable,DRIVE_COM inbound - Unavailable,inbound & ~drive_commuter_available_inbound,,,,,,,,,,,,,,,,,,-999,,, +util_DRIVE_COM_inbound_In_vehicle_time,DRIVE_COM inbound - In-vehicle time,@df.inbound * odt_skims['WLK_COM_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_In_vehicle_time_on_COM,DRIVE_COM inbound - In-vehicle time on commuter rail (incremental w/ ivt),@df.inbound * (ivt_com_multiplier - 1) * odt_skims['WLK_COM_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_Short_iwait_time,DRIVE_COM inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_COM_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_Long_iwait_time,DRIVE_COM inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_COM_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_transfer_wait_time,DRIVE_COM inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_COM_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_number_of_transfers,DRIVE_COM inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WLK_COM_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_Drive_time,DRIVE_COM inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WLK_COM_DRV_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_Walk_access_time,DRIVE_COM inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_Walk_other_time,DRIVE_COM inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WLK_COM_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_Fare_and_operating_cost,DRIVE_COM inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_COM_DRV_FAR'] + costPerMile * odt_skims['WLK_COM_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_COM inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WLK_COM_DRV_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,,,1,,, +#,Taxi,,,,,,,,,,,,,,,,,,,,,, +util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@odt_skims['HOV2TOLL_TIME'],,,,,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Wait_time,Taxi - Wait time,@ridehail_wait_time_multiplier * df.origTaxiWaitTime,,,,,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_BTOLL'],,,,,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare + odt_skims['HOV2TOLL_DIST'] * Taxi_costPerMile + odt_skims['HOV2TOLL_TIME'] * Taxi_costPerMinute)*100,,,,,,,,,,,,,,,,,,,coef_ivt,, +#,TNC Single,,,,,,,,,,,,,,,,,,,,,, +util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@odt_skims['HOV2TOLL_TIME'] ,,,,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Wait_time,TNC Single - Wait time,@ridehail_wait_time_multiplier * df.origSingleTNCWaitTime,,,,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare + odt_skims['HOV2TOLL_DIST'] * TNC_single_costPerMile + odt_skims['HOV2TOLL_TIME'] * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,coef_ivt, +#,#TNC Shared,,,,,,,,,,,,,,,,,,,,,, +util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@odt_skims['HOV2TOLL_TIME'] * TNC_shared_IVTFactor,,,,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Wait_time,TNC Shared - Wait time,@ridehail_wait_time_multiplier * df.origSharedTNCWaitTime,,,,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare + odt_skims['HOV2TOLL_DIST'] * TNC_shared_costPerMile + odt_skims['HOV2TOLL_TIME']* TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,,coef_ivt +#,,,,,,,,,,,,,,,,,,,,,,, +util_tour_mode_is_auto,Auto tour mode availability,tour_mode_is_auto,,,,,,,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, +util_tour_mode_is_walk,Walk tour mode availability,tour_mode_is_walk,-999,-999,-999,-999,-999,-999,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, +util_tour_mode_is_bike,Bike tour mode availability,tour_mode_is_bike,-999,-999,-999,-999,-999,-999,,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, +util_tour_mode_is_walk_transit,Walk to Transit tour mode availability,tour_mode_is_walk_transit,-999,-999,,,,,,-999,,,,,,-999,-999,-999,-999,-999,,, +util_tour_mode_is_drive_transit,Drive to Transit tour modes availability,tour_mode_is_drive_transit,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,,,,,,, +util_tour_mode_is_ride_hail,Ride hail tour modes availability,tour_mode_is_ride_hail,-999,-999,,,,,,-999,,,,,,-999,-999,-999,-999,-999,,, +,#indiv tour ASCs,,,,,,,,,,,,,,,,,,,,,, +util_Drive_Alone_tour_mode_ASC_shared_ride_2_df_is_indiv,Drive Alone tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,sov_ASC_sr2,sov_ASC_sr2,,,,,,,,,,,,,,,,, +util_Drive_Alone_tour_mode_ASC_shared_ride_3_plus,Drive Alone tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,sov_ASC_sr3p,sov_ASC_sr3p,,,,,,,,,,,,,,, +util_Drive_Alone_tour_mode_ASC_walk,Drive Alone tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,sov_ASC_walk,,,,,,,,,,,,,, +util_Drive_Alone_tour_mode_ASC_ride_hail,Drive Alone tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,,,,,,,,,,,,,sov_ASC_rh,sov_ASC_rh,sov_ASC_rh +util_Shared_Ride_2_tour_mode_ASC_shared_ride_2,Shared Ride 2 tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,sr2_ASC_sr2,sr2_ASC_sr2,,,,,,,,,,,,,,,,, +util_Shared_Ride_2_tour_mode_ASC_shared_ride_3_plus,Shared Ride 2 tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,sr2_ASC_sr3p,sr2_ASC_sr3p,,,,,,,,,,,,,,, +util_Shared_Ride_2_tour_mode_ASC_walk,Shared Ride 2 tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,sr2_ASC_walk,,,,,,,,,,,,,, +util_Shared_Ride_2_tour_mode_ASC_ride_hail,Shared Ride 2 tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,,,,,,,,,,,,,sr2_ASC_rh,sr2_ASC_rh,sr2_ASC_rh +util_Shared_Ride_3_tour_mode_ASC_shared_ride_2,Shared Ride 3+ tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,sr3p_ASC_sr2,sr3p_ASC_sr2,,,,,,,,,,,,,,,,, +util_Shared_Ride_3_tour_mode_ASC_shared_ride_3_plus,Shared Ride 3+ tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,sr3p_ASC_sr3p,sr3p_ASC_sr3p,,,,,,,,,,,,,,, +util_Shared_Ride_3_tour_mode_ASC_walk,Shared Ride 3+ tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,sr3p_ASC_walk,,,,,,,,,,,,,, +util_Shared_Ride_3_tour_mode_ASC_ride_hail,Shared Ride 3+ tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,,,,,,,,,,,,,sr3p_ASC_rh,sr3p_ASC_rh,sr3p_ASC_rh +util_Walk_tour_mode_ASC_ride_hail,Walk tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_WALK_MODE),,,,,,,,,,,,,,,,,,,walk_ASC_rh,walk_ASC_rh,walk_ASC_rh +util_Bike_tour_mode_ASC_walk,Bike tour mode ASC -- walk,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,,,,bike_ASC_walk,,,,,,,,,,,,,, +util_Bike_tour_mode_ASC_ride_hail,Bike tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,,,,bike_ASC_rh,bike_ASC_rh,bike_ASC_rh +util_Walk_to_Transit_tour_mode_ASC_light_rail,Walk to Transit tour mode ASC -- light rail,@(df.is_indiv & df.tour_mode_is_walk_transit & ~df.walk_ferry_available),,,,,,,,,,walk_transit_ASC_lightrail,,,,,,,,,,, +util_Walk_to_Transit_tour_mode_ASC_ferry,Walk to Transit tour mode ASC -- ferry,@(df.is_indiv & df.tour_mode_is_walk_transit & df.walk_ferry_available),,,,,,,,,,walk_transit_ASC_ferry,,,,,,,,,,, +util_Walk_to_Transit_tour_mode_ASC_express_bus,Walk to Transit tour mode ASC -- express bus,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,walk_transit_ASC_express,,,,,,,,,, +util_Walk_to_Transit_tour_mode_ASC_heavy_rail,Walk to Transit tour mode ASC -- heavy rail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,walk_transit_ASC_heavyrail,,,,,,,,, +util_Walk_to_Transit_tour_mode_ASC_commuter_rail,Walk to Transit tour mode ASC -- commuter rail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,,walk_transit_ASC_commuter,,,,,,,, +util_Walk_to_Transit_tour_mode_ASC_shared_ride_2,Walk to Transit tour mode ASC -- shared ride 2,@(df.is_indiv & df.tour_mode_is_walk_transit),,,walk_transit_ASC_sr2,walk_transit_ASC_sr2,,,,,,,,,,,,,,,,, +util_Walk_to_Transit_tour_mode_ASC_shared_ride_3_plus,Walk to Transit tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,walk_transit_ASC_sr3p,walk_transit_ASC_sr3p,,,,,,,,,,,,,,, +util_Walk_to_Transit_tour_mode_ASC_walk,Walk to Transit tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,walk_transit_ASC_walk,,,,,,,,,,,,,, +util_Walk_to_Transit_tour_mode_ASC_ride_hail,Walk to Transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,,,,,,,,walk_transit_ASC_rh,walk_transit_ASC_rh,walk_transit_ASC_rh +util_Drive_to_Transit_tour_mode_ASC_light_rail_skims_differ,Drive to Transit tour mode ASC -- light rail (higher b/c loc d-trn skims differ),@(df.is_indiv & df.tour_mode_is_drive_transit & ~df.drive_ferry_available),,,,,,,,,,,,,,,drive_transit_ASC_lightrail,,,,,, +util_Drive_to_Transit_tour_mode_ASC_ferry,Drive to Transit tour mode ASC -- ferry,@(df.is_indiv & df.tour_mode_is_drive_transit & df.drive_ferry_available),,,,,,,,,,,,,,,drive_transit_ASC_ferry,,,,,, +util_Drive_to_Transit_tour_mode_ASC_express_bus,Drive to Transit tour mode ASC -- express bus,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,drive_transit_ASC_express,,,,, +util_Drive_to_Transit_tour_mode_ASC_heavy_rail,Drive to Transit tour mode ASC -- heavy rail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,drive_transit_ASC_heavyrail,,,, +util_Drive_to_Transit_tour_mode_ASC_commuter_rail,Drive to Transit tour mode ASC -- commuter rail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,drive_transit_ASC_commuter,,, +util_Drive_to_Transit_tour_mode_ASC_ride_hail,Drive to Transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,,drive_transit_ASC_rh,drive_transit_ASC_rh,drive_transit_ASC_rh +util_Ride_Hail_tour_mode_ASC_shared_ride_2,Ride Hail tour mode ASC -- shared ride 2,@(df.is_indiv & df.tour_mode_is_ride_hail),,,ride_hail_ASC_sr2,ride_hail_ASC_sr2,,,,,,,,,,,,,,,,, +util_Ride_Hail_tour_mode_ASC_shared_ride_3_plus,Ride Hail tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,ride_hail_ASC_sr3p,ride_hail_ASC_sr3p,,,,,,,,,,,,,,, +util_Ride_Hail_tour_mode_ASC_walk,Ride Hail tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,ride_hail_ASC_walk,,,,,,,,,,,,,, +util_Ride_Hail_tour_mode_ASC_walk_to_transit,Ride Hail tour mode ASC -- walk to transit,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,,,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,,,,,,,, +util_Ride_Hail_tour_mode_ASC_ride_hail_taxi,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,ride_hail_ASC_taxi,, +util_Ride_Hail_tour_mode_ASC_ride_hail_single,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,ride_hail_ASC_tnc_single, +util_Ride_Hail_tour_mode_ASC_ride_hail_shared,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,,ride_hail_ASC_tnc_shared +#,joint tour ASCs,,,,,,,,,,,,,,,,,,,,,, +util_joint_auto_tour_mode_ASC_shared_ride_2,joint - auto tour mode ASC -- shared ride 2,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,joint_auto_ASC_sr2,joint_auto_ASC_sr2,,,,,,,,,,,,,,,,, +util_joint_auto_tour_mode_ASC_shared_ride_3_,joint - auto tour mode ASC -- shared ride 3+,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,,joint_auto_ASC_sr3p,joint_auto_ASC_sr3p,,,,,,,,,,,,,,, +util_joint_auto_tour_mode_ASC_walk,joint - auto tour mode ASC -- walk,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,,,,joint_auto_ASC_walk,,,,,,,,,,,,,, +util_joint_auto_tour_mode_ASC_ride_hail,joint - auto tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,joint_auto_ASC_rh,joint_auto_ASC_rh,joint_auto_ASC_rh +util_joint_Walk_tour_mode_ASC_ride_hail,joint - Walk tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,joint_walk_ASC_rh,,,,,,,,,,,,,, +util_joint_Bike_tour_mode_ASC_walk,joint - Bike tour mode ASC -- walk,@df.is_joint & (df.i_tour_mode == I_BIKE_MODE),,,,,,,joint_bike_ASC_walk,,,,,,,,,,,,,, +util_joint_Bike_tour_mode_ASC_ride_hail,joint - Bike tour mode ASC -- ride hail,@df.is_joint & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,,,,joint_bike_ASC_rh,joint_bike_ASC_rh,joint_bike_ASC_rh +util_joint_Walk_to_Transit_tour_mode_ASC_light_rail,joint - Walk to Transit tour mode ASC -- light rail,@(df.is_joint & df.tour_mode_is_walk_transit & ~df.walk_ferry_available),,,,,,,,,,joint_walk_transit_ASC_lightrail,,,,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_ferry,joint - Walk to Transit tour mode ASC -- ferry,@(df.is_joint & df.tour_mode_is_walk_transit & df.walk_ferry_available),,,,,,,,,,joint_walk_transit_ASC_ferry,,,,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_express_bus,joint - Walk to Transit tour mode ASC -- express bus,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,joint_walk_transit_ASC_express,,,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_heavy_rail,joint - Walk to Transit tour mode ASC -- heavy rail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,joint_walk_transit_ASC_heavyrail,,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_commuter_rail,joint - Walk to Transit tour mode ASC -- commuter rail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,,joint_walk_transit_ASC_commuter,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_shared_ride_2,joint - Walk to Transit tour mode ASC -- shared ride 2,@(df.is_joint & df.tour_mode_is_walk_transit),,,joint_walk_transit_ASC_sr2,joint_walk_transit_ASC_sr2,,,,,,,,,,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_shared_ride_3_plus,joint - Walk to Transit tour mode ASC -- shared ride 3+,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,joint_walk_transit_ASC_sr3p,joint_walk_transit_ASC_sr3p,,,,,,,,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_walk,joint - Walk to Transit tour mode ASC -- walk,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,joint_walk_transit_ASC_walk,,,,,,,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_ride_hail,joint - Walk to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,,,,,,,,joint_walk_transit_ASC_rh,joint_walk_transit_ASC_rh,joint_walk_transit_ASC_rh +util_joint_Drive_to_Transit_tour_mode_ASC_light_rail_skims_differ,joint - Drive to Transit tour mode ASC -- light rail (higher b/c loc d-trn skims differ),@(df.is_joint & df.tour_mode_is_drive_transit & ~df.drive_ferry_available),,,,,,,,,,,,,,,joint_drive_transit_ASC_lightrail,,,,,, +util_joint_Drive_to_Transit_tour_mode_ASC_ferry,joint - Drive to Transit tour mode ASC -- ferry,@(df.is_joint & df.tour_mode_is_drive_transit & df.drive_ferry_available),,,,,,,,,,,,,,,joint_drive_transit_ASC_ferry,,,,,, +util_joint_Drive_to_Transit_tour_mode_ASC_express_bus,joint - Drive to Transit tour mode ASC -- express bus,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,joint_drive_transit_ASC_express,,,,, +util_joint_Drive_to_Transit_tour_mode_ASC_heavy_rail,joint - Drive to Transit tour mode ASC -- heavy rail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,joint_drive_transit_ASC_heavyrail,,,, +util_joint_Drive_to_Transit_tour_mode_ASC_commuter_rail,joint - Drive to Transit tour mode ASC -- commuter rail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,joint_drive_transit_ASC_commuter,,, +util_joint_Drive_to_Transit_tour_mode_ASC_ride_hail,joint - Drive to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,,joint_drive_transit_ASC_rh,joint_drive_transit_ASC_rh,joint_drive_transit_ASC_rh +util_joint_Ride_Hail_tour_mode_ASC_shared_ride_2,joint - Ride Hail tour mode ASC -- shared ride 2,@(df.is_joint & df.tour_mode_is_ride_hail),,,joint_ride_hail_ASC_sr2,joint_ride_hail_ASC_sr2,,,,,,,,,,,,,,,,, +util_joint_Ride_Hail_tour_mode_ASC_shared_ride_3_plus,joint - Ride Hail tour mode ASC -- shared ride 3+,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,joint_ride_hail_ASC_sr3p,joint_ride_hail_ASC_sr3p,,,,,,,,,,,,,,, +util_joint_Ride_Hail_tour_mode_ASC_walk,joint - Ride Hail tour mode ASC -- walk,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,joint_ride_hail_ASC_walk,,,,,,,,,,,,,, +util_joint_Ride_Hail_tour_mode_ASC_walk_to_transit,joint - Ride Hail tour mode ASC -- walk to transit,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,,,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,,,,,,,, +util_joint_Ride_Hail_tour_mode_ASC_ride_hail_taxi,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_taxi,, +util_joint_Ride_Hail_tour_mode_ASC_ride_hail_single,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_tnc_single, +util_joint_Ride_Hail_tour_mode_ASC_ride_hail_shared,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_tnc_shared +#,#,,,,,,,,,,,,,,,,,,,,,, +util_Walk_not_available_for_long_distances,Walk not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTWALK'] > 3),,,,,,,-999,,,,,,,,,,,,,, +util_Bike_not_available_for_long_distances,Bike not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTBIKE'] > 8),,,,,,,,-999,,,,,,,,,,,,, +util_origin_density_index,Origin density index,@origin_density_applied*(origin_density_index_multiplier*df.origin_density_index).clip(origin_density_index_max),,,,,,,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,,,,,,,coef_ivt,coef_ivt +util_walk_express_penalty,Walk-express penalty for intermediate stops,@walk_express_penalty * ~(df.first_trip | df.first_trip),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_adjust_tnc_shared,TNC shared adjustment,@adjust_tnc_shared,,,,,,,,,,,,,,,,,,,,,coef_ivt diff --git a/activitysim/examples/example_mtc/configs/trip_mode_choice.yaml b/activitysim/examples/placeholder_psrc/configs/trip_mode_choice.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/trip_mode_choice.yaml rename to activitysim/examples/placeholder_psrc/configs/trip_mode_choice.yaml diff --git a/activitysim/examples/example_psrc/configs/trip_mode_choice_annotate_trips_preprocessor.csv b/activitysim/examples/placeholder_psrc/configs/trip_mode_choice_annotate_trips_preprocessor.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/trip_mode_choice_annotate_trips_preprocessor.csv rename to activitysim/examples/placeholder_psrc/configs/trip_mode_choice_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/example_mtc/configs/trip_mode_choice_coefficients.csv b/activitysim/examples/placeholder_psrc/configs/trip_mode_choice_coefficients.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/trip_mode_choice_coefficients.csv rename to activitysim/examples/placeholder_psrc/configs/trip_mode_choice_coefficients.csv diff --git a/activitysim/examples/example_mtc/configs/trip_mode_choice_coefficients_template.csv b/activitysim/examples/placeholder_psrc/configs/trip_mode_choice_coefficients_template.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/trip_mode_choice_coefficients_template.csv rename to activitysim/examples/placeholder_psrc/configs/trip_mode_choice_coefficients_template.csv diff --git a/activitysim/examples/example_psrc/configs/trip_purpose.yaml b/activitysim/examples/placeholder_psrc/configs/trip_purpose.yaml similarity index 94% rename from activitysim/examples/example_psrc/configs/trip_purpose.yaml rename to activitysim/examples/placeholder_psrc/configs/trip_purpose.yaml index 47faf0d7c8..7f9d48b93d 100755 --- a/activitysim/examples/example_psrc/configs/trip_purpose.yaml +++ b/activitysim/examples/placeholder_psrc/configs/trip_purpose.yaml @@ -1,9 +1,9 @@ - -PROBS_SPEC: trip_purpose_probs.csv - -preprocessor: - SPEC: trip_purpose_annotate_trips_preprocessor - DF: trips - TABLES: - - persons - - tours + +PROBS_SPEC: trip_purpose_probs.csv + +preprocessor: + SPEC: trip_purpose_annotate_trips_preprocessor + DF: trips + TABLES: + - persons + - tours diff --git a/activitysim/examples/example_arc/configs/trip_purpose_and_destination.yaml b/activitysim/examples/placeholder_psrc/configs/trip_purpose_and_destination.yaml old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_arc/configs/trip_purpose_and_destination.yaml rename to activitysim/examples/placeholder_psrc/configs/trip_purpose_and_destination.yaml diff --git a/activitysim/examples/example_arc/configs/trip_purpose_annotate_trips_preprocessor.csv b/activitysim/examples/placeholder_psrc/configs/trip_purpose_annotate_trips_preprocessor.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_arc/configs/trip_purpose_annotate_trips_preprocessor.csv rename to activitysim/examples/placeholder_psrc/configs/trip_purpose_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/example_mtc/configs/trip_purpose_probs.csv b/activitysim/examples/placeholder_psrc/configs/trip_purpose_probs.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/trip_purpose_probs.csv rename to activitysim/examples/placeholder_psrc/configs/trip_purpose_probs.csv diff --git a/activitysim/examples/example_psrc/configs/trip_scheduling.yaml b/activitysim/examples/placeholder_psrc/configs/trip_scheduling.yaml similarity index 96% rename from activitysim/examples/example_psrc/configs/trip_scheduling.yaml rename to activitysim/examples/placeholder_psrc/configs/trip_scheduling.yaml index 5526a04cc2..a64326c78b 100755 --- a/activitysim/examples/example_psrc/configs/trip_scheduling.yaml +++ b/activitysim/examples/placeholder_psrc/configs/trip_scheduling.yaml @@ -1,10 +1,10 @@ - -# int to add to probs column index to get time period it represents. -# e.g. depart_alt_base = 5 means first column (column 0) represents 5 am -DEPART_ALT_BASE: 5 - -MAX_ITERATIONS: 100 - -#FAILFIX: drop_and_cleanup -FAILFIX: choose_most_initial - + +# int to add to probs column index to get time period it represents. +# e.g. depart_alt_base = 5 means first column (column 0) represents 5 am +DEPART_ALT_BASE: 5 + +MAX_ITERATIONS: 100 + +#FAILFIX: drop_and_cleanup +FAILFIX: choose_most_initial + diff --git a/activitysim/examples/example_psrc/configs/trip_scheduling_probs.csv b/activitysim/examples/placeholder_psrc/configs/trip_scheduling_probs.csv similarity index 98% rename from activitysim/examples/example_psrc/configs/trip_scheduling_probs.csv rename to activitysim/examples/placeholder_psrc/configs/trip_scheduling_probs.csv index e9244bd9c4..6a0deffede 100755 --- a/activitysim/examples/example_psrc/configs/trip_scheduling_probs.csv +++ b/activitysim/examples/placeholder_psrc/configs/trip_scheduling_probs.csv @@ -1,1369 +1,1369 @@ -primary_purpose,outbound,tour_hour,trip_num,HR5,HR6,HR7,HR8,HR9,HR10,HR11,HR12,HR13,HR14,HR15,HR16,HR17,HR18,HR19,HR20,HR21,HR22,HR23 -work,TRUE,5,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -work,TRUE,5,2,0.249730906,0.477180111,0.215788882,0.02257625,0.009653299,0.001272067,0.002559828,0.005345297,0.012868196,0.000858457,0,0.00130551,0,0.000861198,0,0,0,0,0 -work,TRUE,5,3,0.269166724,0.331378773,0.290398422,0.047428828,0.032211326,0.003681738,0,0.00648104,0.007547054,0.006178507,0,0.005527589,0,0,0,0,0,0,0 -work,TRUE,5,4,0.087782501,0.257488508,0.384088251,0.077346978,0.060562922,0,0,0.049138541,0,0.014538525,0,0,0,0.041701151,0.018235082,0,0.009117541,0,0 -work,TRUE,6,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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activitysim/examples/example_mtc/configs/workplace_location.csv rename to activitysim/examples/placeholder_psrc/configs/workplace_location.csv index ea56a39c9f..3ac8b59e96 --- a/activitysim/examples/example_mtc/configs/workplace_location.csv +++ b/activitysim/examples/placeholder_psrc/configs/workplace_location.csv @@ -1,14 +1,14 @@ -Label,Description,Expression,coefficient -local_dist,,_DIST@skims['DIST'],1 -util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_dist_0_1 -util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_dist_1_2 -util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_dist_2_5 -util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_dist_5_15 -util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_dist_15_up -util_dist_0_5_high,"Distance 0 to 5 mi, high and very high income",@(df['income_segment']>=WORK_HIGH_SEGMENT_ID) * _DIST.clip(upper=5),coef_dist_0_5_high -util_dist_15_up_high,"Distance 5+ mi, high and very high income",@(df['income_segment']>=WORK_HIGH_SEGMENT_ID) * (_DIST-5).clip(0),coef_dist_5_up_high -util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1 -util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1 -util_no_attractions,No attractions,@df['size_term']==0,-999 -util_mode_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum +Label,Description,Expression,coefficient +local_dist,,_DIST@skims['DIST'],1 +util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_dist_0_1 +util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_dist_1_2 +util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_dist_2_5 +util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_dist_5_15 +util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_dist_15_up +util_dist_0_5_high,"Distance 0 to 5 mi, high and very high income",@(df['income_segment']>=WORK_HIGH_SEGMENT_ID) * _DIST.clip(upper=5),coef_dist_0_5_high +util_dist_15_up_high,"Distance 5+ mi, high and very high income",@(df['income_segment']>=WORK_HIGH_SEGMENT_ID) * (_DIST-5).clip(0),coef_dist_5_up_high +util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1 +util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1 +util_no_attractions,No attractions,@df['size_term']==0,-999 +util_mode_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1 \ No newline at end of file diff --git a/activitysim/examples/example_psrc/configs/workplace_location.yaml b/activitysim/examples/placeholder_psrc/configs/workplace_location.yaml similarity index 96% rename from activitysim/examples/example_psrc/configs/workplace_location.yaml rename to activitysim/examples/placeholder_psrc/configs/workplace_location.yaml index 0f2073520c..71c1b74d5f 100755 --- a/activitysim/examples/example_psrc/configs/workplace_location.yaml +++ b/activitysim/examples/placeholder_psrc/configs/workplace_location.yaml @@ -1,73 +1,73 @@ -SAMPLE_SIZE: 30 - -SIMULATE_CHOOSER_COLUMNS: - - income_segment - - home_zone_id - -SAMPLE_SPEC: workplace_location_sample.csv -SPEC: workplace_location.csv -COEFFICIENTS: workplace_location_coeffs.csv - -LOGSUM_SETTINGS: tour_mode_choice.yaml -LOGSUM_PREPROCESSOR: nontour_preprocessor -LOGSUM_TOUR_PURPOSE: work - -# model-specific logsum-related settings -CHOOSER_ORIG_COL_NAME: home_zone_id -ALT_DEST_COL_NAME: alt_dest -IN_PERIOD: 17 -OUT_PERIOD: 8 - -DEST_CHOICE_COLUMN_NAME: workplace_zone_id -# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table -DEST_CHOICE_LOGSUM_COLUMN_NAME: workplace_location_logsum - -# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table -DEST_CHOICE_SAMPLE_TABLE_NAME: workplace_location_sample - - -annotate_persons: - SPEC: annotate_persons_workplace - DF: persons - TABLES: - - land_use - -annotate_households: - SPEC: annotate_households_workplace - DF: households - TABLES: - - persons - -# - shadow pricing - - -# income_segment is in households, but we want to count persons -CHOOSER_TABLE_NAME: persons_merged - -# size_terms model_selector -MODEL_SELECTOR: workplace - -# we can't use use household income_segment as this will also be set for non-workers -CHOOSER_SEGMENT_COLUMN_NAME: income_segment - -# boolean column to filter choosers (True means keep) -CHOOSER_FILTER_COLUMN_NAME: is_worker - -# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this -# FIXME - these are not needed for this model and should be re/factored out -SEGMENT_IDS: - work_low: 1 - work_med: 2 - work_high: 3 - work_veryhigh: 4 - -CONSTANTS: - WORK_HIGH_SEGMENT_ID: 3 - - -# model adds these tables (informational - not added if commented out) -SHADOW_PRICE_TABLE: workplace_shadow_prices -MODELED_SIZE_TABLE: workplace_modeled_size - -# not loaded if commented out -SAVED_SHADOW_PRICE_TABLE_NAME: workplace_shadow_prices.csv +SAMPLE_SIZE: 30 + +SIMULATE_CHOOSER_COLUMNS: + - income_segment + - home_zone_id + +SAMPLE_SPEC: workplace_location_sample.csv +SPEC: workplace_location.csv +COEFFICIENTS: workplace_location_coeffs.csv + +LOGSUM_SETTINGS: tour_mode_choice.yaml +LOGSUM_PREPROCESSOR: nontour_preprocessor +LOGSUM_TOUR_PURPOSE: work + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 17 +OUT_PERIOD: 8 + +DEST_CHOICE_COLUMN_NAME: workplace_zone_id +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +DEST_CHOICE_LOGSUM_COLUMN_NAME: workplace_location_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: workplace_location_sample + + +annotate_persons: + SPEC: annotate_persons_workplace + DF: persons + TABLES: + - land_use + +annotate_households: + SPEC: annotate_households_workplace + DF: households + TABLES: + - persons + +# - shadow pricing + + +# income_segment is in households, but we want to count persons +CHOOSER_TABLE_NAME: persons_merged + +# size_terms model_selector +MODEL_SELECTOR: workplace + +# we can't use use household income_segment as this will also be set for non-workers +CHOOSER_SEGMENT_COLUMN_NAME: income_segment + +# boolean column to filter choosers (True means keep) +CHOOSER_FILTER_COLUMN_NAME: is_worker + +# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this +# FIXME - these are not needed for this model and should be re/factored out +SEGMENT_IDS: + work_low: 1 + work_med: 2 + work_high: 3 + work_veryhigh: 4 + +CONSTANTS: + WORK_HIGH_SEGMENT_ID: 3 + + +# model adds these tables (informational - not added if commented out) +SHADOW_PRICE_TABLE: workplace_shadow_prices +MODELED_SIZE_TABLE: workplace_modeled_size + +# not loaded if commented out +SAVED_SHADOW_PRICE_TABLE_NAME: workplace_shadow_prices.csv diff --git a/activitysim/examples/example_mtc/configs/workplace_location_coefficients.csv b/activitysim/examples/placeholder_psrc/configs/workplace_location_coeffs.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/workplace_location_coefficients.csv rename to activitysim/examples/placeholder_psrc/configs/workplace_location_coeffs.csv diff --git a/activitysim/examples/example_mtc/configs/workplace_location_sample.csv b/activitysim/examples/placeholder_psrc/configs/workplace_location_sample.csv old mode 100644 new mode 100755 similarity index 100% rename from activitysim/examples/example_mtc/configs/workplace_location_sample.csv rename to activitysim/examples/placeholder_psrc/configs/workplace_location_sample.csv diff --git a/activitysim/examples/example_psrc/configs/write_trip_matrices.yaml b/activitysim/examples/placeholder_psrc/configs/write_trip_matrices.yaml similarity index 96% rename from activitysim/examples/example_psrc/configs/write_trip_matrices.yaml rename to activitysim/examples/placeholder_psrc/configs/write_trip_matrices.yaml index 6059d750e9..56828439ef 100755 --- a/activitysim/examples/example_psrc/configs/write_trip_matrices.yaml +++ b/activitysim/examples/placeholder_psrc/configs/write_trip_matrices.yaml @@ -1,274 +1,274 @@ -# read trips table post preprocessor and run expressions to code -# additional data fields, with one data fields for each matrix specified below - -preprocessor: - SPEC: write_trip_matrices_annotate_trips_preprocessor - DF: trips - TABLES: - - tours - -# divide trip counts by household expansion factor -HH_EXPANSION_WEIGHT_COL: sample_rate # added when households read in - -# save preprocessed trips table to pipeline if desired -SAVE_TRIPS_TABLE: False - -MATRICES: - - file_name: trips_ea.omx - tables: - - name: DRIVEALONEFREE_EA - data_field: DRIVEALONEFREE_EA - - name: DRIVEALONEPAY_EA - data_field: DRIVEALONEPAY_EA - - name: SHARED2FREE_EA - data_field: SHARED2FREE_EA - - name: SHARED2PAY_EA - data_field: SHARED2PAY_EA - - name: SHARED3FREE_EA - data_field: SHARED3FREE_EA - - name: SHARED3PAY_EA - data_field: SHARED3PAY_EA - - name: WALK_EA - data_field: WALK_EA - - name: BIKE_EA - data_field: BIKE_EA - - name: WALK_LOC_WALK_EA - data_field: WALK_LOC_WALK_EA - - name: WALK_LRF_WALK_EA - data_field: WALK_LRF_WALK_EA - - name: WALK_EXP_WALK_EA - data_field: WALK_EXP_WALK_EA - - name: WALK_HVY_WALK_EA - data_field: WALK_HVY_WALK_EA - - name: WALK_COM_WALK_EA - data_field: WALK_COM_WALK_EA - - name: DRIVE_LOC_WALK_EA - data_field: DRIVE_LOC_WALK_EA - - name: DRIVE_LRF_WALK_EA - data_field: DRIVE_LRF_WALK_EA - - name: DRIVE_EXP_WALK_EA - data_field: DRIVE_EXP_WALK_EA - - name: DRIVE_HVY_WALK_EA - data_field: DRIVE_HVY_WALK_EA - - name: DRIVE_COM_WALK_EA - data_field: DRIVE_COM_WALK_EA - - name: WALK_LOC_DRIVE_EA - data_field: WALK_LOC_DRIVE_EA - - name: WALK_LRF_DRIVE_EA - data_field: WALK_LRF_DRIVE_EA - - name: WALK_EXP_DRIVE_EA - data_field: WALK_EXP_DRIVE_EA - - name: WALK_DRIVE_HVY_EA - data_field: WALK_DRIVE_HVY_EA - - name: WALK_COM_DRIVE_EA - data_field: WALK_COM_DRIVE_EA - - file_name: trips_am.omx - tables: - - name: DRIVEALONEFREE_AM - data_field: DRIVEALONEFREE_AM - - name: DRIVEALONEPAY_AM - data_field: DRIVEALONEPAY_AM - - name: SHARED2FREE_AM - data_field: SHARED2FREE_AM - - name: SHARED2PAY_AM - data_field: SHARED2PAY_AM - - name: SHARED3FREE_AM - data_field: SHARED3FREE_AM - - name: SHARED3PAY_AM - data_field: SHARED3PAY_AM - - name: WALK_AM - data_field: WALK_AM - - name: BIKE_AM - data_field: BIKE_AM - - name: WALK_LOC_WALK_AM - data_field: WALK_LOC_WALK_AM - - name: WALK_LRF_WALK_AM - data_field: WALK_LRF_WALK_AM - - name: WALK_EXP_WALK_AM - data_field: WALK_EXP_WALK_AM - - name: WALK_HVY_WALK_AM - data_field: WALK_HVY_WALK_AM - - name: WALK_COM_WALK_AM - data_field: WALK_COM_WALK_AM - - name: DRIVE_LOC_WALK_AM - data_field: DRIVE_LOC_WALK_AM - - name: DRIVE_LRF_WALK_AM - data_field: DRIVE_LRF_WALK_AM - - name: DRIVE_EXP_WALK_AM - data_field: DRIVE_EXP_WALK_AM - - name: DRIVE_HVY_WALK_AM - data_field: DRIVE_HVY_WALK_AM - - name: DRIVE_COM_WALK_AM - data_field: DRIVE_COM_WALK_AM - - name: WALK_LOC_DRIVE_AM - data_field: WALK_LOC_DRIVE_AM - - name: WALK_LRF_DRIVE_AM - data_field: WALK_LRF_DRIVE_AM - - name: WALK_EXP_DRIVE_AM - data_field: WALK_EXP_DRIVE_AM - - name: WALK_DRIVE_HVY_AM - data_field: WALK_DRIVE_HVY_AM - - name: WALK_COM_DRIVE_AM - data_field: WALK_COM_DRIVE_AM - - file_name: trips_md.omx - tables: - - name: DRIVEALONEFREE_MD - data_field: DRIVEALONEFREE_MD - - name: DRIVEALONEPAY_MD - data_field: DRIVEALONEPAY_MD - - name: SHARED2FREE_MD - data_field: SHARED2FREE_MD - - name: SHARED2PAY_MD - data_field: SHARED2PAY_MD - - name: SHARED3FREE_MD - data_field: SHARED3FREE_MD - - name: SHARED3PAY_MD - data_field: SHARED3PAY_MD - - name: WALK_MD - data_field: WALK_MD - - name: BIKE_MD - data_field: BIKE_MD - - name: WALK_LOC_WALK_MD - data_field: WALK_LOC_WALK_MD - - name: WALK_LRF_WALK_MD - data_field: WALK_LRF_WALK_MD - - name: WALK_EXP_WALK_MD - data_field: WALK_EXP_WALK_MD - - name: WALK_HVY_WALK_MD - data_field: WALK_HVY_WALK_MD - - name: WALK_COM_WALK_MD - data_field: WALK_COM_WALK_MD - - name: DRIVE_LOC_WALK_MD - data_field: DRIVE_LOC_WALK_MD - - name: DRIVE_LRF_WALK_MD - data_field: DRIVE_LRF_WALK_MD - - name: DRIVE_EXP_WALK_MD - data_field: DRIVE_EXP_WALK_MD - - name: DRIVE_HVY_WALK_MD - data_field: DRIVE_HVY_WALK_MD - - name: DRIVE_COM_WALK_MD - data_field: DRIVE_COM_WALK_MD - - name: WALK_LOC_DRIVE_MD - data_field: WALK_LOC_DRIVE_MD - - name: WALK_LRF_DRIVE_MD - data_field: WALK_LRF_DRIVE_MD - - name: WALK_EXP_DRIVE_MD - data_field: WALK_EXP_DRIVE_MD - - name: WALK_DRIVE_HVY_MD - data_field: WALK_DRIVE_HVY_MD - - name: WALK_COM_DRIVE_MD - data_field: WALK_COM_DRIVE_MD - - file_name: trips_pm.omx - tables: - - name: DRIVEALONEFREE_PM - data_field: DRIVEALONEFREE_PM - - name: DRIVEALONEPAY_PM - data_field: DRIVEALONEPAY_PM - - name: SHARED2FREE_PM - data_field: SHARED2FREE_PM - - name: SHARED2PAY_PM - data_field: SHARED2PAY_PM - - name: SHARED3FREE_PM - data_field: SHARED3FREE_PM - - name: SHARED3PAY_PM - data_field: SHARED3PAY_PM - - name: WALK_PM - data_field: WALK_PM - - name: BIKE_PM - data_field: BIKE_PM - - name: WALK_LOC_WALK_PM - data_field: WALK_LOC_WALK_PM - - name: WALK_LRF_WALK_PM - data_field: WALK_LRF_WALK_PM - - name: WALK_EXP_WALK_PM - data_field: WALK_EXP_WALK_PM - - name: WALK_HVY_WALK_PM - data_field: WALK_HVY_WALK_PM - - name: WALK_COM_WALK_PM - data_field: WALK_COM_WALK_PM - - name: DRIVE_LOC_WALK_PM - data_field: DRIVE_LOC_WALK_PM - - name: DRIVE_LRF_WALK_PM - data_field: DRIVE_LRF_WALK_PM - - name: DRIVE_EXP_WALK_PM - data_field: DRIVE_EXP_WALK_PM - - name: DRIVE_HVY_WALK_PM - data_field: DRIVE_HVY_WALK_PM - - name: DRIVE_COM_WALK_PM - data_field: DRIVE_COM_WALK_PM - - name: WALK_LOC_DRIVE_PM - data_field: WALK_LOC_DRIVE_PM - - name: WALK_LRF_DRIVE_PM - data_field: WALK_LRF_DRIVE_PM - - name: WALK_EXP_DRIVE_PM - data_field: WALK_EXP_DRIVE_PM - - name: WALK_DRIVE_HVY_PM - data_field: WALK_DRIVE_HVY_PM - - name: WALK_COM_DRIVE_PM - data_field: WALK_COM_DRIVE_PM - - file_name: trips_ev.omx - tables: - - name: DRIVEALONEFREE_EV - data_field: DRIVEALONEFREE_EV - - name: DRIVEALONEPAY_EV - data_field: DRIVEALONEPAY_EV - - name: SHARED2FREE_EV - data_field: SHARED2FREE_EV - - name: SHARED2PAY_EV - data_field: SHARED2PAY_EV - - name: SHARED3FREE_EV - data_field: SHARED3FREE_EV - - name: SHARED3PAY_EV - data_field: SHARED3PAY_EV - - name: WALK_EV - data_field: WALK_EV - - name: BIKE_EV - data_field: BIKE_EV - - name: WALK_LOC_WALK_EV - data_field: WALK_LOC_WALK_EV - - name: WALK_LRF_WALK_EV - data_field: WALK_LRF_WALK_EV - - name: WALK_EXP_WALK_EV - data_field: WALK_EXP_WALK_EV - - name: WALK_HVY_WALK_EV - data_field: WALK_HVY_WALK_EV - - name: WALK_COM_WALK_EV - data_field: WALK_COM_WALK_EV - - name: DRIVE_LOC_WALK_EV - data_field: DRIVE_LOC_WALK_EV - - name: DRIVE_LRF_WALK_EV - data_field: DRIVE_LRF_WALK_EV - - name: DRIVE_EXP_WALK_EV - data_field: DRIVE_EXP_WALK_EV - - name: DRIVE_HVY_WALK_EV - data_field: DRIVE_HVY_WALK_EV - - name: DRIVE_COM_WALK_EV - data_field: DRIVE_COM_WALK_EV - - name: WALK_LOC_DRIVE_EV - data_field: WALK_LOC_DRIVE_EV - - name: WALK_LRF_DRIVE_EV - data_field: WALK_LRF_DRIVE_EV - - name: WALK_EXP_DRIVE_EV - data_field: WALK_EXP_DRIVE_EV - - name: WALK_DRIVE_HVY_EV - data_field: WALK_DRIVE_HVY_EV - - name: WALK_COM_DRIVE_EV - data_field: WALK_COM_DRIVE_EV - -CONSTANTS: - time_periods: - EA: - first_hour: 3 - last_hour: 5 - AM: - first_hour: 6 - last_hour: 9 - MD: - first_hour: 10 - last_hour: 14 - PM: - first_hour: 15 - last_hour: 18 - EV: - first_hour: 19 - last_hour: 2 +# read trips table post preprocessor and run expressions to code +# additional data fields, with one data fields for each matrix specified below + +preprocessor: + SPEC: write_trip_matrices_annotate_trips_preprocessor + DF: trips + TABLES: + - tours + +# divide trip counts by household expansion factor +HH_EXPANSION_WEIGHT_COL: sample_rate # added when households read in + +# save preprocessed trips table to pipeline if desired +SAVE_TRIPS_TABLE: False + +MATRICES: + - file_name: trips_ea.omx + tables: + - name: DRIVEALONEFREE_EA + data_field: DRIVEALONEFREE_EA + - name: DRIVEALONEPAY_EA + data_field: DRIVEALONEPAY_EA + - name: SHARED2FREE_EA + data_field: SHARED2FREE_EA + - name: SHARED2PAY_EA + data_field: SHARED2PAY_EA + - name: SHARED3FREE_EA + data_field: SHARED3FREE_EA + - name: SHARED3PAY_EA + data_field: SHARED3PAY_EA + - name: WALK_EA + data_field: WALK_EA + - name: BIKE_EA + data_field: BIKE_EA + - name: WALK_LOC_WALK_EA + data_field: WALK_LOC_WALK_EA + - name: WALK_LRF_WALK_EA + data_field: WALK_LRF_WALK_EA + - name: WALK_EXP_WALK_EA + data_field: WALK_EXP_WALK_EA + - name: WALK_HVY_WALK_EA + data_field: WALK_HVY_WALK_EA + - name: WALK_COM_WALK_EA + data_field: WALK_COM_WALK_EA + - name: DRIVE_LOC_WALK_EA + data_field: DRIVE_LOC_WALK_EA + - name: DRIVE_LRF_WALK_EA + data_field: DRIVE_LRF_WALK_EA + - name: DRIVE_EXP_WALK_EA + data_field: DRIVE_EXP_WALK_EA + - name: DRIVE_HVY_WALK_EA + data_field: DRIVE_HVY_WALK_EA + - name: DRIVE_COM_WALK_EA + data_field: DRIVE_COM_WALK_EA + - name: WALK_LOC_DRIVE_EA + data_field: WALK_LOC_DRIVE_EA + - name: WALK_LRF_DRIVE_EA + data_field: WALK_LRF_DRIVE_EA + - name: WALK_EXP_DRIVE_EA + data_field: WALK_EXP_DRIVE_EA + - name: WALK_DRIVE_HVY_EA + data_field: WALK_DRIVE_HVY_EA + - name: WALK_COM_DRIVE_EA + data_field: WALK_COM_DRIVE_EA + - file_name: trips_am.omx + tables: + - name: DRIVEALONEFREE_AM + data_field: DRIVEALONEFREE_AM + - name: DRIVEALONEPAY_AM + data_field: DRIVEALONEPAY_AM + - name: SHARED2FREE_AM + data_field: SHARED2FREE_AM + - name: SHARED2PAY_AM + data_field: SHARED2PAY_AM + - name: SHARED3FREE_AM + data_field: SHARED3FREE_AM + - name: SHARED3PAY_AM + data_field: SHARED3PAY_AM + - name: WALK_AM + data_field: WALK_AM + - name: BIKE_AM + data_field: BIKE_AM + - name: WALK_LOC_WALK_AM + data_field: WALK_LOC_WALK_AM + - name: WALK_LRF_WALK_AM + data_field: WALK_LRF_WALK_AM + - name: WALK_EXP_WALK_AM + data_field: WALK_EXP_WALK_AM + - name: WALK_HVY_WALK_AM + data_field: WALK_HVY_WALK_AM + - name: WALK_COM_WALK_AM + data_field: WALK_COM_WALK_AM + - name: DRIVE_LOC_WALK_AM + data_field: DRIVE_LOC_WALK_AM + - name: DRIVE_LRF_WALK_AM + data_field: DRIVE_LRF_WALK_AM + - name: DRIVE_EXP_WALK_AM + data_field: DRIVE_EXP_WALK_AM + - name: DRIVE_HVY_WALK_AM + data_field: DRIVE_HVY_WALK_AM + - name: DRIVE_COM_WALK_AM + data_field: DRIVE_COM_WALK_AM + - name: WALK_LOC_DRIVE_AM + data_field: WALK_LOC_DRIVE_AM + - name: WALK_LRF_DRIVE_AM + data_field: WALK_LRF_DRIVE_AM + - name: WALK_EXP_DRIVE_AM + data_field: WALK_EXP_DRIVE_AM + - name: WALK_DRIVE_HVY_AM + data_field: WALK_DRIVE_HVY_AM + - name: WALK_COM_DRIVE_AM + data_field: WALK_COM_DRIVE_AM + - file_name: trips_md.omx + tables: + - name: DRIVEALONEFREE_MD + data_field: DRIVEALONEFREE_MD + - name: DRIVEALONEPAY_MD + data_field: DRIVEALONEPAY_MD + - name: SHARED2FREE_MD + data_field: SHARED2FREE_MD + - name: SHARED2PAY_MD + data_field: SHARED2PAY_MD + - name: SHARED3FREE_MD + data_field: SHARED3FREE_MD + - name: SHARED3PAY_MD + data_field: SHARED3PAY_MD + - name: WALK_MD + data_field: WALK_MD + - name: BIKE_MD + data_field: BIKE_MD + - name: WALK_LOC_WALK_MD + data_field: WALK_LOC_WALK_MD + - name: WALK_LRF_WALK_MD + data_field: WALK_LRF_WALK_MD + - name: WALK_EXP_WALK_MD + data_field: WALK_EXP_WALK_MD + - name: WALK_HVY_WALK_MD + data_field: WALK_HVY_WALK_MD + - name: WALK_COM_WALK_MD + data_field: WALK_COM_WALK_MD + - name: DRIVE_LOC_WALK_MD + data_field: DRIVE_LOC_WALK_MD + - name: DRIVE_LRF_WALK_MD + data_field: DRIVE_LRF_WALK_MD + - name: DRIVE_EXP_WALK_MD + data_field: DRIVE_EXP_WALK_MD + - name: DRIVE_HVY_WALK_MD + data_field: DRIVE_HVY_WALK_MD + - name: DRIVE_COM_WALK_MD + data_field: DRIVE_COM_WALK_MD + - name: WALK_LOC_DRIVE_MD + data_field: WALK_LOC_DRIVE_MD + - name: WALK_LRF_DRIVE_MD + data_field: WALK_LRF_DRIVE_MD + - name: WALK_EXP_DRIVE_MD + data_field: WALK_EXP_DRIVE_MD + - name: WALK_DRIVE_HVY_MD + data_field: WALK_DRIVE_HVY_MD + - name: WALK_COM_DRIVE_MD + data_field: WALK_COM_DRIVE_MD + - file_name: trips_pm.omx + tables: + - name: DRIVEALONEFREE_PM + data_field: DRIVEALONEFREE_PM + - name: DRIVEALONEPAY_PM + data_field: DRIVEALONEPAY_PM + - name: SHARED2FREE_PM + data_field: SHARED2FREE_PM + - name: SHARED2PAY_PM + data_field: SHARED2PAY_PM + - name: SHARED3FREE_PM + data_field: SHARED3FREE_PM + - name: SHARED3PAY_PM + data_field: SHARED3PAY_PM + - name: WALK_PM + data_field: WALK_PM + - name: BIKE_PM + data_field: BIKE_PM + - name: WALK_LOC_WALK_PM + data_field: WALK_LOC_WALK_PM + - name: WALK_LRF_WALK_PM + data_field: WALK_LRF_WALK_PM + - name: WALK_EXP_WALK_PM + data_field: WALK_EXP_WALK_PM + - name: WALK_HVY_WALK_PM + data_field: WALK_HVY_WALK_PM + - name: WALK_COM_WALK_PM + data_field: WALK_COM_WALK_PM + - name: DRIVE_LOC_WALK_PM + data_field: DRIVE_LOC_WALK_PM + - name: DRIVE_LRF_WALK_PM + data_field: DRIVE_LRF_WALK_PM + - name: DRIVE_EXP_WALK_PM + data_field: DRIVE_EXP_WALK_PM + - name: DRIVE_HVY_WALK_PM + data_field: DRIVE_HVY_WALK_PM + - name: DRIVE_COM_WALK_PM + data_field: DRIVE_COM_WALK_PM + - name: WALK_LOC_DRIVE_PM + data_field: WALK_LOC_DRIVE_PM + - name: WALK_LRF_DRIVE_PM + data_field: WALK_LRF_DRIVE_PM + - name: WALK_EXP_DRIVE_PM + data_field: WALK_EXP_DRIVE_PM + - name: WALK_DRIVE_HVY_PM + data_field: WALK_DRIVE_HVY_PM + - name: WALK_COM_DRIVE_PM + data_field: WALK_COM_DRIVE_PM + - file_name: trips_ev.omx + tables: + - name: DRIVEALONEFREE_EV + data_field: DRIVEALONEFREE_EV + - name: DRIVEALONEPAY_EV + data_field: DRIVEALONEPAY_EV + - name: SHARED2FREE_EV + data_field: SHARED2FREE_EV + - name: SHARED2PAY_EV + data_field: SHARED2PAY_EV + - name: SHARED3FREE_EV + data_field: SHARED3FREE_EV + - name: SHARED3PAY_EV + data_field: SHARED3PAY_EV + - name: WALK_EV + data_field: WALK_EV + - name: BIKE_EV + data_field: BIKE_EV + - name: WALK_LOC_WALK_EV + data_field: WALK_LOC_WALK_EV + - name: WALK_LRF_WALK_EV + data_field: WALK_LRF_WALK_EV + - name: WALK_EXP_WALK_EV + data_field: WALK_EXP_WALK_EV + - name: WALK_HVY_WALK_EV + data_field: WALK_HVY_WALK_EV + - name: WALK_COM_WALK_EV + data_field: WALK_COM_WALK_EV + - name: DRIVE_LOC_WALK_EV + data_field: DRIVE_LOC_WALK_EV + - name: DRIVE_LRF_WALK_EV + data_field: DRIVE_LRF_WALK_EV + - name: DRIVE_EXP_WALK_EV + data_field: DRIVE_EXP_WALK_EV + - name: DRIVE_HVY_WALK_EV + data_field: DRIVE_HVY_WALK_EV + - name: DRIVE_COM_WALK_EV + data_field: DRIVE_COM_WALK_EV + - name: WALK_LOC_DRIVE_EV + data_field: WALK_LOC_DRIVE_EV + - name: WALK_LRF_DRIVE_EV + data_field: WALK_LRF_DRIVE_EV + - name: WALK_EXP_DRIVE_EV + data_field: WALK_EXP_DRIVE_EV + - name: WALK_DRIVE_HVY_EV + data_field: WALK_DRIVE_HVY_EV + - name: WALK_COM_DRIVE_EV + data_field: WALK_COM_DRIVE_EV + +CONSTANTS: + time_periods: + EA: + first_hour: 3 + last_hour: 5 + AM: + first_hour: 6 + last_hour: 9 + MD: + first_hour: 10 + last_hour: 14 + PM: + first_hour: 15 + last_hour: 18 + EV: + first_hour: 19 + last_hour: 2 diff --git a/activitysim/examples/example_psrc/configs/write_trip_matrices_annotate_trips_preprocessor.csv b/activitysim/examples/placeholder_psrc/configs/write_trip_matrices_annotate_trips_preprocessor.csv similarity index 98% rename from activitysim/examples/example_psrc/configs/write_trip_matrices_annotate_trips_preprocessor.csv rename to activitysim/examples/placeholder_psrc/configs/write_trip_matrices_annotate_trips_preprocessor.csv index 8f3e386e60..9dc036679f 100755 --- a/activitysim/examples/example_psrc/configs/write_trip_matrices_annotate_trips_preprocessor.csv +++ b/activitysim/examples/placeholder_psrc/configs/write_trip_matrices_annotate_trips_preprocessor.csv @@ -1,130 +1,130 @@ -Description,Target,Expression -# add additional fields,, -,tour_participants,trips.tour_id.map(tours.number_of_participants) -,distance,od_skims['DIST'] -# code time periods,, -,is_ea,"trips.depart.between(time_periods['EA']['first_hour'], time_periods['EA']['last_hour'])" -,is_am,"trips.depart.between(time_periods['AM']['first_hour'], time_periods['AM']['last_hour'])" -,is_md,"trips.depart.between(time_periods['MD']['first_hour'], time_periods['MD']['last_hour'])" -,is_pm,"trips.depart.between(time_periods['PM']['first_hour'], time_periods['PM']['last_hour'])" -,is_ev,(trips.depart >= time_periods['EV']['first_hour']) | (trips.depart <= time_periods['EV']['last_hour']) -# ea trips,, -,DRIVEALONEFREE_EA,((trips.trip_mode == 'DRIVEALONEFREE') & is_ea) * tour_participants -,DRIVEALONEPAY_EA,((trips.trip_mode == 'DRIVEALONEPAY') & is_ea) * tour_participants -,SHARED2FREE_EA,((trips.trip_mode == 'SHARED2FREE') & is_ea) * tour_participants -,SHARED2PAY_EA,((trips.trip_mode == 'SHARED2PAY') & is_ea) * tour_participants -,SHARED3FREE_EA,((trips.trip_mode == 'SHARED3FREE') & is_ea) * tour_participants -,SHARED3PAY_EA,((trips.trip_mode == 'SHARED3PAY') & is_ea) * tour_participants -,WALK_EA,((trips.trip_mode == 'WALK') & is_ea) * tour_participants -,BIKE_EA,((trips.trip_mode == 'BIKE') & is_ea) * tour_participants -,WALK_LOC_WALK_EA,((trips.trip_mode == 'WALK_LOC') & is_ea) * tour_participants -,WALK_LRF_WALK_EA,((trips.trip_mode == 'WALK_LRF') & is_ea) * tour_participants -,WALK_EXP_WALK_EA,((trips.trip_mode == 'WALK_EXP') & is_ea) * tour_participants -,WALK_HVY_WALK_EA,((trips.trip_mode == 'WALK_HVY') & is_ea) * tour_participants -,WALK_COM_WALK_EA,((trips.trip_mode == 'WALK_COM') & is_ea) * tour_participants -,DRIVE_LOC_WALK_EA,((trips.trip_mode == 'DRIVE_LOC') & is_ea & trips.outbound) * tour_participants -,DRIVE_LRF_WALK_EA,((trips.trip_mode == 'DRIVE_LRF') & is_ea & trips.outbound) * tour_participants -,DRIVE_EXP_WALK_EA,((trips.trip_mode == 'DRIVE_EXP') & is_ea & trips.outbound) * tour_participants -,DRIVE_HVY_WALK_EA,((trips.trip_mode == 'DRIVE_HVY') & is_ea & trips.outbound) * tour_participants -,DRIVE_COM_WALK_EA,((trips.trip_mode == 'DRIVE_COM') & is_ea & trips.outbound) * tour_participants -,WALK_LOC_DRIVE_EA,((trips.trip_mode == 'DRIVE_LOC') & is_ea & ~trips.outbound) * tour_participants -,WALK_LRF_DRIVE_EA,((trips.trip_mode == 'DRIVE_LRF') & is_ea & ~trips.outbound) * tour_participants -,WALK_EXP_DRIVE_EA,((trips.trip_mode == 'DRIVE_EXP') & is_ea & ~trips.outbound) * tour_participants -,WALK_DRIVE_HVY_EA,((trips.trip_mode == 'DRIVE_HVY') & is_ea & ~trips.outbound) * tour_participants -,WALK_COM_DRIVE_EA,((trips.trip_mode == 'DRIVE_COM') & is_ea & ~trips.outbound) * tour_participants -# am trips,, -,DRIVEALONEFREE_AM,((trips.trip_mode == 'DRIVEALONEFREE') & is_am) * tour_participants -,DRIVEALONEPAY_AM,((trips.trip_mode == 'DRIVEALONEPAY') & is_am) * tour_participants -,SHARED2FREE_AM,((trips.trip_mode == 'SHARED2FREE') & is_am) * tour_participants -,SHARED2PAY_AM,((trips.trip_mode == 'SHARED2PAY') & is_am) * tour_participants -,SHARED3FREE_AM,((trips.trip_mode == 'SHARED3FREE') & is_am) * tour_participants -,SHARED3PAY_AM,((trips.trip_mode == 'SHARED3PAY') & is_am) * tour_participants -,WALK_AM,((trips.trip_mode == 'WALK') & is_am) * tour_participants -,BIKE_AM,((trips.trip_mode == 'BIKE') & is_am) * tour_participants -,WALK_LOC_WALK_AM,((trips.trip_mode == 'WALK_LOC') & is_am) * tour_participants -,WALK_LRF_WALK_AM,((trips.trip_mode == 'WALK_LRF') & is_am) * tour_participants -,WALK_EXP_WALK_AM,((trips.trip_mode == 'WALK_EXP') & is_am) * tour_participants -,WALK_HVY_WALK_AM,((trips.trip_mode == 'WALK_HVY') & is_am) * tour_participants -,WALK_COM_WALK_AM,((trips.trip_mode == 'WALK_COM') & is_am) * tour_participants -,DRIVE_LOC_WALK_AM,((trips.trip_mode == 'DRIVE_LOC') & is_am & trips.outbound) * tour_participants -,DRIVE_LRF_WALK_AM,((trips.trip_mode == 'DRIVE_LRF') & is_am & trips.outbound) * tour_participants -,DRIVE_EXP_WALK_AM,((trips.trip_mode == 'DRIVE_EXP') & is_am & trips.outbound) * tour_participants -,DRIVE_HVY_WALK_AM,((trips.trip_mode == 'DRIVE_HVY') & is_am & trips.outbound) * tour_participants -,DRIVE_COM_WALK_AM,((trips.trip_mode == 'DRIVE_COM') & is_am & trips.outbound) * tour_participants -,WALK_LOC_DRIVE_AM,((trips.trip_mode == 'DRIVE_LOC') & is_am & ~trips.outbound) * tour_participants -,WALK_LRF_DRIVE_AM,((trips.trip_mode == 'DRIVE_LRF') & is_am & ~trips.outbound) * tour_participants -,WALK_EXP_DRIVE_AM,((trips.trip_mode == 'DRIVE_EXP') & is_am & ~trips.outbound) * tour_participants -,WALK_DRIVE_HVY_AM,((trips.trip_mode == 'DRIVE_HVY') & is_am & ~trips.outbound) * tour_participants -,WALK_COM_DRIVE_AM,((trips.trip_mode == 'DRIVE_COM') & is_am & ~trips.outbound) * tour_participants -# md trips,, -,DRIVEALONEFREE_MD,((trips.trip_mode == 'DRIVEALONEFREE') & is_md) * tour_participants -,DRIVEALONEPAY_MD,((trips.trip_mode == 'DRIVEALONEPAY') & is_md) * tour_participants -,SHARED2FREE_MD,((trips.trip_mode == 'SHARED2FREE') & is_md) * tour_participants -,SHARED2PAY_MD,((trips.trip_mode == 'SHARED2PAY') & is_md) * tour_participants -,SHARED3FREE_MD,((trips.trip_mode == 'SHARED3FREE') & is_md) * tour_participants -,SHARED3PAY_MD,((trips.trip_mode == 'SHARED3PAY') & is_md) * tour_participants -,WALK_MD,((trips.trip_mode == 'WALK') & is_md) * tour_participants -,BIKE_MD,((trips.trip_mode == 'BIKE') & is_md) * tour_participants -,WALK_LOC_WALK_MD,((trips.trip_mode == 'WALK_LOC') & is_md) * tour_participants -,WALK_LRF_WALK_MD,((trips.trip_mode == 'WALK_LRF') & is_md) * tour_participants -,WALK_EXP_WALK_MD,((trips.trip_mode == 'WALK_EXP') & is_md) * tour_participants -,WALK_HVY_WALK_MD,((trips.trip_mode == 'WALK_HVY') & is_md) * tour_participants -,WALK_COM_WALK_MD,((trips.trip_mode == 'WALK_COM') & is_md) * tour_participants -,DRIVE_LOC_WALK_MD,((trips.trip_mode == 'DRIVE_LOC') & is_md & trips.outbound) * tour_participants -,DRIVE_LRF_WALK_MD,((trips.trip_mode == 'DRIVE_LRF') & is_md & trips.outbound) * tour_participants -,DRIVE_EXP_WALK_MD,((trips.trip_mode == 'DRIVE_EXP') & is_md & trips.outbound) * tour_participants -,DRIVE_HVY_WALK_MD,((trips.trip_mode == 'DRIVE_HVY') & is_md & trips.outbound) * tour_participants -,DRIVE_COM_WALK_MD,((trips.trip_mode == 'DRIVE_COM') & is_md & trips.outbound) * tour_participants -,WALK_LOC_DRIVE_MD,((trips.trip_mode == 'DRIVE_LOC') & is_md & ~trips.outbound) * tour_participants -,WALK_LRF_DRIVE_MD,((trips.trip_mode == 'DRIVE_LRF') & is_md & ~trips.outbound) * tour_participants -,WALK_EXP_DRIVE_MD,((trips.trip_mode == 'DRIVE_EXP') & is_md & ~trips.outbound) * tour_participants -,WALK_DRIVE_HVY_MD,((trips.trip_mode == 'DRIVE_HVY') & is_md & ~trips.outbound) * tour_participants -,WALK_COM_DRIVE_MD,((trips.trip_mode == 'DRIVE_COM') & is_md & ~trips.outbound) * tour_participants -# pm trips,, -,DRIVEALONEFREE_PM,((trips.trip_mode == 'DRIVEALONEFREE') & is_pm) * tour_participants -,DRIVEALONEPAY_PM,((trips.trip_mode == 'DRIVEALONEPAY') & is_pm) * tour_participants -,SHARED2FREE_PM,((trips.trip_mode == 'SHARED2FREE') & is_pm) * tour_participants -,SHARED2PAY_PM,((trips.trip_mode == 'SHARED2PAY') & is_pm) * tour_participants -,SHARED3FREE_PM,((trips.trip_mode == 'SHARED3FREE') & is_pm) * tour_participants -,SHARED3PAY_PM,((trips.trip_mode == 'SHARED3PAY') & is_pm) * tour_participants -,WALK_PM,((trips.trip_mode == 'WALK') & is_pm) * tour_participants -,BIKE_PM,((trips.trip_mode == 'BIKE') & is_pm) * tour_participants -,WALK_LOC_WALK_PM,((trips.trip_mode == 'WALK_LOC') & is_pm) * tour_participants -,WALK_LRF_WALK_PM,((trips.trip_mode == 'WALK_LRF') & is_pm) * tour_participants -,WALK_EXP_WALK_PM,((trips.trip_mode == 'WALK_EXP') & is_pm) * tour_participants -,WALK_HVY_WALK_PM,((trips.trip_mode == 'WALK_HVY') & is_pm) * tour_participants -,WALK_COM_WALK_PM,((trips.trip_mode == 'WALK_COM') & is_pm) * tour_participants -,DRIVE_LOC_WALK_PM,((trips.trip_mode == 'DRIVE_LOC') & is_pm & trips.outbound) * tour_participants -,DRIVE_LRF_WALK_PM,((trips.trip_mode == 'DRIVE_LRF') & is_pm & trips.outbound) * tour_participants -,DRIVE_EXP_WALK_PM,((trips.trip_mode == 'DRIVE_EXP') & is_pm & trips.outbound) * tour_participants -,DRIVE_HVY_WALK_PM,((trips.trip_mode == 'DRIVE_HVY') & is_pm & trips.outbound) * tour_participants -,DRIVE_COM_WALK_PM,((trips.trip_mode == 'DRIVE_COM') & is_pm & trips.outbound) * tour_participants -,WALK_LOC_DRIVE_PM,((trips.trip_mode == 'DRIVE_LOC') & is_pm & ~trips.outbound) * tour_participants -,WALK_LRF_DRIVE_PM,((trips.trip_mode == 'DRIVE_LRF') & is_pm & ~trips.outbound) * tour_participants -,WALK_EXP_DRIVE_PM,((trips.trip_mode == 'DRIVE_EXP') & is_pm & ~trips.outbound) * tour_participants -,WALK_DRIVE_HVY_PM,((trips.trip_mode == 'DRIVE_HVY') & is_pm & ~trips.outbound) * tour_participants -,WALK_COM_DRIVE_PM,((trips.trip_mode == 'DRIVE_COM') & is_pm & ~trips.outbound) * tour_participants -# ev trips,, -,DRIVEALONEFREE_EV,((trips.trip_mode == 'DRIVEALONEFREE') & is_ev) * tour_participants -,DRIVEALONEPAY_EV,((trips.trip_mode == 'DRIVEALONEPAY') & is_ev) * tour_participants -,SHARED2FREE_EV,((trips.trip_mode == 'SHARED2FREE') & is_ev) * tour_participants -,SHARED2PAY_EV,((trips.trip_mode == 'SHARED2PAY') & is_ev) * tour_participants -,SHARED3FREE_EV,((trips.trip_mode == 'SHARED3FREE') & is_ev) * tour_participants -,SHARED3PAY_EV,((trips.trip_mode == 'SHARED3PAY') & is_ev) * tour_participants -,WALK_EV,((trips.trip_mode == 'WALK') & is_ev) * tour_participants -,BIKE_EV,((trips.trip_mode == 'BIKE') & is_ev) * tour_participants -,WALK_LOC_WALK_EV,((trips.trip_mode == 'WALK_LOC') & is_ev) * tour_participants -,WALK_LRF_WALK_EV,((trips.trip_mode == 'WALK_LRF') & is_ev) * tour_participants -,WALK_EXP_WALK_EV,((trips.trip_mode == 'WALK_EXP') & is_ev) * tour_participants -,WALK_HVY_WALK_EV,((trips.trip_mode == 'WALK_HVY') & is_ev) * tour_participants -,WALK_COM_WALK_EV,((trips.trip_mode == 'WALK_COM') & is_ev) * tour_participants -,DRIVE_LOC_WALK_EV,((trips.trip_mode == 'DRIVE_LOC') & is_ev & trips.outbound) * tour_participants -,DRIVE_LRF_WALK_EV,((trips.trip_mode == 'DRIVE_LRF') & is_ev & trips.outbound) * tour_participants -,DRIVE_EXP_WALK_EV,((trips.trip_mode == 'DRIVE_EXP') & is_ev & trips.outbound) * tour_participants -,DRIVE_HVY_WALK_EV,((trips.trip_mode == 'DRIVE_HVY') & is_ev & trips.outbound) * tour_participants -,DRIVE_COM_WALK_EV,((trips.trip_mode == 'DRIVE_COM') & is_ev & trips.outbound) * tour_participants -,WALK_LOC_DRIVE_EV,((trips.trip_mode == 'DRIVE_LOC') & is_ev & ~trips.outbound) * tour_participants -,WALK_LRF_DRIVE_EV,((trips.trip_mode == 'DRIVE_LRF') & is_ev & ~trips.outbound) * tour_participants -,WALK_EXP_DRIVE_EV,((trips.trip_mode == 'DRIVE_EXP') & is_ev & ~trips.outbound) * tour_participants -,WALK_DRIVE_HVY_EV,((trips.trip_mode == 'DRIVE_HVY') & is_ev & ~trips.outbound) * tour_participants -,WALK_COM_DRIVE_EV,((trips.trip_mode == 'DRIVE_COM') & is_ev & ~trips.outbound) * tour_participants +Description,Target,Expression +# add additional fields,, +,tour_participants,trips.tour_id.map(tours.number_of_participants) +,distance,od_skims['DIST'] +# code time periods,, +,is_ea,"trips.depart.between(time_periods['EA']['first_hour'], time_periods['EA']['last_hour'])" +,is_am,"trips.depart.between(time_periods['AM']['first_hour'], time_periods['AM']['last_hour'])" +,is_md,"trips.depart.between(time_periods['MD']['first_hour'], time_periods['MD']['last_hour'])" +,is_pm,"trips.depart.between(time_periods['PM']['first_hour'], time_periods['PM']['last_hour'])" +,is_ev,(trips.depart >= time_periods['EV']['first_hour']) | (trips.depart <= time_periods['EV']['last_hour']) +# ea trips,, +,DRIVEALONEFREE_EA,((trips.trip_mode == 'DRIVEALONEFREE') & is_ea) * tour_participants +,DRIVEALONEPAY_EA,((trips.trip_mode == 'DRIVEALONEPAY') & is_ea) * tour_participants +,SHARED2FREE_EA,((trips.trip_mode == 'SHARED2FREE') & is_ea) * tour_participants +,SHARED2PAY_EA,((trips.trip_mode == 'SHARED2PAY') & is_ea) * tour_participants +,SHARED3FREE_EA,((trips.trip_mode == 'SHARED3FREE') & is_ea) * tour_participants +,SHARED3PAY_EA,((trips.trip_mode == 'SHARED3PAY') & is_ea) * tour_participants +,WALK_EA,((trips.trip_mode == 'WALK') & is_ea) * tour_participants +,BIKE_EA,((trips.trip_mode == 'BIKE') & is_ea) * tour_participants +,WALK_LOC_WALK_EA,((trips.trip_mode == 'WALK_LOC') & is_ea) * tour_participants +,WALK_LRF_WALK_EA,((trips.trip_mode == 'WALK_LRF') & is_ea) * tour_participants +,WALK_EXP_WALK_EA,((trips.trip_mode == 'WALK_EXP') & is_ea) * tour_participants +,WALK_HVY_WALK_EA,((trips.trip_mode == 'WALK_HVY') & is_ea) * tour_participants +,WALK_COM_WALK_EA,((trips.trip_mode == 'WALK_COM') & is_ea) * tour_participants +,DRIVE_LOC_WALK_EA,((trips.trip_mode == 'DRIVE_LOC') & is_ea & trips.outbound) * tour_participants +,DRIVE_LRF_WALK_EA,((trips.trip_mode == 'DRIVE_LRF') & is_ea & trips.outbound) * tour_participants +,DRIVE_EXP_WALK_EA,((trips.trip_mode == 'DRIVE_EXP') & is_ea & trips.outbound) * tour_participants +,DRIVE_HVY_WALK_EA,((trips.trip_mode == 'DRIVE_HVY') & is_ea & trips.outbound) * tour_participants +,DRIVE_COM_WALK_EA,((trips.trip_mode == 'DRIVE_COM') & is_ea & trips.outbound) * tour_participants +,WALK_LOC_DRIVE_EA,((trips.trip_mode == 'DRIVE_LOC') & is_ea & ~trips.outbound) * tour_participants +,WALK_LRF_DRIVE_EA,((trips.trip_mode == 'DRIVE_LRF') & is_ea & ~trips.outbound) * tour_participants +,WALK_EXP_DRIVE_EA,((trips.trip_mode == 'DRIVE_EXP') & is_ea & ~trips.outbound) * tour_participants +,WALK_DRIVE_HVY_EA,((trips.trip_mode == 'DRIVE_HVY') & is_ea & ~trips.outbound) * tour_participants +,WALK_COM_DRIVE_EA,((trips.trip_mode == 'DRIVE_COM') & is_ea & ~trips.outbound) * tour_participants +# am trips,, +,DRIVEALONEFREE_AM,((trips.trip_mode == 'DRIVEALONEFREE') & is_am) * tour_participants +,DRIVEALONEPAY_AM,((trips.trip_mode == 'DRIVEALONEPAY') & is_am) * tour_participants +,SHARED2FREE_AM,((trips.trip_mode == 'SHARED2FREE') & is_am) * tour_participants +,SHARED2PAY_AM,((trips.trip_mode == 'SHARED2PAY') & is_am) * tour_participants +,SHARED3FREE_AM,((trips.trip_mode == 'SHARED3FREE') & is_am) * tour_participants +,SHARED3PAY_AM,((trips.trip_mode == 'SHARED3PAY') & is_am) * tour_participants +,WALK_AM,((trips.trip_mode == 'WALK') & is_am) * tour_participants +,BIKE_AM,((trips.trip_mode == 'BIKE') & is_am) * tour_participants +,WALK_LOC_WALK_AM,((trips.trip_mode == 'WALK_LOC') & is_am) * tour_participants +,WALK_LRF_WALK_AM,((trips.trip_mode == 'WALK_LRF') & is_am) * tour_participants +,WALK_EXP_WALK_AM,((trips.trip_mode == 'WALK_EXP') & is_am) * tour_participants +,WALK_HVY_WALK_AM,((trips.trip_mode == 'WALK_HVY') & is_am) * tour_participants +,WALK_COM_WALK_AM,((trips.trip_mode == 'WALK_COM') & is_am) * tour_participants +,DRIVE_LOC_WALK_AM,((trips.trip_mode == 'DRIVE_LOC') & is_am & trips.outbound) * tour_participants +,DRIVE_LRF_WALK_AM,((trips.trip_mode == 'DRIVE_LRF') & is_am & trips.outbound) * tour_participants +,DRIVE_EXP_WALK_AM,((trips.trip_mode == 'DRIVE_EXP') & is_am & trips.outbound) * tour_participants +,DRIVE_HVY_WALK_AM,((trips.trip_mode == 'DRIVE_HVY') & is_am & trips.outbound) * tour_participants +,DRIVE_COM_WALK_AM,((trips.trip_mode == 'DRIVE_COM') & is_am & trips.outbound) * tour_participants +,WALK_LOC_DRIVE_AM,((trips.trip_mode == 'DRIVE_LOC') & is_am & ~trips.outbound) * tour_participants +,WALK_LRF_DRIVE_AM,((trips.trip_mode == 'DRIVE_LRF') & is_am & ~trips.outbound) * tour_participants +,WALK_EXP_DRIVE_AM,((trips.trip_mode == 'DRIVE_EXP') & is_am & ~trips.outbound) * tour_participants +,WALK_DRIVE_HVY_AM,((trips.trip_mode == 'DRIVE_HVY') & is_am & ~trips.outbound) * tour_participants +,WALK_COM_DRIVE_AM,((trips.trip_mode == 'DRIVE_COM') & is_am & ~trips.outbound) * tour_participants +# md trips,, +,DRIVEALONEFREE_MD,((trips.trip_mode == 'DRIVEALONEFREE') & is_md) * tour_participants +,DRIVEALONEPAY_MD,((trips.trip_mode == 'DRIVEALONEPAY') & is_md) * tour_participants +,SHARED2FREE_MD,((trips.trip_mode == 'SHARED2FREE') & is_md) * tour_participants +,SHARED2PAY_MD,((trips.trip_mode == 'SHARED2PAY') & is_md) * tour_participants +,SHARED3FREE_MD,((trips.trip_mode == 'SHARED3FREE') & is_md) * tour_participants +,SHARED3PAY_MD,((trips.trip_mode == 'SHARED3PAY') & is_md) * tour_participants +,WALK_MD,((trips.trip_mode == 'WALK') & is_md) * tour_participants +,BIKE_MD,((trips.trip_mode == 'BIKE') & is_md) * tour_participants +,WALK_LOC_WALK_MD,((trips.trip_mode == 'WALK_LOC') & is_md) * tour_participants +,WALK_LRF_WALK_MD,((trips.trip_mode == 'WALK_LRF') & is_md) * tour_participants +,WALK_EXP_WALK_MD,((trips.trip_mode == 'WALK_EXP') & is_md) * tour_participants +,WALK_HVY_WALK_MD,((trips.trip_mode == 'WALK_HVY') & is_md) * tour_participants +,WALK_COM_WALK_MD,((trips.trip_mode == 'WALK_COM') & is_md) * tour_participants +,DRIVE_LOC_WALK_MD,((trips.trip_mode == 'DRIVE_LOC') & is_md & trips.outbound) * tour_participants +,DRIVE_LRF_WALK_MD,((trips.trip_mode == 'DRIVE_LRF') & is_md & trips.outbound) * tour_participants +,DRIVE_EXP_WALK_MD,((trips.trip_mode == 'DRIVE_EXP') & is_md & trips.outbound) * tour_participants +,DRIVE_HVY_WALK_MD,((trips.trip_mode == 'DRIVE_HVY') & is_md & trips.outbound) * tour_participants +,DRIVE_COM_WALK_MD,((trips.trip_mode == 'DRIVE_COM') & is_md & trips.outbound) * tour_participants +,WALK_LOC_DRIVE_MD,((trips.trip_mode == 'DRIVE_LOC') & is_md & ~trips.outbound) * tour_participants +,WALK_LRF_DRIVE_MD,((trips.trip_mode == 'DRIVE_LRF') & is_md & ~trips.outbound) * tour_participants +,WALK_EXP_DRIVE_MD,((trips.trip_mode == 'DRIVE_EXP') & is_md & ~trips.outbound) * tour_participants +,WALK_DRIVE_HVY_MD,((trips.trip_mode == 'DRIVE_HVY') & is_md & ~trips.outbound) * tour_participants +,WALK_COM_DRIVE_MD,((trips.trip_mode == 'DRIVE_COM') & is_md & ~trips.outbound) * tour_participants +# pm trips,, +,DRIVEALONEFREE_PM,((trips.trip_mode == 'DRIVEALONEFREE') & is_pm) * tour_participants +,DRIVEALONEPAY_PM,((trips.trip_mode == 'DRIVEALONEPAY') & is_pm) * tour_participants +,SHARED2FREE_PM,((trips.trip_mode == 'SHARED2FREE') & is_pm) * tour_participants +,SHARED2PAY_PM,((trips.trip_mode == 'SHARED2PAY') & is_pm) * tour_participants +,SHARED3FREE_PM,((trips.trip_mode == 'SHARED3FREE') & is_pm) * tour_participants +,SHARED3PAY_PM,((trips.trip_mode == 'SHARED3PAY') & is_pm) * tour_participants +,WALK_PM,((trips.trip_mode == 'WALK') & is_pm) * tour_participants +,BIKE_PM,((trips.trip_mode == 'BIKE') & is_pm) * tour_participants +,WALK_LOC_WALK_PM,((trips.trip_mode == 'WALK_LOC') & is_pm) * tour_participants +,WALK_LRF_WALK_PM,((trips.trip_mode == 'WALK_LRF') & is_pm) * tour_participants +,WALK_EXP_WALK_PM,((trips.trip_mode == 'WALK_EXP') & is_pm) * tour_participants +,WALK_HVY_WALK_PM,((trips.trip_mode == 'WALK_HVY') & is_pm) * tour_participants +,WALK_COM_WALK_PM,((trips.trip_mode == 'WALK_COM') & is_pm) * tour_participants +,DRIVE_LOC_WALK_PM,((trips.trip_mode == 'DRIVE_LOC') & is_pm & trips.outbound) * tour_participants +,DRIVE_LRF_WALK_PM,((trips.trip_mode == 'DRIVE_LRF') & is_pm & trips.outbound) * tour_participants +,DRIVE_EXP_WALK_PM,((trips.trip_mode == 'DRIVE_EXP') & is_pm & trips.outbound) * tour_participants +,DRIVE_HVY_WALK_PM,((trips.trip_mode == 'DRIVE_HVY') & is_pm & trips.outbound) * tour_participants +,DRIVE_COM_WALK_PM,((trips.trip_mode == 'DRIVE_COM') & is_pm & trips.outbound) * tour_participants +,WALK_LOC_DRIVE_PM,((trips.trip_mode == 'DRIVE_LOC') & is_pm & ~trips.outbound) * tour_participants +,WALK_LRF_DRIVE_PM,((trips.trip_mode == 'DRIVE_LRF') & is_pm & ~trips.outbound) * tour_participants +,WALK_EXP_DRIVE_PM,((trips.trip_mode == 'DRIVE_EXP') & is_pm & ~trips.outbound) * tour_participants +,WALK_DRIVE_HVY_PM,((trips.trip_mode == 'DRIVE_HVY') & is_pm & ~trips.outbound) * tour_participants +,WALK_COM_DRIVE_PM,((trips.trip_mode == 'DRIVE_COM') & is_pm & ~trips.outbound) * tour_participants +# ev trips,, +,DRIVEALONEFREE_EV,((trips.trip_mode == 'DRIVEALONEFREE') & is_ev) * tour_participants +,DRIVEALONEPAY_EV,((trips.trip_mode == 'DRIVEALONEPAY') & is_ev) * tour_participants +,SHARED2FREE_EV,((trips.trip_mode == 'SHARED2FREE') & is_ev) * tour_participants +,SHARED2PAY_EV,((trips.trip_mode == 'SHARED2PAY') & is_ev) * tour_participants +,SHARED3FREE_EV,((trips.trip_mode == 'SHARED3FREE') & is_ev) * tour_participants +,SHARED3PAY_EV,((trips.trip_mode == 'SHARED3PAY') & is_ev) * tour_participants +,WALK_EV,((trips.trip_mode == 'WALK') & is_ev) * tour_participants +,BIKE_EV,((trips.trip_mode == 'BIKE') & is_ev) * tour_participants +,WALK_LOC_WALK_EV,((trips.trip_mode == 'WALK_LOC') & is_ev) * tour_participants +,WALK_LRF_WALK_EV,((trips.trip_mode == 'WALK_LRF') & is_ev) * tour_participants +,WALK_EXP_WALK_EV,((trips.trip_mode == 'WALK_EXP') & is_ev) * tour_participants +,WALK_HVY_WALK_EV,((trips.trip_mode == 'WALK_HVY') & is_ev) * tour_participants +,WALK_COM_WALK_EV,((trips.trip_mode == 'WALK_COM') & is_ev) * tour_participants +,DRIVE_LOC_WALK_EV,((trips.trip_mode == 'DRIVE_LOC') & is_ev & trips.outbound) * tour_participants +,DRIVE_LRF_WALK_EV,((trips.trip_mode == 'DRIVE_LRF') & is_ev & trips.outbound) * tour_participants +,DRIVE_EXP_WALK_EV,((trips.trip_mode == 'DRIVE_EXP') & is_ev & trips.outbound) * tour_participants +,DRIVE_HVY_WALK_EV,((trips.trip_mode == 'DRIVE_HVY') & is_ev & trips.outbound) * tour_participants +,DRIVE_COM_WALK_EV,((trips.trip_mode == 'DRIVE_COM') & is_ev & trips.outbound) * tour_participants +,WALK_LOC_DRIVE_EV,((trips.trip_mode == 'DRIVE_LOC') & is_ev & ~trips.outbound) * tour_participants +,WALK_LRF_DRIVE_EV,((trips.trip_mode == 'DRIVE_LRF') & is_ev & ~trips.outbound) * tour_participants +,WALK_EXP_DRIVE_EV,((trips.trip_mode == 'DRIVE_EXP') & is_ev & ~trips.outbound) * tour_participants +,WALK_DRIVE_HVY_EV,((trips.trip_mode == 'DRIVE_HVY') & is_ev & ~trips.outbound) * tour_participants +,WALK_COM_DRIVE_EV,((trips.trip_mode == 'DRIVE_COM') & is_ev & ~trips.outbound) * tour_participants diff --git a/activitysim/examples/example_psrc/configs_accessibility/settings.yaml b/activitysim/examples/placeholder_psrc/configs_accessibility/settings.yaml similarity index 96% rename from activitysim/examples/example_psrc/configs_accessibility/settings.yaml rename to activitysim/examples/placeholder_psrc/configs_accessibility/settings.yaml index 68b0fb5a66..499c8e055a 100755 --- a/activitysim/examples/example_psrc/configs_accessibility/settings.yaml +++ b/activitysim/examples/placeholder_psrc/configs_accessibility/settings.yaml @@ -1,42 +1,42 @@ -inherit_settings: True - -# number of households to simulate -households_sample_size: 10 -# simulate all households -# households_sample_size: 0 - -chunk_size: 0 - -# - shadow pricing global switches - -# turn shadow_pricing on and off for all models (e.g. school and work) -# shadow pricing is deprecated for less than full samples -# see shadow_pricing.yaml for additional settings -use_shadow_pricing: False - -# turn writing of sample_tables on and off for all models -# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) -want_dest_choice_sample_tables: False - -# global switch to turn on or off presampling of destination alternatives at TAZ level (multizone models only) -#want_dest_choice_presampling: True - -# - tracing -# trace household id; comment out or leave empty for no trace -trace_hh_id: 594396 - -# trace origin, destination in accessibility calculation; comment out or leave empty for no trace -# trace_od: [5, 11] -trace_od: - -# to resume after last successful checkpoint, specify resume_after: _ -#resume_after: tour_mode_choice_simulate -#resume_after: - -output_tables: - h5_store: False - action: include - prefix: final_ - tables: - - accessibility - +inherit_settings: True + +# number of households to simulate +households_sample_size: 10 +# simulate all households +# households_sample_size: 0 + +chunk_size: 0 + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +# global switch to turn on or off presampling of destination alternatives at TAZ level (multizone models only) +#want_dest_choice_presampling: True + +# - tracing +# trace household id; comment out or leave empty for no trace +trace_hh_id: 594396 + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +# trace_od: [5, 11] +trace_od: + +# to resume after last successful checkpoint, specify resume_after: _ +#resume_after: tour_mode_choice_simulate +#resume_after: + +output_tables: + h5_store: False + action: include + prefix: final_ + tables: + - accessibility + diff --git a/activitysim/examples/example_psrc/configs_accessibility/settings_mp.yaml b/activitysim/examples/placeholder_psrc/configs_accessibility/settings_mp.yaml similarity index 100% rename from activitysim/examples/example_psrc/configs_accessibility/settings_mp.yaml rename to activitysim/examples/placeholder_psrc/configs_accessibility/settings_mp.yaml diff --git a/activitysim/examples/example_psrc/configs_skip_accessibility/settings.yaml b/activitysim/examples/placeholder_psrc/configs_skip_accessibility/settings.yaml similarity index 100% rename from activitysim/examples/example_psrc/configs_skip_accessibility/settings.yaml rename to activitysim/examples/placeholder_psrc/configs_skip_accessibility/settings.yaml diff --git a/activitysim/examples/example_psrc/configs_skip_accessibility/settings_mp.yaml b/activitysim/examples/placeholder_psrc/configs_skip_accessibility/settings_mp.yaml similarity index 100% rename from activitysim/examples/example_psrc/configs_skip_accessibility/settings_mp.yaml rename to activitysim/examples/placeholder_psrc/configs_skip_accessibility/settings_mp.yaml diff --git a/activitysim/examples/example_psrc/data/.gitignore b/activitysim/examples/placeholder_psrc/data/.gitignore similarity index 100% rename from activitysim/examples/example_psrc/data/.gitignore rename to activitysim/examples/placeholder_psrc/data/.gitignore diff --git a/activitysim/examples/example_psrc/data/households.csv b/activitysim/examples/placeholder_psrc/data/households.csv similarity index 100% rename from activitysim/examples/example_psrc/data/households.csv rename to activitysim/examples/placeholder_psrc/data/households.csv diff --git a/activitysim/examples/example_psrc/data/land_use.csv b/activitysim/examples/placeholder_psrc/data/land_use.csv similarity index 100% rename from activitysim/examples/example_psrc/data/land_use.csv rename to activitysim/examples/placeholder_psrc/data/land_use.csv diff --git a/activitysim/examples/example_psrc/data/maz.csv b/activitysim/examples/placeholder_psrc/data/maz.csv similarity index 100% rename from activitysim/examples/example_psrc/data/maz.csv rename to activitysim/examples/placeholder_psrc/data/maz.csv diff --git a/activitysim/examples/example_psrc/data/maz_to_maz_bike.csv b/activitysim/examples/placeholder_psrc/data/maz_to_maz_bike.csv similarity index 100% rename from activitysim/examples/example_psrc/data/maz_to_maz_bike.csv rename to activitysim/examples/placeholder_psrc/data/maz_to_maz_bike.csv diff --git a/activitysim/examples/example_psrc/data/maz_to_maz_walk.csv b/activitysim/examples/placeholder_psrc/data/maz_to_maz_walk.csv similarity index 100% rename from activitysim/examples/example_psrc/data/maz_to_maz_walk.csv rename to activitysim/examples/placeholder_psrc/data/maz_to_maz_walk.csv diff --git a/activitysim/examples/example_psrc/data/persons.csv b/activitysim/examples/placeholder_psrc/data/persons.csv similarity index 100% rename from activitysim/examples/example_psrc/data/persons.csv rename to activitysim/examples/placeholder_psrc/data/persons.csv diff --git a/activitysim/examples/example_psrc/data/skims.omx b/activitysim/examples/placeholder_psrc/data/skims.omx similarity index 100% rename from activitysim/examples/example_psrc/data/skims.omx rename to activitysim/examples/placeholder_psrc/data/skims.omx diff --git a/activitysim/examples/example_psrc/data/taz.csv b/activitysim/examples/placeholder_psrc/data/taz.csv similarity index 100% rename from activitysim/examples/example_psrc/data/taz.csv rename to activitysim/examples/placeholder_psrc/data/taz.csv diff --git a/activitysim/examples/example_multiple_zone/output_1/.gitignore b/activitysim/examples/placeholder_psrc/output/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/output_1/.gitignore rename to activitysim/examples/placeholder_psrc/output/.gitignore diff --git a/activitysim/examples/example_mtc/output/cache/.gitignore b/activitysim/examples/placeholder_psrc/output/cache/.gitignore similarity index 100% rename from activitysim/examples/example_mtc/output/cache/.gitignore rename to activitysim/examples/placeholder_psrc/output/cache/.gitignore diff --git a/activitysim/examples/example_mtc/output/log/.gitignore b/activitysim/examples/placeholder_psrc/output/log/.gitignore similarity index 100% rename from activitysim/examples/example_mtc/output/log/.gitignore rename to activitysim/examples/placeholder_psrc/output/log/.gitignore diff --git a/activitysim/examples/example_mtc/output/trace/.gitignore b/activitysim/examples/placeholder_psrc/output/trace/.gitignore similarity index 100% rename from activitysim/examples/example_mtc/output/trace/.gitignore rename to activitysim/examples/placeholder_psrc/output/trace/.gitignore diff --git a/activitysim/examples/example_psrc/scripts/integrity.py b/activitysim/examples/placeholder_psrc/scripts/integrity.py similarity index 100% rename from activitysim/examples/example_psrc/scripts/integrity.py rename to activitysim/examples/placeholder_psrc/scripts/integrity.py diff --git a/activitysim/examples/example_psrc/scripts/psrc_crop.py b/activitysim/examples/placeholder_psrc/scripts/psrc_crop.py similarity index 100% rename from activitysim/examples/example_psrc/scripts/psrc_crop.py rename to activitysim/examples/placeholder_psrc/scripts/psrc_crop.py diff --git a/activitysim/examples/example_psrc/test/configs/settings.yaml b/activitysim/examples/placeholder_psrc/test/configs/settings.yaml similarity index 100% rename from activitysim/examples/example_psrc/test/configs/settings.yaml rename to activitysim/examples/placeholder_psrc/test/configs/settings.yaml diff --git a/activitysim/examples/example_multiple_zone/output_2/.gitignore b/activitysim/examples/placeholder_psrc/test/output/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/output_2/.gitignore rename to activitysim/examples/placeholder_psrc/test/output/.gitignore diff --git a/activitysim/examples/example_mtc/test/output/cache/.gitignore b/activitysim/examples/placeholder_psrc/test/output/cache/.gitignore similarity index 100% rename from activitysim/examples/example_mtc/test/output/cache/.gitignore rename to activitysim/examples/placeholder_psrc/test/output/cache/.gitignore diff --git a/activitysim/examples/example_mtc/test/output/trace/.gitignore b/activitysim/examples/placeholder_psrc/test/output/trace/.gitignore similarity index 100% rename from activitysim/examples/example_mtc/test/output/trace/.gitignore rename to activitysim/examples/placeholder_psrc/test/output/trace/.gitignore diff --git a/activitysim/examples/example_psrc/test/regress/final_trips.csv b/activitysim/examples/placeholder_psrc/test/regress/final_trips.csv similarity index 100% rename from activitysim/examples/example_psrc/test/regress/final_trips.csv rename to activitysim/examples/placeholder_psrc/test/regress/final_trips.csv diff --git a/activitysim/examples/example_marin/test/simulation.py b/activitysim/examples/placeholder_psrc/test/simulation.py similarity index 94% rename from activitysim/examples/example_marin/test/simulation.py rename to activitysim/examples/placeholder_psrc/test/simulation.py index ec6a1181b1..97ca6b6483 100755 --- a/activitysim/examples/example_marin/test/simulation.py +++ b/activitysim/examples/placeholder_psrc/test/simulation.py @@ -1,15 +1,15 @@ -# ActivitySim -# See full license in LICENSE.txt. - -import sys -import argparse - -from activitysim.cli.run import add_run_args, run - -if __name__ == '__main__': - - parser = argparse.ArgumentParser() - add_run_args(parser) - args = parser.parse_args() - - sys.exit(run(args)) +# ActivitySim +# See full license in LICENSE.txt. + +import sys +import argparse + +from activitysim.cli.run import add_run_args, run + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + add_run_args(parser) + args = parser.parse_args() + + sys.exit(run(args)) diff --git a/activitysim/examples/example_psrc/test/test_psrc.py b/activitysim/examples/placeholder_psrc/test/test_psrc.py similarity index 94% rename from activitysim/examples/example_psrc/test/test_psrc.py rename to activitysim/examples/placeholder_psrc/test/test_psrc.py index 1aa9a2c7dd..958e67215e 100644 --- a/activitysim/examples/example_psrc/test/test_psrc.py +++ b/activitysim/examples/placeholder_psrc/test/test_psrc.py @@ -18,7 +18,7 @@ def teardown_function(func): def test_psrc(): def example_path(dirname): - resource = os.path.join('examples', 'example_psrc', dirname) + resource = os.path.join('examples', 'placeholder_psrc', dirname) return pkg_resources.resource_filename('activitysim', resource) def test_path(dirname): diff --git a/activitysim/examples/example_sandag/3_zone_change_log.txt b/activitysim/examples/placeholder_sandag/3_zone_change_log.txt similarity index 100% rename from activitysim/examples/example_sandag/3_zone_change_log.txt rename to activitysim/examples/placeholder_sandag/3_zone_change_log.txt diff --git a/activitysim/examples/example_sandag/configs_1_zone/constants.yaml b/activitysim/examples/placeholder_sandag/configs_1_zone/constants.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_1_zone/constants.yaml rename to activitysim/examples/placeholder_sandag/configs_1_zone/constants.yaml diff --git a/activitysim/examples/example_sandag/configs_1_zone/network_los.yaml b/activitysim/examples/placeholder_sandag/configs_1_zone/network_los.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_1_zone/network_los.yaml rename to activitysim/examples/placeholder_sandag/configs_1_zone/network_los.yaml diff --git a/activitysim/examples/example_sandag/configs_1_zone/settings.yaml b/activitysim/examples/placeholder_sandag/configs_1_zone/settings.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_1_zone/settings.yaml rename to activitysim/examples/placeholder_sandag/configs_1_zone/settings.yaml diff --git a/activitysim/examples/example_sandag/configs_1_zone/settings_mp.yaml b/activitysim/examples/placeholder_sandag/configs_1_zone/settings_mp.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_1_zone/settings_mp.yaml rename to activitysim/examples/placeholder_sandag/configs_1_zone/settings_mp.yaml diff --git a/activitysim/examples/example_sandag/configs_2_zone/constants.yaml b/activitysim/examples/placeholder_sandag/configs_2_zone/constants.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_2_zone/constants.yaml rename to activitysim/examples/placeholder_sandag/configs_2_zone/constants.yaml diff --git a/activitysim/examples/example_sandag/configs_2_zone/network_los.yaml b/activitysim/examples/placeholder_sandag/configs_2_zone/network_los.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_2_zone/network_los.yaml rename to activitysim/examples/placeholder_sandag/configs_2_zone/network_los.yaml diff --git a/activitysim/examples/example_sandag/configs_2_zone/settings.yaml b/activitysim/examples/placeholder_sandag/configs_2_zone/settings.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_2_zone/settings.yaml rename to activitysim/examples/placeholder_sandag/configs_2_zone/settings.yaml diff --git a/activitysim/examples/example_sandag/configs_2_zone/settings_mp.yaml b/activitysim/examples/placeholder_sandag/configs_2_zone/settings_mp.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_2_zone/settings_mp.yaml rename to activitysim/examples/placeholder_sandag/configs_2_zone/settings_mp.yaml diff --git a/activitysim/examples/example_mtc/configs/tour_departure_and_duration_segments.csv b/activitysim/examples/placeholder_sandag/configs_2_zone/tour_departure_and_duration_segments.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/tour_departure_and_duration_segments.csv rename to activitysim/examples/placeholder_sandag/configs_2_zone/tour_departure_and_duration_segments.csv diff --git a/activitysim/examples/example_sandag/configs_3_zone/_bugs.txt b/activitysim/examples/placeholder_sandag/configs_3_zone/_bugs.txt similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/_bugs.txt rename to activitysim/examples/placeholder_sandag/configs_3_zone/_bugs.txt diff --git a/activitysim/examples/example_sandag/configs_3_zone/accessibility.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/accessibility.csv similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/accessibility.csv rename to activitysim/examples/placeholder_sandag/configs_3_zone/accessibility.csv diff --git a/activitysim/examples/example_sandag/configs_3_zone/annotate_households_workplace.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/annotate_households_workplace.csv similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/annotate_households_workplace.csv rename to activitysim/examples/placeholder_sandag/configs_3_zone/annotate_households_workplace.csv diff --git a/activitysim/examples/example_sandag/configs_3_zone/annotate_persons_workplace.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/annotate_persons_workplace.csv similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/annotate_persons_workplace.csv rename to activitysim/examples/placeholder_sandag/configs_3_zone/annotate_persons_workplace.csv diff --git a/activitysim/examples/example_mtc/configs/auto_ownership.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/auto_ownership.csv similarity index 99% rename from activitysim/examples/example_mtc/configs/auto_ownership.csv rename to activitysim/examples/placeholder_sandag/configs_3_zone/auto_ownership.csv index a2f5a32910..aa30bdb4db 100644 --- a/activitysim/examples/example_mtc/configs/auto_ownership.csv +++ b/activitysim/examples/placeholder_sandag/configs_3_zone/auto_ownership.csv @@ -1,30 +1,30 @@ -Label,Description,Expression,cars0,cars1,cars2,cars3,cars4 -util_drivers_2,2 Adults (age 16+),num_drivers==2,,coef_cars1_drivers_2,coef_cars2_drivers_2,coef_cars3_drivers_2,coef_cars4_drivers_2 -util_drivers_3,3 Adults (age 16+),num_drivers==3,,coef_cars1_drivers_3,coef_cars2_drivers_3,coef_cars3_drivers_3,coef_cars4_drivers_3 -util_drivers_4_up,4+ Adults (age 16+),num_drivers>3,,coef_cars1_drivers_4_up,coef_cars2_drivers_4_up,coef_cars3_drivers_4_up,coef_cars4_drivers_4_up -util_persons_16_17,Persons age 16-17,num_children_16_to_17,,coef_cars1_persons_16_17,coef_cars2_persons_16_17,coef_cars34_persons_16_17,coef_cars34_persons_16_17 -util_persons_18_24,Persons age 18-24,num_college_age,,coef_cars1_persons_18_24,coef_cars2_persons_18_24,coef_cars34_persons_18_24,coef_cars34_persons_18_24 -util_persons_25_34,Persons age 35-34,num_young_adults,,coef_cars1_persons_25_34,coef_cars2_persons_25_34,coef_cars34_persons_25_34,coef_cars34_persons_25_34 -util_presence_children_0_4,Presence of children age 0-4,num_young_children>0,,coef_cars1_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4 -util_presence_children_5_17,Presence of children age 5-17,(num_children_5_to_15+num_children_16_to_17)>0,,coef_cars1_presence_children_5_17,coef_cars2_presence_children_5_17,coef_cars34_presence_children_5_17,coef_cars34_presence_children_5_17 -util_num_workers_clip_3,"Number of workers, capped at 3",@df.num_workers.clip(upper=3),,coef_cars1_num_workers_clip_3,coef_cars2_num_workers_clip_3,coef_cars3_num_workers_clip_3,coef_cars4_num_workers_clip_3 -util_hh_income_0_30k,"Piecewise Linear household income, $0-30k","@df.income_in_thousands.clip(0, 30)",,coef_cars1_hh_income_0_30k,coef_cars2_hh_income_0_30k,coef_cars3_hh_income_0_30k,coef_cars4_hh_income_0_30k -util_hh_income_30_75k,"Piecewise Linear household income, $30-75k","@(df.income_in_thousands-30).clip(0, 45)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up -util_hh_income_75k_up,"Piecewise Linear household income, $75k+, capped at $125k","@(df.income_in_thousands-75).clip(0, 50)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up -util_density_0_10_no_workers,"Density index up to 10, if 0 workers","@(df.num_workers==0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers -util_density_10_up_no_workers,"Density index in excess of 10, if 0 workers",@(df.num_workers==0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_no_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers -util_density_0_10_workers,"Density index up to 10, if 1+ workers","@(df.num_workers>0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers -util_density_10_up_workers,"Density index in excess of 10, if 1+ workers",@(df.num_workers>0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers -util_asc,Constants,1,,coef_cars1_asc,coef_cars2_asc,coef_cars3_asc,coef_cars4_asc -util_asc_san_francisco,San Francisco county,@df.county_id == ID_SAN_FRANCISCO,,coef_cars1_asc_san_francisco,coef_cars2_asc_san_francisco,coef_cars34_asc_san_francisco,coef_cars34_asc_san_francisco -util_asc_solano,Solano county,@df.county_id == ID_SOLANO,,coef_cars1_asc_county,coef_cars2_asc_county,coef_cars34_asc_county,coef_cars34_asc_county -util_asc_napa,Napa county,@df.county_id == ID_NAPA,,coef_cars1_asc_county,coef_cars2_asc_county,coef_cars34_asc_county,coef_cars34_asc_county -util_asc_sonoma,Sonoma county,@df.county_id == ID_SONOMA,,coef_cars1_asc_county,coef_cars2_asc_county,coef_cars34_asc_county,coef_cars34_asc_county -util_asc_marin,Marin county,@df.county_id == ID_MARIN,,coef_cars1_asc_marin,coef_cars234_asc_marin,coef_cars234_asc_marin,coef_cars234_asc_marin -util_retail_auto_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 0 workers",(num_workers==0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers -util_retail_auto_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 1+ workers",(num_workers>0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers -util_retail_transit_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 0 workers",(num_workers==0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers -util_retail_transit_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 1+ workers",(num_workers>0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers -util_retail_non_motor_no_workers,"Retail accessibility by non-motorized, if 0 workers",(num_workers==0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor -util_retail_non_motor_workers,"Retail accessibility by non-motorized, if 1+ workers",(num_workers>0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor -util_auto_time_saving_per_worker,Auto time savings per worker to work,"@np.where(df.num_workers > 0, df.hh_work_auto_savings_ratio / df.num_workers, 0)",,coef_cars1_auto_time_saving_per_worker,coef_cars2_auto_time_saving_per_worker,coef_cars3_auto_time_saving_per_worker,coef_cars4_auto_time_saving_per_worker +Label,Description,Expression,cars0,cars1,cars2,cars3,cars4 +util_drivers_2,2 Adults (age 16+),num_drivers==2,,coef_cars1_drivers_2,coef_cars2_drivers_2,coef_cars3_drivers_2,coef_cars4_drivers_2 +util_drivers_3,3 Adults (age 16+),num_drivers==3,,coef_cars1_drivers_3,coef_cars2_drivers_3,coef_cars3_drivers_3,coef_cars4_drivers_3 +util_drivers_4_up,4+ Adults (age 16+),num_drivers>3,,coef_cars1_drivers_4_up,coef_cars2_drivers_4_up,coef_cars3_drivers_4_up,coef_cars4_drivers_4_up +util_persons_16_17,Persons age 16-17,num_children_16_to_17,,coef_cars1_persons_16_17,coef_cars2_persons_16_17,coef_cars34_persons_16_17,coef_cars34_persons_16_17 +util_persons_18_24,Persons age 18-24,num_college_age,,coef_cars1_persons_18_24,coef_cars2_persons_18_24,coef_cars34_persons_18_24,coef_cars34_persons_18_24 +util_persons_25_34,Persons age 35-34,num_young_adults,,coef_cars1_persons_25_34,coef_cars2_persons_25_34,coef_cars34_persons_25_34,coef_cars34_persons_25_34 +util_presence_children_0_4,Presence of children age 0-4,num_young_children>0,,coef_cars1_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4 +util_presence_children_5_17,Presence of children age 5-17,(num_children_5_to_15+num_children_16_to_17)>0,,coef_cars1_presence_children_5_17,coef_cars2_presence_children_5_17,coef_cars34_presence_children_5_17,coef_cars34_presence_children_5_17 +util_num_workers_clip_3,"Number of workers, capped at 3",@df.num_workers.clip(upper=3),,coef_cars1_num_workers_clip_3,coef_cars2_num_workers_clip_3,coef_cars3_num_workers_clip_3,coef_cars4_num_workers_clip_3 +util_hh_income_0_30k,"Piecewise Linear household income, $0-30k","@df.income_in_thousands.clip(0, 30)",,coef_cars1_hh_income_0_30k,coef_cars2_hh_income_0_30k,coef_cars3_hh_income_0_30k,coef_cars4_hh_income_0_30k +util_hh_income_30_75k,"Piecewise Linear household income, $30-75k","@(df.income_in_thousands-30).clip(0, 45)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up +util_hh_income_75k_up,"Piecewise Linear household income, $75k+, capped at $125k","@(df.income_in_thousands-75).clip(0, 50)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up +util_density_0_10_no_workers,"Density index up to 10, if 0 workers","@(df.num_workers==0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers +util_density_10_up_no_workers,"Density index in excess of 10, if 0 workers",@(df.num_workers==0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_no_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers +util_density_0_10_workers,"Density index up to 10, if 1+ workers","@(df.num_workers>0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers +util_density_10_up_workers,"Density index in excess of 10, if 1+ workers",@(df.num_workers>0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers +util_asc,Constants,1,,coef_cars1_asc,coef_cars2_asc,coef_cars3_asc,coef_cars4_asc +util_asc_san_francisco,San Francisco county,@df.county_id == ID_SAN_FRANCISCO,,coef_cars1_asc_san_francisco,coef_cars2_asc_san_francisco,coef_cars34_asc_san_francisco,coef_cars34_asc_san_francisco +util_asc_solano,Solano county,@df.county_id == ID_SOLANO,,coef_cars1_asc_county,coef_cars2_asc_county,coef_cars34_asc_county,coef_cars34_asc_county +util_asc_napa,Napa county,@df.county_id == ID_NAPA,,coef_cars1_asc_county,coef_cars2_asc_county,coef_cars34_asc_county,coef_cars34_asc_county +util_asc_sonoma,Sonoma county,@df.county_id == ID_SONOMA,,coef_cars1_asc_county,coef_cars2_asc_county,coef_cars34_asc_county,coef_cars34_asc_county +util_asc_marin,Marin county,@df.county_id == ID_MARIN,,coef_cars1_asc_marin,coef_cars234_asc_marin,coef_cars234_asc_marin,coef_cars234_asc_marin +util_retail_auto_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 0 workers",(num_workers==0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers +util_retail_auto_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 1+ workers",(num_workers>0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers +util_retail_transit_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 0 workers",(num_workers==0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers +util_retail_transit_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 1+ workers",(num_workers>0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers +util_retail_non_motor_no_workers,"Retail accessibility by non-motorized, if 0 workers",(num_workers==0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor +util_retail_non_motor_workers,"Retail accessibility by non-motorized, if 1+ workers",(num_workers>0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor +util_auto_time_saving_per_worker,Auto time savings per worker to work,"@np.where(df.num_workers > 0, df.hh_work_auto_savings_ratio / df.num_workers, 0)",,coef_cars1_auto_time_saving_per_worker,coef_cars2_auto_time_saving_per_worker,coef_cars3_auto_time_saving_per_worker,coef_cars4_auto_time_saving_per_worker diff --git a/activitysim/examples/example_sandag/configs_3_zone/constants.yaml b/activitysim/examples/placeholder_sandag/configs_3_zone/constants.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/constants.yaml rename to activitysim/examples/placeholder_sandag/configs_3_zone/constants.yaml diff --git a/activitysim/examples/example_sandag/configs_3_zone/destination_choice_size_terms.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/destination_choice_size_terms.csv similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/destination_choice_size_terms.csv rename to activitysim/examples/placeholder_sandag/configs_3_zone/destination_choice_size_terms.csv diff --git a/activitysim/examples/example_sandag/configs_3_zone/logging.yaml b/activitysim/examples/placeholder_sandag/configs_3_zone/logging.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/logging.yaml rename to activitysim/examples/placeholder_sandag/configs_3_zone/logging.yaml diff --git a/activitysim/examples/example_sandag/configs_3_zone/network_los.yaml b/activitysim/examples/placeholder_sandag/configs_3_zone/network_los.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/network_los.yaml rename to activitysim/examples/placeholder_sandag/configs_3_zone/network_los.yaml diff --git a/activitysim/examples/placeholder_sandag/configs_3_zone/notes.txt b/activitysim/examples/placeholder_sandag/configs_3_zone/notes.txt new file mode 100644 index 0000000000..97da329c1b --- /dev/null +++ b/activitysim/examples/placeholder_sandag/configs_3_zone/notes.txt @@ -0,0 +1,10 @@ + +# test +# activitysim run -c configs_local -c configs -c configs_3_zone -c ../prototype_mtc/configs -o output_test -d data_test +# activitysim run -c configs_local -c configs -c configs_3_zone -c ../prototype_mtc/configs -o output_test -d data_test -s settings_mp.yaml + +# full dataset single-process 100K HH skip_accessibility +# activitysim run -c configs_local -c configs_skip_accessibility -c configs -c configs_3_zone -c ../prototype_mtc/configs -o output_full -d data_full + +# full run multiprocess +# activitysim run -c configs -c configs_3_zone -c ../prototype_mtc/configs -o output_full -d data_full -s settings_mp.yaml diff --git a/activitysim/examples/example_sandag/configs_3_zone/settings.yaml b/activitysim/examples/placeholder_sandag/configs_3_zone/settings.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/settings.yaml rename to activitysim/examples/placeholder_sandag/configs_3_zone/settings.yaml diff --git a/activitysim/examples/example_sandag/configs_3_zone/settings_mp.yaml b/activitysim/examples/placeholder_sandag/configs_3_zone/settings_mp.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/settings_mp.yaml rename to activitysim/examples/placeholder_sandag/configs_3_zone/settings_mp.yaml diff --git a/activitysim/examples/example_sandag/configs_3_zone/stop_frequency.yaml b/activitysim/examples/placeholder_sandag/configs_3_zone/stop_frequency.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/stop_frequency.yaml rename to activitysim/examples/placeholder_sandag/configs_3_zone/stop_frequency.yaml diff --git a/activitysim/examples/example_sandag/configs_3_zone/tour_mode_choice.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/tour_mode_choice.csv similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/tour_mode_choice.csv rename to activitysim/examples/placeholder_sandag/configs_3_zone/tour_mode_choice.csv diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tour_mode_choice.yaml b/activitysim/examples/placeholder_sandag/configs_3_zone/tour_mode_choice.yaml similarity index 95% rename from activitysim/examples/example_multiple_zone/configs_3_zone/tour_mode_choice.yaml rename to activitysim/examples/placeholder_sandag/configs_3_zone/tour_mode_choice.yaml index 09c7469555..7e2f915bfa 100644 --- a/activitysim/examples/example_multiple_zone/configs_3_zone/tour_mode_choice.yaml +++ b/activitysim/examples/placeholder_sandag/configs_3_zone/tour_mode_choice.yaml @@ -1,186 +1,186 @@ -LOGIT_TYPE: NL -#LOGIT_TYPE: MNL - -tvpb_mode_path_types: - DRIVE_TRANSIT: - od: DTW - do: WTD - WALK_TRANSIT: - od: WTW - do: WTW - -NESTS: - name: root - coefficient: coef_nest_root - alternatives: - - name: AUTO - coefficient: coef_nest_AUTO - alternatives: - - name: DRIVEALONE - coefficient: coef_nest_AUTO_DRIVEALONE - alternatives: - - DRIVEALONEFREE - - DRIVEALONEPAY - - name: SHAREDRIDE2 - coefficient: coef_nest_AUTO_SHAREDRIDE2 - alternatives: - - SHARED2FREE - - SHARED2PAY - - name: SHAREDRIDE3 - coefficient: coef_nest_AUTO_SHAREDRIDE3 - alternatives: - - SHARED3FREE - - SHARED3PAY - - name: NONMOTORIZED - coefficient: coef_nest_NONMOTORIZED - alternatives: - - WALK - - BIKE - - name: TRANSIT - coefficient: coef_nest_TRANSIT - alternatives: - - WALK_TRANSIT - - DRIVE_TRANSIT - - name: RIDEHAIL - coefficient: coef_nest_RIDEHAIL - alternatives: - - TAXI - - TNC_SINGLE - - TNC_SHARED - -SPEC: tour_mode_choice.csv -COEFFICIENTS: tour_mode_choice_coefficients.csv -COEFFICIENT_TEMPLATE: tour_mode_choice_coefficients_template.csv - -CONSTANTS: - #valueOfTime: 8.00 - costPerMile: 18.29 - costShareSr2: 1.75 - costShareSr3: 2.50 - waitThresh: 10.00 - walkThresh: 1.50 - shortWalk: 0.333 - longWalk: 0.667 - walkSpeed: 3.00 - bikeThresh: 6.00 - bikeSpeed: 12.00 - maxCbdAreaTypeThresh: 2 - indivTour: 1.00000 - upperEA: 5 - upperAM: 10 - upperMD: 15 - upperPM: 19 - # RIDEHAIL Settings - Taxi_baseFare: 2.20 - Taxi_costPerMile: 2.30 - Taxi_costPerMinute: 0.10 - Taxi_waitTime_mean: - 1: 5.5 - 2: 9.5 - 3: 13.3 - 4: 17.3 - 5: 26.5 - Taxi_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - TNC_single_baseFare: 2.20 - TNC_single_costPerMile: 1.33 - TNC_single_costPerMinute: 0.24 - TNC_single_costMinimum: 7.20 - TNC_single_waitTime_mean: - 1: 3.0 - 2: 6.3 - 3: 8.4 - 4: 8.5 - 5: 10.3 - TNC_single_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - TNC_shared_baseFare: 2.20 - TNC_shared_costPerMile: 0.53 - TNC_shared_costPerMinute: 0.10 - TNC_shared_costMinimum: 3.00 - TNC_shared_IVTFactor: 1.5 - TNC_shared_waitTime_mean: - 1: 5.0 - 2: 8.0 - 3: 11.0 - 4: 15.0 - 5: 15.0 - TNC_shared_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - min_waitTime: 0 - max_waitTime: 50 - - ivt_cost_multiplier: 0.6 - ivt_lrt_multiplier: 0.9 - ivt_ferry_multiplier: 0.8 - ivt_exp_multiplier: 1 - ivt_hvy_multiplier: 0.8 - ivt_com_multiplier: 0.7 - walktimeshort_multiplier: 2 - walktimelong_multiplier: 10 - biketimeshort_multiplier: 4 - biketimelong_multiplier: 20 - short_i_wait_multiplier: 2 - long_i_wait_multiplier: 1 - wacc_multiplier: 2 - wegr_multiplier: 2 - waux_multiplier: 2 - dtim_multiplier: 2 - xwait_multiplier: 2 - dacc_ratio: 0 - xfers_wlk_multiplier: 10 - xfers_drv_multiplier: 20 - drvtrn_distpen_0_multiplier: 270 - drvtrn_distpen_max: 15 - density_index_multiplier: -0.2 -# joint_sr2_ASC_no_auto: 0 -# joint_sr2_ASC_auto_deficient: 0 -# joint_sr2_ASC_auto_sufficient: 0 -# joint_drive_transit_ASC_no_auto: 0 - -# so far, we can use the same spec as for non-joint tours -preprocessor: - SPEC: tour_mode_choice_annotate_choosers_preprocessor - DF: choosers - TABLES: - - land_use - - tours - -nontour_preprocessor: - SPEC: tour_mode_choice_annotate_choosers_preprocessor - DF: choosers - TABLES: - - land_use - -# to reduce memory needs filter chooser table to these fields -LOGSUM_CHOOSER_COLUMNS: - - tour_type - - hhsize - - density_index - - age - - age_16_p - - age_16_to_19 - - auto_ownership - - number_of_participants - - tour_category - - num_workers - - value_of_time - - free_parking_at_work - - income_segment - - demographic_segment - - c_ivt_for_segment - - c_cost_for_segment - -MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum +LOGIT_TYPE: NL +#LOGIT_TYPE: MNL + +tvpb_mode_path_types: + DRIVE_TRANSIT: + od: DTW + do: WTD + WALK_TRANSIT: + od: WTW + do: WTW + +NESTS: + name: root + coefficient: coef_nest_root + alternatives: + - name: AUTO + coefficient: coef_nest_AUTO + alternatives: + - name: DRIVEALONE + coefficient: coef_nest_AUTO_DRIVEALONE + alternatives: + - DRIVEALONEFREE + - DRIVEALONEPAY + - name: SHAREDRIDE2 + coefficient: coef_nest_AUTO_SHAREDRIDE2 + alternatives: + - SHARED2FREE + - SHARED2PAY + - name: SHAREDRIDE3 + coefficient: coef_nest_AUTO_SHAREDRIDE3 + alternatives: + - SHARED3FREE + - SHARED3PAY + - name: NONMOTORIZED + coefficient: coef_nest_NONMOTORIZED + alternatives: + - WALK + - BIKE + - name: TRANSIT + coefficient: coef_nest_TRANSIT + alternatives: + - WALK_TRANSIT + - DRIVE_TRANSIT + - name: RIDEHAIL + coefficient: coef_nest_RIDEHAIL + alternatives: + - TAXI + - TNC_SINGLE + - TNC_SHARED + +SPEC: tour_mode_choice.csv +COEFFICIENTS: tour_mode_choice_coefficients.csv +COEFFICIENT_TEMPLATE: tour_mode_choice_coefficients_template.csv + +CONSTANTS: + #valueOfTime: 8.00 + costPerMile: 18.29 + costShareSr2: 1.75 + costShareSr3: 2.50 + waitThresh: 10.00 + walkThresh: 1.50 + shortWalk: 0.333 + longWalk: 0.667 + walkSpeed: 3.00 + bikeThresh: 6.00 + bikeSpeed: 12.00 + maxCbdAreaTypeThresh: 2 + indivTour: 1.00000 + upperEA: 5 + upperAM: 10 + upperMD: 15 + upperPM: 19 + # RIDEHAIL Settings + Taxi_baseFare: 2.20 + Taxi_costPerMile: 2.30 + Taxi_costPerMinute: 0.10 + Taxi_waitTime_mean: + 1: 5.5 + 2: 9.5 + 3: 13.3 + 4: 17.3 + 5: 26.5 + Taxi_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_single_baseFare: 2.20 + TNC_single_costPerMile: 1.33 + TNC_single_costPerMinute: 0.24 + TNC_single_costMinimum: 7.20 + TNC_single_waitTime_mean: + 1: 3.0 + 2: 6.3 + 3: 8.4 + 4: 8.5 + 5: 10.3 + TNC_single_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_shared_baseFare: 2.20 + TNC_shared_costPerMile: 0.53 + TNC_shared_costPerMinute: 0.10 + TNC_shared_costMinimum: 3.00 + TNC_shared_IVTFactor: 1.5 + TNC_shared_waitTime_mean: + 1: 5.0 + 2: 8.0 + 3: 11.0 + 4: 15.0 + 5: 15.0 + TNC_shared_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + min_waitTime: 0 + max_waitTime: 50 + + ivt_cost_multiplier: 0.6 + ivt_lrt_multiplier: 0.9 + ivt_ferry_multiplier: 0.8 + ivt_exp_multiplier: 1 + ivt_hvy_multiplier: 0.8 + ivt_com_multiplier: 0.7 + walktimeshort_multiplier: 2 + walktimelong_multiplier: 10 + biketimeshort_multiplier: 4 + biketimelong_multiplier: 20 + short_i_wait_multiplier: 2 + long_i_wait_multiplier: 1 + wacc_multiplier: 2 + wegr_multiplier: 2 + waux_multiplier: 2 + dtim_multiplier: 2 + xwait_multiplier: 2 + dacc_ratio: 0 + xfers_wlk_multiplier: 10 + xfers_drv_multiplier: 20 + drvtrn_distpen_0_multiplier: 270 + drvtrn_distpen_max: 15 + density_index_multiplier: -0.2 +# joint_sr2_ASC_no_auto: 0 +# joint_sr2_ASC_auto_deficient: 0 +# joint_sr2_ASC_auto_sufficient: 0 +# joint_drive_transit_ASC_no_auto: 0 + +# so far, we can use the same spec as for non-joint tours +preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + - tours + +nontour_preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + +# to reduce memory needs filter chooser table to these fields +LOGSUM_CHOOSER_COLUMNS: + - tour_type + - hhsize + - density_index + - age + - age_16_p + - age_16_to_19 + - auto_ownership + - number_of_participants + - tour_category + - num_workers + - value_of_time + - free_parking_at_work + - income_segment + - demographic_segment + - c_ivt_for_segment + - c_cost_for_segment + +MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum diff --git a/activitysim/examples/example_sandag/configs_3_zone/tour_mode_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/tour_mode_choice_annotate_choosers_preprocessor.csv similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/tour_mode_choice_annotate_choosers_preprocessor.csv rename to activitysim/examples/placeholder_sandag/configs_3_zone/tour_mode_choice_annotate_choosers_preprocessor.csv diff --git a/activitysim/examples/example_sandag/configs_3_zone/trip_mode_choice.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice.csv similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/trip_mode_choice.csv rename to activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice.csv diff --git a/activitysim/examples/example_sandag/configs_3_zone/trip_mode_choice.yaml b/activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/trip_mode_choice.yaml rename to activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice.yaml diff --git a/activitysim/examples/example_sandag/configs_3_zone/trip_mode_choice_annotate_trips_preprocessor.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice_annotate_trips_preprocessor.csv similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/trip_mode_choice_annotate_trips_preprocessor.csv rename to activitysim/examples/placeholder_sandag/configs_3_zone/trip_mode_choice_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/example_sandag/configs_3_zone/tvpb_accessibility_tap_tap_.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/tvpb_accessibility_tap_tap_.csv similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/tvpb_accessibility_tap_tap_.csv rename to activitysim/examples/placeholder_sandag/configs_3_zone/tvpb_accessibility_tap_tap_.csv diff --git a/activitysim/examples/example_sandag/configs_3_zone/tvpb_accessibility_walk_maz_tap.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/tvpb_accessibility_walk_maz_tap.csv similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/tvpb_accessibility_walk_maz_tap.csv rename to activitysim/examples/placeholder_sandag/configs_3_zone/tvpb_accessibility_walk_maz_tap.csv diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_drive_maz_tap.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/tvpb_utility_drive_maz_tap.csv similarity index 99% rename from activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_drive_maz_tap.csv rename to activitysim/examples/placeholder_sandag/configs_3_zone/tvpb_utility_drive_maz_tap.csv index 9d402a2976..b601a743f8 100644 --- a/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_drive_maz_tap.csv +++ b/activitysim/examples/placeholder_sandag/configs_3_zone/tvpb_utility_drive_maz_tap.csv @@ -1,4 +1,4 @@ -Label,Description,Expression,utility -util_drive_available,walk available,@df.drive_time.isna() * C_UNAVAILABLE,1 -util_drive_time,drive time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_drive * df.drive_time",1 -util_drive_cost,drive cost,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_cost_high_income, c_cost_low_income) * df.DIST * c_auto_operating_cost_per_mile",1 +Label,Description,Expression,utility +util_drive_available,walk available,@df.drive_time.isna() * C_UNAVAILABLE,1 +util_drive_time,drive time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_drive * df.drive_time",1 +util_drive_cost,drive cost,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_cost_high_income, c_cost_low_income) * df.DIST * c_auto_operating_cost_per_mile",1 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_tap_tap.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/tvpb_utility_tap_tap.csv similarity index 99% rename from activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_tap_tap.csv rename to activitysim/examples/placeholder_sandag/configs_3_zone/tvpb_utility_tap_tap.csv index afd50f1642..0d2d6ebaf5 100644 --- a/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_tap_tap.csv +++ b/activitysim/examples/placeholder_sandag/configs_3_zone/tvpb_utility_tap_tap.csv @@ -1,69 +1,69 @@ -Label,Description,Expression,fastest,cheapest,shortest -# fastest,,,,, -util_transit_available_fastest,transit_available,@~df.transit_available_fastest * C_UNAVAILABLE,1,, -#,,, FIXME demonstrate that we can use path inor (access and egress modes here),, -util_bus_xfer_fastest,number of transfers,"@C_DRIVE_TRANSFER_PENALTY * (access_mode == 'drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_BOARDS_FAST')-1).clip(0)",1,, -#,,, local bus,, -util_bus_ivt_fastest,local bus in vehicle time,"@C_FASTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_FAST')",1,, -util_bus_wait_fastest,local bus wait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_IWAIT_FAST')",1,, -util_bus_xwait_fastest,local bus xwait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_XWAIT_FAST')",1,, -util_bus_fare_fastest,local bus fare,"@C_FASTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_FAR_FAST')",1,, -##,,, commuter bus,, -#util_com_ivt_fastest,commuter bus in vehicle time,"@C_FASTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_FAST')",1,, -#util_com_wait_fastest,commuter bus wait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_IWAIT_FAST')",1,, -#util_com_xwait_fastest,commuter bus xwait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_XWAIT_FAST')",1,, -#util_com_fare_fastest,commuter bus fare,"@C_FASTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_FAR_FAST')",1,, -##,,, express,, -#util_exp_ivt_fastest,express in vehicle time,"@C_FASTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_FAST')",1,, -#util_exp_wait_fastest,express wait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_IWAIT_FAST')",1,, -#util_exp_xwait_fastest,express bus xwait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_XWAIT_FAST')",1,, -#util_exp_fare_fastest,express fare,"@C_FASTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_FAR_FAST')",1,, -##,,, heavy,, -#util_hvy_ivt_fastest,heavy in vehicle time,"@C_FASTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_FAST')",1,, -#util_hvy_wait_fastest,heavy wait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_IWAIT_FAST')",1,, -#util_hvy_xwait_fastest,heavy bus xwait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_XWAIT_FAST')",1,, -#util_hvy_fare_fastest,heavy fare,"@C_FASTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_FAR_FAST')",1,, -## cheapest,,,,, -#util_transit_available_cheapest,transit_available,@~df.transit_available_cheapest * C_UNAVAILABLE,,1, -#,,,, local bus, -util_bus_ivt_cheapest,local bus in vehicle time,"@C_CHEAPEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_CHEAP')",,1 -util_bus_wait_cheapest,local bus wait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_IWAIT_CHEAP')",,1 -util_bus_xwait_cheapest,local bus xwait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_XWAIT_CHEAP')",,1 -util_bus_fare_cheapest,local bus fare,"@C_CHEAPEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_FAR_CHEAP')",,1 -##,,,, commuter bus, -#util_com_ivt_cheapest,commuter bus in vehicle time,"@C_CHEAPEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_CHEAP')",,1, -#util_com_wait_cheapest,commuter bus wait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_IWAIT_CHEAP')",,1, -#util_com_xwait_cheapest,commuter bus xwait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_XWAIT_CHEAP')",,1, -#util_com_fare_cheapest,commuter bus fare,"@C_CHEAPEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_FAR_CHEAP')",,1, -##,,,, express, -#util_exp_ivt_cheapest,express in vehicle time,"@C_CHEAPEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_CHEAP')",,1, -#util_exp_wait_cheapest,express wait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_IWAIT_CHEAP')",,1, -#util_exp_xwait_cheapest,express bus xwait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_XWAIT_CHEAP')",,1, -#util_exp_fare_cheapest,express fare,"@C_CHEAPEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_FAR_CHEAP')",,1, -##,,,, heavy, -#util_hvy_ivt_cheapest,heavy in vehicle time,"@C_CHEAPEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_CHEAP')",,1, -#util_hvy_wait_cheapest,heavy wait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_IWAIT_CHEAP')",,1, -#util_hvy_xwait_cheapest,heavy bus xwait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_XWAIT_CHEAP')",,1, -#util_hvy_fare_cheapest,heavy fare,"@C_CHEAPEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_FAR_CHEAP')",,1, -## shortest,,,,, -#util_transit_available_shortest,transit_available,@~df.transit_available_shortest * C_UNAVAILABLE,,,1 -#,,,,, local bus -util_bus_ivt_shortest,local bus in vehicle time,"@C_SHORTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_SHORT')",,,1 -util_bus_wait_shortest,local bus wait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_IWAIT_SHORT')",,,1 -util_bus_xwait_shortest,local bus xwait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_XWAIT_SHORT')",,,1 -util_bus_fare_shortest,local bus fare,"@C_SHORTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_FAR_SHORT')",,,1 -##,,,,, commuter bus -#util_com_ivt_shortest,commuter bus in vehicle time,"@C_SHORTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_SHORT')",,,1 -#util_com_wait_shortest,commuter bus wait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_IWAIT_SHORT')",,,1 -#util_com_xwait_shortest,commuter bus xwait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_XWAIT_SHORT')",,,1 -#util_com_fare_shortest,commuter bus fare,"@C_SHORTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_FAR_SHORT')",,,1 -##,,,,, express -#util_exp_ivt_shortest,express in vehicle time,"@C_SHORTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_SHORT')",,,1 -#util_exp_wait_shortest,express wait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_IWAIT_SHORT')",,,1 -#util_exp_xwait_shortest,express bus xwait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_XWAIT_SHORT')",,,1 -#util_exp_fare_shortest,express fare,"@C_SHORTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_FAR_SHORT')",,,1 -##,,,,, heav -#util_hvy_ivt_shortest,heavy in vehicle time,"@C_SHORTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_SHORT')",,,1 -#util_hvy_wait_shortest,heavy wait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_IWAIT_SHORT')",,,1 -#util_hvy_xwait_shortest,heavy bus xwait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_XWAIT_SHORT')",,,1 -#util_hvy_fare_shortest,heavy fare,"@C_SHORTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_FAR_SHORT')",,,1 +Label,Description,Expression,fastest,cheapest,shortest +# fastest,,,,, +util_transit_available_fastest,transit_available,@~df.transit_available_fastest * C_UNAVAILABLE,1,, +#,,, FIXME demonstrate that we can use path inor (access and egress modes here),, +util_bus_xfer_fastest,number of transfers,"@C_DRIVE_TRANSFER_PENALTY * (access_mode == 'drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_BOARDS_FAST')-1).clip(0)",1,, +#,,, local bus,, +util_bus_ivt_fastest,local bus in vehicle time,"@C_FASTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_FAST')",1,, +util_bus_wait_fastest,local bus wait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_IWAIT_FAST')",1,, +util_bus_xwait_fastest,local bus xwait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_XWAIT_FAST')",1,, +util_bus_fare_fastest,local bus fare,"@C_FASTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_FAR_FAST')",1,, +##,,, commuter bus,, +#util_com_ivt_fastest,commuter bus in vehicle time,"@C_FASTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_FAST')",1,, +#util_com_wait_fastest,commuter bus wait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_IWAIT_FAST')",1,, +#util_com_xwait_fastest,commuter bus xwait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_XWAIT_FAST')",1,, +#util_com_fare_fastest,commuter bus fare,"@C_FASTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_FAR_FAST')",1,, +##,,, express,, +#util_exp_ivt_fastest,express in vehicle time,"@C_FASTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_FAST')",1,, +#util_exp_wait_fastest,express wait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_IWAIT_FAST')",1,, +#util_exp_xwait_fastest,express bus xwait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_XWAIT_FAST')",1,, +#util_exp_fare_fastest,express fare,"@C_FASTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_FAR_FAST')",1,, +##,,, heavy,, +#util_hvy_ivt_fastest,heavy in vehicle time,"@C_FASTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_FAST')",1,, +#util_hvy_wait_fastest,heavy wait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_IWAIT_FAST')",1,, +#util_hvy_xwait_fastest,heavy bus xwait time,"@C_FASTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_XWAIT_FAST')",1,, +#util_hvy_fare_fastest,heavy fare,"@C_FASTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_FAR_FAST')",1,, +## cheapest,,,,, +#util_transit_available_cheapest,transit_available,@~df.transit_available_cheapest * C_UNAVAILABLE,,1, +#,,,, local bus, +util_bus_ivt_cheapest,local bus in vehicle time,"@C_CHEAPEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_CHEAP')",,1 +util_bus_wait_cheapest,local bus wait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_IWAIT_CHEAP')",,1 +util_bus_xwait_cheapest,local bus xwait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_XWAIT_CHEAP')",,1 +util_bus_fare_cheapest,local bus fare,"@C_CHEAPEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_FAR_CHEAP')",,1 +##,,,, commuter bus, +#util_com_ivt_cheapest,commuter bus in vehicle time,"@C_CHEAPEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_CHEAP')",,1, +#util_com_wait_cheapest,commuter bus wait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_IWAIT_CHEAP')",,1, +#util_com_xwait_cheapest,commuter bus xwait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_XWAIT_CHEAP')",,1, +#util_com_fare_cheapest,commuter bus fare,"@C_CHEAPEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_FAR_CHEAP')",,1, +##,,,, express, +#util_exp_ivt_cheapest,express in vehicle time,"@C_CHEAPEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_CHEAP')",,1, +#util_exp_wait_cheapest,express wait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_IWAIT_CHEAP')",,1, +#util_exp_xwait_cheapest,express bus xwait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_XWAIT_CHEAP')",,1, +#util_exp_fare_cheapest,express fare,"@C_CHEAPEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_FAR_CHEAP')",,1, +##,,,, heavy, +#util_hvy_ivt_cheapest,heavy in vehicle time,"@C_CHEAPEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_CHEAP')",,1, +#util_hvy_wait_cheapest,heavy wait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_IWAIT_CHEAP')",,1, +#util_hvy_xwait_cheapest,heavy bus xwait time,"@C_CHEAPEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_XWAIT_CHEAP')",,1, +#util_hvy_fare_cheapest,heavy fare,"@C_CHEAPEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_FAR_CHEAP')",,1, +## shortest,,,,, +#util_transit_available_shortest,transit_available,@~df.transit_available_shortest * C_UNAVAILABLE,,,1 +#,,,,, local bus +util_bus_ivt_shortest,local bus in vehicle time,"@C_SHORTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_SHORT')",,,1 +util_bus_wait_shortest,local bus wait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_IWAIT_SHORT')",,,1 +util_bus_xwait_shortest,local bus xwait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_XWAIT_SHORT')",,,1 +util_bus_fare_shortest,local bus fare,"@C_SHORTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_FAR_SHORT')",,,1 +##,,,,, commuter bus +#util_com_ivt_shortest,commuter bus in vehicle time,"@C_SHORTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_SHORT')",,,1 +#util_com_wait_shortest,commuter bus wait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_IWAIT_SHORT')",,,1 +#util_com_xwait_shortest,commuter bus xwait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_XWAIT_SHORT')",,,1 +#util_com_fare_shortest,commuter bus fare,"@C_SHORTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_FAR_SHORT')",,,1 +##,,,,, express +#util_exp_ivt_shortest,express in vehicle time,"@C_SHORTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_SHORT')",,,1 +#util_exp_wait_shortest,express wait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_IWAIT_SHORT')",,,1 +#util_exp_xwait_shortest,express bus xwait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_XWAIT_SHORT')",,,1 +#util_exp_fare_shortest,express fare,"@C_SHORTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_FAR_SHORT')",,,1 +##,,,,, heav +#util_hvy_ivt_shortest,heavy in vehicle time,"@C_SHORTEST_IVT_MULTIPLIER * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_SHORT')",,,1 +#util_hvy_wait_shortest,heavy wait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_IWAIT_SHORT')",,,1 +#util_hvy_xwait_shortest,heavy bus xwait time,"@C_SHORTEST_IVT_MULTIPLIER * c_wait * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_XWAIT_SHORT')",,,1 +#util_hvy_fare_shortest,heavy fare,"@C_SHORTEST_COST_MULTIPLIER * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_FAR_SHORT')",,,1 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv similarity index 98% rename from activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv rename to activitysim/examples/placeholder_sandag/configs_3_zone/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv index cbac046cf0..0ff843ec8e 100644 --- a/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv +++ b/activitysim/examples/placeholder_sandag/configs_3_zone/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv @@ -1,26 +1,26 @@ -Description,Target,Expression -# demographic segment,, -,c_ivt_for_segment,"np.where(df.demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_ivt_low_income, c_ivt_high_income)" -,c_cost_for_segment,"np.where(df.demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_cost_low_income, c_cost_high_income)" -# fastest,, -,_bus_available_fastest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_FAST')>0" -,_com_available_fastest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_FAST')>0" -,_exp_available_fastest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_FAST')>0" -,_hvy_available_fastest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_FAST')>0" -,_lrf_available_fastest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LRF_TOTIVT_FAST')>0" -,transit_available_fastest,_bus_available_fastest | _com_available_fastest | _exp_available_fastest | _hvy_available_fastest | _lrf_available_fastest -,not_transit_available_fastest,~transit_available_fastest -# cheapest,, -,_bus_available_cheapest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_CHEAP')>0" -,_com_available_cheapest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_CHEAP')>0" -,_exp_available_cheapest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_CHEAP')>0" -,_hvy_available_cheapest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_CHEAP')>0" -,_lrf_available_cheapest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LRF_TOTIVT_CHEAP')>0" -,transit_available_cheapest,_bus_available_cheapest | _com_available_cheapest | _exp_available_cheapest | _hvy_available_cheapest | _lrf_available_cheapest -# shortest,, -,_bus_available_shortest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_SHORT')>0" -,_com_available_shortest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_SHORT')>0" -,_exp_available_shortest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_SHORT')>0" -,_hvy_available_shortest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_SHORT')>0" -,_lrf_available_shortest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LRF_TOTIVT_SHORT')>0" -,transit_available_shortest,_bus_available_shortest | _com_available_shortest | _exp_available_shortest | _hvy_available_shortest | _lrf_available_shortest +Description,Target,Expression +# demographic segment,, +,c_ivt_for_segment,"np.where(df.demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_ivt_low_income, c_ivt_high_income)" +,c_cost_for_segment,"np.where(df.demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_cost_low_income, c_cost_high_income)" +# fastest,, +,_bus_available_fastest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_FAST')>0" +,_com_available_fastest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_FAST')>0" +,_exp_available_fastest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_FAST')>0" +,_hvy_available_fastest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_FAST')>0" +,_lrf_available_fastest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LRF_TOTIVT_FAST')>0" +,transit_available_fastest,_bus_available_fastest | _com_available_fastest | _exp_available_fastest | _hvy_available_fastest | _lrf_available_fastest +,not_transit_available_fastest,~transit_available_fastest +# cheapest,, +,_bus_available_cheapest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_CHEAP')>0" +,_com_available_cheapest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_CHEAP')>0" +,_exp_available_cheapest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_CHEAP')>0" +,_hvy_available_cheapest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_CHEAP')>0" +,_lrf_available_cheapest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LRF_TOTIVT_CHEAP')>0" +,transit_available_cheapest,_bus_available_cheapest | _com_available_cheapest | _exp_available_cheapest | _hvy_available_cheapest | _lrf_available_cheapest +# shortest,, +,_bus_available_shortest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LOC_TOTIVT_SHORT')>0" +,_com_available_shortest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'COM_TOTIVT_SHORT')>0" +,_exp_available_shortest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'EXP_TOTIVT_SHORT')>0" +,_hvy_available_shortest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HVY_TOTIVT_SHORT')>0" +,_lrf_available_shortest,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LRF_TOTIVT_SHORT')>0" +,transit_available_shortest,_bus_available_shortest | _com_available_shortest | _exp_available_shortest | _hvy_available_shortest | _lrf_available_shortest diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_walk_maz_tap.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/tvpb_utility_walk_maz_tap.csv similarity index 98% rename from activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_walk_maz_tap.csv rename to activitysim/examples/placeholder_sandag/configs_3_zone/tvpb_utility_walk_maz_tap.csv index 69ff955b0f..bb9e26b98b 100644 --- a/activitysim/examples/example_multiple_zone/configs_3_zone/tvpb_utility_walk_maz_tap.csv +++ b/activitysim/examples/placeholder_sandag/configs_3_zone/tvpb_utility_walk_maz_tap.csv @@ -1,3 +1,3 @@ -Label,Description,Expression,utility -util_walk_available,walk available,@df.walk_time.isna() * C_UNAVAILABLE,1 -util_walk_time,walk time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_walk * df.walk_time",1 +Label,Description,Expression,utility +util_walk_available,walk available,@df.walk_time.isna() * C_UNAVAILABLE,1 +util_walk_time,walk time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_walk * df.walk_time",1 diff --git a/activitysim/examples/example_sandag/configs_3_zone/write_trip_matrices.yaml b/activitysim/examples/placeholder_sandag/configs_3_zone/write_trip_matrices.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/write_trip_matrices.yaml rename to activitysim/examples/placeholder_sandag/configs_3_zone/write_trip_matrices.yaml diff --git a/activitysim/examples/example_sandag/configs_3_zone/write_trip_matrices_annotate_trips_preprocessor.csv b/activitysim/examples/placeholder_sandag/configs_3_zone/write_trip_matrices_annotate_trips_preprocessor.csv similarity index 100% rename from activitysim/examples/example_sandag/configs_3_zone/write_trip_matrices_annotate_trips_preprocessor.csv rename to activitysim/examples/placeholder_sandag/configs_3_zone/write_trip_matrices_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/example_sandag/configs_benchmarking/settings.yaml b/activitysim/examples/placeholder_sandag/configs_benchmarking/settings.yaml similarity index 100% rename from activitysim/examples/example_sandag/configs_benchmarking/settings.yaml rename to activitysim/examples/placeholder_sandag/configs_benchmarking/settings.yaml diff --git a/activitysim/examples/example_sandag/configs_skip_accessibility/settings.yaml b/activitysim/examples/placeholder_sandag/configs_skip_accessibility/settings.yaml similarity index 98% rename from activitysim/examples/example_sandag/configs_skip_accessibility/settings.yaml rename to activitysim/examples/placeholder_sandag/configs_skip_accessibility/settings.yaml index 08ab8d1dcd..50cc909e17 100755 --- a/activitysim/examples/example_sandag/configs_skip_accessibility/settings.yaml +++ b/activitysim/examples/placeholder_sandag/configs_skip_accessibility/settings.yaml @@ -1,6 +1,6 @@ inherit_settings: True -# activitysim run -c configs_skip_accessibility -c configs_3_zone -c ../example_mtc/configs -o output_3 -d data_3 +# activitysim run -c configs_skip_accessibility -c configs_3_zone -c ../prototype_mtc/configs -o output_3 -d data_3 # input tables input_table_list: diff --git a/activitysim/examples/example_sandag/configs_skip_accessibility/settings_mp.yaml b/activitysim/examples/placeholder_sandag/configs_skip_accessibility/settings_mp.yaml similarity index 86% rename from activitysim/examples/example_sandag/configs_skip_accessibility/settings_mp.yaml rename to activitysim/examples/placeholder_sandag/configs_skip_accessibility/settings_mp.yaml index 1246a0d8ec..d2264c3923 100755 --- a/activitysim/examples/example_sandag/configs_skip_accessibility/settings_mp.yaml +++ b/activitysim/examples/placeholder_sandag/configs_skip_accessibility/settings_mp.yaml @@ -1,6 +1,6 @@ inherit_settings: True -# activitysim run -c configs_skip_accessibility -c configs_3_zone -c ../example_mtc/configs -o output_3 -d data_3 -s settings_mp.yaml +# activitysim run -c configs_skip_accessibility -c configs_3_zone -c ../prototype_mtc/configs -o output_3 -d data_3 -s settings_mp.yaml multiprocess: True diff --git a/activitysim/examples/example_sandag/data_1/households.csv b/activitysim/examples/placeholder_sandag/data_1/households.csv similarity index 100% rename from activitysim/examples/example_sandag/data_1/households.csv rename to activitysim/examples/placeholder_sandag/data_1/households.csv diff --git a/activitysim/examples/example_sandag/data_1/land_use.csv b/activitysim/examples/placeholder_sandag/data_1/land_use.csv similarity index 100% rename from activitysim/examples/example_sandag/data_1/land_use.csv rename to activitysim/examples/placeholder_sandag/data_1/land_use.csv diff --git a/activitysim/examples/example_sandag/data_1/persons.csv b/activitysim/examples/placeholder_sandag/data_1/persons.csv similarity index 100% rename from activitysim/examples/example_sandag/data_1/persons.csv rename to activitysim/examples/placeholder_sandag/data_1/persons.csv diff --git a/activitysim/examples/example_sandag/data_1/skims1.omx b/activitysim/examples/placeholder_sandag/data_1/skims1.omx similarity index 100% rename from activitysim/examples/example_sandag/data_1/skims1.omx rename to activitysim/examples/placeholder_sandag/data_1/skims1.omx diff --git a/activitysim/examples/example_sandag/data_2/households.csv b/activitysim/examples/placeholder_sandag/data_2/households.csv similarity index 100% rename from activitysim/examples/example_sandag/data_2/households.csv rename to activitysim/examples/placeholder_sandag/data_2/households.csv diff --git a/activitysim/examples/example_sandag/data_2/land_use.csv b/activitysim/examples/placeholder_sandag/data_2/land_use.csv similarity index 100% rename from activitysim/examples/example_sandag/data_2/land_use.csv rename to activitysim/examples/placeholder_sandag/data_2/land_use.csv diff --git a/activitysim/examples/example_sandag/data_2/maz.csv b/activitysim/examples/placeholder_sandag/data_2/maz.csv similarity index 100% rename from activitysim/examples/example_sandag/data_2/maz.csv rename to activitysim/examples/placeholder_sandag/data_2/maz.csv diff --git a/activitysim/examples/example_sandag/data_2/maz_to_maz_bike.csv b/activitysim/examples/placeholder_sandag/data_2/maz_to_maz_bike.csv similarity index 100% rename from activitysim/examples/example_sandag/data_2/maz_to_maz_bike.csv rename to activitysim/examples/placeholder_sandag/data_2/maz_to_maz_bike.csv diff --git a/activitysim/examples/example_sandag/data_2/maz_to_maz_walk.csv b/activitysim/examples/placeholder_sandag/data_2/maz_to_maz_walk.csv similarity index 100% rename from activitysim/examples/example_sandag/data_2/maz_to_maz_walk.csv rename to activitysim/examples/placeholder_sandag/data_2/maz_to_maz_walk.csv diff --git a/activitysim/examples/example_sandag/data_2/persons.csv b/activitysim/examples/placeholder_sandag/data_2/persons.csv similarity index 100% rename from activitysim/examples/example_sandag/data_2/persons.csv rename to activitysim/examples/placeholder_sandag/data_2/persons.csv diff --git a/activitysim/examples/example_sandag/data_2/skims1.omx b/activitysim/examples/placeholder_sandag/data_2/skims1.omx similarity index 100% rename from activitysim/examples/example_sandag/data_2/skims1.omx rename to activitysim/examples/placeholder_sandag/data_2/skims1.omx diff --git a/activitysim/examples/example_sandag/data_2/taz.csv b/activitysim/examples/placeholder_sandag/data_2/taz.csv similarity index 100% rename from activitysim/examples/example_sandag/data_2/taz.csv rename to activitysim/examples/placeholder_sandag/data_2/taz.csv diff --git a/activitysim/examples/example_sandag/data_3/cached_accessibility.csv.gz b/activitysim/examples/placeholder_sandag/data_3/cached_accessibility.csv.gz similarity index 100% rename from activitysim/examples/example_sandag/data_3/cached_accessibility.csv.gz rename to activitysim/examples/placeholder_sandag/data_3/cached_accessibility.csv.gz diff --git a/activitysim/examples/example_sandag/data_3/households.csv b/activitysim/examples/placeholder_sandag/data_3/households.csv similarity index 100% rename from activitysim/examples/example_sandag/data_3/households.csv rename to activitysim/examples/placeholder_sandag/data_3/households.csv diff --git a/activitysim/examples/example_sandag/data_3/land_use.csv b/activitysim/examples/placeholder_sandag/data_3/land_use.csv similarity index 100% rename from activitysim/examples/example_sandag/data_3/land_use.csv rename to activitysim/examples/placeholder_sandag/data_3/land_use.csv diff --git a/activitysim/examples/example_sandag/data_3/maz.csv b/activitysim/examples/placeholder_sandag/data_3/maz.csv similarity index 100% rename from activitysim/examples/example_sandag/data_3/maz.csv rename to activitysim/examples/placeholder_sandag/data_3/maz.csv diff --git a/activitysim/examples/example_sandag/data_3/maz_to_maz_bike.csv b/activitysim/examples/placeholder_sandag/data_3/maz_to_maz_bike.csv similarity index 100% rename from activitysim/examples/example_sandag/data_3/maz_to_maz_bike.csv rename to activitysim/examples/placeholder_sandag/data_3/maz_to_maz_bike.csv diff --git a/activitysim/examples/example_sandag/data_3/maz_to_maz_walk.csv b/activitysim/examples/placeholder_sandag/data_3/maz_to_maz_walk.csv similarity index 100% rename from activitysim/examples/example_sandag/data_3/maz_to_maz_walk.csv rename to activitysim/examples/placeholder_sandag/data_3/maz_to_maz_walk.csv diff --git a/activitysim/examples/example_sandag/data_3/maz_to_tap_drive.csv b/activitysim/examples/placeholder_sandag/data_3/maz_to_tap_drive.csv similarity index 100% rename from activitysim/examples/example_sandag/data_3/maz_to_tap_drive.csv rename to activitysim/examples/placeholder_sandag/data_3/maz_to_tap_drive.csv diff --git a/activitysim/examples/example_sandag/data_3/maz_to_tap_walk.csv b/activitysim/examples/placeholder_sandag/data_3/maz_to_tap_walk.csv similarity index 100% rename from activitysim/examples/example_sandag/data_3/maz_to_tap_walk.csv rename to activitysim/examples/placeholder_sandag/data_3/maz_to_tap_walk.csv diff --git a/activitysim/examples/example_sandag/data_3/persons.csv b/activitysim/examples/placeholder_sandag/data_3/persons.csv similarity index 100% rename from activitysim/examples/example_sandag/data_3/persons.csv rename to activitysim/examples/placeholder_sandag/data_3/persons.csv diff --git a/activitysim/examples/example_sandag/data_3/tap.csv b/activitysim/examples/placeholder_sandag/data_3/tap.csv similarity index 100% rename from activitysim/examples/example_sandag/data_3/tap.csv rename to activitysim/examples/placeholder_sandag/data_3/tap.csv diff --git a/activitysim/examples/example_sandag/data_3/tap_lines.csv b/activitysim/examples/placeholder_sandag/data_3/tap_lines.csv similarity index 100% rename from activitysim/examples/example_sandag/data_3/tap_lines.csv rename to activitysim/examples/placeholder_sandag/data_3/tap_lines.csv diff --git a/activitysim/examples/example_sandag/data_3/tap_skims1.omx b/activitysim/examples/placeholder_sandag/data_3/tap_skims1.omx similarity index 100% rename from activitysim/examples/example_sandag/data_3/tap_skims1.omx rename to activitysim/examples/placeholder_sandag/data_3/tap_skims1.omx diff --git a/activitysim/examples/example_sandag/data_3/taz.csv b/activitysim/examples/placeholder_sandag/data_3/taz.csv similarity index 100% rename from activitysim/examples/example_sandag/data_3/taz.csv rename to activitysim/examples/placeholder_sandag/data_3/taz.csv diff --git a/activitysim/examples/example_sandag/data_3/taz_skims1.omx b/activitysim/examples/placeholder_sandag/data_3/taz_skims1.omx similarity index 100% rename from activitysim/examples/example_sandag/data_3/taz_skims1.omx rename to activitysim/examples/placeholder_sandag/data_3/taz_skims1.omx diff --git a/activitysim/examples/example_multiple_zone/output_3/.gitignore b/activitysim/examples/placeholder_sandag/output_1/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/output_3/.gitignore rename to activitysim/examples/placeholder_sandag/output_1/.gitignore diff --git a/activitysim/examples/example_mtc_extended/output/cache/.gitignore b/activitysim/examples/placeholder_sandag/output_1/cache/.gitignore similarity index 100% rename from activitysim/examples/example_mtc_extended/output/cache/.gitignore rename to activitysim/examples/placeholder_sandag/output_1/cache/.gitignore diff --git a/activitysim/examples/example_mtc_extended/output/log/.gitignore b/activitysim/examples/placeholder_sandag/output_1/log/.gitignore similarity index 100% rename from activitysim/examples/example_mtc_extended/output/log/.gitignore rename to activitysim/examples/placeholder_sandag/output_1/log/.gitignore diff --git a/activitysim/examples/example_mtc_extended/output/trace/.gitignore b/activitysim/examples/placeholder_sandag/output_1/trace/.gitignore similarity index 100% rename from activitysim/examples/example_mtc_extended/output/trace/.gitignore rename to activitysim/examples/placeholder_sandag/output_1/trace/.gitignore diff --git a/activitysim/examples/example_multiple_zone/output_3_example_marin_mp/.gitignore b/activitysim/examples/placeholder_sandag/output_2/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/output_3_example_marin_mp/.gitignore rename to activitysim/examples/placeholder_sandag/output_2/.gitignore diff --git a/activitysim/examples/example_mtc_extended/test/output/cache/.gitignore b/activitysim/examples/placeholder_sandag/output_2/cache/.gitignore similarity index 100% rename from activitysim/examples/example_mtc_extended/test/output/cache/.gitignore rename to activitysim/examples/placeholder_sandag/output_2/cache/.gitignore diff --git a/activitysim/examples/example_mtc_extended/test/output/trace/.gitignore b/activitysim/examples/placeholder_sandag/output_2/log/.gitignore similarity index 100% rename from activitysim/examples/example_mtc_extended/test/output/trace/.gitignore rename to activitysim/examples/placeholder_sandag/output_2/log/.gitignore diff --git a/activitysim/examples/example_multiple_zone/output_1/trace/.gitignore b/activitysim/examples/placeholder_sandag/output_2/trace/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/output_1/trace/.gitignore rename to activitysim/examples/placeholder_sandag/output_2/trace/.gitignore diff --git a/activitysim/examples/example_multiple_zone/output_3_mp/.gitignore b/activitysim/examples/placeholder_sandag/output_3/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/output_3_mp/.gitignore rename to activitysim/examples/placeholder_sandag/output_3/.gitignore diff --git a/activitysim/examples/example_multiple_zone/output_3/cache/.gitignore b/activitysim/examples/placeholder_sandag/output_3/cache/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/output_3/cache/.gitignore rename to activitysim/examples/placeholder_sandag/output_3/cache/.gitignore diff --git a/activitysim/examples/example_multiple_zone/output_2/trace/.gitignore b/activitysim/examples/placeholder_sandag/output_3/log/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/output_2/trace/.gitignore rename to activitysim/examples/placeholder_sandag/output_3/log/.gitignore diff --git a/activitysim/examples/example_multiple_zone/output_3/trace/.gitignore b/activitysim/examples/placeholder_sandag/output_3/trace/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/output_3/trace/.gitignore rename to activitysim/examples/placeholder_sandag/output_3/trace/.gitignore diff --git a/activitysim/examples/placeholder_sandag/run_sandag.txt b/activitysim/examples/placeholder_sandag/run_sandag.txt new file mode 100644 index 0000000000..a1c70a3b0f --- /dev/null +++ b/activitysim/examples/placeholder_sandag/run_sandag.txt @@ -0,0 +1,14 @@ +### +### 1 Zone +### +activitysim run -c configs_1_zone -c prototype_mtc/configs -d data_1 -o output_1 -s settings_mp.yaml + +### +### 2 Zone +### +activitysim run -c configs_2_zone -c placeholder_psrc/configs -d data_2 -o output_2 -s settings_mp.yaml + +### +### 3 Zone +### +activitysim run -c configs_3_zone -c prototype_mtc/configs -d data_3 -o output_3 -s settings_mp.yaml \ No newline at end of file diff --git a/activitysim/examples/example_sandag/scripts/sandag_crop_1_zone.py b/activitysim/examples/placeholder_sandag/scripts/sandag_crop_1_zone.py similarity index 100% rename from activitysim/examples/example_sandag/scripts/sandag_crop_1_zone.py rename to activitysim/examples/placeholder_sandag/scripts/sandag_crop_1_zone.py diff --git a/activitysim/examples/example_sandag/scripts/sandag_crop_2_zone.py b/activitysim/examples/placeholder_sandag/scripts/sandag_crop_2_zone.py similarity index 100% rename from activitysim/examples/example_sandag/scripts/sandag_crop_2_zone.py rename to activitysim/examples/placeholder_sandag/scripts/sandag_crop_2_zone.py diff --git a/activitysim/examples/example_sandag/scripts/sandag_crop_3_zone.py b/activitysim/examples/placeholder_sandag/scripts/sandag_crop_3_zone.py similarity index 100% rename from activitysim/examples/example_sandag/scripts/sandag_crop_3_zone.py rename to activitysim/examples/placeholder_sandag/scripts/sandag_crop_3_zone.py diff --git a/activitysim/examples/example_mtc/test/configs_chunkless/network_los.yaml b/activitysim/examples/placeholder_sandag/test/configs_1_zone/network_los.yaml similarity index 100% rename from activitysim/examples/example_mtc/test/configs_chunkless/network_los.yaml rename to activitysim/examples/placeholder_sandag/test/configs_1_zone/network_los.yaml diff --git a/activitysim/examples/example_sandag/test/configs_1_zone/settings.yaml b/activitysim/examples/placeholder_sandag/test/configs_1_zone/settings.yaml similarity index 100% rename from activitysim/examples/example_sandag/test/configs_1_zone/settings.yaml rename to activitysim/examples/placeholder_sandag/test/configs_1_zone/settings.yaml diff --git a/activitysim/examples/example_sandag/test/configs_1_zone/settings_mp.yaml b/activitysim/examples/placeholder_sandag/test/configs_1_zone/settings_mp.yaml similarity index 100% rename from activitysim/examples/example_sandag/test/configs_1_zone/settings_mp.yaml rename to activitysim/examples/placeholder_sandag/test/configs_1_zone/settings_mp.yaml diff --git a/activitysim/examples/example_mtc/test/configs_mp/network_los.yaml b/activitysim/examples/placeholder_sandag/test/configs_2_zone/network_los.yaml similarity index 100% rename from activitysim/examples/example_mtc/test/configs_mp/network_los.yaml rename to activitysim/examples/placeholder_sandag/test/configs_2_zone/network_los.yaml diff --git a/activitysim/examples/example_sandag/test/configs_2_zone/settings.yaml b/activitysim/examples/placeholder_sandag/test/configs_2_zone/settings.yaml similarity index 100% rename from activitysim/examples/example_sandag/test/configs_2_zone/settings.yaml rename to activitysim/examples/placeholder_sandag/test/configs_2_zone/settings.yaml diff --git a/activitysim/examples/example_sandag/test/configs_2_zone/settings_mp.yaml b/activitysim/examples/placeholder_sandag/test/configs_2_zone/settings_mp.yaml similarity index 100% rename from activitysim/examples/example_sandag/test/configs_2_zone/settings_mp.yaml rename to activitysim/examples/placeholder_sandag/test/configs_2_zone/settings_mp.yaml diff --git a/activitysim/examples/example_sandag/test/configs_3_zone/network_los.yaml b/activitysim/examples/placeholder_sandag/test/configs_3_zone/network_los.yaml similarity index 100% rename from activitysim/examples/example_sandag/test/configs_3_zone/network_los.yaml rename to activitysim/examples/placeholder_sandag/test/configs_3_zone/network_los.yaml diff --git a/activitysim/examples/example_sandag/test/configs_3_zone/settings.yaml b/activitysim/examples/placeholder_sandag/test/configs_3_zone/settings.yaml similarity index 100% rename from activitysim/examples/example_sandag/test/configs_3_zone/settings.yaml rename to activitysim/examples/placeholder_sandag/test/configs_3_zone/settings.yaml diff --git a/activitysim/examples/example_sandag/test/configs_3_zone/settings_mp.yaml b/activitysim/examples/placeholder_sandag/test/configs_3_zone/settings_mp.yaml similarity index 100% rename from activitysim/examples/example_sandag/test/configs_3_zone/settings_mp.yaml rename to activitysim/examples/placeholder_sandag/test/configs_3_zone/settings_mp.yaml diff --git a/activitysim/examples/example_multiple_zone/test/output/.gitignore b/activitysim/examples/placeholder_sandag/test/output/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/test/output/.gitignore rename to activitysim/examples/placeholder_sandag/test/output/.gitignore diff --git a/activitysim/examples/example_multiple_zone/output_3_example_marin_mp/cache/.gitignore b/activitysim/examples/placeholder_sandag/test/output/cache/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/output_3_example_marin_mp/cache/.gitignore rename to activitysim/examples/placeholder_sandag/test/output/cache/.gitignore diff --git a/activitysim/examples/example_multiple_zone/output_3_example_marin_mp/trace/.gitignore b/activitysim/examples/placeholder_sandag/test/output/trace/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/output_3_example_marin_mp/trace/.gitignore rename to activitysim/examples/placeholder_sandag/test/output/trace/.gitignore diff --git a/activitysim/examples/example_sandag/test/regress/final_1_zone_tours.csv b/activitysim/examples/placeholder_sandag/test/regress/final_1_zone_tours.csv similarity index 100% rename from activitysim/examples/example_sandag/test/regress/final_1_zone_tours.csv rename to activitysim/examples/placeholder_sandag/test/regress/final_1_zone_tours.csv diff --git a/activitysim/examples/example_sandag/test/regress/final_1_zone_trips.csv b/activitysim/examples/placeholder_sandag/test/regress/final_1_zone_trips.csv similarity index 100% rename from activitysim/examples/example_sandag/test/regress/final_1_zone_trips.csv rename to activitysim/examples/placeholder_sandag/test/regress/final_1_zone_trips.csv diff --git a/activitysim/examples/example_sandag/test/regress/final_2_zone_tours.csv b/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_tours.csv similarity index 100% rename from activitysim/examples/example_sandag/test/regress/final_2_zone_tours.csv rename to activitysim/examples/placeholder_sandag/test/regress/final_2_zone_tours.csv diff --git a/activitysim/examples/example_sandag/test/regress/final_2_zone_trips.csv b/activitysim/examples/placeholder_sandag/test/regress/final_2_zone_trips.csv similarity index 100% rename from activitysim/examples/example_sandag/test/regress/final_2_zone_trips.csv rename to activitysim/examples/placeholder_sandag/test/regress/final_2_zone_trips.csv diff --git a/activitysim/examples/example_sandag/test/regress/final_3_zone_tours.csv b/activitysim/examples/placeholder_sandag/test/regress/final_3_zone_tours.csv similarity index 100% rename from activitysim/examples/example_sandag/test/regress/final_3_zone_tours.csv rename to activitysim/examples/placeholder_sandag/test/regress/final_3_zone_tours.csv diff --git a/activitysim/examples/example_sandag/test/regress/final_3_zone_trips.csv b/activitysim/examples/placeholder_sandag/test/regress/final_3_zone_trips.csv similarity index 100% rename from activitysim/examples/example_sandag/test/regress/final_3_zone_trips.csv rename to activitysim/examples/placeholder_sandag/test/regress/final_3_zone_trips.csv diff --git a/activitysim/examples/example_mtc/test/simulation.py b/activitysim/examples/placeholder_sandag/test/simulation.py similarity index 94% rename from activitysim/examples/example_mtc/test/simulation.py rename to activitysim/examples/placeholder_sandag/test/simulation.py index ec6a1181b1..97ca6b6483 100755 --- a/activitysim/examples/example_mtc/test/simulation.py +++ b/activitysim/examples/placeholder_sandag/test/simulation.py @@ -1,15 +1,15 @@ -# ActivitySim -# See full license in LICENSE.txt. - -import sys -import argparse - -from activitysim.cli.run import add_run_args, run - -if __name__ == '__main__': - - parser = argparse.ArgumentParser() - add_run_args(parser) - args = parser.parse_args() - - sys.exit(run(args)) +# ActivitySim +# See full license in LICENSE.txt. + +import sys +import argparse + +from activitysim.cli.run import add_run_args, run + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + add_run_args(parser) + args = parser.parse_args() + + sys.exit(run(args)) diff --git a/activitysim/examples/example_sandag/test/test_sandag.py b/activitysim/examples/placeholder_sandag/test/test_sandag.py similarity index 93% rename from activitysim/examples/example_sandag/test/test_sandag.py rename to activitysim/examples/placeholder_sandag/test/test_sandag.py index 75e75aa976..7a373defd5 100644 --- a/activitysim/examples/example_sandag/test/test_sandag.py +++ b/activitysim/examples/placeholder_sandag/test/test_sandag.py @@ -17,17 +17,17 @@ def teardown_function(func): def example_path(dirname): - resource = os.path.join('examples', 'example_sandag', dirname) + resource = os.path.join('examples', 'placeholder_sandag', dirname) return pkg_resources.resource_filename('activitysim', resource) def mtc_example_path(dirname): - resource = os.path.join('examples', 'example_mtc', dirname) + resource = os.path.join('examples', 'prototype_mtc', dirname) return pkg_resources.resource_filename('activitysim', resource) def psrc_example_path(dirname): - resource = os.path.join('examples', 'example_psrc', dirname) + resource = os.path.join('examples', 'placeholder_psrc', dirname) return pkg_resources.resource_filename('activitysim', resource) diff --git a/activitysim/examples/example_psrc/.gitignore b/activitysim/examples/prototype_arc/.gitignore similarity index 100% rename from activitysim/examples/example_psrc/.gitignore rename to activitysim/examples/prototype_arc/.gitignore diff --git a/activitysim/examples/example_arc/README.MD b/activitysim/examples/prototype_arc/README.MD similarity index 100% rename from activitysim/examples/example_arc/README.MD rename to activitysim/examples/prototype_arc/README.MD diff --git a/activitysim/examples/example_arc/change_log.txt b/activitysim/examples/prototype_arc/change_log.txt similarity index 100% rename from activitysim/examples/example_arc/change_log.txt rename to activitysim/examples/prototype_arc/change_log.txt diff --git a/activitysim/examples/example_psrc/configs/_dummy_coefficients.csv b/activitysim/examples/prototype_arc/configs/_dummy_coefficients.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/_dummy_coefficients.csv rename to activitysim/examples/prototype_arc/configs/_dummy_coefficients.csv diff --git a/activitysim/examples/example_arc/configs/accessibility.csv b/activitysim/examples/prototype_arc/configs/accessibility.csv similarity index 98% rename from activitysim/examples/example_arc/configs/accessibility.csv rename to activitysim/examples/prototype_arc/configs/accessibility.csv index 1171554c10..cef9a0ae5d 100644 --- a/activitysim/examples/example_arc/configs/accessibility.csv +++ b/activitysim/examples/prototype_arc/configs/accessibility.csv @@ -1,77 +1,77 @@ -Description,Target,Expression -#,, -#,, auto peak -#,, -#AM - ALL TRANSIT,, -AM OVT (MW[31]),_amOvt,"skim_od[('WLK_ALLTRN_WLK_WALK', 'AM')] + skim_od[('WLK_ALLTRN_WLK_IWAIT','AM')]+ skim_od[('WLK_ALLTRN_WLK_XWAIT', 'AM')]" -AM OVT (Transposed) (MW[32]),_amOvt_T,"skim_do[('WLK_ALLTRN_WLK_WALK', 'AM')] + skim_do[('WLK_ALLTRN_WLK_IWAIT','AM')]+ skim_do[('WLK_ALLTRN_WLK_XWAIT', 'AM')]" -AM IVT (MW[33]),_amIvt,"skim_od[('WLK_ALLTRN_WLK_LOCAL', 'AM')]+skim_od[('WLK_ALLTRN_WLK_XBUS', 'AM')]+skim_od[('WLK_ALLTRN_WLK_BRT', 'AM')]+skim_od[('WLK_ALLTRN_WLK_HRT', 'AM')]+skim_od[('WLK_ALLTRN_WLK_LRT', 'AM')]+skim_od[('WLK_ALLTRN_WLK_COMRAIL', 'AM')]" -AM IVT (Transposed) (MW[34]),_amIvt_T,"skim_do[('WLK_ALLTRN_WLK_LOCAL', 'AM')]+skim_do[('WLK_ALLTRN_WLK_XBUS', 'AM')]+skim_do[('WLK_ALLTRN_WLK_BRT', 'AM')]+skim_do[('WLK_ALLTRN_WLK_HRT', 'AM')]+skim_do[('WLK_ALLTRN_WLK_LRT', 'AM')]+skim_od[('WLK_ALLTRN_WLK_COMRAIL', 'AM')]" -#,, -#MD - ALL TRANSIT,, -MD OVT (MW[41]),_mdOvt,"skim_od[('WLK_ALLTRN_WLK_WALK', 'MD')] + skim_od[('WLK_ALLTRN_WLK_IWAIT','MD')]+ skim_od[('WLK_ALLTRN_WLK_XWAIT', 'MD')]" -MD OVT (Transposed) (MW[42]),_mdOvt_T,"skim_do[('WLK_ALLTRN_WLK_WALK', 'MD')] + skim_do[('WLK_ALLTRN_WLK_IWAIT','MD')]+ skim_do[('WLK_ALLTRN_WLK_XWAIT', 'MD')]" -MD IVT (MW[43]),_mdIvt,"skim_od[('WLK_ALLTRN_WLK_LOCAL', 'MD')]+skim_od[('WLK_ALLTRN_WLK_XBUS', 'MD')]+skim_od[('WLK_ALLTRN_WLK_BRT', 'MD')]+skim_od[('WLK_ALLTRN_WLK_HRT', 'MD')]+skim_od[('WLK_ALLTRN_WLK_LRT', 'MD')]+skim_od[('WLK_ALLTRN_WLK_COMRAIL', 'MD')]" -MD IVT (Transposed) (MW[44]),_mdIvt_T,"skim_do[('WLK_ALLTRN_WLK_LOCAL', 'MD')]+skim_do[('WLK_ALLTRN_WLK_XBUS', 'MD')]+skim_do[('WLK_ALLTRN_WLK_BRT', 'MD')]+skim_do[('WLK_ALLTRN_WLK_HRT', 'MD')]+skim_do[('WLK_ALLTRN_WLK_LRT', 'MD')]+skim_od[('WLK_ALLTRN_WLK_COMRAIL', 'MD')]" -#,, -#AM - PREMIUM TRANSIT,, -AM OVT (MW[51]),_amOvtPrem,"skim_od[('WLK_PRMTRN_WLK_WALK', 'AM')] + skim_od[('WLK_PRMTRN_WLK_IWAIT','AM')]+ skim_od[('WLK_PRMTRN_WLK_XWAIT', 'AM')]" -AM OVT (Transposed) (MW[52]),_amOvtPrem_T,"skim_do[('WLK_PRMTRN_WLK_WALK', 'AM')] + skim_do[('WLK_PRMTRN_WLK_IWAIT','AM')]+ skim_do[('WLK_PRMTRN_WLK_XWAIT', 'AM')]" -AM IVT (MW[53]),_amIvtPrem,"skim_od[('WLK_PRMTRN_WLK_LOCAL', 'AM')]+skim_od[('WLK_PRMTRN_WLK_XBUS', 'AM')]+skim_od[('WLK_PRMTRN_WLK_BRT', 'AM')]+skim_od[('WLK_PRMTRN_WLK_HRT', 'AM')]+skim_od[('WLK_PRMTRN_WLK_LRT', 'AM')]+skim_od[('WLK_PRMTRN_WLK_COMRAIL', 'AM')]" -AM IVT (Transposed) (MW[54]),_amIvtPrem_T,"skim_do[('WLK_PRMTRN_WLK_LOCAL', 'AM')]+skim_do[('WLK_PRMTRN_WLK_XBUS', 'AM')]+skim_do[('WLK_PRMTRN_WLK_BRT', 'AM')]+skim_do[('WLK_PRMTRN_WLK_HRT', 'AM')]+skim_do[('WLK_PRMTRN_WLK_LRT', 'AM')]+skim_od[('WLK_PRMTRN_WLK_COMRAIL', 'AM')]" -#,, -#MD - PREMIUM TRANSIT,, -MD OVT (MW[61]),_mdOvtPrem,"skim_od[('WLK_PRMTRN_WLK_WALK', 'MD')] + skim_od[('WLK_PRMTRN_WLK_IWAIT','MD')]+ skim_od[('WLK_PRMTRN_WLK_XWAIT', 'MD')]" -MD OVT (Transposed) (MW[62]),_mdOvtPrem_T,"skim_do[('WLK_PRMTRN_WLK_WALK', 'MD')] + skim_do[('WLK_PRMTRN_WLK_IWAIT','MD')]+ skim_do[('WLK_PRMTRN_WLK_XWAIT', 'MD')]" -MD IVT (MW[63]),_mdIvtPrem,"skim_od[('WLK_PRMTRN_WLK_LOCAL', 'MD')]+skim_od[('WLK_PRMTRN_WLK_XBUS', 'MD')]+skim_od[('WLK_PRMTRN_WLK_BRT', 'MD')]+skim_od[('WLK_PRMTRN_WLK_HRT', 'MD')]+skim_od[('WLK_PRMTRN_WLK_LRT', 'MD')]+skim_od[('WLK_PRMTRN_WLK_COMRAIL', 'MD')]" -MD IVT (Transposed) (MW[64]),_mdIvtPrem_T,"skim_do[('WLK_PRMTRN_WLK_LOCAL', 'MD')]+skim_do[('WLK_PRMTRN_WLK_XBUS', 'MD')]+skim_do[('WLK_PRMTRN_WLK_BRT', 'MD')]+skim_do[('WLK_PRMTRN_WLK_HRT', 'MD')]+skim_do[('WLK_PRMTRN_WLK_LRT', 'MD')]+skim_od[('WLK_PRMTRN_WLK_COMRAIL', 'MD')]" -#,, -# get the destination zone retail employment from dbf file,, -Retail Employment,_retailEmp,df.retail + df.service -Total Employment,_totEmp,df.emp -#,, -#,,auto peak -# assume peak occurs in AM for outbound and PM for inbound,, -peak round trip distance,_auPkTime,"skim_od[('SOV_FREE_TIME', 'AM')] + skim_do[('SOV_FREE_TIME', 'AM')]" -decay function,_decay,exp(_auPkTime * dispersion_parameter_automobile) -auto peak retail,auPkRetail,_retailEmp * _decay -auto peak total,auPkTotal,_totEmp * _decay -#,, -#,, auto off-peak -#,, -#,, assume midday occurs entirely in the midday period -off-peak round trip distance,_auOpTime,"skim_od[('SOV_FREE_TIME', 'MD')] + skim_do[('SOV_FREE_TIME', 'MD')]" -decay function,_decay, exp(_auOpTime * dispersion_parameter_automobile) -auto off-peak retail,auOpRetail,_retailEmp * _decay -auto off-peak total,auOpTotal,_totEmp * _decay -#,, -#,, -#,, -# am peak transit,, -local travel time,_ltt,2 * (_amOvt+_amOvt_T) + (_amIvt+_amIvt_T) -premium travel time,_ptt,2 * (_amOvtPrem+_amOvtPrem_T) + (_amIvtPrem+_amIvtPrem_T) -local travel time 2,_ltt2,_amOvt + _amOvt_T + _amIvt + _amIvt_T -premium travel time 2,_ptt2,_amOvtPrem + _amOvtPrem_T + _amIvtPrem + _amIvtPrem_T -#,, -Transit Decay ,_tr_decay,"np.where((_ltt > 0), np.where((_ltt < _ptt), exp(_ltt2 * dispersion_parameter_transit), 0), np.where((_ptt > 0), exp(_ptt2 * dispersion_parameter_transit), 0))" -transit peak retail,trPkRetail,_retailEmp * _tr_decay -transit peak total,trPkTotal,_totEmp * _tr_decay -#,, -#,, -# midday transit,, -local travel time off-peak,_lttop,2 * (_mdOvt+_mdOvt_T) + (_mdIvt+_mdIvt_T) -premium travel time off-peak,_pttop,2 * (_mdOvtPrem+_mdOvtPrem_T) + (_mdIvtPrem+_mdIvtPrem_T) -local travel time off-peak 2,_ltt2op,_mdOvt + _mdOvt_T + _mdIvt + _mdIvt_T -premium travel time off-peak 2,_ptt2op,_mdOvtPrem + _mdOvtPrem_T + _mdIvtPrem + _mdIvtPrem_T -#,, -Transit Decay ,_tr_op_decay,"np.where((_lttop > 0), np.where((_lttop < _pttop), exp(_ltt2op * dispersion_parameter_transit), 0), np.where((_pttop > 0), exp(_ptt2op * dispersion_parameter_transit), 0))" -transit off-peak retail,trOpRetail,_retailEmp * _tr_op_decay -transit off-peak total,trOpTotal,_totEmp * _tr_op_decay -#,, -#,, -# walk,, -non-motorized round trip distance,_nmDist,"skim_od[('SOV_FREE_DISTANCE', 'MD')] + skim_do[('SOV_FREE_DISTANCE', 'MD')]" -round trip path is available,_rt_available,_nmDist <= (maximum_walk_distance * 2) -decay function,_decay,_rt_available * exp(_nmDist * dispersion_parameter_walk) -non-motorized retail accessibility,nmRetail,_retailEmp * _decay -non-motorized total accessibility,nmTotal,_totEmp * _decay +Description,Target,Expression +#,, +#,, auto peak +#,, +#AM - ALL TRANSIT,, +AM OVT (MW[31]),_amOvt,"skim_od[('WLK_ALLTRN_WLK_WALK', 'AM')] + skim_od[('WLK_ALLTRN_WLK_IWAIT','AM')]+ skim_od[('WLK_ALLTRN_WLK_XWAIT', 'AM')]" +AM OVT (Transposed) (MW[32]),_amOvt_T,"skim_do[('WLK_ALLTRN_WLK_WALK', 'AM')] + skim_do[('WLK_ALLTRN_WLK_IWAIT','AM')]+ skim_do[('WLK_ALLTRN_WLK_XWAIT', 'AM')]" +AM IVT (MW[33]),_amIvt,"skim_od[('WLK_ALLTRN_WLK_LOCAL', 'AM')]+skim_od[('WLK_ALLTRN_WLK_XBUS', 'AM')]+skim_od[('WLK_ALLTRN_WLK_BRT', 'AM')]+skim_od[('WLK_ALLTRN_WLK_HRT', 'AM')]+skim_od[('WLK_ALLTRN_WLK_LRT', 'AM')]+skim_od[('WLK_ALLTRN_WLK_COMRAIL', 'AM')]" +AM IVT (Transposed) (MW[34]),_amIvt_T,"skim_do[('WLK_ALLTRN_WLK_LOCAL', 'AM')]+skim_do[('WLK_ALLTRN_WLK_XBUS', 'AM')]+skim_do[('WLK_ALLTRN_WLK_BRT', 'AM')]+skim_do[('WLK_ALLTRN_WLK_HRT', 'AM')]+skim_do[('WLK_ALLTRN_WLK_LRT', 'AM')]+skim_od[('WLK_ALLTRN_WLK_COMRAIL', 'AM')]" +#,, +#MD - ALL TRANSIT,, +MD OVT (MW[41]),_mdOvt,"skim_od[('WLK_ALLTRN_WLK_WALK', 'MD')] + skim_od[('WLK_ALLTRN_WLK_IWAIT','MD')]+ skim_od[('WLK_ALLTRN_WLK_XWAIT', 'MD')]" +MD OVT (Transposed) (MW[42]),_mdOvt_T,"skim_do[('WLK_ALLTRN_WLK_WALK', 'MD')] + skim_do[('WLK_ALLTRN_WLK_IWAIT','MD')]+ skim_do[('WLK_ALLTRN_WLK_XWAIT', 'MD')]" +MD IVT (MW[43]),_mdIvt,"skim_od[('WLK_ALLTRN_WLK_LOCAL', 'MD')]+skim_od[('WLK_ALLTRN_WLK_XBUS', 'MD')]+skim_od[('WLK_ALLTRN_WLK_BRT', 'MD')]+skim_od[('WLK_ALLTRN_WLK_HRT', 'MD')]+skim_od[('WLK_ALLTRN_WLK_LRT', 'MD')]+skim_od[('WLK_ALLTRN_WLK_COMRAIL', 'MD')]" +MD IVT (Transposed) (MW[44]),_mdIvt_T,"skim_do[('WLK_ALLTRN_WLK_LOCAL', 'MD')]+skim_do[('WLK_ALLTRN_WLK_XBUS', 'MD')]+skim_do[('WLK_ALLTRN_WLK_BRT', 'MD')]+skim_do[('WLK_ALLTRN_WLK_HRT', 'MD')]+skim_do[('WLK_ALLTRN_WLK_LRT', 'MD')]+skim_od[('WLK_ALLTRN_WLK_COMRAIL', 'MD')]" +#,, +#AM - PREMIUM TRANSIT,, +AM OVT (MW[51]),_amOvtPrem,"skim_od[('WLK_PRMTRN_WLK_WALK', 'AM')] + skim_od[('WLK_PRMTRN_WLK_IWAIT','AM')]+ skim_od[('WLK_PRMTRN_WLK_XWAIT', 'AM')]" +AM OVT (Transposed) (MW[52]),_amOvtPrem_T,"skim_do[('WLK_PRMTRN_WLK_WALK', 'AM')] + skim_do[('WLK_PRMTRN_WLK_IWAIT','AM')]+ skim_do[('WLK_PRMTRN_WLK_XWAIT', 'AM')]" +AM IVT (MW[53]),_amIvtPrem,"skim_od[('WLK_PRMTRN_WLK_LOCAL', 'AM')]+skim_od[('WLK_PRMTRN_WLK_XBUS', 'AM')]+skim_od[('WLK_PRMTRN_WLK_BRT', 'AM')]+skim_od[('WLK_PRMTRN_WLK_HRT', 'AM')]+skim_od[('WLK_PRMTRN_WLK_LRT', 'AM')]+skim_od[('WLK_PRMTRN_WLK_COMRAIL', 'AM')]" +AM IVT (Transposed) (MW[54]),_amIvtPrem_T,"skim_do[('WLK_PRMTRN_WLK_LOCAL', 'AM')]+skim_do[('WLK_PRMTRN_WLK_XBUS', 'AM')]+skim_do[('WLK_PRMTRN_WLK_BRT', 'AM')]+skim_do[('WLK_PRMTRN_WLK_HRT', 'AM')]+skim_do[('WLK_PRMTRN_WLK_LRT', 'AM')]+skim_od[('WLK_PRMTRN_WLK_COMRAIL', 'AM')]" +#,, +#MD - PREMIUM TRANSIT,, +MD OVT (MW[61]),_mdOvtPrem,"skim_od[('WLK_PRMTRN_WLK_WALK', 'MD')] + skim_od[('WLK_PRMTRN_WLK_IWAIT','MD')]+ skim_od[('WLK_PRMTRN_WLK_XWAIT', 'MD')]" +MD OVT (Transposed) (MW[62]),_mdOvtPrem_T,"skim_do[('WLK_PRMTRN_WLK_WALK', 'MD')] + skim_do[('WLK_PRMTRN_WLK_IWAIT','MD')]+ skim_do[('WLK_PRMTRN_WLK_XWAIT', 'MD')]" +MD IVT (MW[63]),_mdIvtPrem,"skim_od[('WLK_PRMTRN_WLK_LOCAL', 'MD')]+skim_od[('WLK_PRMTRN_WLK_XBUS', 'MD')]+skim_od[('WLK_PRMTRN_WLK_BRT', 'MD')]+skim_od[('WLK_PRMTRN_WLK_HRT', 'MD')]+skim_od[('WLK_PRMTRN_WLK_LRT', 'MD')]+skim_od[('WLK_PRMTRN_WLK_COMRAIL', 'MD')]" +MD IVT (Transposed) (MW[64]),_mdIvtPrem_T,"skim_do[('WLK_PRMTRN_WLK_LOCAL', 'MD')]+skim_do[('WLK_PRMTRN_WLK_XBUS', 'MD')]+skim_do[('WLK_PRMTRN_WLK_BRT', 'MD')]+skim_do[('WLK_PRMTRN_WLK_HRT', 'MD')]+skim_do[('WLK_PRMTRN_WLK_LRT', 'MD')]+skim_od[('WLK_PRMTRN_WLK_COMRAIL', 'MD')]" +#,, +# get the destination zone retail employment from dbf file,, +Retail Employment,_retailEmp,df.retail + df.service +Total Employment,_totEmp,df.emp +#,, +#,,auto peak +# assume peak occurs in AM for outbound and PM for inbound,, +peak round trip distance,_auPkTime,"skim_od[('SOV_FREE_TIME', 'AM')] + skim_do[('SOV_FREE_TIME', 'AM')]" +decay function,_decay,exp(_auPkTime * dispersion_parameter_automobile) +auto peak retail,auPkRetail,_retailEmp * _decay +auto peak total,auPkTotal,_totEmp * _decay +#,, +#,, auto off-peak +#,, +#,, assume midday occurs entirely in the midday period +off-peak round trip distance,_auOpTime,"skim_od[('SOV_FREE_TIME', 'MD')] + skim_do[('SOV_FREE_TIME', 'MD')]" +decay function,_decay, exp(_auOpTime * dispersion_parameter_automobile) +auto off-peak retail,auOpRetail,_retailEmp * _decay +auto off-peak total,auOpTotal,_totEmp * _decay +#,, +#,, +#,, +# am peak transit,, +local travel time,_ltt,2 * (_amOvt+_amOvt_T) + (_amIvt+_amIvt_T) +premium travel time,_ptt,2 * (_amOvtPrem+_amOvtPrem_T) + (_amIvtPrem+_amIvtPrem_T) +local travel time 2,_ltt2,_amOvt + _amOvt_T + _amIvt + _amIvt_T +premium travel time 2,_ptt2,_amOvtPrem + _amOvtPrem_T + _amIvtPrem + _amIvtPrem_T +#,, +Transit Decay ,_tr_decay,"np.where((_ltt > 0), np.where((_ltt < _ptt), exp(_ltt2 * dispersion_parameter_transit), 0), np.where((_ptt > 0), exp(_ptt2 * dispersion_parameter_transit), 0))" +transit peak retail,trPkRetail,_retailEmp * _tr_decay +transit peak total,trPkTotal,_totEmp * _tr_decay +#,, +#,, +# midday transit,, +local travel time off-peak,_lttop,2 * (_mdOvt+_mdOvt_T) + (_mdIvt+_mdIvt_T) +premium travel time off-peak,_pttop,2 * (_mdOvtPrem+_mdOvtPrem_T) + (_mdIvtPrem+_mdIvtPrem_T) +local travel time off-peak 2,_ltt2op,_mdOvt + _mdOvt_T + _mdIvt + _mdIvt_T +premium travel time off-peak 2,_ptt2op,_mdOvtPrem + _mdOvtPrem_T + _mdIvtPrem + _mdIvtPrem_T +#,, +Transit Decay ,_tr_op_decay,"np.where((_lttop > 0), np.where((_lttop < _pttop), exp(_ltt2op * dispersion_parameter_transit), 0), np.where((_pttop > 0), exp(_ptt2op * dispersion_parameter_transit), 0))" +transit off-peak retail,trOpRetail,_retailEmp * _tr_op_decay +transit off-peak total,trOpTotal,_totEmp * _tr_op_decay +#,, +#,, +# walk,, +non-motorized round trip distance,_nmDist,"skim_od[('SOV_FREE_DISTANCE', 'MD')] + skim_do[('SOV_FREE_DISTANCE', 'MD')]" +round trip path is available,_rt_available,_nmDist <= (maximum_walk_distance * 2) +decay function,_decay,_rt_available * exp(_nmDist * dispersion_parameter_walk) +non-motorized retail accessibility,nmRetail,_retailEmp * _decay +non-motorized total accessibility,nmTotal,_totEmp * _decay diff --git a/activitysim/examples/example_arc/configs/accessibility.yaml b/activitysim/examples/prototype_arc/configs/accessibility.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/accessibility.yaml rename to activitysim/examples/prototype_arc/configs/accessibility.yaml diff --git a/activitysim/examples/example_arc/configs/annotate_households.csv b/activitysim/examples/prototype_arc/configs/annotate_households.csv similarity index 100% rename from activitysim/examples/example_arc/configs/annotate_households.csv rename to activitysim/examples/prototype_arc/configs/annotate_households.csv diff --git a/activitysim/examples/example_arc/configs/annotate_households_cdap.csv b/activitysim/examples/prototype_arc/configs/annotate_households_cdap.csv similarity index 100% rename from activitysim/examples/example_arc/configs/annotate_households_cdap.csv rename to activitysim/examples/prototype_arc/configs/annotate_households_cdap.csv diff --git a/activitysim/examples/example_arc/configs/annotate_households_workplace.csv b/activitysim/examples/prototype_arc/configs/annotate_households_workplace.csv similarity index 100% rename from activitysim/examples/example_arc/configs/annotate_households_workplace.csv rename to activitysim/examples/prototype_arc/configs/annotate_households_workplace.csv diff --git a/activitysim/examples/example_arc/configs/annotate_landuse.csv b/activitysim/examples/prototype_arc/configs/annotate_landuse.csv similarity index 100% rename from activitysim/examples/example_arc/configs/annotate_landuse.csv rename to activitysim/examples/prototype_arc/configs/annotate_landuse.csv diff --git a/activitysim/examples/example_arc/configs/annotate_persons.csv b/activitysim/examples/prototype_arc/configs/annotate_persons.csv similarity index 100% rename from activitysim/examples/example_arc/configs/annotate_persons.csv rename to activitysim/examples/prototype_arc/configs/annotate_persons.csv diff --git a/activitysim/examples/example_arc/configs/annotate_persons_after_hh.csv b/activitysim/examples/prototype_arc/configs/annotate_persons_after_hh.csv similarity index 100% rename from activitysim/examples/example_arc/configs/annotate_persons_after_hh.csv rename to activitysim/examples/prototype_arc/configs/annotate_persons_after_hh.csv diff --git a/activitysim/examples/example_arc/configs/annotate_persons_cdap.csv b/activitysim/examples/prototype_arc/configs/annotate_persons_cdap.csv similarity index 100% rename from activitysim/examples/example_arc/configs/annotate_persons_cdap.csv rename to activitysim/examples/prototype_arc/configs/annotate_persons_cdap.csv diff --git a/activitysim/examples/example_arc/configs/annotate_persons_jtp.csv b/activitysim/examples/prototype_arc/configs/annotate_persons_jtp.csv similarity index 100% rename from activitysim/examples/example_arc/configs/annotate_persons_jtp.csv rename to activitysim/examples/prototype_arc/configs/annotate_persons_jtp.csv diff --git a/activitysim/examples/example_arc/configs/annotate_persons_mtf.csv b/activitysim/examples/prototype_arc/configs/annotate_persons_mtf.csv similarity index 100% rename from activitysim/examples/example_arc/configs/annotate_persons_mtf.csv rename to activitysim/examples/prototype_arc/configs/annotate_persons_mtf.csv diff --git a/activitysim/examples/example_arc/configs/annotate_persons_nmtf.csv b/activitysim/examples/prototype_arc/configs/annotate_persons_nmtf.csv similarity index 100% rename from activitysim/examples/example_arc/configs/annotate_persons_nmtf.csv rename to activitysim/examples/prototype_arc/configs/annotate_persons_nmtf.csv diff --git a/activitysim/examples/example_arc/configs/annotate_persons_school.csv b/activitysim/examples/prototype_arc/configs/annotate_persons_school.csv similarity index 100% rename from activitysim/examples/example_arc/configs/annotate_persons_school.csv rename to activitysim/examples/prototype_arc/configs/annotate_persons_school.csv diff --git a/activitysim/examples/example_arc/configs/annotate_persons_workplace.csv b/activitysim/examples/prototype_arc/configs/annotate_persons_workplace.csv similarity index 100% rename from activitysim/examples/example_arc/configs/annotate_persons_workplace.csv rename to activitysim/examples/prototype_arc/configs/annotate_persons_workplace.csv diff --git a/activitysim/examples/example_arc/configs/atwork_subtour_destination.csv b/activitysim/examples/prototype_arc/configs/atwork_subtour_destination.csv similarity index 100% rename from activitysim/examples/example_arc/configs/atwork_subtour_destination.csv rename to activitysim/examples/prototype_arc/configs/atwork_subtour_destination.csv diff --git a/activitysim/examples/example_arc/configs/atwork_subtour_destination.yaml b/activitysim/examples/prototype_arc/configs/atwork_subtour_destination.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/atwork_subtour_destination.yaml rename to activitysim/examples/prototype_arc/configs/atwork_subtour_destination.yaml diff --git a/activitysim/examples/example_arc/configs/atwork_subtour_destination_coeffs.csv b/activitysim/examples/prototype_arc/configs/atwork_subtour_destination_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/atwork_subtour_destination_coeffs.csv rename to activitysim/examples/prototype_arc/configs/atwork_subtour_destination_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/atwork_subtour_destination_sample.csv b/activitysim/examples/prototype_arc/configs/atwork_subtour_destination_sample.csv similarity index 100% rename from activitysim/examples/example_arc/configs/atwork_subtour_destination_sample.csv rename to activitysim/examples/prototype_arc/configs/atwork_subtour_destination_sample.csv diff --git a/activitysim/examples/example_arc/configs/atwork_subtour_frequency.csv b/activitysim/examples/prototype_arc/configs/atwork_subtour_frequency.csv similarity index 100% rename from activitysim/examples/example_arc/configs/atwork_subtour_frequency.csv rename to activitysim/examples/prototype_arc/configs/atwork_subtour_frequency.csv diff --git a/activitysim/examples/example_semcog/configs/atwork_subtour_frequency.yaml b/activitysim/examples/prototype_arc/configs/atwork_subtour_frequency.yaml old mode 100755 new mode 100644 similarity index 95% rename from activitysim/examples/example_semcog/configs/atwork_subtour_frequency.yaml rename to activitysim/examples/prototype_arc/configs/atwork_subtour_frequency.yaml index 125bf2c1fd..1b5d27101d --- a/activitysim/examples/example_semcog/configs/atwork_subtour_frequency.yaml +++ b/activitysim/examples/prototype_arc/configs/atwork_subtour_frequency.yaml @@ -1,11 +1,11 @@ - -SPEC: atwork_subtour_frequency.csv -COEFFICIENTS: atwork_subtour_frequency_coeffs.csv - -preprocessor: - SPEC: atwork_subtour_frequency_annotate_tours_preprocessor - DF: df - TABLES: - - land_use - - tours - - joint_tour_participants + +SPEC: atwork_subtour_frequency.csv +COEFFICIENTS: atwork_subtour_frequency_coeffs.csv + +preprocessor: + SPEC: atwork_subtour_frequency_annotate_tours_preprocessor + DF: df + TABLES: + - land_use + - tours + - joint_tour_participants diff --git a/activitysim/examples/example_arc/configs/atwork_subtour_frequency_alternatives.csv b/activitysim/examples/prototype_arc/configs/atwork_subtour_frequency_alternatives.csv similarity index 100% rename from activitysim/examples/example_arc/configs/atwork_subtour_frequency_alternatives.csv rename to activitysim/examples/prototype_arc/configs/atwork_subtour_frequency_alternatives.csv diff --git a/activitysim/examples/example_arc/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_arc/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/atwork_subtour_frequency_coeffs.csv b/activitysim/examples/prototype_arc/configs/atwork_subtour_frequency_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/atwork_subtour_frequency_coeffs.csv rename to activitysim/examples/prototype_arc/configs/atwork_subtour_frequency_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/auto_ownership.csv b/activitysim/examples/prototype_arc/configs/auto_ownership.csv similarity index 100% rename from activitysim/examples/example_arc/configs/auto_ownership.csv rename to activitysim/examples/prototype_arc/configs/auto_ownership.csv diff --git a/activitysim/examples/example_arc/configs/auto_ownership.yaml b/activitysim/examples/prototype_arc/configs/auto_ownership.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/auto_ownership.yaml rename to activitysim/examples/prototype_arc/configs/auto_ownership.yaml diff --git a/activitysim/examples/example_arc/configs/auto_ownership_coeffs.csv b/activitysim/examples/prototype_arc/configs/auto_ownership_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/auto_ownership_coeffs.csv rename to activitysim/examples/prototype_arc/configs/auto_ownership_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/cdap.yaml b/activitysim/examples/prototype_arc/configs/cdap.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/cdap.yaml rename to activitysim/examples/prototype_arc/configs/cdap.yaml diff --git a/activitysim/examples/example_mtc/configs/cdap_fixed_relative_proportions.csv b/activitysim/examples/prototype_arc/configs/cdap_fixed_relative_proportions.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/cdap_fixed_relative_proportions.csv rename to activitysim/examples/prototype_arc/configs/cdap_fixed_relative_proportions.csv diff --git a/activitysim/examples/example_arc/configs/cdap_indiv_and_hhsize1.csv b/activitysim/examples/prototype_arc/configs/cdap_indiv_and_hhsize1.csv similarity index 100% rename from activitysim/examples/example_arc/configs/cdap_indiv_and_hhsize1.csv rename to activitysim/examples/prototype_arc/configs/cdap_indiv_and_hhsize1.csv diff --git a/activitysim/examples/example_arc/configs/cdap_interaction_coefficients.csv b/activitysim/examples/prototype_arc/configs/cdap_interaction_coefficients.csv similarity index 100% rename from activitysim/examples/example_arc/configs/cdap_interaction_coefficients.csv rename to activitysim/examples/prototype_arc/configs/cdap_interaction_coefficients.csv diff --git a/activitysim/examples/example_arc/configs/constants.yaml b/activitysim/examples/prototype_arc/configs/constants.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/constants.yaml rename to activitysim/examples/prototype_arc/configs/constants.yaml diff --git a/activitysim/examples/example_arc/configs/destination_choice_size_terms.csv b/activitysim/examples/prototype_arc/configs/destination_choice_size_terms.csv similarity index 100% rename from activitysim/examples/example_arc/configs/destination_choice_size_terms.csv rename to activitysim/examples/prototype_arc/configs/destination_choice_size_terms.csv diff --git a/activitysim/examples/example_arc/configs/free_parking.csv b/activitysim/examples/prototype_arc/configs/free_parking.csv similarity index 100% rename from activitysim/examples/example_arc/configs/free_parking.csv rename to activitysim/examples/prototype_arc/configs/free_parking.csv diff --git a/activitysim/examples/example_arc/configs/free_parking.yaml b/activitysim/examples/prototype_arc/configs/free_parking.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/free_parking.yaml rename to activitysim/examples/prototype_arc/configs/free_parking.yaml diff --git a/activitysim/examples/example_arc/configs/free_parking_coeffs.csv b/activitysim/examples/prototype_arc/configs/free_parking_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/free_parking_coeffs.csv rename to activitysim/examples/prototype_arc/configs/free_parking_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/initialize_households.yaml b/activitysim/examples/prototype_arc/configs/initialize_households.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/initialize_households.yaml rename to activitysim/examples/prototype_arc/configs/initialize_households.yaml diff --git a/activitysim/examples/example_arc/configs/initialize_landuse.yaml b/activitysim/examples/prototype_arc/configs/initialize_landuse.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/initialize_landuse.yaml rename to activitysim/examples/prototype_arc/configs/initialize_landuse.yaml diff --git a/activitysim/examples/example_arc/configs/joint_tour_composition.csv b/activitysim/examples/prototype_arc/configs/joint_tour_composition.csv similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_composition.csv rename to activitysim/examples/prototype_arc/configs/joint_tour_composition.csv diff --git a/activitysim/examples/example_semcog/configs/joint_tour_composition.yaml b/activitysim/examples/prototype_arc/configs/joint_tour_composition.yaml old mode 100755 new mode 100644 similarity index 95% rename from activitysim/examples/example_semcog/configs/joint_tour_composition.yaml rename to activitysim/examples/prototype_arc/configs/joint_tour_composition.yaml index 55ee2015ec..a699bb337d --- a/activitysim/examples/example_semcog/configs/joint_tour_composition.yaml +++ b/activitysim/examples/prototype_arc/configs/joint_tour_composition.yaml @@ -1,11 +1,11 @@ -LOGIT_TYPE: MNL - -SPEC: joint_tour_composition.csv -COEFFICIENTS: joint_tour_composition_coeffs.csv - -preprocessor: - SPEC: joint_tour_composition_annotate_households_preprocessor - DF: households -# TABLES: -# - persons -# - accessibility +LOGIT_TYPE: MNL + +SPEC: joint_tour_composition.csv +COEFFICIENTS: joint_tour_composition_coeffs.csv + +preprocessor: + SPEC: joint_tour_composition_annotate_households_preprocessor + DF: households +# TABLES: +# - persons +# - accessibility diff --git a/activitysim/examples/example_arc/configs/joint_tour_composition_annotate_households_preprocessor.csv b/activitysim/examples/prototype_arc/configs/joint_tour_composition_annotate_households_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_composition_annotate_households_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/joint_tour_composition_annotate_households_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/joint_tour_composition_coeffs.csv b/activitysim/examples/prototype_arc/configs/joint_tour_composition_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_composition_coeffs.csv rename to activitysim/examples/prototype_arc/configs/joint_tour_composition_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/joint_tour_destination.yaml b/activitysim/examples/prototype_arc/configs/joint_tour_destination.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_destination.yaml rename to activitysim/examples/prototype_arc/configs/joint_tour_destination.yaml diff --git a/activitysim/examples/example_arc/configs/joint_tour_destination_coeffs.csv b/activitysim/examples/prototype_arc/configs/joint_tour_destination_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_destination_coeffs.csv rename to activitysim/examples/prototype_arc/configs/joint_tour_destination_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/joint_tour_frequency.csv b/activitysim/examples/prototype_arc/configs/joint_tour_frequency.csv similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_frequency.csv rename to activitysim/examples/prototype_arc/configs/joint_tour_frequency.csv diff --git a/activitysim/examples/example_semcog/configs/joint_tour_frequency.yaml b/activitysim/examples/prototype_arc/configs/joint_tour_frequency.yaml old mode 100755 new mode 100644 similarity index 95% rename from activitysim/examples/example_semcog/configs/joint_tour_frequency.yaml rename to activitysim/examples/prototype_arc/configs/joint_tour_frequency.yaml index d5a70b1dfc..61e1f1bdcf --- a/activitysim/examples/example_semcog/configs/joint_tour_frequency.yaml +++ b/activitysim/examples/prototype_arc/configs/joint_tour_frequency.yaml @@ -1,11 +1,11 @@ -LOGIT_TYPE: MNL - -SPEC: joint_tour_frequency.csv -COEFFICIENTS: joint_tour_frequency_coeffs.csv - -preprocessor: - SPEC: joint_tour_frequency_annotate_households_preprocessor - DF: households - TABLES: - #- persons - - accessibility +LOGIT_TYPE: MNL + +SPEC: joint_tour_frequency.csv +COEFFICIENTS: joint_tour_frequency_coeffs.csv + +preprocessor: + SPEC: joint_tour_frequency_annotate_households_preprocessor + DF: households + TABLES: + #- persons + - accessibility diff --git a/activitysim/examples/example_arc/configs/joint_tour_frequency_alternatives.csv b/activitysim/examples/prototype_arc/configs/joint_tour_frequency_alternatives.csv similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_frequency_alternatives.csv rename to activitysim/examples/prototype_arc/configs/joint_tour_frequency_alternatives.csv diff --git a/activitysim/examples/example_arc/configs/joint_tour_frequency_annotate_households_preprocessor.csv b/activitysim/examples/prototype_arc/configs/joint_tour_frequency_annotate_households_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_frequency_annotate_households_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/joint_tour_frequency_annotate_households_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/joint_tour_frequency_coeffs.csv b/activitysim/examples/prototype_arc/configs/joint_tour_frequency_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_frequency_coeffs.csv rename to activitysim/examples/prototype_arc/configs/joint_tour_frequency_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/joint_tour_participation.csv b/activitysim/examples/prototype_arc/configs/joint_tour_participation.csv similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_participation.csv rename to activitysim/examples/prototype_arc/configs/joint_tour_participation.csv diff --git a/activitysim/examples/example_arc/configs/joint_tour_participation.yaml b/activitysim/examples/prototype_arc/configs/joint_tour_participation.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_participation.yaml rename to activitysim/examples/prototype_arc/configs/joint_tour_participation.yaml diff --git a/activitysim/examples/example_arc/configs/joint_tour_participation_annotate_participants_preprocessor.csv b/activitysim/examples/prototype_arc/configs/joint_tour_participation_annotate_participants_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_participation_annotate_participants_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/joint_tour_participation_annotate_participants_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/joint_tour_participation_coeffs.csv b/activitysim/examples/prototype_arc/configs/joint_tour_participation_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_participation_coeffs.csv rename to activitysim/examples/prototype_arc/configs/joint_tour_participation_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/joint_tour_scheduling.yaml b/activitysim/examples/prototype_arc/configs/joint_tour_scheduling.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_scheduling.yaml rename to activitysim/examples/prototype_arc/configs/joint_tour_scheduling.yaml diff --git a/activitysim/examples/example_arc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_arc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/joint_tour_scheduling_coeffs.csv b/activitysim/examples/prototype_arc/configs/joint_tour_scheduling_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/joint_tour_scheduling_coeffs.csv rename to activitysim/examples/prototype_arc/configs/joint_tour_scheduling_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/logging.yaml b/activitysim/examples/prototype_arc/configs/logging.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/logging.yaml rename to activitysim/examples/prototype_arc/configs/logging.yaml diff --git a/activitysim/examples/example_arc/configs/mandatory_tour_frequency.csv b/activitysim/examples/prototype_arc/configs/mandatory_tour_frequency.csv similarity index 100% rename from activitysim/examples/example_arc/configs/mandatory_tour_frequency.csv rename to activitysim/examples/prototype_arc/configs/mandatory_tour_frequency.csv diff --git a/activitysim/examples/example_arc/configs/mandatory_tour_frequency.yaml b/activitysim/examples/prototype_arc/configs/mandatory_tour_frequency.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/mandatory_tour_frequency.yaml rename to activitysim/examples/prototype_arc/configs/mandatory_tour_frequency.yaml diff --git a/activitysim/examples/example_arc/configs/mandatory_tour_frequency_alternatives.csv b/activitysim/examples/prototype_arc/configs/mandatory_tour_frequency_alternatives.csv similarity index 100% rename from activitysim/examples/example_arc/configs/mandatory_tour_frequency_alternatives.csv rename to activitysim/examples/prototype_arc/configs/mandatory_tour_frequency_alternatives.csv diff --git a/activitysim/examples/example_arc/configs/mandatory_tour_frequency_coeffs.csv b/activitysim/examples/prototype_arc/configs/mandatory_tour_frequency_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/mandatory_tour_frequency_coeffs.csv rename to activitysim/examples/prototype_arc/configs/mandatory_tour_frequency_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/mandatory_tour_scheduling.yaml b/activitysim/examples/prototype_arc/configs/mandatory_tour_scheduling.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/mandatory_tour_scheduling.yaml rename to activitysim/examples/prototype_arc/configs/mandatory_tour_scheduling.yaml diff --git a/activitysim/examples/example_arc/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv b/activitysim/examples/prototype_arc/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/mandatory_tour_schedulings_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_arc/configs/mandatory_tour_schedulings_annotate_tours_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/mandatory_tour_schedulings_annotate_tours_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/mandatory_tour_schedulings_annotate_tours_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/network_los.yaml b/activitysim/examples/prototype_arc/configs/network_los.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/network_los.yaml rename to activitysim/examples/prototype_arc/configs/network_los.yaml diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_destination.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_destination.csv similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_destination.csv rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_destination.csv diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_destination.yaml b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_destination.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_destination.yaml rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_destination.yaml diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_destination_coeffs.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_destination_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_destination_coeffs.csv rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_destination_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_destination_sample.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_destination_sample.csv similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_destination_sample.csv rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_destination_sample.csv diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_frequency.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency.csv similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_frequency.csv rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency.csv diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency.yaml b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency.yaml old mode 100755 new mode 100644 similarity index 96% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency.yaml rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency.yaml index bd1c6f12d3..239b5a38a6 --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency.yaml +++ b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency.yaml @@ -1,42 +1,42 @@ - -SEGMENT_COL: ptype -SPEC: non_mandatory_tour_frequency.csv - -SPEC_SEGMENTS: - - NAME: PTYPE_FULL - PTYPE: 1 - COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv - - NAME: PTYPE_PART - PTYPE: 2 - COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv - - NAME: PTYPE_UNIVERSITY - PTYPE: 3 - COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv - - NAME: PTYPE_NONWORK - PTYPE: 4 - COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv - - NAME: PTYPE_RETIRED - PTYPE: 5 - COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv - - NAME: PTYPE_DRIVING - PTYPE: 6 - COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv - - NAME: PTYPE_SCHOOL - PTYPE: 7 - COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv - - NAME: PTYPE_PRESCHOOL - PTYPE: 8 - COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv - -annotate_persons: - SPEC: annotate_persons_nmtf - DF: persons - TABLES: - - tours - -preprocessor: - SPEC: non_mandatory_tour_frequency_annotate_persons_preprocessor - DF: persons - TABLES: - - tours -# - accessibility + +SEGMENT_COL: ptype +SPEC: non_mandatory_tour_frequency.csv + +SPEC_SEGMENTS: + - NAME: PTYPE_FULL + PTYPE: 1 + COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv + - NAME: PTYPE_PART + PTYPE: 2 + COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv + - NAME: PTYPE_UNIVERSITY + PTYPE: 3 + COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv + - NAME: PTYPE_NONWORK + PTYPE: 4 + COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv + - NAME: PTYPE_RETIRED + PTYPE: 5 + COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv + - NAME: PTYPE_DRIVING + PTYPE: 6 + COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv + - NAME: PTYPE_SCHOOL + PTYPE: 7 + COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv + - NAME: PTYPE_PRESCHOOL + PTYPE: 8 + COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv + +annotate_persons: + SPEC: annotate_persons_nmtf + DF: persons + TABLES: + - tours + +preprocessor: + SPEC: non_mandatory_tour_frequency_annotate_persons_preprocessor + DF: persons + TABLES: + - tours +# - accessibility diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_alternatives.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_alternatives.csv similarity index 92% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_alternatives.csv rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_alternatives.csv index 1c0052f963..b9765aa75a 100644 --- a/activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_alternatives.csv +++ b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_alternatives.csv @@ -1,97 +1,97 @@ -escort,shopping,othmaint,othdiscr,eatout,social -0,0,0,0,0,0 -0,0,0,1,0,0 -0,0,0,0,0,1 -0,0,0,1,0,1 -0,0,0,0,1,0 -0,0,0,1,1,0 -0,0,0,0,1,1 -0,0,0,1,1,1 -0,0,1,0,0,0 -0,0,1,1,0,0 -0,0,1,0,0,1 -0,0,1,1,0,1 -0,0,1,0,1,0 -0,0,1,1,1,0 -0,0,1,0,1,1 -0,0,1,1,1,1 -0,1,0,0,0,0 -0,1,0,1,0,0 -0,1,0,0,0,1 -0,1,0,1,0,1 -0,1,0,0,1,0 -0,1,0,1,1,0 -0,1,0,0,1,1 -0,1,0,1,1,1 -0,1,1,0,0,0 -0,1,1,1,0,0 -0,1,1,0,0,1 -0,1,1,1,0,1 -0,1,1,0,1,0 -0,1,1,1,1,0 -0,1,1,0,1,1 -0,1,1,1,1,1 -1,0,0,0,0,0 -1,0,0,1,0,0 -1,0,0,0,0,1 -1,0,0,1,0,1 -1,0,0,0,1,0 -1,0,0,1,1,0 -1,0,0,0,1,1 -1,0,0,1,1,1 -1,0,1,0,0,0 -1,0,1,1,0,0 -1,0,1,0,0,1 -1,0,1,1,0,1 -1,0,1,0,1,0 -1,0,1,1,1,0 -1,0,1,0,1,1 -1,0,1,1,1,1 -1,1,0,0,0,0 -1,1,0,1,0,0 -1,1,0,0,0,1 -1,1,0,1,0,1 -1,1,0,0,1,0 -1,1,0,1,1,0 -1,1,0,0,1,1 -1,1,0,1,1,1 -1,1,1,0,0,0 -1,1,1,1,0,0 -1,1,1,0,0,1 -1,1,1,1,0,1 -1,1,1,0,1,0 -1,1,1,1,1,0 -1,1,1,0,1,1 -1,1,1,1,1,1 -2,0,0,0,0,0 -2,0,0,1,0,0 -2,0,0,0,0,1 -2,0,0,1,0,1 -2,0,0,0,1,0 -2,0,0,1,1,0 -2,0,0,0,1,1 -2,0,0,1,1,1 -2,0,1,0,0,0 -2,0,1,1,0,0 -2,0,1,0,0,1 -2,0,1,1,0,1 -2,0,1,0,1,0 -2,0,1,1,1,0 -2,0,1,0,1,1 -2,0,1,1,1,1 -2,1,0,0,0,0 -2,1,0,1,0,0 -2,1,0,0,0,1 -2,1,0,1,0,1 -2,1,0,0,1,0 -2,1,0,1,1,0 -2,1,0,0,1,1 -2,1,0,1,1,1 -2,1,1,0,0,0 -2,1,1,1,0,0 -2,1,1,0,0,1 -2,1,1,1,0,1 -2,1,1,0,1,0 -2,1,1,1,1,0 -2,1,1,0,1,1 -2,1,1,1,1,1 +escort,shopping,othmaint,othdiscr,eatout,social +0,0,0,0,0,0 +0,0,0,1,0,0 +0,0,0,0,0,1 +0,0,0,1,0,1 +0,0,0,0,1,0 +0,0,0,1,1,0 +0,0,0,0,1,1 +0,0,0,1,1,1 +0,0,1,0,0,0 +0,0,1,1,0,0 +0,0,1,0,0,1 +0,0,1,1,0,1 +0,0,1,0,1,0 +0,0,1,1,1,0 +0,0,1,0,1,1 +0,0,1,1,1,1 +0,1,0,0,0,0 +0,1,0,1,0,0 +0,1,0,0,0,1 +0,1,0,1,0,1 +0,1,0,0,1,0 +0,1,0,1,1,0 +0,1,0,0,1,1 +0,1,0,1,1,1 +0,1,1,0,0,0 +0,1,1,1,0,0 +0,1,1,0,0,1 +0,1,1,1,0,1 +0,1,1,0,1,0 +0,1,1,1,1,0 +0,1,1,0,1,1 +0,1,1,1,1,1 +1,0,0,0,0,0 +1,0,0,1,0,0 +1,0,0,0,0,1 +1,0,0,1,0,1 +1,0,0,0,1,0 +1,0,0,1,1,0 +1,0,0,0,1,1 +1,0,0,1,1,1 +1,0,1,0,0,0 +1,0,1,1,0,0 +1,0,1,0,0,1 +1,0,1,1,0,1 +1,0,1,0,1,0 +1,0,1,1,1,0 +1,0,1,0,1,1 +1,0,1,1,1,1 +1,1,0,0,0,0 +1,1,0,1,0,0 +1,1,0,0,0,1 +1,1,0,1,0,1 +1,1,0,0,1,0 +1,1,0,1,1,0 +1,1,0,0,1,1 +1,1,0,1,1,1 +1,1,1,0,0,0 +1,1,1,1,0,0 +1,1,1,0,0,1 +1,1,1,1,0,1 +1,1,1,0,1,0 +1,1,1,1,1,0 +1,1,1,0,1,1 +1,1,1,1,1,1 +2,0,0,0,0,0 +2,0,0,1,0,0 +2,0,0,0,0,1 +2,0,0,1,0,1 +2,0,0,0,1,0 +2,0,0,1,1,0 +2,0,0,0,1,1 +2,0,0,1,1,1 +2,0,1,0,0,0 +2,0,1,1,0,0 +2,0,1,0,0,1 +2,0,1,1,0,1 +2,0,1,0,1,0 +2,0,1,1,1,0 +2,0,1,0,1,1 +2,0,1,1,1,1 +2,1,0,0,0,0 +2,1,0,1,0,0 +2,1,0,0,0,1 +2,1,0,1,0,1 +2,1,0,0,1,0 +2,1,0,1,1,0 +2,1,0,0,1,1 +2,1,0,1,1,1 +2,1,1,0,0,0 +2,1,1,1,0,0 +2,1,1,0,0,1 +2,1,1,1,0,1 +2,1,1,0,1,0 +2,1,1,1,1,0 +2,1,1,0,1,1 +2,1,1,1,1,1 diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_extension_probs.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_extension_probs.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency_extension_probs.csv rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_frequency_extension_probs.csv diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_scheduling.yaml b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_scheduling.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_scheduling.yaml rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_scheduling.yaml diff --git a/activitysim/examples/example_arc/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_arc/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/parking_location_choice.csv b/activitysim/examples/prototype_arc/configs/parking_location_choice.csv similarity index 100% rename from activitysim/examples/example_arc/configs/parking_location_choice.csv rename to activitysim/examples/prototype_arc/configs/parking_location_choice.csv diff --git a/activitysim/examples/example_arc/configs/parking_location_choice.yaml b/activitysim/examples/prototype_arc/configs/parking_location_choice.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/parking_location_choice.yaml rename to activitysim/examples/prototype_arc/configs/parking_location_choice.yaml diff --git a/activitysim/examples/example_arc/configs/parking_location_choice_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_arc/configs/parking_location_choice_annotate_trips_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/parking_location_choice_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/parking_location_choice_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/parking_location_choice_coeffs.csv b/activitysim/examples/prototype_arc/configs/parking_location_choice_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/parking_location_choice_coeffs.csv rename to activitysim/examples/prototype_arc/configs/parking_location_choice_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/school_location.csv b/activitysim/examples/prototype_arc/configs/school_location.csv similarity index 100% rename from activitysim/examples/example_arc/configs/school_location.csv rename to activitysim/examples/prototype_arc/configs/school_location.csv diff --git a/activitysim/examples/example_arc/configs/school_location.yaml b/activitysim/examples/prototype_arc/configs/school_location.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/school_location.yaml rename to activitysim/examples/prototype_arc/configs/school_location.yaml diff --git a/activitysim/examples/example_arc/configs/school_location_coeffs.csv b/activitysim/examples/prototype_arc/configs/school_location_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/school_location_coeffs.csv rename to activitysim/examples/prototype_arc/configs/school_location_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/school_location_sample.csv b/activitysim/examples/prototype_arc/configs/school_location_sample.csv similarity index 98% rename from activitysim/examples/example_arc/configs/school_location_sample.csv rename to activitysim/examples/prototype_arc/configs/school_location_sample.csv index 8527304458..601d61bae1 100644 --- a/activitysim/examples/example_arc/configs/school_location_sample.csv +++ b/activitysim/examples/prototype_arc/configs/school_location_sample.csv @@ -1,4 +1,4 @@ -Description,Expression,univ,k12_predrive,k12_drive -Distance,"@skims[('SOV_FREE_DISTANCE', 'MD')]",-0.2,-0.2,-0.2 -Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1,1,1 -No attractions,@df['size_term']==0,-999,-999,-999 +Description,Expression,univ,k12_predrive,k12_drive +Distance,"@skims[('SOV_FREE_DISTANCE', 'MD')]",-0.2,-0.2,-0.2 +Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1,1,1 +No attractions,@df['size_term']==0,-999,-999,-999 diff --git a/activitysim/examples/example_arc/configs/settings.yaml b/activitysim/examples/prototype_arc/configs/settings.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/settings.yaml rename to activitysim/examples/prototype_arc/configs/settings.yaml diff --git a/activitysim/examples/example_arc/configs/settings_mp.yaml b/activitysim/examples/prototype_arc/configs/settings_mp.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/settings_mp.yaml rename to activitysim/examples/prototype_arc/configs/settings_mp.yaml diff --git a/activitysim/examples/example_arc/configs/shadow_pricing.yaml b/activitysim/examples/prototype_arc/configs/shadow_pricing.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/shadow_pricing.yaml rename to activitysim/examples/prototype_arc/configs/shadow_pricing.yaml diff --git a/activitysim/examples/example_arc/configs/stop_frequency.yaml b/activitysim/examples/prototype_arc/configs/stop_frequency.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/stop_frequency.yaml rename to activitysim/examples/prototype_arc/configs/stop_frequency.yaml diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_alternatives.csv b/activitysim/examples/prototype_arc/configs/stop_frequency_alternatives.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_alternatives.csv rename to activitysim/examples/prototype_arc/configs/stop_frequency_alternatives.csv diff --git a/activitysim/examples/example_arc/configs/stop_frequency_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_arc/configs/stop_frequency_annotate_tours_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/stop_frequency_annotate_tours_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/stop_frequency_annotate_tours_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/stop_frequency_atwork.csv b/activitysim/examples/prototype_arc/configs/stop_frequency_atwork.csv similarity index 100% rename from activitysim/examples/example_arc/configs/stop_frequency_atwork.csv rename to activitysim/examples/prototype_arc/configs/stop_frequency_atwork.csv diff --git a/activitysim/examples/example_arc/configs/stop_frequency_eatout.csv b/activitysim/examples/prototype_arc/configs/stop_frequency_eatout.csv similarity index 100% rename from activitysim/examples/example_arc/configs/stop_frequency_eatout.csv rename to activitysim/examples/prototype_arc/configs/stop_frequency_eatout.csv diff --git a/activitysim/examples/example_arc/configs/stop_frequency_escort.csv b/activitysim/examples/prototype_arc/configs/stop_frequency_escort.csv similarity index 100% rename from activitysim/examples/example_arc/configs/stop_frequency_escort.csv rename to activitysim/examples/prototype_arc/configs/stop_frequency_escort.csv diff --git a/activitysim/examples/example_arc/configs/stop_frequency_othdiscr.csv b/activitysim/examples/prototype_arc/configs/stop_frequency_othdiscr.csv similarity index 100% rename from activitysim/examples/example_arc/configs/stop_frequency_othdiscr.csv rename to activitysim/examples/prototype_arc/configs/stop_frequency_othdiscr.csv diff --git a/activitysim/examples/example_arc/configs/stop_frequency_othmaint.csv b/activitysim/examples/prototype_arc/configs/stop_frequency_othmaint.csv similarity index 100% rename from activitysim/examples/example_arc/configs/stop_frequency_othmaint.csv rename to activitysim/examples/prototype_arc/configs/stop_frequency_othmaint.csv diff --git a/activitysim/examples/example_arc/configs/stop_frequency_school.csv b/activitysim/examples/prototype_arc/configs/stop_frequency_school.csv similarity index 100% rename from activitysim/examples/example_arc/configs/stop_frequency_school.csv rename to activitysim/examples/prototype_arc/configs/stop_frequency_school.csv diff --git a/activitysim/examples/example_arc/configs/stop_frequency_shopping.csv b/activitysim/examples/prototype_arc/configs/stop_frequency_shopping.csv similarity index 100% rename from activitysim/examples/example_arc/configs/stop_frequency_shopping.csv rename to activitysim/examples/prototype_arc/configs/stop_frequency_shopping.csv diff --git a/activitysim/examples/example_arc/configs/stop_frequency_social.csv b/activitysim/examples/prototype_arc/configs/stop_frequency_social.csv similarity index 100% rename from activitysim/examples/example_arc/configs/stop_frequency_social.csv rename to activitysim/examples/prototype_arc/configs/stop_frequency_social.csv diff --git a/activitysim/examples/example_arc/configs/stop_frequency_univ.csv b/activitysim/examples/prototype_arc/configs/stop_frequency_univ.csv similarity index 100% rename from activitysim/examples/example_arc/configs/stop_frequency_univ.csv rename to activitysim/examples/prototype_arc/configs/stop_frequency_univ.csv diff --git a/activitysim/examples/example_arc/configs/stop_frequency_work.csv b/activitysim/examples/prototype_arc/configs/stop_frequency_work.csv similarity index 100% rename from activitysim/examples/example_arc/configs/stop_frequency_work.csv rename to activitysim/examples/prototype_arc/configs/stop_frequency_work.csv diff --git a/activitysim/examples/example_arc/configs/tour_departure_and_duration_alternatives.csv b/activitysim/examples/prototype_arc/configs/tour_departure_and_duration_alternatives.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_departure_and_duration_alternatives.csv rename to activitysim/examples/prototype_arc/configs/tour_departure_and_duration_alternatives.csv diff --git a/activitysim/examples/example_arc/configs/tour_mode_choice.csv b/activitysim/examples/prototype_arc/configs/tour_mode_choice.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_mode_choice.csv rename to activitysim/examples/prototype_arc/configs/tour_mode_choice.csv diff --git a/activitysim/examples/example_arc/configs/tour_mode_choice.yaml b/activitysim/examples/prototype_arc/configs/tour_mode_choice.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/tour_mode_choice.yaml rename to activitysim/examples/prototype_arc/configs/tour_mode_choice.yaml diff --git a/activitysim/examples/example_arc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/prototype_arc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/tour_mode_choice_coeffs.csv b/activitysim/examples/prototype_arc/configs/tour_mode_choice_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_mode_choice_coeffs.csv rename to activitysim/examples/prototype_arc/configs/tour_mode_choice_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/tour_mode_choice_coeffs_template.csv b/activitysim/examples/prototype_arc/configs/tour_mode_choice_coeffs_template.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_mode_choice_coeffs_template.csv rename to activitysim/examples/prototype_arc/configs/tour_mode_choice_coeffs_template.csv diff --git a/activitysim/examples/example_arc/configs/tour_scheduling_atwork.csv b/activitysim/examples/prototype_arc/configs/tour_scheduling_atwork.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_scheduling_atwork.csv rename to activitysim/examples/prototype_arc/configs/tour_scheduling_atwork.csv diff --git a/activitysim/examples/example_arc/configs/tour_scheduling_atwork.yaml b/activitysim/examples/prototype_arc/configs/tour_scheduling_atwork.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/tour_scheduling_atwork.yaml rename to activitysim/examples/prototype_arc/configs/tour_scheduling_atwork.yaml diff --git a/activitysim/examples/example_arc/configs/tour_scheduling_atwork_coeffs.csv b/activitysim/examples/prototype_arc/configs/tour_scheduling_atwork_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_scheduling_atwork_coeffs.csv rename to activitysim/examples/prototype_arc/configs/tour_scheduling_atwork_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/tour_scheduling_atwork_preprocessor.csv b/activitysim/examples/prototype_arc/configs/tour_scheduling_atwork_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_scheduling_atwork_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/tour_scheduling_atwork_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/tour_scheduling_joint.csv b/activitysim/examples/prototype_arc/configs/tour_scheduling_joint.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_scheduling_joint.csv rename to activitysim/examples/prototype_arc/configs/tour_scheduling_joint.csv diff --git a/activitysim/examples/example_arc/configs/tour_scheduling_joint_coeffs.csv b/activitysim/examples/prototype_arc/configs/tour_scheduling_joint_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_scheduling_joint_coeffs.csv rename to activitysim/examples/prototype_arc/configs/tour_scheduling_joint_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/tour_scheduling_nonmandatory.csv b/activitysim/examples/prototype_arc/configs/tour_scheduling_nonmandatory.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_scheduling_nonmandatory.csv rename to activitysim/examples/prototype_arc/configs/tour_scheduling_nonmandatory.csv diff --git a/activitysim/examples/example_arc/configs/tour_scheduling_nonmandatory_coeffs.csv b/activitysim/examples/prototype_arc/configs/tour_scheduling_nonmandatory_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_scheduling_nonmandatory_coeffs.csv rename to activitysim/examples/prototype_arc/configs/tour_scheduling_nonmandatory_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/tour_scheduling_school.csv b/activitysim/examples/prototype_arc/configs/tour_scheduling_school.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_scheduling_school.csv rename to activitysim/examples/prototype_arc/configs/tour_scheduling_school.csv diff --git a/activitysim/examples/example_arc/configs/tour_scheduling_school_coeffs.csv b/activitysim/examples/prototype_arc/configs/tour_scheduling_school_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_scheduling_school_coeffs.csv rename to activitysim/examples/prototype_arc/configs/tour_scheduling_school_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/tour_scheduling_university.csv b/activitysim/examples/prototype_arc/configs/tour_scheduling_university.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_scheduling_university.csv rename to activitysim/examples/prototype_arc/configs/tour_scheduling_university.csv diff --git a/activitysim/examples/example_arc/configs/tour_scheduling_university_coeffs.csv b/activitysim/examples/prototype_arc/configs/tour_scheduling_university_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_scheduling_university_coeffs.csv rename to activitysim/examples/prototype_arc/configs/tour_scheduling_university_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/tour_scheduling_work.csv b/activitysim/examples/prototype_arc/configs/tour_scheduling_work.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_scheduling_work.csv rename to activitysim/examples/prototype_arc/configs/tour_scheduling_work.csv diff --git a/activitysim/examples/example_arc/configs/tour_scheduling_work_coeffs.csv b/activitysim/examples/prototype_arc/configs/tour_scheduling_work_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/tour_scheduling_work_coeffs.csv rename to activitysim/examples/prototype_arc/configs/tour_scheduling_work_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/trip_departure_choice.csv b/activitysim/examples/prototype_arc/configs/trip_departure_choice.csv similarity index 100% rename from activitysim/examples/example_arc/configs/trip_departure_choice.csv rename to activitysim/examples/prototype_arc/configs/trip_departure_choice.csv diff --git a/activitysim/examples/example_arc/configs/trip_departure_choice.yaml b/activitysim/examples/prototype_arc/configs/trip_departure_choice.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/trip_departure_choice.yaml rename to activitysim/examples/prototype_arc/configs/trip_departure_choice.yaml diff --git a/activitysim/examples/example_arc/configs/trip_departure_choice_preprocessor.csv b/activitysim/examples/prototype_arc/configs/trip_departure_choice_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/trip_departure_choice_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/trip_departure_choice_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/trip_departure_sample_patterns.csv b/activitysim/examples/prototype_arc/configs/trip_departure_sample_patterns.csv similarity index 100% rename from activitysim/examples/example_arc/configs/trip_departure_sample_patterns.csv rename to activitysim/examples/prototype_arc/configs/trip_departure_sample_patterns.csv diff --git a/activitysim/examples/example_arc/configs/trip_destination.csv b/activitysim/examples/prototype_arc/configs/trip_destination.csv similarity index 100% rename from activitysim/examples/example_arc/configs/trip_destination.csv rename to activitysim/examples/prototype_arc/configs/trip_destination.csv diff --git a/activitysim/examples/example_arc/configs/trip_destination.yaml b/activitysim/examples/prototype_arc/configs/trip_destination.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/trip_destination.yaml rename to activitysim/examples/prototype_arc/configs/trip_destination.yaml diff --git a/activitysim/examples/example_arc/configs/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_arc/configs/trip_destination_annotate_trips_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/trip_destination_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/trip_destination_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/trip_destination_sample.csv b/activitysim/examples/prototype_arc/configs/trip_destination_sample.csv similarity index 100% rename from activitysim/examples/example_arc/configs/trip_destination_sample.csv rename to activitysim/examples/prototype_arc/configs/trip_destination_sample.csv diff --git a/activitysim/examples/example_arc/configs/trip_mode_choice.csv b/activitysim/examples/prototype_arc/configs/trip_mode_choice.csv similarity index 100% rename from activitysim/examples/example_arc/configs/trip_mode_choice.csv rename to activitysim/examples/prototype_arc/configs/trip_mode_choice.csv diff --git a/activitysim/examples/example_arc/configs/trip_mode_choice.yaml b/activitysim/examples/prototype_arc/configs/trip_mode_choice.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/trip_mode_choice.yaml rename to activitysim/examples/prototype_arc/configs/trip_mode_choice.yaml diff --git a/activitysim/examples/example_arc/configs/trip_mode_choice_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_arc/configs/trip_mode_choice_annotate_trips_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/trip_mode_choice_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/trip_mode_choice_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/trip_mode_choice_coeffs.csv b/activitysim/examples/prototype_arc/configs/trip_mode_choice_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/trip_mode_choice_coeffs.csv rename to activitysim/examples/prototype_arc/configs/trip_mode_choice_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/trip_purpose.yaml b/activitysim/examples/prototype_arc/configs/trip_purpose.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/trip_purpose.yaml rename to activitysim/examples/prototype_arc/configs/trip_purpose.yaml diff --git a/activitysim/examples/example_mtc/configs/trip_purpose_and_destination.yaml b/activitysim/examples/prototype_arc/configs/trip_purpose_and_destination.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/trip_purpose_and_destination.yaml rename to activitysim/examples/prototype_arc/configs/trip_purpose_and_destination.yaml diff --git a/activitysim/examples/example_mtc/configs/trip_purpose_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_arc/configs/trip_purpose_annotate_trips_preprocessor.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/trip_purpose_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/trip_purpose_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/trip_purpose_probs.csv b/activitysim/examples/prototype_arc/configs/trip_purpose_probs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/trip_purpose_probs.csv rename to activitysim/examples/prototype_arc/configs/trip_purpose_probs.csv diff --git a/activitysim/examples/example_arc/configs/trip_scheduling_choice.csv b/activitysim/examples/prototype_arc/configs/trip_scheduling_choice.csv similarity index 100% rename from activitysim/examples/example_arc/configs/trip_scheduling_choice.csv rename to activitysim/examples/prototype_arc/configs/trip_scheduling_choice.csv diff --git a/activitysim/examples/example_arc/configs/trip_scheduling_choice.yaml b/activitysim/examples/prototype_arc/configs/trip_scheduling_choice.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/trip_scheduling_choice.yaml rename to activitysim/examples/prototype_arc/configs/trip_scheduling_choice.yaml diff --git a/activitysim/examples/example_arc/configs/trip_scheduling_choice_preprocessor.csv b/activitysim/examples/prototype_arc/configs/trip_scheduling_choice_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/trip_scheduling_choice_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/trip_scheduling_choice_preprocessor.csv diff --git a/activitysim/examples/example_arc/configs/workplace_location.csv b/activitysim/examples/prototype_arc/configs/workplace_location.csv similarity index 100% rename from activitysim/examples/example_arc/configs/workplace_location.csv rename to activitysim/examples/prototype_arc/configs/workplace_location.csv diff --git a/activitysim/examples/example_arc/configs/workplace_location.yaml b/activitysim/examples/prototype_arc/configs/workplace_location.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/workplace_location.yaml rename to activitysim/examples/prototype_arc/configs/workplace_location.yaml diff --git a/activitysim/examples/example_arc/configs/workplace_location_coeffs.csv b/activitysim/examples/prototype_arc/configs/workplace_location_coeffs.csv similarity index 100% rename from activitysim/examples/example_arc/configs/workplace_location_coeffs.csv rename to activitysim/examples/prototype_arc/configs/workplace_location_coeffs.csv diff --git a/activitysim/examples/example_arc/configs/workplace_location_sample.csv b/activitysim/examples/prototype_arc/configs/workplace_location_sample.csv similarity index 100% rename from activitysim/examples/example_arc/configs/workplace_location_sample.csv rename to activitysim/examples/prototype_arc/configs/workplace_location_sample.csv diff --git a/activitysim/examples/example_arc/configs/write_trip_matrices.yaml b/activitysim/examples/prototype_arc/configs/write_trip_matrices.yaml similarity index 100% rename from activitysim/examples/example_arc/configs/write_trip_matrices.yaml rename to activitysim/examples/prototype_arc/configs/write_trip_matrices.yaml diff --git a/activitysim/examples/example_arc/configs/write_trip_matrices_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_arc/configs/write_trip_matrices_annotate_trips_preprocessor.csv similarity index 100% rename from activitysim/examples/example_arc/configs/write_trip_matrices_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_arc/configs/write_trip_matrices_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/example_arc/data/households.csv b/activitysim/examples/prototype_arc/data/households.csv similarity index 100% rename from activitysim/examples/example_arc/data/households.csv rename to activitysim/examples/prototype_arc/data/households.csv diff --git a/activitysim/examples/example_arc/data/land_use.csv b/activitysim/examples/prototype_arc/data/land_use.csv similarity index 100% rename from activitysim/examples/example_arc/data/land_use.csv rename to activitysim/examples/prototype_arc/data/land_use.csv diff --git a/activitysim/examples/example_arc/data/persons.csv b/activitysim/examples/prototype_arc/data/persons.csv similarity index 100% rename from activitysim/examples/example_arc/data/persons.csv rename to activitysim/examples/prototype_arc/data/persons.csv diff --git a/activitysim/examples/example_arc/data/skims.omx b/activitysim/examples/prototype_arc/data/skims.omx similarity index 100% rename from activitysim/examples/example_arc/data/skims.omx rename to activitysim/examples/prototype_arc/data/skims.omx diff --git a/activitysim/examples/example_arc/output/.gitignore b/activitysim/examples/prototype_arc/output/.gitignore similarity index 100% rename from activitysim/examples/example_arc/output/.gitignore rename to activitysim/examples/prototype_arc/output/.gitignore diff --git a/activitysim/examples/example_multiple_zone/output_3_mp/cache/.gitignore b/activitysim/examples/prototype_arc/output/cache/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/output_3_mp/cache/.gitignore rename to activitysim/examples/prototype_arc/output/cache/.gitignore diff --git a/activitysim/examples/example_multiple_zone/output_3_mp/trace/.gitignore b/activitysim/examples/prototype_arc/output/log/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/output_3_mp/trace/.gitignore rename to activitysim/examples/prototype_arc/output/log/.gitignore diff --git a/activitysim/examples/example_multiple_zone/test/output/trace/.gitignore b/activitysim/examples/prototype_arc/output/trace/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/test/output/trace/.gitignore rename to activitysim/examples/prototype_arc/output/trace/.gitignore diff --git a/activitysim/examples/example_arc/scripts/arc_crop.py b/activitysim/examples/prototype_arc/scripts/arc_crop.py similarity index 100% rename from activitysim/examples/example_arc/scripts/arc_crop.py rename to activitysim/examples/prototype_arc/scripts/arc_crop.py diff --git a/activitysim/examples/example_arc/simulation.py b/activitysim/examples/prototype_arc/simulation.py similarity index 100% rename from activitysim/examples/example_arc/simulation.py rename to activitysim/examples/prototype_arc/simulation.py diff --git a/activitysim/examples/example_arc/test/configs/settings.yaml b/activitysim/examples/prototype_arc/test/configs/settings.yaml similarity index 100% rename from activitysim/examples/example_arc/test/configs/settings.yaml rename to activitysim/examples/prototype_arc/test/configs/settings.yaml diff --git a/activitysim/examples/example_psrc/output/.gitignore b/activitysim/examples/prototype_arc/test/output/.gitignore similarity index 100% rename from activitysim/examples/example_psrc/output/.gitignore rename to activitysim/examples/prototype_arc/test/output/.gitignore diff --git a/activitysim/examples/example_multiple_zone/test/output/cache/.gitignore b/activitysim/examples/prototype_arc/test/output/cache/.gitignore similarity index 100% rename from activitysim/examples/example_multiple_zone/test/output/cache/.gitignore rename to activitysim/examples/prototype_arc/test/output/cache/.gitignore diff --git a/activitysim/examples/example_psrc/output/log/.gitignore b/activitysim/examples/prototype_arc/test/output/trace/.gitignore similarity index 100% rename from activitysim/examples/example_psrc/output/log/.gitignore rename to activitysim/examples/prototype_arc/test/output/trace/.gitignore diff --git a/activitysim/examples/example_arc/test/regress/final_trips.csv b/activitysim/examples/prototype_arc/test/regress/final_trips.csv similarity index 100% rename from activitysim/examples/example_arc/test/regress/final_trips.csv rename to activitysim/examples/prototype_arc/test/regress/final_trips.csv diff --git a/activitysim/examples/example_arc/test/simulation.py b/activitysim/examples/prototype_arc/test/simulation.py similarity index 94% rename from activitysim/examples/example_arc/test/simulation.py rename to activitysim/examples/prototype_arc/test/simulation.py index ec6a1181b1..97ca6b6483 100755 --- a/activitysim/examples/example_arc/test/simulation.py +++ b/activitysim/examples/prototype_arc/test/simulation.py @@ -1,15 +1,15 @@ -# ActivitySim -# See full license in LICENSE.txt. - -import sys -import argparse - -from activitysim.cli.run import add_run_args, run - -if __name__ == '__main__': - - parser = argparse.ArgumentParser() - add_run_args(parser) - args = parser.parse_args() - - sys.exit(run(args)) +# ActivitySim +# See full license in LICENSE.txt. + +import sys +import argparse + +from activitysim.cli.run import add_run_args, run + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + add_run_args(parser) + args = parser.parse_args() + + sys.exit(run(args)) diff --git a/activitysim/examples/example_arc/test/test_arc.py b/activitysim/examples/prototype_arc/test/test_arc.py similarity index 94% rename from activitysim/examples/example_arc/test/test_arc.py rename to activitysim/examples/prototype_arc/test/test_arc.py index e747f632a1..2bb33dc5d0 100644 --- a/activitysim/examples/example_arc/test/test_arc.py +++ b/activitysim/examples/prototype_arc/test/test_arc.py @@ -18,7 +18,7 @@ def teardown_function(func): def test_arc(): def example_path(dirname): - resource = os.path.join('examples', 'example_arc', dirname) + resource = os.path.join('examples', 'prototype_arc', dirname) return pkg_resources.resource_filename('activitysim', resource) def test_path(dirname): diff --git a/activitysim/examples/example_marin/.gitignore b/activitysim/examples/prototype_marin/.gitignore similarity index 100% rename from activitysim/examples/example_marin/.gitignore rename to activitysim/examples/prototype_marin/.gitignore diff --git a/activitysim/examples/example_multiple_zone/README.MD b/activitysim/examples/prototype_marin/README.MD similarity index 100% rename from activitysim/examples/example_multiple_zone/README.MD rename to activitysim/examples/prototype_marin/README.MD diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/annotate_households.csv b/activitysim/examples/prototype_marin/configs/annotate_households.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/annotate_households.csv rename to activitysim/examples/prototype_marin/configs/annotate_households.csv diff --git a/activitysim/examples/example_marin/configs/annotate_persons.csv b/activitysim/examples/prototype_marin/configs/annotate_persons.csv similarity index 100% rename from activitysim/examples/example_marin/configs/annotate_persons.csv rename to activitysim/examples/prototype_marin/configs/annotate_persons.csv diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/annotate_tours.csv b/activitysim/examples/prototype_marin/configs/annotate_tours.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/annotate_tours.csv rename to activitysim/examples/prototype_marin/configs/annotate_tours.csv diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/constants.yaml b/activitysim/examples/prototype_marin/configs/constants.yaml similarity index 95% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/constants.yaml rename to activitysim/examples/prototype_marin/configs/constants.yaml index 626a0c415e..6199378b42 100755 --- a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/constants.yaml +++ b/activitysim/examples/prototype_marin/configs/constants.yaml @@ -1,64 +1,64 @@ -## ActivitySim -## See full license in LICENSE.txt. - -walk_speed: 3.00 - -#HHT_NONE: 0 -#HHT_FAMILY_MARRIED: 1 -#HHT_FAMILY_MALE: 2 -#HHT_FAMILY_FEMALE: 3 -#HHT_NONFAMILY_MALE_ALONE: 4 -#HHT_NONFAMILY_MALE_NOTALONE: 5 -#HHT_NONFAMILY_FEMALE_ALONE: 6 -#HHT_NONFAMILY_FEMALE_NOTALONE: 7 - -# convenience for expression files -HHT_NONFAMILY: [4, 5, 6, 7] -HHT_FAMILY: [1, 2, 3] - -PSTUDENT_GRADE_OR_HIGH: 1 -PSTUDENT_UNIVERSITY: 2 -PSTUDENT_NOT: 3 - -GRADE_SCHOOL_MAX_AGE: 14 -GRADE_SCHOOL_MIN_AGE: 5 - -SCHOOL_SEGMENT_NONE: 0 -SCHOOL_SEGMENT_GRADE: 1 -SCHOOL_SEGMENT_HIGH: 2 -SCHOOL_SEGMENT_UNIV: 3 - -#INCOME_SEGMENT_LOW: 1 -#INCOME_SEGMENT_MED: 2 -#INCOME_SEGMENT_HIGH: 3 -#INCOME_SEGMENT_VERYHIGH: 4 - -PEMPLOY_FULL: 1 -PEMPLOY_PART: 2 -PEMPLOY_NOT: 3 -PEMPLOY_CHILD: 4 - -PTYPE_FULL: &ptype_full 1 -PTYPE_PART: &ptype_part 2 -PTYPE_UNIVERSITY: &ptype_university 3 -PTYPE_NONWORK: &ptype_nonwork 4 -PTYPE_RETIRED: &ptype_retired 5 -PTYPE_DRIVING: &ptype_driving 6 -PTYPE_SCHOOL: &ptype_school 7 -PTYPE_PRESCHOOL: &ptype_preschool 8 - -# these appear as column headers in non_mandatory_tour_frequency.csv -PTYPE_NAME: - *ptype_full: PTYPE_FULL - *ptype_part: PTYPE_PART - *ptype_university: PTYPE_UNIVERSITY - *ptype_nonwork: PTYPE_NONWORK - *ptype_retired: PTYPE_RETIRED - *ptype_driving: PTYPE_DRIVING - *ptype_school: PTYPE_SCHOOL - *ptype_preschool: PTYPE_PRESCHOOL - - -CDAP_ACTIVITY_MANDATORY: M -CDAP_ACTIVITY_NONMANDATORY: N -CDAP_ACTIVITY_HOME: H +## ActivitySim +## See full license in LICENSE.txt. + +walk_speed: 3.00 + +#HHT_NONE: 0 +#HHT_FAMILY_MARRIED: 1 +#HHT_FAMILY_MALE: 2 +#HHT_FAMILY_FEMALE: 3 +#HHT_NONFAMILY_MALE_ALONE: 4 +#HHT_NONFAMILY_MALE_NOTALONE: 5 +#HHT_NONFAMILY_FEMALE_ALONE: 6 +#HHT_NONFAMILY_FEMALE_NOTALONE: 7 + +# convenience for expression files +HHT_NONFAMILY: [4, 5, 6, 7] +HHT_FAMILY: [1, 2, 3] + +PSTUDENT_GRADE_OR_HIGH: 1 +PSTUDENT_UNIVERSITY: 2 +PSTUDENT_NOT: 3 + +GRADE_SCHOOL_MAX_AGE: 14 +GRADE_SCHOOL_MIN_AGE: 5 + +SCHOOL_SEGMENT_NONE: 0 +SCHOOL_SEGMENT_GRADE: 1 +SCHOOL_SEGMENT_HIGH: 2 +SCHOOL_SEGMENT_UNIV: 3 + +#INCOME_SEGMENT_LOW: 1 +#INCOME_SEGMENT_MED: 2 +#INCOME_SEGMENT_HIGH: 3 +#INCOME_SEGMENT_VERYHIGH: 4 + +PEMPLOY_FULL: 1 +PEMPLOY_PART: 2 +PEMPLOY_NOT: 3 +PEMPLOY_CHILD: 4 + +PTYPE_FULL: &ptype_full 1 +PTYPE_PART: &ptype_part 2 +PTYPE_UNIVERSITY: &ptype_university 3 +PTYPE_NONWORK: &ptype_nonwork 4 +PTYPE_RETIRED: &ptype_retired 5 +PTYPE_DRIVING: &ptype_driving 6 +PTYPE_SCHOOL: &ptype_school 7 +PTYPE_PRESCHOOL: &ptype_preschool 8 + +# these appear as column headers in non_mandatory_tour_frequency.csv +PTYPE_NAME: + *ptype_full: PTYPE_FULL + *ptype_part: PTYPE_PART + *ptype_university: PTYPE_UNIVERSITY + *ptype_nonwork: PTYPE_NONWORK + *ptype_retired: PTYPE_RETIRED + *ptype_driving: PTYPE_DRIVING + *ptype_school: PTYPE_SCHOOL + *ptype_preschool: PTYPE_PRESCHOOL + + +CDAP_ACTIVITY_MANDATORY: M +CDAP_ACTIVITY_NONMANDATORY: N +CDAP_ACTIVITY_HOME: H diff --git a/activitysim/examples/example_mtc/configs/destination_choice_size_terms.csv b/activitysim/examples/prototype_marin/configs/destination_choice_size_terms.csv similarity index 98% rename from activitysim/examples/example_mtc/configs/destination_choice_size_terms.csv rename to activitysim/examples/prototype_marin/configs/destination_choice_size_terms.csv index 8c72d2a78f..7f70421e85 100644 --- a/activitysim/examples/example_mtc/configs/destination_choice_size_terms.csv +++ b/activitysim/examples/prototype_marin/configs/destination_choice_size_terms.csv @@ -1,28 +1,28 @@ -model_selector,segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTE -workplace,work_low,0,0.129,0.193,0.383,0.12,0.01,0.164,0,0,0,0 -workplace,work_med,0,0.12,0.197,0.325,0.139,0.008,0.21,0,0,0,0 -workplace,work_high,0,0.11,0.207,0.284,0.154,0.006,0.239,0,0,0,0 -workplace,work_veryhigh,0,0.093,0.27,0.241,0.146,0.004,0.246,0,0,0,0 -school,university,0,0,0,0,0,0,0,0,0,0.592,0.408 -school,gradeschool,0,0,0,0,0,0,0,1,0,0,0 -school,highschool,0,0,0,0,0,0,0,0,1,0,0 -non_mandatory,escort,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 -#non_mandatory,escort_kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 -#non_mandatory,escort_nokids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 -non_mandatory,shopping,0,1,0,0,0,0,0,0,0,0,0 -non_mandatory,eatout,0,0.742,0,0.258,0,0,0,0,0,0,0 -non_mandatory,othmaint,0,0.482,0,0.518,0,0,0,0,0,0,0 -non_mandatory,social,0,0.522,0,0.478,0,0,0,0,0,0,0 -non_mandatory,othdiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0 -atwork,atwork,0,0.742,0,0.258,0,0,0,0,0,0,0 -trip,work,0,1,1,1,1,1,1,0,0,0,0 -trip,escort,0.001,0.225,0,0.144,0,0,0,0.464,0.166,0,0 -trip,shopping,0.001,0.999,0,0,0,0,0,0,0,0,0 -trip,eatout,0,0.742,0,0.258,0,0,0,0,0,0,0 -trip,othmaint,0.001,0.481,0,0.518,0,0,0,0,0,0,0 -trip,social,0.001,0.521,0,0.478,0,0,0,0,0,0,0 -trip,othdiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0 -trip,univ,0.001,0,0,0,0,0,0,0,0,0.592,0.408 -# not needed as school is not chosen as an intermediate trip destination,,,,,,,,,,,, -#trip,gradeschool,0,0,0,0,0,0,0,1,0,0,0 -#trip,highschool,0,0,0,0,0,0,0,0,1,0,0 +model_selector,segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTE +workplace,work_low,0,0.129,0.193,0.383,0.12,0.01,0.164,0,0,0,0 +workplace,work_med,0,0.12,0.197,0.325,0.139,0.008,0.21,0,0,0,0 +workplace,work_high,0,0.11,0.207,0.284,0.154,0.006,0.239,0,0,0,0 +workplace,work_veryhigh,0,0.093,0.27,0.241,0.146,0.004,0.246,0,0,0,0 +school,university,0,0,0,0,0,0,0,0,0,0.592,0.408 +school,gradeschool,0,0,0,0,0,0,0,1,0,0,0 +school,highschool,0,0,0,0,0,0,0,0,1,0,0 +non_mandatory,escort,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 +#non_mandatory,escort_kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 +#non_mandatory,escort_nokids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 +non_mandatory,shopping,0,1,0,0,0,0,0,0,0,0,0 +non_mandatory,eatout,0,0.742,0,0.258,0,0,0,0,0,0,0 +non_mandatory,othmaint,0,0.482,0,0.518,0,0,0,0,0,0,0 +non_mandatory,social,0,0.522,0,0.478,0,0,0,0,0,0,0 +non_mandatory,othdiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0 +atwork,atwork,0,0.742,0,0.258,0,0,0,0,0,0,0 +trip,work,0,1,1,1,1,1,1,0,0,0,0 +trip,escort,0.001,0.225,0,0.144,0,0,0,0.464,0.166,0,0 +trip,shopping,0.001,0.999,0,0,0,0,0,0,0,0,0 +trip,eatout,0,0.742,0,0.258,0,0,0,0,0,0,0 +trip,othmaint,0.001,0.481,0,0.518,0,0,0,0,0,0,0 +trip,social,0.001,0.521,0,0.478,0,0,0,0,0,0,0 +trip,othdiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0 +trip,univ,0.001,0,0,0,0,0,0,0,0,0.592,0.408 +# not needed as school is not chosen as an intermediate trip destination,,,,,,,,,,,, +#trip,gradeschool,0,0,0,0,0,0,0,1,0,0,0 +#trip,highschool,0,0,0,0,0,0,0,0,1,0,0 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/initialize_households.yaml b/activitysim/examples/prototype_marin/configs/initialize_households.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/initialize_households.yaml rename to activitysim/examples/prototype_marin/configs/initialize_households.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/initialize_landuse.yaml b/activitysim/examples/prototype_marin/configs/initialize_landuse.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/initialize_landuse.yaml rename to activitysim/examples/prototype_marin/configs/initialize_landuse.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/initialize_tours.yaml b/activitysim/examples/prototype_marin/configs/initialize_tours.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/initialize_tours.yaml rename to activitysim/examples/prototype_marin/configs/initialize_tours.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/logging.yaml b/activitysim/examples/prototype_marin/configs/logging.yaml similarity index 95% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/logging.yaml rename to activitysim/examples/prototype_marin/configs/logging.yaml index 33b6a4b1cc..df20cf0c7e 100755 --- a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/logging.yaml +++ b/activitysim/examples/prototype_marin/configs/logging.yaml @@ -1,54 +1,54 @@ -# Config for logging -# ------------------ -# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema - -logging: - version: 1 - disable_existing_loggers: true - - - # Configuring the default (root) logger is highly recommended - root: - level: NOTSET - handlers: [console, logfile] - - loggers: - - activitysim: - level: DEBUG - handlers: [console, logfile] - propagate: false - - orca: - level: WARN - handlers: [console, logfile] - propagate: false - - handlers: - - logfile: - class: logging.FileHandler - filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] - mode: w - formatter: fileFormatter - level: NOTSET - - console: - class: logging.StreamHandler - stream: ext://sys.stdout - formatter: simpleFormatter - level: NOTSET - - formatters: - - simpleFormatter: - class: logging.Formatter - # format: '%(levelname)s - %(name)s - %(message)s' - format: '%(levelname)s - %(message)s' - datefmt: '%d/%m/%Y %H:%M:%S' - - fileFormatter: - class: logging.Formatter - format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' - datefmt: '%d/%m/%Y %H:%M:%S' - +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console, logfile] + + loggers: + + activitysim: + level: DEBUG + handlers: [console, logfile] + propagate: false + + orca: + level: WARN + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + level: NOTSET + + formatters: + + simpleFormatter: + class: logging.Formatter + # format: '%(levelname)s - %(name)s - %(message)s' + format: '%(levelname)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + diff --git a/activitysim/examples/example_marin/configs/network_los.yaml b/activitysim/examples/prototype_marin/configs/network_los.yaml similarity index 96% rename from activitysim/examples/example_marin/configs/network_los.yaml rename to activitysim/examples/prototype_marin/configs/network_los.yaml index 7c99f84eca..bee302b941 100755 --- a/activitysim/examples/example_marin/configs/network_los.yaml +++ b/activitysim/examples/prototype_marin/configs/network_los.yaml @@ -1,170 +1,170 @@ -inherit_settings: True - -zone_system: 3 - -skim_dict_factory: NumpyArraySkimFactory -#skim_dict_factory: MemMapSkimFactory - -# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) -read_skim_cache: True -# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs -write_skim_cache: True - -# rebuild and overwrite existing tap_tap_utilities cache -rebuild_tvpb_cache: True - - -# when checkpointing cache. also write a csv version of tvpb cache for tracing -# (writes csv file when writing/checkpointing cache (i.e. when cached changed) even if rebuild_tvpb_cache is False) -# (n.b. csv file could be quite large if cache is STATIC!) -trace_tvpb_cache_as_csv: False - -taz_skims: - - highway_skims_AM.omx - - highway_skims_EA.omx - - highway_skims_EV.omx - - highway_skims_MD.omx - - highway_skims_PM.omx - -tap_skims: - # we require that skims for all tap_tap sets have unique names - # and cso an share a single skim_dict without name collision - # e.g. TRN_XWAIT_FAST__AM, TRN_XWAIT_SHORT__AM, TRN_XWAIT_CHEAP__AM - - transit_skims_SET1.omx - - transit_skims_SET2.omx - - transit_skims_SET3.omx - -maz: maz_taz.csv - -tap: tap.csv - -tap_lines: tap_lines.csv - -maz_to_maz: - tables: - - maz_maz_walk.csv - - maz_maz_bike.csv - - # maz_to_maz blending distance (missing or 0 means no blending) - max_blend_distance: - # blend distance of 0 means no blending - WALK_DIST: 0 - BIKE_DIST: 0 - - -maz_to_tap: - walk: - table: maz_tap_walk.csv - # if provided, this column will be used (together with tap_lines table) to trim the near tap set - # to only include the nearest tap to origin when more than one tap serves the same line - tap_line_distance_col: WALK_TRANSIT_DIST - max_dist: 1.2 - drive: - table: maz_taz_tap_drive.csv - # not trimming because drive_maz_tap utility calculations take into account both drive and walk time and cost - # though some sort of trimming appears to have been done as there are not so many of these in marin data - #tap_line_distance_col: DDIST - - -skim_time_periods: - time_window: 1440 - period_minutes: 30 - periods: [0, 12, 20, 30, 38, 48] - labels: &skim_time_period_labels ['EA', 'AM', 'MD', 'PM', 'EV'] - -demographic_segments: &demographic_segments - - &low_income_segment_id 0 - - &high_income_segment_id 1 - - -# transit virtual path builder settings -TVPB_SETTINGS: - - tour_mode_choice: - units: utility - path_types: - WTW: - access: walk - egress: walk - max_paths_across_tap_sets: 3 - max_paths_per_tap_set: 1 - paths_nest_nesting_coefficient: 1 - DTW: - access: drive - egress: walk - max_paths_across_tap_sets: 3 - max_paths_per_tap_set: 1 - paths_nest_nesting_coefficient: 1 - WTD: - access: walk - egress: drive - max_paths_across_tap_sets: 3 - max_paths_per_tap_set: 1 - paths_nest_nesting_coefficient: 1 - tap_tap_settings: - SPEC: tvpb_utility_tap_tap.csv - PREPROCESSOR: - SPEC: tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv - DF: df - # FIXME this has to be explicitly specified, since e.g. attribute columns are assigned in expression files - attribute_segments: - demographic_segment: *demographic_segments - tod: *skim_time_period_labels - access_mode: ['drive', 'walk'] - attributes_as_columns: - - demographic_segment - - tod - - maz_tap_settings: - walk: - SPEC: tvpb_utility_walk_maz_tap.csv - CHOOSER_COLUMNS: - #- demographic_segment - - WALK_TRANSIT_DIST - drive: - SPEC: tvpb_utility_drive_maz_tap.csv - CHOOSER_COLUMNS: - #- demographic_segment - - DDIST - - DTIME - - WDIST - - CONSTANTS: - C_LOW_INCOME_SEGMENT_ID: *low_income_segment_id - C_HIGH_INCOME_SEGMENT_ID: *high_income_segment_id - TVPB_demographic_segments_by_income_segment: - 1: *low_income_segment_id - 2: *low_income_segment_id - 3: *high_income_segment_id - 4: *high_income_segment_id - c_ivt_high_income: -0.016 # use tour constant from TM2 - c_ivt_low_income: -0.016 # use tour constant from TM2 - c_cost_high_income: -0.00112 - c_cost_low_income: -0.00112 - c_auto_operating_cost_per_mile: 18.29 - # constants used in maz_tap and tap_tap utility expressions - c_drive: 1.5 - c_walk: 1.7 - c_fwt: 1.5 - c_waux: 3.677 - c_xwt: 2 - c_xfers1: 30 - c_xfers2: 45 - c_xfers3: 47.026 - # no Express bus alternative-specific constant - c_lrt_asc: -17 # LRT alternative-specific constant - c_fr_asc: -35 # FR alternative-specific constant - c_hr_asc: -22 # Heavy Rail alternative-specific constant - c_cr_asc: -15 # Commuter Rail alternative-specific constant - c_cr20_40: -20 # Commuter Rail distance 20-40 miles - c_cr40plus: -30 # Commuter Rail distance >40 miles - c_drvExpress: -26 # drive to EB constant - c_drvLRT: 2 # drive to LRT constant - c_drvFR: -52 # drive to FR constant - c_drvHeavy: -41 # drive to HR constant - c_drvCR: -52 # drive to CR constant - #"max(IVT/Drive time - 0.3,0)",drvRatio,c_ivt* 6 - C_UNAVAILABLE: -999 - c_walkAcc: 3.0783 # walk to tap time - c_dtim: 2.5724 # drive to tap time - +inherit_settings: True + +zone_system: 3 + +skim_dict_factory: NumpyArraySkimFactory +#skim_dict_factory: MemMapSkimFactory + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: True +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: True + +# rebuild and overwrite existing tap_tap_utilities cache +rebuild_tvpb_cache: True + + +# when checkpointing cache. also write a csv version of tvpb cache for tracing +# (writes csv file when writing/checkpointing cache (i.e. when cached changed) even if rebuild_tvpb_cache is False) +# (n.b. csv file could be quite large if cache is STATIC!) +trace_tvpb_cache_as_csv: False + +taz_skims: + - highway_skims_AM.omx + - highway_skims_EA.omx + - highway_skims_EV.omx + - highway_skims_MD.omx + - highway_skims_PM.omx + +tap_skims: + # we require that skims for all tap_tap sets have unique names + # and cso an share a single skim_dict without name collision + # e.g. TRN_XWAIT_FAST__AM, TRN_XWAIT_SHORT__AM, TRN_XWAIT_CHEAP__AM + - transit_skims_SET1.omx + - transit_skims_SET2.omx + - transit_skims_SET3.omx + +maz: maz_taz.csv + +tap: tap.csv + +tap_lines: tap_lines.csv + +maz_to_maz: + tables: + - maz_maz_walk.csv + - maz_maz_bike.csv + + # maz_to_maz blending distance (missing or 0 means no blending) + max_blend_distance: + # blend distance of 0 means no blending + WALK_DIST: 0 + BIKE_DIST: 0 + + +maz_to_tap: + walk: + table: maz_tap_walk.csv + # if provided, this column will be used (together with tap_lines table) to trim the near tap set + # to only include the nearest tap to origin when more than one tap serves the same line + tap_line_distance_col: WALK_TRANSIT_DIST + max_dist: 1.2 + drive: + table: maz_taz_tap_drive.csv + # not trimming because drive_maz_tap utility calculations take into account both drive and walk time and cost + # though some sort of trimming appears to have been done as there are not so many of these in marin data + #tap_line_distance_col: DDIST + + +skim_time_periods: + time_window: 1440 + period_minutes: 30 + periods: [0, 12, 20, 30, 38, 48] + labels: &skim_time_period_labels ['EA', 'AM', 'MD', 'PM', 'EV'] + +demographic_segments: &demographic_segments + - &low_income_segment_id 0 + - &high_income_segment_id 1 + + +# transit virtual path builder settings +TVPB_SETTINGS: + + tour_mode_choice: + units: utility + path_types: + WTW: + access: walk + egress: walk + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + paths_nest_nesting_coefficient: 1 + DTW: + access: drive + egress: walk + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + paths_nest_nesting_coefficient: 1 + WTD: + access: walk + egress: drive + max_paths_across_tap_sets: 3 + max_paths_per_tap_set: 1 + paths_nest_nesting_coefficient: 1 + tap_tap_settings: + SPEC: tvpb_utility_tap_tap.csv + PREPROCESSOR: + SPEC: tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv + DF: df + # FIXME this has to be explicitly specified, since e.g. attribute columns are assigned in expression files + attribute_segments: + demographic_segment: *demographic_segments + tod: *skim_time_period_labels + access_mode: ['drive', 'walk'] + attributes_as_columns: + - demographic_segment + - tod + + maz_tap_settings: + walk: + SPEC: tvpb_utility_walk_maz_tap.csv + CHOOSER_COLUMNS: + #- demographic_segment + - WALK_TRANSIT_DIST + drive: + SPEC: tvpb_utility_drive_maz_tap.csv + CHOOSER_COLUMNS: + #- demographic_segment + - DDIST + - DTIME + - WDIST + + CONSTANTS: + C_LOW_INCOME_SEGMENT_ID: *low_income_segment_id + C_HIGH_INCOME_SEGMENT_ID: *high_income_segment_id + TVPB_demographic_segments_by_income_segment: + 1: *low_income_segment_id + 2: *low_income_segment_id + 3: *high_income_segment_id + 4: *high_income_segment_id + c_ivt_high_income: -0.016 # use tour constant from TM2 + c_ivt_low_income: -0.016 # use tour constant from TM2 + c_cost_high_income: -0.00112 + c_cost_low_income: -0.00112 + c_auto_operating_cost_per_mile: 18.29 + # constants used in maz_tap and tap_tap utility expressions + c_drive: 1.5 + c_walk: 1.7 + c_fwt: 1.5 + c_waux: 3.677 + c_xwt: 2 + c_xfers1: 30 + c_xfers2: 45 + c_xfers3: 47.026 + # no Express bus alternative-specific constant + c_lrt_asc: -17 # LRT alternative-specific constant + c_fr_asc: -35 # FR alternative-specific constant + c_hr_asc: -22 # Heavy Rail alternative-specific constant + c_cr_asc: -15 # Commuter Rail alternative-specific constant + c_cr20_40: -20 # Commuter Rail distance 20-40 miles + c_cr40plus: -30 # Commuter Rail distance >40 miles + c_drvExpress: -26 # drive to EB constant + c_drvLRT: 2 # drive to LRT constant + c_drvFR: -52 # drive to FR constant + c_drvHeavy: -41 # drive to HR constant + c_drvCR: -52 # drive to CR constant + #"max(IVT/Drive time - 0.3,0)",drvRatio,c_ivt* 6 + C_UNAVAILABLE: -999 + c_walkAcc: 3.0783 # walk to tap time + c_dtim: 2.5724 # drive to tap time + diff --git a/activitysim/examples/example_marin/configs/settings.yaml b/activitysim/examples/prototype_marin/configs/settings.yaml similarity index 95% rename from activitysim/examples/example_marin/configs/settings.yaml rename to activitysim/examples/prototype_marin/configs/settings.yaml index 2d2eae9d31..5624cd0c97 100755 --- a/activitysim/examples/example_marin/configs/settings.yaml +++ b/activitysim/examples/prototype_marin/configs/settings.yaml @@ -1,240 +1,240 @@ -inherit_settings: True - -# number of households to simulate -#households_sample_size: 200000 -households_sample_size: 500 - -chunk_size: 0 - -# assume enough RAM to not chunk -chunk_training_mode: disabled - -#trace_hh_id: 662398 -trace_hh_id: - -# input tables -input_table_list: - - tablename: households - filename: households.csv - index_col: household_id - rename_columns: - HHID: household_id - MAZ: home_zone_id - keep_columns: - - home_zone_id - - HHINCADJ - - NWRKRS_ESR - - VEH - - NP - #- MTCCountyID - #- HHT - #- BLD - #- TYPE - - - tablename: persons - filename: persons.csv - index_col: person_id - rename_columns: - HHID: household_id - PERID: person_id - keep_columns: - - AGEP - - household_id - - type - - value_of_time - - fp_choice - - SEX - #- SCHL - #- OCCP - #- WKHP - #- WKW - #- EMPLOYED - #- ESR - #- SCHG - - - tablename: land_use - filename: land_use.csv - index_col: zone_id - rename_columns: - MAZ: zone_id - CountyID: county_id - keep_columns: - - TAZ - - DistID - - ACRES - - POP - - emp_total - - hparkcost - - TERMINALTIME - - county_id - - TotInt - - EmpDen - - RetEmpDen - - DUDen -# - level_0 -# - index -# - MAZ_ORIGINAL -# - TAZ_ORIGINAL -# - DistName -# - CountyID -# - CountyName -# - HH -# - ag -# - art_rec -# - constr -# - eat -# - ed_high -# - ed_k12 -# - ed_oth -# - fire -# - gov -# - health -# - hotel -# - info -# - lease -# - logis -# - man_bio -# - man_lgt -# - man_hvy -# - man_tech -# - natres -# - prof -# - ret_loc -# - ret_reg -# - serv_bus -# - serv_pers -# - serv_soc -# - transp -# - util -# - publicEnrollGradeKto8 -# - privateEnrollGradeKto8 -# - publicEnrollGrade9to12 -# - privateEnrollGrade9to12 -# - comm_coll_enroll -# - EnrollGradeKto8 -# - EnrollGrade9to12 -# - collegeEnroll -# - otherCollegeEnroll -# - AdultSchEnrl -# - hstallsoth -# - hstallssam -# - dstallsoth -# - dstallssam -# - mstallsoth -# - mstallssam -# - park_area -# - numfreehrs -# - dparkcost -# - mparkcost -# - ech_dist -# - hch_dist -# - parkarea -# - MAZ_X -# - MAZ_Y -# - PopDen -# - IntDenBin -# - EmpDenBin -# - DuDenBin -# - PopEmpDenPerMi -# - mgra -# - mgraParkArea -# - lsWgtAvgCostM -# - lsWgtAvgCostD -# - lsWgtAvgCostH - - - tablename: tours - filename: work_tours.csv - # since tours has a canonical index name 'tour_id', we must explicitly indicate that no index should be assigned - # canonical index_col 'tour_id' will be assigned by initialize_tours - index_col: - rename_columns: - hh_id: household_id - start_period: start - end_period: end - tour_id: tm2_tour_id - tour_mode: tm2_tour_mode - out_btap: tm2_out_btap - out_atap: tm2_out_atap - in_btap: tm2_in_btap - in_atap: tm2_in_atap - out_set: tm2_out_set - in_set: tm2_in_set - keep_columns: - - person_id - - household_id - - tour_category - - tour_purpose - - orig_mgra - - dest_mgra - - start - - end - # ctramp tm2 fields for validation - - tm2_tour_id # really just ordinal position in ctramp tour file, put probably will be useful for validation - - tm2_tour_mode - - tm2_out_btap - - tm2_out_atap - - tm2_in_btap - - tm2_in_atap - - tm2_out_set - - tm2_in_set -# - person_num -# - person_type -# - tour_distance -# - tour_time -# - atWork_freq -# - num_ob_stops -# - num_ib_stops - - -# set false to disable variability check in simple_simulate and interaction_simulate -check_for_variability: False - -# - shadow pricing global switches - -# turn shadow_pricing on and off for all models (e.g. school and work) -# shadow pricing is deprecated for less than full samples -# see shadow_pricing.yaml for additional settings -use_shadow_pricing: False - -# turn writing of sample_tables on and off for all models -# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) -want_dest_choice_sample_tables: False - -#resume_after: initialize_tvpb - -models: - - initialize_landuse - - initialize_households - - initialize_tours - # --- STATIC cache prebuild steps - # single-process step to create attribute_combination list - - initialize_los - # multi-processable step to build STATIC cache - # (this step is a NOP if cache already exists and network_los.rebuild_tvpb_cache setting is False) - - initialize_tvpb - # --- - - tour_mode_choice_simulate - - write_data_dictionary - - track_skim_usage - - write_tables - -output_tables: - h5_store: False - action: include - prefix: final_ - # FIXME sort is an undocumented feature - sorts table by best index or ref_col according to traceable_table_indexes - sort: True - tables: - - checkpoints - - accessibility - - land_use - - households - - persons - - tours - - attribute_combinations - -output_summaries: - tours: - - tour_mode - - od_path_set - - do_path_set +inherit_settings: True + +# number of households to simulate +#households_sample_size: 200000 +households_sample_size: 500 + +chunk_size: 0 + +# assume enough RAM to not chunk +chunk_training_mode: disabled + +#trace_hh_id: 662398 +trace_hh_id: + +# input tables +input_table_list: + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + HHID: household_id + MAZ: home_zone_id + keep_columns: + - home_zone_id + - HHINCADJ + - NWRKRS_ESR + - VEH + - NP + #- MTCCountyID + #- HHT + #- BLD + #- TYPE + + - tablename: persons + filename: persons.csv + index_col: person_id + rename_columns: + HHID: household_id + PERID: person_id + keep_columns: + - AGEP + - household_id + - type + - value_of_time + - fp_choice + - SEX + #- SCHL + #- OCCP + #- WKHP + #- WKW + #- EMPLOYED + #- ESR + #- SCHG + + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + MAZ: zone_id + CountyID: county_id + keep_columns: + - TAZ + - DistID + - ACRES + - POP + - emp_total + - hparkcost + - TERMINALTIME + - county_id + - TotInt + - EmpDen + - RetEmpDen + - DUDen +# - level_0 +# - index +# - MAZ_ORIGINAL +# - TAZ_ORIGINAL +# - DistName +# - CountyID +# - CountyName +# - HH +# - ag +# - art_rec +# - constr +# - eat +# - ed_high +# - ed_k12 +# - ed_oth +# - fire +# - gov +# - health +# - hotel +# - info +# - lease +# - logis +# - man_bio +# - man_lgt +# - man_hvy +# - man_tech +# - natres +# - prof +# - ret_loc +# - ret_reg +# - serv_bus +# - serv_pers +# - serv_soc +# - transp +# - util +# - publicEnrollGradeKto8 +# - privateEnrollGradeKto8 +# - publicEnrollGrade9to12 +# - privateEnrollGrade9to12 +# - comm_coll_enroll +# - EnrollGradeKto8 +# - EnrollGrade9to12 +# - collegeEnroll +# - otherCollegeEnroll +# - AdultSchEnrl +# - hstallsoth +# - hstallssam +# - dstallsoth +# - dstallssam +# - mstallsoth +# - mstallssam +# - park_area +# - numfreehrs +# - dparkcost +# - mparkcost +# - ech_dist +# - hch_dist +# - parkarea +# - MAZ_X +# - MAZ_Y +# - PopDen +# - IntDenBin +# - EmpDenBin +# - DuDenBin +# - PopEmpDenPerMi +# - mgra +# - mgraParkArea +# - lsWgtAvgCostM +# - lsWgtAvgCostD +# - lsWgtAvgCostH + + - tablename: tours + filename: work_tours.csv + # since tours has a canonical index name 'tour_id', we must explicitly indicate that no index should be assigned + # canonical index_col 'tour_id' will be assigned by initialize_tours + index_col: + rename_columns: + hh_id: household_id + start_period: start + end_period: end + tour_id: tm2_tour_id + tour_mode: tm2_tour_mode + out_btap: tm2_out_btap + out_atap: tm2_out_atap + in_btap: tm2_in_btap + in_atap: tm2_in_atap + out_set: tm2_out_set + in_set: tm2_in_set + keep_columns: + - person_id + - household_id + - tour_category + - tour_purpose + - orig_mgra + - dest_mgra + - start + - end + # ctramp tm2 fields for validation + - tm2_tour_id # really just ordinal position in ctramp tour file, put probably will be useful for validation + - tm2_tour_mode + - tm2_out_btap + - tm2_out_atap + - tm2_in_btap + - tm2_in_atap + - tm2_out_set + - tm2_in_set +# - person_num +# - person_type +# - tour_distance +# - tour_time +# - atWork_freq +# - num_ob_stops +# - num_ib_stops + + +# set false to disable variability check in simple_simulate and interaction_simulate +check_for_variability: False + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False + +#resume_after: initialize_tvpb + +models: + - initialize_landuse + - initialize_households + - initialize_tours + # --- STATIC cache prebuild steps + # single-process step to create attribute_combination list + - initialize_los + # multi-processable step to build STATIC cache + # (this step is a NOP if cache already exists and network_los.rebuild_tvpb_cache setting is False) + - initialize_tvpb + # --- + - tour_mode_choice_simulate + - write_data_dictionary + - track_skim_usage + - write_tables + +output_tables: + h5_store: False + action: include + prefix: final_ + # FIXME sort is an undocumented feature - sorts table by best index or ref_col according to traceable_table_indexes + sort: True + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - attribute_combinations + +output_summaries: + tours: + - tour_mode + - od_path_set + - do_path_set diff --git a/activitysim/examples/example_marin/configs/settings_mp.yaml b/activitysim/examples/prototype_marin/configs/settings_mp.yaml similarity index 100% rename from activitysim/examples/example_marin/configs/settings_mp.yaml rename to activitysim/examples/prototype_marin/configs/settings_mp.yaml diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/shadow_pricing.yaml b/activitysim/examples/prototype_marin/configs/shadow_pricing.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/shadow_pricing.yaml rename to activitysim/examples/prototype_marin/configs/shadow_pricing.yaml diff --git a/activitysim/examples/example_mtc/configs/tour_departure_and_duration_alternatives.csv b/activitysim/examples/prototype_marin/configs/tour_departure_and_duration_alternatives.csv similarity index 84% rename from activitysim/examples/example_mtc/configs/tour_departure_and_duration_alternatives.csv rename to activitysim/examples/prototype_marin/configs/tour_departure_and_duration_alternatives.csv index 05f02b7964..bddab06b9d 100644 --- a/activitysim/examples/example_mtc/configs/tour_departure_and_duration_alternatives.csv +++ b/activitysim/examples/prototype_marin/configs/tour_departure_and_duration_alternatives.csv @@ -1,191 +1,191 @@ -start,end -5,5 -5,6 -5,7 -5,8 -5,9 -5,10 -5,11 -5,12 -5,13 -5,14 -5,15 -5,16 -5,17 -5,18 -5,19 -5,20 -5,21 -5,22 -5,23 -6,6 -6,7 -6,8 -6,9 -6,10 -6,11 -6,12 -6,13 -6,14 -6,15 -6,16 -6,17 -6,18 -6,19 -6,20 -6,21 -6,22 -6,23 -7,7 -7,8 -7,9 -7,10 -7,11 -7,12 -7,13 -7,14 -7,15 -7,16 -7,17 -7,18 -7,19 -7,20 -7,21 -7,22 -7,23 -8,8 -8,9 -8,10 -8,11 -8,12 -8,13 -8,14 -8,15 -8,16 -8,17 -8,18 -8,19 -8,20 -8,21 -8,22 -8,23 -9,9 -9,10 -9,11 -9,12 -9,13 -9,14 -9,15 -9,16 -9,17 -9,18 -9,19 -9,20 -9,21 -9,22 -9,23 -10,10 -10,11 -10,12 -10,13 -10,14 -10,15 -10,16 -10,17 -10,18 -10,19 -10,20 -10,21 -10,22 -10,23 -11,11 -11,12 -11,13 -11,14 -11,15 -11,16 -11,17 -11,18 -11,19 -11,20 -11,21 -11,22 -11,23 -12,12 -12,13 -12,14 -12,15 -12,16 -12,17 -12,18 -12,19 -12,20 -12,21 -12,22 -12,23 -13,13 -13,14 -13,15 -13,16 -13,17 -13,18 -13,19 -13,20 -13,21 -13,22 -13,23 -14,14 -14,15 -14,16 -14,17 -14,18 -14,19 -14,20 -14,21 -14,22 -14,23 -15,15 -15,16 -15,17 -15,18 -15,19 -15,20 -15,21 -15,22 -15,23 -16,16 -16,17 -16,18 -16,19 -16,20 -16,21 -16,22 -16,23 -17,17 -17,18 -17,19 -17,20 -17,21 -17,22 -17,23 -18,18 -18,19 -18,20 -18,21 -18,22 -18,23 -19,19 -19,20 -19,21 -19,22 -19,23 -20,20 -20,21 -20,22 -20,23 -21,21 -21,22 -21,23 -22,22 -22,23 +start,end +5,5 +5,6 +5,7 +5,8 +5,9 +5,10 +5,11 +5,12 +5,13 +5,14 +5,15 +5,16 +5,17 +5,18 +5,19 +5,20 +5,21 +5,22 +5,23 +6,6 +6,7 +6,8 +6,9 +6,10 +6,11 +6,12 +6,13 +6,14 +6,15 +6,16 +6,17 +6,18 +6,19 +6,20 +6,21 +6,22 +6,23 +7,7 +7,8 +7,9 +7,10 +7,11 +7,12 +7,13 +7,14 +7,15 +7,16 +7,17 +7,18 +7,19 +7,20 +7,21 +7,22 +7,23 +8,8 +8,9 +8,10 +8,11 +8,12 +8,13 +8,14 +8,15 +8,16 +8,17 +8,18 +8,19 +8,20 +8,21 +8,22 +8,23 +9,9 +9,10 +9,11 +9,12 +9,13 +9,14 +9,15 +9,16 +9,17 +9,18 +9,19 +9,20 +9,21 +9,22 +9,23 +10,10 +10,11 +10,12 +10,13 +10,14 +10,15 +10,16 +10,17 +10,18 +10,19 +10,20 +10,21 +10,22 +10,23 +11,11 +11,12 +11,13 +11,14 +11,15 +11,16 +11,17 +11,18 +11,19 +11,20 +11,21 +11,22 +11,23 +12,12 +12,13 +12,14 +12,15 +12,16 +12,17 +12,18 +12,19 +12,20 +12,21 +12,22 +12,23 +13,13 +13,14 +13,15 +13,16 +13,17 +13,18 +13,19 +13,20 +13,21 +13,22 +13,23 +14,14 +14,15 +14,16 +14,17 +14,18 +14,19 +14,20 +14,21 +14,22 +14,23 +15,15 +15,16 +15,17 +15,18 +15,19 +15,20 +15,21 +15,22 +15,23 +16,16 +16,17 +16,18 +16,19 +16,20 +16,21 +16,22 +16,23 +17,17 +17,18 +17,19 +17,20 +17,21 +17,22 +17,23 +18,18 +18,19 +18,20 +18,21 +18,22 +18,23 +19,19 +19,20 +19,21 +19,22 +19,23 +20,20 +20,21 +20,22 +20,23 +21,21 +21,22 +21,23 +22,22 +22,23 23,23 \ No newline at end of file diff --git a/activitysim/examples/example_marin/configs/tour_mode_choice.csv b/activitysim/examples/prototype_marin/configs/tour_mode_choice.csv similarity index 99% rename from activitysim/examples/example_marin/configs/tour_mode_choice.csv rename to activitysim/examples/prototype_marin/configs/tour_mode_choice.csv index 386e2e37c1..364648ceaa 100755 --- a/activitysim/examples/example_marin/configs/tour_mode_choice.csv +++ b/activitysim/examples/prototype_marin/configs/tour_mode_choice.csv @@ -1,226 +1,226 @@ -Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_TRANSIT,DRIVE_TRANSIT,TAXI,TNC_SINGLE,TNC_SHARED -#,Drive alone no toll,,,,,,,,,,,,,, -#util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,VEH == 0,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,AGEP < 16,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,, -util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['TIMEDA'] + dot_skims['TIMEDA'],coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_TERMINALTIME,DRIVEALONEFREE - Terminal time,@df.origin_terminal_time,coef_walk_access_time,,,,,,,,,,,, -util_DRIVEALONEFREE_TERMINALTIME,DRIVEALONEFREE - Terminal time,@df.dest_terminal_time,coef_walk_egress_time,,,,,,,,,,,, -util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['DISTDA'] + dot_skims['DISTDA']),coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,coef_ivt,,,,,,,,,,,, -util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['BTOLLDA'] + dot_skims['BTOLLDA']),coef_ivt,,,,,,,,,,,, -#,Drive alone toll,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,VEH == 0,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,AGEP < 16,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,, -util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['TOLLTIMEDA'] + dot_skims['TOLLTIMEDA'],,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_TERMINALTIME,DRIVEALONEPAY - Terminal time,@df.origin_terminal_time,,coef_walk_access_time,,,,,,,,,,, -util_DRIVEALONEPAY_TERMINALTIME,DRIVEALONEPAY - Terminal time,@df.dest_terminal_time,,coef_walk_egress_time,,,,,,,,,,, -util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['TOLLDISTDA'] + dot_skims['TOLLDISTDA']),,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLDA'] + dot_skims['TOLLBTOLLDA']),,coef_ivt,,,,,,,,,,, -util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLDA'] + dot_skims['TOLLVTOLLDA']),,coef_ivt,,,,,,,,,,, -#,Shared ride 2,,,,,,,,,,,,,, -util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,, -util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,, -util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@(odt_skims['TIMES2'] + dot_skims['TIMES2']),,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_TERMINALTIME,SHARED2FREE - Terminal time,@df.origin_terminal_time,,,coef_walk_access_time,,,,,,,,,, -util_SHARED2FREE_TERMINALTIME,SHARED2FREE - Terminal time,@df.dest_terminal_time,,,coef_walk_egress_time,,,,,,,,,, -util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['DISTS2'] + dot_skims['DISTS2']),,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['BTOLLS2'] + dot_skims['BTOLLS2']) / costShareSr2,,,coef_ivt,,,,,,,,,, -util_SHARED2FREE_Person_is_between_16_and_24_years_old,SHARED2FREE_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,c_age1624_sr2,,,,,,,,,, -util_SHARED2FREE_Person_is_between_41_and_55_years_old,SHARED2FREE_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,c_age4155_sr2,,,,,,,,,, -util_SHARED2FREE_Person_is_between_56_and_64_years_old,SHARED2FREE_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,c_age5664_sr2,,,,,,,,,, -util_SHARED2FREE_Person_is_between_65plus_years_old,SHARED2FREE_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,c_age65pl_sr2,,,,,,,,,, -util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.NP == 2),,,c_size2_sr2,,,,,,,,,, -util_SHARED2FREE_Three_person_household,SHARED2FREE - Three person household,@(df.NP == 3),,,c_size3_sr2,,,,,,,,,, -util_SHARED2FREE_Four_person_household,SHARED2FREE - Four person household,@(df.NP >= 4),,,c_size4p_sr2,,,,,,,,,, -util_SHARED2FREE_Female,SHARED2FREE - Female,@~df.is_male,,,c_female_sr2,,,,,,,,,, -#,Shared ride 2 toll,,,,,,,,,,,,,, -util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,, -util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,, -util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']),,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_TERMINALTIME,SHARED2PAY - Terminal time,@df.origin_terminal_time,,,,coef_walk_access_time,,,,,,,,, -util_SHARED2PAY_TERMINALTIME,SHARED2PAY - Terminal time,@df.dest_terminal_time,,,,coef_walk_egress_time,,,,,,,,, -util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']),,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2']) / costShareSr2,,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']) / costShareSr2,,,,coef_ivt,,,,,,,,, -util_SHARED2PAY_Person_is_between_16_and_24_years_old,SHARED2PAY_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,c_age1624_sr2,,,,,,,,, -util_SHARED2PAY_Person_is_between_41_and_55_years_old,SHARED2PAY_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,c_age4155_sr2,,,,,,,,, -util_SHARED2PAY_Person_is_between_56_and_64_years_old,SHARED2PAY_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,c_age5664_sr2,,,,,,,,, -util_SHARED2PAY_Person_is_between_65plus_years_old,SHARED2PAY_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,c_age65pl_sr2,,,,,,,,, -util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.NP == 2),,,,c_size2_sr2,,,,,,,,, -util_SHARED2PAY_Three_person_household,SHARED2PAY - Three person household,@(df.NP == 3),,,,c_size3_sr2,,,,,,,,, -util_SHARED2PAY_Four_person_household,SHARED2PAY - Four person household,@(df.NP >= 4),,,,c_size4p_sr2,,,,,,,,, -util_SHARED2PAY_Female,SHARED2PAY - Female,@~df.is_male,,,,c_female_sr2,,,,,,,,, -#,Shared ride 3+,,,,,,,,,,,,,, -util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,, -util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@(odt_skims['TIMES3'] + dot_skims['TIMES3']),,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_TERMINALTIME,SHARED3FREE - Terminal time,@df.origin_terminal_time,,,,,coef_walk_access_time,,,,,,,, -util_SHARED3FREE_TERMINALTIME,SHARED3FREE - Terminal time,@df.dest_terminal_time,,,,,coef_walk_egress_time,,,,,,,, -util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['DISTS3'] + dot_skims['DISTS3']),,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['BTOLLS3'] + dot_skims['BTOLLS3']) / costShareSr3,,,,,coef_ivt,,,,,,,, -util_SHARED3FREE_Person_is_between_16_and_24_years_old,SHARED3FREE_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,c_age1624_sr3,,,,,,,, -util_SHARED3FREE_Person_is_between_41_and_55_years_old,SHARED3FREE_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,c_age4155_sr3,,,,,,,, -util_SHARED3FREE_Person_is_between_56_and_64_years_old,SHARED3FREE_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,c_age5664_sr3,,,,,,,, -util_SHARED3FREE_Person_is_between_65plus_years_old,SHARED3FREE_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,c_age65pl_sr3,,,,,,,, -util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.NP == 2),,,,,c_size2_sr3,,,,,,,, -util_SHARED3FREE_Three_person_household,SHARED3FREE - Three person household,@(df.NP == 3),,,,,c_size3_sr3,,,,,,,, -util_SHARED3FREE_Four_person_household,SHARED3FREE - Four person household,@(df.NP >= 4),,,,,c_size4p_sr3,,,,,,,, -util_SHARED3FREE_Female,SHARED3FREE - Female,@~df.is_male,,,,,c_female_sr3,,,,,,,, -#,Shared ride 3+ toll,,,,,,,,,,,,,, -util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,, -util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@(odt_skims['TOLLTIMES3'] + dot_skims['TOLLTIMES3']),,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_TERMINALTIME,SHARED3PAY - Terminal time,@df.origin_terminal_time,,,,,,coef_walk_access_time,,,,,,, -util_SHARED3PAY_TERMINALTIME,SHARED3PAY - Terminal time,@df.dest_terminal_time,,,,,,coef_walk_egress_time,,,,,,, -util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['TOLLDISTS3'] + dot_skims['TOLLDISTS3']),,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS3'] + dot_skims['TOLLBTOLLS3']) / costShareSr3,,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS3'] + dot_skims['TOLLVTOLLS3']) / costShareSr3,,,,,,coef_ivt,,,,,,, -util_SHARED3PAY_Person_is_between_16_and_24_years_old,SHARED3PAY_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,,c_age1624_sr3,,,,,,, -util_SHARED3PAY_Person_is_between_41_and_55_years_old,SHARED3PAY_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,,c_age4155_sr3,,,,,,, -util_SHARED3PAY_Person_is_between_56_and_64_years_old,SHARED3PAY_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,,c_age5664_sr3,,,,,,, -util_SHARED3PAY_Person_is_between_65plus_years_old,SHARED3PAY_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,,c_age65pl_sr3,,,,,,, -util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.NP == 2),,,,,,c_size2_sr3,,,,,,, -util_SHARED3PAY_Three_person_household,SHARED3PAY - Three person household,@(df.NP == 3),,,,,,c_size3_sr3,,,,,,, -util_SHARED3PAY_Four_person_household,SHARED3PAY - Four person household,@(df.NP >= 4),,,,,,c_size4p_sr3,,,,,,, -util_SHARED3PAY_Female,SHARED3PAY - Female,@~df.is_male,,,,,,c_female_sr3,,,,,,, -#,Walk,,,,,,,,,,,,,, -util_WALK_Unavailable,WALK - Unavailable,walk_available == False,,,,,,,-999,,,,,, -util_WALK_Time,WALK - walk time,@(od_skims.lookup('WALK_DIST') + od_skims.reverse('WALK_DIST'))*60/walkSpeed,,,,,,,c_walkTime,,,,,, -util_WALK_Person_is_between_16_and_24_years_old,WALK_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,,,c_age1624_nmot,,,,,, -util_WALK_Person_is_between_41_and_55_years_old,WALK_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,,,c_age4155_nmot,,,,,, -util_WALK_Person_is_between_56_and_64_years_old,WALK_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,,,c_age5664_nmot,,,,,, -util_WALK_Person_is_between_65plus_years_old,WALK_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,,,c_age65pl_nmot,,,,,, -util_WALK_Female,WALK - Female,@~df.is_male,,,,,,,c_female_nmot,,,,,, -util_WALK_Origin_Mix,WALK_Origin_Mix,@df.origin_Mix,,,,,,,c_oMix_nmot,,,,,, -util_WALK_Origin_Intersection_Density,WALK_Origin_Intersection_Density,@df.origin_TotInt,,,,,,,c_oIntDen_nmot,,,,,, -util_WALK_Destination_Employment_Density,WALK_Destination_Employment_Density,@df.dest_EmpDen,,,,,,,c_dEmpDen_nmot,,,,,, -#,Bike,,,,,,,,,,,,,, -util_BIKE_Unavailable,BIKE - Unavailable,bike_available == False,,,,,,,,-999,,,,, -util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,, -util_BIKE_Time,BIKE - bike time,@(od_skims.lookup('BIKE_DIST') + od_skims.reverse('BIKE_DIST'))*60/bikeSpeed,,,,,,,,c_bikeTime,,,,, -util_BIKE_Person_is_between_16_and_24_years_old,BIKE_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,,,,c_age1624_nmot,,,,, -util_BIKE_Person_is_between_41_and_55_years_old,BIKE_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,,,,c_age4155_nmot,,,,, -util_BIKE_Person_is_between_56_and_64_years_old,BIKE_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,,,,c_age5664_nmot,,,,, -util_BIKE_Person_is_between_65plus_years_old,BIKE_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,,,,c_age65pl_nmot,,,,, -util_BIKE_Female,BIKE - Female,@~df.is_male,,,,,,,,c_female_nmot,,,,, -util_BIKE_Origin_Mix,BIKE_Origin_Mix,@df.origin_Mix,,,,,,,,c_oMix_nmot,,,,, -util_BIKE_Origin_Intersection_Density,BIKE_Origin_Intersection_Density,@df.origin_TotInt,,,,,,,,c_oIntDen_nmot,,,,, -util_BIKE_Destination_Employment_Density,BIKE_Destination_Employment_Density,@df.dest_EmpDen,,,,,,,,c_dEmpDen_nmot,,,,, -#,Walk to Local,,,,,,,,,,,,,, -util_WALK_TRANSIT_Paths_logsums,WALK_TRANSIT - Path logsums,@tvpb_logsum_odt['WTW'] + tvpb_logsum_dot['WTW'],,,,,,,,,coef_one,,,, -util_WALK_TRANSIT_Person_is_between_16_and_24_years_old,WALK_TRANSIT_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,,,,,c_age1624_tran,,,, -util_WALK_TRANSIT_Person_is_between_41_and_55_years_old,WALK_TRANSIT_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,,,,,c_age4155_tran,,,, -util_WALK_TRANSIT_Person_is_between_56_and_64_years_old,WALK_TRANSIT_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,,,,,c_age5664_tran,,,, -util_WALK_TRANSIT_Person_is_between_65plus_years_old,WALK_TRANSIT_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,,,,,c_age65pl_tran,,,, -util_WALK_TRANSIT_Female,BIKE - Female,@~df.is_male,,,,,,,,,c_female_tran,,,, -util_WALK_TRANSIT_Origin_Mix,WALK_TRANSIT_Origin_Mix,@df.origin_Mix,,,,,,,,,c_oMix_wtran,,,, -util_WALK_TRANSIT_Origin_Intersection_Density,WALK_TRANSIT_Origin_Intersection_Density,@df.origin_TotInt,,,,,,,,,c_oIntDen_wtran,,,, -util_WALK_TRANSIT_Destination_Employment_Density,WALK_TRANSIT_Destination_Employment_Density,@df.dest_EmpDen,,,,,,,,,c_dEmpDen_wtran,,,, -#,Drive to Local,,,,,,,,,,,,,, -util_DRIVE_TRANSIT_Unavailable_for_zero_auto_households,DRIVE_TRANSIT - Unavailable for zero auto households,VEH == 0,,,,,,,,,,-999,,, -util_DRIVE_TRANSIT_Unavailable_for_persons_less_than_16,DRIVE_TRANSIT - Unavailable for persons less than 16,AGEP < 16,,,,,,,,,,-999,,, -util_DRIVE_TRANSIT_Paths_logsums,DRIVE_TRANSIT - Path logsums,@tvpb_logsum_odt['DTW'] + tvpb_logsum_dot['WTD'],,,,,,,,,,coef_one,,, -util_DRIVE_TRANSIT_Person_is_between_16_and_24_years_old,DRIVE_TRANSIT_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,,,,,,c_age1624_tran,,, -util_DRIVE_TRANSIT_Person_is_between_41_and_55_years_old,DRIVE_TRANSIT_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,,,,,,c_age4155_tran,,, -util_DRIVE_TRANSIT_Person_is_between_56_and_64_years_old,DRIVE_TRANSIT_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,,,,,,c_age5664_tran,,, -util_DRIVE_TRANSIT_Person_is_between_65plus_years_old,DRIVE_TRANSIT_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,,,,,,c_age65pl_tran,,, -util_DRIVE_TRANSIT_Female,BIKE - Female,@~df.is_male,,,,,,,,,,c_female_tran,,, -util_DRIVE_TRANSIT_Destination_Employment_Density,DRIVE_TRANSIT_Destination_Employment_Density,@df.dest_EmpDen,,,,,,,,,,c_dEmpDen_dtran,,, -#,Taxi,,,,,,,,,,,,,, -util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']),,,,,,,,,,,coef_ivt,, -#, FIXME magic constant 1.5,,,,,,,,,,,,,, -util_Taxi_Wait_time,Taxi - Wait time,@1.5 * df.totalWaitTaxi,,,,,,,,,,,coef_ivt,, -util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']),,,,,,,,,,,coef_ivt,, -util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2']),,,,,,,,,,,coef_ivt,, -util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare * 2 + (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']) * Taxi_costPerMile +(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * Taxi_costPerMinute)*100,,,,,,,,,,,coef_ivt,, -#,TNC Single,,,,,,,,,,,,,, -util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']),,,,,,,,,,,,coef_ivt, -util_TNC_Single_Wait_time,TNC Single - Wait time,@1.5 * df.totalWaitSingleTNC,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']),,,,,,,,,,,,coef_ivt, -util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + odr_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2'] + dor_skims['TOLLBTOLLS2']),,,,,,,,,,,,coef_ivt, -util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare * 2 + (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']) * TNC_single_costPerMile + (odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,coef_ivt, -#,TNC Shared,,,,,,,,,,,,,, -util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * TNC_shared_IVTFactor,,,,,,,,,,,,,coef_ivt -#, FIXME magic constant 1.5,,,,,,,,,,,,,, -util_TNC_Shared_Wait_time,TNC Shared - Wait time,@1.5 * df.totalWaitSharedTNC,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']),,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + odr_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2'] + dor_skims['TOLLBTOLLS2']),,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare * 2 + (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']) * TNC_shared_costPerMile + (odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,coef_ivt -#,indiv tour ASCs,,,,,,,,,,,,,, -util_Walk_ASC_Zero_auto,Walk ASC - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,zeroAutoHH_walk,,,,,, -util_Walk_ASC_Auto_deficient,Walk ASC - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,autoDeficientHH_walk,,,,,, -util_Walk_ASC_Auto_sufficient,Walk ASC - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,autoSufficientHH_walk,,,,,, -util_Bike_ASC_Zero_auto,Bike ASC - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,zeroAutoHH_bike,,,,, -util_Bike_ASC_Auto_deficient,Bike ASC - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,0,,,,, -util_Bike_ASC_Auto_sufficient,Bike ASC - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,autoSufficientHH_bike,,,,, -util_Shared_ride_2_ASC_Zero_auto,Shared ride 2 ASC - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,0,0,,,,,,,,, -util_Shared_ride_2_ASC_Auto_deficient,Shared ride 2 ASC - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,autoDeficientHH_sr2,autoDeficientHH_sr2,,,,,,,,, -util_Shared_ride_2_ASC_Auto_sufficient,Shared ride 2 ASC - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,autoSufficientHH_sr2,autoSufficientHH_sr2,,,,,,,,, -util_Shared_ride_3p_Zero_auto,Shared ride 3+ - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,zeroAutoHH_sr3,zeroAutoHH_sr3,,,,,,, -util_Shared_ride_3p_Auto_deficient,Shared ride 3+ - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,autoDeficientHH_sr3,autoDeficientHH_sr3,,,,,,, -util_Shared_ride_3p_Auto_sufficient,Shared ride 3+ - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,autoSufficientHH_sr3,autoSufficientHH_sr3,,,,,,, -util_Walk_to_Transit_Zero_auto,Walk to Transit - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,zeroAutoHH_wt,,,, -util_Walk_to_Transit_Auto_deficient,Walk to Transit - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,autoDeficientHH_wt,,,, -util_Walk_to_Transit_Auto_sufficient,Walk to Transit - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,autoSufficientHH_wt,,,, -util_Drive_to_Transit_Zero_auto,Drive to Transit - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,zeroAutoHH_kt,,, -util_Drive_to_Transit_Auto_deficient,Drive to Transit - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,autoDeficientHH_dt,,, -util_Drive_to_Transit_Auto_sufficient,Drive to Transit - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,autoSufficientHH_dt,,, -util_Taxi_Zero_auto,Taxi - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,,0,, -util_Taxi_Auto_deficient,Taxi - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,0,, -util_Taxi_Auto_sufficient,Taxi - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,0,, -util_TNC_Single_Zero_auto,TNC Single - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,,,0, -util_TNC_Single_Auto_deficient,TNC Single - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,0, -util_TNC_Single_Auto_sufficient,TNC Single - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,0, -util_TNC_Shared_Zero_auto,TNC Shared - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,,,,0 -util_TNC_Shared_Auto_deficient,TNC Shared - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,,0 -util_TNC_Shared_Auto_sufficient,TNC Shared - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,,0 -#,joint tour ASCs,,,,,,,,,,,,,, -util_Joint_Walk_ASC_Zero_auto,Joint - Walk ASC - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,0,,,,,, -util_Joint_Walk_ASC_Auto_deficient,Joint - Walk ASC - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,0,,,,,, -util_Joint_Walk_ASC_Auto_sufficient,Joint - Walk ASC - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,0,,,,,, -util_Joint_Bike_ASC_Zero_auto,Joint - Bike ASC - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,0,,,,, -util_Joint_Bike_ASC_Auto_deficient,Joint - Bike ASC - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,0,,,,, -util_Joint_Bike_ASC_Auto_sufficient,Joint - Bike ASC - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,0,,,,, -util_Joint_Shared_ride_2_ASC_Zero_auto,Joint - Shared ride 2 ASC - Zero auto,@(df.is_joint & (df.VEH == 0)),,,0,0,,,,,,,,, -util_Joint_Shared_ride_2_ASC_Auto_deficient,Joint - Shared ride 2 ASC - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,0,0,,,,,,,,, -util_Joint_Shared_ride_2_ASC_Auto_sufficient,Joint - Shared ride 2 ASC - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,0,0,,,,,,,,, -util_Joint_Shared_ride_3p_Zero_auto,Joint - Shared ride 3+ - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,0,0,,,,,,, -util_Joint_Shared_ride_3p_Auto_deficient,Joint - Shared ride 3+ - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,0,0,,,,,,, -util_Joint_Shared_ride_3p_Auto_sufficient,Joint - Shared ride 3+ - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,0,0,,,,,,, -util_Joint_Walk_to_Transit_Zero_auto,Joint - Walk to Transit - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,0,,,, -util_Joint_Walk_to_Transit_Auto_deficient,Joint - Walk to Transit - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,0,,,, -util_Joint_Walk_to_Transit_Auto_sufficient,Joint - Walk to Transit - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,0,,,, -util_Joint_Drive_to_Transit_Zero_auto,Joint - Drive to Transit - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,0,,, -util_Joint_Drive_to_Transit_Auto_deficient,Joint - Drive to Transit - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,0,,, -util_Joint_Drive_to_Transit_Auto_sufficient,Joint - Drive to Transit - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,0,,, -util_Joint_Taxi_Zero_auto,Joint - Taxi - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,,0,, -util_Joint_Taxi_Auto_deficient,Joint - Taxi - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,0,, -util_Joint_Taxi_Auto_sufficient,Joint - Taxi - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,0,, -util_Joint_TNC_Single_Zero_auto,Joint - TNC Single - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,,,0, -util_Joint_TNC_Single_Auto_deficient,Joint - TNC Single - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,0, -util_Joint_TNC_Single_Auto_sufficient,Joint - TNC Single - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,0, -util_Joint_TNC_Shared_Zero_auto,Joint - TNC Shared - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,,,,0 -util_Joint_TNC_Shared_Auto_deficient,Joint - TNC Shared - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,,0 -util_Joint_TNC_Shared_Auto_sufficient,Joint - TNC Shared - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,,0 -#,calibration,,,,,,,,,,,,,, -util_Walk_to_Transit_dest_CBD_SF,Walk to Transit dest CBD SF,@df.destination_in_cbd_sf,,,,,,,,,asc_wtransit_cbd_sf,,,, -util_Walk_to_Transit_dest_NW_SF,walk to Transit dest NW SF,@df.destination_in_nw_sf,,,,,,,,,asc_wtransit_nw_sf,,,, -util_Walk_to_Transit_dest_SE_SF,Walk to Transit dest SE SF,@df.destination_in_se_sf,,,,,,,,,asc_wtransit_se_sf,,,, -util_Drive_to_Transit_dest_CBD_SF,Drive to Transit dest CBD SF,@df.destination_in_cbd_sf,,,,,,,,,,asc_dtransit_cbd_sf,,, -util_Drive_to_Transit_distance_penalty,Drive to Transit - distance penalty,@(50-2.5*odt_skims['DISTDA']).clip(lower=0),,,,,,,,,,coef_ivt,,, -util_Walk_to_Transit_distance_penalty,Walk to Transit - distance penalty,@(200-133*odt_skims['DISTDA']).clip(lower=0),,,,,,,,,coef_ivt,,,, -util_Transit_Pseudo_area_type_constant,Transit - Pseudo area type constant,@asc_Transit_Pseudo_area_type_constant * (df.daily_parking_cost>0),,,,,,,,,coef_ivt,coef_ivt,,, -util_TM2_Round_2_ASC_adjustment_for_0_Autos_HHs,TM2_Round_2_ASC_adjustment_for_0_Autos_HHs,@(df.is_indiv & (df.VEH == 0)),,,zeroAutoHH_SHARED2HOV,zeroAutoHH_SHARED2PAY,zeroAutoHH_SHARED3HOV,zeroAutoHH_SHARED3PAY,zeroAutoHH_WALK,zeroAutoHH_BIKE,zeroAutoHH_WALK_SET,zeroAutoHH_PNR_SET,,, -util_TM2_Round_2_ASC_adjustment_for_Auto_Defecient_HHs,TM2_Round_2_ASC_adjustment_for_Auto_Defecient_HHs,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,autoDeficientHH_SHARED2HOV,autoDeficientHH_SHARED2PAY,autoDeficientHH_SHARED3HOV,autoDeficientHH_SHARED3PAY,autoDeficientHH_WALK,autoDeficientHH_BIKE,autoDeficientHH_WALK_SET,autoDeficientHH_PNR_SET,,, -util_TM2_Round_2_ASC_adjustment_for_Auto_Sufficient_HHs,TM2_Round_2_ASC_adjustment_for_Auto_Sufficient_HHs,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,autoSufficientHH_SHARED2HOV,autoSufficientHH_SHARED2PAY,autoSufficientHH_SHARED3HOV,autoSufficientHH_SHARED3PAY,autoSufficientHH_WALK,autoSufficientHH_BIKE,autoSufficientHH_WALK_SET,autoSufficientHH_PNR_SET,,, -util_taxi_penalty,taxi penalty,@asc_taxi_penalty,,,,,,,,,,,coef_one,, -util_no_tnc,turn off tnc,1,,,,,,,,,,,,-999,-999 +Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_TRANSIT,DRIVE_TRANSIT,TAXI,TNC_SINGLE,TNC_SHARED +#,Drive alone no toll,,,,,,,,,,,,,, +#util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,VEH == 0,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,AGEP < 16,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,, +util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['TIMEDA'] + dot_skims['TIMEDA'],coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_TERMINALTIME,DRIVEALONEFREE - Terminal time,@df.origin_terminal_time,coef_walk_access_time,,,,,,,,,,,, +util_DRIVEALONEFREE_TERMINALTIME,DRIVEALONEFREE - Terminal time,@df.dest_terminal_time,coef_walk_egress_time,,,,,,,,,,,, +util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['DISTDA'] + dot_skims['DISTDA']),coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,coef_ivt,,,,,,,,,,,, +util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['BTOLLDA'] + dot_skims['BTOLLDA']),coef_ivt,,,,,,,,,,,, +#,Drive alone toll,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,VEH == 0,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,AGEP < 16,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,, +util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['TOLLTIMEDA'] + dot_skims['TOLLTIMEDA'],,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_TERMINALTIME,DRIVEALONEPAY - Terminal time,@df.origin_terminal_time,,coef_walk_access_time,,,,,,,,,,, +util_DRIVEALONEPAY_TERMINALTIME,DRIVEALONEPAY - Terminal time,@df.dest_terminal_time,,coef_walk_egress_time,,,,,,,,,,, +util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['TOLLDISTDA'] + dot_skims['TOLLDISTDA']),,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLDA'] + dot_skims['TOLLBTOLLDA']),,coef_ivt,,,,,,,,,,, +util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLDA'] + dot_skims['TOLLVTOLLDA']),,coef_ivt,,,,,,,,,,, +#,Shared ride 2,,,,,,,,,,,,,, +util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,, +util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,, +util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@(odt_skims['TIMES2'] + dot_skims['TIMES2']),,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_TERMINALTIME,SHARED2FREE - Terminal time,@df.origin_terminal_time,,,coef_walk_access_time,,,,,,,,,, +util_SHARED2FREE_TERMINALTIME,SHARED2FREE - Terminal time,@df.dest_terminal_time,,,coef_walk_egress_time,,,,,,,,,, +util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['DISTS2'] + dot_skims['DISTS2']),,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['BTOLLS2'] + dot_skims['BTOLLS2']) / costShareSr2,,,coef_ivt,,,,,,,,,, +util_SHARED2FREE_Person_is_between_16_and_24_years_old,SHARED2FREE_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,c_age1624_sr2,,,,,,,,,, +util_SHARED2FREE_Person_is_between_41_and_55_years_old,SHARED2FREE_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,c_age4155_sr2,,,,,,,,,, +util_SHARED2FREE_Person_is_between_56_and_64_years_old,SHARED2FREE_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,c_age5664_sr2,,,,,,,,,, +util_SHARED2FREE_Person_is_between_65plus_years_old,SHARED2FREE_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,c_age65pl_sr2,,,,,,,,,, +util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.NP == 2),,,c_size2_sr2,,,,,,,,,, +util_SHARED2FREE_Three_person_household,SHARED2FREE - Three person household,@(df.NP == 3),,,c_size3_sr2,,,,,,,,,, +util_SHARED2FREE_Four_person_household,SHARED2FREE - Four person household,@(df.NP >= 4),,,c_size4p_sr2,,,,,,,,,, +util_SHARED2FREE_Female,SHARED2FREE - Female,@~df.is_male,,,c_female_sr2,,,,,,,,,, +#,Shared ride 2 toll,,,,,,,,,,,,,, +util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,, +util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,, +util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']),,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_TERMINALTIME,SHARED2PAY - Terminal time,@df.origin_terminal_time,,,,coef_walk_access_time,,,,,,,,, +util_SHARED2PAY_TERMINALTIME,SHARED2PAY - Terminal time,@df.dest_terminal_time,,,,coef_walk_egress_time,,,,,,,,, +util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']),,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2']) / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']) / costShareSr2,,,,coef_ivt,,,,,,,,, +util_SHARED2PAY_Person_is_between_16_and_24_years_old,SHARED2PAY_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,c_age1624_sr2,,,,,,,,, +util_SHARED2PAY_Person_is_between_41_and_55_years_old,SHARED2PAY_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,c_age4155_sr2,,,,,,,,, +util_SHARED2PAY_Person_is_between_56_and_64_years_old,SHARED2PAY_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,c_age5664_sr2,,,,,,,,, +util_SHARED2PAY_Person_is_between_65plus_years_old,SHARED2PAY_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,c_age65pl_sr2,,,,,,,,, +util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.NP == 2),,,,c_size2_sr2,,,,,,,,, +util_SHARED2PAY_Three_person_household,SHARED2PAY - Three person household,@(df.NP == 3),,,,c_size3_sr2,,,,,,,,, +util_SHARED2PAY_Four_person_household,SHARED2PAY - Four person household,@(df.NP >= 4),,,,c_size4p_sr2,,,,,,,,, +util_SHARED2PAY_Female,SHARED2PAY - Female,@~df.is_male,,,,c_female_sr2,,,,,,,,, +#,Shared ride 3+,,,,,,,,,,,,,, +util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,, +util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@(odt_skims['TIMES3'] + dot_skims['TIMES3']),,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_TERMINALTIME,SHARED3FREE - Terminal time,@df.origin_terminal_time,,,,,coef_walk_access_time,,,,,,,, +util_SHARED3FREE_TERMINALTIME,SHARED3FREE - Terminal time,@df.dest_terminal_time,,,,,coef_walk_egress_time,,,,,,,, +util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['DISTS3'] + dot_skims['DISTS3']),,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['BTOLLS3'] + dot_skims['BTOLLS3']) / costShareSr3,,,,,coef_ivt,,,,,,,, +util_SHARED3FREE_Person_is_between_16_and_24_years_old,SHARED3FREE_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,c_age1624_sr3,,,,,,,, +util_SHARED3FREE_Person_is_between_41_and_55_years_old,SHARED3FREE_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,c_age4155_sr3,,,,,,,, +util_SHARED3FREE_Person_is_between_56_and_64_years_old,SHARED3FREE_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,c_age5664_sr3,,,,,,,, +util_SHARED3FREE_Person_is_between_65plus_years_old,SHARED3FREE_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,c_age65pl_sr3,,,,,,,, +util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.NP == 2),,,,,c_size2_sr3,,,,,,,, +util_SHARED3FREE_Three_person_household,SHARED3FREE - Three person household,@(df.NP == 3),,,,,c_size3_sr3,,,,,,,, +util_SHARED3FREE_Four_person_household,SHARED3FREE - Four person household,@(df.NP >= 4),,,,,c_size4p_sr3,,,,,,,, +util_SHARED3FREE_Female,SHARED3FREE - Female,@~df.is_male,,,,,c_female_sr3,,,,,,,, +#,Shared ride 3+ toll,,,,,,,,,,,,,, +util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,, +util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@(odt_skims['TOLLTIMES3'] + dot_skims['TOLLTIMES3']),,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_TERMINALTIME,SHARED3PAY - Terminal time,@df.origin_terminal_time,,,,,,coef_walk_access_time,,,,,,, +util_SHARED3PAY_TERMINALTIME,SHARED3PAY - Terminal time,@df.dest_terminal_time,,,,,,coef_walk_egress_time,,,,,,, +util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['TOLLDISTS3'] + dot_skims['TOLLDISTS3']),,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS3'] + dot_skims['TOLLBTOLLS3']) / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS3'] + dot_skims['TOLLVTOLLS3']) / costShareSr3,,,,,,coef_ivt,,,,,,, +util_SHARED3PAY_Person_is_between_16_and_24_years_old,SHARED3PAY_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,,c_age1624_sr3,,,,,,, +util_SHARED3PAY_Person_is_between_41_and_55_years_old,SHARED3PAY_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,,c_age4155_sr3,,,,,,, +util_SHARED3PAY_Person_is_between_56_and_64_years_old,SHARED3PAY_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,,c_age5664_sr3,,,,,,, +util_SHARED3PAY_Person_is_between_65plus_years_old,SHARED3PAY_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,,c_age65pl_sr3,,,,,,, +util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.NP == 2),,,,,,c_size2_sr3,,,,,,, +util_SHARED3PAY_Three_person_household,SHARED3PAY - Three person household,@(df.NP == 3),,,,,,c_size3_sr3,,,,,,, +util_SHARED3PAY_Four_person_household,SHARED3PAY - Four person household,@(df.NP >= 4),,,,,,c_size4p_sr3,,,,,,, +util_SHARED3PAY_Female,SHARED3PAY - Female,@~df.is_male,,,,,,c_female_sr3,,,,,,, +#,Walk,,,,,,,,,,,,,, +util_WALK_Unavailable,WALK - Unavailable,walk_available == False,,,,,,,-999,,,,,, +util_WALK_Time,WALK - walk time,@(od_skims.lookup('WALK_DIST') + od_skims.reverse('WALK_DIST'))*60/walkSpeed,,,,,,,c_walkTime,,,,,, +util_WALK_Person_is_between_16_and_24_years_old,WALK_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,,,c_age1624_nmot,,,,,, +util_WALK_Person_is_between_41_and_55_years_old,WALK_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,,,c_age4155_nmot,,,,,, +util_WALK_Person_is_between_56_and_64_years_old,WALK_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,,,c_age5664_nmot,,,,,, +util_WALK_Person_is_between_65plus_years_old,WALK_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,,,c_age65pl_nmot,,,,,, +util_WALK_Female,WALK - Female,@~df.is_male,,,,,,,c_female_nmot,,,,,, +util_WALK_Origin_Mix,WALK_Origin_Mix,@df.origin_Mix,,,,,,,c_oMix_nmot,,,,,, +util_WALK_Origin_Intersection_Density,WALK_Origin_Intersection_Density,@df.origin_TotInt,,,,,,,c_oIntDen_nmot,,,,,, +util_WALK_Destination_Employment_Density,WALK_Destination_Employment_Density,@df.dest_EmpDen,,,,,,,c_dEmpDen_nmot,,,,,, +#,Bike,,,,,,,,,,,,,, +util_BIKE_Unavailable,BIKE - Unavailable,bike_available == False,,,,,,,,-999,,,,, +util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,, +util_BIKE_Time,BIKE - bike time,@(od_skims.lookup('BIKE_DIST') + od_skims.reverse('BIKE_DIST'))*60/bikeSpeed,,,,,,,,c_bikeTime,,,,, +util_BIKE_Person_is_between_16_and_24_years_old,BIKE_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,,,,c_age1624_nmot,,,,, +util_BIKE_Person_is_between_41_and_55_years_old,BIKE_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,,,,c_age4155_nmot,,,,, +util_BIKE_Person_is_between_56_and_64_years_old,BIKE_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,,,,c_age5664_nmot,,,,, +util_BIKE_Person_is_between_65plus_years_old,BIKE_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,,,,c_age65pl_nmot,,,,, +util_BIKE_Female,BIKE - Female,@~df.is_male,,,,,,,,c_female_nmot,,,,, +util_BIKE_Origin_Mix,BIKE_Origin_Mix,@df.origin_Mix,,,,,,,,c_oMix_nmot,,,,, +util_BIKE_Origin_Intersection_Density,BIKE_Origin_Intersection_Density,@df.origin_TotInt,,,,,,,,c_oIntDen_nmot,,,,, +util_BIKE_Destination_Employment_Density,BIKE_Destination_Employment_Density,@df.dest_EmpDen,,,,,,,,c_dEmpDen_nmot,,,,, +#,Walk to Local,,,,,,,,,,,,,, +util_WALK_TRANSIT_Paths_logsums,WALK_TRANSIT - Path logsums,@tvpb_logsum_odt['WTW'] + tvpb_logsum_dot['WTW'],,,,,,,,,coef_one,,,, +util_WALK_TRANSIT_Person_is_between_16_and_24_years_old,WALK_TRANSIT_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,,,,,c_age1624_tran,,,, +util_WALK_TRANSIT_Person_is_between_41_and_55_years_old,WALK_TRANSIT_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,,,,,c_age4155_tran,,,, +util_WALK_TRANSIT_Person_is_between_56_and_64_years_old,WALK_TRANSIT_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,,,,,c_age5664_tran,,,, +util_WALK_TRANSIT_Person_is_between_65plus_years_old,WALK_TRANSIT_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,,,,,c_age65pl_tran,,,, +util_WALK_TRANSIT_Female,BIKE - Female,@~df.is_male,,,,,,,,,c_female_tran,,,, +util_WALK_TRANSIT_Origin_Mix,WALK_TRANSIT_Origin_Mix,@df.origin_Mix,,,,,,,,,c_oMix_wtran,,,, +util_WALK_TRANSIT_Origin_Intersection_Density,WALK_TRANSIT_Origin_Intersection_Density,@df.origin_TotInt,,,,,,,,,c_oIntDen_wtran,,,, +util_WALK_TRANSIT_Destination_Employment_Density,WALK_TRANSIT_Destination_Employment_Density,@df.dest_EmpDen,,,,,,,,,c_dEmpDen_wtran,,,, +#,Drive to Local,,,,,,,,,,,,,, +util_DRIVE_TRANSIT_Unavailable_for_zero_auto_households,DRIVE_TRANSIT - Unavailable for zero auto households,VEH == 0,,,,,,,,,,-999,,, +util_DRIVE_TRANSIT_Unavailable_for_persons_less_than_16,DRIVE_TRANSIT - Unavailable for persons less than 16,AGEP < 16,,,,,,,,,,-999,,, +util_DRIVE_TRANSIT_Paths_logsums,DRIVE_TRANSIT - Path logsums,@tvpb_logsum_odt['DTW'] + tvpb_logsum_dot['WTD'],,,,,,,,,,coef_one,,, +util_DRIVE_TRANSIT_Person_is_between_16_and_24_years_old,DRIVE_TRANSIT_Person_is_between_16_and_24_years_old,@(df.AGEP >= 16) & (df.AGEP <= 24),,,,,,,,,,c_age1624_tran,,, +util_DRIVE_TRANSIT_Person_is_between_41_and_55_years_old,DRIVE_TRANSIT_Person_is_between_41_and_55_years_old,@(df.AGEP >= 41) & (df.AGEP <= 55),,,,,,,,,,c_age4155_tran,,, +util_DRIVE_TRANSIT_Person_is_between_56_and_64_years_old,DRIVE_TRANSIT_Person_is_between_56_and_64_years_old,@(df.AGEP >= 56) & (df.AGEP <= 64),,,,,,,,,,c_age5664_tran,,, +util_DRIVE_TRANSIT_Person_is_between_65plus_years_old,DRIVE_TRANSIT_Person_is_between_65plus_years_old,@(df.AGEP >= 65),,,,,,,,,,c_age65pl_tran,,, +util_DRIVE_TRANSIT_Female,BIKE - Female,@~df.is_male,,,,,,,,,,c_female_tran,,, +util_DRIVE_TRANSIT_Destination_Employment_Density,DRIVE_TRANSIT_Destination_Employment_Density,@df.dest_EmpDen,,,,,,,,,,c_dEmpDen_dtran,,, +#,Taxi,,,,,,,,,,,,,, +util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']),,,,,,,,,,,coef_ivt,, +#, FIXME magic constant 1.5,,,,,,,,,,,,,, +util_Taxi_Wait_time,Taxi - Wait time,@1.5 * df.totalWaitTaxi,,,,,,,,,,,coef_ivt,, +util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']),,,,,,,,,,,coef_ivt,, +util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2']),,,,,,,,,,,coef_ivt,, +util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare * 2 + (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']) * Taxi_costPerMile +(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * Taxi_costPerMinute)*100,,,,,,,,,,,coef_ivt,, +#,TNC Single,,,,,,,,,,,,,, +util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']),,,,,,,,,,,,coef_ivt, +util_TNC_Single_Wait_time,TNC Single - Wait time,@1.5 * df.totalWaitSingleTNC,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']),,,,,,,,,,,,coef_ivt, +util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + odr_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2'] + dor_skims['TOLLBTOLLS2']),,,,,,,,,,,,coef_ivt, +util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare * 2 + (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']) * TNC_single_costPerMile + (odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,coef_ivt, +#,TNC Shared,,,,,,,,,,,,,, +util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@(odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * TNC_shared_IVTFactor,,,,,,,,,,,,,coef_ivt +#, FIXME magic constant 1.5,,,,,,,,,,,,,, +util_TNC_Shared_Wait_time,TNC Shared - Wait time,@1.5 * df.totalWaitSharedTNC,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2']),,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['TOLLBTOLLS2'] + odr_skims['TOLLBTOLLS2'] + dot_skims['TOLLBTOLLS2'] + dor_skims['TOLLBTOLLS2']),,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare * 2 + (odt_skims['TOLLDISTS2'] + dot_skims['TOLLDISTS2']) * TNC_shared_costPerMile + (odt_skims['TOLLTIMES2'] + dot_skims['TOLLTIMES2']) * TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,coef_ivt +#,indiv tour ASCs,,,,,,,,,,,,,, +util_Walk_ASC_Zero_auto,Walk ASC - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,zeroAutoHH_walk,,,,,, +util_Walk_ASC_Auto_deficient,Walk ASC - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,autoDeficientHH_walk,,,,,, +util_Walk_ASC_Auto_sufficient,Walk ASC - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,autoSufficientHH_walk,,,,,, +util_Bike_ASC_Zero_auto,Bike ASC - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,zeroAutoHH_bike,,,,, +util_Bike_ASC_Auto_deficient,Bike ASC - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,0,,,,, +util_Bike_ASC_Auto_sufficient,Bike ASC - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,autoSufficientHH_bike,,,,, +util_Shared_ride_2_ASC_Zero_auto,Shared ride 2 ASC - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,0,0,,,,,,,,, +util_Shared_ride_2_ASC_Auto_deficient,Shared ride 2 ASC - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,autoDeficientHH_sr2,autoDeficientHH_sr2,,,,,,,,, +util_Shared_ride_2_ASC_Auto_sufficient,Shared ride 2 ASC - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,autoSufficientHH_sr2,autoSufficientHH_sr2,,,,,,,,, +util_Shared_ride_3p_Zero_auto,Shared ride 3+ - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,zeroAutoHH_sr3,zeroAutoHH_sr3,,,,,,, +util_Shared_ride_3p_Auto_deficient,Shared ride 3+ - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,autoDeficientHH_sr3,autoDeficientHH_sr3,,,,,,, +util_Shared_ride_3p_Auto_sufficient,Shared ride 3+ - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,autoSufficientHH_sr3,autoSufficientHH_sr3,,,,,,, +util_Walk_to_Transit_Zero_auto,Walk to Transit - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,zeroAutoHH_wt,,,, +util_Walk_to_Transit_Auto_deficient,Walk to Transit - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,autoDeficientHH_wt,,,, +util_Walk_to_Transit_Auto_sufficient,Walk to Transit - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,autoSufficientHH_wt,,,, +util_Drive_to_Transit_Zero_auto,Drive to Transit - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,zeroAutoHH_kt,,, +util_Drive_to_Transit_Auto_deficient,Drive to Transit - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,autoDeficientHH_dt,,, +util_Drive_to_Transit_Auto_sufficient,Drive to Transit - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,autoSufficientHH_dt,,, +util_Taxi_Zero_auto,Taxi - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,,0,, +util_Taxi_Auto_deficient,Taxi - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,0,, +util_Taxi_Auto_sufficient,Taxi - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,0,, +util_TNC_Single_Zero_auto,TNC Single - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,,,0, +util_TNC_Single_Auto_deficient,TNC Single - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,0, +util_TNC_Single_Auto_sufficient,TNC Single - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,0, +util_TNC_Shared_Zero_auto,TNC Shared - Zero auto,@(df.is_indiv & (df.VEH == 0)),,,,,,,,,,,,,0 +util_TNC_Shared_Auto_deficient,TNC Shared - Auto deficient,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,,0 +util_TNC_Shared_Auto_sufficient,TNC Shared - Auto sufficient,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,,0 +#,joint tour ASCs,,,,,,,,,,,,,, +util_Joint_Walk_ASC_Zero_auto,Joint - Walk ASC - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,0,,,,,, +util_Joint_Walk_ASC_Auto_deficient,Joint - Walk ASC - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,0,,,,,, +util_Joint_Walk_ASC_Auto_sufficient,Joint - Walk ASC - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,0,,,,,, +util_Joint_Bike_ASC_Zero_auto,Joint - Bike ASC - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,0,,,,, +util_Joint_Bike_ASC_Auto_deficient,Joint - Bike ASC - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,0,,,,, +util_Joint_Bike_ASC_Auto_sufficient,Joint - Bike ASC - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,0,,,,, +util_Joint_Shared_ride_2_ASC_Zero_auto,Joint - Shared ride 2 ASC - Zero auto,@(df.is_joint & (df.VEH == 0)),,,0,0,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Auto_deficient,Joint - Shared ride 2 ASC - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,0,0,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Auto_sufficient,Joint - Shared ride 2 ASC - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,0,0,,,,,,,,, +util_Joint_Shared_ride_3p_Zero_auto,Joint - Shared ride 3+ - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,0,0,,,,,,, +util_Joint_Shared_ride_3p_Auto_deficient,Joint - Shared ride 3+ - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,0,0,,,,,,, +util_Joint_Shared_ride_3p_Auto_sufficient,Joint - Shared ride 3+ - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,0,0,,,,,,, +util_Joint_Walk_to_Transit_Zero_auto,Joint - Walk to Transit - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,0,,,, +util_Joint_Walk_to_Transit_Auto_deficient,Joint - Walk to Transit - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,0,,,, +util_Joint_Walk_to_Transit_Auto_sufficient,Joint - Walk to Transit - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,0,,,, +util_Joint_Drive_to_Transit_Zero_auto,Joint - Drive to Transit - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,0,,, +util_Joint_Drive_to_Transit_Auto_deficient,Joint - Drive to Transit - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,0,,, +util_Joint_Drive_to_Transit_Auto_sufficient,Joint - Drive to Transit - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,0,,, +util_Joint_Taxi_Zero_auto,Joint - Taxi - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,,0,, +util_Joint_Taxi_Auto_deficient,Joint - Taxi - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,0,, +util_Joint_Taxi_Auto_sufficient,Joint - Taxi - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,0,, +util_Joint_TNC_Single_Zero_auto,Joint - TNC Single - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,,,0, +util_Joint_TNC_Single_Auto_deficient,Joint - TNC Single - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,0, +util_Joint_TNC_Single_Auto_sufficient,Joint - TNC Single - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,0, +util_Joint_TNC_Shared_Zero_auto,Joint - TNC Shared - Zero auto,@(df.is_joint & (df.VEH == 0)),,,,,,,,,,,,,0 +util_Joint_TNC_Shared_Auto_deficient,Joint - TNC Shared - Auto deficient,@(df.is_joint & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,,,,,,,,,,,0 +util_Joint_TNC_Shared_Auto_sufficient,Joint - TNC Shared - Auto sufficient,@(df.is_joint & (df.VEH >= df.NWRKRS_ESR)),,,,,,,,,,,,,0 +#,calibration,,,,,,,,,,,,,, +util_Walk_to_Transit_dest_CBD_SF,Walk to Transit dest CBD SF,@df.destination_in_cbd_sf,,,,,,,,,asc_wtransit_cbd_sf,,,, +util_Walk_to_Transit_dest_NW_SF,walk to Transit dest NW SF,@df.destination_in_nw_sf,,,,,,,,,asc_wtransit_nw_sf,,,, +util_Walk_to_Transit_dest_SE_SF,Walk to Transit dest SE SF,@df.destination_in_se_sf,,,,,,,,,asc_wtransit_se_sf,,,, +util_Drive_to_Transit_dest_CBD_SF,Drive to Transit dest CBD SF,@df.destination_in_cbd_sf,,,,,,,,,,asc_dtransit_cbd_sf,,, +util_Drive_to_Transit_distance_penalty,Drive to Transit - distance penalty,@(50-2.5*odt_skims['DISTDA']).clip(lower=0),,,,,,,,,,coef_ivt,,, +util_Walk_to_Transit_distance_penalty,Walk to Transit - distance penalty,@(200-133*odt_skims['DISTDA']).clip(lower=0),,,,,,,,,coef_ivt,,,, +util_Transit_Pseudo_area_type_constant,Transit - Pseudo area type constant,@asc_Transit_Pseudo_area_type_constant * (df.daily_parking_cost>0),,,,,,,,,coef_ivt,coef_ivt,,, +util_TM2_Round_2_ASC_adjustment_for_0_Autos_HHs,TM2_Round_2_ASC_adjustment_for_0_Autos_HHs,@(df.is_indiv & (df.VEH == 0)),,,zeroAutoHH_SHARED2HOV,zeroAutoHH_SHARED2PAY,zeroAutoHH_SHARED3HOV,zeroAutoHH_SHARED3PAY,zeroAutoHH_WALK,zeroAutoHH_BIKE,zeroAutoHH_WALK_SET,zeroAutoHH_PNR_SET,,, +util_TM2_Round_2_ASC_adjustment_for_Auto_Defecient_HHs,TM2_Round_2_ASC_adjustment_for_Auto_Defecient_HHs,@(df.is_indiv & (df.VEH < df.NWRKRS_ESR) & (df.VEH > 0)),,,autoDeficientHH_SHARED2HOV,autoDeficientHH_SHARED2PAY,autoDeficientHH_SHARED3HOV,autoDeficientHH_SHARED3PAY,autoDeficientHH_WALK,autoDeficientHH_BIKE,autoDeficientHH_WALK_SET,autoDeficientHH_PNR_SET,,, +util_TM2_Round_2_ASC_adjustment_for_Auto_Sufficient_HHs,TM2_Round_2_ASC_adjustment_for_Auto_Sufficient_HHs,@(df.is_indiv & (df.VEH >= df.NWRKRS_ESR)),,,autoSufficientHH_SHARED2HOV,autoSufficientHH_SHARED2PAY,autoSufficientHH_SHARED3HOV,autoSufficientHH_SHARED3PAY,autoSufficientHH_WALK,autoSufficientHH_BIKE,autoSufficientHH_WALK_SET,autoSufficientHH_PNR_SET,,, +util_taxi_penalty,taxi penalty,@asc_taxi_penalty,,,,,,,,,,,coef_one,, +util_no_tnc,turn off tnc,1,,,,,,,,,,,,-999,-999 diff --git a/activitysim/examples/example_marin/configs/tour_mode_choice.yaml b/activitysim/examples/prototype_marin/configs/tour_mode_choice.yaml similarity index 95% rename from activitysim/examples/example_marin/configs/tour_mode_choice.yaml rename to activitysim/examples/prototype_marin/configs/tour_mode_choice.yaml index 5aadac97d7..61e0ab6772 100755 --- a/activitysim/examples/example_marin/configs/tour_mode_choice.yaml +++ b/activitysim/examples/prototype_marin/configs/tour_mode_choice.yaml @@ -1,188 +1,188 @@ -LOGIT_TYPE: NL -#LOGIT_TYPE: MNL - -tvpb_mode_path_types: - DRIVE_TRANSIT: - od: DTW - do: WTD - WALK_TRANSIT: - od: WTW - do: WTW - -NESTS: - name: root - coefficient: coef_nest_root - alternatives: - - name: AUTO - coefficient: coef_nest_AUTO - alternatives: - - name: DRIVEALONE - coefficient: coef_nest_AUTO_DRIVEALONE - alternatives: - - DRIVEALONEFREE - - DRIVEALONEPAY - - name: SHAREDRIDE2 - coefficient: coef_nest_AUTO_SHAREDRIDE2 - alternatives: - - SHARED2FREE - - SHARED2PAY - - name: SHAREDRIDE3 - coefficient: coef_nest_AUTO_SHAREDRIDE3 - alternatives: - - SHARED3FREE - - SHARED3PAY - - name: NONMOTORIZED - coefficient: coef_nest_NONMOTORIZED - alternatives: - - WALK - - BIKE - - name: TRANSIT - coefficient: coef_nest_TRANSIT - alternatives: - - WALK_TRANSIT - - DRIVE_TRANSIT - - name: RIDEHAIL - coefficient: coef_nest_RIDEHAIL - alternatives: - - TAXI - - TNC_SINGLE - - TNC_SHARED - -SPEC: tour_mode_choice.csv -COEFFICIENTS: tour_mode_choice_coefficients.csv -COEFFICIENT_TEMPLATE: tour_mode_choice_coefficients_template.csv - -CONSTANTS: - #valueOfTime: 8.00 - costPerMile: 17.23 - costShareSr2: 1.11 - costShareSr3: 1.25 -# waitThresh: 10.00 - walkThresh: 3.0 -# shortWalk: 0.333 -# longWalk: 0.667 - walkSpeed: 3.00 - bikeThresh: 12.00 - bikeSpeed: 12.00 -# maxCbdAreaTypeThresh: 2 -# indivTour: 1.00000 -# upperEA: 5 -# upperAM: 10 -# upperMD: 15 -# upperPM: 19 - # RIDEHAIL Settings - Taxi_baseFare: 2.20 - Taxi_costPerMile: 2.30 - Taxi_costPerMinute: 0.10 - Taxi_waitTime_mean: - 1: 5.5 - 2: 9.5 - 3: 13.3 - 4: 17.3 - 5: 26.5 - Taxi_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - TNC_single_baseFare: 2.20 - TNC_single_costPerMile: 1.33 - TNC_single_costPerMinute: 0.24 - TNC_single_costMinimum: 7.20 - TNC_single_waitTime_mean: - 1: 3.0 - 2: 6.3 - 3: 8.4 - 4: 8.5 - 5: 10.3 - TNC_single_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - TNC_shared_baseFare: 2.20 - TNC_shared_costPerMile: 0.53 - TNC_shared_costPerMinute: 0.10 - TNC_shared_costMinimum: 3.00 - TNC_shared_IVTFactor: 1.5 - TNC_shared_waitTime_mean: - 1: 5.0 - 2: 8.0 - 3: 11.0 - 4: 15.0 - 5: 15.0 - TNC_shared_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - min_waitTime: 0 - max_waitTime: 50 -# - ivt_cost_multiplier: 0.6 -# ivt_lrt_multiplier: 0.9 -# ivt_ferry_multiplier: 0.8 -# ivt_exp_multiplier: 1 -# ivt_hvy_multiplier: 0.8 -# ivt_com_multiplier: 0.7 - walktimeshort_multiplier: 2 -# walktimelong_multiplier: 10 -# biketimeshort_multiplier: 4 -# biketimelong_multiplier: 20 -# short_i_wait_multiplier: 2 -# long_i_wait_multiplier: 1 -# wacc_multiplier: 2 -# wegr_multiplier: 2 -# waux_multiplier: 2 -# dtim_multiplier: 2 -# xwait_multiplier: 2 -# dacc_ratio: 0 -# xfers_wlk_multiplier: 10 -# xfers_drv_multiplier: 20 - drvtrn_distpen_0_multiplier: 270 - drvtrn_distpen_max: 15 -# density_index_multiplier: -0.2 - joint_sr2_ASC_no_auto: 0 - joint_sr2_ASC_auto_deficient: 0 - joint_sr2_ASC_auto_sufficient: 0 - joint_drive_transit_ASC_no_auto: 0 - c_auto_operating_cost_per_mile: 17.23 - - -# so far, we can use the same spec as for non-joint tours -preprocessor: - SPEC: tour_mode_choice_annotate_choosers_preprocessor - DF: choosers - TABLES: - - land_use - - tours - -nontour_preprocessor: - SPEC: tour_mode_choice_annotate_choosers_preprocessor - DF: choosers - TABLES: - - land_use - -# to reduce memory needs filter chooser table to these fields -LOGSUM_CHOOSER_COLUMNS: - - tour_type - - hhsize - - density_index - - age - - age_16_p - - age_16_to_19 - - auto_ownership - - number_of_participants - - tour_category - - num_workers - - value_of_time - - free_parking_at_work - - income_segment - - demographic_segment - - c_ivt_for_segment - - c_cost_for_segment - -MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum +LOGIT_TYPE: NL +#LOGIT_TYPE: MNL + +tvpb_mode_path_types: + DRIVE_TRANSIT: + od: DTW + do: WTD + WALK_TRANSIT: + od: WTW + do: WTW + +NESTS: + name: root + coefficient: coef_nest_root + alternatives: + - name: AUTO + coefficient: coef_nest_AUTO + alternatives: + - name: DRIVEALONE + coefficient: coef_nest_AUTO_DRIVEALONE + alternatives: + - DRIVEALONEFREE + - DRIVEALONEPAY + - name: SHAREDRIDE2 + coefficient: coef_nest_AUTO_SHAREDRIDE2 + alternatives: + - SHARED2FREE + - SHARED2PAY + - name: SHAREDRIDE3 + coefficient: coef_nest_AUTO_SHAREDRIDE3 + alternatives: + - SHARED3FREE + - SHARED3PAY + - name: NONMOTORIZED + coefficient: coef_nest_NONMOTORIZED + alternatives: + - WALK + - BIKE + - name: TRANSIT + coefficient: coef_nest_TRANSIT + alternatives: + - WALK_TRANSIT + - DRIVE_TRANSIT + - name: RIDEHAIL + coefficient: coef_nest_RIDEHAIL + alternatives: + - TAXI + - TNC_SINGLE + - TNC_SHARED + +SPEC: tour_mode_choice.csv +COEFFICIENTS: tour_mode_choice_coefficients.csv +COEFFICIENT_TEMPLATE: tour_mode_choice_coefficients_template.csv + +CONSTANTS: + #valueOfTime: 8.00 + costPerMile: 17.23 + costShareSr2: 1.11 + costShareSr3: 1.25 +# waitThresh: 10.00 + walkThresh: 3.0 +# shortWalk: 0.333 +# longWalk: 0.667 + walkSpeed: 3.00 + bikeThresh: 12.00 + bikeSpeed: 12.00 +# maxCbdAreaTypeThresh: 2 +# indivTour: 1.00000 +# upperEA: 5 +# upperAM: 10 +# upperMD: 15 +# upperPM: 19 + # RIDEHAIL Settings + Taxi_baseFare: 2.20 + Taxi_costPerMile: 2.30 + Taxi_costPerMinute: 0.10 + Taxi_waitTime_mean: + 1: 5.5 + 2: 9.5 + 3: 13.3 + 4: 17.3 + 5: 26.5 + Taxi_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_single_baseFare: 2.20 + TNC_single_costPerMile: 1.33 + TNC_single_costPerMinute: 0.24 + TNC_single_costMinimum: 7.20 + TNC_single_waitTime_mean: + 1: 3.0 + 2: 6.3 + 3: 8.4 + 4: 8.5 + 5: 10.3 + TNC_single_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_shared_baseFare: 2.20 + TNC_shared_costPerMile: 0.53 + TNC_shared_costPerMinute: 0.10 + TNC_shared_costMinimum: 3.00 + TNC_shared_IVTFactor: 1.5 + TNC_shared_waitTime_mean: + 1: 5.0 + 2: 8.0 + 3: 11.0 + 4: 15.0 + 5: 15.0 + TNC_shared_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + min_waitTime: 0 + max_waitTime: 50 +# + ivt_cost_multiplier: 0.6 +# ivt_lrt_multiplier: 0.9 +# ivt_ferry_multiplier: 0.8 +# ivt_exp_multiplier: 1 +# ivt_hvy_multiplier: 0.8 +# ivt_com_multiplier: 0.7 + walktimeshort_multiplier: 2 +# walktimelong_multiplier: 10 +# biketimeshort_multiplier: 4 +# biketimelong_multiplier: 20 +# short_i_wait_multiplier: 2 +# long_i_wait_multiplier: 1 +# wacc_multiplier: 2 +# wegr_multiplier: 2 +# waux_multiplier: 2 +# dtim_multiplier: 2 +# xwait_multiplier: 2 +# dacc_ratio: 0 +# xfers_wlk_multiplier: 10 +# xfers_drv_multiplier: 20 + drvtrn_distpen_0_multiplier: 270 + drvtrn_distpen_max: 15 +# density_index_multiplier: -0.2 + joint_sr2_ASC_no_auto: 0 + joint_sr2_ASC_auto_deficient: 0 + joint_sr2_ASC_auto_sufficient: 0 + joint_drive_transit_ASC_no_auto: 0 + c_auto_operating_cost_per_mile: 17.23 + + +# so far, we can use the same spec as for non-joint tours +preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + - tours + +nontour_preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + +# to reduce memory needs filter chooser table to these fields +LOGSUM_CHOOSER_COLUMNS: + - tour_type + - hhsize + - density_index + - age + - age_16_p + - age_16_to_19 + - auto_ownership + - number_of_participants + - tour_category + - num_workers + - value_of_time + - free_parking_at_work + - income_segment + - demographic_segment + - c_ivt_for_segment + - c_cost_for_segment + +MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum diff --git a/activitysim/examples/example_marin/configs/tour_mode_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/prototype_marin/configs/tour_mode_choice_annotate_choosers_preprocessor.csv similarity index 98% rename from activitysim/examples/example_marin/configs/tour_mode_choice_annotate_choosers_preprocessor.csv rename to activitysim/examples/prototype_marin/configs/tour_mode_choice_annotate_choosers_preprocessor.csv index 1e9f1978a6..0f87f09e16 100755 --- a/activitysim/examples/example_marin/configs/tour_mode_choice_annotate_choosers_preprocessor.csv +++ b/activitysim/examples/prototype_marin/configs/tour_mode_choice_annotate_choosers_preprocessor.csv @@ -1,87 +1,87 @@ -Description,Target,Expression, -#,,, -,number_of_participants,1, -,is_joint,False, -#,,, -,_HAVE_PARENT_TOURS,False, -,_parent_tour_mode,False, -,work_tour_is_drive,False, -,work_tour_is_bike,False, -,work_tour_is_SOV,False, -#,,, -,is_mandatory,True, -,is_joint,False, -,is_indiv,~is_joint, -,is_atwork_subtour,False, -,is_escort,False, -#,,, -income_in_thousands,income_in_thousands,(df.HHINCADJ / 1000).clip(lower=0), -income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[1, 2, 3, 4]).astype(int)", -,demographic_segment,income_segment.map(TVPB_demographic_segments_by_income_segment), -#,c_ivt_for_segment,"np.where(demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_ivt_low_income, c_ivt_high_income)", -#,c_cost_for_segment,"np.where(demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_cost_low_income, c_cost_high_income)", -#,,, -#,c_cost,(0.60 * c_ivt) / df.value_of_time, -# ivot * (c_ivt_cost_multiplier * c_ivt),,, -,ivot,1.0 / df.value_of_time, -# RIDEHAIL,,, -,origin_density_measure,"(reindex(land_use.POP, df[orig_col_name]) + reindex(land_use.emp_total, df[orig_col_name])) / (reindex(land_use.ACRES, df[orig_col_name]) / 640)", -,dest_density_measure,"(reindex(land_use.POP, df[dest_col_name]) + reindex(land_use.emp_total, df[dest_col_name])) / (reindex(land_use.ACRES, df[dest_col_name]) / 640)", -,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", -,dest_density,"pd.cut(dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", -,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", -,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", -,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", -,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", -# ,, Note that the mean and standard deviation are not the values for the distribution itself, but of the underlying normal distribution it is derived from -,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", -,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", -,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", -,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", -,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", -,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", -,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", -,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", -,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime, -,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime, -,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime, -#,,, -,_free_parking_available,(df.tour_type == 'work') & (df.fp_choice == 1), -,_dest_hourly_peak_parking_cost,"reindex(land_use.hparkcost, df[dest_col_name])", -,_dest_hourly_offpeak_parking_cost,"reindex(land_use.hparkcost, df[dest_col_name])", -,_hourly_peak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_peak_parking_cost)", -,_hourly_offpeak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_offpeak_parking_cost)", -just hourly instead of times duration for now,daily_parking_cost,"np.where(is_mandatory, _hourly_peak_parking_cost, _hourly_offpeak_parking_cost)", -#,,, -,distance,(odt_skims['DISTDA']), -,sov_available,(odt_skims['TIMEDA']>0) & (dot_skims['TIMEDA']>0), -,sovtoll_available,(odt_skims['TOLLVTOLLDA']>0) | (dot_skims['TOLLVTOLLDA']>0), -,hov2_available,(odt_skims['TIMES2'] + dot_skims['TIMES2'])>0, -,hov2toll_available,(odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2'])>0, -,hov3_available,(odt_skims['TIMES3']>0) & (dot_skims['TIMES3']>0), -,hov3toll_available,(odt_skims['TOLLVTOLLS3'] + dot_skims['TOLLVTOLLS3'])>0, -,walk_available,"od_skims.lookup('WALK_DIST').between(0.01, walkThresh) & od_skims.reverse('WALK_DIST').between(0.01, walkThresh)", -,bike_available,"od_skims.lookup('BIKE_DIST').between(0.01, bikeThresh) & od_skims.reverse('BIKE_DIST').between(0.01, bikeThresh)", -#,,, -destination district,destination_in_cbd_sf,"reindex(land_use.DistID, df[dest_col_name])==1", -destination district,destination_in_nw_sf,"reindex(land_use.DistID, df[dest_col_name])==2", -destination district,destination_in_se_sf,"reindex(land_use.DistID, df[dest_col_name])==3", -#,,, -,origin_terminal_time,"reindex(land_use.TERMINALTIME, df[orig_col_name])", -,dest_terminal_time,"reindex(land_use.TERMINALTIME, df[dest_col_name])", -,origin_DUDen,"reindex(land_use.DUDen, df[orig_col_name])", -,dest_DUDen,"reindex(land_use.DUDen, df[dest_col_name])", -,origin_EmpDen,"reindex(land_use.EmpDen, df[orig_col_name])", -,dest_EmpDen,"reindex(land_use.EmpDen, df[dest_col_name])", -,origin_TotInt,"reindex(land_use.TotInt, df[orig_col_name])", -,dest_TotInt,"reindex(land_use.TotInt, df[dest_col_name])", -,origin_Mix,"(origin_DUDen * origin_EmpDen) / np.where((origin_DUDen + origin_EmpDen) > 0, (origin_DUDen + origin_EmpDen), 0.001)", -,dest_Mix,"(dest_DUDen * dest_EmpDen) / np.where((dest_DUDen + dest_EmpDen) > 0, (dest_DUDen + dest_EmpDen), 0.001)", -# diagnostic,,, -#,sov_dist_roundtrip,(odt_skims['DISTDA'] + dot_skims['DISTDA']), +Description,Target,Expression, +#,,, +,number_of_participants,1, +,is_joint,False, +#,,, +,_HAVE_PARENT_TOURS,False, +,_parent_tour_mode,False, +,work_tour_is_drive,False, +,work_tour_is_bike,False, +,work_tour_is_SOV,False, +#,,, +,is_mandatory,True, +,is_joint,False, +,is_indiv,~is_joint, +,is_atwork_subtour,False, +,is_escort,False, +#,,, +income_in_thousands,income_in_thousands,(df.HHINCADJ / 1000).clip(lower=0), +income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[1, 2, 3, 4]).astype(int)", +,demographic_segment,income_segment.map(TVPB_demographic_segments_by_income_segment), +#,c_ivt_for_segment,"np.where(demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_ivt_low_income, c_ivt_high_income)", +#,c_cost_for_segment,"np.where(demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_cost_low_income, c_cost_high_income)", +#,,, +#,c_cost,(0.60 * c_ivt) / df.value_of_time, +# ivot * (c_ivt_cost_multiplier * c_ivt),,, +,ivot,1.0 / df.value_of_time, +# RIDEHAIL,,, +,origin_density_measure,"(reindex(land_use.POP, df[orig_col_name]) + reindex(land_use.emp_total, df[orig_col_name])) / (reindex(land_use.ACRES, df[orig_col_name]) / 640)", +,dest_density_measure,"(reindex(land_use.POP, df[dest_col_name]) + reindex(land_use.emp_total, df[dest_col_name])) / (reindex(land_use.ACRES, df[dest_col_name]) / 640)", +,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", +,dest_density,"pd.cut(dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", +,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", +,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", +,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", +,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", +# ,, Note that the mean and standard deviation are not the values for the distribution itself, but of the underlying normal distribution it is derived from +,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", +,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", +,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", +,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", +,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", +,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", +,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", +,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", +,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime, +,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime, +,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime, +#,,, +,_free_parking_available,(df.tour_type == 'work') & (df.fp_choice == 1), +,_dest_hourly_peak_parking_cost,"reindex(land_use.hparkcost, df[dest_col_name])", +,_dest_hourly_offpeak_parking_cost,"reindex(land_use.hparkcost, df[dest_col_name])", +,_hourly_peak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_peak_parking_cost)", +,_hourly_offpeak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_offpeak_parking_cost)", +just hourly instead of times duration for now,daily_parking_cost,"np.where(is_mandatory, _hourly_peak_parking_cost, _hourly_offpeak_parking_cost)", +#,,, +,distance,(odt_skims['DISTDA']), +,sov_available,(odt_skims['TIMEDA']>0) & (dot_skims['TIMEDA']>0), +,sovtoll_available,(odt_skims['TOLLVTOLLDA']>0) | (dot_skims['TOLLVTOLLDA']>0), +,hov2_available,(odt_skims['TIMES2'] + dot_skims['TIMES2'])>0, +,hov2toll_available,(odt_skims['TOLLVTOLLS2'] + dot_skims['TOLLVTOLLS2'])>0, +,hov3_available,(odt_skims['TIMES3']>0) & (dot_skims['TIMES3']>0), +,hov3toll_available,(odt_skims['TOLLVTOLLS3'] + dot_skims['TOLLVTOLLS3'])>0, +,walk_available,"od_skims.lookup('WALK_DIST').between(0.01, walkThresh) & od_skims.reverse('WALK_DIST').between(0.01, walkThresh)", +,bike_available,"od_skims.lookup('BIKE_DIST').between(0.01, bikeThresh) & od_skims.reverse('BIKE_DIST').between(0.01, bikeThresh)", +#,,, +destination district,destination_in_cbd_sf,"reindex(land_use.DistID, df[dest_col_name])==1", +destination district,destination_in_nw_sf,"reindex(land_use.DistID, df[dest_col_name])==2", +destination district,destination_in_se_sf,"reindex(land_use.DistID, df[dest_col_name])==3", +#,,, +,origin_terminal_time,"reindex(land_use.TERMINALTIME, df[orig_col_name])", +,dest_terminal_time,"reindex(land_use.TERMINALTIME, df[dest_col_name])", +,origin_DUDen,"reindex(land_use.DUDen, df[orig_col_name])", +,dest_DUDen,"reindex(land_use.DUDen, df[dest_col_name])", +,origin_EmpDen,"reindex(land_use.EmpDen, df[orig_col_name])", +,dest_EmpDen,"reindex(land_use.EmpDen, df[dest_col_name])", +,origin_TotInt,"reindex(land_use.TotInt, df[orig_col_name])", +,dest_TotInt,"reindex(land_use.TotInt, df[dest_col_name])", +,origin_Mix,"(origin_DUDen * origin_EmpDen) / np.where((origin_DUDen + origin_EmpDen) > 0, (origin_DUDen + origin_EmpDen), 0.001)", +,dest_Mix,"(dest_DUDen * dest_EmpDen) / np.where((dest_DUDen + dest_EmpDen) > 0, (dest_DUDen + dest_EmpDen), 0.001)", +# diagnostic,,, +#,sov_dist_roundtrip,(odt_skims['DISTDA'] + dot_skims['DISTDA']), diff --git a/activitysim/examples/example_marin/configs/tour_mode_choice_coefficients.csv b/activitysim/examples/prototype_marin/configs/tour_mode_choice_coefficients.csv similarity index 97% rename from activitysim/examples/example_marin/configs/tour_mode_choice_coefficients.csv rename to activitysim/examples/prototype_marin/configs/tour_mode_choice_coefficients.csv index 22e88732da..ed7ee8e220 100755 --- a/activitysim/examples/example_marin/configs/tour_mode_choice_coefficients.csv +++ b/activitysim/examples/prototype_marin/configs/tour_mode_choice_coefficients.csv @@ -1,394 +1,394 @@ -coefficient_name,value,constrain -coef_one,1,T -coef_nest_root,1,T -coef_nest_AUTO,0.72,T -coef_nest_AUTO_DRIVEALONE,0.35,T -coef_nest_AUTO_SHAREDRIDE2,0.35,T -coef_nest_AUTO_SHAREDRIDE3,0.35,T -coef_nest_NONMOTORIZED,0.72,T -coef_nest_TRANSIT,0.72,T -coef_nest_TRANSIT_WALKACCESS,0.5,T -coef_nest_TRANSIT_DRIVEACCESS,0.5,T -coef_nest_RIDEHAIL,0.36,T -coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,-0.0175,F -coef_ivt_school_univ,-0.0224,F -coef_ivt_work,-0.016,F -coef_ivt_atwork,-0.0188,F -coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,15,F -coef_topology_walk_multiplier_atwork,7.5,F -coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,20,F -coef_topology_bike_multiplier_atwork,10,F -coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,2.2,F -coef_topology_trn_multiplier_atwork,2,F -coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F -coef_age1619_da_multiplier_school_univ,-1.3813,F -coef_age1619_da_multiplier_atwork,0.0032336,F -coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F -coef_age010_trn_multiplier_school_univ,-1.5548,F -coef_age010_trn_multiplier_atwork,0.000722,F -coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,-1.366,F -coef_age16p_sr_multiplier_school_univ_work_atwork,0,F -coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,0,F -coef_hhsize1_sr_multiplier_work,-0.734588,F -coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,0,F -coef_hhsize2_sr_multiplier_school_univ,-0.6359,F -coef_walk_access_time,-0.03,T -coef_walk_egress_time,-0.03,T -c_age1624_sr2,-0.21388,T -c_age1624_sr3,-1.79023,T -c_age1624_nmot,0.30322,T -c_age1624_tran,0.79472,T -c_age4155_sr2,-0.30638,T -c_age4155_sr3,-0.41025,T -c_age4155_nmot,-0.17752,T -c_age4155_tran,-0.42301,T -c_age5664_sr2,-1.02962,T -c_age5664_sr3,-0.85641,T -c_age5664_nmot,-0.64534,T -c_age5664_tran,-0.44991,T -c_age65pl_sr2,-0.67111,T -c_age65pl_sr3,-1.43462,T -c_age65pl_nmot,-1.45334,T -c_age65pl_tran,-1.1231,T -c_female_sr2,0.59473,T -c_female_sr3,0.84806,T -c_female_tran,0.15779,T -c_female_nmot,0,T -c_size2_sr2,1.06964,T -c_size2_sr3,-0.46736,T -c_size3_sr2,1.58018,T -c_size3_sr3,0.65463,T -c_size4p_sr2,1.68839,T -c_size4p_sr3,1.4987,T -c_walkTime,-0.059,T -c_bikeTime,-0.0492,T -c_oMix_nmot,0.21014,T -c_oMix_wtran,0,T -c_oIntDen_nmot,0.003,T -c_oIntDen_wtran,0,T -c_dEmpDen_nmot,0.02071,T -c_dEmpDen_wtran,0,T -c_dEmpDen_dtran,0,T -walk_ASC_no_auto_eatout,5.1251173,F -walk_ASC_no_auto_escort,2.8012068,F -walk_ASC_no_auto_othdiscr,3.2665946,F -walk_ASC_no_auto_othmaint,1.287299,F -walk_ASC_no_auto_school,18.414557,F -walk_ASC_no_auto_shopping,2.3768773,F -walk_ASC_no_auto_social,1.8680915,F -walk_ASC_no_auto_univ,6.408967,F -walk_ASC_no_auto_work,5.7672157,F -walk_ASC_no_auto_atwork,6.669213,F -walk_ASC_auto_deficient_eatout,3.274605,F -walk_ASC_auto_deficient_escort,-0.90204656,F -walk_ASC_auto_deficient_othdiscr,2.2494075,F -walk_ASC_auto_deficient_othmaint,1.3690404,F -walk_ASC_auto_deficient_school,3.2573624,F -walk_ASC_auto_deficient_shopping,2.2701733,F -walk_ASC_auto_deficient_social,2.870184,F -walk_ASC_auto_deficient_univ,4.50591,F -walk_ASC_auto_deficient_work,2.4010417,F -walk_ASC_auto_deficient_atwork,0.92546093,F -walk_ASC_auto_sufficient_eatout,1.5516903,F -walk_ASC_auto_sufficient_escort,-0.8116066,F -walk_ASC_auto_sufficient_othdiscr,1.2633476,F -walk_ASC_auto_sufficient_othmaint,0.7999634,F -walk_ASC_auto_sufficient_school,0.6476856,F -walk_ASC_auto_sufficient_shopping,0.7312663,F -walk_ASC_auto_sufficient_social,1.7072186,F -walk_ASC_auto_sufficient_univ,1.0607665,F -walk_ASC_auto_sufficient_work,0.053265337,F -walk_ASC_auto_sufficient_atwork,0.677216,F -bike_ASC_no_auto_eatout,0.86807096,F -bike_ASC_no_auto_escort,-0.716212,F -bike_ASC_no_auto_othdiscr,-0.3764232,F -bike_ASC_no_auto_othmaint,1.5394334,F -bike_ASC_no_auto_school,12.098735,F -bike_ASC_no_auto_shopping,0.8341555,F -bike_ASC_no_auto_social,0.02058321,F -bike_ASC_no_auto_univ,4.2945156,F -bike_ASC_no_auto_work,3.1940088,F -bike_ASC_no_auto_atwork,-0.90725845,F -bike_ASC_auto_deficient_eatout,-1.5691106,F -bike_ASC_auto_deficient_escort,-4.527928,F -bike_ASC_auto_deficient_othdiscr,-0.09246834,F -bike_ASC_auto_deficient_othmaint,-1.5184649,F -bike_ASC_auto_deficient_school,-0.5280678,F -bike_ASC_auto_deficient_shopping,-0.87584466,F -bike_ASC_auto_deficient_social,0.6345214,F -bike_ASC_auto_deficient_univ,-0.669235,F -bike_ASC_auto_deficient_work,0.25318968,F -bike_ASC_auto_deficient_atwork,-0.8074083,F -bike_ASC_auto_sufficient_eatout,-1.2003471,F -bike_ASC_auto_sufficient_escort,-5.0631084,F -bike_ASC_auto_sufficient_othdiscr,-1.0714597,F -bike_ASC_auto_sufficient_othmaint,-2.8083024,F -bike_ASC_auto_sufficient_school,-2.1134686,F -bike_ASC_auto_sufficient_shopping,-2.5662103,F -bike_ASC_auto_sufficient_social,-1.368071,F -bike_ASC_auto_sufficient_univ,-1.9397832,F -bike_ASC_auto_sufficient_work,-1.5800232,F -bike_ASC_auto_sufficient_atwork,15.72017,F -sr2_ASC_no_auto_all,0,F -sr2_ASC_auto_deficient_eatout,0.5882345,F -sr2_ASC_auto_deficient_escort,0,F -sr2_ASC_auto_deficient_othdiscr,0.6601513,F -sr2_ASC_auto_deficient_othmaint,0.2621527,F -sr2_ASC_auto_deficient_school,0.12474365,F -sr2_ASC_auto_deficient_shopping,0.24409756,F -sr2_ASC_auto_deficient_social,1.8558528,F -sr2_ASC_auto_deficient_univ,-1.6922346,F -sr2_ASC_auto_deficient_work,-0.33803123,F -sr2_ASC_auto_deficient_atwork,-2.1102421,F -sr2_ASC_auto_sufficient_eatout,0.86280555,F -sr2_ASC_auto_sufficient_escort,0,F -sr2_ASC_auto_sufficient_othdiscr,0.49684617,F -sr2_ASC_auto_sufficient_othmaint,0.25817883,F -sr2_ASC_auto_sufficient_school,-1.6062657,F -sr2_ASC_auto_sufficient_shopping,0.19770707,F -sr2_ASC_auto_sufficient_social,0.5236025,F -sr2_ASC_auto_sufficient_univ,-1.859427,F -sr2_ASC_auto_sufficient_work,-1.0857458,F -sr2_ASC_auto_sufficient_atwork,-1.4450618,F -sr3p_ASC_no_auto_eatout,0.3219998,F -sr3p_ASC_no_auto_escort,-1.8129267,F -sr3p_ASC_no_auto_othdiscr,0.27216902,F -sr3p_ASC_no_auto_othmaint,-0.8031854,F -sr3p_ASC_no_auto_school,-6.0240827,F -sr3p_ASC_no_auto_shopping,-0.27978948,F -sr3p_ASC_no_auto_social,-1.4036902,F -sr3p_ASC_no_auto_univ,-6.056001,F -sr3p_ASC_no_auto_work,-0.5831269,F -sr3p_ASC_no_auto_atwork,0.5826626,F -sr3p_ASC_auto_deficient_eatout,0.04605236,F -sr3p_ASC_auto_deficient_escort,-0.40818766,F -sr3p_ASC_auto_deficient_othdiscr,1.0470966,F -sr3p_ASC_auto_deficient_othmaint,-1.3493925,F -sr3p_ASC_auto_deficient_school,0.7149571,F -sr3p_ASC_auto_deficient_shopping,-0.073370166,F -sr3p_ASC_auto_deficient_social,1.5007243,F -sr3p_ASC_auto_deficient_univ,-1.7277422,F -sr3p_ASC_auto_deficient_work,-0.8527042,F -sr3p_ASC_auto_deficient_atwork,-2.514658,F -sr3p_ASC_auto_sufficient_eatout,0.8468596,F -sr3p_ASC_auto_sufficient_escort,-0.05741253,F -sr3p_ASC_auto_sufficient_othdiscr,0.58850205,F -sr3p_ASC_auto_sufficient_othmaint,-0.07549867,F -sr3p_ASC_auto_sufficient_school,-1.0201935,F -sr3p_ASC_auto_sufficient_shopping,-0.077571295,F -sr3p_ASC_auto_sufficient_social,0.50617886,F -sr3p_ASC_auto_sufficient_univ,-1.9047098,F -sr3p_ASC_auto_sufficient_work,-1.4699702,F -sr3p_ASC_auto_sufficient_atwork,-1.652174,F -walk_transit_ASC_no_auto_eatout,2.5936368,F -walk_transit_ASC_no_auto_escort,-2.2172081,F -walk_transit_ASC_no_auto_othdiscr,2.2437785,F -walk_transit_ASC_no_auto_othmaint,2.5643456,F -walk_transit_ASC_no_auto_school,21.383749,F -walk_transit_ASC_no_auto_shopping,2.1067476,F -walk_transit_ASC_no_auto_social,1.3814651,F -walk_transit_ASC_no_auto_univ,8.786037,F -walk_transit_ASC_no_auto_work,5.0354166,F -walk_transit_ASC_no_auto_atwork,2.7041876,F -walk_transit_ASC_auto_deficient_eatout,-0.03896324,F -walk_transit_ASC_auto_deficient_escort,-4.960704,F -walk_transit_ASC_auto_deficient_othdiscr,0.9530884,F -walk_transit_ASC_auto_deficient_othmaint,-3.0597258,F -walk_transit_ASC_auto_deficient_school,4.120708,F -walk_transit_ASC_auto_deficient_shopping,-0.8476569,F -walk_transit_ASC_auto_deficient_social,0.97444487,F -walk_transit_ASC_auto_deficient_univ,3.1362555,F -walk_transit_ASC_auto_deficient_work,0.65302855,F -walk_transit_ASC_auto_deficient_atwork,-2.9988291,F -walk_transit_ASC_auto_sufficient_eatout,-1.1126906,F -walk_transit_ASC_auto_sufficient_escort,-4.934847,F -walk_transit_ASC_auto_sufficient_othdiscr,-0.80636793,F -walk_transit_ASC_auto_sufficient_othmaint,-1.5471172,F -walk_transit_ASC_auto_sufficient_school,0.74590874,F -walk_transit_ASC_auto_sufficient_shopping,-2.2036798,F -walk_transit_ASC_auto_sufficient_social,-0.3453759,F -walk_transit_ASC_auto_sufficient_univ,0.4731163,F -walk_transit_ASC_auto_sufficient_work,-0.8916507,F -walk_transit_ASC_auto_sufficient_atwork,-3.401027,F -drive_transit_ASC_no_auto_all,0,F -drive_transit_ASC_auto_deficient_eatout,0.5998061,F -drive_transit_ASC_auto_deficient_escort,-1.1537067,F -drive_transit_ASC_auto_deficient_othdiscr,0.3199308,F -drive_transit_ASC_auto_deficient_othmaint,-0.29943228,F -drive_transit_ASC_auto_deficient_school,5.3252654,F -drive_transit_ASC_auto_deficient_shopping,-0.41849178,F -drive_transit_ASC_auto_deficient_social,1.5627195,F -drive_transit_ASC_auto_deficient_univ,1.8501176,F -drive_transit_ASC_auto_deficient_work,0.10081567,F -drive_transit_ASC_auto_deficient_atwork,-998.8196,F -drive_transit_ASC_auto_sufficient_eatout,-0.96951586,F -drive_transit_ASC_auto_sufficient_escort,-4.6014247,F -drive_transit_ASC_auto_sufficient_othdiscr,-0.3785917,F -drive_transit_ASC_auto_sufficient_othmaint,-2.6249478,F -drive_transit_ASC_auto_sufficient_school,1.40135,F -drive_transit_ASC_auto_sufficient_shopping,-2.1718938,F -drive_transit_ASC_auto_sufficient_social,-0.61585575,F -drive_transit_ASC_auto_sufficient_univ,1.3587753,F -drive_transit_ASC_auto_sufficient_work,-1.0045459,F -drive_transit_ASC_auto_sufficient_atwork,-999.21466,F -taxi_ASC_no_auto_eatout_othdiscr_social,0.9923,F -taxi_ASC_no_auto_escort_othmaint_shopping,1.8939,F -taxi_ASC_no_auto_school_univ,-7,T -taxi_ASC_no_auto_work,4.7291,F -taxi_ASC_no_auto_atwork,4.1021,F -taxi_ASC_auto_deficient_eatout_othdiscr_social,-3.1317,F -taxi_ASC_auto_deficient_escort_othmaint_shopping,0.1766,F -taxi_ASC_auto_deficient_school,-0.3338,F -taxi_ASC_auto_deficient_univ,4.2492,F -taxi_ASC_auto_deficient_work,-1.4766,F -taxi_ASC_auto_deficient_atwork,-4.4046,F -taxi_ASC_auto_sufficient_eatout_othdiscr_social,-3.0374,F -taxi_ASC_auto_sufficient_escort_othmaint_shopping,-1.8055,F -taxi_ASC_auto_sufficient_school,-2.4294,F -taxi_ASC_auto_sufficient_univ,-0.3131,F -taxi_ASC_auto_sufficient_work,-4.8509,F -taxi_ASC_auto_sufficient_atwork,-2.8804,F -tnc_single_ASC_no_auto_eatout_othdiscr_social,1.6852,F -tnc_single_ASC_no_auto_escort_othmaint_shopping,1.8605,F -tnc_single_ASC_no_auto_school,-7,T -tnc_single_ASC_no_auto_univ,-2.519,F -tnc_single_ASC_no_auto_work,5.7855,F -tnc_single_ASC_no_auto_atwork,4.4982,F -tnc_single_ASC_auto_deficient_eatout_othdiscr_social,-2.9623,F -tnc_single_ASC_auto_deficient_escort_othmaint_shopping,0.6748,F -tnc_single_ASC_auto_deficient_school,-0.5524,F -tnc_single_ASC_auto_deficient_univ,1.0221,F -tnc_single_ASC_auto_deficient_work,-0.8013,F -tnc_single_ASC_auto_deficient_atwork,-3.7626,F -tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,-2.3239,F -tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,-1.45,F -tnc_single_ASC_auto_sufficient_school,-2.8375,F -tnc_single_ASC_auto_sufficient_univ,0.2088,F -tnc_single_ASC_auto_sufficient_work,-4.1946,F -tnc_single_ASC_auto_sufficient_atwork,-2.7988,F -tnc_shared_ASC_no_auto_eatout_othdiscr_social,0.6464,F -tnc_shared_ASC_no_auto_escort_othmaint_shopping,0.9361,F -tnc_shared_ASC_no_auto_school,-7,T -tnc_shared_ASC_no_auto_univ,-5.8116,F -tnc_shared_ASC_no_auto_work,3.2429,F -tnc_shared_ASC_no_auto_atwork,3.3672,F -tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,-4.3576,F -tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,-0.3863,F -tnc_shared_ASC_auto_deficient_school,-1.4746,F -tnc_shared_ASC_auto_deficient_univ,3.25,F -tnc_shared_ASC_auto_deficient_work,-2.1435,F -tnc_shared_ASC_auto_deficient_atwork,-4.5089,F -tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,-3.6638,F -tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,-2.4365,F -tnc_shared_ASC_auto_sufficient_school,-3.7219,F -tnc_shared_ASC_auto_sufficient_univ,-0.9068,F -tnc_shared_ASC_auto_sufficient_work,-5.3575,F -tnc_shared_ASC_auto_sufficient_atwork,-3.5397,F -joint_walk_ASC_no_auto_all,-0.21274701,F -joint_walk_ASC_auto_deficient_all,-1.9607706,F -joint_walk_ASC_auto_sufficient_all,-3.2352157,F -joint_bike_ASC_no_auto_all,-2.8671598,F -joint_bike_ASC_auto_deficient_all,-6.076415,F -joint_bike_ASC_auto_sufficient_all,-6.3760657,F -joint_sr2_ASC_no_auto_all,0,T -joint_sr2_ASC_auto_deficient_all,0,T -joint_sr2_ASC_auto_sufficient_all,0,T -joint_sr3p_ASC_no_auto_all,0.5630671,F -joint_sr3p_ASC_auto_deficient_all,-1.8841692,F -joint_sr3p_ASC_auto_sufficient_all,-2.234826,F -joint_walk_transit_ASC_no_auto_all,0.62292415,F -joint_walk_transit_ASC_auto_deficient_all,-5.1634483,F -joint_walk_transit_ASC_auto_sufficient_all,-18.264534,F -joint_drive_transit_ASC_no_auto_all,0,T -joint_drive_transit_ASC_auto_deficient_all,-5.9632215,F -joint_drive_transit_ASC_auto_sufficient_all,-8.045285,F -joint_taxi_ASC_no_auto_all,-4.5792,F -joint_taxi_ASC_auto_deficient_all,-9.8157,F -joint_taxi_ASC_auto_sufficient_all,-11.7099,T -joint_tnc_single_ASC_no_auto_all,-4.4917,F -joint_tnc_single_ASC_auto_deficient_all,-9.8961,F -joint_tnc_single_ASC_auto_sufficient_all,-14.0159,T -joint_tnc_shared_ASC_no_auto_all,-4.3002,F -joint_tnc_shared_ASC_auto_deficient_all,-11.1572,F -joint_tnc_shared_ASC_auto_sufficient_all,-13.205,T -local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,-0.090703264,F -local_bus_ASC_school_univ,-0.06508621,F -local_bus_ASC_work,0.06689507,F -walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F -walk_light_rail_ASC_school_univ,1.6814003,F -walk_light_rail_ASC_work,0.8255567,F -drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F -drive_light_rail_ASC_school_univ,1.6814003,F -drive_light_rail_ASC_work,0.8255567,F -walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F -walk_ferry_ASC_school_univ,2.0202317,F -walk_ferry_ASC_work,0.93322605,F -drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F -drive_ferry_ASC_school_univ,2.0202317,F -drive_ferry_ASC_work,0.93322605,F -express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9692316,F -express_bus_ASC_school_univ,0.32496938,F -express_bus_ASC_work,-0.5165474,F -heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7706121,F -heavy_rail_ASC_school_univ,0.96200377,F -heavy_rail_ASC_work,0.64772975,F -commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7270185,F -commuter_rail_ASC_school_univ,1.0336206,F -commuter_rail_ASC_work,0.725503,F -walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F -walk_transit_CBD_ASC_school_univ,0.672,F -walk_transit_CBD_ASC_work,0.804,F -walk_transit_CBD_ASC_atwork,0.564,F -drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F -drive_transit_CBD_ASC_school_univ,0.672,F -drive_transit_CBD_ASC_work,1.1,F -drive_transit_CBD_ASC_atwork,0.564,F -zeroAutoHH_sr3,-0.466,F -zeroAutoHH_walk,6.823,F -zeroAutoHH_bike,3.536,F -zeroAutoHH_wt,10.326,F -zeroAutoHH_kt,8.281,F -autoDeficientHH_sr2,-2.166,F -autoDeficientHH_sr3,-2.580,F -autoDeficientHH_walk,2.794,F -autoDeficientHH_bike,-0.015,F -autoDeficientHH_wt,-0.940,F -autoDeficientHH_dt,-1.706,F -autoDeficientHH_kt,-2.229,F -autoSufficientHH_sr2,-2.582,F -autoSufficientHH_sr3,-2.580,F -autoSufficientHH_walk,0.729,F -autoSufficientHH_bike,-1.434,F -autoSufficientHH_wt,-2.582,F -autoSufficientHH_dt,-2.923,F -autoSufficientHH_kt,-3.493,F -asc_wtransit_cbd_sf,2.0000,F -asc_wtransit_nw_sf,1.2500,F -asc_wtransit_se_sf,1.2500,F -asc_dtransit_cbd_sf,1.2000,F -asc_Transit_Pseudo_area_type_constant,-55.0000,F -asc_taxi_penalty,-10.0000,F -zeroAutoHH_SHARED2HOV,0,F -autoDeficientHH_SHARED2HOV,0.2369,F -autoSufficientHH_SHARED2HOV,0.1127,F -zeroAutoHH_SHARED2PAY,0,F -autoDeficientHH_SHARED2PAY,0.2369,F -autoSufficientHH_SHARED2PAY,0.1127,F -zeroAutoHH_SHARED3HOV,-2.3789,F -autoDeficientHH_SHARED3HOV,0.2982,F -autoSufficientHH_SHARED3HOV,0.1494,F -zeroAutoHH_SHARED3PAY,-2.3789,F -autoDeficientHH_SHARED3PAY,0.2982,F -autoSufficientHH_SHARED3PAY,0.1494,F -zeroAutoHH_WALK,-9.6191,F -autoDeficientHH_WALK,-1.5999,F -autoSufficientHH_WALK,-2.6212,F -zeroAutoHH_BIKE,-8.1503,F -autoDeficientHH_BIKE,-2.1993,F -autoSufficientHH_BIKE,-3.3049,F -zeroAutoHH_WALK_SET,-0.4471,F -autoDeficientHH_WALK_SET,3.6328,F -autoSufficientHH_WALK_SET,1.4325,F -zeroAutoHH_PNR_SET,0,F -autoDeficientHH_PNR_SET,3.0458,F -autoSufficientHH_PNR_SET,1.6645,F +coefficient_name,value,constrain +coef_one,1,T +coef_nest_root,1,T +coef_nest_AUTO,0.72,T +coef_nest_AUTO_DRIVEALONE,0.35,T +coef_nest_AUTO_SHAREDRIDE2,0.35,T +coef_nest_AUTO_SHAREDRIDE3,0.35,T +coef_nest_NONMOTORIZED,0.72,T +coef_nest_TRANSIT,0.72,T +coef_nest_TRANSIT_WALKACCESS,0.5,T +coef_nest_TRANSIT_DRIVEACCESS,0.5,T +coef_nest_RIDEHAIL,0.36,T +coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,-0.0175,F +coef_ivt_school_univ,-0.0224,F +coef_ivt_work,-0.016,F +coef_ivt_atwork,-0.0188,F +coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,15,F +coef_topology_walk_multiplier_atwork,7.5,F +coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,20,F +coef_topology_bike_multiplier_atwork,10,F +coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,2.2,F +coef_topology_trn_multiplier_atwork,2,F +coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F +coef_age1619_da_multiplier_school_univ,-1.3813,F +coef_age1619_da_multiplier_atwork,0.0032336,F +coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F +coef_age010_trn_multiplier_school_univ,-1.5548,F +coef_age010_trn_multiplier_atwork,0.000722,F +coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,-1.366,F +coef_age16p_sr_multiplier_school_univ_work_atwork,0,F +coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,0,F +coef_hhsize1_sr_multiplier_work,-0.734588,F +coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,0,F +coef_hhsize2_sr_multiplier_school_univ,-0.6359,F +coef_walk_access_time,-0.03,T +coef_walk_egress_time,-0.03,T +c_age1624_sr2,-0.21388,T +c_age1624_sr3,-1.79023,T +c_age1624_nmot,0.30322,T +c_age1624_tran,0.79472,T +c_age4155_sr2,-0.30638,T +c_age4155_sr3,-0.41025,T +c_age4155_nmot,-0.17752,T +c_age4155_tran,-0.42301,T +c_age5664_sr2,-1.02962,T +c_age5664_sr3,-0.85641,T +c_age5664_nmot,-0.64534,T +c_age5664_tran,-0.44991,T +c_age65pl_sr2,-0.67111,T +c_age65pl_sr3,-1.43462,T +c_age65pl_nmot,-1.45334,T +c_age65pl_tran,-1.1231,T +c_female_sr2,0.59473,T +c_female_sr3,0.84806,T +c_female_tran,0.15779,T +c_female_nmot,0,T +c_size2_sr2,1.06964,T +c_size2_sr3,-0.46736,T +c_size3_sr2,1.58018,T +c_size3_sr3,0.65463,T +c_size4p_sr2,1.68839,T +c_size4p_sr3,1.4987,T +c_walkTime,-0.059,T +c_bikeTime,-0.0492,T +c_oMix_nmot,0.21014,T +c_oMix_wtran,0,T +c_oIntDen_nmot,0.003,T +c_oIntDen_wtran,0,T +c_dEmpDen_nmot,0.02071,T +c_dEmpDen_wtran,0,T +c_dEmpDen_dtran,0,T +walk_ASC_no_auto_eatout,5.1251173,F +walk_ASC_no_auto_escort,2.8012068,F +walk_ASC_no_auto_othdiscr,3.2665946,F +walk_ASC_no_auto_othmaint,1.287299,F +walk_ASC_no_auto_school,18.414557,F +walk_ASC_no_auto_shopping,2.3768773,F +walk_ASC_no_auto_social,1.8680915,F +walk_ASC_no_auto_univ,6.408967,F +walk_ASC_no_auto_work,5.7672157,F +walk_ASC_no_auto_atwork,6.669213,F +walk_ASC_auto_deficient_eatout,3.274605,F +walk_ASC_auto_deficient_escort,-0.90204656,F +walk_ASC_auto_deficient_othdiscr,2.2494075,F +walk_ASC_auto_deficient_othmaint,1.3690404,F +walk_ASC_auto_deficient_school,3.2573624,F +walk_ASC_auto_deficient_shopping,2.2701733,F +walk_ASC_auto_deficient_social,2.870184,F +walk_ASC_auto_deficient_univ,4.50591,F +walk_ASC_auto_deficient_work,2.4010417,F +walk_ASC_auto_deficient_atwork,0.92546093,F +walk_ASC_auto_sufficient_eatout,1.5516903,F +walk_ASC_auto_sufficient_escort,-0.8116066,F +walk_ASC_auto_sufficient_othdiscr,1.2633476,F +walk_ASC_auto_sufficient_othmaint,0.7999634,F +walk_ASC_auto_sufficient_school,0.6476856,F +walk_ASC_auto_sufficient_shopping,0.7312663,F +walk_ASC_auto_sufficient_social,1.7072186,F +walk_ASC_auto_sufficient_univ,1.0607665,F +walk_ASC_auto_sufficient_work,0.053265337,F +walk_ASC_auto_sufficient_atwork,0.677216,F +bike_ASC_no_auto_eatout,0.86807096,F +bike_ASC_no_auto_escort,-0.716212,F +bike_ASC_no_auto_othdiscr,-0.3764232,F +bike_ASC_no_auto_othmaint,1.5394334,F +bike_ASC_no_auto_school,12.098735,F +bike_ASC_no_auto_shopping,0.8341555,F +bike_ASC_no_auto_social,0.02058321,F +bike_ASC_no_auto_univ,4.2945156,F +bike_ASC_no_auto_work,3.1940088,F +bike_ASC_no_auto_atwork,-0.90725845,F +bike_ASC_auto_deficient_eatout,-1.5691106,F +bike_ASC_auto_deficient_escort,-4.527928,F +bike_ASC_auto_deficient_othdiscr,-0.09246834,F +bike_ASC_auto_deficient_othmaint,-1.5184649,F +bike_ASC_auto_deficient_school,-0.5280678,F +bike_ASC_auto_deficient_shopping,-0.87584466,F +bike_ASC_auto_deficient_social,0.6345214,F +bike_ASC_auto_deficient_univ,-0.669235,F +bike_ASC_auto_deficient_work,0.25318968,F +bike_ASC_auto_deficient_atwork,-0.8074083,F +bike_ASC_auto_sufficient_eatout,-1.2003471,F +bike_ASC_auto_sufficient_escort,-5.0631084,F +bike_ASC_auto_sufficient_othdiscr,-1.0714597,F +bike_ASC_auto_sufficient_othmaint,-2.8083024,F +bike_ASC_auto_sufficient_school,-2.1134686,F +bike_ASC_auto_sufficient_shopping,-2.5662103,F +bike_ASC_auto_sufficient_social,-1.368071,F +bike_ASC_auto_sufficient_univ,-1.9397832,F +bike_ASC_auto_sufficient_work,-1.5800232,F +bike_ASC_auto_sufficient_atwork,15.72017,F +sr2_ASC_no_auto_all,0,F +sr2_ASC_auto_deficient_eatout,0.5882345,F +sr2_ASC_auto_deficient_escort,0,F +sr2_ASC_auto_deficient_othdiscr,0.6601513,F +sr2_ASC_auto_deficient_othmaint,0.2621527,F +sr2_ASC_auto_deficient_school,0.12474365,F +sr2_ASC_auto_deficient_shopping,0.24409756,F +sr2_ASC_auto_deficient_social,1.8558528,F +sr2_ASC_auto_deficient_univ,-1.6922346,F +sr2_ASC_auto_deficient_work,-0.33803123,F +sr2_ASC_auto_deficient_atwork,-2.1102421,F +sr2_ASC_auto_sufficient_eatout,0.86280555,F +sr2_ASC_auto_sufficient_escort,0,F +sr2_ASC_auto_sufficient_othdiscr,0.49684617,F +sr2_ASC_auto_sufficient_othmaint,0.25817883,F +sr2_ASC_auto_sufficient_school,-1.6062657,F +sr2_ASC_auto_sufficient_shopping,0.19770707,F +sr2_ASC_auto_sufficient_social,0.5236025,F +sr2_ASC_auto_sufficient_univ,-1.859427,F +sr2_ASC_auto_sufficient_work,-1.0857458,F +sr2_ASC_auto_sufficient_atwork,-1.4450618,F +sr3p_ASC_no_auto_eatout,0.3219998,F +sr3p_ASC_no_auto_escort,-1.8129267,F +sr3p_ASC_no_auto_othdiscr,0.27216902,F +sr3p_ASC_no_auto_othmaint,-0.8031854,F +sr3p_ASC_no_auto_school,-6.0240827,F +sr3p_ASC_no_auto_shopping,-0.27978948,F +sr3p_ASC_no_auto_social,-1.4036902,F +sr3p_ASC_no_auto_univ,-6.056001,F +sr3p_ASC_no_auto_work,-0.5831269,F +sr3p_ASC_no_auto_atwork,0.5826626,F +sr3p_ASC_auto_deficient_eatout,0.04605236,F +sr3p_ASC_auto_deficient_escort,-0.40818766,F +sr3p_ASC_auto_deficient_othdiscr,1.0470966,F +sr3p_ASC_auto_deficient_othmaint,-1.3493925,F +sr3p_ASC_auto_deficient_school,0.7149571,F +sr3p_ASC_auto_deficient_shopping,-0.073370166,F +sr3p_ASC_auto_deficient_social,1.5007243,F +sr3p_ASC_auto_deficient_univ,-1.7277422,F +sr3p_ASC_auto_deficient_work,-0.8527042,F +sr3p_ASC_auto_deficient_atwork,-2.514658,F +sr3p_ASC_auto_sufficient_eatout,0.8468596,F +sr3p_ASC_auto_sufficient_escort,-0.05741253,F +sr3p_ASC_auto_sufficient_othdiscr,0.58850205,F +sr3p_ASC_auto_sufficient_othmaint,-0.07549867,F +sr3p_ASC_auto_sufficient_school,-1.0201935,F +sr3p_ASC_auto_sufficient_shopping,-0.077571295,F +sr3p_ASC_auto_sufficient_social,0.50617886,F +sr3p_ASC_auto_sufficient_univ,-1.9047098,F +sr3p_ASC_auto_sufficient_work,-1.4699702,F +sr3p_ASC_auto_sufficient_atwork,-1.652174,F +walk_transit_ASC_no_auto_eatout,2.5936368,F +walk_transit_ASC_no_auto_escort,-2.2172081,F +walk_transit_ASC_no_auto_othdiscr,2.2437785,F +walk_transit_ASC_no_auto_othmaint,2.5643456,F +walk_transit_ASC_no_auto_school,21.383749,F +walk_transit_ASC_no_auto_shopping,2.1067476,F +walk_transit_ASC_no_auto_social,1.3814651,F +walk_transit_ASC_no_auto_univ,8.786037,F +walk_transit_ASC_no_auto_work,5.0354166,F +walk_transit_ASC_no_auto_atwork,2.7041876,F +walk_transit_ASC_auto_deficient_eatout,-0.03896324,F +walk_transit_ASC_auto_deficient_escort,-4.960704,F +walk_transit_ASC_auto_deficient_othdiscr,0.9530884,F +walk_transit_ASC_auto_deficient_othmaint,-3.0597258,F +walk_transit_ASC_auto_deficient_school,4.120708,F +walk_transit_ASC_auto_deficient_shopping,-0.8476569,F +walk_transit_ASC_auto_deficient_social,0.97444487,F +walk_transit_ASC_auto_deficient_univ,3.1362555,F +walk_transit_ASC_auto_deficient_work,0.65302855,F +walk_transit_ASC_auto_deficient_atwork,-2.9988291,F +walk_transit_ASC_auto_sufficient_eatout,-1.1126906,F +walk_transit_ASC_auto_sufficient_escort,-4.934847,F +walk_transit_ASC_auto_sufficient_othdiscr,-0.80636793,F +walk_transit_ASC_auto_sufficient_othmaint,-1.5471172,F +walk_transit_ASC_auto_sufficient_school,0.74590874,F +walk_transit_ASC_auto_sufficient_shopping,-2.2036798,F +walk_transit_ASC_auto_sufficient_social,-0.3453759,F +walk_transit_ASC_auto_sufficient_univ,0.4731163,F +walk_transit_ASC_auto_sufficient_work,-0.8916507,F +walk_transit_ASC_auto_sufficient_atwork,-3.401027,F +drive_transit_ASC_no_auto_all,0,F +drive_transit_ASC_auto_deficient_eatout,0.5998061,F +drive_transit_ASC_auto_deficient_escort,-1.1537067,F +drive_transit_ASC_auto_deficient_othdiscr,0.3199308,F +drive_transit_ASC_auto_deficient_othmaint,-0.29943228,F +drive_transit_ASC_auto_deficient_school,5.3252654,F +drive_transit_ASC_auto_deficient_shopping,-0.41849178,F +drive_transit_ASC_auto_deficient_social,1.5627195,F +drive_transit_ASC_auto_deficient_univ,1.8501176,F +drive_transit_ASC_auto_deficient_work,0.10081567,F +drive_transit_ASC_auto_deficient_atwork,-998.8196,F +drive_transit_ASC_auto_sufficient_eatout,-0.96951586,F +drive_transit_ASC_auto_sufficient_escort,-4.6014247,F +drive_transit_ASC_auto_sufficient_othdiscr,-0.3785917,F +drive_transit_ASC_auto_sufficient_othmaint,-2.6249478,F +drive_transit_ASC_auto_sufficient_school,1.40135,F +drive_transit_ASC_auto_sufficient_shopping,-2.1718938,F +drive_transit_ASC_auto_sufficient_social,-0.61585575,F +drive_transit_ASC_auto_sufficient_univ,1.3587753,F +drive_transit_ASC_auto_sufficient_work,-1.0045459,F +drive_transit_ASC_auto_sufficient_atwork,-999.21466,F +taxi_ASC_no_auto_eatout_othdiscr_social,0.9923,F +taxi_ASC_no_auto_escort_othmaint_shopping,1.8939,F +taxi_ASC_no_auto_school_univ,-7,T +taxi_ASC_no_auto_work,4.7291,F +taxi_ASC_no_auto_atwork,4.1021,F +taxi_ASC_auto_deficient_eatout_othdiscr_social,-3.1317,F +taxi_ASC_auto_deficient_escort_othmaint_shopping,0.1766,F +taxi_ASC_auto_deficient_school,-0.3338,F +taxi_ASC_auto_deficient_univ,4.2492,F +taxi_ASC_auto_deficient_work,-1.4766,F +taxi_ASC_auto_deficient_atwork,-4.4046,F +taxi_ASC_auto_sufficient_eatout_othdiscr_social,-3.0374,F +taxi_ASC_auto_sufficient_escort_othmaint_shopping,-1.8055,F +taxi_ASC_auto_sufficient_school,-2.4294,F +taxi_ASC_auto_sufficient_univ,-0.3131,F +taxi_ASC_auto_sufficient_work,-4.8509,F +taxi_ASC_auto_sufficient_atwork,-2.8804,F +tnc_single_ASC_no_auto_eatout_othdiscr_social,1.6852,F +tnc_single_ASC_no_auto_escort_othmaint_shopping,1.8605,F +tnc_single_ASC_no_auto_school,-7,T +tnc_single_ASC_no_auto_univ,-2.519,F +tnc_single_ASC_no_auto_work,5.7855,F +tnc_single_ASC_no_auto_atwork,4.4982,F +tnc_single_ASC_auto_deficient_eatout_othdiscr_social,-2.9623,F +tnc_single_ASC_auto_deficient_escort_othmaint_shopping,0.6748,F +tnc_single_ASC_auto_deficient_school,-0.5524,F +tnc_single_ASC_auto_deficient_univ,1.0221,F +tnc_single_ASC_auto_deficient_work,-0.8013,F +tnc_single_ASC_auto_deficient_atwork,-3.7626,F +tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,-2.3239,F +tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,-1.45,F +tnc_single_ASC_auto_sufficient_school,-2.8375,F +tnc_single_ASC_auto_sufficient_univ,0.2088,F +tnc_single_ASC_auto_sufficient_work,-4.1946,F +tnc_single_ASC_auto_sufficient_atwork,-2.7988,F +tnc_shared_ASC_no_auto_eatout_othdiscr_social,0.6464,F +tnc_shared_ASC_no_auto_escort_othmaint_shopping,0.9361,F +tnc_shared_ASC_no_auto_school,-7,T +tnc_shared_ASC_no_auto_univ,-5.8116,F +tnc_shared_ASC_no_auto_work,3.2429,F +tnc_shared_ASC_no_auto_atwork,3.3672,F +tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,-4.3576,F +tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,-0.3863,F +tnc_shared_ASC_auto_deficient_school,-1.4746,F +tnc_shared_ASC_auto_deficient_univ,3.25,F +tnc_shared_ASC_auto_deficient_work,-2.1435,F +tnc_shared_ASC_auto_deficient_atwork,-4.5089,F +tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,-3.6638,F +tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,-2.4365,F +tnc_shared_ASC_auto_sufficient_school,-3.7219,F +tnc_shared_ASC_auto_sufficient_univ,-0.9068,F +tnc_shared_ASC_auto_sufficient_work,-5.3575,F +tnc_shared_ASC_auto_sufficient_atwork,-3.5397,F +joint_walk_ASC_no_auto_all,-0.21274701,F +joint_walk_ASC_auto_deficient_all,-1.9607706,F +joint_walk_ASC_auto_sufficient_all,-3.2352157,F +joint_bike_ASC_no_auto_all,-2.8671598,F +joint_bike_ASC_auto_deficient_all,-6.076415,F +joint_bike_ASC_auto_sufficient_all,-6.3760657,F +joint_sr2_ASC_no_auto_all,0,T +joint_sr2_ASC_auto_deficient_all,0,T +joint_sr2_ASC_auto_sufficient_all,0,T +joint_sr3p_ASC_no_auto_all,0.5630671,F +joint_sr3p_ASC_auto_deficient_all,-1.8841692,F +joint_sr3p_ASC_auto_sufficient_all,-2.234826,F +joint_walk_transit_ASC_no_auto_all,0.62292415,F +joint_walk_transit_ASC_auto_deficient_all,-5.1634483,F +joint_walk_transit_ASC_auto_sufficient_all,-18.264534,F +joint_drive_transit_ASC_no_auto_all,0,T +joint_drive_transit_ASC_auto_deficient_all,-5.9632215,F +joint_drive_transit_ASC_auto_sufficient_all,-8.045285,F +joint_taxi_ASC_no_auto_all,-4.5792,F +joint_taxi_ASC_auto_deficient_all,-9.8157,F +joint_taxi_ASC_auto_sufficient_all,-11.7099,T +joint_tnc_single_ASC_no_auto_all,-4.4917,F +joint_tnc_single_ASC_auto_deficient_all,-9.8961,F +joint_tnc_single_ASC_auto_sufficient_all,-14.0159,T +joint_tnc_shared_ASC_no_auto_all,-4.3002,F +joint_tnc_shared_ASC_auto_deficient_all,-11.1572,F +joint_tnc_shared_ASC_auto_sufficient_all,-13.205,T +local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,-0.090703264,F +local_bus_ASC_school_univ,-0.06508621,F +local_bus_ASC_work,0.06689507,F +walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F +walk_light_rail_ASC_school_univ,1.6814003,F +walk_light_rail_ASC_work,0.8255567,F +drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F +drive_light_rail_ASC_school_univ,1.6814003,F +drive_light_rail_ASC_work,0.8255567,F +walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F +walk_ferry_ASC_school_univ,2.0202317,F +walk_ferry_ASC_work,0.93322605,F +drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F +drive_ferry_ASC_school_univ,2.0202317,F +drive_ferry_ASC_work,0.93322605,F +express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9692316,F +express_bus_ASC_school_univ,0.32496938,F +express_bus_ASC_work,-0.5165474,F +heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7706121,F +heavy_rail_ASC_school_univ,0.96200377,F +heavy_rail_ASC_work,0.64772975,F +commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7270185,F +commuter_rail_ASC_school_univ,1.0336206,F +commuter_rail_ASC_work,0.725503,F +walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F +walk_transit_CBD_ASC_school_univ,0.672,F +walk_transit_CBD_ASC_work,0.804,F +walk_transit_CBD_ASC_atwork,0.564,F +drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F +drive_transit_CBD_ASC_school_univ,0.672,F +drive_transit_CBD_ASC_work,1.1,F +drive_transit_CBD_ASC_atwork,0.564,F +zeroAutoHH_sr3,-0.466,F +zeroAutoHH_walk,6.823,F +zeroAutoHH_bike,3.536,F +zeroAutoHH_wt,10.326,F +zeroAutoHH_kt,8.281,F +autoDeficientHH_sr2,-2.166,F +autoDeficientHH_sr3,-2.580,F +autoDeficientHH_walk,2.794,F +autoDeficientHH_bike,-0.015,F +autoDeficientHH_wt,-0.940,F +autoDeficientHH_dt,-1.706,F +autoDeficientHH_kt,-2.229,F +autoSufficientHH_sr2,-2.582,F +autoSufficientHH_sr3,-2.580,F +autoSufficientHH_walk,0.729,F +autoSufficientHH_bike,-1.434,F +autoSufficientHH_wt,-2.582,F +autoSufficientHH_dt,-2.923,F +autoSufficientHH_kt,-3.493,F +asc_wtransit_cbd_sf,2.0000,F +asc_wtransit_nw_sf,1.2500,F +asc_wtransit_se_sf,1.2500,F +asc_dtransit_cbd_sf,1.2000,F +asc_Transit_Pseudo_area_type_constant,-55.0000,F +asc_taxi_penalty,-10.0000,F +zeroAutoHH_SHARED2HOV,0,F +autoDeficientHH_SHARED2HOV,0.2369,F +autoSufficientHH_SHARED2HOV,0.1127,F +zeroAutoHH_SHARED2PAY,0,F +autoDeficientHH_SHARED2PAY,0.2369,F +autoSufficientHH_SHARED2PAY,0.1127,F +zeroAutoHH_SHARED3HOV,-2.3789,F +autoDeficientHH_SHARED3HOV,0.2982,F +autoSufficientHH_SHARED3HOV,0.1494,F +zeroAutoHH_SHARED3PAY,-2.3789,F +autoDeficientHH_SHARED3PAY,0.2982,F +autoSufficientHH_SHARED3PAY,0.1494,F +zeroAutoHH_WALK,-9.6191,F +autoDeficientHH_WALK,-1.5999,F +autoSufficientHH_WALK,-2.6212,F +zeroAutoHH_BIKE,-8.1503,F +autoDeficientHH_BIKE,-2.1993,F +autoSufficientHH_BIKE,-3.3049,F +zeroAutoHH_WALK_SET,-0.4471,F +autoDeficientHH_WALK_SET,3.6328,F +autoSufficientHH_WALK_SET,1.4325,F +zeroAutoHH_PNR_SET,0,F +autoDeficientHH_PNR_SET,3.0458,F +autoSufficientHH_PNR_SET,1.6645,F diff --git a/activitysim/examples/example_marin/configs/tour_mode_choice_coefficients_template.csv b/activitysim/examples/prototype_marin/configs/tour_mode_choice_coefficients_template.csv similarity index 99% rename from activitysim/examples/example_marin/configs/tour_mode_choice_coefficients_template.csv rename to activitysim/examples/prototype_marin/configs/tour_mode_choice_coefficients_template.csv index f73dd5fd77..07808b2837 100755 --- a/activitysim/examples/example_marin/configs/tour_mode_choice_coefficients_template.csv +++ b/activitysim/examples/prototype_marin/configs/tour_mode_choice_coefficients_template.csv @@ -1,174 +1,174 @@ -coefficient_name,eatout,escort,othdiscr,othmaint,school,shopping,social,univ,work,atwork -#same for all segments,,,,,,,,,, -coef_one,,,,,,,,,, -coef_nest_root,,,,,,,,,, -coef_nest_AUTO,,,,,,,,,, -coef_nest_AUTO_DRIVEALONE,,,,,,,,,, -coef_nest_AUTO_SHAREDRIDE2,,,,,,,,,, -coef_nest_AUTO_SHAREDRIDE3,,,,,,,,,, -coef_nest_NONMOTORIZED,,,,,,,,,, -coef_nest_TRANSIT,,,,,,,,,, -coef_nest_TRANSIT_WALKACCESS,,,,,,,,,, -coef_nest_TRANSIT_DRIVEACCESS,,,,,,,,,, -coef_nest_RIDEHAIL,,,,,,,,,, -#,,,,,,,,,, -coef_ivt,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_work,coef_ivt_atwork -coef_topology_walk_multiplier,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_atwork -coef_topology_bike_multiplier,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_atwork -coef_topology_trn_multiplier,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_atwork -coef_age1619_da_multiplier,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_atwork -coef_age010_trn_multiplier,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_atwork -coef_age16p_sr_multiplier,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork -coef_hhsize1_sr_multiplier,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_work,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork -coef_hhsize2_sr_multiplier,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork -walk_ASC_no_auto,walk_ASC_no_auto_eatout,walk_ASC_no_auto_escort,walk_ASC_no_auto_othdiscr,walk_ASC_no_auto_othmaint,walk_ASC_no_auto_school,walk_ASC_no_auto_shopping,walk_ASC_no_auto_social,walk_ASC_no_auto_univ,walk_ASC_no_auto_work,walk_ASC_no_auto_atwork -walk_ASC_auto_deficient,walk_ASC_auto_deficient_eatout,walk_ASC_auto_deficient_escort,walk_ASC_auto_deficient_othdiscr,walk_ASC_auto_deficient_othmaint,walk_ASC_auto_deficient_school,walk_ASC_auto_deficient_shopping,walk_ASC_auto_deficient_social,walk_ASC_auto_deficient_univ,walk_ASC_auto_deficient_work,walk_ASC_auto_deficient_atwork -walk_ASC_auto_sufficient,walk_ASC_auto_sufficient_eatout,walk_ASC_auto_sufficient_escort,walk_ASC_auto_sufficient_othdiscr,walk_ASC_auto_sufficient_othmaint,walk_ASC_auto_sufficient_school,walk_ASC_auto_sufficient_shopping,walk_ASC_auto_sufficient_social,walk_ASC_auto_sufficient_univ,walk_ASC_auto_sufficient_work,walk_ASC_auto_sufficient_atwork -bike_ASC_no_auto,bike_ASC_no_auto_eatout,bike_ASC_no_auto_escort,bike_ASC_no_auto_othdiscr,bike_ASC_no_auto_othmaint,bike_ASC_no_auto_school,bike_ASC_no_auto_shopping,bike_ASC_no_auto_social,bike_ASC_no_auto_univ,bike_ASC_no_auto_work,bike_ASC_no_auto_atwork -bike_ASC_auto_deficient,bike_ASC_auto_deficient_eatout,bike_ASC_auto_deficient_escort,bike_ASC_auto_deficient_othdiscr,bike_ASC_auto_deficient_othmaint,bike_ASC_auto_deficient_school,bike_ASC_auto_deficient_shopping,bike_ASC_auto_deficient_social,bike_ASC_auto_deficient_univ,bike_ASC_auto_deficient_work,bike_ASC_auto_deficient_atwork -bike_ASC_auto_sufficient,bike_ASC_auto_sufficient_eatout,bike_ASC_auto_sufficient_escort,bike_ASC_auto_sufficient_othdiscr,bike_ASC_auto_sufficient_othmaint,bike_ASC_auto_sufficient_school,bike_ASC_auto_sufficient_shopping,bike_ASC_auto_sufficient_social,bike_ASC_auto_sufficient_univ,bike_ASC_auto_sufficient_work,bike_ASC_auto_sufficient_atwork -sr2_ASC_no_auto,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all -sr2_ASC_auto_deficient,sr2_ASC_auto_deficient_eatout,sr2_ASC_auto_deficient_escort,sr2_ASC_auto_deficient_othdiscr,sr2_ASC_auto_deficient_othmaint,sr2_ASC_auto_deficient_school,sr2_ASC_auto_deficient_shopping,sr2_ASC_auto_deficient_social,sr2_ASC_auto_deficient_univ,sr2_ASC_auto_deficient_work,sr2_ASC_auto_deficient_atwork -sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient_eatout,sr2_ASC_auto_sufficient_escort,sr2_ASC_auto_sufficient_othdiscr,sr2_ASC_auto_sufficient_othmaint,sr2_ASC_auto_sufficient_school,sr2_ASC_auto_sufficient_shopping,sr2_ASC_auto_sufficient_social,sr2_ASC_auto_sufficient_univ,sr2_ASC_auto_sufficient_work,sr2_ASC_auto_sufficient_atwork -sr3p_ASC_no_auto,sr3p_ASC_no_auto_eatout,sr3p_ASC_no_auto_escort,sr3p_ASC_no_auto_othdiscr,sr3p_ASC_no_auto_othmaint,sr3p_ASC_no_auto_school,sr3p_ASC_no_auto_shopping,sr3p_ASC_no_auto_social,sr3p_ASC_no_auto_univ,sr3p_ASC_no_auto_work,sr3p_ASC_no_auto_atwork -sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient_eatout,sr3p_ASC_auto_deficient_escort,sr3p_ASC_auto_deficient_othdiscr,sr3p_ASC_auto_deficient_othmaint,sr3p_ASC_auto_deficient_school,sr3p_ASC_auto_deficient_shopping,sr3p_ASC_auto_deficient_social,sr3p_ASC_auto_deficient_univ,sr3p_ASC_auto_deficient_work,sr3p_ASC_auto_deficient_atwork -sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient_eatout,sr3p_ASC_auto_sufficient_escort,sr3p_ASC_auto_sufficient_othdiscr,sr3p_ASC_auto_sufficient_othmaint,sr3p_ASC_auto_sufficient_school,sr3p_ASC_auto_sufficient_shopping,sr3p_ASC_auto_sufficient_social,sr3p_ASC_auto_sufficient_univ,sr3p_ASC_auto_sufficient_work,sr3p_ASC_auto_sufficient_atwork -walk_transit_ASC_no_auto,walk_transit_ASC_no_auto_eatout,walk_transit_ASC_no_auto_escort,walk_transit_ASC_no_auto_othdiscr,walk_transit_ASC_no_auto_othmaint,walk_transit_ASC_no_auto_school,walk_transit_ASC_no_auto_shopping,walk_transit_ASC_no_auto_social,walk_transit_ASC_no_auto_univ,walk_transit_ASC_no_auto_work,walk_transit_ASC_no_auto_atwork -walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient_eatout,walk_transit_ASC_auto_deficient_escort,walk_transit_ASC_auto_deficient_othdiscr,walk_transit_ASC_auto_deficient_othmaint,walk_transit_ASC_auto_deficient_school,walk_transit_ASC_auto_deficient_shopping,walk_transit_ASC_auto_deficient_social,walk_transit_ASC_auto_deficient_univ,walk_transit_ASC_auto_deficient_work,walk_transit_ASC_auto_deficient_atwork -walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient_eatout,walk_transit_ASC_auto_sufficient_escort,walk_transit_ASC_auto_sufficient_othdiscr,walk_transit_ASC_auto_sufficient_othmaint,walk_transit_ASC_auto_sufficient_school,walk_transit_ASC_auto_sufficient_shopping,walk_transit_ASC_auto_sufficient_social,walk_transit_ASC_auto_sufficient_univ,walk_transit_ASC_auto_sufficient_work,walk_transit_ASC_auto_sufficient_atwork -drive_transit_ASC_no_auto,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all -drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient_eatout,drive_transit_ASC_auto_deficient_escort,drive_transit_ASC_auto_deficient_othdiscr,drive_transit_ASC_auto_deficient_othmaint,drive_transit_ASC_auto_deficient_school,drive_transit_ASC_auto_deficient_shopping,drive_transit_ASC_auto_deficient_social,drive_transit_ASC_auto_deficient_univ,drive_transit_ASC_auto_deficient_work,drive_transit_ASC_auto_deficient_atwork -drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient_eatout,drive_transit_ASC_auto_sufficient_escort,drive_transit_ASC_auto_sufficient_othdiscr,drive_transit_ASC_auto_sufficient_othmaint,drive_transit_ASC_auto_sufficient_school,drive_transit_ASC_auto_sufficient_shopping,drive_transit_ASC_auto_sufficient_social,drive_transit_ASC_auto_sufficient_univ,drive_transit_ASC_auto_sufficient_work,drive_transit_ASC_auto_sufficient_atwork -taxi_ASC_no_auto,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_work,taxi_ASC_no_auto_atwork -taxi_ASC_auto_deficient,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_school,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_univ,taxi_ASC_auto_deficient_work,taxi_ASC_auto_deficient_atwork -taxi_ASC_auto_sufficient,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_school,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_univ,taxi_ASC_auto_sufficient_work,taxi_ASC_auto_sufficient_atwork -tnc_single_ASC_no_auto,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_school,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_univ,tnc_single_ASC_no_auto_work,tnc_single_ASC_no_auto_atwork -tnc_single_ASC_auto_deficient,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_school,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_univ,tnc_single_ASC_auto_deficient_work,tnc_single_ASC_auto_deficient_atwork -tnc_single_ASC_auto_sufficient,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_school,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_univ,tnc_single_ASC_auto_sufficient_work,tnc_single_ASC_auto_sufficient_atwork -tnc_shared_ASC_no_auto,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_school,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_univ,tnc_shared_ASC_no_auto_work,tnc_shared_ASC_no_auto_atwork -tnc_shared_ASC_auto_deficient,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_school,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_univ,tnc_shared_ASC_auto_deficient_work,tnc_shared_ASC_auto_deficient_atwork -tnc_shared_ASC_auto_sufficient,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_school,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_univ,tnc_shared_ASC_auto_sufficient_work,tnc_shared_ASC_auto_sufficient_atwork -joint_walk_ASC_no_auto,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all -joint_walk_ASC_auto_deficient,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all -joint_walk_ASC_auto_sufficient,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all -joint_bike_ASC_no_auto,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all -joint_bike_ASC_auto_deficient,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all -joint_bike_ASC_auto_sufficient,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all -joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all -joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all -joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all -joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all -joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all -joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all -joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all -joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all -joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all -joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all -joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all -joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all -joint_taxi_ASC_no_auto,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all -joint_taxi_ASC_auto_deficient,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all -joint_taxi_ASC_auto_sufficient,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all -joint_tnc_single_ASC_no_auto,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all -joint_tnc_single_ASC_auto_deficient,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all -joint_tnc_single_ASC_auto_sufficient,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all -joint_tnc_shared_ASC_no_auto,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all -joint_tnc_shared_ASC_auto_deficient,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all -joint_tnc_shared_ASC_auto_sufficient,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all -local_bus_ASC,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_school_univ,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_school_univ,local_bus_ASC_work,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -walk_light_rail_ASC,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_work,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -drive_light_rail_ASC,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_work,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -walk_ferry_ASC,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_work,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -drive_ferry_ASC,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_work,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -express_bus_ASC,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_work,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -heavy_rail_ASC,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_work,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -commuter_rail_ASC,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_work,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -walk_transit_CBD_ASC,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_work,walk_transit_CBD_ASC_atwork -drive_transit_CBD_ASC,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_work,drive_transit_CBD_ASC_atwork -#same for all segments,,,,,,,,,, -coef_walk_access_time,,,,,,,,,, -coef_walk_egress_time,,,,,,,,,, -c_age1624_sr2,,,,,,,,,, -c_age1624_sr3,,,,,,,,,, -c_age1624_nmot,,,,,,,,,, -c_age1624_tran,,,,,,,,,, -c_age4155_sr2,,,,,,,,,, -c_age4155_sr3,,,,,,,,,, -c_age4155_nmot,,,,,,,,,, -c_age4155_tran,,,,,,,,,, -c_age5664_sr2,,,,,,,,,, -c_age5664_sr3,,,,,,,,,, -c_age5664_nmot,,,,,,,,,, -c_age5664_tran,,,,,,,,,, -c_age65pl_sr2,,,,,,,,,, -c_age65pl_sr3,,,,,,,,,, -c_age65pl_nmot,,,,,,,,,, -c_age65pl_tran,,,,,,,,,, -c_female_sr2,,,,,,,,,, -c_female_sr3,,,,,,,,,, -c_female_tran,,,,,,,,,, -c_female_nmot,,,,,,,,,, -c_size2_sr2,,,,,,,,,, -c_size2_sr3,,,,,,,,,, -c_size3_sr2,,,,,,,,,, -c_size3_sr3,,,,,,,,,, -c_size4p_sr2,,,,,,,,,, -c_size4p_sr3,,,,,,,,,, -c_walkTime,,,,,,,,,, -c_bikeTime,,,,,,,,,, -c_oMix_nmot,,,,,,,,,, -c_oMix_wtran,,,,,,,,,, -c_oIntDen_nmot,,,,,,,,,, -c_oIntDen_wtran,,,,,,,,,, -c_dEmpDen_nmot,,,,,,,,,, -c_dEmpDen_wtran,,,,,,,,,, -c_dEmpDen_dtran,,,,,,,,,, -zeroAutoHH_sr3,,,,,,,,,, -zeroAutoHH_walk,,,,,,,,,, -zeroAutoHH_bike,,,,,,,,,, -zeroAutoHH_wt,,,,,,,,,, -zeroAutoHH_kt,,,,,,,,,, -autoDeficientHH_sr2,,,,,,,,,, -autoDeficientHH_sr3,,,,,,,,,, -autoDeficientHH_walk,,,,,,,,,, -autoDeficientHH_bike,,,,,,,,,, -autoDeficientHH_wt,,,,,,,,,, -autoDeficientHH_dt,,,,,,,,,, -autoDeficientHH_kt,,,,,,,,,, -autoSufficientHH_sr2,,,,,,,,,, -autoSufficientHH_sr3,,,,,,,,,, -autoSufficientHH_walk,,,,,,,,,, -autoSufficientHH_bike,,,,,,,,,, -autoSufficientHH_wt,,,,,,,,,, -autoSufficientHH_dt,,,,,,,,,, -autoSufficientHH_kt,,,,,,,,,, -asc_wtransit_cbd_sf,,,,,,,,,, -asc_wtransit_nw_sf,,,,,,,,,, -asc_wtransit_se_sf,,,,,,,,,, -asc_dtransit_cbd_sf,,,,,,,,,, -asc_Transit_Pseudo_area_type_constant,,,,,,,,,, -asc_taxi_penalty,,,,,,,,,, -zeroAutoHH_SHARED2HOV,,,,,,,,,, -autoDeficientHH_SHARED2HOV,,,,,,,,,, -autoSufficientHH_SHARED2HOV,,,,,,,,,, -zeroAutoHH_SHARED2PAY,,,,,,,,,, -autoDeficientHH_SHARED2PAY,,,,,,,,,, -autoSufficientHH_SHARED2PAY,,,,,,,,,, -zeroAutoHH_SHARED3HOV,,,,,,,,,, -autoDeficientHH_SHARED3HOV,,,,,,,,,, -autoSufficientHH_SHARED3HOV,,,,,,,,,, -zeroAutoHH_SHARED3PAY,,,,,,,,,, -autoDeficientHH_SHARED3PAY,,,,,,,,,, -autoSufficientHH_SHARED3PAY,,,,,,,,,, -zeroAutoHH_WALK,,,,,,,,,, -autoDeficientHH_WALK,,,,,,,,,, -autoSufficientHH_WALK,,,,,,,,,, -zeroAutoHH_BIKE,,,,,,,,,, -autoDeficientHH_BIKE,,,,,,,,,, -autoSufficientHH_BIKE,,,,,,,,,, -zeroAutoHH_WALK_SET,,,,,,,,,, -autoDeficientHH_WALK_SET,,,,,,,,,, -autoSufficientHH_WALK_SET,,,,,,,,,, -zeroAutoHH_PNR_SET,,,,,,,,,, -autoDeficientHH_PNR_SET,,,,,,,,,, +coefficient_name,eatout,escort,othdiscr,othmaint,school,shopping,social,univ,work,atwork +#same for all segments,,,,,,,,,, +coef_one,,,,,,,,,, +coef_nest_root,,,,,,,,,, +coef_nest_AUTO,,,,,,,,,, +coef_nest_AUTO_DRIVEALONE,,,,,,,,,, +coef_nest_AUTO_SHAREDRIDE2,,,,,,,,,, +coef_nest_AUTO_SHAREDRIDE3,,,,,,,,,, +coef_nest_NONMOTORIZED,,,,,,,,,, +coef_nest_TRANSIT,,,,,,,,,, +coef_nest_TRANSIT_WALKACCESS,,,,,,,,,, +coef_nest_TRANSIT_DRIVEACCESS,,,,,,,,,, +coef_nest_RIDEHAIL,,,,,,,,,, +#,,,,,,,,,, +coef_ivt,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_work,coef_ivt_atwork +coef_topology_walk_multiplier,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_atwork +coef_topology_bike_multiplier,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_atwork +coef_topology_trn_multiplier,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_atwork +coef_age1619_da_multiplier,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_atwork +coef_age010_trn_multiplier,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_atwork +coef_age16p_sr_multiplier,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork +coef_hhsize1_sr_multiplier,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_work,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork +coef_hhsize2_sr_multiplier,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork +walk_ASC_no_auto,walk_ASC_no_auto_eatout,walk_ASC_no_auto_escort,walk_ASC_no_auto_othdiscr,walk_ASC_no_auto_othmaint,walk_ASC_no_auto_school,walk_ASC_no_auto_shopping,walk_ASC_no_auto_social,walk_ASC_no_auto_univ,walk_ASC_no_auto_work,walk_ASC_no_auto_atwork +walk_ASC_auto_deficient,walk_ASC_auto_deficient_eatout,walk_ASC_auto_deficient_escort,walk_ASC_auto_deficient_othdiscr,walk_ASC_auto_deficient_othmaint,walk_ASC_auto_deficient_school,walk_ASC_auto_deficient_shopping,walk_ASC_auto_deficient_social,walk_ASC_auto_deficient_univ,walk_ASC_auto_deficient_work,walk_ASC_auto_deficient_atwork +walk_ASC_auto_sufficient,walk_ASC_auto_sufficient_eatout,walk_ASC_auto_sufficient_escort,walk_ASC_auto_sufficient_othdiscr,walk_ASC_auto_sufficient_othmaint,walk_ASC_auto_sufficient_school,walk_ASC_auto_sufficient_shopping,walk_ASC_auto_sufficient_social,walk_ASC_auto_sufficient_univ,walk_ASC_auto_sufficient_work,walk_ASC_auto_sufficient_atwork +bike_ASC_no_auto,bike_ASC_no_auto_eatout,bike_ASC_no_auto_escort,bike_ASC_no_auto_othdiscr,bike_ASC_no_auto_othmaint,bike_ASC_no_auto_school,bike_ASC_no_auto_shopping,bike_ASC_no_auto_social,bike_ASC_no_auto_univ,bike_ASC_no_auto_work,bike_ASC_no_auto_atwork +bike_ASC_auto_deficient,bike_ASC_auto_deficient_eatout,bike_ASC_auto_deficient_escort,bike_ASC_auto_deficient_othdiscr,bike_ASC_auto_deficient_othmaint,bike_ASC_auto_deficient_school,bike_ASC_auto_deficient_shopping,bike_ASC_auto_deficient_social,bike_ASC_auto_deficient_univ,bike_ASC_auto_deficient_work,bike_ASC_auto_deficient_atwork +bike_ASC_auto_sufficient,bike_ASC_auto_sufficient_eatout,bike_ASC_auto_sufficient_escort,bike_ASC_auto_sufficient_othdiscr,bike_ASC_auto_sufficient_othmaint,bike_ASC_auto_sufficient_school,bike_ASC_auto_sufficient_shopping,bike_ASC_auto_sufficient_social,bike_ASC_auto_sufficient_univ,bike_ASC_auto_sufficient_work,bike_ASC_auto_sufficient_atwork +sr2_ASC_no_auto,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all +sr2_ASC_auto_deficient,sr2_ASC_auto_deficient_eatout,sr2_ASC_auto_deficient_escort,sr2_ASC_auto_deficient_othdiscr,sr2_ASC_auto_deficient_othmaint,sr2_ASC_auto_deficient_school,sr2_ASC_auto_deficient_shopping,sr2_ASC_auto_deficient_social,sr2_ASC_auto_deficient_univ,sr2_ASC_auto_deficient_work,sr2_ASC_auto_deficient_atwork +sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient_eatout,sr2_ASC_auto_sufficient_escort,sr2_ASC_auto_sufficient_othdiscr,sr2_ASC_auto_sufficient_othmaint,sr2_ASC_auto_sufficient_school,sr2_ASC_auto_sufficient_shopping,sr2_ASC_auto_sufficient_social,sr2_ASC_auto_sufficient_univ,sr2_ASC_auto_sufficient_work,sr2_ASC_auto_sufficient_atwork +sr3p_ASC_no_auto,sr3p_ASC_no_auto_eatout,sr3p_ASC_no_auto_escort,sr3p_ASC_no_auto_othdiscr,sr3p_ASC_no_auto_othmaint,sr3p_ASC_no_auto_school,sr3p_ASC_no_auto_shopping,sr3p_ASC_no_auto_social,sr3p_ASC_no_auto_univ,sr3p_ASC_no_auto_work,sr3p_ASC_no_auto_atwork +sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient_eatout,sr3p_ASC_auto_deficient_escort,sr3p_ASC_auto_deficient_othdiscr,sr3p_ASC_auto_deficient_othmaint,sr3p_ASC_auto_deficient_school,sr3p_ASC_auto_deficient_shopping,sr3p_ASC_auto_deficient_social,sr3p_ASC_auto_deficient_univ,sr3p_ASC_auto_deficient_work,sr3p_ASC_auto_deficient_atwork +sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient_eatout,sr3p_ASC_auto_sufficient_escort,sr3p_ASC_auto_sufficient_othdiscr,sr3p_ASC_auto_sufficient_othmaint,sr3p_ASC_auto_sufficient_school,sr3p_ASC_auto_sufficient_shopping,sr3p_ASC_auto_sufficient_social,sr3p_ASC_auto_sufficient_univ,sr3p_ASC_auto_sufficient_work,sr3p_ASC_auto_sufficient_atwork +walk_transit_ASC_no_auto,walk_transit_ASC_no_auto_eatout,walk_transit_ASC_no_auto_escort,walk_transit_ASC_no_auto_othdiscr,walk_transit_ASC_no_auto_othmaint,walk_transit_ASC_no_auto_school,walk_transit_ASC_no_auto_shopping,walk_transit_ASC_no_auto_social,walk_transit_ASC_no_auto_univ,walk_transit_ASC_no_auto_work,walk_transit_ASC_no_auto_atwork +walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient_eatout,walk_transit_ASC_auto_deficient_escort,walk_transit_ASC_auto_deficient_othdiscr,walk_transit_ASC_auto_deficient_othmaint,walk_transit_ASC_auto_deficient_school,walk_transit_ASC_auto_deficient_shopping,walk_transit_ASC_auto_deficient_social,walk_transit_ASC_auto_deficient_univ,walk_transit_ASC_auto_deficient_work,walk_transit_ASC_auto_deficient_atwork +walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient_eatout,walk_transit_ASC_auto_sufficient_escort,walk_transit_ASC_auto_sufficient_othdiscr,walk_transit_ASC_auto_sufficient_othmaint,walk_transit_ASC_auto_sufficient_school,walk_transit_ASC_auto_sufficient_shopping,walk_transit_ASC_auto_sufficient_social,walk_transit_ASC_auto_sufficient_univ,walk_transit_ASC_auto_sufficient_work,walk_transit_ASC_auto_sufficient_atwork +drive_transit_ASC_no_auto,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all +drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient_eatout,drive_transit_ASC_auto_deficient_escort,drive_transit_ASC_auto_deficient_othdiscr,drive_transit_ASC_auto_deficient_othmaint,drive_transit_ASC_auto_deficient_school,drive_transit_ASC_auto_deficient_shopping,drive_transit_ASC_auto_deficient_social,drive_transit_ASC_auto_deficient_univ,drive_transit_ASC_auto_deficient_work,drive_transit_ASC_auto_deficient_atwork +drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient_eatout,drive_transit_ASC_auto_sufficient_escort,drive_transit_ASC_auto_sufficient_othdiscr,drive_transit_ASC_auto_sufficient_othmaint,drive_transit_ASC_auto_sufficient_school,drive_transit_ASC_auto_sufficient_shopping,drive_transit_ASC_auto_sufficient_social,drive_transit_ASC_auto_sufficient_univ,drive_transit_ASC_auto_sufficient_work,drive_transit_ASC_auto_sufficient_atwork +taxi_ASC_no_auto,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_work,taxi_ASC_no_auto_atwork +taxi_ASC_auto_deficient,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_school,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_univ,taxi_ASC_auto_deficient_work,taxi_ASC_auto_deficient_atwork +taxi_ASC_auto_sufficient,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_school,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_univ,taxi_ASC_auto_sufficient_work,taxi_ASC_auto_sufficient_atwork +tnc_single_ASC_no_auto,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_school,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_univ,tnc_single_ASC_no_auto_work,tnc_single_ASC_no_auto_atwork +tnc_single_ASC_auto_deficient,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_school,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_univ,tnc_single_ASC_auto_deficient_work,tnc_single_ASC_auto_deficient_atwork +tnc_single_ASC_auto_sufficient,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_school,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_univ,tnc_single_ASC_auto_sufficient_work,tnc_single_ASC_auto_sufficient_atwork +tnc_shared_ASC_no_auto,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_school,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_univ,tnc_shared_ASC_no_auto_work,tnc_shared_ASC_no_auto_atwork +tnc_shared_ASC_auto_deficient,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_school,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_univ,tnc_shared_ASC_auto_deficient_work,tnc_shared_ASC_auto_deficient_atwork +tnc_shared_ASC_auto_sufficient,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_school,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_univ,tnc_shared_ASC_auto_sufficient_work,tnc_shared_ASC_auto_sufficient_atwork +joint_walk_ASC_no_auto,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all +joint_walk_ASC_auto_deficient,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all +joint_walk_ASC_auto_sufficient,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all +joint_bike_ASC_no_auto,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all +joint_bike_ASC_auto_deficient,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all +joint_bike_ASC_auto_sufficient,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all +joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all +joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all +joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all +joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all +joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all +joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all +joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all +joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all +joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all +joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all +joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all +joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all +joint_taxi_ASC_no_auto,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all +joint_taxi_ASC_auto_deficient,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all +joint_taxi_ASC_auto_sufficient,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all +joint_tnc_single_ASC_no_auto,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all +joint_tnc_single_ASC_auto_deficient,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all +joint_tnc_single_ASC_auto_sufficient,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all +joint_tnc_shared_ASC_no_auto,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all +joint_tnc_shared_ASC_auto_deficient,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all +joint_tnc_shared_ASC_auto_sufficient,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all +local_bus_ASC,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_school_univ,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_school_univ,local_bus_ASC_work,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +walk_light_rail_ASC,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_work,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +drive_light_rail_ASC,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_work,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +walk_ferry_ASC,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_work,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +drive_ferry_ASC,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_work,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +express_bus_ASC,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_work,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +heavy_rail_ASC,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_work,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +commuter_rail_ASC,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_work,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +walk_transit_CBD_ASC,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_work,walk_transit_CBD_ASC_atwork +drive_transit_CBD_ASC,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_work,drive_transit_CBD_ASC_atwork +#same for all segments,,,,,,,,,, +coef_walk_access_time,,,,,,,,,, +coef_walk_egress_time,,,,,,,,,, +c_age1624_sr2,,,,,,,,,, +c_age1624_sr3,,,,,,,,,, +c_age1624_nmot,,,,,,,,,, +c_age1624_tran,,,,,,,,,, +c_age4155_sr2,,,,,,,,,, +c_age4155_sr3,,,,,,,,,, +c_age4155_nmot,,,,,,,,,, +c_age4155_tran,,,,,,,,,, +c_age5664_sr2,,,,,,,,,, +c_age5664_sr3,,,,,,,,,, +c_age5664_nmot,,,,,,,,,, +c_age5664_tran,,,,,,,,,, +c_age65pl_sr2,,,,,,,,,, +c_age65pl_sr3,,,,,,,,,, +c_age65pl_nmot,,,,,,,,,, +c_age65pl_tran,,,,,,,,,, +c_female_sr2,,,,,,,,,, +c_female_sr3,,,,,,,,,, +c_female_tran,,,,,,,,,, +c_female_nmot,,,,,,,,,, +c_size2_sr2,,,,,,,,,, +c_size2_sr3,,,,,,,,,, +c_size3_sr2,,,,,,,,,, +c_size3_sr3,,,,,,,,,, +c_size4p_sr2,,,,,,,,,, +c_size4p_sr3,,,,,,,,,, +c_walkTime,,,,,,,,,, +c_bikeTime,,,,,,,,,, +c_oMix_nmot,,,,,,,,,, +c_oMix_wtran,,,,,,,,,, +c_oIntDen_nmot,,,,,,,,,, +c_oIntDen_wtran,,,,,,,,,, +c_dEmpDen_nmot,,,,,,,,,, +c_dEmpDen_wtran,,,,,,,,,, +c_dEmpDen_dtran,,,,,,,,,, +zeroAutoHH_sr3,,,,,,,,,, +zeroAutoHH_walk,,,,,,,,,, +zeroAutoHH_bike,,,,,,,,,, +zeroAutoHH_wt,,,,,,,,,, +zeroAutoHH_kt,,,,,,,,,, +autoDeficientHH_sr2,,,,,,,,,, +autoDeficientHH_sr3,,,,,,,,,, +autoDeficientHH_walk,,,,,,,,,, +autoDeficientHH_bike,,,,,,,,,, +autoDeficientHH_wt,,,,,,,,,, +autoDeficientHH_dt,,,,,,,,,, +autoDeficientHH_kt,,,,,,,,,, +autoSufficientHH_sr2,,,,,,,,,, +autoSufficientHH_sr3,,,,,,,,,, +autoSufficientHH_walk,,,,,,,,,, +autoSufficientHH_bike,,,,,,,,,, +autoSufficientHH_wt,,,,,,,,,, +autoSufficientHH_dt,,,,,,,,,, +autoSufficientHH_kt,,,,,,,,,, +asc_wtransit_cbd_sf,,,,,,,,,, +asc_wtransit_nw_sf,,,,,,,,,, +asc_wtransit_se_sf,,,,,,,,,, +asc_dtransit_cbd_sf,,,,,,,,,, +asc_Transit_Pseudo_area_type_constant,,,,,,,,,, +asc_taxi_penalty,,,,,,,,,, +zeroAutoHH_SHARED2HOV,,,,,,,,,, +autoDeficientHH_SHARED2HOV,,,,,,,,,, +autoSufficientHH_SHARED2HOV,,,,,,,,,, +zeroAutoHH_SHARED2PAY,,,,,,,,,, +autoDeficientHH_SHARED2PAY,,,,,,,,,, +autoSufficientHH_SHARED2PAY,,,,,,,,,, +zeroAutoHH_SHARED3HOV,,,,,,,,,, +autoDeficientHH_SHARED3HOV,,,,,,,,,, +autoSufficientHH_SHARED3HOV,,,,,,,,,, +zeroAutoHH_SHARED3PAY,,,,,,,,,, +autoDeficientHH_SHARED3PAY,,,,,,,,,, +autoSufficientHH_SHARED3PAY,,,,,,,,,, +zeroAutoHH_WALK,,,,,,,,,, +autoDeficientHH_WALK,,,,,,,,,, +autoSufficientHH_WALK,,,,,,,,,, +zeroAutoHH_BIKE,,,,,,,,,, +autoDeficientHH_BIKE,,,,,,,,,, +autoSufficientHH_BIKE,,,,,,,,,, +zeroAutoHH_WALK_SET,,,,,,,,,, +autoDeficientHH_WALK_SET,,,,,,,,,, +autoSufficientHH_WALK_SET,,,,,,,,,, +zeroAutoHH_PNR_SET,,,,,,,,,, +autoDeficientHH_PNR_SET,,,,,,,,,, autoSufficientHH_PNR_SET,,,,,,,,,, \ No newline at end of file diff --git a/activitysim/examples/example_marin/configs/tvpb_utility_drive_maz_tap.csv b/activitysim/examples/prototype_marin/configs/tvpb_utility_drive_maz_tap.csv similarity index 99% rename from activitysim/examples/example_marin/configs/tvpb_utility_drive_maz_tap.csv rename to activitysim/examples/prototype_marin/configs/tvpb_utility_drive_maz_tap.csv index 04ba9016a7..c4abf674f7 100755 --- a/activitysim/examples/example_marin/configs/tvpb_utility_drive_maz_tap.csv +++ b/activitysim/examples/prototype_marin/configs/tvpb_utility_drive_maz_tap.csv @@ -1,3 +1,3 @@ -Label,Description,Expression,utility -util_drive_time,drive time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_dtim * (df.DTIME + (df.WDIST / 5280 / walk_speed * 60))",1 -util_drive_cost,drive cost,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_cost_high_income, c_cost_low_income) * (df.DDIST + (df.WDIST / 5280)) * c_auto_operating_cost_per_mile",1 +Label,Description,Expression,utility +util_drive_time,drive time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_dtim * (df.DTIME + (df.WDIST / 5280 / walk_speed * 60))",1 +util_drive_cost,drive cost,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_cost_high_income, c_cost_low_income) * (df.DDIST + (df.WDIST / 5280)) * c_auto_operating_cost_per_mile",1 diff --git a/activitysim/examples/example_marin/configs/tvpb_utility_tap_tap.csv b/activitysim/examples/prototype_marin/configs/tvpb_utility_tap_tap.csv similarity index 99% rename from activitysim/examples/example_marin/configs/tvpb_utility_tap_tap.csv rename to activitysim/examples/prototype_marin/configs/tvpb_utility_tap_tap.csv index b7456c856b..495cad7f01 100755 --- a/activitysim/examples/example_marin/configs/tvpb_utility_tap_tap.csv +++ b/activitysim/examples/prototype_marin/configs/tvpb_utility_tap_tap.csv @@ -1,82 +1,82 @@ -Label,Description,Expression,set1,set2,set3 -# Set 1,,,,, -set1_unavailable,Shut off set if unavailable,@df.not_transit_available_set1,C_UNAVAILABLE,, -set1_ivt,set In-Vehicle Time,@~df.not_transit_available_set1 * df.c_ivt_for_segment * df.totalIVT_set1,1,, -set1_first_wait_time,First wait time,"@~df.not_transit_available_set1 * c_fwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'IWAIT_SET1')",1,, -set1_xfer_wait_time,set Transfer Wait Time,"@~df.not_transit_available_set1 * c_xwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWAIT_SET1')",1,, -set1_xfer_walk_time,set Walk transfer time,"@~df.not_transit_available_set1 * c_waux * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWTIME_SET1')",1,, -set1_fare,set Fare,"@~df.not_transit_available_set1 * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'FARE_SET1') * 100",1,, -set1_xfers1,0-1 transfers constant,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers1_set1 * c_xfers1 * df.c_ivt_for_segment,1,, -set1_xfers2,1-2 transfers constant,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers2_set1 * c_xfers2 * df.c_ivt_for_segment,1,, -set1_sfers3,>2 transfers constant,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers3_set1 * c_xfers3 * df.c_ivt_for_segment,1,, -set1_xfers1_drive,0-1 transfers penalty for drive access,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers1_set1 * (access_mode=='drive') * (df.c_ivt_for_segment * 15),1,, -set1_xfers2_drive,1-2 transfers penalty for drive access,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers2_set1 * (access_mode=='drive') * (df.c_ivt_for_segment * 15),1,, -set1_sfers3_drive,>2 transfers penalty for drive access,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers3_set1 * (access_mode=='drive') * (df.c_ivt_for_segment * 15),1,, -set1_xfers1_bart,0-1 transfers constant when using only BART,@~df.not_transit_available_set1 * df.bartOnly_set1 * df.xfers1_set1 * (df.c_ivt_for_segment * 5),1,, -set1_xfers2_bart,1-2 transfers constant when using only BART,@~df.not_transit_available_set1 * df.bartOnly_set1 * df.xfers2_set1 * (df.c_ivt_for_segment * 5),1,, -set1_sfers3_bart,>2 transfers constant when using only BART,@~df.not_transit_available_set1 * df.bartOnly_set1 * df.xfers3_set1 * (df.c_ivt_for_segment * 5),1,, -set1_cr_20_40,CR distance 20-40 miles,@~df.not_transit_available_set1 * (df.crDistance_set1>20) * (df.crDistance_set1<=40) * c_cr20_40 * df.c_ivt_for_segment,1,, -set1_cr_40_plus,CR distance > 40 miles,@~df.not_transit_available_set1 * (df.crDistance_set1>40) * c_cr40plus * df.c_ivt_for_segment,1,, -set1_CR_drive,drive access to CR,"@~df.not_transit_available_set1 * (access_mode=='drive') * c_drvCR * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1')>0)",1,, -set1_HR_drive,drive access to HR,"@~df.not_transit_available_set1 * (access_mode=='drive') * c_drvHeavy * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')>0)",1,, -set1_FR_drive,drive access to FR,"@~df.not_transit_available_set1 * (access_mode=='drive') * c_drvFR * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET1')>0)",1,, -set1_LRT_drive,drive access to LRT,"@~df.not_transit_available_set1 * (access_mode=='drive') * c_drvLRT * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1')>0)",1,, -set1_EB_drive,drive access to EB,"@~df.not_transit_available_set1 * (access_mode=='drive') * c_drvExpress * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET1')>0)",1,, -set1_ASC_CR,ASC CR,"@~df.not_transit_available_set1 * c_cr_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1')>0) * (np.where(df.premWithXfer_set1, 0.333, 1.0))",1,, -set1_ASC_HR,ASC HR,"@~df.not_transit_available_set1 * c_hr_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')>0) * (np.where(df.premWithXfer_set1, 0.333, 1.0))",1,, -set1_ASC_FR,ASC FR,"@~df.not_transit_available_set1 * c_fr_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET1')>0) * (np.where(df.premWithXfer_set1, 0.333, 1.0))",1,, -set1_ASC_LRT,ASC LRT,"@~df.not_transit_available_set1 * c_lrt_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1')>0) * (np.where(df.premWithXfer_set1, 0.333, 1.0))",1,, -# Set 2,,,,, -set2_unavailable,Shut off set if unavailable,@df.not_transit_available_set2,,C_UNAVAILABLE, -set2_ivt,set In-Vehicle Time,@~df.not_transit_available_set2 * df.c_ivt_for_segment * df.totalIVT_set2,,1, -set2_first_wait_time,First wait time,"@~df.not_transit_available_set2 * c_fwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'IWAIT_SET2')",,1, -set2_xfer_wait_time,set Transfer Wait Time,"@~df.not_transit_available_set2 * c_xwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWAIT_SET2')",,1, -set2_xfer_walk_time,set Walk transfer time,"@~df.not_transit_available_set2 * c_waux * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWTIME_SET2')",,1, -set2_fare,set Fare,"@~df.not_transit_available_set2 * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'FARE_SET2') * 100",,1, -set2_xfers1,0-1 transfers constant,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * df.xfers1_set2 * c_xfers1 * df.c_ivt_for_segment,,1, -set2_xfers2,1-2 transfers constant,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * df.xfers2_set2 * c_xfers2 * df.c_ivt_for_segment,,1, -set2_sfers3,>2 transfers constant,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * df.xfers3_set2 * c_xfers3 * df.c_ivt_for_segment,,1, -set2_xfers1_drive,0-1 transfers penalty for drive access,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * df.xfers1_set2 * (access_mode=='drive') * (df.c_ivt_for_segment * 15),,1, -set2_xfers2_drive,1-2 transfers penalty for drive access,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * df.xfers2_set2 * (access_mode=='drive') * (df.c_ivt_for_segment * 15),,1, -set2_sfers3_drive,>2 transfers penalty for drive access,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * (access_mode=='drive') * df.xfers3_set2 * (df.c_ivt_for_segment * 15),,1, -set2_xfers1_bart,0-1 transfers constant when using only BART,@~df.not_transit_available_set2 * df.bartOnly_set2 * df.xfers1_set2 * (df.c_ivt_for_segment * 5),,1, -set2_xfers2_bart,1-2 transfers constant when using only BART,@~df.not_transit_available_set2 * df.bartOnly_set2 * df.xfers2_set2 * (df.c_ivt_for_segment * 5),,1, -set2_sfers3_bart,>2 transfers constant when using only BART,@~df.not_transit_available_set2 * df.bartOnly_set2 * df.xfers3_set2 * (df.c_ivt_for_segment * 5),,1, -set2_cr_20_40,CR distance 20-40 miles,@~df.not_transit_available_set2 * (df.crDistance_set2>20) * (df.crDistance_set2<=40) * c_cr20_40 * df.c_ivt_for_segment,,1, -set2_cr_40_plus,CR distance > 40 miles,@~df.not_transit_available_set2 * (df.crDistance_set2>40) * c_cr40plus * df.c_ivt_for_segment,,1, -set2_CR_drive,drive access to CR,"@~df.not_transit_available_set2 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2')>0) * c_drvCR * df.c_ivt_for_segment",,1, -set2_HR_drive,drive access to HR,"@~df.not_transit_available_set2 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')>0) * c_drvHeavy * df.c_ivt_for_segment",,1, -set2_FR_drive,drive access to FR,"@~df.not_transit_available_set2 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET2')>0) * c_drvFR * df.c_ivt_for_segment",,1, -set2_LRT_drive,drive access to LRT,"@~df.not_transit_available_set2 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2')>0) * c_drvLRT * df.c_ivt_for_segment",,1, -set2_EB_drive,drive access to EB,"@~df.not_transit_available_set2 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET2')>0) * c_drvExpress * df.c_ivt_for_segment",,1, -set2_ASC_CR,ASC CR,"@~df.not_transit_available_set2 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2')>0) * c_cr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set2, 0.333, 1.0))",,1, -set2_ASC_HR,ASC HR,"@~df.not_transit_available_set2 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')>0) * c_hr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set2, 0.333, 1.0))",,1, -set2_ASC_FR,ASC FR,"@~df.not_transit_available_set2 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET2')>0) * c_fr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set2, 0.333, 1.0))",,1, -set2_ASC_LRT,ASC LRT,"@~df.not_transit_available_set2 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2')>0) * c_lrt_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set2, 0.333, 1.0))",,1, -# Set 3,,,,, -set3_unavailable,Shut off set if unavailable,@df.not_transit_available_set3,,,C_UNAVAILABLE -set3_ivt,set In-Vehicle Time,@~df.not_transit_available_set3 * df.c_ivt_for_segment * df.totalIVT_set3,,,1 -set3_first_wait_time,First wait time,"@~df.not_transit_available_set3 * c_fwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'IWAIT_SET3')",,,1 -set3_xfer_wait_time,set Transfer Wait Time,"@~df.not_transit_available_set3 * c_xwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWAIT_SET3')",,,1 -set3_xfer_walk_time,set Walk transfer time,"@~df.not_transit_available_set3 * c_waux * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWTIME_SET3')",,,1 -set3_fare,set Fare,"@~df.not_transit_available_set3 * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'FARE_SET3') * 100",,,1 -set3_xfers1,0-1 transfers constant,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * df.xfers1_set3 * c_xfers1 * df.c_ivt_for_segment,,,1 -set3_xfers2,1-2 transfers constant,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * df.xfers2_set3 * c_xfers2 * df.c_ivt_for_segment,,,1 -set3_sfers3,>2 transfers constant,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * df.xfers3_set3 * c_xfers3 * df.c_ivt_for_segment,,,1 -set3_xfers1_drive,0-1 transfers penalty for drive access,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * (access_mode=='drive') * df.xfers1_set3 * (df.c_ivt_for_segment * 15),,,1 -set3_xfers2_drive,1-2 transfers penalty for drive access,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * (access_mode=='drive') * df.xfers2_set3 * (df.c_ivt_for_segment * 15),,,1 -set3_sfers3_drive,>2 transfers penalty for drive access,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * (access_mode=='drive') * df.xfers3_set3 * (df.c_ivt_for_segment * 15),,,1 -set3_xfers1_bart,0-1 transfers constant when using only BART,@~df.not_transit_available_set3 * df.bartOnly_set3 * df.xfers1_set3 * (df.c_ivt_for_segment * 5),,,1 -set3_xfers2_bart,1-2 transfers constant when using only BART,@~df.not_transit_available_set3 * df.bartOnly_set3 * df.xfers2_set3 * (df.c_ivt_for_segment * 5),,,1 -set3_sfers3_bart,>2 transfers constant when using only BART,@~df.not_transit_available_set3 * df.bartOnly_set3 * df.xfers3_set3 * (df.c_ivt_for_segment * 5),,,1 -set3_cr_20_40,CR distance 20-40 miles,@~df.not_transit_available_set3 * (df.crDistance_set3>20) * (df.crDistance_set3<=40) * c_cr20_40 * df.c_ivt_for_segment,,,1 -set3_cr_40_plus,CR distance > 40 miles,@~df.not_transit_available_set3 * (df.crDistance_set3>40) * c_cr40plus * df.c_ivt_for_segment,,,1 -set3_CR_drive,drive access to CR,"@~df.not_transit_available_set3 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3')>0) * c_drvCR * df.c_ivt_for_segment",,,1 -set3_HR_drive,drive access to HR,"@~df.not_transit_available_set3 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')>0) * c_drvHeavy * df.c_ivt_for_segment",,,1 -set3_FR_drive,drive access to FR,"@~df.not_transit_available_set3 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET3')>0) * c_drvFR * df.c_ivt_for_segment",,,1 -set3_LRT_drive,drive access to LRT,"@~df.not_transit_available_set3 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3')>0) * c_drvLRT * df.c_ivt_for_segment",,,1 -set3_EB_drive,drive access to EB,"@~df.not_transit_available_set3 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET3')>0) * c_drvExpress* df.c_ivt_for_segment",,,1 -set3_ASC_CR,ASC CR,"@~df.not_transit_available_set3 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3')>0) * c_cr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set3, 0.333, 1.0))",,,1 -set3_ASC_HR,ASC HR,"@~df.not_transit_available_set3 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')>0) * c_hr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set3, 0.333, 1.0))",,,1 -set3_ASC_FR,ASC FR,"@~df.not_transit_available_set3 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET3')>0) * c_fr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set3, 0.333, 1.0))",,,1 -set3_ASC_LRT,ASC LRT,"@~df.not_transit_available_set3 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3')>0) * c_lrt_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set3, 0.333, 1.0))",,,1 +Label,Description,Expression,set1,set2,set3 +# Set 1,,,,, +set1_unavailable,Shut off set if unavailable,@df.not_transit_available_set1,C_UNAVAILABLE,, +set1_ivt,set In-Vehicle Time,@~df.not_transit_available_set1 * df.c_ivt_for_segment * df.totalIVT_set1,1,, +set1_first_wait_time,First wait time,"@~df.not_transit_available_set1 * c_fwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'IWAIT_SET1')",1,, +set1_xfer_wait_time,set Transfer Wait Time,"@~df.not_transit_available_set1 * c_xwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWAIT_SET1')",1,, +set1_xfer_walk_time,set Walk transfer time,"@~df.not_transit_available_set1 * c_waux * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWTIME_SET1')",1,, +set1_fare,set Fare,"@~df.not_transit_available_set1 * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'FARE_SET1') * 100",1,, +set1_xfers1,0-1 transfers constant,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers1_set1 * c_xfers1 * df.c_ivt_for_segment,1,, +set1_xfers2,1-2 transfers constant,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers2_set1 * c_xfers2 * df.c_ivt_for_segment,1,, +set1_sfers3,>2 transfers constant,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers3_set1 * c_xfers3 * df.c_ivt_for_segment,1,, +set1_xfers1_drive,0-1 transfers penalty for drive access,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers1_set1 * (access_mode=='drive') * (df.c_ivt_for_segment * 15),1,, +set1_xfers2_drive,1-2 transfers penalty for drive access,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers2_set1 * (access_mode=='drive') * (df.c_ivt_for_segment * 15),1,, +set1_sfers3_drive,>2 transfers penalty for drive access,@~df.not_transit_available_set1 * ~df.bartOnly_set1 * df.xfers3_set1 * (access_mode=='drive') * (df.c_ivt_for_segment * 15),1,, +set1_xfers1_bart,0-1 transfers constant when using only BART,@~df.not_transit_available_set1 * df.bartOnly_set1 * df.xfers1_set1 * (df.c_ivt_for_segment * 5),1,, +set1_xfers2_bart,1-2 transfers constant when using only BART,@~df.not_transit_available_set1 * df.bartOnly_set1 * df.xfers2_set1 * (df.c_ivt_for_segment * 5),1,, +set1_sfers3_bart,>2 transfers constant when using only BART,@~df.not_transit_available_set1 * df.bartOnly_set1 * df.xfers3_set1 * (df.c_ivt_for_segment * 5),1,, +set1_cr_20_40,CR distance 20-40 miles,@~df.not_transit_available_set1 * (df.crDistance_set1>20) * (df.crDistance_set1<=40) * c_cr20_40 * df.c_ivt_for_segment,1,, +set1_cr_40_plus,CR distance > 40 miles,@~df.not_transit_available_set1 * (df.crDistance_set1>40) * c_cr40plus * df.c_ivt_for_segment,1,, +set1_CR_drive,drive access to CR,"@~df.not_transit_available_set1 * (access_mode=='drive') * c_drvCR * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1')>0)",1,, +set1_HR_drive,drive access to HR,"@~df.not_transit_available_set1 * (access_mode=='drive') * c_drvHeavy * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')>0)",1,, +set1_FR_drive,drive access to FR,"@~df.not_transit_available_set1 * (access_mode=='drive') * c_drvFR * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET1')>0)",1,, +set1_LRT_drive,drive access to LRT,"@~df.not_transit_available_set1 * (access_mode=='drive') * c_drvLRT * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1')>0)",1,, +set1_EB_drive,drive access to EB,"@~df.not_transit_available_set1 * (access_mode=='drive') * c_drvExpress * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET1')>0)",1,, +set1_ASC_CR,ASC CR,"@~df.not_transit_available_set1 * c_cr_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1')>0) * (np.where(df.premWithXfer_set1, 0.333, 1.0))",1,, +set1_ASC_HR,ASC HR,"@~df.not_transit_available_set1 * c_hr_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')>0) * (np.where(df.premWithXfer_set1, 0.333, 1.0))",1,, +set1_ASC_FR,ASC FR,"@~df.not_transit_available_set1 * c_fr_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET1')>0) * (np.where(df.premWithXfer_set1, 0.333, 1.0))",1,, +set1_ASC_LRT,ASC LRT,"@~df.not_transit_available_set1 * c_lrt_asc * df.c_ivt_for_segment * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1')>0) * (np.where(df.premWithXfer_set1, 0.333, 1.0))",1,, +# Set 2,,,,, +set2_unavailable,Shut off set if unavailable,@df.not_transit_available_set2,,C_UNAVAILABLE, +set2_ivt,set In-Vehicle Time,@~df.not_transit_available_set2 * df.c_ivt_for_segment * df.totalIVT_set2,,1, +set2_first_wait_time,First wait time,"@~df.not_transit_available_set2 * c_fwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'IWAIT_SET2')",,1, +set2_xfer_wait_time,set Transfer Wait Time,"@~df.not_transit_available_set2 * c_xwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWAIT_SET2')",,1, +set2_xfer_walk_time,set Walk transfer time,"@~df.not_transit_available_set2 * c_waux * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWTIME_SET2')",,1, +set2_fare,set Fare,"@~df.not_transit_available_set2 * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'FARE_SET2') * 100",,1, +set2_xfers1,0-1 transfers constant,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * df.xfers1_set2 * c_xfers1 * df.c_ivt_for_segment,,1, +set2_xfers2,1-2 transfers constant,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * df.xfers2_set2 * c_xfers2 * df.c_ivt_for_segment,,1, +set2_sfers3,>2 transfers constant,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * df.xfers3_set2 * c_xfers3 * df.c_ivt_for_segment,,1, +set2_xfers1_drive,0-1 transfers penalty for drive access,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * df.xfers1_set2 * (access_mode=='drive') * (df.c_ivt_for_segment * 15),,1, +set2_xfers2_drive,1-2 transfers penalty for drive access,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * df.xfers2_set2 * (access_mode=='drive') * (df.c_ivt_for_segment * 15),,1, +set2_sfers3_drive,>2 transfers penalty for drive access,@~df.not_transit_available_set2 * ~df.bartOnly_set2 * (access_mode=='drive') * df.xfers3_set2 * (df.c_ivt_for_segment * 15),,1, +set2_xfers1_bart,0-1 transfers constant when using only BART,@~df.not_transit_available_set2 * df.bartOnly_set2 * df.xfers1_set2 * (df.c_ivt_for_segment * 5),,1, +set2_xfers2_bart,1-2 transfers constant when using only BART,@~df.not_transit_available_set2 * df.bartOnly_set2 * df.xfers2_set2 * (df.c_ivt_for_segment * 5),,1, +set2_sfers3_bart,>2 transfers constant when using only BART,@~df.not_transit_available_set2 * df.bartOnly_set2 * df.xfers3_set2 * (df.c_ivt_for_segment * 5),,1, +set2_cr_20_40,CR distance 20-40 miles,@~df.not_transit_available_set2 * (df.crDistance_set2>20) * (df.crDistance_set2<=40) * c_cr20_40 * df.c_ivt_for_segment,,1, +set2_cr_40_plus,CR distance > 40 miles,@~df.not_transit_available_set2 * (df.crDistance_set2>40) * c_cr40plus * df.c_ivt_for_segment,,1, +set2_CR_drive,drive access to CR,"@~df.not_transit_available_set2 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2')>0) * c_drvCR * df.c_ivt_for_segment",,1, +set2_HR_drive,drive access to HR,"@~df.not_transit_available_set2 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')>0) * c_drvHeavy * df.c_ivt_for_segment",,1, +set2_FR_drive,drive access to FR,"@~df.not_transit_available_set2 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET2')>0) * c_drvFR * df.c_ivt_for_segment",,1, +set2_LRT_drive,drive access to LRT,"@~df.not_transit_available_set2 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2')>0) * c_drvLRT * df.c_ivt_for_segment",,1, +set2_EB_drive,drive access to EB,"@~df.not_transit_available_set2 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET2')>0) * c_drvExpress * df.c_ivt_for_segment",,1, +set2_ASC_CR,ASC CR,"@~df.not_transit_available_set2 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2')>0) * c_cr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set2, 0.333, 1.0))",,1, +set2_ASC_HR,ASC HR,"@~df.not_transit_available_set2 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')>0) * c_hr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set2, 0.333, 1.0))",,1, +set2_ASC_FR,ASC FR,"@~df.not_transit_available_set2 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET2')>0) * c_fr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set2, 0.333, 1.0))",,1, +set2_ASC_LRT,ASC LRT,"@~df.not_transit_available_set2 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2')>0) * c_lrt_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set2, 0.333, 1.0))",,1, +# Set 3,,,,, +set3_unavailable,Shut off set if unavailable,@df.not_transit_available_set3,,,C_UNAVAILABLE +set3_ivt,set In-Vehicle Time,@~df.not_transit_available_set3 * df.c_ivt_for_segment * df.totalIVT_set3,,,1 +set3_first_wait_time,First wait time,"@~df.not_transit_available_set3 * c_fwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'IWAIT_SET3')",,,1 +set3_xfer_wait_time,set Transfer Wait Time,"@~df.not_transit_available_set3 * c_xwt * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWAIT_SET3')",,,1 +set3_xfer_walk_time,set Walk transfer time,"@~df.not_transit_available_set3 * c_waux * df.c_ivt_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'XWTIME_SET3')",,,1 +set3_fare,set Fare,"@~df.not_transit_available_set3 * df.c_cost_for_segment * los.get_tappairs3d(df.btap, df.atap, df.tod, 'FARE_SET3') * 100",,,1 +set3_xfers1,0-1 transfers constant,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * df.xfers1_set3 * c_xfers1 * df.c_ivt_for_segment,,,1 +set3_xfers2,1-2 transfers constant,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * df.xfers2_set3 * c_xfers2 * df.c_ivt_for_segment,,,1 +set3_sfers3,>2 transfers constant,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * df.xfers3_set3 * c_xfers3 * df.c_ivt_for_segment,,,1 +set3_xfers1_drive,0-1 transfers penalty for drive access,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * (access_mode=='drive') * df.xfers1_set3 * (df.c_ivt_for_segment * 15),,,1 +set3_xfers2_drive,1-2 transfers penalty for drive access,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * (access_mode=='drive') * df.xfers2_set3 * (df.c_ivt_for_segment * 15),,,1 +set3_sfers3_drive,>2 transfers penalty for drive access,@~df.not_transit_available_set3 * ~df.bartOnly_set3 * (access_mode=='drive') * df.xfers3_set3 * (df.c_ivt_for_segment * 15),,,1 +set3_xfers1_bart,0-1 transfers constant when using only BART,@~df.not_transit_available_set3 * df.bartOnly_set3 * df.xfers1_set3 * (df.c_ivt_for_segment * 5),,,1 +set3_xfers2_bart,1-2 transfers constant when using only BART,@~df.not_transit_available_set3 * df.bartOnly_set3 * df.xfers2_set3 * (df.c_ivt_for_segment * 5),,,1 +set3_sfers3_bart,>2 transfers constant when using only BART,@~df.not_transit_available_set3 * df.bartOnly_set3 * df.xfers3_set3 * (df.c_ivt_for_segment * 5),,,1 +set3_cr_20_40,CR distance 20-40 miles,@~df.not_transit_available_set3 * (df.crDistance_set3>20) * (df.crDistance_set3<=40) * c_cr20_40 * df.c_ivt_for_segment,,,1 +set3_cr_40_plus,CR distance > 40 miles,@~df.not_transit_available_set3 * (df.crDistance_set3>40) * c_cr40plus * df.c_ivt_for_segment,,,1 +set3_CR_drive,drive access to CR,"@~df.not_transit_available_set3 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3')>0) * c_drvCR * df.c_ivt_for_segment",,,1 +set3_HR_drive,drive access to HR,"@~df.not_transit_available_set3 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')>0) * c_drvHeavy * df.c_ivt_for_segment",,,1 +set3_FR_drive,drive access to FR,"@~df.not_transit_available_set3 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET3')>0) * c_drvFR * df.c_ivt_for_segment",,,1 +set3_LRT_drive,drive access to LRT,"@~df.not_transit_available_set3 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3')>0) * c_drvLRT * df.c_ivt_for_segment",,,1 +set3_EB_drive,drive access to EB,"@~df.not_transit_available_set3 * (access_mode=='drive') * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET3')>0) * c_drvExpress* df.c_ivt_for_segment",,,1 +set3_ASC_CR,ASC CR,"@~df.not_transit_available_set3 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3')>0) * c_cr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set3, 0.333, 1.0))",,,1 +set3_ASC_HR,ASC HR,"@~df.not_transit_available_set3 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')>0) * c_hr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set3, 0.333, 1.0))",,,1 +set3_ASC_FR,ASC FR,"@~df.not_transit_available_set3 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET3')>0) * c_fr_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set3, 0.333, 1.0))",,,1 +set3_ASC_LRT,ASC LRT,"@~df.not_transit_available_set3 * (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3')>0) * c_lrt_asc * df.c_ivt_for_segment * (np.where(df.premWithXfer_set3, 0.333, 1.0))",,,1 diff --git a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv b/activitysim/examples/prototype_marin/configs/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv similarity index 99% rename from activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv rename to activitysim/examples/prototype_marin/configs/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv index ba5cbe1d30..f2661a897b 100755 --- a/activitysim/examples/example_multiple_zone/configs_3_zone_marin/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv +++ b/activitysim/examples/prototype_marin/configs/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv @@ -1,44 +1,44 @@ -Description,Target,Expression -# time of day,,SHOULD BE PASSED IN -# demographic segment,, -,c_ivt_for_segment,"np.where(df.demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_ivt_low_income, c_ivt_high_income)" -,c_cost_for_segment,"np.where(df.demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_cost_low_income, c_cost_high_income)" -# set1,, -,not_transit_available_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BEST_MODE_SET1')==0" -Total IVT,totalIVT_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1') " -IVT on BART,bartIVT_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')" -premium modes used,premiumMode_set1,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET1')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1')>0)" -only travel by BART,bartOnly_set1,bartIVT_set1 == totalIVT_set1 -Set contains only BART with Xfers,bartWithXfer_set1,"(bartIVT_set1 == totalIVT_set1) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET1')>0)" -Set contains premium mode with transfers to LB,premWithXfer_set1,"(premiumMode_set1>0) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET1')>0)" -Number transfers,transfers_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET1')" -0-1 transfers,xfers1_set1,(transfers_set1>0) & (transfers_set1 <=1) -1-2 transfers,xfers2_set1,(transfers_set1>1) & (transfers_set1 <=2) ->2 transfers,xfers3_set1,(transfers_set1>2) -Commuter Rail Distance in miles [35 mph],crDistance_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1') * (35/60)" -# set2,, -,not_transit_available_set2,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'BEST_MODE_SET2')==0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET2')==0)" -Total IVT,totalIVT_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2') " -IVT on BART,bartIVT_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')" -premium modes used,premiumMode_set2,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET2')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2')>0)" -only travel by BART,bartOnly_set2,bartIVT_set2 == totalIVT_set2 -Set contains only BART with Xfers,bartWithXfer_set2,"(bartIVT_set2 == totalIVT_set2) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET2')>0)" -Set contains premium mode with transfers to LB,premWithXfer_set2,"(premiumMode_set2>0) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET2')>0)" -Number transfers,transfers_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET2')" -0-1 transfers,xfers1_set2,(transfers_set2>0) & (transfers_set2 <=1) -1-2 transfers,xfers2_set2,(transfers_set2>1) & (transfers_set2 <=2) ->2 transfers,xfers3_set2,(transfers_set2>2) -Commuter Rail Distance in miles [35 mph],crDistance_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2') * (35/60)" -# set3,, -,not_transit_available_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BEST_MODE_SET3')==0" -Total IVT,totalIVT_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3') " -IVT on BART,bartIVT_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')" -premium modes used,premiumMode_set3,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET3')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3')>0)" -only travel by BART,bartOnly_set3,bartIVT_set3 == totalIVT_set3 -Set contains only BART with Xfers,bartWithXfer_set3,"(bartIVT_set3 == totalIVT_set3) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET3')>0)" -Set contains premium mode with transfers to LB,premWithXfer_set3,"(premiumMode_set3>0) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET3')>0)" -Number transfers,transfers_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET3')" -0-1 transfers,xfers1_set3,(transfers_set3>0) & (transfers_set3 <=1) -1-2 transfers,xfers2_set3,(transfers_set3>1) & (transfers_set3 <=2) ->2 transfers,xfers3_set3,(transfers_set3>2) -Commuter Rail Distance in miles [35 mph],crDistance_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3') * (35/60)" +Description,Target,Expression +# time of day,,SHOULD BE PASSED IN +# demographic segment,, +,c_ivt_for_segment,"np.where(df.demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_ivt_low_income, c_ivt_high_income)" +,c_cost_for_segment,"np.where(df.demographic_segment==C_LOW_INCOME_SEGMENT_ID,c_cost_low_income, c_cost_high_income)" +# set1,, +,not_transit_available_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BEST_MODE_SET1')==0" +Total IVT,totalIVT_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1') " +IVT on BART,bartIVT_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')" +premium modes used,premiumMode_set1,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET1')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET1')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET1')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1')>0)" +only travel by BART,bartOnly_set1,bartIVT_set1 == totalIVT_set1 +Set contains only BART with Xfers,bartWithXfer_set1,"(bartIVT_set1 == totalIVT_set1) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET1')>0)" +Set contains premium mode with transfers to LB,premWithXfer_set1,"(premiumMode_set1>0) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET1')>0)" +Number transfers,transfers_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET1')" +0-1 transfers,xfers1_set1,(transfers_set1>0) & (transfers_set1 <=1) +1-2 transfers,xfers2_set1,(transfers_set1>1) & (transfers_set1 <=2) +>2 transfers,xfers3_set1,(transfers_set1>2) +Commuter Rail Distance in miles [35 mph],crDistance_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET1') * (35/60)" +# set2,, +,not_transit_available_set2,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'BEST_MODE_SET2')==0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET2')==0)" +Total IVT,totalIVT_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2') " +IVT on BART,bartIVT_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')" +premium modes used,premiumMode_set2,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET2')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET2')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET2')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2')>0)" +only travel by BART,bartOnly_set2,bartIVT_set2 == totalIVT_set2 +Set contains only BART with Xfers,bartWithXfer_set2,"(bartIVT_set2 == totalIVT_set2) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET2')>0)" +Set contains premium mode with transfers to LB,premWithXfer_set2,"(premiumMode_set2>0) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET2')>0)" +Number transfers,transfers_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET2')" +0-1 transfers,xfers1_set2,(transfers_set2>0) & (transfers_set2 <=1) +1-2 transfers,xfers2_set2,(transfers_set2>1) & (transfers_set2 <=2) +>2 transfers,xfers3_set2,(transfers_set2>2) +Commuter Rail Distance in miles [35 mph],crDistance_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET2') * (35/60)" +# set3,, +,not_transit_available_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BEST_MODE_SET3')==0" +Total IVT,totalIVT_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'FR_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3') + los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3') " +IVT on BART,bartIVT_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')" +premium modes used,premiumMode_set3,"(los.get_tappairs3d(df.btap, df.atap, df.tod, 'EB_TIME_SET3')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'HR_TIME_SET3')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LR_TIME_SET3')>0) | (los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3')>0)" +only travel by BART,bartOnly_set3,bartIVT_set3 == totalIVT_set3 +Set contains only BART with Xfers,bartWithXfer_set3,"(bartIVT_set3 == totalIVT_set3) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET3')>0)" +Set contains premium mode with transfers to LB,premWithXfer_set3,"(premiumMode_set3>0) & (los.get_tappairs3d(df.btap, df.atap, df.tod, 'LB_TIME_SET3')>0)" +Number transfers,transfers_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'XFERS_SET3')" +0-1 transfers,xfers1_set3,(transfers_set3>0) & (transfers_set3 <=1) +1-2 transfers,xfers2_set3,(transfers_set3>1) & (transfers_set3 <=2) +>2 transfers,xfers3_set3,(transfers_set3>2) +Commuter Rail Distance in miles [35 mph],crDistance_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'CR_TIME_SET3') * (35/60)" diff --git a/activitysim/examples/example_marin/configs/tvpb_utility_walk_maz_tap.csv b/activitysim/examples/prototype_marin/configs/tvpb_utility_walk_maz_tap.csv similarity index 98% rename from activitysim/examples/example_marin/configs/tvpb_utility_walk_maz_tap.csv rename to activitysim/examples/prototype_marin/configs/tvpb_utility_walk_maz_tap.csv index fdb64bd1e9..7bc72761c8 100755 --- a/activitysim/examples/example_marin/configs/tvpb_utility_walk_maz_tap.csv +++ b/activitysim/examples/prototype_marin/configs/tvpb_utility_walk_maz_tap.csv @@ -1,4 +1,4 @@ -Label,Description,Expression,utility -#,,,FIXME column values shouldn't ever be na if different moides have different tables? -#util_walk_available,walk available,@df.walk_time.isna() * C_UNAVAILABLE,1 -util_walk_time,walk time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_walkAcc * df.WALK_TRANSIT_DIST*(60/walk_speed)",1 +Label,Description,Expression,utility +#,,,FIXME column values shouldn't ever be na if different moides have different tables? +#util_walk_available,walk available,@df.walk_time.isna() * C_UNAVAILABLE,1 +util_walk_time,walk time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_walkAcc * df.WALK_TRANSIT_DIST*(60/walk_speed)",1 diff --git a/activitysim/examples/example_marin/data/accessibility.csv b/activitysim/examples/prototype_marin/data/accessibility.csv similarity index 100% rename from activitysim/examples/example_marin/data/accessibility.csv rename to activitysim/examples/prototype_marin/data/accessibility.csv diff --git a/activitysim/examples/example_marin/data/highway_skims_AM.omx b/activitysim/examples/prototype_marin/data/highway_skims_AM.omx similarity index 100% rename from activitysim/examples/example_marin/data/highway_skims_AM.omx rename to activitysim/examples/prototype_marin/data/highway_skims_AM.omx diff --git a/activitysim/examples/example_marin/data/highway_skims_EA.omx b/activitysim/examples/prototype_marin/data/highway_skims_EA.omx similarity index 100% rename from activitysim/examples/example_marin/data/highway_skims_EA.omx rename to activitysim/examples/prototype_marin/data/highway_skims_EA.omx diff --git a/activitysim/examples/example_marin/data/highway_skims_EV.omx b/activitysim/examples/prototype_marin/data/highway_skims_EV.omx similarity index 100% rename from activitysim/examples/example_marin/data/highway_skims_EV.omx rename to activitysim/examples/prototype_marin/data/highway_skims_EV.omx diff --git a/activitysim/examples/example_marin/data/highway_skims_MD.omx b/activitysim/examples/prototype_marin/data/highway_skims_MD.omx similarity index 100% rename from activitysim/examples/example_marin/data/highway_skims_MD.omx rename to activitysim/examples/prototype_marin/data/highway_skims_MD.omx diff --git a/activitysim/examples/example_marin/data/highway_skims_PM.omx b/activitysim/examples/prototype_marin/data/highway_skims_PM.omx similarity index 100% rename from activitysim/examples/example_marin/data/highway_skims_PM.omx rename to activitysim/examples/prototype_marin/data/highway_skims_PM.omx diff --git a/activitysim/examples/example_marin/data/households.csv b/activitysim/examples/prototype_marin/data/households.csv similarity index 100% rename from activitysim/examples/example_marin/data/households.csv rename to activitysim/examples/prototype_marin/data/households.csv diff --git a/activitysim/examples/example_marin/data/land_use.csv b/activitysim/examples/prototype_marin/data/land_use.csv similarity index 100% rename from activitysim/examples/example_marin/data/land_use.csv rename to activitysim/examples/prototype_marin/data/land_use.csv diff --git a/activitysim/examples/example_marin/data/maz_maz_bike.csv b/activitysim/examples/prototype_marin/data/maz_maz_bike.csv similarity index 100% rename from activitysim/examples/example_marin/data/maz_maz_bike.csv rename to activitysim/examples/prototype_marin/data/maz_maz_bike.csv diff --git a/activitysim/examples/example_marin/data/maz_maz_walk.csv b/activitysim/examples/prototype_marin/data/maz_maz_walk.csv similarity index 100% rename from activitysim/examples/example_marin/data/maz_maz_walk.csv rename to activitysim/examples/prototype_marin/data/maz_maz_walk.csv diff --git a/activitysim/examples/example_marin/data/maz_tap_walk.csv b/activitysim/examples/prototype_marin/data/maz_tap_walk.csv similarity index 100% rename from activitysim/examples/example_marin/data/maz_tap_walk.csv rename to activitysim/examples/prototype_marin/data/maz_tap_walk.csv diff --git a/activitysim/examples/example_marin/data/maz_taz.csv b/activitysim/examples/prototype_marin/data/maz_taz.csv similarity index 100% rename from activitysim/examples/example_marin/data/maz_taz.csv rename to activitysim/examples/prototype_marin/data/maz_taz.csv diff --git a/activitysim/examples/example_marin/data/maz_taz_tap_drive.csv b/activitysim/examples/prototype_marin/data/maz_taz_tap_drive.csv similarity index 100% rename from activitysim/examples/example_marin/data/maz_taz_tap_drive.csv rename to activitysim/examples/prototype_marin/data/maz_taz_tap_drive.csv diff --git a/activitysim/examples/example_marin/data/persons.csv b/activitysim/examples/prototype_marin/data/persons.csv similarity index 100% rename from activitysim/examples/example_marin/data/persons.csv rename to activitysim/examples/prototype_marin/data/persons.csv diff --git a/activitysim/examples/example_marin/data/tap.csv b/activitysim/examples/prototype_marin/data/tap.csv similarity index 100% rename from activitysim/examples/example_marin/data/tap.csv rename to activitysim/examples/prototype_marin/data/tap.csv diff --git a/activitysim/examples/example_marin/data/tap_lines.csv b/activitysim/examples/prototype_marin/data/tap_lines.csv similarity index 100% rename from activitysim/examples/example_marin/data/tap_lines.csv rename to activitysim/examples/prototype_marin/data/tap_lines.csv diff --git a/activitysim/examples/example_marin/data/taz_skims.omx b/activitysim/examples/prototype_marin/data/taz_skims.omx similarity index 100% rename from activitysim/examples/example_marin/data/taz_skims.omx rename to activitysim/examples/prototype_marin/data/taz_skims.omx diff --git a/activitysim/examples/example_marin/data/transit_skims_SET1.omx b/activitysim/examples/prototype_marin/data/transit_skims_SET1.omx similarity index 100% rename from activitysim/examples/example_marin/data/transit_skims_SET1.omx rename to activitysim/examples/prototype_marin/data/transit_skims_SET1.omx diff --git a/activitysim/examples/example_marin/data/transit_skims_SET2.omx b/activitysim/examples/prototype_marin/data/transit_skims_SET2.omx similarity index 100% rename from activitysim/examples/example_marin/data/transit_skims_SET2.omx rename to activitysim/examples/prototype_marin/data/transit_skims_SET2.omx diff --git a/activitysim/examples/example_marin/data/transit_skims_SET3.omx b/activitysim/examples/prototype_marin/data/transit_skims_SET3.omx similarity index 100% rename from activitysim/examples/example_marin/data/transit_skims_SET3.omx rename to activitysim/examples/prototype_marin/data/transit_skims_SET3.omx diff --git a/activitysim/examples/example_marin/data/work_tours.csv b/activitysim/examples/prototype_marin/data/work_tours.csv similarity index 100% rename from activitysim/examples/example_marin/data/work_tours.csv rename to activitysim/examples/prototype_marin/data/work_tours.csv diff --git a/activitysim/examples/example_psrc/test/output/.gitignore b/activitysim/examples/prototype_marin/output/.gitignore similarity index 100% rename from activitysim/examples/example_psrc/test/output/.gitignore rename to activitysim/examples/prototype_marin/output/.gitignore diff --git a/activitysim/examples/example_psrc/output/cache/.gitignore b/activitysim/examples/prototype_marin/output/cache/.gitignore similarity index 100% rename from activitysim/examples/example_psrc/output/cache/.gitignore rename to activitysim/examples/prototype_marin/output/cache/.gitignore diff --git a/activitysim/examples/example_psrc/output/trace/.gitignore b/activitysim/examples/prototype_marin/output/log/.gitignore similarity index 100% rename from activitysim/examples/example_psrc/output/trace/.gitignore rename to activitysim/examples/prototype_marin/output/log/.gitignore diff --git a/activitysim/examples/example_psrc/test/output/trace/.gitignore b/activitysim/examples/prototype_marin/output/trace/.gitignore similarity index 100% rename from activitysim/examples/example_psrc/test/output/trace/.gitignore rename to activitysim/examples/prototype_marin/output/trace/.gitignore diff --git a/activitysim/examples/example_marin/scripts/marin_crop.py b/activitysim/examples/prototype_marin/scripts/marin_crop.py similarity index 96% rename from activitysim/examples/example_marin/scripts/marin_crop.py rename to activitysim/examples/prototype_marin/scripts/marin_crop.py index f3a92cc0eb..7eaddc554f 100644 --- a/activitysim/examples/example_marin/scripts/marin_crop.py +++ b/activitysim/examples/prototype_marin/scripts/marin_crop.py @@ -1,244 +1,244 @@ -# crop marin tvpb example data processing to one county -# Ben Stabler, ben.stabler@rsginc.com, 09/17/20 - -import os -import pandas as pd -import openmatrix as omx -import argparse -import numpy as np - -MAZ_OFFSET = 100000 - -segments = { - 'test': {'DistName': ["Downtown SF"]}, - 'marin_sf': {'CountyName': ["Marin", "San Francisco"]}, - 'full': {}, -} - -parser = argparse.ArgumentParser(description='crop Marin raw_data') -parser.add_argument('segment_name', metavar='segment_name', type=str, nargs=1, - help=f"geography segmentation (e.g. full)") - -parser.add_argument('-c', '--check_geography', - default=False, - action='store_true', - help='check consistency of MAZ, TAZ, TAP zone_ids and foreign keys & write orphan_households file') - -args = parser.parse_args() - - -segment_name = args.segment_name[0] -check_geography = args.check_geography - -assert segment_name in segments.keys(), f"Unknown seg: {segment_name}" - -input_dir = './data_raw' -output_dir = f'./data_{segment_name}' - - -print(f"segment_name {segment_name}") - -print(f"input_dir {input_dir}") -print(f"output_dir {output_dir}") - -print(f"check_geography {check_geography}") - -if not os.path.isdir(output_dir): - print(f"creating output directory {output_dir}") - os.mkdir(output_dir) - - -def input_path(file_name): - return os.path.join(input_dir, file_name) - - -def output_path(file_name): - return os.path.join(output_dir, file_name) - - -def patch_maz(df, maz_offset): - for c in df.columns: - if c in ['MAZ', 'OMAZ', 'DMAZ', 'mgra', 'orig_mgra', 'dest_mgra']: - df[c] += maz_offset - return df - - -def read_csv(file_name): - df = pd.read_csv(input_path(file_name)) - if MAZ_OFFSET: - df = patch_maz(df, MAZ_OFFSET) - print(f"read {file_name} {df.shape}") - return df - - -def to_csv(df, file_name): - df.to_csv(output_path(file_name), index=False) - print(f"write {file_name} {df.shape}") - - -# non-standard input file names -LAND_USE = "maz_data_asim.csv" -HOUSEHOLDS = "households_asim.csv" -PERSONS = "persons_asim.csv" -MAZ_TAZ = "maz_taz.csv" -TAP_MAZ = "tap_data.csv" -ACCESSIBILITY = "access.csv" -WORK_TOURS = "work_tours.csv" - -if check_geography: - - # ######## check for orphan_households not in any maz in land_use - land_use = read_csv(LAND_USE) - land_use = land_use[['MAZ', 'TAZ']] - land_use = land_use.sort_values(['TAZ', 'MAZ']) - - households = read_csv(HOUSEHOLDS) - orphan_households = households[~households.MAZ.isin(land_use.MAZ)] - print(f"{len(orphan_households)} orphan_households") - - # write orphan_households to INPUT directory (since it doesn't belong in output) - file_name = "orphan_households.csv" - print(f"writing {file_name} {orphan_households.shape} to {input_path(file_name)}") - orphan_households.to_csv(input_path(file_name), index=False) - - # ######## check that land_use and maz and taz tables have same MAZs and TAZs - - # could just build maz and taz files, but want to make sure PSRC data is right - - land_use = read_csv(LAND_USE) - land_use = land_use.sort_values('MAZ') - maz = read_csv(MAZ_TAZ).sort_values('MAZ') - - # ### FATAL ### - if not land_use.MAZ.isin(maz.MAZ).all(): - print(f"land_use.MAZ not in maz.MAZ\n{land_use.MAZ[~land_use.MAZ.isin(maz.MAZ)]}") - raise RuntimeError(f"land_use.MAZ not in maz.MAZ") - - if not maz.MAZ.isin(land_use.MAZ).all(): - print(f"maz.MAZ not in land_use.MAZ\n{maz.MAZ[~maz.MAZ.isin(land_use.MAZ)]}") - - # ### FATAL ### - if not land_use.TAZ.isin(maz.TAZ).all(): - print(f"land_use.TAZ not in maz.TAZ\n{land_use.TAZ[~land_use.TAZ.isin(maz.TAZ)]}") - raise RuntimeError(f"land_use.TAZ not in maz.TAZ") - - if not maz.TAZ.isin(land_use.TAZ).all(): - print(f"maz.TAZ not in land_use.TAZ\n{maz.TAZ[~maz.TAZ.isin(land_use.TAZ)]}") - - -# land_use - -land_use = read_csv(LAND_USE) - -slicer = segments[segment_name] -for slice_col, slice_values in slicer.items(): - print(f"slice {slice_col}: {slice_values}") - - land_use = land_use[land_use[slice_col].isin(slice_values)] - -print(f"land_use shape after slicing {land_use.shape}") -to_csv(land_use, 'land_use.csv') - -# maz_taz, tazs, taps - -maz_taz = land_use[['MAZ', 'TAZ']] -to_csv(maz_taz, "maz_taz.csv") - -tazs = land_use["TAZ"].unique() -tazs.sort() - -taps = read_csv(TAP_MAZ) -taps = taps[['TAP', 'TAZ']].sort_values(by='TAP') -taps = taps[taps["TAZ"].isin(tazs)] -to_csv(taps, "tap.csv") - -# maz to tap walk, bike - -maz_tap_walk = read_csv("maz_tap_walk.csv") -maz_maz_walk = read_csv("maz_maz_walk.csv") -maz_maz_bike = read_csv("maz_maz_bike.csv") - -maz_tap_walk = maz_tap_walk[maz_tap_walk["MAZ"].isin(land_use["MAZ"]) & maz_tap_walk["TAP"].isin(taps["TAP"])] -maz_maz_walk = maz_maz_walk[maz_maz_walk["OMAZ"].isin(land_use["MAZ"]) & maz_maz_walk["DMAZ"].isin(land_use["MAZ"])] -maz_maz_bike = maz_maz_bike[maz_maz_bike["OMAZ"].isin(land_use["MAZ"]) & maz_maz_bike["DMAZ"].isin(land_use["MAZ"])] - -to_csv(maz_tap_walk, "maz_tap_walk.csv") -to_csv(maz_maz_walk, "maz_maz_walk.csv") -to_csv(maz_maz_bike, "maz_maz_bike.csv") - -tap_lines = read_csv("tap_lines.csv") -tap_lines = tap_lines[tap_lines['TAP'].isin(taps["TAP"])] -to_csv(tap_lines, "tap_lines.csv") - -# taz to tap drive data - -taz_tap_drive = read_csv("maz_taz_tap_drive.csv") -taz_tap_drive = taz_tap_drive[taz_tap_drive["MAZ"].isin(land_use["MAZ"]) & taz_tap_drive["TAP"].isin(taps["TAP"])] -to_csv(taz_tap_drive, "maz_taz_tap_drive.csv") - - -# accessibility data - -access = read_csv(ACCESSIBILITY) -access = access[access["mgra"].isin(land_use["MAZ"])] -to_csv(access, "accessibility.csv") - - -# households - -households = read_csv(HOUSEHOLDS) -households = households[households["MAZ"].isin(land_use["MAZ"])] -to_csv(households, "households.csv") - -# persons - -persons = read_csv(PERSONS) -persons = persons[persons["HHID"].isin(households["HHID"])] -to_csv(persons, "persons.csv") - -# tours file - -work_tours = read_csv(WORK_TOURS) -work_tours = work_tours[work_tours["hh_id"].isin(households["HHID"])] -work_tours = work_tours[work_tours["orig_mgra"].isin(land_use["MAZ"]) & work_tours["dest_mgra"].isin(land_use["MAZ"])] -to_csv(work_tours, "work_tours.csv") - -# skims - -taz_indexes = (tazs - 1).tolist() # offset_map -tap_indexes = (taps["TAP"] - 1).tolist() # offset_map -time_periods = ["AM", "EA", "EV", "MD", "PM"] -skim_data_type = np.float32 - -# taz skims with skim_data_type np.float32 are under 2GB - otherwise we would need to further segment them - -for tp in time_periods: - in_file_name = f'HWYSKM{tp}_taz_rename.omx' - taz_file_in = omx.open_file(input_path(in_file_name)) - out_file_name = f'highway_skims_{tp}.omx' - taz_file_out = omx.open_file(output_path(out_file_name), 'w') - taz_file_out.create_mapping('ZONE', tazs.tolist()) - for mat_name in taz_file_in.list_matrices(): - # make sure we have a vanilla numpy array, not a CArray - m = np.asanyarray(taz_file_in[mat_name]).astype(skim_data_type) - m = m[taz_indexes, :][:, taz_indexes] - taz_file_out[mat_name] = m - print(f"taz {mat_name} {m.shape}") - taz_file_in.close() - taz_file_out.close() - -for skim_set in ["SET1", "SET2", "SET3"]: - out_file_name = f'transit_skims_{skim_set}.omx' - tap_file_out = omx.open_file(output_path(out_file_name), 'w') - tap_file_out.create_mapping('TAP', taps["TAP"].tolist()) - for tp in time_periods: - in_file_name = f'transit_skims_{tp}_{skim_set}_rename.omx' - tap_file_in = omx.open_file(input_path(in_file_name)) - for mat_name in tap_file_in.list_matrices(): - # make sure we have a vanilla numpy array, not a CArray - m = np.asanyarray(tap_file_in[mat_name]).astype(skim_data_type) - m = m[tap_indexes, :][:, tap_indexes] - tap_file_out[mat_name] = m - print(f"tap {skim_set} {mat_name} {m.shape}") - tap_file_in.close() - tap_file_out.close() +# crop marin tvpb example data processing to one county +# Ben Stabler, ben.stabler@rsginc.com, 09/17/20 + +import os +import pandas as pd +import openmatrix as omx +import argparse +import numpy as np + +MAZ_OFFSET = 100000 + +segments = { + 'test': {'DistName': ["Downtown SF"]}, + 'marin_sf': {'CountyName': ["Marin", "San Francisco"]}, + 'full': {}, +} + +parser = argparse.ArgumentParser(description='crop Marin raw_data') +parser.add_argument('segment_name', metavar='segment_name', type=str, nargs=1, + help=f"geography segmentation (e.g. full)") + +parser.add_argument('-c', '--check_geography', + default=False, + action='store_true', + help='check consistency of MAZ, TAZ, TAP zone_ids and foreign keys & write orphan_households file') + +args = parser.parse_args() + + +segment_name = args.segment_name[0] +check_geography = args.check_geography + +assert segment_name in segments.keys(), f"Unknown seg: {segment_name}" + +input_dir = './data_raw' +output_dir = f'./data_{segment_name}' + + +print(f"segment_name {segment_name}") + +print(f"input_dir {input_dir}") +print(f"output_dir {output_dir}") + +print(f"check_geography {check_geography}") + +if not os.path.isdir(output_dir): + print(f"creating output directory {output_dir}") + os.mkdir(output_dir) + + +def input_path(file_name): + return os.path.join(input_dir, file_name) + + +def output_path(file_name): + return os.path.join(output_dir, file_name) + + +def patch_maz(df, maz_offset): + for c in df.columns: + if c in ['MAZ', 'OMAZ', 'DMAZ', 'mgra', 'orig_mgra', 'dest_mgra']: + df[c] += maz_offset + return df + + +def read_csv(file_name): + df = pd.read_csv(input_path(file_name)) + if MAZ_OFFSET: + df = patch_maz(df, MAZ_OFFSET) + print(f"read {file_name} {df.shape}") + return df + + +def to_csv(df, file_name): + df.to_csv(output_path(file_name), index=False) + print(f"write {file_name} {df.shape}") + + +# non-standard input file names +LAND_USE = "maz_data_asim.csv" +HOUSEHOLDS = "households_asim.csv" +PERSONS = "persons_asim.csv" +MAZ_TAZ = "maz_taz.csv" +TAP_MAZ = "tap_data.csv" +ACCESSIBILITY = "access.csv" +WORK_TOURS = "work_tours.csv" + +if check_geography: + + # ######## check for orphan_households not in any maz in land_use + land_use = read_csv(LAND_USE) + land_use = land_use[['MAZ', 'TAZ']] + land_use = land_use.sort_values(['TAZ', 'MAZ']) + + households = read_csv(HOUSEHOLDS) + orphan_households = households[~households.MAZ.isin(land_use.MAZ)] + print(f"{len(orphan_households)} orphan_households") + + # write orphan_households to INPUT directory (since it doesn't belong in output) + file_name = "orphan_households.csv" + print(f"writing {file_name} {orphan_households.shape} to {input_path(file_name)}") + orphan_households.to_csv(input_path(file_name), index=False) + + # ######## check that land_use and maz and taz tables have same MAZs and TAZs + + # could just build maz and taz files, but want to make sure PSRC data is right + + land_use = read_csv(LAND_USE) + land_use = land_use.sort_values('MAZ') + maz = read_csv(MAZ_TAZ).sort_values('MAZ') + + # ### FATAL ### + if not land_use.MAZ.isin(maz.MAZ).all(): + print(f"land_use.MAZ not in maz.MAZ\n{land_use.MAZ[~land_use.MAZ.isin(maz.MAZ)]}") + raise RuntimeError(f"land_use.MAZ not in maz.MAZ") + + if not maz.MAZ.isin(land_use.MAZ).all(): + print(f"maz.MAZ not in land_use.MAZ\n{maz.MAZ[~maz.MAZ.isin(land_use.MAZ)]}") + + # ### FATAL ### + if not land_use.TAZ.isin(maz.TAZ).all(): + print(f"land_use.TAZ not in maz.TAZ\n{land_use.TAZ[~land_use.TAZ.isin(maz.TAZ)]}") + raise RuntimeError(f"land_use.TAZ not in maz.TAZ") + + if not maz.TAZ.isin(land_use.TAZ).all(): + print(f"maz.TAZ not in land_use.TAZ\n{maz.TAZ[~maz.TAZ.isin(land_use.TAZ)]}") + + +# land_use + +land_use = read_csv(LAND_USE) + +slicer = segments[segment_name] +for slice_col, slice_values in slicer.items(): + print(f"slice {slice_col}: {slice_values}") + + land_use = land_use[land_use[slice_col].isin(slice_values)] + +print(f"land_use shape after slicing {land_use.shape}") +to_csv(land_use, 'land_use.csv') + +# maz_taz, tazs, taps + +maz_taz = land_use[['MAZ', 'TAZ']] +to_csv(maz_taz, "maz_taz.csv") + +tazs = land_use["TAZ"].unique() +tazs.sort() + +taps = read_csv(TAP_MAZ) +taps = taps[['TAP', 'TAZ']].sort_values(by='TAP') +taps = taps[taps["TAZ"].isin(tazs)] +to_csv(taps, "tap.csv") + +# maz to tap walk, bike + +maz_tap_walk = read_csv("maz_tap_walk.csv") +maz_maz_walk = read_csv("maz_maz_walk.csv") +maz_maz_bike = read_csv("maz_maz_bike.csv") + +maz_tap_walk = maz_tap_walk[maz_tap_walk["MAZ"].isin(land_use["MAZ"]) & maz_tap_walk["TAP"].isin(taps["TAP"])] +maz_maz_walk = maz_maz_walk[maz_maz_walk["OMAZ"].isin(land_use["MAZ"]) & maz_maz_walk["DMAZ"].isin(land_use["MAZ"])] +maz_maz_bike = maz_maz_bike[maz_maz_bike["OMAZ"].isin(land_use["MAZ"]) & maz_maz_bike["DMAZ"].isin(land_use["MAZ"])] + +to_csv(maz_tap_walk, "maz_tap_walk.csv") +to_csv(maz_maz_walk, "maz_maz_walk.csv") +to_csv(maz_maz_bike, "maz_maz_bike.csv") + +tap_lines = read_csv("tap_lines.csv") +tap_lines = tap_lines[tap_lines['TAP'].isin(taps["TAP"])] +to_csv(tap_lines, "tap_lines.csv") + +# taz to tap drive data + +taz_tap_drive = read_csv("maz_taz_tap_drive.csv") +taz_tap_drive = taz_tap_drive[taz_tap_drive["MAZ"].isin(land_use["MAZ"]) & taz_tap_drive["TAP"].isin(taps["TAP"])] +to_csv(taz_tap_drive, "maz_taz_tap_drive.csv") + + +# accessibility data + +access = read_csv(ACCESSIBILITY) +access = access[access["mgra"].isin(land_use["MAZ"])] +to_csv(access, "accessibility.csv") + + +# households + +households = read_csv(HOUSEHOLDS) +households = households[households["MAZ"].isin(land_use["MAZ"])] +to_csv(households, "households.csv") + +# persons + +persons = read_csv(PERSONS) +persons = persons[persons["HHID"].isin(households["HHID"])] +to_csv(persons, "persons.csv") + +# tours file + +work_tours = read_csv(WORK_TOURS) +work_tours = work_tours[work_tours["hh_id"].isin(households["HHID"])] +work_tours = work_tours[work_tours["orig_mgra"].isin(land_use["MAZ"]) & work_tours["dest_mgra"].isin(land_use["MAZ"])] +to_csv(work_tours, "work_tours.csv") + +# skims + +taz_indexes = (tazs - 1).tolist() # offset_map +tap_indexes = (taps["TAP"] - 1).tolist() # offset_map +time_periods = ["AM", "EA", "EV", "MD", "PM"] +skim_data_type = np.float32 + +# taz skims with skim_data_type np.float32 are under 2GB - otherwise we would need to further segment them + +for tp in time_periods: + in_file_name = f'HWYSKM{tp}_taz_rename.omx' + taz_file_in = omx.open_file(input_path(in_file_name)) + out_file_name = f'highway_skims_{tp}.omx' + taz_file_out = omx.open_file(output_path(out_file_name), 'w') + taz_file_out.create_mapping('ZONE', tazs.tolist()) + for mat_name in taz_file_in.list_matrices(): + # make sure we have a vanilla numpy array, not a CArray + m = np.asanyarray(taz_file_in[mat_name]).astype(skim_data_type) + m = m[taz_indexes, :][:, taz_indexes] + taz_file_out[mat_name] = m + print(f"taz {mat_name} {m.shape}") + taz_file_in.close() + taz_file_out.close() + +for skim_set in ["SET1", "SET2", "SET3"]: + out_file_name = f'transit_skims_{skim_set}.omx' + tap_file_out = omx.open_file(output_path(out_file_name), 'w') + tap_file_out.create_mapping('TAP', taps["TAP"].tolist()) + for tp in time_periods: + in_file_name = f'transit_skims_{tp}_{skim_set}_rename.omx' + tap_file_in = omx.open_file(input_path(in_file_name)) + for mat_name in tap_file_in.list_matrices(): + # make sure we have a vanilla numpy array, not a CArray + m = np.asanyarray(tap_file_in[mat_name]).astype(skim_data_type) + m = m[tap_indexes, :][:, tap_indexes] + tap_file_out[mat_name] = m + print(f"tap {skim_set} {mat_name} {m.shape}") + tap_file_in.close() + tap_file_out.close() diff --git a/activitysim/examples/example_multiple_zone/marin_fix.py b/activitysim/examples/prototype_marin/scripts/marin_fix.py similarity index 100% rename from activitysim/examples/example_multiple_zone/marin_fix.py rename to activitysim/examples/prototype_marin/scripts/marin_fix.py diff --git a/activitysim/examples/example_marin/scripts/marin_work_tour_mode_choice_data.py b/activitysim/examples/prototype_marin/scripts/marin_work_tour_mode_choice_data.py similarity index 100% rename from activitysim/examples/example_marin/scripts/marin_work_tour_mode_choice_data.py rename to activitysim/examples/prototype_marin/scripts/marin_work_tour_mode_choice_data.py diff --git a/activitysim/examples/example_marin/scripts/notes.txt b/activitysim/examples/prototype_marin/scripts/notes.txt similarity index 100% rename from activitysim/examples/example_marin/scripts/notes.txt rename to activitysim/examples/prototype_marin/scripts/notes.txt diff --git a/activitysim/examples/example_marin/scripts/tvpb_validation.R b/activitysim/examples/prototype_marin/scripts/tvpb_validation.R similarity index 80% rename from activitysim/examples/example_marin/scripts/tvpb_validation.R rename to activitysim/examples/prototype_marin/scripts/tvpb_validation.R index de8eb01356..5ca979797e 100644 --- a/activitysim/examples/example_marin/scripts/tvpb_validation.R +++ b/activitysim/examples/prototype_marin/scripts/tvpb_validation.R @@ -1,6 +1,6 @@ #mode share -x=read.csv("C:/projects/activitysim/activitysim/examples/example_multiple_zone/output_3_marin_full/final_tours.csv") +x=read.csv("C:/projects/activitysim/activitysim/examples/placeholder_multiple_zone/output_3_marin_full/final_tours.csv") tm2_mode_codes = c("DRIVEALONEFREE","SHARED2FREE","SHARED2FREE","SHARED3FREE","SHARED3FREE","WALK","BIKE","WALK_TRANSIT","DRIVE_TRANSIT","DRIVE_TRANSIT","TAXI") names(tm2_mode_codes) = c(1,3,4,6,7,9,10,11,12,13,15) @@ -14,7 +14,7 @@ write.csv(asim_ms, "c:/projects/asim_ms.csv", row.names=F) #taps -taps=read.csv("C:/projects/activitysim/activitysim/examples/example_multiple_zone/data_3_marin_full/tap_data.csv") +taps=read.csv("C:/projects/activitysim/activitysim/examples/placeholder_multiple_zone/data_3_marin_full/tap_data.csv") tm2_out_btap = sort(table(x$tm2_out_btap)) asim_out_btap = sort(table(x$od_btap)) diff --git a/activitysim/examples/example_marin/test/configs/network_los.yaml b/activitysim/examples/prototype_marin/test/configs/network_los.yaml similarity index 100% rename from activitysim/examples/example_marin/test/configs/network_los.yaml rename to activitysim/examples/prototype_marin/test/configs/network_los.yaml diff --git a/activitysim/examples/example_marin/test/configs/settings.yaml b/activitysim/examples/prototype_marin/test/configs/settings.yaml similarity index 100% rename from activitysim/examples/example_marin/test/configs/settings.yaml rename to activitysim/examples/prototype_marin/test/configs/settings.yaml diff --git a/activitysim/examples/example_sandag/output_1/.gitignore b/activitysim/examples/prototype_marin/test/output/.gitignore similarity index 100% rename from activitysim/examples/example_sandag/output_1/.gitignore rename to activitysim/examples/prototype_marin/test/output/.gitignore diff --git a/activitysim/examples/example_psrc/test/output/cache/.gitignore b/activitysim/examples/prototype_marin/test/output/cache/.gitignore similarity index 100% rename from activitysim/examples/example_psrc/test/output/cache/.gitignore rename to activitysim/examples/prototype_marin/test/output/cache/.gitignore diff --git a/activitysim/examples/example_sandag/output_1/log/.gitignore b/activitysim/examples/prototype_marin/test/output/trace/.gitignore similarity index 100% rename from activitysim/examples/example_sandag/output_1/log/.gitignore rename to activitysim/examples/prototype_marin/test/output/trace/.gitignore diff --git a/activitysim/examples/example_marin/test/regress/final_tours.csv b/activitysim/examples/prototype_marin/test/regress/final_tours.csv similarity index 100% rename from activitysim/examples/example_marin/test/regress/final_tours.csv rename to activitysim/examples/prototype_marin/test/regress/final_tours.csv diff --git a/activitysim/examples/example_multiple_zone/test/simulation.py b/activitysim/examples/prototype_marin/test/simulation.py similarity index 94% rename from activitysim/examples/example_multiple_zone/test/simulation.py rename to activitysim/examples/prototype_marin/test/simulation.py index ec6a1181b1..97ca6b6483 100755 --- a/activitysim/examples/example_multiple_zone/test/simulation.py +++ b/activitysim/examples/prototype_marin/test/simulation.py @@ -1,15 +1,15 @@ -# ActivitySim -# See full license in LICENSE.txt. - -import sys -import argparse - -from activitysim.cli.run import add_run_args, run - -if __name__ == '__main__': - - parser = argparse.ArgumentParser() - add_run_args(parser) - args = parser.parse_args() - - sys.exit(run(args)) +# ActivitySim +# See full license in LICENSE.txt. + +import sys +import argparse + +from activitysim.cli.run import add_run_args, run + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + add_run_args(parser) + args = parser.parse_args() + + sys.exit(run(args)) diff --git a/activitysim/examples/example_marin/test/test_marin.py b/activitysim/examples/prototype_marin/test/test_marin.py similarity index 94% rename from activitysim/examples/example_marin/test/test_marin.py rename to activitysim/examples/prototype_marin/test/test_marin.py index 6217e8b690..ab87a47086 100644 --- a/activitysim/examples/example_marin/test/test_marin.py +++ b/activitysim/examples/prototype_marin/test/test_marin.py @@ -18,7 +18,7 @@ def teardown_function(func): def test_marin(): def example_path(dirname): - resource = os.path.join('examples', 'example_marin', dirname) + resource = os.path.join('examples', 'prototype_marin', dirname) return pkg_resources.resource_filename('activitysim', resource) def test_path(dirname): diff --git a/activitysim/examples/example_mtc/.gitignore b/activitysim/examples/prototype_mtc/.gitignore similarity index 100% rename from activitysim/examples/example_mtc/.gitignore rename to activitysim/examples/prototype_mtc/.gitignore diff --git a/activitysim/examples/example_mtc/README.MD b/activitysim/examples/prototype_mtc/README.MD similarity index 80% rename from activitysim/examples/example_mtc/README.MD rename to activitysim/examples/prototype_mtc/README.MD index 486dc9cd37..b9110fe219 100644 --- a/activitysim/examples/example_mtc/README.MD +++ b/activitysim/examples/prototype_mtc/README.MD @@ -1,4 +1,4 @@ -### MTC Example +### Prototype MTC Example The primary ActivitySim example model. See https://activitysim.github.io/activitysim for more information. diff --git a/activitysim/examples/example_psrc/configs/accessibility.csv b/activitysim/examples/prototype_mtc/configs/accessibility.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/accessibility.csv rename to activitysim/examples/prototype_mtc/configs/accessibility.csv index c40a672b18..db2275546a --- a/activitysim/examples/example_psrc/configs/accessibility.csv +++ b/activitysim/examples/prototype_mtc/configs/accessibility.csv @@ -1,59 +1,59 @@ -Description,Target,Expression -#,, -#,, auto peak -#,, -#,, assume peak occurs in AM for outbound and PM for inbound -peak round trip distance,_auPkTime,"skim_od[('SOVTOLL_TIME', 'AM')] + skim_do[('SOVTOLL_TIME', 'PM')]" -decay function,_decay, exp(_auPkTime * dispersion_parameter_automobile) -auto peak retail,auPkRetail,df.RETEMPN * _decay -auto peak total,auPkTotal,df.TOTEMP * _decay -#,, -#,, auto off-peak -#,, -#,, assume midday occurs entirely in the midday period -off-peak round trip distance,_auOpTime,"skim_od[('SOVTOLL_TIME', 'MD')] + skim_do[('SOVTOLL_TIME', 'MD')]" -decay function,_decay, exp(_auOpTime * dispersion_parameter_automobile) -auto off-peak retail,auOpRetail,df.RETEMPN * _decay -auto off-peak total,auOpTotal,df.TOTEMP * _decay -#,, -#,, transit peak -#,, -#,, assume peak outbound transit occurs in AM -o-d peak transit ivt,_inVehicleTime,"skim_od[('WLK_TRN_WLK_IVT', 'AM')]" -o-d peak transit ovt,_outOfVehicleTime,"skim_od[('WLK_TRN_WLK_IWAIT', 'AM')] + skim_od[('WLK_TRN_WLK_XWAIT', 'AM')] + skim_od[('WLK_TRN_WLK_WACC', 'AM')] + skim_od[('WLK_TRN_WLK_WAUX', 'AM')] + skim_od[('WLK_TRN_WLK_WEGR', 'AM')]" -o-d peak transit time,_trPkTime_od,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) / TRANSIT_SCALE_FACTOR -#,, assume peak inbound transit occurs in PM -d-o peak transit ivt,_inVehicleTime,"skim_do[('WLK_TRN_WLK_IVT', 'PM')]" -d-o peak transit ovt,_outOfVehicleTime,"skim_do[('WLK_TRN_WLK_IWAIT', 'PM')] + skim_do[('WLK_TRN_WLK_XWAIT', 'PM')] + skim_do[('WLK_TRN_WLK_WACC', 'PM')] + skim_do[('WLK_TRN_WLK_WAUX', 'PM')] + skim_do[('WLK_TRN_WLK_WEGR', 'PM')]" -d-o peak transit time,_trPkTime_do,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) / TRANSIT_SCALE_FACTOR -peak transit time,_trPkTime,(_trPkTime_od + _trPkTime_do).clip(0) -round trip path is available,_rt_available,(_trPkTime_od > 0) & (_trPkTime_do > 0) -decay function,_decay,_rt_available * exp(_trPkTime * dispersion_parameter_transit) -transit peak retail,trPkRetail,df.RETEMPN * _decay -transit peak total,trPkTotal,df.TOTEMP * _decay -#,, -#,, transit off-peak -#,, -#,, assume off-peak outbound transit occurs in the MD time period -o-d off-peak transit ivt,_inVehicleTime,"skim_od[('WLK_TRN_WLK_IVT', 'MD')]" -o-d off-peak transit ovt,_outOfVehicleTime,"skim_od[('WLK_TRN_WLK_IWAIT', 'MD')] + skim_od[('WLK_TRN_WLK_XWAIT', 'MD')] + skim_od[('WLK_TRN_WLK_WACC', 'MD')] + skim_od[('WLK_TRN_WLK_WAUX', 'MD')] + skim_od[('WLK_TRN_WLK_WEGR', 'MD')]" -o-d off-peak transit time,_trOpTime_od,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) / TRANSIT_SCALE_FACTOR -#,, assume off-peak inbound transit occurs in the MD time period -d-o off-peak transit ivt,_inVehicleTime,"skim_do[('WLK_TRN_WLK_IVT', 'MD')]" -d-o off-peak transit ovt,_outOfVehicleTime,"skim_do[('WLK_TRN_WLK_IWAIT', 'MD')] + skim_do[('WLK_TRN_WLK_XWAIT', 'MD')] + skim_do[('WLK_TRN_WLK_WACC', 'MD')] + skim_do[('WLK_TRN_WLK_WAUX', 'MD')] + skim_do[('WLK_TRN_WLK_WEGR', 'MD')]" -d-o off-peak transit time,_trOpTime_do,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) / TRANSIT_SCALE_FACTOR -peak transit time,_trOpTime,(_trOpTime_od + _trOpTime_do).clip(0) -#,,FIXME - _rt_available calculation appears to be wrong in mtctm1 accessibility.job -#round trip path is available,_rt_available,(_trOpTime > 0) -round trip path is available,_rt_available,(_trOpTime_od > 0) & (_trOpTime_do > 0) -decay function,_decay,_rt_available * exp(_trOpTime * dispersion_parameter_transit) -transit off-peak retail,trOpRetail,df.RETEMPN * _decay -transit off-peak total,trOpTotal,df.TOTEMP * _decay -#,, -#,, non motorized -#,, -non-motorized round trip distance,_nmDist,skim_od['DISTWALK'] + skim_do['DISTWALK'] -round trip path is available,_rt_available,_nmDist <= maximum_walk_distance -decay function,_decay,_rt_available * exp(_nmDist * dispersion_parameter_walk) -retail accessibility,nmRetail,df.RETEMPN * _decay -total accessibility,nmTotal,df.TOTEMP * _decay +Description,Target,Expression +#,, +#,, auto peak +#,, +#,, assume peak occurs in AM for outbound and PM for inbound +peak round trip distance,_auPkTime,"skim_od[('SOVTOLL_TIME', 'AM')] + skim_do[('SOVTOLL_TIME', 'PM')]" +decay function,_decay, exp(_auPkTime * dispersion_parameter_automobile) +auto peak retail,auPkRetail,df.RETEMPN * _decay +auto peak total,auPkTotal,df.TOTEMP * _decay +#,, +#,, auto off-peak +#,, +#,, assume midday occurs entirely in the midday period +off-peak round trip distance,_auOpTime,"skim_od[('SOVTOLL_TIME', 'MD')] + skim_do[('SOVTOLL_TIME', 'MD')]" +decay function,_decay, exp(_auOpTime * dispersion_parameter_automobile) +auto off-peak retail,auOpRetail,df.RETEMPN * _decay +auto off-peak total,auOpTotal,df.TOTEMP * _decay +#,, +#,, transit peak +#,, +#,, assume peak outbound transit occurs in AM +o-d peak transit ivt,_inVehicleTime,"skim_od[('WLK_TRN_WLK_IVT', 'AM')]" +o-d peak transit ovt,_outOfVehicleTime,"skim_od[('WLK_TRN_WLK_IWAIT', 'AM')] + skim_od[('WLK_TRN_WLK_XWAIT', 'AM')] + skim_od[('WLK_TRN_WLK_WACC', 'AM')] + skim_od[('WLK_TRN_WLK_WAUX', 'AM')] + skim_od[('WLK_TRN_WLK_WEGR', 'AM')]" +o-d peak transit time,_trPkTime_od,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) / TRANSIT_SCALE_FACTOR +#,, assume peak inbound transit occurs in PM +d-o peak transit ivt,_inVehicleTime,"skim_do[('WLK_TRN_WLK_IVT', 'PM')]" +d-o peak transit ovt,_outOfVehicleTime,"skim_do[('WLK_TRN_WLK_IWAIT', 'PM')] + skim_do[('WLK_TRN_WLK_XWAIT', 'PM')] + skim_do[('WLK_TRN_WLK_WACC', 'PM')] + skim_do[('WLK_TRN_WLK_WAUX', 'PM')] + skim_do[('WLK_TRN_WLK_WEGR', 'PM')]" +d-o peak transit time,_trPkTime_do,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) / TRANSIT_SCALE_FACTOR +peak transit time,_trPkTime,(_trPkTime_od + _trPkTime_do).clip(0) +round trip path is available,_rt_available,(_trPkTime_od > 0) & (_trPkTime_do > 0) +decay function,_decay,_rt_available * exp(_trPkTime * dispersion_parameter_transit) +transit peak retail,trPkRetail,df.RETEMPN * _decay +transit peak total,trPkTotal,df.TOTEMP * _decay +#,, +#,, transit off-peak +#,, +#,, assume off-peak outbound transit occurs in the MD time period +o-d off-peak transit ivt,_inVehicleTime,"skim_od[('WLK_TRN_WLK_IVT', 'MD')]" +o-d off-peak transit ovt,_outOfVehicleTime,"skim_od[('WLK_TRN_WLK_IWAIT', 'MD')] + skim_od[('WLK_TRN_WLK_XWAIT', 'MD')] + skim_od[('WLK_TRN_WLK_WACC', 'MD')] + skim_od[('WLK_TRN_WLK_WAUX', 'MD')] + skim_od[('WLK_TRN_WLK_WEGR', 'MD')]" +o-d off-peak transit time,_trOpTime_od,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) / TRANSIT_SCALE_FACTOR +#,, assume off-peak inbound transit occurs in the MD time period +d-o off-peak transit ivt,_inVehicleTime,"skim_do[('WLK_TRN_WLK_IVT', 'MD')]" +d-o off-peak transit ovt,_outOfVehicleTime,"skim_do[('WLK_TRN_WLK_IWAIT', 'MD')] + skim_do[('WLK_TRN_WLK_XWAIT', 'MD')] + skim_do[('WLK_TRN_WLK_WACC', 'MD')] + skim_do[('WLK_TRN_WLK_WAUX', 'MD')] + skim_do[('WLK_TRN_WLK_WEGR', 'MD')]" +d-o off-peak transit time,_trOpTime_do,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) / TRANSIT_SCALE_FACTOR +peak transit time,_trOpTime,(_trOpTime_od + _trOpTime_do).clip(0) +#,,FIXME - _rt_available calculation appears to be wrong in mtctm1 accessibility.job +#round trip path is available,_rt_available,(_trOpTime > 0) +round trip path is available,_rt_available,(_trOpTime_od > 0) & (_trOpTime_do > 0) +decay function,_decay,_rt_available * exp(_trOpTime * dispersion_parameter_transit) +transit off-peak retail,trOpRetail,df.RETEMPN * _decay +transit off-peak total,trOpTotal,df.TOTEMP * _decay +#,, +#,, non motorized +#,, +non-motorized round trip distance,_nmDist,skim_od['DISTWALK'] + skim_do['DISTWALK'] +round trip path is available,_rt_available,_nmDist <= maximum_walk_distance +decay function,_decay,_rt_available * exp(_nmDist * dispersion_parameter_walk) +retail accessibility,nmRetail,df.RETEMPN * _decay +total accessibility,nmTotal,df.TOTEMP * _decay diff --git a/activitysim/examples/example_psrc/configs/accessibility.yaml b/activitysim/examples/prototype_mtc/configs/accessibility.yaml old mode 100755 new mode 100644 similarity index 96% rename from activitysim/examples/example_psrc/configs/accessibility.yaml rename to activitysim/examples/prototype_mtc/configs/accessibility.yaml index 0ba32fd50a..d8c1e038c5 --- a/activitysim/examples/example_psrc/configs/accessibility.yaml +++ b/activitysim/examples/prototype_mtc/configs/accessibility.yaml @@ -1,13 +1,13 @@ - -# columns from land_use table to add to df -land_use_columns: ['RETEMPN', 'TOTEMP'] - -CONSTANTS: - # dispersion parameters - dispersion_parameter_automobile: -0.05 - dispersion_parameter_transit: -0.05 - dispersion_parameter_walk: -1.00 - # maximum walk distance in miles - maximum_walk_distance: 3.0 - # perceived minute of in-vehicle time for every minute of out-of-vehicle time - out_of_vehicle_time_weight: 2.0 + +# columns from land_use table to add to df +land_use_columns: ['RETEMPN', 'TOTEMP'] + +CONSTANTS: + # dispersion parameters + dispersion_parameter_automobile: -0.05 + dispersion_parameter_transit: -0.05 + dispersion_parameter_walk: -1.00 + # maximum walk distance in miles + maximum_walk_distance: 3.0 + # perceived minute of in-vehicle time for every minute of out-of-vehicle time + out_of_vehicle_time_weight: 2.0 diff --git a/activitysim/examples/example_mtc/configs/annotate_households.csv b/activitysim/examples/prototype_mtc/configs/annotate_households.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/annotate_households.csv rename to activitysim/examples/prototype_mtc/configs/annotate_households.csv diff --git a/activitysim/examples/example_mtc/configs/annotate_households_cdap.csv b/activitysim/examples/prototype_mtc/configs/annotate_households_cdap.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/annotate_households_cdap.csv rename to activitysim/examples/prototype_mtc/configs/annotate_households_cdap.csv diff --git a/activitysim/examples/example_psrc/configs/annotate_households_workplace.csv b/activitysim/examples/prototype_mtc/configs/annotate_households_workplace.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/annotate_households_workplace.csv rename to activitysim/examples/prototype_mtc/configs/annotate_households_workplace.csv index 29ff416845..a28f92732b --- a/activitysim/examples/example_psrc/configs/annotate_households_workplace.csv +++ b/activitysim/examples/prototype_mtc/configs/annotate_households_workplace.csv @@ -1,5 +1,5 @@ -Description,Target,Expression -#,, annotate households table after workplace_location model has run -#,, hh_work_auto_savings_ratio is sum of persons work_auto_savings_ratio -,hh_work_auto_savings_ratio,persons.work_auto_savings_ratio.groupby(persons.household_id).sum().reindex(households.index).fillna(0.0) -#,,handle persons with no location +Description,Target,Expression +#,, annotate households table after workplace_location model has run +#,, hh_work_auto_savings_ratio is sum of persons work_auto_savings_ratio +,hh_work_auto_savings_ratio,persons.work_auto_savings_ratio.groupby(persons.household_id).sum().reindex(households.index).fillna(0.0) +#,,handle persons with no location diff --git a/activitysim/examples/example_mtc/configs/annotate_landuse.csv b/activitysim/examples/prototype_mtc/configs/annotate_landuse.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/annotate_landuse.csv rename to activitysim/examples/prototype_mtc/configs/annotate_landuse.csv diff --git a/activitysim/examples/example_psrc/configs/annotate_persons.csv b/activitysim/examples/prototype_mtc/configs/annotate_persons.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/annotate_persons.csv rename to activitysim/examples/prototype_mtc/configs/annotate_persons.csv index 037dc3fe5d..6162b53a82 --- a/activitysim/examples/example_psrc/configs/annotate_persons.csv +++ b/activitysim/examples/prototype_mtc/configs/annotate_persons.csv @@ -1,38 +1,38 @@ -Description,Target,Expression -#,, annotate persons table after import -age_16_to_19,age_16_to_19,"persons.age.between(16, 19)" -age_16_p,age_16_p,persons.age >= 16 -adult,adult,persons.age >= 18 -male,male,persons.sex == 1 -female,female,persons.sex == 2 -presence of non_worker other than self in household,has_non_worker,"other_than(persons.household_id, persons.ptype == PTYPE_NONWORK)" -presence of retiree other than self in household,has_retiree,"other_than(persons.household_id, persons.ptype == PTYPE_RETIRED)" -presence of preschooler other than self in household,has_preschool_kid,"other_than(persons.household_id, persons.ptype == PTYPE_PRESCHOOL)" -presence of driving_kid other than self in household,has_driving_kid,"other_than(persons.household_id, persons.ptype == PTYPE_DRIVING)" -presence of school_kid other than self in household,has_school_kid,"other_than(persons.household_id, persons.ptype == PTYPE_SCHOOL)" -presence of full_time worker other than self in household (independent of person type),has_full_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_FULL)" -presence of part_time worker other than self in household (independent of person type),has_part_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_PART)" -presence of university student other than self in household,has_university,"other_than(persons.household_id, persons.ptype == PTYPE_UNIVERSITY)" -student_is_employed,student_is_employed,"(persons.ptype.isin([PTYPE_UNIVERSITY, PTYPE_DRIVING]) & persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART]))" -nonstudent_to_school,nonstudent_to_school,"(persons.ptype.isin([PTYPE_FULL, PTYPE_PART, PTYPE_NONWORK, PTYPE_RETIRED]) & persons.pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY]))" -#,, -#,, FIXME - if person is a university student but has school age student category value then reset student category value -,pstudent,"persons.pstudent.where(persons.ptype!=PTYPE_UNIVERSITY, PSTUDENT_UNIVERSITY)" -#,, FIXME if person is a student of any kind but has full-time employment status then reset student category value to non-student -,pstudent,"pstudent.where(persons.ptype!=PTYPE_FULL, PSTUDENT_NOT)" -#,, FIXME if student category is non-student and employment is student then reset student category value to student -,pstudent,"pstudent.where((persons.ptype!=PTYPE_DRIVING) & (persons.ptype!=PTYPE_SCHOOL), PSTUDENT_GRADE_OR_HIGH)" -#,, -is_student,is_student,"pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY])" -preschool age can go to preschool,is_student,"is_student.where(persons.age > GRADE_SCHOOL_MIN_AGE, True)" -preschool age can go to preschool,pstudent,"pstudent.where(persons.age > GRADE_SCHOOL_MIN_AGE, PSTUDENT_GRADE_OR_HIGH)" -is_gradeschool,is_gradeschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MAX_AGE) -is_highschool,is_highschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age > GRADE_SCHOOL_MAX_AGE) -is_university,is_university,pstudent == PSTUDENT_UNIVERSITY -school_segment gradeschool,school_segment,"np.where(is_gradeschool, SCHOOL_SEGMENT_GRADE, SCHOOL_SEGMENT_NONE)" -school_segment highschool,school_segment,"np.where(is_highschool, SCHOOL_SEGMENT_HIGH, school_segment)" -school_segment university,school_segment,"np.where(is_university, SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)" -#,, -is_worker,is_worker,"persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART])" -#,, -home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)" +Description,Target,Expression +#,, annotate persons table after import +age_16_to_19,age_16_to_19,"persons.age.between(16, 19)" +age_16_p,age_16_p,persons.age >= 16 +adult,adult,persons.age >= 18 +male,male,persons.sex == 1 +female,female,persons.sex == 2 +presence of non_worker other than self in household,has_non_worker,"other_than(persons.household_id, persons.ptype == PTYPE_NONWORK)" +presence of retiree other than self in household,has_retiree,"other_than(persons.household_id, persons.ptype == PTYPE_RETIRED)" +presence of preschooler other than self in household,has_preschool_kid,"other_than(persons.household_id, persons.ptype == PTYPE_PRESCHOOL)" +presence of driving_kid other than self in household,has_driving_kid,"other_than(persons.household_id, persons.ptype == PTYPE_DRIVING)" +presence of school_kid other than self in household,has_school_kid,"other_than(persons.household_id, persons.ptype == PTYPE_SCHOOL)" +presence of full_time worker other than self in household (independent of person type),has_full_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_FULL)" +presence of part_time worker other than self in household (independent of person type),has_part_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_PART)" +presence of university student other than self in household,has_university,"other_than(persons.household_id, persons.ptype == PTYPE_UNIVERSITY)" +student_is_employed,student_is_employed,"(persons.ptype.isin([PTYPE_UNIVERSITY, PTYPE_DRIVING]) & persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART]))" +nonstudent_to_school,nonstudent_to_school,"(persons.ptype.isin([PTYPE_FULL, PTYPE_PART, PTYPE_NONWORK, PTYPE_RETIRED]) & persons.pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY]))" +#,, +#,, FIXME - if person is a university student but has school age student category value then reset student category value +,pstudent,"persons.pstudent.where(persons.ptype!=PTYPE_UNIVERSITY, PSTUDENT_UNIVERSITY)" +#,, FIXME if person is a student of any kind but has full-time employment status then reset student category value to non-student +,pstudent,"pstudent.where(persons.ptype!=PTYPE_FULL, PSTUDENT_NOT)" +#,, FIXME if student category is non-student and employment is student then reset student category value to student +,pstudent,"pstudent.where((persons.ptype!=PTYPE_DRIVING) & (persons.ptype!=PTYPE_SCHOOL), PSTUDENT_GRADE_OR_HIGH)" +#,, +is_student,is_student,"pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY])" +preschool age can go to preschool,is_student,"is_student.where(persons.age > GRADE_SCHOOL_MIN_AGE, True)" +preschool age can go to preschool,pstudent,"pstudent.where(persons.age > GRADE_SCHOOL_MIN_AGE, PSTUDENT_GRADE_OR_HIGH)" +is_gradeschool,is_gradeschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MAX_AGE) +is_highschool,is_highschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age > GRADE_SCHOOL_MAX_AGE) +is_university,is_university,pstudent == PSTUDENT_UNIVERSITY +school_segment gradeschool,school_segment,"np.where(is_gradeschool, SCHOOL_SEGMENT_GRADE, SCHOOL_SEGMENT_NONE)" +school_segment highschool,school_segment,"np.where(is_highschool, SCHOOL_SEGMENT_HIGH, school_segment)" +school_segment university,school_segment,"np.where(is_university, SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)" +#,, +is_worker,is_worker,"persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART])" +#,, +home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)" diff --git a/activitysim/examples/example_psrc/configs/annotate_persons_after_hh.csv b/activitysim/examples/prototype_mtc/configs/annotate_persons_after_hh.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/annotate_persons_after_hh.csv rename to activitysim/examples/prototype_mtc/configs/annotate_persons_after_hh.csv index 905aca7531..59374d5bf2 --- a/activitysim/examples/example_psrc/configs/annotate_persons_after_hh.csv +++ b/activitysim/examples/prototype_mtc/configs/annotate_persons_after_hh.csv @@ -1,5 +1,5 @@ -Description,Target,Expression -#,, annotate persons table after annotate_households -#,, adults get full hh_value_of_time and children get 60% -,_hh_vot,"reindex(households.hh_value_of_time, persons.household_id)" -,value_of_time,"_hh_vot.where(persons.age>=18, _hh_vot * 0.667)" +Description,Target,Expression +#,, annotate persons table after annotate_households +#,, adults get full hh_value_of_time and children get 60% +,_hh_vot,"reindex(households.hh_value_of_time, persons.household_id)" +,value_of_time,"_hh_vot.where(persons.age>=18, _hh_vot * 0.667)" diff --git a/activitysim/examples/example_psrc/configs/annotate_persons_cdap.csv b/activitysim/examples/prototype_mtc/configs/annotate_persons_cdap.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_psrc/configs/annotate_persons_cdap.csv rename to activitysim/examples/prototype_mtc/configs/annotate_persons_cdap.csv index 10ebafbc73..2ad5e56a6b --- a/activitysim/examples/example_psrc/configs/annotate_persons_cdap.csv +++ b/activitysim/examples/prototype_mtc/configs/annotate_persons_cdap.csv @@ -1,6 +1,6 @@ -Description,Target,Expression -#,, annotate persons table after cdap model has run -travel_active,travel_active,persons.cdap_activity != CDAP_ACTIVITY_HOME -under16_not_at_school,under16_not_at_school,"persons.ptype.isin([PTYPE_SCHOOL, PTYPE_PRESCHOOL]) & persons.cdap_activity.isin(['N', 'H'])" -has_preschool_kid_at_home,has_preschool_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_PRESCHOOL) & (persons.cdap_activity == 'H'))" -has_school_kid_at_home,has_school_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_SCHOOL) & (persons.cdap_activity == 'H'))" +Description,Target,Expression +#,, annotate persons table after cdap model has run +travel_active,travel_active,persons.cdap_activity != CDAP_ACTIVITY_HOME +under16_not_at_school,under16_not_at_school,"persons.ptype.isin([PTYPE_SCHOOL, PTYPE_PRESCHOOL]) & persons.cdap_activity.isin(['N', 'H'])" +has_preschool_kid_at_home,has_preschool_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_PRESCHOOL) & (persons.cdap_activity == 'H'))" +has_school_kid_at_home,has_school_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_SCHOOL) & (persons.cdap_activity == 'H'))" diff --git a/activitysim/examples/example_semcog/configs/annotate_persons_jtp.csv b/activitysim/examples/prototype_mtc/configs/annotate_persons_jtp.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_semcog/configs/annotate_persons_jtp.csv rename to activitysim/examples/prototype_mtc/configs/annotate_persons_jtp.csv index 3d5e08db0c..a72c866057 --- a/activitysim/examples/example_semcog/configs/annotate_persons_jtp.csv +++ b/activitysim/examples/prototype_mtc/configs/annotate_persons_jtp.csv @@ -1,3 +1,3 @@ -Description,Target,Expression -#,, annotate persons table after joint_tour_participation model has run -num_joint_tours,num_joint_tours,"joint_tour_participants.groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" +Description,Target,Expression +#,, annotate persons table after joint_tour_participation model has run +num_joint_tours,num_joint_tours,"joint_tour_participants.groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" diff --git a/activitysim/examples/example_psrc/configs/annotate_persons_mtf.csv b/activitysim/examples/prototype_mtc/configs/annotate_persons_mtf.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_psrc/configs/annotate_persons_mtf.csv rename to activitysim/examples/prototype_mtc/configs/annotate_persons_mtf.csv index e94a8776ca..00e0de1014 --- a/activitysim/examples/example_psrc/configs/annotate_persons_mtf.csv +++ b/activitysim/examples/prototype_mtc/configs/annotate_persons_mtf.csv @@ -1,8 +1,8 @@ -Description,Target,Expression -#,, annotate persons table after mandatory_tour_frequency model has run -,_PERSON_TOUR_COUNT,"lambda exp, persons, tours: tours.query(exp).groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" -,_Q,lambda s: "'{}'".format(s) -work_and_school_and_worker,work_and_school_and_worker,(persons.mandatory_tour_frequency == 'work_and_school') & persons.is_worker -work_and_school_and_student,work_and_school_and_student,(persons.mandatory_tour_frequency == 'work_and_school') & persons.is_student -number of mandatory tours for each person,num_mand,"_PERSON_TOUR_COUNT('tour_category==%s' % _Q('mandatory'), persons, tours).fillna(0)" -number of work tours for each person,num_work_tours,"_PERSON_TOUR_COUNT('tour_type==%s' % _Q('work'), persons, tours).fillna(0)" +Description,Target,Expression +#,, annotate persons table after mandatory_tour_frequency model has run +,_PERSON_TOUR_COUNT,"lambda exp, persons, tours: tours.query(exp).groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" +,_Q,lambda s: "'{}'".format(s) +work_and_school_and_worker,work_and_school_and_worker,(persons.mandatory_tour_frequency == 'work_and_school') & persons.is_worker +work_and_school_and_student,work_and_school_and_student,(persons.mandatory_tour_frequency == 'work_and_school') & persons.is_student +number of mandatory tours for each person,num_mand,"_PERSON_TOUR_COUNT('tour_category==%s' % _Q('mandatory'), persons, tours).fillna(0)" +number of work tours for each person,num_work_tours,"_PERSON_TOUR_COUNT('tour_type==%s' % _Q('work'), persons, tours).fillna(0)" diff --git a/activitysim/examples/example_psrc/configs/annotate_persons_nmtf.csv b/activitysim/examples/prototype_mtc/configs/annotate_persons_nmtf.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_psrc/configs/annotate_persons_nmtf.csv rename to activitysim/examples/prototype_mtc/configs/annotate_persons_nmtf.csv index 3305b4ecb6..07890f2379 --- a/activitysim/examples/example_psrc/configs/annotate_persons_nmtf.csv +++ b/activitysim/examples/prototype_mtc/configs/annotate_persons_nmtf.csv @@ -1,10 +1,10 @@ -Description,Target,Expression -#,, annotate persons table after non_mandatory_tour_frequency model has run -num_non_mand,num_non_mand,tours[tours.tour_category=='non_mandatory'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) -num_escort_tours,num_escort_tours,tours[tours.tour_type == 'escort'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) -num_eatout_tours,num_eatout_tours,tours[tours.tour_type == 'eatout'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) -num_shop_tours,num_shop_tours,tours[tours.tour_type == 'shopping'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) -num_maint_tours,num_maint_tours,tours[tours.tour_type == 'othmaint'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) -num_discr_tours,num_discr_tours,tours[tours.tour_type == 'othdiscr'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) -num_social_tours,num_social_tours,tours[tours.tour_type == 'social'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) -num_non_escort_tours,num_non_escort_tours,num_non_mand-num_escort_tours +Description,Target,Expression +#,, annotate persons table after non_mandatory_tour_frequency model has run +num_non_mand,num_non_mand,tours[tours.tour_category=='non_mandatory'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_escort_tours,num_escort_tours,tours[tours.tour_type == 'escort'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_eatout_tours,num_eatout_tours,tours[tours.tour_type == 'eatout'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_shop_tours,num_shop_tours,tours[tours.tour_type == 'shopping'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_maint_tours,num_maint_tours,tours[tours.tour_type == 'othmaint'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_discr_tours,num_discr_tours,tours[tours.tour_type == 'othdiscr'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_social_tours,num_social_tours,tours[tours.tour_type == 'social'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_non_escort_tours,num_non_escort_tours,num_non_mand-num_escort_tours diff --git a/activitysim/examples/example_psrc/configs/annotate_persons_school.csv b/activitysim/examples/prototype_mtc/configs/annotate_persons_school.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_psrc/configs/annotate_persons_school.csv rename to activitysim/examples/prototype_mtc/configs/annotate_persons_school.csv index 43625623f8..553b124c3b --- a/activitysim/examples/example_psrc/configs/annotate_persons_school.csv +++ b/activitysim/examples/prototype_mtc/configs/annotate_persons_school.csv @@ -1,7 +1,7 @@ -Description,Target,Expression -#,, annotate persons table after school_location model has run -,distance_to_school,"np.where(persons.school_zone_id>=0,skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, 'DIST'),np.nan)" -#,, this uses the free flow travel time in both directions. MTC TM1 was MD and MD -temp auto_time_to_school,_auto_time_to_school,"skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, ('SOV_TIME', 'MD'))" -temp auto_time_return,_auto_time_return,"skim_dict.lookup(persons.school_zone_id, persons.home_zone_id, ('SOV_TIME', 'MD'))" -free flow roundtrip_auto_time_to_school,roundtrip_auto_time_to_school,"np.where(persons.school_zone_id>=0,_auto_time_to_school + _auto_time_return,0)" +Description,Target,Expression +#,, annotate persons table after school_location model has run +,distance_to_school,"np.where(persons.school_zone_id>=0,skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, 'DIST'),np.nan)" +#,, this uses the free flow travel time in both directions. MTC TM1 was MD and MD +temp auto_time_to_school,_auto_time_to_school,"skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, ('SOV_TIME', 'MD'))" +temp auto_time_return,_auto_time_return,"skim_dict.lookup(persons.school_zone_id, persons.home_zone_id, ('SOV_TIME', 'MD'))" +free flow roundtrip_auto_time_to_school,roundtrip_auto_time_to_school,"np.where(persons.school_zone_id>=0,_auto_time_to_school + _auto_time_return,0)" diff --git a/activitysim/examples/example_psrc/configs/annotate_persons_workplace.csv b/activitysim/examples/prototype_mtc/configs/annotate_persons_workplace.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_psrc/configs/annotate_persons_workplace.csv rename to activitysim/examples/prototype_mtc/configs/annotate_persons_workplace.csv index f215886a5e..87a5853d63 --- a/activitysim/examples/example_psrc/configs/annotate_persons_workplace.csv +++ b/activitysim/examples/prototype_mtc/configs/annotate_persons_workplace.csv @@ -1,32 +1,32 @@ -Description,Target,Expression -#,, annotate persons table after workplace_location model has run -,distance_to_work,"np.where(persons.workplace_zone_id>=0,skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, 'DIST'),np.nan)" -workplace_in_cbd,workplace_in_cbd,"reindex(land_use.area_type, persons.workplace_zone_id) < setting('cbd_threshold')" -work_zone_area_type,work_zone_area_type,"reindex(land_use.area_type, persons.workplace_zone_id)" -#,, auto time to work - free flow travel time in both directions. MTC TM1 was MD and MD -#,,roundtrip_auto_time_to_work -,_auto_time_home_to_work,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('SOV_TIME', 'MD'))" -,_auto_time_work_to_home,"skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('SOV_TIME', 'MD'))" -,roundtrip_auto_time_to_work,"np.where(persons.workplace_zone_id>=0,_auto_time_home_to_work + _auto_time_work_to_home,0)" -#,,_roundtrip_walk_time_to_work -,_MAX_TIME_TO_WORK,999 -,_WALK_SPEED_MPH,3 -,_walk_time_home_to_work,"60 * skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, 'DISTWALK')/_WALK_SPEED_MPH" -,_walk_time_work_to_home,"60 * skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, 'DISTWALK')/_WALK_SPEED_MPH" -,_work_walk_available,(_walk_time_home_to_work > 0) & (_walk_time_work_to_home > 0) -,_roundtrip_walk_time_to_work,"np.where(_work_walk_available, _walk_time_home_to_work + _walk_time_work_to_home, _MAX_TIME_TO_WORK)" -#,,_roundtrip_transit_time_to_work -,_transit_ivt_home_to_work,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_IVT', 'MD'))/TRANSIT_SCALE_FACTOR" -,_transit_ivt_work_to_home,"skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_IVT', 'MD'))/TRANSIT_SCALE_FACTOR" -,_work_transit_available,(_transit_ivt_home_to_work > 0) & (_transit_ivt_work_to_home > 0) -,_transit_iwait,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_IWAIT', 'MD'))/TRANSIT_SCALE_FACTOR + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_IWAIT', 'MD'))/TRANSIT_SCALE_FACTOR" -,_transit_xwait,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_XWAIT', 'MD'))/TRANSIT_SCALE_FACTOR + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_XWAIT', 'MD'))/TRANSIT_SCALE_FACTOR" -,_transit_waux,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_WAUX', 'MD'))/TRANSIT_SCALE_FACTOR + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_WAUX', 'MD'))/TRANSIT_SCALE_FACTOR" -,_transit_wacc,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_WACC', 'MD'))/TRANSIT_SCALE_FACTOR + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_WACC', 'MD'))/TRANSIT_SCALE_FACTOR" -,_transit_wegr,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_WEGR', 'MD'))/TRANSIT_SCALE_FACTOR + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_WEGR', 'MD'))/TRANSIT_SCALE_FACTOR" -,_roundtrip_transit_time_to_work,_transit_ivt_home_to_work + _transit_ivt_work_to_home + _transit_iwait + _transit_xwait + _transit_waux + _transit_wacc + _transit_wegr -#,,work_auto_savings_ratio -,_min_work_walk_transit,"np.where(_work_transit_available, np.minimum(_roundtrip_transit_time_to_work, _roundtrip_walk_time_to_work), _roundtrip_walk_time_to_work)" -,work_auto_savings,"np.where(persons.is_worker, _min_work_walk_transit - roundtrip_auto_time_to_work, 0)" -#,,auto savings over walk or transit capped at 120 and normalized to unity -,work_auto_savings_ratio,"(work_auto_savings / 120.0).clip(-1.0, 1.0)" +Description,Target,Expression +#,, annotate persons table after workplace_location model has run +,distance_to_work,"np.where(persons.workplace_zone_id>=0,skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, 'DIST'),np.nan)" +workplace_in_cbd,workplace_in_cbd,"reindex(land_use.area_type, persons.workplace_zone_id) < setting('cbd_threshold')" +work_zone_area_type,work_zone_area_type,"reindex(land_use.area_type, persons.workplace_zone_id)" +#,, auto time to work - free flow travel time in both directions. MTC TM1 was MD and MD +#,,roundtrip_auto_time_to_work +,_auto_time_home_to_work,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('SOV_TIME', 'MD'))" +,_auto_time_work_to_home,"skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('SOV_TIME', 'MD'))" +,roundtrip_auto_time_to_work,"np.where(persons.workplace_zone_id>=0,_auto_time_home_to_work + _auto_time_work_to_home,0)" +#,,_roundtrip_walk_time_to_work +,_MAX_TIME_TO_WORK,999 +,_WALK_SPEED_MPH,3 +,_walk_time_home_to_work,"60 * skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, 'DISTWALK')/_WALK_SPEED_MPH" +,_walk_time_work_to_home,"60 * skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, 'DISTWALK')/_WALK_SPEED_MPH" +,_work_walk_available,(_walk_time_home_to_work > 0) & (_walk_time_work_to_home > 0) +,_roundtrip_walk_time_to_work,"np.where(_work_walk_available, _walk_time_home_to_work + _walk_time_work_to_home, _MAX_TIME_TO_WORK)" +#,,_roundtrip_transit_time_to_work +,_transit_ivt_home_to_work,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_IVT', 'MD'))/TRANSIT_SCALE_FACTOR" +,_transit_ivt_work_to_home,"skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_IVT', 'MD'))/TRANSIT_SCALE_FACTOR" +,_work_transit_available,(_transit_ivt_home_to_work > 0) & (_transit_ivt_work_to_home > 0) +,_transit_iwait,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_IWAIT', 'MD'))/TRANSIT_SCALE_FACTOR + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_IWAIT', 'MD'))/TRANSIT_SCALE_FACTOR" +,_transit_xwait,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_XWAIT', 'MD'))/TRANSIT_SCALE_FACTOR + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_XWAIT', 'MD'))/TRANSIT_SCALE_FACTOR" +,_transit_waux,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_WAUX', 'MD'))/TRANSIT_SCALE_FACTOR + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_WAUX', 'MD'))/TRANSIT_SCALE_FACTOR" +,_transit_wacc,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_WACC', 'MD'))/TRANSIT_SCALE_FACTOR + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_WACC', 'MD'))/TRANSIT_SCALE_FACTOR" +,_transit_wegr,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WLK_WEGR', 'MD'))/TRANSIT_SCALE_FACTOR + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WLK_WEGR', 'MD'))/TRANSIT_SCALE_FACTOR" +,_roundtrip_transit_time_to_work,_transit_ivt_home_to_work + _transit_ivt_work_to_home + _transit_iwait + _transit_xwait + _transit_waux + _transit_wacc + _transit_wegr +#,,work_auto_savings_ratio +,_min_work_walk_transit,"np.where(_work_transit_available, np.minimum(_roundtrip_transit_time_to_work, _roundtrip_walk_time_to_work), _roundtrip_walk_time_to_work)" +,work_auto_savings,"np.where(persons.is_worker, _min_work_walk_transit - roundtrip_auto_time_to_work, 0)" +#,,auto savings over walk or transit capped at 120 and normalized to unity +,work_auto_savings_ratio,"(work_auto_savings / 120.0).clip(-1.0, 1.0)" diff --git a/activitysim/examples/example_mtc/configs/atwork_subtour_destination.csv b/activitysim/examples/prototype_mtc/configs/atwork_subtour_destination.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/atwork_subtour_destination.csv rename to activitysim/examples/prototype_mtc/configs/atwork_subtour_destination.csv diff --git a/activitysim/examples/example_mtc/configs/atwork_subtour_destination.yaml b/activitysim/examples/prototype_mtc/configs/atwork_subtour_destination.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/atwork_subtour_destination.yaml rename to activitysim/examples/prototype_mtc/configs/atwork_subtour_destination.yaml diff --git a/activitysim/examples/example_psrc/configs/atwork_subtour_destination_coeffs.csv b/activitysim/examples/prototype_mtc/configs/atwork_subtour_destination_coefficients.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/atwork_subtour_destination_coeffs.csv rename to activitysim/examples/prototype_mtc/configs/atwork_subtour_destination_coefficients.csv index 83e9368c63..689fff660f --- a/activitysim/examples/example_psrc/configs/atwork_subtour_destination_coeffs.csv +++ b/activitysim/examples/prototype_mtc/configs/atwork_subtour_destination_coefficients.csv @@ -1,10 +1,10 @@ -coefficient_name,value,constrain -coef_distance_piecewise_linear_from_0_to_1_miles,-0.7926,F -coef_distance_piecewise_linear_from_1_to_2_miles,-0.7926,F -coef_distance_piecewise_linear_from_2_to_5_miles,-0.5197,F -coef_distance_piecewise_linear_from_5_to_15_miles,-0.2045,F -coef_distance_piecewise_linear_for_15_plus_miles,-0.2045,F -coef_size_variable_atwork,1,T -coef_no_attractions_atwork_size_variable_is_0,-999,T -coef_mode_choice_logsum,0.5136,F +coefficient_name,value,constrain +coef_distance_piecewise_linear_from_0_to_1_miles,-0.7926,F +coef_distance_piecewise_linear_from_1_to_2_miles,-0.7926,F +coef_distance_piecewise_linear_from_2_to_5_miles,-0.5197,F +coef_distance_piecewise_linear_from_5_to_15_miles,-0.2045,F +coef_distance_piecewise_linear_for_15_plus_miles,-0.2045,F +coef_size_variable_atwork,1,T +coef_no_attractions_atwork_size_variable_is_0,-999,T +coef_mode_choice_logsum,0.5136,F coef_sample_of_alternatives_correction_factor,1,T \ No newline at end of file diff --git a/activitysim/examples/example_mtc/configs/atwork_subtour_destination_sample.csv b/activitysim/examples/prototype_mtc/configs/atwork_subtour_destination_sample.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/atwork_subtour_destination_sample.csv rename to activitysim/examples/prototype_mtc/configs/atwork_subtour_destination_sample.csv diff --git a/activitysim/examples/example_mtc/configs/atwork_subtour_frequency.csv b/activitysim/examples/prototype_mtc/configs/atwork_subtour_frequency.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/atwork_subtour_frequency.csv rename to activitysim/examples/prototype_mtc/configs/atwork_subtour_frequency.csv diff --git a/activitysim/examples/example_mtc/configs/atwork_subtour_frequency.yaml b/activitysim/examples/prototype_mtc/configs/atwork_subtour_frequency.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/atwork_subtour_frequency.yaml rename to activitysim/examples/prototype_mtc/configs/atwork_subtour_frequency.yaml diff --git a/activitysim/examples/example_psrc/configs/atwork_subtour_frequency_alternatives.csv b/activitysim/examples/prototype_mtc/configs/atwork_subtour_frequency_alternatives.csv old mode 100755 new mode 100644 similarity index 96% rename from activitysim/examples/example_psrc/configs/atwork_subtour_frequency_alternatives.csv rename to activitysim/examples/prototype_mtc/configs/atwork_subtour_frequency_alternatives.csv index ed7c13c58d..ba9941919d --- a/activitysim/examples/example_psrc/configs/atwork_subtour_frequency_alternatives.csv +++ b/activitysim/examples/prototype_mtc/configs/atwork_subtour_frequency_alternatives.csv @@ -1,8 +1,8 @@ -#,,,alt file for building tours even though simulation is simple_simulate not interaction_simulate -alt,eat,business,maint -no_subtours,0,0,0 -eat,1,0,0 -business1,0,1,0 -maint,0,0,1 -business2,0,2,0 -eat_business,1,1,0 +#,,,alt file for building tours even though simulation is simple_simulate not interaction_simulate +alt,eat,business,maint +no_subtours,0,0,0 +eat,1,0,0 +business1,0,1,0 +maint,0,0,1 +business2,0,2,0 +eat_business,1,1,0 diff --git a/activitysim/examples/example_psrc/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_psrc/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv rename to activitysim/examples/prototype_mtc/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv index bf51e5bd78..3e4f151877 --- a/activitysim/examples/example_psrc/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv +++ b/activitysim/examples/prototype_mtc/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv @@ -1,9 +1,9 @@ -Description,Target,Expression -,num_maint_shop_escort,df.num_maint_tours+df.num_shop_tours+df.num_escort_tours -#,num_joint_maint_shop_eat,"reindex_i(tours[(tours.tour_category=='joint') & tours.tour_type.isin(['othmaint', 'shopping', 'eatout'])].groupby('person_id').size(), df.person_id)" -#,num_eatout_tours,"reindex_i(tours[~tours.is_joint & (tours.tour_type==EATOUT_TOUR)].groupby('person_id').size(), df.person_id)" -joint tour participants annotated with tour type,_PARTICIPANTS,"pd.merge(joint_tour_participants[['tour_id', 'person_id']], tours[['tour_type']], left_on='tour_id', right_index=True, how='left')" -,num_joint_discr,"reindex_i(_PARTICIPANTS[_PARTICIPANTS.tour_type=='othdiscr'].groupby('person_id').size(), df.person_id)" -,num_joint_maint_shop_eat,"reindex_i(_PARTICIPANTS[_PARTICIPANTS.tour_type.isin(['othmaint', 'shopping', 'eatout'])].groupby('person_id').size(), df.person_id)" -#,, -,work_tour_is_SOV,"df.tour_mode.isin(['DRIVEALONEFREE','DRIVEALONEPAY'])" +Description,Target,Expression +,num_maint_shop_escort,df.num_maint_tours+df.num_shop_tours+df.num_escort_tours +#,num_joint_maint_shop_eat,"reindex_i(tours[(tours.tour_category=='joint') & tours.tour_type.isin(['othmaint', 'shopping', 'eatout'])].groupby('person_id').size(), df.person_id)" +#,num_eatout_tours,"reindex_i(tours[~tours.is_joint & (tours.tour_type==EATOUT_TOUR)].groupby('person_id').size(), df.person_id)" +joint tour participants annotated with tour type,_PARTICIPANTS,"pd.merge(joint_tour_participants[['tour_id', 'person_id']], tours[['tour_type']], left_on='tour_id', right_index=True, how='left')" +,num_joint_discr,"reindex_i(_PARTICIPANTS[_PARTICIPANTS.tour_type=='othdiscr'].groupby('person_id').size(), df.person_id)" +,num_joint_maint_shop_eat,"reindex_i(_PARTICIPANTS[_PARTICIPANTS.tour_type.isin(['othmaint', 'shopping', 'eatout'])].groupby('person_id').size(), df.person_id)" +#,, +,work_tour_is_SOV,"df.tour_mode.isin(['DRIVEALONEFREE','DRIVEALONEPAY'])" diff --git a/activitysim/examples/example_mtc/configs/atwork_subtour_frequency_coefficients.csv b/activitysim/examples/prototype_mtc/configs/atwork_subtour_frequency_coefficients.csv similarity index 98% rename from activitysim/examples/example_mtc/configs/atwork_subtour_frequency_coefficients.csv rename to activitysim/examples/prototype_mtc/configs/atwork_subtour_frequency_coefficients.csv index 0e5de6ba7a..f719a691c7 100644 --- a/activitysim/examples/example_mtc/configs/atwork_subtour_frequency_coefficients.csv +++ b/activitysim/examples/prototype_mtc/configs/atwork_subtour_frequency_coefficients.csv @@ -1,133 +1,133 @@ -coefficient_name,value,constrain -coefficient_dummy_for_full_time_worker_business1,-7.375,F -coefficient_dummy_for_full_time_worker_business2,-14.28,F -coefficient_dummy_for_full_time_worker_eat,-7.28,F -coefficient_dummy_for_full_time_worker_eat_business,-14.79,F -coefficient_dummy_for_full_time_worker_maint,-8.093,F -coefficient_dummy_for_full_time_worker_no_subtours,-0.6,F -coefficient_dummy_for_non_full_time_worker_business1,-8.319,F -coefficient_dummy_for_non_full_time_worker_business2,-14.28,F -coefficient_dummy_for_non_full_time_worker_eat,-8.604,F -coefficient_dummy_for_non_full_time_worker_eat_business,-14.79,F -coefficient_dummy_for_non_full_time_worker_maint,-8.214,F -coefficient_dummy_for_non_full_time_worker_no_subtours,-0.6,F -coefficient_dummy_for_non_workers_business1,-5,T -coefficient_dummy_for_non_workers_business2,-5,T -coefficient_dummy_for_non_workers_eat,0,T -coefficient_dummy_for_non_workers_eat_business,-5,T -coefficient_dummy_for_non_workers_maint,-5,T -coefficient_dummy_for_non_workers_no_subtours,0,T -coefficient_medium_hh_income_dummy_business1,0.5555,F -coefficient_medium_hh_income_dummy_business2,1.111,F -coefficient_medium_hh_income_dummy_eat,0.61,F -coefficient_medium_hh_income_dummy_eat_business,1.1655,F -coefficient_medium_hh_income_dummy_maint,0.1527,F -coefficient_medium_hh_income_dummy_no_subtours,0,T -coefficient_high_hh_income_dummy_business1,1.066,F -coefficient_high_hh_income_dummy_business2,2.132,F -coefficient_high_hh_income_dummy_eat,0.8693,F -coefficient_high_hh_income_dummy_eat_business,1.9353,F -coefficient_high_hh_income_dummy_maint,0.1651,F -coefficient_high_hh_income_dummy_no_subtours,0,T -coefficient_zero_cars_owned_by_hh_dummy_business1,-0.3391,F -coefficient_zero_cars_owned_by_hh_dummy_business2,0,T -coefficient_zero_cars_owned_by_hh_dummy_eat,0,T -coefficient_zero_cars_owned_by_hh_dummy_eat_business,-0.3391,F -coefficient_zero_cars_owned_by_hh_dummy_maint,0.1762,F -coefficient_zero_cars_owned_by_hh_dummy_no_subtours,0,T -coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,0.7045,F -coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,1.409,F -coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,0.2334,F -coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business,0.9379,F -coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,0.5061,F -coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,0,T -coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,0.7045,F -coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,1.409,F -coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,0.6776,F -coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business,1.3821,F -coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,0.5061,F -coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,0,T -coefficient_individual_eating_out_tours_made_by_person_business1,0.5434,F -coefficient_individual_eating_out_tours_made_by_person_business2,1.0868,F -coefficient_individual_eating_out_tours_made_by_person_eat,0.5491,F -coefficient_individual_eating_out_tours_made_by_person_eat_business,1.0925,F -coefficient_individual_eating_out_tours_made_by_person_maint,0.9166,F -coefficient_individual_eating_out_tours_made_by_person_no_subtours,0,T -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,-0.1903,F -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,-0.3806,F -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,0.052,F -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business,-0.2423,F -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,0.1446,F -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,0,T -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,-0.1903,F -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,-0.3806,F -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,-0.3099,F -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business,-0.5002,F -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,-0.2723,F -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,0,T -coefficient_participation_in_joint_shop_main_eat_tours_business1,0.083,F -coefficient_participation_in_joint_shop_main_eat_tours_business2,0.166,F -coefficient_participation_in_joint_shop_main_eat_tours_eat,0.2458,F -coefficient_participation_in_joint_shop_main_eat_tours_eat_business,0.3288,F -coefficient_participation_in_joint_shop_main_eat_tours_maint,0.0803,F -coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,0,T -coefficient_participation_in_joint_discretionary_tours_business1,-0.2637,F -coefficient_participation_in_joint_discretionary_tours_business2,-0.5274,F -coefficient_participation_in_joint_discretionary_tours_eat,0.3588,F -coefficient_participation_in_joint_discretionary_tours_eat_business,0.0951,F -coefficient_participation_in_joint_discretionary_tours_maint,0.5822,F -coefficient_participation_in_joint_discretionary_tours_no_subtours,0,T -coefficient_log_of_the_work_tour_duration_business1,1.142,F -coefficient_log_of_the_work_tour_duration_business2,2.284,F -coefficient_log_of_the_work_tour_duration_eat,1.55,F -coefficient_log_of_the_work_tour_duration_eat_business,2.692,F -coefficient_log_of_the_work_tour_duration_maint,1.659,F -coefficient_log_of_the_work_tour_duration_no_subtours,0,T -coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,0.9901,F -coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,1.9802,F -coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,0.4804,F -coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business,1.4705,F -coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,1.153,F -coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,0,T -coefficient_two_work_tours_by_person_business1,0.3753,F -coefficient_two_work_tours_by_person_business2,0.7506,F -coefficient_two_work_tours_by_person_eat,-0.9862,F -coefficient_two_work_tours_by_person_eat_business,-0.6109,F -coefficient_two_work_tours_by_person_maint,-0.2312,F -coefficient_two_work_tours_by_person_no_subtours,0,T -coefficient_workplace_urban_area_dummy_business1,-0.2235,F -coefficient_workplace_urban_area_dummy_business2,-0.447,F -coefficient_workplace_urban_area_dummy_eat,-0.4182,F -coefficient_workplace_urban_area_dummy_eat_business,-0.6417,F -coefficient_workplace_urban_area_dummy_maint,-0.1479,F -coefficient_workplace_urban_area_dummy_no_subtours,0,T -coefficient_workplace_suburban_area_dummy_business1,-0.1102,F -coefficient_workplace_suburban_area_dummy_business2,-0.2204,F -coefficient_workplace_suburban_area_dummy_eat,-0.2916,F -coefficient_workplace_suburban_area_dummy_eat_business,-0.4018,F -coefficient_workplace_suburban_area_dummy_maint,0,T -coefficient_workplace_suburban_area_dummy_no_subtours,0,T -coefficient_auto_accessibility_to_retail_for_work_taz_business1,0.0534,F -coefficient_auto_accessibility_to_retail_for_work_taz_business2,0.1067,F -coefficient_auto_accessibility_to_retail_for_work_taz_eat,0.015,F -coefficient_auto_accessibility_to_retail_for_work_taz_eat_business,0.0683,F -coefficient_auto_accessibility_to_retail_for_work_taz_maint,0.0265,F -coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,0,T -coefficient_walk_accessibility_to_retail_for_work_taz_business1,0,T -coefficient_walk_accessibility_to_retail_for_work_taz_business2,0,T -coefficient_walk_accessibility_to_retail_for_work_taz_eat,0.06,F -coefficient_walk_accessibility_to_retail_for_work_taz_eat_business,0.06,F -coefficient_walk_accessibility_to_retail_for_work_taz_maint,0.04,F -coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,0,T -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,0,T -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,0,T -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,0,T -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business,0,T -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,-0.3573,F -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,0,T -coefficient_at_work_sub_tour_asc_business1,-0.5372,F -coefficient_at_work_sub_tour_asc_business2,-2.1337,F -coefficient_at_work_sub_tour_asc_eat,0.8576,F -coefficient_at_work_sub_tour_asc_eat_business,-0.9721,F -coefficient_at_work_sub_tour_asc_maint,-0.6198,F -coefficient_at_work_sub_tour_asc_no_subtours,0,T +coefficient_name,value,constrain +coefficient_dummy_for_full_time_worker_business1,-7.375,F +coefficient_dummy_for_full_time_worker_business2,-14.28,F +coefficient_dummy_for_full_time_worker_eat,-7.28,F +coefficient_dummy_for_full_time_worker_eat_business,-14.79,F +coefficient_dummy_for_full_time_worker_maint,-8.093,F +coefficient_dummy_for_full_time_worker_no_subtours,-0.6,F +coefficient_dummy_for_non_full_time_worker_business1,-8.319,F +coefficient_dummy_for_non_full_time_worker_business2,-14.28,F +coefficient_dummy_for_non_full_time_worker_eat,-8.604,F +coefficient_dummy_for_non_full_time_worker_eat_business,-14.79,F +coefficient_dummy_for_non_full_time_worker_maint,-8.214,F +coefficient_dummy_for_non_full_time_worker_no_subtours,-0.6,F +coefficient_dummy_for_non_workers_business1,-5,T +coefficient_dummy_for_non_workers_business2,-5,T +coefficient_dummy_for_non_workers_eat,0,T +coefficient_dummy_for_non_workers_eat_business,-5,T +coefficient_dummy_for_non_workers_maint,-5,T +coefficient_dummy_for_non_workers_no_subtours,0,T +coefficient_medium_hh_income_dummy_business1,0.5555,F +coefficient_medium_hh_income_dummy_business2,1.111,F +coefficient_medium_hh_income_dummy_eat,0.61,F +coefficient_medium_hh_income_dummy_eat_business,1.1655,F +coefficient_medium_hh_income_dummy_maint,0.1527,F +coefficient_medium_hh_income_dummy_no_subtours,0,T +coefficient_high_hh_income_dummy_business1,1.066,F +coefficient_high_hh_income_dummy_business2,2.132,F +coefficient_high_hh_income_dummy_eat,0.8693,F +coefficient_high_hh_income_dummy_eat_business,1.9353,F +coefficient_high_hh_income_dummy_maint,0.1651,F +coefficient_high_hh_income_dummy_no_subtours,0,T +coefficient_zero_cars_owned_by_hh_dummy_business1,-0.3391,F +coefficient_zero_cars_owned_by_hh_dummy_business2,0,T +coefficient_zero_cars_owned_by_hh_dummy_eat,0,T +coefficient_zero_cars_owned_by_hh_dummy_eat_business,-0.3391,F +coefficient_zero_cars_owned_by_hh_dummy_maint,0.1762,F +coefficient_zero_cars_owned_by_hh_dummy_no_subtours,0,T +coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,0.7045,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,1.409,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,0.2334,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business,0.9379,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,0.5061,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,0,T +coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,0.7045,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,1.409,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,0.6776,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business,1.3821,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,0.5061,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,0,T +coefficient_individual_eating_out_tours_made_by_person_business1,0.5434,F +coefficient_individual_eating_out_tours_made_by_person_business2,1.0868,F +coefficient_individual_eating_out_tours_made_by_person_eat,0.5491,F +coefficient_individual_eating_out_tours_made_by_person_eat_business,1.0925,F +coefficient_individual_eating_out_tours_made_by_person_maint,0.9166,F +coefficient_individual_eating_out_tours_made_by_person_no_subtours,0,T +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,-0.1903,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,-0.3806,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,0.052,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business,-0.2423,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,0.1446,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,0,T +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,-0.1903,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,-0.3806,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,-0.3099,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business,-0.5002,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,-0.2723,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,0,T +coefficient_participation_in_joint_shop_main_eat_tours_business1,0.083,F +coefficient_participation_in_joint_shop_main_eat_tours_business2,0.166,F +coefficient_participation_in_joint_shop_main_eat_tours_eat,0.2458,F +coefficient_participation_in_joint_shop_main_eat_tours_eat_business,0.3288,F +coefficient_participation_in_joint_shop_main_eat_tours_maint,0.0803,F +coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,0,T +coefficient_participation_in_joint_discretionary_tours_business1,-0.2637,F +coefficient_participation_in_joint_discretionary_tours_business2,-0.5274,F +coefficient_participation_in_joint_discretionary_tours_eat,0.3588,F +coefficient_participation_in_joint_discretionary_tours_eat_business,0.0951,F +coefficient_participation_in_joint_discretionary_tours_maint,0.5822,F +coefficient_participation_in_joint_discretionary_tours_no_subtours,0,T +coefficient_log_of_the_work_tour_duration_business1,1.142,F +coefficient_log_of_the_work_tour_duration_business2,2.284,F +coefficient_log_of_the_work_tour_duration_eat,1.55,F +coefficient_log_of_the_work_tour_duration_eat_business,2.692,F +coefficient_log_of_the_work_tour_duration_maint,1.659,F +coefficient_log_of_the_work_tour_duration_no_subtours,0,T +coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,0.9901,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,1.9802,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,0.4804,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business,1.4705,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,1.153,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,0,T +coefficient_two_work_tours_by_person_business1,0.3753,F +coefficient_two_work_tours_by_person_business2,0.7506,F +coefficient_two_work_tours_by_person_eat,-0.9862,F +coefficient_two_work_tours_by_person_eat_business,-0.6109,F +coefficient_two_work_tours_by_person_maint,-0.2312,F +coefficient_two_work_tours_by_person_no_subtours,0,T +coefficient_workplace_urban_area_dummy_business1,-0.2235,F +coefficient_workplace_urban_area_dummy_business2,-0.447,F +coefficient_workplace_urban_area_dummy_eat,-0.4182,F +coefficient_workplace_urban_area_dummy_eat_business,-0.6417,F +coefficient_workplace_urban_area_dummy_maint,-0.1479,F +coefficient_workplace_urban_area_dummy_no_subtours,0,T +coefficient_workplace_suburban_area_dummy_business1,-0.1102,F +coefficient_workplace_suburban_area_dummy_business2,-0.2204,F +coefficient_workplace_suburban_area_dummy_eat,-0.2916,F +coefficient_workplace_suburban_area_dummy_eat_business,-0.4018,F +coefficient_workplace_suburban_area_dummy_maint,0,T +coefficient_workplace_suburban_area_dummy_no_subtours,0,T +coefficient_auto_accessibility_to_retail_for_work_taz_business1,0.0534,F +coefficient_auto_accessibility_to_retail_for_work_taz_business2,0.1067,F +coefficient_auto_accessibility_to_retail_for_work_taz_eat,0.015,F +coefficient_auto_accessibility_to_retail_for_work_taz_eat_business,0.0683,F +coefficient_auto_accessibility_to_retail_for_work_taz_maint,0.0265,F +coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_business1,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_business2,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_eat,0.06,F +coefficient_walk_accessibility_to_retail_for_work_taz_eat_business,0.06,F +coefficient_walk_accessibility_to_retail_for_work_taz_maint,0.04,F +coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,-0.3573,F +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,0,T +coefficient_at_work_sub_tour_asc_business1,-0.5372,F +coefficient_at_work_sub_tour_asc_business2,-2.1337,F +coefficient_at_work_sub_tour_asc_eat,0.8576,F +coefficient_at_work_sub_tour_asc_eat_business,-0.9721,F +coefficient_at_work_sub_tour_asc_maint,-0.6198,F +coefficient_at_work_sub_tour_asc_no_subtours,0,T diff --git a/activitysim/examples/example_psrc/configs/auto_ownership.csv b/activitysim/examples/prototype_mtc/configs/auto_ownership.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_psrc/configs/auto_ownership.csv rename to activitysim/examples/prototype_mtc/configs/auto_ownership.csv index a2f5a32910..aa30bdb4db --- a/activitysim/examples/example_psrc/configs/auto_ownership.csv +++ b/activitysim/examples/prototype_mtc/configs/auto_ownership.csv @@ -1,30 +1,30 @@ -Label,Description,Expression,cars0,cars1,cars2,cars3,cars4 -util_drivers_2,2 Adults (age 16+),num_drivers==2,,coef_cars1_drivers_2,coef_cars2_drivers_2,coef_cars3_drivers_2,coef_cars4_drivers_2 -util_drivers_3,3 Adults (age 16+),num_drivers==3,,coef_cars1_drivers_3,coef_cars2_drivers_3,coef_cars3_drivers_3,coef_cars4_drivers_3 -util_drivers_4_up,4+ Adults (age 16+),num_drivers>3,,coef_cars1_drivers_4_up,coef_cars2_drivers_4_up,coef_cars3_drivers_4_up,coef_cars4_drivers_4_up -util_persons_16_17,Persons age 16-17,num_children_16_to_17,,coef_cars1_persons_16_17,coef_cars2_persons_16_17,coef_cars34_persons_16_17,coef_cars34_persons_16_17 -util_persons_18_24,Persons age 18-24,num_college_age,,coef_cars1_persons_18_24,coef_cars2_persons_18_24,coef_cars34_persons_18_24,coef_cars34_persons_18_24 -util_persons_25_34,Persons age 35-34,num_young_adults,,coef_cars1_persons_25_34,coef_cars2_persons_25_34,coef_cars34_persons_25_34,coef_cars34_persons_25_34 -util_presence_children_0_4,Presence of children age 0-4,num_young_children>0,,coef_cars1_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4 -util_presence_children_5_17,Presence of children age 5-17,(num_children_5_to_15+num_children_16_to_17)>0,,coef_cars1_presence_children_5_17,coef_cars2_presence_children_5_17,coef_cars34_presence_children_5_17,coef_cars34_presence_children_5_17 -util_num_workers_clip_3,"Number of workers, capped at 3",@df.num_workers.clip(upper=3),,coef_cars1_num_workers_clip_3,coef_cars2_num_workers_clip_3,coef_cars3_num_workers_clip_3,coef_cars4_num_workers_clip_3 -util_hh_income_0_30k,"Piecewise Linear household income, $0-30k","@df.income_in_thousands.clip(0, 30)",,coef_cars1_hh_income_0_30k,coef_cars2_hh_income_0_30k,coef_cars3_hh_income_0_30k,coef_cars4_hh_income_0_30k -util_hh_income_30_75k,"Piecewise Linear household income, $30-75k","@(df.income_in_thousands-30).clip(0, 45)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up -util_hh_income_75k_up,"Piecewise Linear household income, $75k+, capped at $125k","@(df.income_in_thousands-75).clip(0, 50)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up -util_density_0_10_no_workers,"Density index up to 10, if 0 workers","@(df.num_workers==0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers -util_density_10_up_no_workers,"Density index in excess of 10, if 0 workers",@(df.num_workers==0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_no_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers -util_density_0_10_workers,"Density index up to 10, if 1+ workers","@(df.num_workers>0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers -util_density_10_up_workers,"Density index in excess of 10, if 1+ workers",@(df.num_workers>0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers -util_asc,Constants,1,,coef_cars1_asc,coef_cars2_asc,coef_cars3_asc,coef_cars4_asc -util_asc_san_francisco,San Francisco county,@df.county_id == ID_SAN_FRANCISCO,,coef_cars1_asc_san_francisco,coef_cars2_asc_san_francisco,coef_cars34_asc_san_francisco,coef_cars34_asc_san_francisco -util_asc_solano,Solano county,@df.county_id == ID_SOLANO,,coef_cars1_asc_county,coef_cars2_asc_county,coef_cars34_asc_county,coef_cars34_asc_county -util_asc_napa,Napa county,@df.county_id == ID_NAPA,,coef_cars1_asc_county,coef_cars2_asc_county,coef_cars34_asc_county,coef_cars34_asc_county -util_asc_sonoma,Sonoma county,@df.county_id == ID_SONOMA,,coef_cars1_asc_county,coef_cars2_asc_county,coef_cars34_asc_county,coef_cars34_asc_county -util_asc_marin,Marin county,@df.county_id == ID_MARIN,,coef_cars1_asc_marin,coef_cars234_asc_marin,coef_cars234_asc_marin,coef_cars234_asc_marin -util_retail_auto_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 0 workers",(num_workers==0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers -util_retail_auto_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 1+ workers",(num_workers>0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers -util_retail_transit_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 0 workers",(num_workers==0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers -util_retail_transit_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 1+ workers",(num_workers>0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers -util_retail_non_motor_no_workers,"Retail accessibility by non-motorized, if 0 workers",(num_workers==0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor -util_retail_non_motor_workers,"Retail accessibility by non-motorized, if 1+ workers",(num_workers>0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor -util_auto_time_saving_per_worker,Auto time savings per worker to work,"@np.where(df.num_workers > 0, df.hh_work_auto_savings_ratio / df.num_workers, 0)",,coef_cars1_auto_time_saving_per_worker,coef_cars2_auto_time_saving_per_worker,coef_cars3_auto_time_saving_per_worker,coef_cars4_auto_time_saving_per_worker +Label,Description,Expression,cars0,cars1,cars2,cars3,cars4 +util_drivers_2,2 Adults (age 16+),num_drivers==2,,coef_cars1_drivers_2,coef_cars2_drivers_2,coef_cars3_drivers_2,coef_cars4_drivers_2 +util_drivers_3,3 Adults (age 16+),num_drivers==3,,coef_cars1_drivers_3,coef_cars2_drivers_3,coef_cars3_drivers_3,coef_cars4_drivers_3 +util_drivers_4_up,4+ Adults (age 16+),num_drivers>3,,coef_cars1_drivers_4_up,coef_cars2_drivers_4_up,coef_cars3_drivers_4_up,coef_cars4_drivers_4_up +util_persons_16_17,Persons age 16-17,num_children_16_to_17,,coef_cars1_persons_16_17,coef_cars2_persons_16_17,coef_cars34_persons_16_17,coef_cars34_persons_16_17 +util_persons_18_24,Persons age 18-24,num_college_age,,coef_cars1_persons_18_24,coef_cars2_persons_18_24,coef_cars34_persons_18_24,coef_cars34_persons_18_24 +util_persons_25_34,Persons age 35-34,num_young_adults,,coef_cars1_persons_25_34,coef_cars2_persons_25_34,coef_cars34_persons_25_34,coef_cars34_persons_25_34 +util_presence_children_0_4,Presence of children age 0-4,num_young_children>0,,coef_cars1_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4 +util_presence_children_5_17,Presence of children age 5-17,(num_children_5_to_15+num_children_16_to_17)>0,,coef_cars1_presence_children_5_17,coef_cars2_presence_children_5_17,coef_cars34_presence_children_5_17,coef_cars34_presence_children_5_17 +util_num_workers_clip_3,"Number of workers, capped at 3",@df.num_workers.clip(upper=3),,coef_cars1_num_workers_clip_3,coef_cars2_num_workers_clip_3,coef_cars3_num_workers_clip_3,coef_cars4_num_workers_clip_3 +util_hh_income_0_30k,"Piecewise Linear household income, $0-30k","@df.income_in_thousands.clip(0, 30)",,coef_cars1_hh_income_0_30k,coef_cars2_hh_income_0_30k,coef_cars3_hh_income_0_30k,coef_cars4_hh_income_0_30k +util_hh_income_30_75k,"Piecewise Linear household income, $30-75k","@(df.income_in_thousands-30).clip(0, 45)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up +util_hh_income_75k_up,"Piecewise Linear household income, $75k+, capped at $125k","@(df.income_in_thousands-75).clip(0, 50)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up +util_density_0_10_no_workers,"Density index up to 10, if 0 workers","@(df.num_workers==0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers +util_density_10_up_no_workers,"Density index in excess of 10, if 0 workers",@(df.num_workers==0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_no_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers +util_density_0_10_workers,"Density index up to 10, if 1+ workers","@(df.num_workers>0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers +util_density_10_up_workers,"Density index in excess of 10, if 1+ workers",@(df.num_workers>0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers +util_asc,Constants,1,,coef_cars1_asc,coef_cars2_asc,coef_cars3_asc,coef_cars4_asc +util_asc_san_francisco,San Francisco county,@df.county_id == ID_SAN_FRANCISCO,,coef_cars1_asc_san_francisco,coef_cars2_asc_san_francisco,coef_cars34_asc_san_francisco,coef_cars34_asc_san_francisco +util_asc_solano,Solano county,@df.county_id == ID_SOLANO,,coef_cars1_asc_county,coef_cars2_asc_county,coef_cars34_asc_county,coef_cars34_asc_county +util_asc_napa,Napa county,@df.county_id == ID_NAPA,,coef_cars1_asc_county,coef_cars2_asc_county,coef_cars34_asc_county,coef_cars34_asc_county +util_asc_sonoma,Sonoma county,@df.county_id == ID_SONOMA,,coef_cars1_asc_county,coef_cars2_asc_county,coef_cars34_asc_county,coef_cars34_asc_county +util_asc_marin,Marin county,@df.county_id == ID_MARIN,,coef_cars1_asc_marin,coef_cars234_asc_marin,coef_cars234_asc_marin,coef_cars234_asc_marin +util_retail_auto_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 0 workers",(num_workers==0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers +util_retail_auto_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 1+ workers",(num_workers>0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers +util_retail_transit_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 0 workers",(num_workers==0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers +util_retail_transit_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 1+ workers",(num_workers>0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers +util_retail_non_motor_no_workers,"Retail accessibility by non-motorized, if 0 workers",(num_workers==0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor +util_retail_non_motor_workers,"Retail accessibility by non-motorized, if 1+ workers",(num_workers>0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor +util_auto_time_saving_per_worker,Auto time savings per worker to work,"@np.where(df.num_workers > 0, df.hh_work_auto_savings_ratio / df.num_workers, 0)",,coef_cars1_auto_time_saving_per_worker,coef_cars2_auto_time_saving_per_worker,coef_cars3_auto_time_saving_per_worker,coef_cars4_auto_time_saving_per_worker diff --git a/activitysim/examples/example_mtc/configs/auto_ownership.yaml b/activitysim/examples/prototype_mtc/configs/auto_ownership.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/auto_ownership.yaml rename to activitysim/examples/prototype_mtc/configs/auto_ownership.yaml diff --git a/activitysim/examples/example_mtc/configs/auto_ownership_coefficients.csv b/activitysim/examples/prototype_mtc/configs/auto_ownership_coefficients.csv similarity index 97% rename from activitysim/examples/example_mtc/configs/auto_ownership_coefficients.csv rename to activitysim/examples/prototype_mtc/configs/auto_ownership_coefficients.csv index 9c1470b690..b9d7fd07b0 100644 --- a/activitysim/examples/example_mtc/configs/auto_ownership_coefficients.csv +++ b/activitysim/examples/prototype_mtc/configs/auto_ownership_coefficients.csv @@ -1,68 +1,68 @@ -coefficient_name,value,constrain -coef_cars1_drivers_2,0,T -coef_cars1_drivers_3,0,T -coef_cars1_persons_16_17,0,T -coef_cars234_asc_marin,0,T -coef_cars1_persons_25_34,0,T -coef_cars1_num_workers_clip_3,0,T -coef_cars1_hh_income_30_up,0,T -coef_cars1_density_0_10_no_workers,0,T -coef_cars1_density_10_up_workers,-0.0152,F -coef_retail_non_motor,-0.03,T -coef_cars4_asc,-5.313,F -coef_cars3_asc,-3.2502,F -coef_cars34_persons_16_17,-1.7313,F -coef_cars2_asc,-1.0846,F -coef_cars34_persons_18_24,-1.0107,F -coef_cars2_persons_18_24,-1.0095,F -coef_cars2_persons_16_17,-0.881,F -coef_cars34_persons_25_34,-0.8596,F -coef_cars1_asc_county,-0.566,F -coef_retail_transit_workers,-0.5117,F -coef_cars2_persons_25_34,-0.4849,F -coef_cars2_asc_county,-0.4429,F -coef_cars1_persons_18_24,-0.4087,F -coef_cars34_density_0_10_no_workers,-0.3654,F -coef_retail_transit_no_workers,-0.3053,F -coef_cars1_asc_marin,-0.2434,F -coef_cars34_asc_county,-0.2372,F -coef_cars2_density_0_10_no_workers,-0.2028,F -coef_cars34_density_10_up_no_workers,-0.1766,F -coef_cars2_density_10_up_no_workers,-0.1106,F -coef_cars2_density_10_up_workers,-0.1106,F -coef_cars1_density_10_up_no_workers,-0.0152,F -coef_cars2_hh_income_30_up,0.0083,F -coef_cars3_hh_income_30_up,0.011,F -coef_cars4_hh_income_30_up,0.0147,F -coef_cars1_presence_children_5_17,0.0158,F -coef_cars1_hh_income_0_30k,0.0383,F -coef_cars2_hh_income_0_30k,0.054,F -coef_cars3_hh_income_0_30k,0.0559,F -coef_cars4_hh_income_0_30k,0.0619,F -coef_retail_auto_no_workers,0.0626,F -coef_cars34_asc_san_francisco,0.1458,F -coef_retail_auto_workers,0.1646,F -coef_cars2_presence_children_5_17,0.2936,F -coef_cars2_num_workers_clip_3,0.2936,F -coef_cars1_presence_children_0_4,0.3669,F -coef_cars1_asc_san_francisco,0.4259,F -coef_cars2_asc_san_francisco,0.4683,F -coef_cars1_auto_time_saving_per_worker,0.4707,F -coef_cars34_presence_children_5_17,0.4769,F -coef_cars3_auto_time_saving_per_worker,0.5705,F -coef_cars2_auto_time_saving_per_worker,0.6142,F -coef_cars3_num_workers_clip_3,0.6389,F -coef_cars234_presence_children_0_4,0.7627,F -coef_cars4_auto_time_saving_per_worker,0.7693,F -coef_cars4_num_workers_clip_3,0.8797,F -coef_cars1_asc,1.1865,F -coef_cars1_drivers_4_up,2.0107,F -coef_cars4_drivers_2,2.6616,F -coef_cars2_drivers_2,3.0773,F -coef_cars3_drivers_2,3.1962,F -coef_cars2_drivers_3,3.5401,F -coef_cars4_drivers_3,5.208,F -coef_cars3_drivers_3,5.5131,F -coef_cars2_drivers_4_up,6.3662,F -coef_cars3_drivers_4_up,8.5148,F -coef_cars4_drivers_4_up,9.5807,F +coefficient_name,value,constrain +coef_cars1_drivers_2,0,T +coef_cars1_drivers_3,0,T +coef_cars1_persons_16_17,0,T +coef_cars234_asc_marin,0,T +coef_cars1_persons_25_34,0,T +coef_cars1_num_workers_clip_3,0,T +coef_cars1_hh_income_30_up,0,T +coef_cars1_density_0_10_no_workers,0,T +coef_cars1_density_10_up_workers,-0.0152,F +coef_retail_non_motor,-0.03,T +coef_cars4_asc,-5.313,F +coef_cars3_asc,-3.2502,F +coef_cars34_persons_16_17,-1.7313,F +coef_cars2_asc,-1.0846,F +coef_cars34_persons_18_24,-1.0107,F +coef_cars2_persons_18_24,-1.0095,F +coef_cars2_persons_16_17,-0.881,F +coef_cars34_persons_25_34,-0.8596,F +coef_cars1_asc_county,-0.566,F +coef_retail_transit_workers,-0.5117,F +coef_cars2_persons_25_34,-0.4849,F +coef_cars2_asc_county,-0.4429,F +coef_cars1_persons_18_24,-0.4087,F +coef_cars34_density_0_10_no_workers,-0.3654,F +coef_retail_transit_no_workers,-0.3053,F +coef_cars1_asc_marin,-0.2434,F +coef_cars34_asc_county,-0.2372,F +coef_cars2_density_0_10_no_workers,-0.2028,F +coef_cars34_density_10_up_no_workers,-0.1766,F +coef_cars2_density_10_up_no_workers,-0.1106,F +coef_cars2_density_10_up_workers,-0.1106,F +coef_cars1_density_10_up_no_workers,-0.0152,F +coef_cars2_hh_income_30_up,0.0083,F +coef_cars3_hh_income_30_up,0.011,F +coef_cars4_hh_income_30_up,0.0147,F +coef_cars1_presence_children_5_17,0.0158,F +coef_cars1_hh_income_0_30k,0.0383,F +coef_cars2_hh_income_0_30k,0.054,F +coef_cars3_hh_income_0_30k,0.0559,F +coef_cars4_hh_income_0_30k,0.0619,F +coef_retail_auto_no_workers,0.0626,F +coef_cars34_asc_san_francisco,0.1458,F +coef_retail_auto_workers,0.1646,F +coef_cars2_presence_children_5_17,0.2936,F +coef_cars2_num_workers_clip_3,0.2936,F +coef_cars1_presence_children_0_4,0.3669,F +coef_cars1_asc_san_francisco,0.4259,F +coef_cars2_asc_san_francisco,0.4683,F +coef_cars1_auto_time_saving_per_worker,0.4707,F +coef_cars34_presence_children_5_17,0.4769,F +coef_cars3_auto_time_saving_per_worker,0.5705,F +coef_cars2_auto_time_saving_per_worker,0.6142,F +coef_cars3_num_workers_clip_3,0.6389,F +coef_cars234_presence_children_0_4,0.7627,F +coef_cars4_auto_time_saving_per_worker,0.7693,F +coef_cars4_num_workers_clip_3,0.8797,F +coef_cars1_asc,1.1865,F +coef_cars1_drivers_4_up,2.0107,F +coef_cars4_drivers_2,2.6616,F +coef_cars2_drivers_2,3.0773,F +coef_cars3_drivers_2,3.1962,F +coef_cars2_drivers_3,3.5401,F +coef_cars4_drivers_3,5.208,F +coef_cars3_drivers_3,5.5131,F +coef_cars2_drivers_4_up,6.3662,F +coef_cars3_drivers_4_up,8.5148,F +coef_cars4_drivers_4_up,9.5807,F diff --git a/activitysim/examples/example_mtc/configs/cdap.yaml b/activitysim/examples/prototype_mtc/configs/cdap.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/cdap.yaml rename to activitysim/examples/prototype_mtc/configs/cdap.yaml diff --git a/activitysim/examples/example_psrc/configs/cdap_coefficients.csv b/activitysim/examples/prototype_mtc/configs/cdap_coefficients.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/cdap_coefficients.csv rename to activitysim/examples/prototype_mtc/configs/cdap_coefficients.csv diff --git a/activitysim/examples/example_psrc/configs/cdap_fixed_relative_proportions.csv b/activitysim/examples/prototype_mtc/configs/cdap_fixed_relative_proportions.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/cdap_fixed_relative_proportions.csv rename to activitysim/examples/prototype_mtc/configs/cdap_fixed_relative_proportions.csv index c011a597c5..788f398b64 --- a/activitysim/examples/example_psrc/configs/cdap_fixed_relative_proportions.csv +++ b/activitysim/examples/prototype_mtc/configs/cdap_fixed_relative_proportions.csv @@ -1,9 +1,9 @@ -Description,Expression,M,N,H -Full-time worker,ptype == 1,0.79647,0.09368,0.10985 -Part-time worker,ptype == 2,0.61678,0.25757,0.12565 -University student,ptype == 3,0.69229,0.15641,0.1513 -Non-working adult,ptype == 4,0,0.67169,0.32831 -Retired,ptype == 5,0,0.54295,0.45705 -Driving-age child who is in school,ptype == 6,0.77609,0.06004,0.16387 -Pre-driving-age child who is in school,ptype == 7,0.68514,0.09144,0.22342 -Child who is too young for school,ptype == 8,0.14056,0.06512,0.79432 +Description,Expression,M,N,H +Full-time worker,ptype == 1,0.79647,0.09368,0.10985 +Part-time worker,ptype == 2,0.61678,0.25757,0.12565 +University student,ptype == 3,0.69229,0.15641,0.1513 +Non-working adult,ptype == 4,0,0.67169,0.32831 +Retired,ptype == 5,0,0.54295,0.45705 +Driving-age child who is in school,ptype == 6,0.77609,0.06004,0.16387 +Pre-driving-age child who is in school,ptype == 7,0.68514,0.09144,0.22342 +Child who is too young for school,ptype == 8,0.14056,0.06512,0.79432 diff --git a/activitysim/examples/example_mtc/configs/cdap_indiv_and_hhsize1.csv b/activitysim/examples/prototype_mtc/configs/cdap_indiv_and_hhsize1.csv similarity index 99% rename from activitysim/examples/example_mtc/configs/cdap_indiv_and_hhsize1.csv rename to activitysim/examples/prototype_mtc/configs/cdap_indiv_and_hhsize1.csv index 36ff4e67b8..2eeb9fde8e 100644 --- a/activitysim/examples/example_mtc/configs/cdap_indiv_and_hhsize1.csv +++ b/activitysim/examples/prototype_mtc/configs/cdap_indiv_and_hhsize1.csv @@ -1,52 +1,52 @@ -Description,Expression,M,N,H -Full-time worker alternative-specific constants,ptype == 1,coef_full_time_worker_asc_M,coef_full_time_worker_asc_N, -Part-time worker alternative-specific constants,ptype == 2,coef_part_time_worker_asc_M,coef_part_time_worker_asc_N, -University student alternative-specific constants,ptype == 3,coef_university_student_asc_M,coef_university_student_asc_N, -Non-working adult alternative-specific constants,ptype == 4,coef_UNAVAILABLE,coef_non_working_adult_asc_N, -Retired alternative-specific constants,ptype == 5,coef_UNAVAILABLE,coef_retired_asc_N, -Driving-age child who is in school alternative-specific constants,ptype == 6,coef_driving_age_child_who_is_in_school_asc_M,coef_driving_age_child_who_is_in_school_asc_N, -Pre-driving-age child who is in school alternative-specific constants,ptype == 7,coef_pre_driving_age_child_who_is_in_school_asc_M,coef_pre_driving_age_child_who_is_in_school_asc_N, -Pre-driving-age child who is in school interaction with age 6 to 9,(ptype == 7) & (age >= 6) & (age <= 9),coef_pre_driving_age_child_who_is_in_school_interaction_with_age_6_to_9_M,, -Pre-driving-age child who is in school interaction with age 13 to 15,(ptype == 7) & (age >= 13) & (age <= 15),coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_M,coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_N, -Pre-driving-age child who is too young for school alternative-specific constants,ptype == 8,coef_pre_driving_age_child_who_is_too_young_for_school_asc_M,coef_pre_driving_age_child_who_is_too_young_for_school_asc_N, -# corrected tm1 age bug,,,, -Pre-driving-age child who is too young for school interaction with age 0 to 1,(ptype == 8) & (age >= 0) & (age <= 1),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_0_to_1_M,, -Pre-driving-age child who is too young for school interaction with age 4 to 5,(ptype == 8) & (age >= 4) & (age <= 5),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_4_to_5_M,, -#,,,, -Full-time worker interaction with age less than 40,(ptype == 1) & (age < 40),coef_full_time_worker_interaction_with_age_less_than_40_M,, -Retired interaction with age more than 80,(ptype == 5) & (age > 80),,,coef_retired_interaction_with_age_more_than_80_H -Full-time worker interaction with female gender,(ptype == 1) & (sex == 2),coef_full_time_worker_interaction_with_female_gender_M,, -Non-working adult interaction with female gender,(ptype == 4) & (sex == 2),coef_non_working_adult_interaction_with_female_gender_M,, -Retired interaction with female,(ptype == 5) & (sex == 2),coef_retired_interaction_with_female_M,, -Non-working adult interaction with more cars than workers,(ptype == 4) & (auto_ownership > num_workers),coef_non_working_adult_interaction_with_more_cars_than_workers_M,coef_non_working_adult_interaction_with_more_cars_than_workers_N, -Retired interaction with more cars than workers,(ptype == 5) & (auto_ownership > num_workers),coef_retired_interaction_with_more_cars_than_workers_M,coef_retired_interaction_with_more_cars_than_workers_N, -Pre-driving-age child who is too young for school interaction with more cars than workers,(ptype == 8) & (auto_ownership > num_workers),,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_more_cars_than_workers_N, -Full-time worker interaction with fewer cars than workers,(ptype == 1) & (auto_ownership < num_workers),,,coef_full_time_worker_interaction_with_fewer_cars_than_workers_H -Non-working adult interaction with fewer cars than workers,(ptype == 4) & (auto_ownership < num_workers),,,coef_non_working_adult_interaction_with_fewer_cars_than_workers_H -Retired interaction with fewer cars than workers,(ptype == 5) & (auto_ownership < num_workers),,,coef_retired_interaction_with_fewer_cars_than_workers_H -Driving-age child who is in school interaction with fewer cars than workers,(ptype == 6) & (auto_ownership < num_workers),,,coef_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H -Pre-driving-age child who is in school interaction with fewer cars than workers,(ptype == 7) & (auto_ownership < num_workers),,,coef_pre_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H -Pre-driving-age child who is too young for school interaction with fewer cars than workers,(ptype == 8) & (auto_ownership < num_workers),,,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_fewer_cars_than_workers_H -Full-time worker interaction with income less than $20k,(ptype == 1) & (income_in_thousands < 20),,,coef_full_time_worker_interaction_with_income_less_than_20k_H -Retired interaction with income less than $20k,(ptype == 5) & (income_in_thousands < 20),,,coef_retired_interaction_with_income_less_than_20k_H -Part-time worker interaction with income less than $20k,(ptype == 2) & (income_in_thousands < 20),,,coef_part_time_worker_interaction_with_income_less_than_20k_H -Part-time worker interaction with income between $50k and $100k,(ptype == 2) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,coef_part_time_worker_interaction_with_income_between_50k_and_100k_H -Part-time worker interaction with income more than $100k,(ptype == 2) & (income_in_thousands >= 100),,coef_part_time_worker_interaction_with_income_more_than_100k_N,coef_part_time_worker_interaction_with_income_more_than_100k_H -Non-working adult interaction with income between $50k and $100k,(ptype == 4) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,coef_non_working_adult_interaction_with_income_between_50k_and_100k_H -Non-working adult interaction with income more than $100k,(ptype == 4) & (income_in_thousands >= 100),,,coef_non_working_adult_interaction_with_income_more_than_100k_H -Driving-age child who is in school interaction with less than $20k,(ptype == 6) & (income_in_thousands < 20),,,coef_driving_age_child_who_is_in_school_interaction_with_less_than_20k_H -Driving-age child who is in school interaction income between $50k and $100k,(ptype == 6) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,coef_driving_age_child_who_is_in_school_interaction_income_between_50k_and_100k_H -Driving-age child who is in school interaction with income more than $100k,(ptype == 6) & (income_in_thousands >= 100),,,coef_driving_age_child_who_is_in_school_interaction_with_income_more_than_100k_H -Pre-driving-age child who is too young for school interaction with income between $50k and $100k,(ptype == 8) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_between_50k_and_100k_H -Pre-driving-age child who is too young for school interaction with income more than $100k,(ptype == 8) & (income_in_thousands >= 100),,,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_more_than_100k_H -Full-time worker intraction with peak accessibility to all employment,(ptype == 1) * auPkTotal,coef_full_time_worker_intraction_with_peak_accessibility_to_all_employment_M,, -Part-time worker interaction with peak accessibility to all employment,(ptype == 2) * auPkTotal,coef_part_time_worker_interaction_with_peak_accessibility_to_all_employment_M,, -Non-working adult interaction with peak accessibility to all employment,(ptype == 4) * auPkTotal,coef_non_working_adult_interaction_with_peak_accessibility_to_all_employment_M,, -Retired interaction with peak accessibility to all employment,(ptype == 5) * auPkTotal,coef_retired_interaction_with_peak_accessibility_to_all_employment_M,, -Non-working adult interaction with off-peak accessibility to retail,(ptype == 4) * auOpRetail,,coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N, -Retired interaction with off-peak accessibility to retail,(ptype == 5) * auOpRetail,,coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N, -University student interaction with off-peak accessibility to retail,(ptype == 3) * auOpRetail,,coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N, -Driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 6) * auOpRetail,,coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N, -Pre-driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 7) * auOpRetail,,coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N, -Pre-driving-age child who is too young for school interaction with off-peak accessibility to retail,(ptype == 8) * auOpRetail,,coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N, - +Description,Expression,M,N,H +Full-time worker alternative-specific constants,ptype == 1,coef_full_time_worker_asc_M,coef_full_time_worker_asc_N, +Part-time worker alternative-specific constants,ptype == 2,coef_part_time_worker_asc_M,coef_part_time_worker_asc_N, +University student alternative-specific constants,ptype == 3,coef_university_student_asc_M,coef_university_student_asc_N, +Non-working adult alternative-specific constants,ptype == 4,coef_UNAVAILABLE,coef_non_working_adult_asc_N, +Retired alternative-specific constants,ptype == 5,coef_UNAVAILABLE,coef_retired_asc_N, +Driving-age child who is in school alternative-specific constants,ptype == 6,coef_driving_age_child_who_is_in_school_asc_M,coef_driving_age_child_who_is_in_school_asc_N, +Pre-driving-age child who is in school alternative-specific constants,ptype == 7,coef_pre_driving_age_child_who_is_in_school_asc_M,coef_pre_driving_age_child_who_is_in_school_asc_N, +Pre-driving-age child who is in school interaction with age 6 to 9,(ptype == 7) & (age >= 6) & (age <= 9),coef_pre_driving_age_child_who_is_in_school_interaction_with_age_6_to_9_M,, +Pre-driving-age child who is in school interaction with age 13 to 15,(ptype == 7) & (age >= 13) & (age <= 15),coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_M,coef_pre_driving_age_child_who_is_in_school_interaction_with_age_13_to_15_N, +Pre-driving-age child who is too young for school alternative-specific constants,ptype == 8,coef_pre_driving_age_child_who_is_too_young_for_school_asc_M,coef_pre_driving_age_child_who_is_too_young_for_school_asc_N, +# corrected tm1 age bug,,,, +Pre-driving-age child who is too young for school interaction with age 0 to 1,(ptype == 8) & (age >= 0) & (age <= 1),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_0_to_1_M,, +Pre-driving-age child who is too young for school interaction with age 4 to 5,(ptype == 8) & (age >= 4) & (age <= 5),coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_age_4_to_5_M,, +#,,,, +Full-time worker interaction with age less than 40,(ptype == 1) & (age < 40),coef_full_time_worker_interaction_with_age_less_than_40_M,, +Retired interaction with age more than 80,(ptype == 5) & (age > 80),,,coef_retired_interaction_with_age_more_than_80_H +Full-time worker interaction with female gender,(ptype == 1) & (sex == 2),coef_full_time_worker_interaction_with_female_gender_M,, +Non-working adult interaction with female gender,(ptype == 4) & (sex == 2),coef_non_working_adult_interaction_with_female_gender_M,, +Retired interaction with female,(ptype == 5) & (sex == 2),coef_retired_interaction_with_female_M,, +Non-working adult interaction with more cars than workers,(ptype == 4) & (auto_ownership > num_workers),coef_non_working_adult_interaction_with_more_cars_than_workers_M,coef_non_working_adult_interaction_with_more_cars_than_workers_N, +Retired interaction with more cars than workers,(ptype == 5) & (auto_ownership > num_workers),coef_retired_interaction_with_more_cars_than_workers_M,coef_retired_interaction_with_more_cars_than_workers_N, +Pre-driving-age child who is too young for school interaction with more cars than workers,(ptype == 8) & (auto_ownership > num_workers),,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_more_cars_than_workers_N, +Full-time worker interaction with fewer cars than workers,(ptype == 1) & (auto_ownership < num_workers),,,coef_full_time_worker_interaction_with_fewer_cars_than_workers_H +Non-working adult interaction with fewer cars than workers,(ptype == 4) & (auto_ownership < num_workers),,,coef_non_working_adult_interaction_with_fewer_cars_than_workers_H +Retired interaction with fewer cars than workers,(ptype == 5) & (auto_ownership < num_workers),,,coef_retired_interaction_with_fewer_cars_than_workers_H +Driving-age child who is in school interaction with fewer cars than workers,(ptype == 6) & (auto_ownership < num_workers),,,coef_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H +Pre-driving-age child who is in school interaction with fewer cars than workers,(ptype == 7) & (auto_ownership < num_workers),,,coef_pre_driving_age_child_who_is_in_school_interaction_with_fewer_cars_than_workers_H +Pre-driving-age child who is too young for school interaction with fewer cars than workers,(ptype == 8) & (auto_ownership < num_workers),,,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_fewer_cars_than_workers_H +Full-time worker interaction with income less than $20k,(ptype == 1) & (income_in_thousands < 20),,,coef_full_time_worker_interaction_with_income_less_than_20k_H +Retired interaction with income less than $20k,(ptype == 5) & (income_in_thousands < 20),,,coef_retired_interaction_with_income_less_than_20k_H +Part-time worker interaction with income less than $20k,(ptype == 2) & (income_in_thousands < 20),,,coef_part_time_worker_interaction_with_income_less_than_20k_H +Part-time worker interaction with income between $50k and $100k,(ptype == 2) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,coef_part_time_worker_interaction_with_income_between_50k_and_100k_H +Part-time worker interaction with income more than $100k,(ptype == 2) & (income_in_thousands >= 100),,coef_part_time_worker_interaction_with_income_more_than_100k_N,coef_part_time_worker_interaction_with_income_more_than_100k_H +Non-working adult interaction with income between $50k and $100k,(ptype == 4) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,coef_non_working_adult_interaction_with_income_between_50k_and_100k_H +Non-working adult interaction with income more than $100k,(ptype == 4) & (income_in_thousands >= 100),,,coef_non_working_adult_interaction_with_income_more_than_100k_H +Driving-age child who is in school interaction with less than $20k,(ptype == 6) & (income_in_thousands < 20),,,coef_driving_age_child_who_is_in_school_interaction_with_less_than_20k_H +Driving-age child who is in school interaction income between $50k and $100k,(ptype == 6) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,coef_driving_age_child_who_is_in_school_interaction_income_between_50k_and_100k_H +Driving-age child who is in school interaction with income more than $100k,(ptype == 6) & (income_in_thousands >= 100),,,coef_driving_age_child_who_is_in_school_interaction_with_income_more_than_100k_H +Pre-driving-age child who is too young for school interaction with income between $50k and $100k,(ptype == 8) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_between_50k_and_100k_H +Pre-driving-age child who is too young for school interaction with income more than $100k,(ptype == 8) & (income_in_thousands >= 100),,,coef_pre_driving_age_child_who_is_too_young_for_school_interaction_with_income_more_than_100k_H +Full-time worker intraction with peak accessibility to all employment,(ptype == 1) * auPkTotal,coef_full_time_worker_intraction_with_peak_accessibility_to_all_employment_M,, +Part-time worker interaction with peak accessibility to all employment,(ptype == 2) * auPkTotal,coef_part_time_worker_interaction_with_peak_accessibility_to_all_employment_M,, +Non-working adult interaction with peak accessibility to all employment,(ptype == 4) * auPkTotal,coef_non_working_adult_interaction_with_peak_accessibility_to_all_employment_M,, +Retired interaction with peak accessibility to all employment,(ptype == 5) * auPkTotal,coef_retired_interaction_with_peak_accessibility_to_all_employment_M,, +Non-working adult interaction with off-peak accessibility to retail,(ptype == 4) * auOpRetail,,coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N, +Retired interaction with off-peak accessibility to retail,(ptype == 5) * auOpRetail,,coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N, +University student interaction with off-peak accessibility to retail,(ptype == 3) * auOpRetail,,coef_non_working_adult_retired_or_univ_student_interaction_with_off_peak_accessibility_to_all_employment_N, +Driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 6) * auOpRetail,,coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N, +Pre-driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 7) * auOpRetail,,coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N, +Pre-driving-age child who is too young for school interaction with off-peak accessibility to retail,(ptype == 8) * auOpRetail,,coef_child_who_is_in_school_or_too_young_for_school_interaction_with_off_peak_accessibility_to_retail_N, + diff --git a/activitysim/examples/example_psrc/configs/cdap_interaction_coefficients.csv b/activitysim/examples/prototype_mtc/configs/cdap_interaction_coefficients.csv old mode 100755 new mode 100644 similarity index 94% rename from activitysim/examples/example_psrc/configs/cdap_interaction_coefficients.csv rename to activitysim/examples/prototype_mtc/configs/cdap_interaction_coefficients.csv index 854d48ef58..c9d415fe13 --- a/activitysim/examples/example_psrc/configs/cdap_interaction_coefficients.csv +++ b/activitysim/examples/prototype_mtc/configs/cdap_interaction_coefficients.csv @@ -1,138 +1,138 @@ -activity,interaction_ptypes,coefficient -# 2-way interactions,, -H,11,coef_H_11 -H,12,coef_H_12 -H,13,coef_H_13 -H,14,coef_H_14 -H,15,coef_H_15 -H,16,coef_H_16 -H,17,coef_H_17 -H,18,coef_H_18 -H,22,coef_H_22 -H,23,coef_H_23 -H,24,coef_H_24 -H,25,coef_H_25 -H,26,coef_H_26 -H,27,coef_H_27 -H,28,coef_H_28 -H,33,coef_H_33 -H,34,coef_H_34 -H,35,coef_H_35 -H,36,coef_H_36 -H,37,coef_H_37 -H,38,coef_H_38 -H,44,coef_H_44 -H,45,coef_H_45 -H,46,coef_H_46 -H,47,coef_H_47 -H,48,coef_H_48 -H,55,coef_H_55 -H,56,coef_H_56_57_58 -H,57,coef_H_56_57_58 -H,58,coef_H_56_57_58 -H,66,coef_H_66 -H,67,coef_H_67 -H,68,coef_H_68 -H,77,coef_H_77 -H,78,coef_H_78 -H,88,coef_H_88 -M,11,coef_M_11 -M,12,coef_M_12 -M,13,coef_M_13 -M,16,coef_M_16 -M,17,coef_M_17 -M,18,coef_M_18 -M,22,coef_M_22 -M,23,coef_M_23 -M,26,coef_M_26 -M,27,coef_M_27 -M,28,coef_M_28 -M,33,coef_M_33 -M,36,coef_M_36 -M,37,coef_M_37 -M,38,coef_M_38 -M,66,coef_M_66 -M,67,coef_M_67 -M,68,coef_M_68 -M,77,coef_M_77 -M,78,coef_M_78 -M,88,coef_M_88 -N,11,coef_N_11 -N,12,coef_N_12 -N,13,coef_N_13 -N,14,coef_N_14 -N,15,coef_N_15 -N,16,coef_N_16 -N,17,coef_N_17 -N,18,coef_N_18 -N,22,coef_N_22 -N,23,coef_N_23 -N,24,coef_N_24 -N,25,coef_N_25 -N,26,coef_N_26 -N,27,coef_N_27 -N,28,coef_N_28 -N,33,coef_N_33 -N,34,coef_N_34 -N,35,coef_N_35 -N,36,coef_N_36 -N,37,coef_N_37 -N,38,coef_N_38 -N,44,coef_N_44 -N,45,coef_N_45 -N,46,coef_N_46 -N,47,coef_N_47 -N,48,coef_N_48 -N,55,coef_N_55 -N,56,coef_N_56_57_58 -N,57,coef_N_56_57_58 -N,58,coef_N_56_57_58 -N,66,coef_N_66 -N,67,coef_N_67 -N,68,coef_N_68 -N,77,coef_N_77 -N,78,coef_N_78 -N,88,coef_N_88 -# 3-way interactions,, -H,124,coef_H_124_122_144 -H,122,coef_H_124_122_144 -H,144,coef_H_124_122_144 -H,126,coef_H_126_146 -H,146,coef_H_126_146 -H,222,coef_H_222_224_244 -H,224,coef_H_222_224_244 -H,244,coef_H_222_224_244 -H,226,coef_H_226_246_446 -H,246,coef_H_226_246_446 -H,446,coef_H_226_246_446 -H,266,coef_H_266_466 -H,466,coef_H_266_466 -M,111,coef_M_111 -M,112,coef_M_112_114 -M,114,coef_M_112_114 -M,666,coef_M_666 -N,112,coef_N_112_114 -N,114,coef_N_112_114 -N,124,coef_N_124_122_144 -N,122,coef_N_124_122_144 -N,144,coef_N_124_122_144 -N,166,coef_N_166 -N,222,coef_N_222_224_444 -N,224,coef_N_222_224_444 -N,444,coef_N_222_224_444 -N,246,coef_N_246_226_446 -N,226,coef_N_246_226_446 -N,446,coef_N_246_226_446 -# cdap_final_rules,, -M,5,coef_UNAVAILABLE -M,4,coef_UNAVAILABLE -# cdap_all_people,, -M,***,coef_M_xxx -N,***,coef_N_xxx -H,***,coef_H_xxx -M,****,coef_M_xxxx -N,****,coef_N_xxxx -H,****,coef_H_xxxx -M,*****,coef_M_xxxxx -N,*****,coef_N_xxxxx -H,*****,coef_H_xxxxx +activity,interaction_ptypes,coefficient +# 2-way interactions,, +H,11,coef_H_11 +H,12,coef_H_12 +H,13,coef_H_13 +H,14,coef_H_14 +H,15,coef_H_15 +H,16,coef_H_16 +H,17,coef_H_17 +H,18,coef_H_18 +H,22,coef_H_22 +H,23,coef_H_23 +H,24,coef_H_24 +H,25,coef_H_25 +H,26,coef_H_26 +H,27,coef_H_27 +H,28,coef_H_28 +H,33,coef_H_33 +H,34,coef_H_34 +H,35,coef_H_35 +H,36,coef_H_36 +H,37,coef_H_37 +H,38,coef_H_38 +H,44,coef_H_44 +H,45,coef_H_45 +H,46,coef_H_46 +H,47,coef_H_47 +H,48,coef_H_48 +H,55,coef_H_55 +H,56,coef_H_56_57_58 +H,57,coef_H_56_57_58 +H,58,coef_H_56_57_58 +H,66,coef_H_66 +H,67,coef_H_67 +H,68,coef_H_68 +H,77,coef_H_77 +H,78,coef_H_78 +H,88,coef_H_88 +M,11,coef_M_11 +M,12,coef_M_12 +M,13,coef_M_13 +M,16,coef_M_16 +M,17,coef_M_17 +M,18,coef_M_18 +M,22,coef_M_22 +M,23,coef_M_23 +M,26,coef_M_26 +M,27,coef_M_27 +M,28,coef_M_28 +M,33,coef_M_33 +M,36,coef_M_36 +M,37,coef_M_37 +M,38,coef_M_38 +M,66,coef_M_66 +M,67,coef_M_67 +M,68,coef_M_68 +M,77,coef_M_77 +M,78,coef_M_78 +M,88,coef_M_88 +N,11,coef_N_11 +N,12,coef_N_12 +N,13,coef_N_13 +N,14,coef_N_14 +N,15,coef_N_15 +N,16,coef_N_16 +N,17,coef_N_17 +N,18,coef_N_18 +N,22,coef_N_22 +N,23,coef_N_23 +N,24,coef_N_24 +N,25,coef_N_25 +N,26,coef_N_26 +N,27,coef_N_27 +N,28,coef_N_28 +N,33,coef_N_33 +N,34,coef_N_34 +N,35,coef_N_35 +N,36,coef_N_36 +N,37,coef_N_37 +N,38,coef_N_38 +N,44,coef_N_44 +N,45,coef_N_45 +N,46,coef_N_46 +N,47,coef_N_47 +N,48,coef_N_48 +N,55,coef_N_55 +N,56,coef_N_56_57_58 +N,57,coef_N_56_57_58 +N,58,coef_N_56_57_58 +N,66,coef_N_66 +N,67,coef_N_67 +N,68,coef_N_68 +N,77,coef_N_77 +N,78,coef_N_78 +N,88,coef_N_88 +# 3-way interactions,, +H,124,coef_H_124_122_144 +H,122,coef_H_124_122_144 +H,144,coef_H_124_122_144 +H,126,coef_H_126_146 +H,146,coef_H_126_146 +H,222,coef_H_222_224_244 +H,224,coef_H_222_224_244 +H,244,coef_H_222_224_244 +H,226,coef_H_226_246_446 +H,246,coef_H_226_246_446 +H,446,coef_H_226_246_446 +H,266,coef_H_266_466 +H,466,coef_H_266_466 +M,111,coef_M_111 +M,112,coef_M_112_114 +M,114,coef_M_112_114 +M,666,coef_M_666 +N,112,coef_N_112_114 +N,114,coef_N_112_114 +N,124,coef_N_124_122_144 +N,122,coef_N_124_122_144 +N,144,coef_N_124_122_144 +N,166,coef_N_166 +N,222,coef_N_222_224_444 +N,224,coef_N_222_224_444 +N,444,coef_N_222_224_444 +N,246,coef_N_246_226_446 +N,226,coef_N_246_226_446 +N,446,coef_N_246_226_446 +# cdap_final_rules,, +M,5,coef_UNAVAILABLE +M,4,coef_UNAVAILABLE +# cdap_all_people,, +M,***,coef_M_xxx +N,***,coef_N_xxx +H,***,coef_H_xxx +M,****,coef_M_xxxx +N,****,coef_N_xxxx +H,****,coef_H_xxxx +M,*****,coef_M_xxxxx +N,*****,coef_N_xxxxx +H,*****,coef_H_xxxxx diff --git a/activitysim/examples/example_psrc/configs/constants.yaml b/activitysim/examples/prototype_mtc/configs/constants.yaml old mode 100755 new mode 100644 similarity index 95% rename from activitysim/examples/example_psrc/configs/constants.yaml rename to activitysim/examples/prototype_mtc/configs/constants.yaml index b2896e4645..b0bd5a1f37 --- a/activitysim/examples/example_psrc/configs/constants.yaml +++ b/activitysim/examples/prototype_mtc/configs/constants.yaml @@ -1,68 +1,68 @@ -## ActivitySim -## See full license in LICENSE.txt. - - -#HHT_NONE: 0 -#HHT_FAMILY_MARRIED: 1 -#HHT_FAMILY_MALE: 2 -#HHT_FAMILY_FEMALE: 3 -#HHT_NONFAMILY_MALE_ALONE: 4 -#HHT_NONFAMILY_MALE_NOTALONE: 5 -#HHT_NONFAMILY_FEMALE_ALONE: 6 -#HHT_NONFAMILY_FEMALE_NOTALONE: 7 - -# convenience for expression files -HHT_NONFAMILY: [4, 5, 6, 7] -HHT_FAMILY: [1, 2, 3] - -PSTUDENT_GRADE_OR_HIGH: 1 -PSTUDENT_UNIVERSITY: 2 -PSTUDENT_NOT: 3 - -GRADE_SCHOOL_MAX_AGE: 14 -GRADE_SCHOOL_MIN_AGE: 5 - -SCHOOL_SEGMENT_NONE: 0 -SCHOOL_SEGMENT_GRADE: 1 -SCHOOL_SEGMENT_HIGH: 2 -SCHOOL_SEGMENT_UNIV: 3 - -INCOME_SEGMENT_LOW: 1 -INCOME_SEGMENT_MED: 2 -INCOME_SEGMENT_HIGH: 3 -INCOME_SEGMENT_VERYHIGH: 4 - -PEMPLOY_FULL: 1 -PEMPLOY_PART: 2 -PEMPLOY_NOT: 3 -PEMPLOY_CHILD: 4 - -PTYPE_FULL: &ptype_full 1 -PTYPE_PART: &ptype_part 2 -PTYPE_UNIVERSITY: &ptype_university 3 -PTYPE_NONWORK: &ptype_nonwork 4 -PTYPE_RETIRED: &ptype_retired 5 -PTYPE_DRIVING: &ptype_driving 6 -PTYPE_SCHOOL: &ptype_school 7 -PTYPE_PRESCHOOL: &ptype_preschool 8 - -# these appear as column headers in non_mandatory_tour_frequency.csv -PTYPE_NAME: - *ptype_full: PTYPE_FULL - *ptype_part: PTYPE_PART - *ptype_university: PTYPE_UNIVERSITY - *ptype_nonwork: PTYPE_NONWORK - *ptype_retired: PTYPE_RETIRED - *ptype_driving: PTYPE_DRIVING - *ptype_school: PTYPE_SCHOOL - *ptype_preschool: PTYPE_PRESCHOOL - - -CDAP_ACTIVITY_MANDATORY: M -CDAP_ACTIVITY_NONMANDATORY: N -CDAP_ACTIVITY_HOME: H - -# Correction for transit skim expressions -# e.g. MTC transit skims (Cube TRANPLAN skims) use scaled ints and -# therefore need to be divided by the scale factor if used in expressions -TRANSIT_SCALE_FACTOR: 100 +## ActivitySim +## See full license in LICENSE.txt. + + +#HHT_NONE: 0 +#HHT_FAMILY_MARRIED: 1 +#HHT_FAMILY_MALE: 2 +#HHT_FAMILY_FEMALE: 3 +#HHT_NONFAMILY_MALE_ALONE: 4 +#HHT_NONFAMILY_MALE_NOTALONE: 5 +#HHT_NONFAMILY_FEMALE_ALONE: 6 +#HHT_NONFAMILY_FEMALE_NOTALONE: 7 + +# convenience for expression files +HHT_NONFAMILY: [4, 5, 6, 7] +HHT_FAMILY: [1, 2, 3] + +PSTUDENT_GRADE_OR_HIGH: 1 +PSTUDENT_UNIVERSITY: 2 +PSTUDENT_NOT: 3 + +GRADE_SCHOOL_MAX_AGE: 14 +GRADE_SCHOOL_MIN_AGE: 5 + +SCHOOL_SEGMENT_NONE: 0 +SCHOOL_SEGMENT_GRADE: 1 +SCHOOL_SEGMENT_HIGH: 2 +SCHOOL_SEGMENT_UNIV: 3 + +INCOME_SEGMENT_LOW: 1 +INCOME_SEGMENT_MED: 2 +INCOME_SEGMENT_HIGH: 3 +INCOME_SEGMENT_VERYHIGH: 4 + +PEMPLOY_FULL: 1 +PEMPLOY_PART: 2 +PEMPLOY_NOT: 3 +PEMPLOY_CHILD: 4 + +PTYPE_FULL: &ptype_full 1 +PTYPE_PART: &ptype_part 2 +PTYPE_UNIVERSITY: &ptype_university 3 +PTYPE_NONWORK: &ptype_nonwork 4 +PTYPE_RETIRED: &ptype_retired 5 +PTYPE_DRIVING: &ptype_driving 6 +PTYPE_SCHOOL: &ptype_school 7 +PTYPE_PRESCHOOL: &ptype_preschool 8 + +# these appear as column headers in non_mandatory_tour_frequency.csv +PTYPE_NAME: + *ptype_full: PTYPE_FULL + *ptype_part: PTYPE_PART + *ptype_university: PTYPE_UNIVERSITY + *ptype_nonwork: PTYPE_NONWORK + *ptype_retired: PTYPE_RETIRED + *ptype_driving: PTYPE_DRIVING + *ptype_school: PTYPE_SCHOOL + *ptype_preschool: PTYPE_PRESCHOOL + + +CDAP_ACTIVITY_MANDATORY: M +CDAP_ACTIVITY_NONMANDATORY: N +CDAP_ACTIVITY_HOME: H + +# Correction for transit skim expressions +# e.g. MTC transit skims (Cube TRANPLAN skims) use scaled ints and +# therefore need to be divided by the scale factor if used in expressions +TRANSIT_SCALE_FACTOR: 100 diff --git a/activitysim/examples/example_psrc/configs/destination_choice_size_terms.csv b/activitysim/examples/prototype_mtc/configs/destination_choice_size_terms.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/destination_choice_size_terms.csv rename to activitysim/examples/prototype_mtc/configs/destination_choice_size_terms.csv index 8c72d2a78f..7f70421e85 --- a/activitysim/examples/example_psrc/configs/destination_choice_size_terms.csv +++ b/activitysim/examples/prototype_mtc/configs/destination_choice_size_terms.csv @@ -1,28 +1,28 @@ -model_selector,segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTE -workplace,work_low,0,0.129,0.193,0.383,0.12,0.01,0.164,0,0,0,0 -workplace,work_med,0,0.12,0.197,0.325,0.139,0.008,0.21,0,0,0,0 -workplace,work_high,0,0.11,0.207,0.284,0.154,0.006,0.239,0,0,0,0 -workplace,work_veryhigh,0,0.093,0.27,0.241,0.146,0.004,0.246,0,0,0,0 -school,university,0,0,0,0,0,0,0,0,0,0.592,0.408 -school,gradeschool,0,0,0,0,0,0,0,1,0,0,0 -school,highschool,0,0,0,0,0,0,0,0,1,0,0 -non_mandatory,escort,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 -#non_mandatory,escort_kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 -#non_mandatory,escort_nokids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 -non_mandatory,shopping,0,1,0,0,0,0,0,0,0,0,0 -non_mandatory,eatout,0,0.742,0,0.258,0,0,0,0,0,0,0 -non_mandatory,othmaint,0,0.482,0,0.518,0,0,0,0,0,0,0 -non_mandatory,social,0,0.522,0,0.478,0,0,0,0,0,0,0 -non_mandatory,othdiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0 -atwork,atwork,0,0.742,0,0.258,0,0,0,0,0,0,0 -trip,work,0,1,1,1,1,1,1,0,0,0,0 -trip,escort,0.001,0.225,0,0.144,0,0,0,0.464,0.166,0,0 -trip,shopping,0.001,0.999,0,0,0,0,0,0,0,0,0 -trip,eatout,0,0.742,0,0.258,0,0,0,0,0,0,0 -trip,othmaint,0.001,0.481,0,0.518,0,0,0,0,0,0,0 -trip,social,0.001,0.521,0,0.478,0,0,0,0,0,0,0 -trip,othdiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0 -trip,univ,0.001,0,0,0,0,0,0,0,0,0.592,0.408 -# not needed as school is not chosen as an intermediate trip destination,,,,,,,,,,,, -#trip,gradeschool,0,0,0,0,0,0,0,1,0,0,0 -#trip,highschool,0,0,0,0,0,0,0,0,1,0,0 +model_selector,segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTE +workplace,work_low,0,0.129,0.193,0.383,0.12,0.01,0.164,0,0,0,0 +workplace,work_med,0,0.12,0.197,0.325,0.139,0.008,0.21,0,0,0,0 +workplace,work_high,0,0.11,0.207,0.284,0.154,0.006,0.239,0,0,0,0 +workplace,work_veryhigh,0,0.093,0.27,0.241,0.146,0.004,0.246,0,0,0,0 +school,university,0,0,0,0,0,0,0,0,0,0.592,0.408 +school,gradeschool,0,0,0,0,0,0,0,1,0,0,0 +school,highschool,0,0,0,0,0,0,0,0,1,0,0 +non_mandatory,escort,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 +#non_mandatory,escort_kids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 +#non_mandatory,escort_nokids,0,0.225,0,0.144,0,0,0,0.465,0.166,0,0 +non_mandatory,shopping,0,1,0,0,0,0,0,0,0,0,0 +non_mandatory,eatout,0,0.742,0,0.258,0,0,0,0,0,0,0 +non_mandatory,othmaint,0,0.482,0,0.518,0,0,0,0,0,0,0 +non_mandatory,social,0,0.522,0,0.478,0,0,0,0,0,0,0 +non_mandatory,othdiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0 +atwork,atwork,0,0.742,0,0.258,0,0,0,0,0,0,0 +trip,work,0,1,1,1,1,1,1,0,0,0,0 +trip,escort,0.001,0.225,0,0.144,0,0,0,0.464,0.166,0,0 +trip,shopping,0.001,0.999,0,0,0,0,0,0,0,0,0 +trip,eatout,0,0.742,0,0.258,0,0,0,0,0,0,0 +trip,othmaint,0.001,0.481,0,0.518,0,0,0,0,0,0,0 +trip,social,0.001,0.521,0,0.478,0,0,0,0,0,0,0 +trip,othdiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0 +trip,univ,0.001,0,0,0,0,0,0,0,0,0.592,0.408 +# not needed as school is not chosen as an intermediate trip destination,,,,,,,,,,,, +#trip,gradeschool,0,0,0,0,0,0,0,1,0,0,0 +#trip,highschool,0,0,0,0,0,0,0,0,1,0,0 diff --git a/activitysim/examples/example_psrc/configs/free_parking.csv b/activitysim/examples/prototype_mtc/configs/free_parking.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/free_parking.csv rename to activitysim/examples/prototype_mtc/configs/free_parking.csv index 5a1212b5be..588bbbf2c6 --- a/activitysim/examples/example_psrc/configs/free_parking.csv +++ b/activitysim/examples/prototype_mtc/configs/free_parking.csv @@ -1,9 +1,9 @@ -Label,Description,Expression,free,pay -util_asc_san_francisco,,@df.workplace_county_id == ID_SAN_FRANCISCO,coef_asc_san_francisco,0.0 -util_asc_santa_clara,,@df.workplace_county_id == ID_SANTA_CLARA,coef_asc_santa_clara,0.0 -util_asc_alameda,,@df.workplace_county_id == ID_ALAMEDA,coef_asc_alameda,0.0 -util_income_very_high,Very high income household dummy,@df.income>=100000,coef_income_very_high,0.0 -util_income_high,High income housheold dummy,@(df.income>=60000) & (df.income<100000),coef_income_high,0.0 -util_hh_size_4_up,Household size is greater than 3 dummy,@df.hhsize>3,coef_hh_size_4_up,0.0 -util_more_autos_than_workers,More automobiles than workers dummy,@df.auto_ownership>df.num_workers,coef_more_autos_than_workers,0.0 -util_fewer_autos_than_workers,Fewer automobiles than workers dummy,@df.auto_ownership=100000,coef_income_very_high,0.0 +util_income_high,High income housheold dummy,@(df.income>=60000) & (df.income<100000),coef_income_high,0.0 +util_hh_size_4_up,Household size is greater than 3 dummy,@df.hhsize>3,coef_hh_size_4_up,0.0 +util_more_autos_than_workers,More automobiles than workers dummy,@df.auto_ownership>df.num_workers,coef_more_autos_than_workers,0.0 +util_fewer_autos_than_workers,Fewer automobiles than workers dummy,@df.auto_ownership=30) & (income_in_thousands<60),coef_medium_income_households,, -util_household_has_more_cars_than_workers,Household has more cars than workers (dummy),more_cars_than_workers,coef_household_has_more_cars_than_workers_adults,,coef_household_has_more_cars_than_workers_mixed -util_household_in_urban_area,Household is located in an urban area type (dummy),home_is_urban,coef_household_in_urban_area,, -util_household_in_suburban_area,Household is located in a suburban area type (dummy),~(home_is_urban | home_is_rural),coef_household_in_suburban_area_adults,,coef_household_in_suburban_area_mixed -util_log_max_overlap_of_adults_time_windows,Log of max pair-wise overlap of household adults time windows,log_time_window_overlap_adult,coef_log_max_overlap_of_adults_time_windows,, -util_log_max_overlap_of_childrens_time_windows,Log of max pair-wise overlap of household childrens time windows,log_time_window_overlap_child,,coef_log_max_overlap_of_childrens_time_windows, -util_log_max_overlap_of_time_windows,Log of max pair-wise overlap of household adults and childrens time windows,log_time_window_overlap_adult_child,,,coef_log_max_overlap_of_time_windows -util_two_acive_adults,Two adults must have Mand or Non Mand activity patterns to have adult-only joint travel,num_travel_active_adults<2,coef_unavailable,, -util_two_active_children,Two children must have Mand or Non Mand activity patterns to have children-only joint travel,num_travel_active_children<2,,coef_unavailable, +Label,Description,Expression,adults,children,mixed +util_asc,Alternative-specific constant,1,,coef_asc_children,coef_asc_mixed +util_tour_purpose_is_eating_out,Joint tour purpose is eating out (dummy),tour_type=='eat',,coef_tour_purpose_is_eating_out_children,coef_tour_purpose_is_eating_out_mixed +util_tour_purpose_is_discretionary,Joint tour purpose is discretionary (dummy),tour_type=='disc',coef_tour_purpose_is_discretionary_adults,coef_tour_purpose_is_discretionary_children, +util_number_of_full_time_workers,Number of Full-Time Workers in the household,num_full_max3,coef_number_of_full_time_workers_adults,,coef_number_of_full_time_workers_mixed +util_number_of_part_time_workers,Number of Part-Time Workers in the household,num_part_max3,coef_number_of_part_time_workers_adults,,coef_number_of_part_time_workers_mixed +util_number_of_university_students,Number of University students in the household,num_univ_max3,coef_number_of_university_students,, +util_number_of_non_workers,Number of Non-Workers in the household,num_nonwork_max3,coef_number_of_non_workers_adults,,coef_number_of_non_workers_mixed +util_number_of_children_too_young_for_school,Number of Children too Young for School in the household,num_preschool_max3,,coef_number_of_children_too_young_for_school_children,coef_number_of_children_too_young_for_school_mixed +util_number_of_pre_driving_age_children,Number of Pre-driving Age Children in the household,num_school_max3,,coef_number_of_pre_driving_age_children_children,coef_number_of_pre_driving_age_children_mixed +util_number_of_driving_age_children,Number of Driving-age Children in the household,num_driving_max3,,coef_number_of_driving_age_children_children,coef_number_of_driving_age_children_mixed +util_low_income_households,Low income households (dummy),income_in_thousands<30,coef_low_income_households_adults,,coef_low_income_households_mixed +util_medium_income_households,Medium income households (dummy),(income_in_thousands>=30) & (income_in_thousands<60),coef_medium_income_households,, +util_household_has_more_cars_than_workers,Household has more cars than workers (dummy),more_cars_than_workers,coef_household_has_more_cars_than_workers_adults,,coef_household_has_more_cars_than_workers_mixed +util_household_in_urban_area,Household is located in an urban area type (dummy),home_is_urban,coef_household_in_urban_area,, +util_household_in_suburban_area,Household is located in a suburban area type (dummy),~(home_is_urban | home_is_rural),coef_household_in_suburban_area_adults,,coef_household_in_suburban_area_mixed +util_log_max_overlap_of_adults_time_windows,Log of max pair-wise overlap of household adults time windows,log_time_window_overlap_adult,coef_log_max_overlap_of_adults_time_windows,, +util_log_max_overlap_of_childrens_time_windows,Log of max pair-wise overlap of household childrens time windows,log_time_window_overlap_child,,coef_log_max_overlap_of_childrens_time_windows, +util_log_max_overlap_of_time_windows,Log of max pair-wise overlap of household adults and childrens time windows,log_time_window_overlap_adult_child,,,coef_log_max_overlap_of_time_windows +util_two_acive_adults,Two adults must have Mand or Non Mand activity patterns to have adult-only joint travel,num_travel_active_adults<2,coef_unavailable,, +util_two_active_children,Two children must have Mand or Non Mand activity patterns to have children-only joint travel,num_travel_active_children<2,,coef_unavailable, util_travel_active_adult,At least one adult and at least one child must have Mand or Non Mand activity patterns to have adult/child joint travel,(num_travel_active_adults == 0) | (num_travel_active_children == 0),,,coef_unavailable \ No newline at end of file diff --git a/activitysim/examples/example_mtc/configs/joint_tour_composition.yaml b/activitysim/examples/prototype_mtc/configs/joint_tour_composition.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/joint_tour_composition.yaml rename to activitysim/examples/prototype_mtc/configs/joint_tour_composition.yaml diff --git a/activitysim/examples/example_psrc/configs/joint_tour_composition_annotate_households_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/joint_tour_composition_annotate_households_preprocessor.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/joint_tour_composition_annotate_households_preprocessor.csv rename to activitysim/examples/prototype_mtc/configs/joint_tour_composition_annotate_households_preprocessor.csv index 80d442ba51..0dd67e97a9 --- a/activitysim/examples/example_psrc/configs/joint_tour_composition_annotate_households_preprocessor.csv +++ b/activitysim/examples/prototype_mtc/configs/joint_tour_composition_annotate_households_preprocessor.csv @@ -1,22 +1,22 @@ -Description,Target,Expression -#,, -,_HH_OVERLAPS,"hh_time_window_overlap(households, persons)" -,time_window_overlap_adult,_HH_OVERLAPS['aa'] -,time_window_overlap_child,_HH_OVERLAPS['cc'] -,time_window_overlap_adult_child,_HH_OVERLAPS['ac'] -logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) -logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) -logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) -#,, -,_HH_PERSON_COUNT,"lambda exp, households, persons: persons.query(exp).groupby('household_id').size().reindex(households.index).fillna(0)" -,_num_full,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_FULL, households, persons)" -,_num_part,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_PART, households, persons)" -,num_full_max3,"_num_full.clip(0,3)" -,num_part_max3,"_num_part.clip(0,3)" -,num_univ_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_UNIVERSITY, households, persons).clip(0,3)" -,num_nonwork_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_NONWORK, households, persons).clip(0,3)" -,num_preschool_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_PRESCHOOL, households, persons).clip(0,3)" -,num_school_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_SCHOOL, households, persons).clip(0,3)" -,num_driving_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_DRIVING, households, persons).clip(0,3)" -#,, -,more_cars_than_workers,households.auto_ownership > (_num_full + _num_part) +Description,Target,Expression +#,, +,_HH_OVERLAPS,"hh_time_window_overlap(households, persons)" +,time_window_overlap_adult,_HH_OVERLAPS['aa'] +,time_window_overlap_child,_HH_OVERLAPS['cc'] +,time_window_overlap_adult_child,_HH_OVERLAPS['ac'] +logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) +logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) +logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) +#,, +,_HH_PERSON_COUNT,"lambda exp, households, persons: persons.query(exp).groupby('household_id').size().reindex(households.index).fillna(0)" +,_num_full,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_FULL, households, persons)" +,_num_part,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_PART, households, persons)" +,num_full_max3,"_num_full.clip(0,3)" +,num_part_max3,"_num_part.clip(0,3)" +,num_univ_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_UNIVERSITY, households, persons).clip(0,3)" +,num_nonwork_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_NONWORK, households, persons).clip(0,3)" +,num_preschool_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_PRESCHOOL, households, persons).clip(0,3)" +,num_school_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_SCHOOL, households, persons).clip(0,3)" +,num_driving_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_DRIVING, households, persons).clip(0,3)" +#,, +,more_cars_than_workers,households.auto_ownership > (_num_full + _num_part) diff --git a/activitysim/examples/example_psrc/configs/joint_tour_composition_coeffs.csv b/activitysim/examples/prototype_mtc/configs/joint_tour_composition_coefficients.csv old mode 100755 new mode 100644 similarity index 97% rename from activitysim/examples/example_psrc/configs/joint_tour_composition_coeffs.csv rename to activitysim/examples/prototype_mtc/configs/joint_tour_composition_coefficients.csv index ad082c2d1e..4d929c2b5f --- a/activitysim/examples/example_psrc/configs/joint_tour_composition_coeffs.csv +++ b/activitysim/examples/prototype_mtc/configs/joint_tour_composition_coefficients.csv @@ -1,32 +1,32 @@ -coefficient_name,value,constrain -coef_unavailable,-999,T -coef_asc_children,5.3517,F -coef_asc_mixed,5.6290,fF -coef_tour_purpose_is_eating_out_children,-0.9678,F -coef_tour_purpose_is_eating_out_mixed,-0.8027,F -coef_tour_purpose_is_discretionary_adults,0.7648,F -coef_tour_purpose_is_discretionary_children,0.5101,F -coef_number_of_full_time_workers_adults,1.024,F -coef_number_of_full_time_workers_mixed,0.3624,F -coef_number_of_part_time_workers_adults,0.5412,F -coef_number_of_part_time_workers_mixed,0.3164,F -coef_number_of_university_students,0.8245,F -coef_number_of_non_workers_adults,0.6263,F -coef_number_of_non_workers_mixed,-0.3724,F -coef_number_of_children_too_young_for_school_children,0.7306,F -coef_number_of_children_too_young_for_school_mixed,0.7906,F -coef_number_of_pre_driving_age_children_children,0.7306,F -coef_number_of_pre_driving_age_children_mixed,0.3532,F -coef_number_of_driving_age_children_children,-0.2667,F -coef_number_of_driving_age_children_mixed,-0.9399,F -coef_low_income_households_adults,1.248,F -coef_low_income_households_mixed,0.5755,F -coef_medium_income_households,0.8369,F -coef_household_has_more_cars_than_workers_adults,1.386,F -coef_household_has_more_cars_than_workers_mixed,0.751,F -coef_household_in_urban_area,0.5741,F -coef_household_in_suburban_area_adults,0.5105,F -coef_household_in_suburban_area_mixed,0.1283,F -coef_log_max_overlap_of_adults_time_windows,1.192,F -coef_log_max_overlap_of_childrens_time_windows,1.841,F -coef_log_max_overlap_of_time_windows,1.958,F +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_asc_children,5.3517,F +coef_asc_mixed,5.6290,fF +coef_tour_purpose_is_eating_out_children,-0.9678,F +coef_tour_purpose_is_eating_out_mixed,-0.8027,F +coef_tour_purpose_is_discretionary_adults,0.7648,F +coef_tour_purpose_is_discretionary_children,0.5101,F +coef_number_of_full_time_workers_adults,1.024,F +coef_number_of_full_time_workers_mixed,0.3624,F +coef_number_of_part_time_workers_adults,0.5412,F +coef_number_of_part_time_workers_mixed,0.3164,F +coef_number_of_university_students,0.8245,F +coef_number_of_non_workers_adults,0.6263,F +coef_number_of_non_workers_mixed,-0.3724,F +coef_number_of_children_too_young_for_school_children,0.7306,F +coef_number_of_children_too_young_for_school_mixed,0.7906,F +coef_number_of_pre_driving_age_children_children,0.7306,F +coef_number_of_pre_driving_age_children_mixed,0.3532,F +coef_number_of_driving_age_children_children,-0.2667,F +coef_number_of_driving_age_children_mixed,-0.9399,F +coef_low_income_households_adults,1.248,F +coef_low_income_households_mixed,0.5755,F +coef_medium_income_households,0.8369,F +coef_household_has_more_cars_than_workers_adults,1.386,F +coef_household_has_more_cars_than_workers_mixed,0.751,F +coef_household_in_urban_area,0.5741,F +coef_household_in_suburban_area_adults,0.5105,F +coef_household_in_suburban_area_mixed,0.1283,F +coef_log_max_overlap_of_adults_time_windows,1.192,F +coef_log_max_overlap_of_childrens_time_windows,1.841,F +coef_log_max_overlap_of_time_windows,1.958,F diff --git a/activitysim/examples/example_mtc/configs/joint_tour_destination.yaml b/activitysim/examples/prototype_mtc/configs/joint_tour_destination.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/joint_tour_destination.yaml rename to activitysim/examples/prototype_mtc/configs/joint_tour_destination.yaml diff --git a/activitysim/examples/example_psrc/configs/joint_tour_frequency.csv b/activitysim/examples/prototype_mtc/configs/joint_tour_frequency.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_psrc/configs/joint_tour_frequency.csv rename to activitysim/examples/prototype_mtc/configs/joint_tour_frequency.csv index 8405d30649..ed2da406ee --- a/activitysim/examples/example_psrc/configs/joint_tour_frequency.csv +++ b/activitysim/examples/prototype_mtc/configs/joint_tour_frequency.csv @@ -1,77 +1,77 @@ -Label,Description,Expression,0_tours,1_Shop,1_Main,1_Eat,1_Visit,1_Disc,2_SS,2_SM,2_SE,2_SV,2_SD,2_MM,2_ME,2_MV,2_MD,2_EE,2_EV,2_ED,2_VV,2_VD,2_DD -util_alternative_specific_constants,alternative_specific_constants,1,coef_asc_0_tours,coef_asc_1_Shop,coef_asc_1_Main,coef_asc_1_Eat,coef_asc_1_Visit,coef_asc_1_Disc,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours -#_zero_tours,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeHomeMaxThree_zero_tours,fullTimeHomeMaxThree_zero_tours,cdap_home_full_max3,coef_fullTimeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, -util_partTimeHomeMaxThree_zero_tours,partTimeHomeMaxThree_zero_tours,cdap_home_part_max3,coef_partTimeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, -util_nonWorkerHomeMaxThree_zero_tours,nonWorkerHomeMaxThree_zero_tours,cdap_home_nonwork_max3,coef_nonWorkerHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, -util_retireeHomeMaxThree_zero_tours,retireeHomeMaxThree_zero_tours,cdap_home_retired_max3,coef_retireeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, -util_universityHomeMaxThree_univ_and_driving_zero_tours,universityHomeMaxThree_univ_and_driving_zero_tours,cdap_home_univ_driving_max3,coef_universityHomeMaxThree_univ_and_driving_zero_tours,,,,,,,,,,,,,,,,,,,, -util_preDrivingHomeMaxThree_preschool_and_school_zero_tours,preDrivingHomeMaxThree_preschool_and_school_zero_tours,cdap_home_nondriving_child_max3,coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours,,,,,,,,,,,,,,,,,,,, -#_shopping,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeNonMandMaxThree_shopping,fullTimeNonMandMaxThree_shopping,cdap_nonmand_full_max3,,coef_fullTimeNonMandMaxThree_shopping,,,,,2 * coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,,,,,,,,,, -util_partTimeNonMandMaxThree_shopping,partTimeNonMandMaxThree_shopping,cdap_nonmand_part_max3,,coef_partTimeNonMandMaxThree_shopping,,,,,2 * coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,,,,,,,,,, -util_nonWorkerNonMandMaxThree_shopping,nonWorkerNonMandMaxThree_shopping,cdap_nonmand_nonwork_max3,,coef_nonWorkerNonMandMaxThree_shopping,,,,,2 * coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,,,,,,,,,, -util_retireeNonMandMaxThree_shopping,retireeNonMandMaxThree_shopping,cdap_nonmand_retired_max3,,coef_retireeNonMandMaxThree_shopping,,,,,2 * coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,,,,,,,,,, -util_universityNonMandMaxThree_shopping,universityNonMandMaxThree_shopping,cdap_nonmand_univ_driving_max3,,coef_universityNonMandMaxThree_shopping,,,,,2 * coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,,,,,,,,,, -util_preDrivingNonMandMaxThree_shopping,preDrivingNonMandMaxThree_shopping,cdap_nonmand_nondriving_child_max3,,coef_preDrivingNonMandMaxThree_shopping,,,,,2 * coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,,,,,,,,,, -util_fullTimeMandMaxThree_shopping,fullTimeMandMaxThree_shopping,cdap_mand_full_max3,,coef_fullTimeMandMaxThree_shopping,,,,,2 * coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,,,,,,,,,, -util_logTimeWindowOverlapAdult_shopping,logTimeWindowOverlapAdult_shopping,log_time_window_overlap_adult,,coef_logTimeWindowOverlapAdult_shopping,,,,,2 * coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,,,,,,,,,, -util_logTimeWindowOverlapChild_shopping,logTimeWindowOverlapChild_shopping,log_time_window_overlap_child,,coef_logTimeWindowOverlapChild_shopping,,,,,2 * coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,,,,,,,,,, -util_logTimeWindowOverlapAdultChild_shopping,logTimeWindowOverlapAdultChild_shopping,log_time_window_overlap_adult_child,,coef_logTimeWindowOverlapAdultChild_shopping,,,,,2 * coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,,,,,,,,,, -util_fewerCarsThanDrivers_shopping,fewerCarsThanDrivers_shopping,(auto_ownership > 0) & (auto_ownership < num_drivers),,coef_fewerCarsThanDrivers_shopping,,,,,2 * coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,,,,,,,,,, -util_moreCarsThanWorkers_shopping,moreCarsThanWorkers_shopping,auto_ownership > num_workers,,coef_moreCarsThanWorkers_shopping,,,,,2 * coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,,,,,,,,,, -#_Maintenance,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeNonMandMaxThree_Maintenance,fullTimeNonMandMaxThree_Maintenance,cdap_nonmand_full_max3,,,coef_fullTimeNonMandMaxThree_maint,,,,,coef_fullTimeNonMandMaxThree_maint,,,,2 * coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,,,,,, -util_partTimeNonMandMaxThree_Maintenance,partTimeNonMandMaxThree_Maintenance,cdap_nonmand_part_max3,,,coef_partTimeNonMandMaxThree_maint,,,,,coef_partTimeNonMandMaxThree_maint,,,,2 * coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,,,,,, -util_nonWorkerNonMandMaxThree_Maintenance,nonWorkerNonMandMaxThree_Maintenance,cdap_nonmand_nonwork_max3,,,coef_nonWorkerNonMandMaxThree_maint,,,,,coef_nonWorkerNonMandMaxThree_maint,,,,2 * coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,,,,,, -util_retireeNonMandMaxThree_Maintenance,retireeNonMandMaxThree_Maintenance,cdap_nonmand_retired_max3,,,coef_retireeNonMandMaxThree_maint,,,,,coef_retireeNonMandMaxThree_maint,,,,2 * coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,,,,,, -util_universityNonMandMaxThree_Maintenance,universityNonMandMaxThree_Maintenance,cdap_nonmand_univ_driving_max3,,,coef_universityNonMandMaxThree_maint,,,,,coef_universityNonMandMaxThree_maint,,,,2 * coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,,,,,, -util_preDrivingNonMandMaxThree_Maintenance,preDrivingNonMandMaxThree_Maintenance,cdap_nonmand_nondriving_child_max3,,,coef_preDrivingNonMandMaxThree_maint,,,,,coef_preDrivingNonMandMaxThree_maint,,,,2 * coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,,,,,, -util_fullTimeMandMaxThree_Maintenance,fullTimeMandMaxThree_Maintenance,cdap_mand_full_max3,,,coef_fullTimeMandMaxThree_maint,,,,,coef_fullTimeMandMaxThree_maint,,,,2 * coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,,,,,, -util_drivingAgeStuMandMaxThree_Maintenance,drivingAgeStuMandMaxThree_Maintenance,cdap_mand_univ_driving_max3,,,coef_drivingAgeStuMandMaxThree_maint,,,,,coef_drivingAgeStuMandMaxThree_maint,,,,2 * coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,,,,,, -util_preDrivingAgeMandMaxThree_Maintenance,preDrivingAgeMandMaxThree_Maintenance,cdap_mand_nondriving_child_max3,,,coef_preDrivingAgeMandMaxThree_maint,,,,,coef_preDrivingAgeMandMaxThree_maint,,,,2 * coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,,,,,, -util_logTimeWindowOverlapAdult_Maintenance,logTimeWindowOverlapAdult_Maintenance,log_time_window_overlap_adult,,,coef_logTimeWindowOverlapAdult_maint,,,,,coef_logTimeWindowOverlapAdult_maint,,,,2 * coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,,,,,, -util_logTimeWindowOverlapChild_Maintenance,logTimeWindowOverlapChild_Maintenance,log_time_window_overlap_child,,,coef_logTimeWindowOverlapChild_maint,,,,,coef_logTimeWindowOverlapChild_maint,,,,2 * coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,,,,,, -util_logTimeWindowOverlapAdultChild_Maintenance,logTimeWindowOverlapAdultChild_Maintenance,log_time_window_overlap_adult_child,,,coef_logTimeWindowOverlapAdultChild_maint,,,,,coef_logTimeWindowOverlapAdultChild_maint,,,,2 * coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,,,,,, -util_fewerCarsThanDrivers_Maintenance,fewerCarsThanDrivers_Maintenance,(auto_ownership > 0) & (auto_ownership < num_drivers),,,coef_fewerCarsThanDrivers_maint,,,,,coef_fewerCarsThanDrivers_maint,,,,2 * coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,,,,,, -#_eatout,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeNonMandMaxThree_eatout,fullTimeNonMandMaxThree_eatout,cdap_nonmand_full_max3,,,,coef_fullTimeNonMandMaxThree_eatout,,,,,coef_fullTimeNonMandMaxThree_eatout,,,,coef_fullTimeNonMandMaxThree_eatout,,,2 * coef_fullTimeNonMandMaxThree_eatout,coef_fullTimeNonMandMaxThree_eatout,coef_fullTimeNonMandMaxThree_eatout,,, -util_partTimeNonMandMaxThree_eatout,partTimeNonMandMaxThree_eatout,cdap_nonmand_part_max3,,,,coef_partTimeNonMandMaxThree_eatout,,,,,coef_partTimeNonMandMaxThree_eatout,,,,coef_partTimeNonMandMaxThree_eatout,,,2 * coef_partTimeNonMandMaxThree_eatout,coef_partTimeNonMandMaxThree_eatout,coef_partTimeNonMandMaxThree_eatout,,, -util_nonWorkerNonMandMaxThree_eatout,nonWorkerNonMandMaxThree_eatout,cdap_nonmand_nonwork_max3,,,,coef_nonWorkerNonMandMaxThree_eatout,,,,,coef_nonWorkerNonMandMaxThree_eatout,,,,coef_nonWorkerNonMandMaxThree_eatout,,,2 * coef_nonWorkerNonMandMaxThree_eatout,coef_nonWorkerNonMandMaxThree_eatout,coef_nonWorkerNonMandMaxThree_eatout,,, -util_retireeNonMandMaxThree_eatout,retireeNonMandMaxThree_eatout,cdap_nonmand_retired_max3,,,,coef_retireeNonMandMaxThree_eatout,,,,,coef_retireeNonMandMaxThree_eatout,,,,coef_retireeNonMandMaxThree_eatout,,,2 * coef_retireeNonMandMaxThree_eatout,coef_retireeNonMandMaxThree_eatout,coef_retireeNonMandMaxThree_eatout,,, -util_universityNonMandMaxThree_eatout,universityNonMandMaxThree_eatout,cdap_nonmand_univ_driving_max3,,,,coef_universityNonMandMaxThree_eatout,,,,,coef_universityNonMandMaxThree_eatout,,,,coef_universityNonMandMaxThree_eatout,,,2 * coef_universityNonMandMaxThree_eatout,coef_universityNonMandMaxThree_eatout,coef_universityNonMandMaxThree_eatout,,, -util_preDrivingNonMandMaxThree_eatout,preDrivingNonMandMaxThree_eatout,cdap_nonmand_nondriving_child_max3,,,,coef_preDrivingNonMandMaxThree_eatout,,,,,coef_preDrivingNonMandMaxThree_eatout,,,,coef_preDrivingNonMandMaxThree_eatout,,,2 * coef_preDrivingNonMandMaxThree_eatout,coef_preDrivingNonMandMaxThree_eatout,coef_preDrivingNonMandMaxThree_eatout,,, -util_logTimeWindowOverlapAdult_eatout,logTimeWindowOverlapAdult_eatout,log_time_window_overlap_adult,,,,coef_logTimeWindowOverlapAdult_eatout,,,,,coef_logTimeWindowOverlapAdult_eatout,,,,coef_logTimeWindowOverlapAdult_eatout,,,2 * coef_logTimeWindowOverlapAdult_eatout,coef_logTimeWindowOverlapAdult_eatout,coef_logTimeWindowOverlapAdult_eatout,,, -util_logTimeWindowOverlapAdultChild_eatout,logTimeWindowOverlapAdultChild_eatout,log_time_window_overlap_adult_child,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,2 * coef_logTimeWindowOverlapAdultChild_eatout,coef_logTimeWindowOverlapAdultChild_eatout,coef_logTimeWindowOverlapAdultChild_eatout,,, -util_incomeBetween50And100_eatout,incomeBetween50And100_eatout,income_between_50_and_100,,,,coef_incomeBetween50And100_eatout,,,,,coef_incomeBetween50And100_eatout,,,,coef_incomeBetween50And100_eatout,,,2 * coef_incomeBetween50And100_eatout,coef_incomeBetween50And100_eatout,coef_incomeBetween50And100_eatout,,, -util_incomeGreaterThan100_eatout,incomeGreaterThan100_eatout,income_greater_than_100,,,,coef_incomeGreaterThan100_eatout,,,,,coef_incomeGreaterThan100_eatout,,,,coef_incomeGreaterThan100_eatout,,,2 * coef_incomeGreaterThan100_eatout,coef_incomeGreaterThan100_eatout,coef_incomeGreaterThan100_eatout,,, -util_incomeMissing_dummy_always_zero_eatout,incomeMissing_dummy_always_zero_eatout,income_missing,,,,coef_incomeMissing_dummy_always_zero_eatout,,,,,coef_incomeMissing_dummy_always_zero_eatout,,,,coef_incomeMissing_dummy_always_zero_eatout,,,2 * coef_incomeMissing_dummy_always_zero_eatout,coef_incomeMissing_dummy_always_zero_eatout,coef_incomeMissing_dummy_always_zero_eatout,,, -util_moreCarsThanWorkers_eatout,moreCarsThanWorkers_eatout,auto_ownership > num_workers,,,,coef_moreCarsThanWorkers_eatout,,,,,coef_moreCarsThanWorkers_eatout,,,,coef_moreCarsThanWorkers_eatout,,,2 * coef_moreCarsThanWorkers_eatout,coef_moreCarsThanWorkers_eatout,coef_moreCarsThanWorkers_eatout,,, -util_walkRetailAccessibility_eatout,walkRetailAccessibility_eatout,non_motorized_retail_accessibility,,,,coef_walkRetailAccessibility_eatout,,,,,coef_walkRetailAccessibility_eatout,,,,coef_walkRetailAccessibility_eatout,,,2 * coef_walkRetailAccessibility_eatout,coef_walkRetailAccessibility_eatout,coef_walkRetailAccessibility_eatout,,, -#_visiting,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeNonMandMaxThree_visiting,fullTimeNonMandMaxThree_visiting,cdap_nonmand_full_max3,,,,,coef_fullTimeNonMandMaxThree_visiting,,,,,coef_fullTimeNonMandMaxThree_visiting,,,,coef_fullTimeNonMandMaxThree_visiting,,,coef_fullTimeNonMandMaxThree_visiting,,2 * coef_fullTimeNonMandMaxThree_visiting,coef_fullTimeNonMandMaxThree_visiting, -util_partTimeNonMandMaxThree_visiting,partTimeNonMandMaxThree_visiting,cdap_nonmand_part_max3,,,,,coef_partTimeNonMandMaxThree_visiting,,,,,coef_partTimeNonMandMaxThree_visiting,,,,coef_partTimeNonMandMaxThree_visiting,,,coef_partTimeNonMandMaxThree_visiting,,2 * coef_partTimeNonMandMaxThree_visiting,coef_partTimeNonMandMaxThree_visiting, -util_nonWorkerNonMandMaxThree_visiting,nonWorkerNonMandMaxThree_visiting,cdap_nonmand_nonwork_max3,,,,,coef_nonWorkerNonMandMaxThree_visiting,,,,,coef_nonWorkerNonMandMaxThree_visiting,,,,coef_nonWorkerNonMandMaxThree_visiting,,,coef_nonWorkerNonMandMaxThree_visiting,,2 * coef_nonWorkerNonMandMaxThree_visiting,coef_nonWorkerNonMandMaxThree_visiting, -util_retireeNonMandMaxThree_visiting,retireeNonMandMaxThree_visiting,cdap_nonmand_retired_max3,,,,,coef_retireeNonMandMaxThree_visiting,,,,,coef_retireeNonMandMaxThree_visiting,,,,coef_retireeNonMandMaxThree_visiting,,,coef_retireeNonMandMaxThree_visiting,,2 * coef_retireeNonMandMaxThree_visiting,coef_retireeNonMandMaxThree_visiting, -util_universityNonMandMaxThree_visiting,universityNonMandMaxThree_visiting,cdap_nonmand_univ_driving_max3,,,,,coef_universityNonMandMaxThree_visiting,,,,,coef_universityNonMandMaxThree_visiting,,,,coef_universityNonMandMaxThree_visiting,,,coef_universityNonMandMaxThree_visiting,,2 * coef_universityNonMandMaxThree_visiting,coef_universityNonMandMaxThree_visiting, -util_preDrivingNonMandMaxThree_visiting,preDrivingNonMandMaxThree_visiting,cdap_nonmand_nondriving_child_max3,,,,,coef_preDrivingNonMandMaxThree_visiting,,,,,coef_preDrivingNonMandMaxThree_visiting,,,,coef_preDrivingNonMandMaxThree_visiting,,,coef_preDrivingNonMandMaxThree_visiting,,2 * coef_preDrivingNonMandMaxThree_visiting,coef_preDrivingNonMandMaxThree_visiting, -util_timeWindowOverlapAdult_visiting,timeWindowOverlapAdult_visiting,time_window_overlap_adult,,,,,coef_timeWindowOverlapAdult_visiting,,,,,coef_timeWindowOverlapAdult_visiting,,,,coef_timeWindowOverlapAdult_visiting,,,coef_timeWindowOverlapAdult_visiting,,2 * coef_timeWindowOverlapAdult_visiting,coef_timeWindowOverlapAdult_visiting, -util_timeWindowOverlapChild_visiting,timeWindowOverlapChild_visiting,time_window_overlap_child,,,,,coef_timeWindowOverlapChild_visiting,,,,,coef_timeWindowOverlapChild_visiting,,,,coef_timeWindowOverlapChild_visiting,,,coef_timeWindowOverlapChild_visiting,,2 * coef_timeWindowOverlapChild_visiting,coef_timeWindowOverlapChild_visiting, -util_timeWindowOverlapAdultChild_visiting,timeWindowOverlapAdultChild_visiting,time_window_overlap_adult_child,,,,,coef_timeWindowOverlapAdultChild_visiting,,,,,coef_timeWindowOverlapAdultChild_visiting,,,,coef_timeWindowOverlapAdultChild_visiting,,,coef_timeWindowOverlapAdultChild_visiting,,2 * coef_timeWindowOverlapAdultChild_visiting,coef_timeWindowOverlapAdultChild_visiting, -util_zeroAutomobiles_visiting,zeroAutomobiles_visiting,auto_ownership == 0,,,,,coef_zeroAutomobiles_visiting,,,,,coef_zeroAutomobiles_visiting,,,,coef_zeroAutomobiles_visiting,,,coef_zeroAutomobiles_visiting,,2 * coef_zeroAutomobiles_visiting,coef_zeroAutomobiles_visiting, -#_discretionary,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeNonMandMaxThree_disc,fullTimeNonMandMaxThree_disc,cdap_nonmand_full_max3,,,,,,coef_fullTimeNonMandMaxThree_disc,,,,,coef_fullTimeNonMandMaxThree_disc,,,,coef_fullTimeNonMandMaxThree_disc,,,coef_fullTimeNonMandMaxThree_disc,,coef_fullTimeNonMandMaxThree_disc,2 * coef_fullTimeNonMandMaxThree_disc -util_partTimeNonMandMaxThree_disc,partTimeNonMandMaxThree_disc,cdap_nonmand_part_max3,,,,,,coef_partTimeNonMandMaxThree_disc,,,,,coef_partTimeNonMandMaxThree_disc,,,,coef_partTimeNonMandMaxThree_disc,,,coef_partTimeNonMandMaxThree_disc,,coef_partTimeNonMandMaxThree_disc,2 * coef_partTimeNonMandMaxThree_disc -util_nonWorkerNonMandMaxThree_disc,nonWorkerNonMandMaxThree_disc,cdap_nonmand_nonwork_max3,,,,,,coef_nonWorkerNonMandMaxThree_disc,,,,,coef_nonWorkerNonMandMaxThree_disc,,,,coef_nonWorkerNonMandMaxThree_disc,,,coef_nonWorkerNonMandMaxThree_disc,,coef_nonWorkerNonMandMaxThree_disc,2 * coef_nonWorkerNonMandMaxThree_disc -util_retireeNonMandMaxThree_disc,retireeNonMandMaxThree_disc,cdap_nonmand_retired_max3,,,,,,coef_retireeNonMandMaxThree_disc,,,,,coef_retireeNonMandMaxThree_disc,,,,coef_retireeNonMandMaxThree_disc,,,coef_retireeNonMandMaxThree_disc,,coef_retireeNonMandMaxThree_disc,2 * coef_retireeNonMandMaxThree_disc -util_universityNonMandMaxThree_disc,universityNonMandMaxThree_disc,cdap_nonmand_univ_driving_max3,,,,,,coef_universityNonMandMaxThree_disc,,,,,coef_universityNonMandMaxThree_disc,,,,coef_universityNonMandMaxThree_disc,,,coef_universityNonMandMaxThree_disc,,coef_universityNonMandMaxThree_disc,2 * coef_universityNonMandMaxThree_disc -util_preDrivingNonMandMaxThree_disc,preDrivingNonMandMaxThree_disc,cdap_nonmand_nondriving_child_max3,,,,,,coef_preDrivingNonMandMaxThree_disc,,,,,coef_preDrivingNonMandMaxThree_disc,,,,coef_preDrivingNonMandMaxThree_disc,,,coef_preDrivingNonMandMaxThree_disc,,coef_preDrivingNonMandMaxThree_disc,2 * coef_preDrivingNonMandMaxThree_disc -util_drivingAgeStuMandMaxThree_disc,drivingAgeStuMandMaxThree_disc,cdap_mand_univ_driving_max3,,,,,,coef_drivingAgeStuMandMaxThree_disc,,,,,coef_drivingAgeStuMandMaxThree_disc,,,,coef_drivingAgeStuMandMaxThree_disc,,,coef_drivingAgeStuMandMaxThree_disc,,coef_drivingAgeStuMandMaxThree_disc,2 * coef_drivingAgeStuMandMaxThree_disc -util_preDrivingAgeMandMaxThree_disc,preDrivingAgeMandMaxThree_disc,cdap_mand_nondriving_child_max3,,,,,,coef_preDrivingAgeMandMaxThree_disc,,,,,coef_preDrivingAgeMandMaxThree_disc,,,,coef_preDrivingAgeMandMaxThree_disc,,,coef_preDrivingAgeMandMaxThree_disc,,coef_preDrivingAgeMandMaxThree_disc,2 * coef_preDrivingAgeMandMaxThree_disc -util_logTimeWindowOverlapAdult_disc,logTimeWindowOverlapAdult_disc,log_time_window_overlap_adult,,,,,,coef_logTimeWindowOverlapAdult_disc,,,,,coef_logTimeWindowOverlapAdult_disc,,,,coef_logTimeWindowOverlapAdult_disc,,,coef_logTimeWindowOverlapAdult_disc,,coef_logTimeWindowOverlapAdult_disc,2 * coef_logTimeWindowOverlapAdult_disc -util_logTimeWindowOverlapChild_disc,logTimeWindowOverlapChild_disc,log_time_window_overlap_child,,,,,,coef_logTimeWindowOverlapChild_disc,,,,,coef_logTimeWindowOverlapChild_disc,,,,coef_logTimeWindowOverlapChild_disc,,,coef_logTimeWindowOverlapChild_disc,,coef_logTimeWindowOverlapChild_disc,2 * coef_logTimeWindowOverlapChild_disc -util_logTimeWindowOverlapAdultChild_disc,logTimeWindowOverlapAdultChild_disc,log_time_window_overlap_adult_child,,,,,,coef_logTimeWindowOverlapAdultChild_disc,,,,,coef_logTimeWindowOverlapAdultChild_disc,,,,coef_logTimeWindowOverlapAdultChild_disc,,,coef_logTimeWindowOverlapAdultChild_disc,,coef_logTimeWindowOverlapAdultChild_disc,2 * coef_logTimeWindowOverlapAdultChild_disc -util_incomeBetween50And100_disc,incomeBetween50And100_disc,income_between_50_and_100,,,,,,coef_incomeBetween50And100_disc,,,,,coef_incomeBetween50And100_disc,,,,coef_incomeBetween50And100_disc,,,coef_incomeBetween50And100_disc,,coef_incomeBetween50And100_disc,2 * coef_incomeBetween50And100_disc -util_incomeGreaterThan100_disc,incomeGreaterThan100_disc,income_greater_than_100,,,,,,coef_incomeGreaterThan100_disc,,,,,coef_incomeGreaterThan100_disc,,,,coef_incomeGreaterThan100_disc,,,coef_incomeGreaterThan100_disc,,coef_incomeGreaterThan100_disc,2 * coef_incomeGreaterThan100_disc -util_incomeMissing_dummy_always_zero_disc,incomeMissing_dummy_always_zero_disc,income_missing,,,,,,coef_incomeMissing_dummy_always_zero_disc,,,,,coef_incomeMissing_dummy_always_zero_disc,,,,coef_incomeMissing_dummy_always_zero_disc,,,coef_incomeMissing_dummy_always_zero_disc,,coef_incomeMissing_dummy_always_zero_disc,2 * coef_incomeMissing_dummy_always_zero_disc +Label,Description,Expression,0_tours,1_Shop,1_Main,1_Eat,1_Visit,1_Disc,2_SS,2_SM,2_SE,2_SV,2_SD,2_MM,2_ME,2_MV,2_MD,2_EE,2_EV,2_ED,2_VV,2_VD,2_DD +util_alternative_specific_constants,alternative_specific_constants,1,coef_asc_0_tours,coef_asc_1_Shop,coef_asc_1_Main,coef_asc_1_Eat,coef_asc_1_Visit,coef_asc_1_Disc,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours +#_zero_tours,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeHomeMaxThree_zero_tours,fullTimeHomeMaxThree_zero_tours,cdap_home_full_max3,coef_fullTimeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, +util_partTimeHomeMaxThree_zero_tours,partTimeHomeMaxThree_zero_tours,cdap_home_part_max3,coef_partTimeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, +util_nonWorkerHomeMaxThree_zero_tours,nonWorkerHomeMaxThree_zero_tours,cdap_home_nonwork_max3,coef_nonWorkerHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, +util_retireeHomeMaxThree_zero_tours,retireeHomeMaxThree_zero_tours,cdap_home_retired_max3,coef_retireeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, +util_universityHomeMaxThree_univ_and_driving_zero_tours,universityHomeMaxThree_univ_and_driving_zero_tours,cdap_home_univ_driving_max3,coef_universityHomeMaxThree_univ_and_driving_zero_tours,,,,,,,,,,,,,,,,,,,, +util_preDrivingHomeMaxThree_preschool_and_school_zero_tours,preDrivingHomeMaxThree_preschool_and_school_zero_tours,cdap_home_nondriving_child_max3,coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours,,,,,,,,,,,,,,,,,,,, +#_shopping,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeNonMandMaxThree_shopping,fullTimeNonMandMaxThree_shopping,cdap_nonmand_full_max3,,coef_fullTimeNonMandMaxThree_shopping,,,,,2 * coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,,,,,,,,,, +util_partTimeNonMandMaxThree_shopping,partTimeNonMandMaxThree_shopping,cdap_nonmand_part_max3,,coef_partTimeNonMandMaxThree_shopping,,,,,2 * coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,,,,,,,,,, +util_nonWorkerNonMandMaxThree_shopping,nonWorkerNonMandMaxThree_shopping,cdap_nonmand_nonwork_max3,,coef_nonWorkerNonMandMaxThree_shopping,,,,,2 * coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,,,,,,,,,, +util_retireeNonMandMaxThree_shopping,retireeNonMandMaxThree_shopping,cdap_nonmand_retired_max3,,coef_retireeNonMandMaxThree_shopping,,,,,2 * coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,,,,,,,,,, +util_universityNonMandMaxThree_shopping,universityNonMandMaxThree_shopping,cdap_nonmand_univ_driving_max3,,coef_universityNonMandMaxThree_shopping,,,,,2 * coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,,,,,,,,,, +util_preDrivingNonMandMaxThree_shopping,preDrivingNonMandMaxThree_shopping,cdap_nonmand_nondriving_child_max3,,coef_preDrivingNonMandMaxThree_shopping,,,,,2 * coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,,,,,,,,,, +util_fullTimeMandMaxThree_shopping,fullTimeMandMaxThree_shopping,cdap_mand_full_max3,,coef_fullTimeMandMaxThree_shopping,,,,,2 * coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,,,,,,,,,, +util_logTimeWindowOverlapAdult_shopping,logTimeWindowOverlapAdult_shopping,log_time_window_overlap_adult,,coef_logTimeWindowOverlapAdult_shopping,,,,,2 * coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,,,,,,,,,, +util_logTimeWindowOverlapChild_shopping,logTimeWindowOverlapChild_shopping,log_time_window_overlap_child,,coef_logTimeWindowOverlapChild_shopping,,,,,2 * coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,,,,,,,,,, +util_logTimeWindowOverlapAdultChild_shopping,logTimeWindowOverlapAdultChild_shopping,log_time_window_overlap_adult_child,,coef_logTimeWindowOverlapAdultChild_shopping,,,,,2 * coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,,,,,,,,,, +util_fewerCarsThanDrivers_shopping,fewerCarsThanDrivers_shopping,(auto_ownership > 0) & (auto_ownership < num_drivers),,coef_fewerCarsThanDrivers_shopping,,,,,2 * coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,,,,,,,,,, +util_moreCarsThanWorkers_shopping,moreCarsThanWorkers_shopping,auto_ownership > num_workers,,coef_moreCarsThanWorkers_shopping,,,,,2 * coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,,,,,,,,,, +#_Maintenance,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeNonMandMaxThree_Maintenance,fullTimeNonMandMaxThree_Maintenance,cdap_nonmand_full_max3,,,coef_fullTimeNonMandMaxThree_maint,,,,,coef_fullTimeNonMandMaxThree_maint,,,,2 * coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,,,,,, +util_partTimeNonMandMaxThree_Maintenance,partTimeNonMandMaxThree_Maintenance,cdap_nonmand_part_max3,,,coef_partTimeNonMandMaxThree_maint,,,,,coef_partTimeNonMandMaxThree_maint,,,,2 * coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,,,,,, +util_nonWorkerNonMandMaxThree_Maintenance,nonWorkerNonMandMaxThree_Maintenance,cdap_nonmand_nonwork_max3,,,coef_nonWorkerNonMandMaxThree_maint,,,,,coef_nonWorkerNonMandMaxThree_maint,,,,2 * coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,,,,,, +util_retireeNonMandMaxThree_Maintenance,retireeNonMandMaxThree_Maintenance,cdap_nonmand_retired_max3,,,coef_retireeNonMandMaxThree_maint,,,,,coef_retireeNonMandMaxThree_maint,,,,2 * coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,,,,,, +util_universityNonMandMaxThree_Maintenance,universityNonMandMaxThree_Maintenance,cdap_nonmand_univ_driving_max3,,,coef_universityNonMandMaxThree_maint,,,,,coef_universityNonMandMaxThree_maint,,,,2 * coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,,,,,, +util_preDrivingNonMandMaxThree_Maintenance,preDrivingNonMandMaxThree_Maintenance,cdap_nonmand_nondriving_child_max3,,,coef_preDrivingNonMandMaxThree_maint,,,,,coef_preDrivingNonMandMaxThree_maint,,,,2 * coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,,,,,, +util_fullTimeMandMaxThree_Maintenance,fullTimeMandMaxThree_Maintenance,cdap_mand_full_max3,,,coef_fullTimeMandMaxThree_maint,,,,,coef_fullTimeMandMaxThree_maint,,,,2 * coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,,,,,, +util_drivingAgeStuMandMaxThree_Maintenance,drivingAgeStuMandMaxThree_Maintenance,cdap_mand_univ_driving_max3,,,coef_drivingAgeStuMandMaxThree_maint,,,,,coef_drivingAgeStuMandMaxThree_maint,,,,2 * coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,,,,,, +util_preDrivingAgeMandMaxThree_Maintenance,preDrivingAgeMandMaxThree_Maintenance,cdap_mand_nondriving_child_max3,,,coef_preDrivingAgeMandMaxThree_maint,,,,,coef_preDrivingAgeMandMaxThree_maint,,,,2 * coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,,,,,, +util_logTimeWindowOverlapAdult_Maintenance,logTimeWindowOverlapAdult_Maintenance,log_time_window_overlap_adult,,,coef_logTimeWindowOverlapAdult_maint,,,,,coef_logTimeWindowOverlapAdult_maint,,,,2 * coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,,,,,, +util_logTimeWindowOverlapChild_Maintenance,logTimeWindowOverlapChild_Maintenance,log_time_window_overlap_child,,,coef_logTimeWindowOverlapChild_maint,,,,,coef_logTimeWindowOverlapChild_maint,,,,2 * coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,,,,,, +util_logTimeWindowOverlapAdultChild_Maintenance,logTimeWindowOverlapAdultChild_Maintenance,log_time_window_overlap_adult_child,,,coef_logTimeWindowOverlapAdultChild_maint,,,,,coef_logTimeWindowOverlapAdultChild_maint,,,,2 * coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,,,,,, +util_fewerCarsThanDrivers_Maintenance,fewerCarsThanDrivers_Maintenance,(auto_ownership > 0) & (auto_ownership < num_drivers),,,coef_fewerCarsThanDrivers_maint,,,,,coef_fewerCarsThanDrivers_maint,,,,2 * coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,,,,,, +#_eatout,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeNonMandMaxThree_eatout,fullTimeNonMandMaxThree_eatout,cdap_nonmand_full_max3,,,,coef_fullTimeNonMandMaxThree_eatout,,,,,coef_fullTimeNonMandMaxThree_eatout,,,,coef_fullTimeNonMandMaxThree_eatout,,,2 * coef_fullTimeNonMandMaxThree_eatout,coef_fullTimeNonMandMaxThree_eatout,coef_fullTimeNonMandMaxThree_eatout,,, +util_partTimeNonMandMaxThree_eatout,partTimeNonMandMaxThree_eatout,cdap_nonmand_part_max3,,,,coef_partTimeNonMandMaxThree_eatout,,,,,coef_partTimeNonMandMaxThree_eatout,,,,coef_partTimeNonMandMaxThree_eatout,,,2 * coef_partTimeNonMandMaxThree_eatout,coef_partTimeNonMandMaxThree_eatout,coef_partTimeNonMandMaxThree_eatout,,, +util_nonWorkerNonMandMaxThree_eatout,nonWorkerNonMandMaxThree_eatout,cdap_nonmand_nonwork_max3,,,,coef_nonWorkerNonMandMaxThree_eatout,,,,,coef_nonWorkerNonMandMaxThree_eatout,,,,coef_nonWorkerNonMandMaxThree_eatout,,,2 * coef_nonWorkerNonMandMaxThree_eatout,coef_nonWorkerNonMandMaxThree_eatout,coef_nonWorkerNonMandMaxThree_eatout,,, +util_retireeNonMandMaxThree_eatout,retireeNonMandMaxThree_eatout,cdap_nonmand_retired_max3,,,,coef_retireeNonMandMaxThree_eatout,,,,,coef_retireeNonMandMaxThree_eatout,,,,coef_retireeNonMandMaxThree_eatout,,,2 * coef_retireeNonMandMaxThree_eatout,coef_retireeNonMandMaxThree_eatout,coef_retireeNonMandMaxThree_eatout,,, +util_universityNonMandMaxThree_eatout,universityNonMandMaxThree_eatout,cdap_nonmand_univ_driving_max3,,,,coef_universityNonMandMaxThree_eatout,,,,,coef_universityNonMandMaxThree_eatout,,,,coef_universityNonMandMaxThree_eatout,,,2 * coef_universityNonMandMaxThree_eatout,coef_universityNonMandMaxThree_eatout,coef_universityNonMandMaxThree_eatout,,, +util_preDrivingNonMandMaxThree_eatout,preDrivingNonMandMaxThree_eatout,cdap_nonmand_nondriving_child_max3,,,,coef_preDrivingNonMandMaxThree_eatout,,,,,coef_preDrivingNonMandMaxThree_eatout,,,,coef_preDrivingNonMandMaxThree_eatout,,,2 * coef_preDrivingNonMandMaxThree_eatout,coef_preDrivingNonMandMaxThree_eatout,coef_preDrivingNonMandMaxThree_eatout,,, +util_logTimeWindowOverlapAdult_eatout,logTimeWindowOverlapAdult_eatout,log_time_window_overlap_adult,,,,coef_logTimeWindowOverlapAdult_eatout,,,,,coef_logTimeWindowOverlapAdult_eatout,,,,coef_logTimeWindowOverlapAdult_eatout,,,2 * coef_logTimeWindowOverlapAdult_eatout,coef_logTimeWindowOverlapAdult_eatout,coef_logTimeWindowOverlapAdult_eatout,,, +util_logTimeWindowOverlapAdultChild_eatout,logTimeWindowOverlapAdultChild_eatout,log_time_window_overlap_adult_child,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,2 * coef_logTimeWindowOverlapAdultChild_eatout,coef_logTimeWindowOverlapAdultChild_eatout,coef_logTimeWindowOverlapAdultChild_eatout,,, +util_incomeBetween50And100_eatout,incomeBetween50And100_eatout,income_between_50_and_100,,,,coef_incomeBetween50And100_eatout,,,,,coef_incomeBetween50And100_eatout,,,,coef_incomeBetween50And100_eatout,,,2 * coef_incomeBetween50And100_eatout,coef_incomeBetween50And100_eatout,coef_incomeBetween50And100_eatout,,, +util_incomeGreaterThan100_eatout,incomeGreaterThan100_eatout,income_greater_than_100,,,,coef_incomeGreaterThan100_eatout,,,,,coef_incomeGreaterThan100_eatout,,,,coef_incomeGreaterThan100_eatout,,,2 * coef_incomeGreaterThan100_eatout,coef_incomeGreaterThan100_eatout,coef_incomeGreaterThan100_eatout,,, +util_incomeMissing_dummy_always_zero_eatout,incomeMissing_dummy_always_zero_eatout,income_missing,,,,coef_incomeMissing_dummy_always_zero_eatout,,,,,coef_incomeMissing_dummy_always_zero_eatout,,,,coef_incomeMissing_dummy_always_zero_eatout,,,2 * coef_incomeMissing_dummy_always_zero_eatout,coef_incomeMissing_dummy_always_zero_eatout,coef_incomeMissing_dummy_always_zero_eatout,,, +util_moreCarsThanWorkers_eatout,moreCarsThanWorkers_eatout,auto_ownership > num_workers,,,,coef_moreCarsThanWorkers_eatout,,,,,coef_moreCarsThanWorkers_eatout,,,,coef_moreCarsThanWorkers_eatout,,,2 * coef_moreCarsThanWorkers_eatout,coef_moreCarsThanWorkers_eatout,coef_moreCarsThanWorkers_eatout,,, +util_walkRetailAccessibility_eatout,walkRetailAccessibility_eatout,non_motorized_retail_accessibility,,,,coef_walkRetailAccessibility_eatout,,,,,coef_walkRetailAccessibility_eatout,,,,coef_walkRetailAccessibility_eatout,,,2 * coef_walkRetailAccessibility_eatout,coef_walkRetailAccessibility_eatout,coef_walkRetailAccessibility_eatout,,, +#_visiting,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeNonMandMaxThree_visiting,fullTimeNonMandMaxThree_visiting,cdap_nonmand_full_max3,,,,,coef_fullTimeNonMandMaxThree_visiting,,,,,coef_fullTimeNonMandMaxThree_visiting,,,,coef_fullTimeNonMandMaxThree_visiting,,,coef_fullTimeNonMandMaxThree_visiting,,2 * coef_fullTimeNonMandMaxThree_visiting,coef_fullTimeNonMandMaxThree_visiting, +util_partTimeNonMandMaxThree_visiting,partTimeNonMandMaxThree_visiting,cdap_nonmand_part_max3,,,,,coef_partTimeNonMandMaxThree_visiting,,,,,coef_partTimeNonMandMaxThree_visiting,,,,coef_partTimeNonMandMaxThree_visiting,,,coef_partTimeNonMandMaxThree_visiting,,2 * coef_partTimeNonMandMaxThree_visiting,coef_partTimeNonMandMaxThree_visiting, +util_nonWorkerNonMandMaxThree_visiting,nonWorkerNonMandMaxThree_visiting,cdap_nonmand_nonwork_max3,,,,,coef_nonWorkerNonMandMaxThree_visiting,,,,,coef_nonWorkerNonMandMaxThree_visiting,,,,coef_nonWorkerNonMandMaxThree_visiting,,,coef_nonWorkerNonMandMaxThree_visiting,,2 * coef_nonWorkerNonMandMaxThree_visiting,coef_nonWorkerNonMandMaxThree_visiting, +util_retireeNonMandMaxThree_visiting,retireeNonMandMaxThree_visiting,cdap_nonmand_retired_max3,,,,,coef_retireeNonMandMaxThree_visiting,,,,,coef_retireeNonMandMaxThree_visiting,,,,coef_retireeNonMandMaxThree_visiting,,,coef_retireeNonMandMaxThree_visiting,,2 * coef_retireeNonMandMaxThree_visiting,coef_retireeNonMandMaxThree_visiting, +util_universityNonMandMaxThree_visiting,universityNonMandMaxThree_visiting,cdap_nonmand_univ_driving_max3,,,,,coef_universityNonMandMaxThree_visiting,,,,,coef_universityNonMandMaxThree_visiting,,,,coef_universityNonMandMaxThree_visiting,,,coef_universityNonMandMaxThree_visiting,,2 * coef_universityNonMandMaxThree_visiting,coef_universityNonMandMaxThree_visiting, +util_preDrivingNonMandMaxThree_visiting,preDrivingNonMandMaxThree_visiting,cdap_nonmand_nondriving_child_max3,,,,,coef_preDrivingNonMandMaxThree_visiting,,,,,coef_preDrivingNonMandMaxThree_visiting,,,,coef_preDrivingNonMandMaxThree_visiting,,,coef_preDrivingNonMandMaxThree_visiting,,2 * coef_preDrivingNonMandMaxThree_visiting,coef_preDrivingNonMandMaxThree_visiting, +util_timeWindowOverlapAdult_visiting,timeWindowOverlapAdult_visiting,time_window_overlap_adult,,,,,coef_timeWindowOverlapAdult_visiting,,,,,coef_timeWindowOverlapAdult_visiting,,,,coef_timeWindowOverlapAdult_visiting,,,coef_timeWindowOverlapAdult_visiting,,2 * coef_timeWindowOverlapAdult_visiting,coef_timeWindowOverlapAdult_visiting, +util_timeWindowOverlapChild_visiting,timeWindowOverlapChild_visiting,time_window_overlap_child,,,,,coef_timeWindowOverlapChild_visiting,,,,,coef_timeWindowOverlapChild_visiting,,,,coef_timeWindowOverlapChild_visiting,,,coef_timeWindowOverlapChild_visiting,,2 * coef_timeWindowOverlapChild_visiting,coef_timeWindowOverlapChild_visiting, +util_timeWindowOverlapAdultChild_visiting,timeWindowOverlapAdultChild_visiting,time_window_overlap_adult_child,,,,,coef_timeWindowOverlapAdultChild_visiting,,,,,coef_timeWindowOverlapAdultChild_visiting,,,,coef_timeWindowOverlapAdultChild_visiting,,,coef_timeWindowOverlapAdultChild_visiting,,2 * coef_timeWindowOverlapAdultChild_visiting,coef_timeWindowOverlapAdultChild_visiting, +util_zeroAutomobiles_visiting,zeroAutomobiles_visiting,auto_ownership == 0,,,,,coef_zeroAutomobiles_visiting,,,,,coef_zeroAutomobiles_visiting,,,,coef_zeroAutomobiles_visiting,,,coef_zeroAutomobiles_visiting,,2 * coef_zeroAutomobiles_visiting,coef_zeroAutomobiles_visiting, +#_discretionary,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeNonMandMaxThree_disc,fullTimeNonMandMaxThree_disc,cdap_nonmand_full_max3,,,,,,coef_fullTimeNonMandMaxThree_disc,,,,,coef_fullTimeNonMandMaxThree_disc,,,,coef_fullTimeNonMandMaxThree_disc,,,coef_fullTimeNonMandMaxThree_disc,,coef_fullTimeNonMandMaxThree_disc,2 * coef_fullTimeNonMandMaxThree_disc +util_partTimeNonMandMaxThree_disc,partTimeNonMandMaxThree_disc,cdap_nonmand_part_max3,,,,,,coef_partTimeNonMandMaxThree_disc,,,,,coef_partTimeNonMandMaxThree_disc,,,,coef_partTimeNonMandMaxThree_disc,,,coef_partTimeNonMandMaxThree_disc,,coef_partTimeNonMandMaxThree_disc,2 * coef_partTimeNonMandMaxThree_disc +util_nonWorkerNonMandMaxThree_disc,nonWorkerNonMandMaxThree_disc,cdap_nonmand_nonwork_max3,,,,,,coef_nonWorkerNonMandMaxThree_disc,,,,,coef_nonWorkerNonMandMaxThree_disc,,,,coef_nonWorkerNonMandMaxThree_disc,,,coef_nonWorkerNonMandMaxThree_disc,,coef_nonWorkerNonMandMaxThree_disc,2 * coef_nonWorkerNonMandMaxThree_disc +util_retireeNonMandMaxThree_disc,retireeNonMandMaxThree_disc,cdap_nonmand_retired_max3,,,,,,coef_retireeNonMandMaxThree_disc,,,,,coef_retireeNonMandMaxThree_disc,,,,coef_retireeNonMandMaxThree_disc,,,coef_retireeNonMandMaxThree_disc,,coef_retireeNonMandMaxThree_disc,2 * coef_retireeNonMandMaxThree_disc +util_universityNonMandMaxThree_disc,universityNonMandMaxThree_disc,cdap_nonmand_univ_driving_max3,,,,,,coef_universityNonMandMaxThree_disc,,,,,coef_universityNonMandMaxThree_disc,,,,coef_universityNonMandMaxThree_disc,,,coef_universityNonMandMaxThree_disc,,coef_universityNonMandMaxThree_disc,2 * coef_universityNonMandMaxThree_disc +util_preDrivingNonMandMaxThree_disc,preDrivingNonMandMaxThree_disc,cdap_nonmand_nondriving_child_max3,,,,,,coef_preDrivingNonMandMaxThree_disc,,,,,coef_preDrivingNonMandMaxThree_disc,,,,coef_preDrivingNonMandMaxThree_disc,,,coef_preDrivingNonMandMaxThree_disc,,coef_preDrivingNonMandMaxThree_disc,2 * coef_preDrivingNonMandMaxThree_disc +util_drivingAgeStuMandMaxThree_disc,drivingAgeStuMandMaxThree_disc,cdap_mand_univ_driving_max3,,,,,,coef_drivingAgeStuMandMaxThree_disc,,,,,coef_drivingAgeStuMandMaxThree_disc,,,,coef_drivingAgeStuMandMaxThree_disc,,,coef_drivingAgeStuMandMaxThree_disc,,coef_drivingAgeStuMandMaxThree_disc,2 * coef_drivingAgeStuMandMaxThree_disc +util_preDrivingAgeMandMaxThree_disc,preDrivingAgeMandMaxThree_disc,cdap_mand_nondriving_child_max3,,,,,,coef_preDrivingAgeMandMaxThree_disc,,,,,coef_preDrivingAgeMandMaxThree_disc,,,,coef_preDrivingAgeMandMaxThree_disc,,,coef_preDrivingAgeMandMaxThree_disc,,coef_preDrivingAgeMandMaxThree_disc,2 * coef_preDrivingAgeMandMaxThree_disc +util_logTimeWindowOverlapAdult_disc,logTimeWindowOverlapAdult_disc,log_time_window_overlap_adult,,,,,,coef_logTimeWindowOverlapAdult_disc,,,,,coef_logTimeWindowOverlapAdult_disc,,,,coef_logTimeWindowOverlapAdult_disc,,,coef_logTimeWindowOverlapAdult_disc,,coef_logTimeWindowOverlapAdult_disc,2 * coef_logTimeWindowOverlapAdult_disc +util_logTimeWindowOverlapChild_disc,logTimeWindowOverlapChild_disc,log_time_window_overlap_child,,,,,,coef_logTimeWindowOverlapChild_disc,,,,,coef_logTimeWindowOverlapChild_disc,,,,coef_logTimeWindowOverlapChild_disc,,,coef_logTimeWindowOverlapChild_disc,,coef_logTimeWindowOverlapChild_disc,2 * coef_logTimeWindowOverlapChild_disc +util_logTimeWindowOverlapAdultChild_disc,logTimeWindowOverlapAdultChild_disc,log_time_window_overlap_adult_child,,,,,,coef_logTimeWindowOverlapAdultChild_disc,,,,,coef_logTimeWindowOverlapAdultChild_disc,,,,coef_logTimeWindowOverlapAdultChild_disc,,,coef_logTimeWindowOverlapAdultChild_disc,,coef_logTimeWindowOverlapAdultChild_disc,2 * coef_logTimeWindowOverlapAdultChild_disc +util_incomeBetween50And100_disc,incomeBetween50And100_disc,income_between_50_and_100,,,,,,coef_incomeBetween50And100_disc,,,,,coef_incomeBetween50And100_disc,,,,coef_incomeBetween50And100_disc,,,coef_incomeBetween50And100_disc,,coef_incomeBetween50And100_disc,2 * coef_incomeBetween50And100_disc +util_incomeGreaterThan100_disc,incomeGreaterThan100_disc,income_greater_than_100,,,,,,coef_incomeGreaterThan100_disc,,,,,coef_incomeGreaterThan100_disc,,,,coef_incomeGreaterThan100_disc,,,coef_incomeGreaterThan100_disc,,coef_incomeGreaterThan100_disc,2 * coef_incomeGreaterThan100_disc +util_incomeMissing_dummy_always_zero_disc,incomeMissing_dummy_always_zero_disc,income_missing,,,,,,coef_incomeMissing_dummy_always_zero_disc,,,,,coef_incomeMissing_dummy_always_zero_disc,,,,coef_incomeMissing_dummy_always_zero_disc,,,coef_incomeMissing_dummy_always_zero_disc,,coef_incomeMissing_dummy_always_zero_disc,2 * coef_incomeMissing_dummy_always_zero_disc util_zeroAutomobiles_dis,zeroAutomobiles_disc,auto_ownership == 0,,,,,,coef_zeroAutomobiles_disc,,,,,coef_zeroAutomobiles_disc,,,,coef_zeroAutomobiles_disc,,,coef_zeroAutomobiles_disc,,coef_zeroAutomobiles_disc,2 * coef_zeroAutomobiles_disc \ No newline at end of file diff --git a/activitysim/examples/example_mtc/configs/joint_tour_frequency.yaml b/activitysim/examples/prototype_mtc/configs/joint_tour_frequency.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/joint_tour_frequency.yaml rename to activitysim/examples/prototype_mtc/configs/joint_tour_frequency.yaml diff --git a/activitysim/examples/example_psrc/configs/joint_tour_frequency_alternatives.csv b/activitysim/examples/prototype_mtc/configs/joint_tour_frequency_alternatives.csv old mode 100755 new mode 100644 similarity index 94% rename from activitysim/examples/example_psrc/configs/joint_tour_frequency_alternatives.csv rename to activitysim/examples/prototype_mtc/configs/joint_tour_frequency_alternatives.csv index fefa93432c..7bf93731f9 --- a/activitysim/examples/example_psrc/configs/joint_tour_frequency_alternatives.csv +++ b/activitysim/examples/prototype_mtc/configs/joint_tour_frequency_alternatives.csv @@ -1,23 +1,23 @@ -#,,,,,alt file for building joint tours -alt,shopping,othmaint,eatout,social,othdiscr -0_tours,0,0,0,0,0 -1_Shop,1,0,0,0,0 -1_Main,0,1,0,0,0 -1_Eat,0,0,1,0,0 -1_Visit,0,0,0,1,0 -1_Disc,0,0,0,0,1 -2_SS,2,0,0,0,0 -2_SM,1,1,0,0,0 -2_SE,1,0,1,0,0 -2_SV,1,0,0,1,0 -2_SD,1,0,0,0,1 -2_MM,0,2,0,0,0 -2_ME,0,1,1,0,0 -2_MV,0,1,0,1,0 -2_MD,0,1,0,0,1 -2_EE,0,0,2,0,0 -2_EV,0,0,1,1,0 -2_ED,0,0,1,0,1 -2_VV,0,0,0,2,0 -2_VD,0,0,0,1,1 -2_DD,0,0,0,0,2 +#,,,,,alt file for building joint tours +alt,shopping,othmaint,eatout,social,othdiscr +0_tours,0,0,0,0,0 +1_Shop,1,0,0,0,0 +1_Main,0,1,0,0,0 +1_Eat,0,0,1,0,0 +1_Visit,0,0,0,1,0 +1_Disc,0,0,0,0,1 +2_SS,2,0,0,0,0 +2_SM,1,1,0,0,0 +2_SE,1,0,1,0,0 +2_SV,1,0,0,1,0 +2_SD,1,0,0,0,1 +2_MM,0,2,0,0,0 +2_ME,0,1,1,0,0 +2_MV,0,1,0,1,0 +2_MD,0,1,0,0,1 +2_EE,0,0,2,0,0 +2_EV,0,0,1,1,0 +2_ED,0,0,1,0,1 +2_VV,0,0,0,2,0 +2_VD,0,0,0,1,1 +2_DD,0,0,0,0,2 diff --git a/activitysim/examples/example_psrc/configs/joint_tour_frequency_annotate_households_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/joint_tour_frequency_annotate_households_preprocessor.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/joint_tour_frequency_annotate_households_preprocessor.csv rename to activitysim/examples/prototype_mtc/configs/joint_tour_frequency_annotate_households_preprocessor.csv index a466c43f65..af42d07490 --- a/activitysim/examples/example_psrc/configs/joint_tour_frequency_annotate_households_preprocessor.csv +++ b/activitysim/examples/prototype_mtc/configs/joint_tour_frequency_annotate_households_preprocessor.csv @@ -1,32 +1,32 @@ -Description,Target,Expression -,_PTYPE_CDAP_PATTERN_COUNT,"lambda ptype, activity, households, persons: persons.query('ptype == %s and cdap_activity==\'%s\'' % (ptype, activity)).groupby('household_id').size().reindex(households.index).fillna(0)" -,_PEMPLOY_CDAP_PATTERN_COUNT,"lambda pemploy, activity, households, persons: persons.query('pemploy == %s and cdap_activity==\'%s\'' % (pemploy, activity)).groupby('household_id').size().reindex(households.index).fillna(0)" -,_2_PTYPE_CDAP_PATTERN_COUNT,"lambda ptype1, ptype2, activity, households, persons: persons.query('(ptype == %s or ptype == %s) and cdap_activity==\'%s\'' % (ptype1, ptype2, activity)).groupby('household_id').size().reindex(households.index).fillna(0)" -#,, -,_HH_OVERLAPS,"hh_time_window_overlap(households, persons)" -,time_window_overlap_adult,_HH_OVERLAPS['aa'] -,time_window_overlap_child,_HH_OVERLAPS['cc'] -,time_window_overlap_adult_child,_HH_OVERLAPS['ac'] -#,, -,cdap_home_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'H', households, persons).clip(0,3)" -,cdap_home_part_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_PART, 'H', households, persons).clip(0,3)" -,cdap_home_nonwork_max3,"_PTYPE_CDAP_PATTERN_COUNT(4, 'H', households, persons).clip(0,3)" -,cdap_home_retired_max3,"_PTYPE_CDAP_PATTERN_COUNT(5, 'H', households, persons).clip(0,3)" -,cdap_home_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'H', households, persons).clip(0,3)" -,cdap_home_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'H', households, persons).clip(0,3)" -,cdap_nonmand_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'N', households, persons).clip(0,3)" -,cdap_nonmand_part_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_PART, 'N', households, persons).clip(0,3)" -,cdap_nonmand_nonwork_max3,"_PTYPE_CDAP_PATTERN_COUNT(4, 'N', households, persons).clip(0,3)" -,cdap_nonmand_retired_max3,"_PTYPE_CDAP_PATTERN_COUNT(5, 'N', households, persons).clip(0,3)" -,cdap_nonmand_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'N', households, persons).clip(0,3)" -,cdap_nonmand_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'N', households, persons).clip(0,3)" -,cdap_mand_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'M', households, persons).clip(0,3)" -,cdap_mand_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'M', households, persons).clip(0,3)" -,cdap_mand_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'M', households, persons).clip(0,3)" -,income_between_50_and_100,(households.income > 50000) & (households.income <= 100000) -,income_greater_than_100,households.income > 100000 -,income_missing,0 -logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) -logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) -logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) -nmRetail,non_motorized_retail_accessibility,"reindex(accessibility.nmRetail, households.home_zone_id)" +Description,Target,Expression +,_PTYPE_CDAP_PATTERN_COUNT,"lambda ptype, activity, households, persons: persons.query('ptype == %s and cdap_activity==\'%s\'' % (ptype, activity)).groupby('household_id').size().reindex(households.index).fillna(0)" +,_PEMPLOY_CDAP_PATTERN_COUNT,"lambda pemploy, activity, households, persons: persons.query('pemploy == %s and cdap_activity==\'%s\'' % (pemploy, activity)).groupby('household_id').size().reindex(households.index).fillna(0)" +,_2_PTYPE_CDAP_PATTERN_COUNT,"lambda ptype1, ptype2, activity, households, persons: persons.query('(ptype == %s or ptype == %s) and cdap_activity==\'%s\'' % (ptype1, ptype2, activity)).groupby('household_id').size().reindex(households.index).fillna(0)" +#,, +,_HH_OVERLAPS,"hh_time_window_overlap(households, persons)" +,time_window_overlap_adult,_HH_OVERLAPS['aa'] +,time_window_overlap_child,_HH_OVERLAPS['cc'] +,time_window_overlap_adult_child,_HH_OVERLAPS['ac'] +#,, +,cdap_home_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'H', households, persons).clip(0,3)" +,cdap_home_part_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_PART, 'H', households, persons).clip(0,3)" +,cdap_home_nonwork_max3,"_PTYPE_CDAP_PATTERN_COUNT(4, 'H', households, persons).clip(0,3)" +,cdap_home_retired_max3,"_PTYPE_CDAP_PATTERN_COUNT(5, 'H', households, persons).clip(0,3)" +,cdap_home_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'H', households, persons).clip(0,3)" +,cdap_home_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'H', households, persons).clip(0,3)" +,cdap_nonmand_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'N', households, persons).clip(0,3)" +,cdap_nonmand_part_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_PART, 'N', households, persons).clip(0,3)" +,cdap_nonmand_nonwork_max3,"_PTYPE_CDAP_PATTERN_COUNT(4, 'N', households, persons).clip(0,3)" +,cdap_nonmand_retired_max3,"_PTYPE_CDAP_PATTERN_COUNT(5, 'N', households, persons).clip(0,3)" +,cdap_nonmand_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'N', households, persons).clip(0,3)" +,cdap_nonmand_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'N', households, persons).clip(0,3)" +,cdap_mand_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'M', households, persons).clip(0,3)" +,cdap_mand_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'M', households, persons).clip(0,3)" +,cdap_mand_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'M', households, persons).clip(0,3)" +,income_between_50_and_100,(households.income > 50000) & (households.income <= 100000) +,income_greater_than_100,households.income > 100000 +,income_missing,0 +logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) +logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) +logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) +nmRetail,non_motorized_retail_accessibility,"reindex(accessibility.nmRetail, households.home_zone_id)" diff --git a/activitysim/examples/example_psrc/configs/joint_tour_frequency_coeffs.csv b/activitysim/examples/prototype_mtc/configs/joint_tour_frequency_coefficients.csv old mode 100755 new mode 100644 similarity index 97% rename from activitysim/examples/example_psrc/configs/joint_tour_frequency_coeffs.csv rename to activitysim/examples/prototype_mtc/configs/joint_tour_frequency_coefficients.csv index b716326ee5..ae244d3df6 --- a/activitysim/examples/example_psrc/configs/joint_tour_frequency_coeffs.csv +++ b/activitysim/examples/prototype_mtc/configs/joint_tour_frequency_coefficients.csv @@ -1,84 +1,84 @@ -coefficient_name,value,constrain -# asc,, -coef_asc_0_tours,0,T -coef_asc_1_Shop,-6.0149,F -coef_asc_1_Main,-5.7389,F -coef_asc_1_Eat,-6.3757,F -coef_asc_1_Visit,-5.8818,F -coef_asc_1_Disc,-5.4806,F -coef_asc_2_tours,-14.4576,F -# zero_tours,, -coef_fullTimeHomeMaxThree_zero_tours,1.175,F -coef_partTimeHomeMaxThree_zero_tours,1.447,F -coef_nonWorkerHomeMaxThree_zero_tours,1.514,F -coef_retireeHomeMaxThree_zero_tours,0.6053,F -coef_universityHomeMaxThree_univ_and_driving_zero_tours,0.5685,F -coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours,0.530,F -# shopping,, -coef_fullTimeNonMandMaxThree_shopping,0.2052,F -coef_partTimeNonMandMaxThree_shopping,0.1866,F -coef_nonWorkerNonMandMaxThree_shopping,0.7078,F -coef_retireeNonMandMaxThree_shopping,0.941,F -coef_universityNonMandMaxThree_shopping,0.7648,F -coef_preDrivingNonMandMaxThree_shopping,0.5474,F -coef_fullTimeMandMaxThree_shopping,-0.2424,F -coef_logTimeWindowOverlapAdult_shopping,0.5945,F -coef_logTimeWindowOverlapChild_shopping,0.1416,F -coef_logTimeWindowOverlapAdultChild_shopping,0.1086,F -coef_fewerCarsThanDrivers_shopping,0.2523,F -coef_moreCarsThanWorkers_shopping,-0.3027,F -# maintenance,, -coef_fullTimeNonMandMaxThree_maint,0.3173,F -coef_partTimeNonMandMaxThree_maint,0.2452,F -coef_nonWorkerNonMandMaxThree_maint,0.4643,F -coef_retireeNonMandMaxThree_maint,0.905,F -coef_universityNonMandMaxThree_maint,0.2643,F -coef_preDrivingNonMandMaxThree_maint,0.6482,F -coef_fullTimeMandMaxThree_maint,-0.3009,F -coef_drivingAgeStuMandMaxThree_maint,-0.3237,F -coef_preDrivingAgeMandMaxThree_maint,0.2299,F -coef_logTimeWindowOverlapAdult_maint,0.3714,F -coef_logTimeWindowOverlapChild_maint,0.176,F -coef_logTimeWindowOverlapAdultChild_maint,0.2443,F -coef_fewerCarsThanDrivers_maint,0.461,F -# eatout,, -coef_fullTimeNonMandMaxThree_eatout,0.2275,F -coef_partTimeNonMandMaxThree_eatout,0.3765,F -coef_nonWorkerNonMandMaxThree_eatout,0.182,F -coef_retireeNonMandMaxThree_eatout,0.4264,F -coef_universityNonMandMaxThree_eatout,0.4097,F -coef_preDrivingNonMandMaxThree_eatout,0.3851,F -coef_logTimeWindowOverlapAdult_eatout,0.4856,F -coef_logTimeWindowOverlapAdultChild_eatout,0.0921,F -coef_incomeBetween50And100_eatout,0.2977,F -coef_incomeGreaterThan100_eatout,0.4492,F -coef_incomeMissing_dummy_always_zero_eatout,0.278,F -coef_moreCarsThanWorkers_eatout,0.3825,F -coef_walkRetailAccessibility_eatout,0.062,F -# visiting,, -coef_fullTimeNonMandMaxThree_visiting,0.6445,F -coef_partTimeNonMandMaxThree_visiting,0.1332,F -coef_nonWorkerNonMandMaxThree_visiting,0.5475,F -coef_retireeNonMandMaxThree_visiting,0.5579,F -coef_universityNonMandMaxThree_visiting,0.2809,F -coef_preDrivingNonMandMaxThree_visiting,0.6008,F -coef_timeWindowOverlapAdult_visiting,0.0596,F -coef_timeWindowOverlapChild_visiting,0.0092,F -coef_timeWindowOverlapAdultChild_visiting,0.0256,F -coef_zeroAutomobiles_visiting,-0.980,F -# discretionary,, -coef_fullTimeNonMandMaxThree_disc,0.1275,F -coef_partTimeNonMandMaxThree_disc,0.4979,F -coef_nonWorkerNonMandMaxThree_disc,0.2871,F -coef_retireeNonMandMaxThree_disc,0.6136,F -coef_universityNonMandMaxThree_disc,0.7546,F -coef_preDrivingNonMandMaxThree_disc,0.5331,F -coef_drivingAgeStuMandMaxThree_disc,0.1932,F -coef_preDrivingAgeMandMaxThree_disc,0.3862,F -coef_logTimeWindowOverlapAdult_disc,0.3428,F -coef_logTimeWindowOverlapChild_disc,0.1162,F -coef_logTimeWindowOverlapAdultChild_disc,0.2212,F -coef_incomeBetween50And100_disc,0.3167,F -coef_incomeGreaterThan100_disc,0.486,F -coef_incomeMissing_dummy_always_zero_disc,0.3723,F +coefficient_name,value,constrain +# asc,, +coef_asc_0_tours,0,T +coef_asc_1_Shop,-6.0149,F +coef_asc_1_Main,-5.7389,F +coef_asc_1_Eat,-6.3757,F +coef_asc_1_Visit,-5.8818,F +coef_asc_1_Disc,-5.4806,F +coef_asc_2_tours,-14.4576,F +# zero_tours,, +coef_fullTimeHomeMaxThree_zero_tours,1.175,F +coef_partTimeHomeMaxThree_zero_tours,1.447,F +coef_nonWorkerHomeMaxThree_zero_tours,1.514,F +coef_retireeHomeMaxThree_zero_tours,0.6053,F +coef_universityHomeMaxThree_univ_and_driving_zero_tours,0.5685,F +coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours,0.530,F +# shopping,, +coef_fullTimeNonMandMaxThree_shopping,0.2052,F +coef_partTimeNonMandMaxThree_shopping,0.1866,F +coef_nonWorkerNonMandMaxThree_shopping,0.7078,F +coef_retireeNonMandMaxThree_shopping,0.941,F +coef_universityNonMandMaxThree_shopping,0.7648,F +coef_preDrivingNonMandMaxThree_shopping,0.5474,F +coef_fullTimeMandMaxThree_shopping,-0.2424,F +coef_logTimeWindowOverlapAdult_shopping,0.5945,F +coef_logTimeWindowOverlapChild_shopping,0.1416,F +coef_logTimeWindowOverlapAdultChild_shopping,0.1086,F +coef_fewerCarsThanDrivers_shopping,0.2523,F +coef_moreCarsThanWorkers_shopping,-0.3027,F +# maintenance,, +coef_fullTimeNonMandMaxThree_maint,0.3173,F +coef_partTimeNonMandMaxThree_maint,0.2452,F +coef_nonWorkerNonMandMaxThree_maint,0.4643,F +coef_retireeNonMandMaxThree_maint,0.905,F +coef_universityNonMandMaxThree_maint,0.2643,F +coef_preDrivingNonMandMaxThree_maint,0.6482,F +coef_fullTimeMandMaxThree_maint,-0.3009,F +coef_drivingAgeStuMandMaxThree_maint,-0.3237,F +coef_preDrivingAgeMandMaxThree_maint,0.2299,F +coef_logTimeWindowOverlapAdult_maint,0.3714,F +coef_logTimeWindowOverlapChild_maint,0.176,F +coef_logTimeWindowOverlapAdultChild_maint,0.2443,F +coef_fewerCarsThanDrivers_maint,0.461,F +# eatout,, +coef_fullTimeNonMandMaxThree_eatout,0.2275,F +coef_partTimeNonMandMaxThree_eatout,0.3765,F +coef_nonWorkerNonMandMaxThree_eatout,0.182,F +coef_retireeNonMandMaxThree_eatout,0.4264,F +coef_universityNonMandMaxThree_eatout,0.4097,F +coef_preDrivingNonMandMaxThree_eatout,0.3851,F +coef_logTimeWindowOverlapAdult_eatout,0.4856,F +coef_logTimeWindowOverlapAdultChild_eatout,0.0921,F +coef_incomeBetween50And100_eatout,0.2977,F +coef_incomeGreaterThan100_eatout,0.4492,F +coef_incomeMissing_dummy_always_zero_eatout,0.278,F +coef_moreCarsThanWorkers_eatout,0.3825,F +coef_walkRetailAccessibility_eatout,0.062,F +# visiting,, +coef_fullTimeNonMandMaxThree_visiting,0.6445,F +coef_partTimeNonMandMaxThree_visiting,0.1332,F +coef_nonWorkerNonMandMaxThree_visiting,0.5475,F +coef_retireeNonMandMaxThree_visiting,0.5579,F +coef_universityNonMandMaxThree_visiting,0.2809,F +coef_preDrivingNonMandMaxThree_visiting,0.6008,F +coef_timeWindowOverlapAdult_visiting,0.0596,F +coef_timeWindowOverlapChild_visiting,0.0092,F +coef_timeWindowOverlapAdultChild_visiting,0.0256,F +coef_zeroAutomobiles_visiting,-0.980,F +# discretionary,, +coef_fullTimeNonMandMaxThree_disc,0.1275,F +coef_partTimeNonMandMaxThree_disc,0.4979,F +coef_nonWorkerNonMandMaxThree_disc,0.2871,F +coef_retireeNonMandMaxThree_disc,0.6136,F +coef_universityNonMandMaxThree_disc,0.7546,F +coef_preDrivingNonMandMaxThree_disc,0.5331,F +coef_drivingAgeStuMandMaxThree_disc,0.1932,F +coef_preDrivingAgeMandMaxThree_disc,0.3862,F +coef_logTimeWindowOverlapAdult_disc,0.3428,F +coef_logTimeWindowOverlapChild_disc,0.1162,F +coef_logTimeWindowOverlapAdultChild_disc,0.2212,F +coef_incomeBetween50And100_disc,0.3167,F +coef_incomeGreaterThan100_disc,0.486,F +coef_incomeMissing_dummy_always_zero_disc,0.3723,F coef_zeroAutomobiles_disc,-0.909,F \ No newline at end of file diff --git a/activitysim/examples/example_semcog/configs/joint_tour_participation.csv b/activitysim/examples/prototype_mtc/configs/joint_tour_participation.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_semcog/configs/joint_tour_participation.csv rename to activitysim/examples/prototype_mtc/configs/joint_tour_participation.csv index da2e65d1ea..cd692d8d27 --- a/activitysim/examples/example_semcog/configs/joint_tour_participation.csv +++ b/activitysim/examples/prototype_mtc/configs/joint_tour_participation.csv @@ -1,67 +1,67 @@ -Label,Description,Expression,participate,not_participate -util_full_time_worker_mixed_party,"Full-Time Worker, mixed party",person_is_full & tour_composition_is_mixed,coef_full_time_worker_mixed_party,coef_full_time_worker_mixed_party_not -util_part_time_worker_adults_only_party,"Part-Time Worker, adults-only party",person_is_part & tour_composition_is_adults,coef_part_time_worker_adults_only_party,coef_part_time_worker_adults_only_party_not -util_part_time_worker_mixed_party,"Part-Time Worker, mixed party",person_is_part & tour_composition_is_mixed,coef_part_time_worker_mixed_party, -util_university_student_mixed_party,"University Student, mixed party",person_is_univ & tour_composition_is_mixed,coef_university_student_mixed_party, -util_non_worker_adults_only_party,"Non-Worker, adults-only party",person_is_nonwork & tour_composition_is_adults,coef_non_worker_adults_only_party, -util_non_worker_mixed_party,"Non-Worker, mixed party",person_is_nonwork & tour_composition_is_mixed,coef_non_worker_mixed_party, -util_child_too_young_for_school_children_only_party,"Child too Young for School, children-only party",person_is_preschool & tour_composition_is_children,coef_child_too_young_for_school_children_only_party, -util_child_too_young_for_school_mixed_party,"Child too Young for School, mixed party",person_is_preschool & tour_composition_is_mixed,coef_child_too_young_for_school_mixed_party, -util_pre_driving_age_student_children_only_party,"Pre-driving age Student, children-only party",person_is_school & tour_composition_is_children,coef_pre_driving_age_student_children_only_party, -util_pre_driving_age_student_mixed_party,"Pre-driving age Student, mixed party",person_is_school & tour_composition_is_mixed,coef_pre_driving_age_student_mixed_party, -util_driving_age_student_children_only_party,"Driving-age Student, children-only party",person_is_driving & tour_composition_is_children,coef_driving_age_student_children_only_party, -util_driving_age_student_mixed_party,"Driving-age Student, mixed party",person_is_driving & tour_composition_is_mixed,coef_driving_age_student_mixed_party, -#,,,, -util_full_time_worker_specific_to_eating_out_joint_tours,"Full-Time Worker, specific to eating out joint tours",person_is_full & tour_type_is_eat,coef_full_time_worker_specific_to_eating_out_joint_tours,coef_full_time_worker_specific_to_eating_out_joint_tours_not -util_full_time_worker_specific_to_discretionary_joint_tours,"Full-Time Worker, specific to discretionary joint tours",person_is_full & tour_type_is_disc,coef_full_time_worker_specific_to_discretionary_joint_tours,coef_full_time_worker_specific_to_discretionary_joint_tours_not -util_part_time_worker_specific_to_eating_out_joint_tours,"Part-Time Worker, specific to eating out joint tours",person_is_part & tour_type_is_eat,coef_part_time_worker_specific_to_eating_out_joint_tours, -util_part_time_worker_specific_to_discretionary_joint_tours,"Part-Time Worker, specific to discretionary joint tours",person_is_part & tour_type_is_disc,coef_part_time_worker_specific_to_discretionary_joint_tours, -util_university_student_specific_to_eating_out_joint_tours,"University Student, specific to eating out joint tours",person_is_univ & tour_type_is_eat,coef_university_student_specific_to_eating_out_joint_tours, -util_university_student_specific_to_discretionary_joint_tours,"University Student, specific to discretionary joint tours",person_is_univ & tour_type_is_disc,coef_university_student_specific_to_discretionary_joint_tours, -util_non_worker_specific_to_eating_out_joint_tours,"Non-worker, specific to eating out joint tours",person_is_nonwork & tour_type_is_eat,coef_non_worker_specific_to_eating_out_joint_tours, -util_non_worker_specific_to_discretionary_joint_tours,"Non-worker, specific to discretionary joint tours",person_is_nonwork & tour_type_is_disc,coef_non_worker_specific_to_discretionary_joint_tours, -util_child_too_young_for_school_specific_to_eating_out_joint_tours,"Child too Young for School, specific to eating out joint tours",person_is_preschool & tour_type_is_eat,coef_child_too_young_for_school_specific_to_eating_out_joint_tours, -util_child_too_young_for_school_specific_to_discretionary_joint_tours,"Child too Young for School, specific to discretionary joint tours",person_is_preschool & tour_type_is_disc,coef_child_too_young_for_school_specific_to_discretionary_joint_tours, -util_pre_driving_age_student_specific_to_eating_out_joint_tours,"Pre-driving Age Student, specific to eating out joint tours",person_is_school & tour_type_is_eat,coef_pre_driving_age_student_specific_to_eating_out_joint_tours, -util_pre_driving_age_student_specific_to_discretionary_joint_tours,"Pre-driving age Student, specific to discretionary joint tours",person_is_school & tour_type_is_disc,coef_pre_driving_age_student_specific_to_discretionary_joint_tours, -util_driving_age_student_specific_to_eating_out_joint_tours,"Driving-age Student, specific to eating out joint tours",person_is_driving & tour_type_is_eat,coef_driving_age_student_specific_to_eating_out_joint_tours, -util_driving_age_student_specific_to_discretionary_joint_tours,"Driving-age Student, specific to discretionary joint tours",person_is_driving & tour_type_is_disc,coef_driving_age_student_specific_to_discretionary_joint_tours, -#,,,, -util_household_in_urban_area_adult_adult_only_party,"Household in urban area, adult, adult-only party",home_is_urban & adult & tour_composition_is_adults,coef_household_in_urban_area_adult_adult_only_party, -util_household_in_urban_area_adult_mixed_party,"Household in urban area, adult, mixed party",home_is_urban & adult & tour_composition_is_mixed,coef_household_in_urban_area_adult_mixed_party, -util_household_in_urban_area_child_child_only_party,"Household in urban area, child, child-only party",home_is_urban & ~adult & tour_composition_is_children,coef_household_in_urban_area_child_child_only_party, -util_household_in_urban_area_child_mixed_party,"Household in urban area, child, mixed party",home_is_urban & ~adult & tour_composition_is_mixed,coef_household_in_urban_area_child_mixed_party, -util_household_in_suburban_area_adult_adult_only_party,"Household in suburban area, adult, adult-only party",home_is_suburban & adult & tour_composition_is_adults,coef_household_in_suburban_area_adult_adult_only_party, -util_household_in_suburban_area_adult_mixed_party,"Household in suburban area, adult, mixed party",home_is_suburban & adult & tour_composition_is_mixed,coef_household_in_suburban_area_adult_mixed_party, -util_household_in_suburban_area_child_child_only_party,"Household in suburban area, child, child-only party",home_is_suburban & ~adult & tour_composition_is_children,coef_household_in_suburban_area_child_child_only_party, -util_household_in_suburban_area_child_mixed_party,"Household in suburban area, child, mixed party",home_is_suburban & ~adult & tour_composition_is_mixed,coef_household_in_suburban_area_child_mixed_party, -util_adult_more_automobiles_than_workers_adult_only_party,"Adult, more automobiles than workers, adult-only party",adult & more_cars_than_workers & tour_composition_is_adults,coef_adult_more_automobiles_than_workers_adult_only_party, -util_adult_more_automobiles_than_workers_mixed_party,"Adult, more automobiles than workers, mixed party",adult & more_cars_than_workers & tour_composition_is_mixed,coef_adult_more_automobiles_than_workers_mixed_party, -util_child_more_automobiles_than_workers_child_only_party,"Child, more automobiles than workers, child-only party",adult & more_cars_than_workers & tour_composition_is_children,coef_child_more_automobiles_than_workers_child_only_party, -util_child_more_automobiles_than_workers_mixed_party,"Child, more automobiles than workers, mixed party",adult & more_cars_than_workers & tour_composition_is_mixed,coef_child_more_automobiles_than_workers_mixed_party, -#,,,, -util_dummy_for_high_income_for_adult_in_adult_party,Dummy for high income for adult in adult party,high_income & tour_composition_is_adults,coef_dummy_for_high_income_for_adult_in_adult_party, -util_dummy_for_high_income_for_adult_in_mixed_party,Dummy for high income for adult in mixed party,high_income & tour_composition_is_mixed,coef_dummy_for_high_income_for_adult_in_mixed_party, -util_dummy_for_high_income_for_child_in_children_party,Dummy for high income for child in children party,high_income & tour_composition_is_children,coef_dummy_for_high_income_for_child_in_children_party, -util_dummy_for_high_income_for_child_in_mixed_party,Dummy for high income for child in mixed party,high_income & tour_composition_is_mixed,coef_dummy_for_high_income_for_child_in_mixed_party, -util_adult_number_of_joint_tours_adult_only,"Adult, number of joint tours, adult-only",(adult & tour_composition_is_adults) * num_hh_joint_tours,coef_adult_number_of_joint_tours_adult_only, -util_adult_number_of_joint_tours_mixed,"Adult, number of joint tours, mixed",(adult & tour_composition_is_mixed) * num_hh_joint_tours,coef_adult_number_of_joint_tours_mixed, -util_child_number_of_joint_tours_child_only,"Child, number of joint tours, child only",(~adult & tour_composition_is_children) * num_hh_joint_tours,coef_child_number_of_joint_tours_child_only, -util_child_number_of_joint_tours_mixed,"Child, number of joint tours, mixed",(~adult & tour_composition_is_mixed) * num_hh_joint_tours,coef_child_number_of_joint_tours_mixed, -util_adult_number_of_other_adults_in_the_household_adults_only_party,"Adult, number of other adults in the household, adults-only party",(adult & tour_composition_is_adults) * (num_adults - 1),coef_adult_number_of_other_adults_in_the_household_adults_only_party, -util_adult_number_of_other_adults_in_the_household_mixed_party,"Adult, number of other adults in the household, mixed party",(adult & tour_composition_is_mixed) * (num_adults - 1),coef_adult_number_of_other_adults_in_the_household_mixed_party, -util_child_number_of_other_children_in_the_household_child_only_party,"Child, number of other children in the household, child-only party",(~adult & tour_composition_is_children) * (num_children - 1),coef_child_number_of_other_children_in_the_household_child_only_party, -util_child_number_of_other_children_in_the_household_mixed,"Child, number of other children in the household, mixed",(~adult & tour_composition_is_mixed) * (num_children - 1),coef_child_number_of_other_children_in_the_household_mixed, -#,,,, -util_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,"Adult, log of max window overlap with an adult, adult-only party",(adult & tour_composition_is_adults) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party, -util_adult_log_of_max_window_overlap_with_a_child_mixed,"Adult, log of max window overlap with a child, mixed",(adult & tour_composition_is_mixed) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_a_child_mixed, -util_child_log_of_max_window_overlap_with_an_adult_mixed,"Child, log of max window overlap with an adult, mixed",(~adult & tour_composition_is_mixed) * log_time_window_overlap_adult,coef_child_log_of_max_window_overlap_with_an_adult_mixed, -util_child_log_of_max_window_overlap_with_a_child_child,"Child, log of max window overlap with a child, child",(~adult & tour_composition_is_children) * log_time_window_overlap_adult,coef_child_log_of_max_window_overlap_with_a_child_child, -#,,,, -util_adults_are_prohibited_in_participating_in_child_only_tours,Adults are prohibited in participating in child-only tours,adult & tour_composition_is_children,coef_unavailable, -util_children_are_prohibited_in_participating_in_adult_only_tours,Children are prohibited in participating in adult-only tours,~adult & tour_composition_is_adults,coef_unavailable, -util_persons_with_home_activity_patterns_are_prohibilted_from_participating,Persons with Home activity patterns are prohibilted from participating,~travel_active,coef_unavailable, -util_if_only_two_available_adults_both_must_participate_in_adult_only_tour,"If only two available adults, both must participate in adult-only tour",adult & travel_active & tour_composition_is_adults & (num_travel_active_adults<3),,coef_unavailable -util_if_only_one_available_adult_traveler_must_participate_in_mixed_tour,"If only one available adult, traveler must participate in mixed tour",adult & travel_active & tour_composition_is_mixed & (num_travel_active_adults<2),,coef_unavailable -util_if_only_two_available_children_both_must_participate_in_child_only_tour,"If only two available children, both must participate in child-only tour",~adult & travel_active & tour_composition_is_children & (num_travel_active_children<3),,coef_unavailable -util_if_only_one_available_child_traveler_must_participate_in_mixed_tour,"If only one available child, traveler must participate in mixed tour",~adult & travel_active & tour_composition_is_mixed & (num_travel_active_children<2),,coef_unavailable +Label,Description,Expression,participate,not_participate +util_full_time_worker_mixed_party,"Full-Time Worker, mixed party",person_is_full & tour_composition_is_mixed,coef_full_time_worker_mixed_party,coef_full_time_worker_mixed_party_not +util_part_time_worker_adults_only_party,"Part-Time Worker, adults-only party",person_is_part & tour_composition_is_adults,coef_part_time_worker_adults_only_party,coef_part_time_worker_adults_only_party_not +util_part_time_worker_mixed_party,"Part-Time Worker, mixed party",person_is_part & tour_composition_is_mixed,coef_part_time_worker_mixed_party, +util_university_student_mixed_party,"University Student, mixed party",person_is_univ & tour_composition_is_mixed,coef_university_student_mixed_party, +util_non_worker_adults_only_party,"Non-Worker, adults-only party",person_is_nonwork & tour_composition_is_adults,coef_non_worker_adults_only_party, +util_non_worker_mixed_party,"Non-Worker, mixed party",person_is_nonwork & tour_composition_is_mixed,coef_non_worker_mixed_party, +util_child_too_young_for_school_children_only_party,"Child too Young for School, children-only party",person_is_preschool & tour_composition_is_children,coef_child_too_young_for_school_children_only_party, +util_child_too_young_for_school_mixed_party,"Child too Young for School, mixed party",person_is_preschool & tour_composition_is_mixed,coef_child_too_young_for_school_mixed_party, +util_pre_driving_age_student_children_only_party,"Pre-driving age Student, children-only party",person_is_school & tour_composition_is_children,coef_pre_driving_age_student_children_only_party, +util_pre_driving_age_student_mixed_party,"Pre-driving age Student, mixed party",person_is_school & tour_composition_is_mixed,coef_pre_driving_age_student_mixed_party, +util_driving_age_student_children_only_party,"Driving-age Student, children-only party",person_is_driving & tour_composition_is_children,coef_driving_age_student_children_only_party, +util_driving_age_student_mixed_party,"Driving-age Student, mixed party",person_is_driving & tour_composition_is_mixed,coef_driving_age_student_mixed_party, +#,,,, +util_full_time_worker_specific_to_eating_out_joint_tours,"Full-Time Worker, specific to eating out joint tours",person_is_full & tour_type_is_eat,coef_full_time_worker_specific_to_eating_out_joint_tours,coef_full_time_worker_specific_to_eating_out_joint_tours_not +util_full_time_worker_specific_to_discretionary_joint_tours,"Full-Time Worker, specific to discretionary joint tours",person_is_full & tour_type_is_disc,coef_full_time_worker_specific_to_discretionary_joint_tours,coef_full_time_worker_specific_to_discretionary_joint_tours_not +util_part_time_worker_specific_to_eating_out_joint_tours,"Part-Time Worker, specific to eating out joint tours",person_is_part & tour_type_is_eat,coef_part_time_worker_specific_to_eating_out_joint_tours, +util_part_time_worker_specific_to_discretionary_joint_tours,"Part-Time Worker, specific to discretionary joint tours",person_is_part & tour_type_is_disc,coef_part_time_worker_specific_to_discretionary_joint_tours, +util_university_student_specific_to_eating_out_joint_tours,"University Student, specific to eating out joint tours",person_is_univ & tour_type_is_eat,coef_university_student_specific_to_eating_out_joint_tours, +util_university_student_specific_to_discretionary_joint_tours,"University Student, specific to discretionary joint tours",person_is_univ & tour_type_is_disc,coef_university_student_specific_to_discretionary_joint_tours, +util_non_worker_specific_to_eating_out_joint_tours,"Non-worker, specific to eating out joint tours",person_is_nonwork & tour_type_is_eat,coef_non_worker_specific_to_eating_out_joint_tours, +util_non_worker_specific_to_discretionary_joint_tours,"Non-worker, specific to discretionary joint tours",person_is_nonwork & tour_type_is_disc,coef_non_worker_specific_to_discretionary_joint_tours, +util_child_too_young_for_school_specific_to_eating_out_joint_tours,"Child too Young for School, specific to eating out joint tours",person_is_preschool & tour_type_is_eat,coef_child_too_young_for_school_specific_to_eating_out_joint_tours, +util_child_too_young_for_school_specific_to_discretionary_joint_tours,"Child too Young for School, specific to discretionary joint tours",person_is_preschool & tour_type_is_disc,coef_child_too_young_for_school_specific_to_discretionary_joint_tours, +util_pre_driving_age_student_specific_to_eating_out_joint_tours,"Pre-driving Age Student, specific to eating out joint tours",person_is_school & tour_type_is_eat,coef_pre_driving_age_student_specific_to_eating_out_joint_tours, +util_pre_driving_age_student_specific_to_discretionary_joint_tours,"Pre-driving age Student, specific to discretionary joint tours",person_is_school & tour_type_is_disc,coef_pre_driving_age_student_specific_to_discretionary_joint_tours, +util_driving_age_student_specific_to_eating_out_joint_tours,"Driving-age Student, specific to eating out joint tours",person_is_driving & tour_type_is_eat,coef_driving_age_student_specific_to_eating_out_joint_tours, +util_driving_age_student_specific_to_discretionary_joint_tours,"Driving-age Student, specific to discretionary joint tours",person_is_driving & tour_type_is_disc,coef_driving_age_student_specific_to_discretionary_joint_tours, +#,,,, +util_household_in_urban_area_adult_adult_only_party,"Household in urban area, adult, adult-only party",home_is_urban & adult & tour_composition_is_adults,coef_household_in_urban_area_adult_adult_only_party, +util_household_in_urban_area_adult_mixed_party,"Household in urban area, adult, mixed party",home_is_urban & adult & tour_composition_is_mixed,coef_household_in_urban_area_adult_mixed_party, +util_household_in_urban_area_child_child_only_party,"Household in urban area, child, child-only party",home_is_urban & ~adult & tour_composition_is_children,coef_household_in_urban_area_child_child_only_party, +util_household_in_urban_area_child_mixed_party,"Household in urban area, child, mixed party",home_is_urban & ~adult & tour_composition_is_mixed,coef_household_in_urban_area_child_mixed_party, +util_household_in_suburban_area_adult_adult_only_party,"Household in suburban area, adult, adult-only party",home_is_suburban & adult & tour_composition_is_adults,coef_household_in_suburban_area_adult_adult_only_party, +util_household_in_suburban_area_adult_mixed_party,"Household in suburban area, adult, mixed party",home_is_suburban & adult & tour_composition_is_mixed,coef_household_in_suburban_area_adult_mixed_party, +util_household_in_suburban_area_child_child_only_party,"Household in suburban area, child, child-only party",home_is_suburban & ~adult & tour_composition_is_children,coef_household_in_suburban_area_child_child_only_party, +util_household_in_suburban_area_child_mixed_party,"Household in suburban area, child, mixed party",home_is_suburban & ~adult & tour_composition_is_mixed,coef_household_in_suburban_area_child_mixed_party, +util_adult_more_automobiles_than_workers_adult_only_party,"Adult, more automobiles than workers, adult-only party",adult & more_cars_than_workers & tour_composition_is_adults,coef_adult_more_automobiles_than_workers_adult_only_party, +util_adult_more_automobiles_than_workers_mixed_party,"Adult, more automobiles than workers, mixed party",adult & more_cars_than_workers & tour_composition_is_mixed,coef_adult_more_automobiles_than_workers_mixed_party, +util_child_more_automobiles_than_workers_child_only_party,"Child, more automobiles than workers, child-only party",adult & more_cars_than_workers & tour_composition_is_children,coef_child_more_automobiles_than_workers_child_only_party, +util_child_more_automobiles_than_workers_mixed_party,"Child, more automobiles than workers, mixed party",adult & more_cars_than_workers & tour_composition_is_mixed,coef_child_more_automobiles_than_workers_mixed_party, +#,,,, +util_dummy_for_high_income_for_adult_in_adult_party,Dummy for high income for adult in adult party,high_income & tour_composition_is_adults,coef_dummy_for_high_income_for_adult_in_adult_party, +util_dummy_for_high_income_for_adult_in_mixed_party,Dummy for high income for adult in mixed party,high_income & tour_composition_is_mixed,coef_dummy_for_high_income_for_adult_in_mixed_party, +util_dummy_for_high_income_for_child_in_children_party,Dummy for high income for child in children party,high_income & tour_composition_is_children,coef_dummy_for_high_income_for_child_in_children_party, +util_dummy_for_high_income_for_child_in_mixed_party,Dummy for high income for child in mixed party,high_income & tour_composition_is_mixed,coef_dummy_for_high_income_for_child_in_mixed_party, +util_adult_number_of_joint_tours_adult_only,"Adult, number of joint tours, adult-only",(adult & tour_composition_is_adults) * num_hh_joint_tours,coef_adult_number_of_joint_tours_adult_only, +util_adult_number_of_joint_tours_mixed,"Adult, number of joint tours, mixed",(adult & tour_composition_is_mixed) * num_hh_joint_tours,coef_adult_number_of_joint_tours_mixed, +util_child_number_of_joint_tours_child_only,"Child, number of joint tours, child only",(~adult & tour_composition_is_children) * num_hh_joint_tours,coef_child_number_of_joint_tours_child_only, +util_child_number_of_joint_tours_mixed,"Child, number of joint tours, mixed",(~adult & tour_composition_is_mixed) * num_hh_joint_tours,coef_child_number_of_joint_tours_mixed, +util_adult_number_of_other_adults_in_the_household_adults_only_party,"Adult, number of other adults in the household, adults-only party",(adult & tour_composition_is_adults) * (num_adults - 1),coef_adult_number_of_other_adults_in_the_household_adults_only_party, +util_adult_number_of_other_adults_in_the_household_mixed_party,"Adult, number of other adults in the household, mixed party",(adult & tour_composition_is_mixed) * (num_adults - 1),coef_adult_number_of_other_adults_in_the_household_mixed_party, +util_child_number_of_other_children_in_the_household_child_only_party,"Child, number of other children in the household, child-only party",(~adult & tour_composition_is_children) * (num_children - 1),coef_child_number_of_other_children_in_the_household_child_only_party, +util_child_number_of_other_children_in_the_household_mixed,"Child, number of other children in the household, mixed",(~adult & tour_composition_is_mixed) * (num_children - 1),coef_child_number_of_other_children_in_the_household_mixed, +#,,,, +util_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,"Adult, log of max window overlap with an adult, adult-only party",(adult & tour_composition_is_adults) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party, +util_adult_log_of_max_window_overlap_with_a_child_mixed,"Adult, log of max window overlap with a child, mixed",(adult & tour_composition_is_mixed) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_a_child_mixed, +util_child_log_of_max_window_overlap_with_an_adult_mixed,"Child, log of max window overlap with an adult, mixed",(~adult & tour_composition_is_mixed) * log_time_window_overlap_adult,coef_child_log_of_max_window_overlap_with_an_adult_mixed, +util_child_log_of_max_window_overlap_with_a_child_child,"Child, log of max window overlap with a child, child",(~adult & tour_composition_is_children) * log_time_window_overlap_adult,coef_child_log_of_max_window_overlap_with_a_child_child, +#,,,, +util_adults_are_prohibited_in_participating_in_child_only_tours,Adults are prohibited in participating in child-only tours,adult & tour_composition_is_children,coef_unavailable, +util_children_are_prohibited_in_participating_in_adult_only_tours,Children are prohibited in participating in adult-only tours,~adult & tour_composition_is_adults,coef_unavailable, +util_persons_with_home_activity_patterns_are_prohibilted_from_participating,Persons with Home activity patterns are prohibilted from participating,~travel_active,coef_unavailable, +util_if_only_two_available_adults_both_must_participate_in_adult_only_tour,"If only two available adults, both must participate in adult-only tour",adult & travel_active & tour_composition_is_adults & (num_travel_active_adults<3),,coef_unavailable +util_if_only_one_available_adult_traveler_must_participate_in_mixed_tour,"If only one available adult, traveler must participate in mixed tour",adult & travel_active & tour_composition_is_mixed & (num_travel_active_adults<2),,coef_unavailable +util_if_only_two_available_children_both_must_participate_in_child_only_tour,"If only two available children, both must participate in child-only tour",~adult & travel_active & tour_composition_is_children & (num_travel_active_children<3),,coef_unavailable +util_if_only_one_available_child_traveler_must_participate_in_mixed_tour,"If only one available child, traveler must participate in mixed tour",~adult & travel_active & tour_composition_is_mixed & (num_travel_active_children<2),,coef_unavailable diff --git a/activitysim/examples/example_mtc/configs/joint_tour_participation.yaml b/activitysim/examples/prototype_mtc/configs/joint_tour_participation.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/joint_tour_participation.yaml rename to activitysim/examples/prototype_mtc/configs/joint_tour_participation.yaml diff --git a/activitysim/examples/example_psrc/configs/joint_tour_participation_annotate_participants_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/joint_tour_participation_annotate_participants_preprocessor.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/joint_tour_participation_annotate_participants_preprocessor.csv rename to activitysim/examples/prototype_mtc/configs/joint_tour_participation_annotate_participants_preprocessor.csv index 5d08da36e7..be9b64a944 --- a/activitysim/examples/example_psrc/configs/joint_tour_participation_annotate_participants_preprocessor.csv +++ b/activitysim/examples/prototype_mtc/configs/joint_tour_participation_annotate_participants_preprocessor.csv @@ -1,24 +1,24 @@ -Description,Target,Expression -,_P_OVERLAPS,person_time_window_overlap(persons) -,time_window_overlap_adult,"reindex(_P_OVERLAPS.aa, participants.person_id)" -,time_window_overlap_child,"reindex(_P_OVERLAPS.cc, participants.person_id)" -,time_window_overlap_adult_child,"reindex(_P_OVERLAPS.ac, participants.person_id)" -logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) -logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) -logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) -#,, -,person_is_full,participants.ptype == PTYPE_FULL -,person_is_part,participants.ptype == PTYPE_PART -,person_is_univ,participants.ptype == PTYPE_UNIVERSITY -,person_is_nonwork,participants.ptype == PTYPE_NONWORK -,person_is_driving,participants.ptype == PTYPE_DRIVING -,person_is_school,participants.ptype == PTYPE_SCHOOL -,person_is_preschool,participants.ptype == PTYPE_PRESCHOOL -,tour_type_is_eat,participants.tour_type=='eat' -,tour_type_is_disc,participants.tour_type=='disc' -,tour_composition_is_adults,participants.composition=='adults' -,tour_composition_is_children,participants.composition=='children' -,tour_composition_is_mixed,participants.composition=='mixed' -,home_is_suburban,~(participants.home_is_urban | participants.home_is_rural) -,high_income,participants.income_in_thousands > 60 -,more_cars_than_workers,participants.auto_ownership > participants.num_workers +Description,Target,Expression +,_P_OVERLAPS,person_time_window_overlap(persons) +,time_window_overlap_adult,"reindex(_P_OVERLAPS.aa, participants.person_id)" +,time_window_overlap_child,"reindex(_P_OVERLAPS.cc, participants.person_id)" +,time_window_overlap_adult_child,"reindex(_P_OVERLAPS.ac, participants.person_id)" +logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) +logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) +logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) +#,, +,person_is_full,participants.ptype == PTYPE_FULL +,person_is_part,participants.ptype == PTYPE_PART +,person_is_univ,participants.ptype == PTYPE_UNIVERSITY +,person_is_nonwork,participants.ptype == PTYPE_NONWORK +,person_is_driving,participants.ptype == PTYPE_DRIVING +,person_is_school,participants.ptype == PTYPE_SCHOOL +,person_is_preschool,participants.ptype == PTYPE_PRESCHOOL +,tour_type_is_eat,participants.tour_type=='eat' +,tour_type_is_disc,participants.tour_type=='disc' +,tour_composition_is_adults,participants.composition=='adults' +,tour_composition_is_children,participants.composition=='children' +,tour_composition_is_mixed,participants.composition=='mixed' +,home_is_suburban,~(participants.home_is_urban | participants.home_is_rural) +,high_income,participants.income_in_thousands > 60 +,more_cars_than_workers,participants.auto_ownership > participants.num_workers diff --git a/activitysim/examples/example_semcog/configs/joint_tour_participation_coeffs.csv b/activitysim/examples/prototype_mtc/configs/joint_tour_participation_coefficients.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_semcog/configs/joint_tour_participation_coeffs.csv rename to activitysim/examples/prototype_mtc/configs/joint_tour_participation_coefficients.csv index 604c0acb69..455f08be9e --- a/activitysim/examples/example_semcog/configs/joint_tour_participation_coeffs.csv +++ b/activitysim/examples/prototype_mtc/configs/joint_tour_participation_coefficients.csv @@ -1,68 +1,68 @@ -coefficient_name,value,constrain -coef_unavailable,-999,T -coef_full_time_worker_mixed_party,-3.566,F -coef_full_time_worker_mixed_party_not,0.5,T -coef_part_time_worker_adults_only_party,-3.566,F -coef_part_time_worker_adults_only_party_not,0.5,T -coef_part_time_worker_mixed_party,-0.3655,F -coef_university_student_mixed_party,-3.041,F -coef_non_worker_adults_only_party,-3.164,F -coef_non_worker_mixed_party,0.7152,F -coef_child_too_young_for_school_children_only_party,-2.786,F -coef_child_too_young_for_school_mixed_party,-1.893,F -coef_pre_driving_age_student_children_only_party,-0.7217,F -coef_pre_driving_age_student_mixed_party,-1.752,F -coef_driving_age_student_children_only_party,-1.822,F -coef_driving_age_student_mixed_party,-1.353,F -#,, -coef_full_time_worker_specific_to_eating_out_joint_tours,0.7157,F -coef_full_time_worker_specific_to_eating_out_joint_tours_not,0.5,T -coef_full_time_worker_specific_to_discretionary_joint_tours,0.4392,F -coef_full_time_worker_specific_to_discretionary_joint_tours_not,0.5,T -coef_part_time_worker_specific_to_eating_out_joint_tours,2.188,F -coef_part_time_worker_specific_to_discretionary_joint_tours,0.285,F -coef_university_student_specific_to_eating_out_joint_tours,-0.82,F -coef_university_student_specific_to_discretionary_joint_tours,0,T -coef_non_worker_specific_to_eating_out_joint_tours,0.1617,F -coef_non_worker_specific_to_discretionary_joint_tours,-0.1835,F -coef_child_too_young_for_school_specific_to_eating_out_joint_tours,0.6589,F -coef_child_too_young_for_school_specific_to_discretionary_joint_tours,0.1284,F -coef_pre_driving_age_student_specific_to_eating_out_joint_tours,1.391,F -coef_pre_driving_age_student_specific_to_discretionary_joint_tours,0.6626,F -coef_driving_age_student_specific_to_eating_out_joint_tours,2.344,F -coef_driving_age_student_specific_to_discretionary_joint_tours,-0.6675,F -#,, -coef_household_in_urban_area_adult_adult_only_party,0,T -coef_household_in_urban_area_adult_mixed_party,-0.137,F -coef_household_in_urban_area_child_child_only_party,1.21,F -coef_household_in_urban_area_child_mixed_party,0.6265,F -coef_household_in_suburban_area_adult_adult_only_party,0,T -coef_household_in_suburban_area_adult_mixed_party,-0.06007,F -coef_household_in_suburban_area_child_child_only_party,0,T -coef_household_in_suburban_area_child_mixed_party,0,T -coef_adult_more_automobiles_than_workers_adult_only_party,-0.2133,F -coef_adult_more_automobiles_than_workers_mixed_party,-0.6031,F -coef_child_more_automobiles_than_workers_child_only_party,-0.4214,F -coef_child_more_automobiles_than_workers_mixed_party,-0.3776,F -#,, -coef_dummy_for_high_income_for_adult_in_adult_party,-0.1682,F -coef_dummy_for_high_income_for_adult_in_mixed_party,-0.02613,F -coef_dummy_for_high_income_for_child_in_children_party,-0.5619,F -coef_dummy_for_high_income_for_child_in_mixed_party,-0.1528,F -coef_adult_number_of_joint_tours_adult_only,-0.3242,F -coef_adult_number_of_joint_tours_mixed,-0.3584,F -coef_child_number_of_joint_tours_child_only,0.1047,F -coef_child_number_of_joint_tours_mixed,-0.5089,F -coef_adult_number_of_other_adults_in_the_household_adults_only_party,0,T -coef_adult_number_of_other_adults_in_the_household_mixed_party,0,T -coef_child_number_of_other_children_in_the_household_child_only_party,0,T -coef_child_number_of_other_children_in_the_household_mixed,0,T -#,, -coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,0.8436,F -coef_adult_log_of_max_window_overlap_with_a_child_mixed,2.189,F -coef_child_log_of_max_window_overlap_with_an_adult_mixed,1.538,F -coef_child_log_of_max_window_overlap_with_a_child_child,1.296,F - - - - +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_full_time_worker_mixed_party,-3.566,F +coef_full_time_worker_mixed_party_not,0.5,T +coef_part_time_worker_adults_only_party,-3.566,F +coef_part_time_worker_adults_only_party_not,0.5,T +coef_part_time_worker_mixed_party,-0.3655,F +coef_university_student_mixed_party,-3.041,F +coef_non_worker_adults_only_party,-3.164,F +coef_non_worker_mixed_party,0.7152,F +coef_child_too_young_for_school_children_only_party,-2.786,F +coef_child_too_young_for_school_mixed_party,-1.893,F +coef_pre_driving_age_student_children_only_party,-0.7217,F +coef_pre_driving_age_student_mixed_party,-1.752,F +coef_driving_age_student_children_only_party,-1.822,F +coef_driving_age_student_mixed_party,-1.353,F +#,, +coef_full_time_worker_specific_to_eating_out_joint_tours,0.7157,F +coef_full_time_worker_specific_to_eating_out_joint_tours_not,0.5,T +coef_full_time_worker_specific_to_discretionary_joint_tours,0.4392,F +coef_full_time_worker_specific_to_discretionary_joint_tours_not,0.5,T +coef_part_time_worker_specific_to_eating_out_joint_tours,2.188,F +coef_part_time_worker_specific_to_discretionary_joint_tours,0.285,F +coef_university_student_specific_to_eating_out_joint_tours,-0.82,F +coef_university_student_specific_to_discretionary_joint_tours,0,T +coef_non_worker_specific_to_eating_out_joint_tours,0.1617,F +coef_non_worker_specific_to_discretionary_joint_tours,-0.1835,F +coef_child_too_young_for_school_specific_to_eating_out_joint_tours,0.6589,F +coef_child_too_young_for_school_specific_to_discretionary_joint_tours,0.1284,F +coef_pre_driving_age_student_specific_to_eating_out_joint_tours,1.391,F +coef_pre_driving_age_student_specific_to_discretionary_joint_tours,0.6626,F +coef_driving_age_student_specific_to_eating_out_joint_tours,2.344,F +coef_driving_age_student_specific_to_discretionary_joint_tours,-0.6675,F +#,, +coef_household_in_urban_area_adult_adult_only_party,0,T +coef_household_in_urban_area_adult_mixed_party,-0.137,F +coef_household_in_urban_area_child_child_only_party,1.21,F +coef_household_in_urban_area_child_mixed_party,0.6265,F +coef_household_in_suburban_area_adult_adult_only_party,0,T +coef_household_in_suburban_area_adult_mixed_party,-0.06007,F +coef_household_in_suburban_area_child_child_only_party,0,T +coef_household_in_suburban_area_child_mixed_party,0,T +coef_adult_more_automobiles_than_workers_adult_only_party,-0.2133,F +coef_adult_more_automobiles_than_workers_mixed_party,-0.6031,F +coef_child_more_automobiles_than_workers_child_only_party,-0.4214,F +coef_child_more_automobiles_than_workers_mixed_party,-0.3776,F +#,, +coef_dummy_for_high_income_for_adult_in_adult_party,-0.1682,F +coef_dummy_for_high_income_for_adult_in_mixed_party,-0.02613,F +coef_dummy_for_high_income_for_child_in_children_party,-0.5619,F +coef_dummy_for_high_income_for_child_in_mixed_party,-0.1528,F +coef_adult_number_of_joint_tours_adult_only,-0.3242,F +coef_adult_number_of_joint_tours_mixed,-0.3584,F +coef_child_number_of_joint_tours_child_only,0.1047,F +coef_child_number_of_joint_tours_mixed,-0.5089,F +coef_adult_number_of_other_adults_in_the_household_adults_only_party,0,T +coef_adult_number_of_other_adults_in_the_household_mixed_party,0,T +coef_child_number_of_other_children_in_the_household_child_only_party,0,T +coef_child_number_of_other_children_in_the_household_mixed,0,T +#,, +coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,0.8436,F +coef_adult_log_of_max_window_overlap_with_a_child_mixed,2.189,F +coef_child_log_of_max_window_overlap_with_an_adult_mixed,1.538,F +coef_child_log_of_max_window_overlap_with_a_child_child,1.296,F + + + + diff --git a/activitysim/examples/example_mtc/configs/joint_tour_scheduling.yaml b/activitysim/examples/prototype_mtc/configs/joint_tour_scheduling.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/joint_tour_scheduling.yaml rename to activitysim/examples/prototype_mtc/configs/joint_tour_scheduling.yaml diff --git a/activitysim/examples/example_psrc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_psrc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv rename to activitysim/examples/prototype_mtc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv index 2256d317d6..d239c39ca6 --- a/activitysim/examples/example_psrc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv +++ b/activitysim/examples/prototype_mtc/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv @@ -1,8 +1,8 @@ -Description,Target,Expression -destination in central business district,destination_in_cbd,"(reindex(land_use.area_type, joint_tours.destination) < setting('cbd_threshold')) * 1" -#,, this uses the free flow travel time in both directions. MTC TM1 was MD and MD -temp auto_time_to_destination,_auto_time_to_destination,"skim_dict.lookup(joint_tours.origin, joint_tours.destination, ('SOV_TIME', 'MD'))" -temp auto_time_return,_auto_time_return,"skim_dict.lookup(joint_tours.destination, joint_tours.origin, ('SOV_TIME', 'MD'))" -free flow roundtrip_auto_time,roundtrip_auto_time,"_auto_time_to_destination + _auto_time_return" -#"number of joint tours that this joint tours point_person participates in",, -,num_person_joint_tours,"reindex_i(joint_tour_participants.groupby('person_id').size(), joint_tours.person_id)" +Description,Target,Expression +destination in central business district,destination_in_cbd,"(reindex(land_use.area_type, joint_tours.destination) < setting('cbd_threshold')) * 1" +#,, this uses the free flow travel time in both directions. MTC TM1 was MD and MD +temp auto_time_to_destination,_auto_time_to_destination,"skim_dict.lookup(joint_tours.origin, joint_tours.destination, ('SOV_TIME', 'MD'))" +temp auto_time_return,_auto_time_return,"skim_dict.lookup(joint_tours.destination, joint_tours.origin, ('SOV_TIME', 'MD'))" +free flow roundtrip_auto_time,roundtrip_auto_time,"_auto_time_to_destination + _auto_time_return" +#"number of joint tours that this joint tours point_person participates in",, +,num_person_joint_tours,"reindex_i(joint_tour_participants.groupby('person_id').size(), joint_tours.person_id)" diff --git a/activitysim/examples/example_mtc/configs/logging.yaml b/activitysim/examples/prototype_mtc/configs/logging.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/logging.yaml rename to activitysim/examples/prototype_mtc/configs/logging.yaml diff --git a/activitysim/examples/example_semcog/configs/mandatory_tour_frequency.csv b/activitysim/examples/prototype_mtc/configs/mandatory_tour_frequency.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_semcog/configs/mandatory_tour_frequency.csv rename to activitysim/examples/prototype_mtc/configs/mandatory_tour_frequency.csv index 17f68125ab..848bbf77aa --- a/activitysim/examples/example_semcog/configs/mandatory_tour_frequency.csv +++ b/activitysim/examples/prototype_mtc/configs/mandatory_tour_frequency.csv @@ -1,101 +1,101 @@ -Label,Description,Expression,work1,work2,school1,school2,work_and_school -util_ft_worker,Full-time worker alternative-specific constants,ptype == 1,0,coef_ft_worker_work2_asc,,, -util_pt_worker,Part-time worker alternative-specific constants,ptype == 2,0,coef_pt_worker_work2_asc,,, -util_univ,University student alternative-specific constants,ptype == 3,coef_univ_work1_asc,coef_univ_work2_asc,0,coef_univ_school2_asc,coef_univ_work_and_school_asc -util_non_working_adult,Non-working adult alternative-specific constants,ptype == 4,,,,, -util_retired,Retired alternative-specific constants,ptype == 5,,,,, -util_driving_age_child,Driving-age child alternative-specific constants,ptype == 6,,,0,coef_driving_age_child_school2_asc,coef_driving_age_child_work_and_school_asc -util_pre_driving_age_child,Pre-driving age child who is in school alternative-specific constants,ptype == 7,,,0,coef_pre_driving_age_child_school2_asc, -util_female_ft_worker,Female - Full-time worker interaction,(ptype == 1) & female,0,coef_female_work2,coef_female_school1,,coef_female_work_and_school -util_female_pt_worker,Female - Part-time worker interaction,(ptype == 2) & female,0,coef_female_work2,coef_female_school1,,coef_female_work_and_school -util_female_univ,Female - University student interaction,(ptype == 3) & female,coef_female_work1,coef_female_work2,coef_female_school1,coef_female_school2,coef_female_work_and_school -util_female_non_working_adult,Female - Non-working adult interaction,(ptype == 4) & female,0,coef_female_work2,coef_female_school1,, -util_female_retired,Female - Retired interaction,(ptype == 5) & female,0,coef_female_work2,coef_female_school1,, -util_female_driving_age_child,Female - Driving-age child interaction,(ptype == 6) & female,coef_female_work1,,0,coef_female_school2,coef_female_work_and_school -util_female_pre_driving,Female - Pre-driving age child who is in school interaction,(ptype == 7) & female,coef_female_work1,,0,coef_female_school2, -util_under_35_ft,Under 35 - Full-time worker interaction,(ptype == 1) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,,coef_under_35_work_and_school -util_under_35_pt,Under 35 - Part-time worker interaction,(ptype == 2) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,,coef_under_35_work_and_school -util_under_35_univ,Under 35 - University student interaction,(ptype == 3) & (age <= 35),coef_under_35_work1,coef_under_35_work2,0,coef_under_35_school2,coef_under_35_work_and_school -util_under_35_non_working,Under 35 - Non-working adult interaction,(ptype == 4) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,, -util_can_walk_to_work_ft,Can walk to work - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, -util_can_walk_to_work_pt,Can walk to work - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, -util_can_walk_to_work_univ,Can walk to work - University student interaction,(ptype == 3) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, -util_can_walk_to_work_non_working_adult,Can walk to work - Non-working adult interaction,(ptype == 4) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, -util_can_walk_to_work_retired,Can walk to work - Retired interaction,(ptype == 5) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, -util_can_walk_to_school_univ,Can walk to school - University student interaction,(ptype == 3) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, -util_can_walk_to_school_driving_age_child,Can walk to school - Driving-age child interaction,(ptype == 6) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, -util_can_walk_to_school_pre_driving_age_child,Can walk to school - Pre-driving age child who is in school interaction,(ptype == 7) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, -util_can_walk_to_work_or_school_ft,Can walk to work or school - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_pt,Can walk to work or school - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_univ,Can walk to work or school - University student interaction,(ptype == 3) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_driving_age_child,Can walk to work or school - Driving-age child interaction,(ptype == 6) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_round_trip_auto_time_to_work_ft,Round trip auto time to work - Full-time worker interaction,(ptype == 1) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 -util_round_trip_auto_time_to_work_pt,Round trip auto time to work - Part-time worker interaction,(ptype == 2) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 -util_round_trip_auto_time_to_work_univ,Round trip auto time to work - University student interaction,(ptype == 3) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 -util_round_trip_auto_time_to_work_non_working_adult,Round trip auto time to work - Non-working adult interaction,(ptype == 4) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,, -util_round_trip_auto_time_to_work_retired,Round trip auto time to work - Retired,(ptype == 5) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,, -util_round_trip_auto_time_to_school_univ,Round trip auto time to school - University student interaction,(ptype == 3) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2,coef_round_trip_auto_time_to_work_work_and_school -util_round_trip_auto_time_to_school_driving_age_child,Round trip auto time to school - Driving-age child interaction,(ptype == 6) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2,coef_round_trip_auto_time_to_work_work_and_school -util_round_trip_auto_time_to_school_pre_driving_age_child,Round trip auto time to school - Pre-driving age child who is in school interaction,(ptype == 7) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2, -util_student_employted_univ,Student is employed - University student interaction,(ptype == 3) & student_is_employed,coef_student_employed,coef_student_employed,,,coef_student_employed -util_student_employted_driving_age_child,Student is employed - Driving-age child interaction,(ptype == 6) & student_is_employed,coef_student_employed,coef_student_employed,,,coef_student_employed -util_non_student_goes_to_school_ft,Non-student goes to school - Full-time worker interaction,(ptype == 1) & nonstudent_to_school,,,coef_non_student_goes_to_school,,coef_non_student_goes_to_school -util_non_student_goes_to_school_pt,Non-student goes to school - Part-time worker interaction,(ptype == 2) & nonstudent_to_school,,,coef_non_student_goes_to_school,,coef_non_student_goes_to_school -util_non_student_goes_to_school_non_working_adult,Non-student goes to school - Non-working adult interaction,(ptype == 4) & nonstudent_to_school,,,coef_non_student_goes_to_school,, -util_non_student_goes_to_school_retired,Non-student goes to school - Retired interaction,(ptype == 5) & nonstudent_to_school,,,coef_non_student_goes_to_school,, -util_no_cars_in_hh_ft,No cars in household - Full-time worker interaction,(ptype == 1) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,,coef_no_cars_in_hh_work_and_school -util_no_cars_in_hh_pt,No cars in household - Part-time worker interaction,(ptype == 2) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,,coef_no_cars_in_hh_work_and_school -util_no_cars_in_hh_unif,No cars in household - University student interaction,(ptype == 3) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,coef_no_cars_in_hh_school2,coef_no_cars_in_hh_work_and_school -util_no_cars_in_hh_non_working_adult,No cars in household - Non-working adult interaction,(ptype == 4) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,, -util_no_cars_in_hh_retired,No cars in household - Retired interaction,(ptype == 5) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,, -util_no_cars_in_hh_driving_age_student,No cars in household - Driving-age student interaction,(ptype == 6) & (auto_ownership == 0),,,,coef_no_cars_in_hh_school2,coef_no_cars_in_hh_work_and_school -util_no_cars_in_hh_pre_driving_age,No cars in household - Pre-driving age child who is in school interaction,(ptype == 7) & (auto_ownership == 0),,,,coef_no_cars_in_hh_school2, -util_fewer_cars_than_drivers_univ,Fewer cars than drivers in household - University student interaction,(ptype == 3) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2, -util_fewer_cars_than_drivers_driving_age_student,Fewer cars than drivers in household - Driving-age student interaction,(ptype == 6) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2, -util_fewer_cars_than_drivers_pre_driving_age,Fewer cars than drivers in household - Pre-driving age child who is in school interaction,(ptype == 7) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2, -util_num_preschool_in_hh_ft,Number of preschool children in household - Full-time worker interaction,(ptype == 1) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,,coef_num_preschool_in_hh_work_and_school -util_num_preschool_in_hh_pt,Number of preschool children in household - Part-time worker interaction,(ptype == 2) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,,coef_num_preschool_in_hh_work_and_school -util_num_preschool_in_hh_univ,Number of preschool children in household - University student interaction,(ptype == 3) * (num_young_children),coef_num_preschool_in_hh_work1,coef_num_preschool_in_hh_work2,0,coef_num_preschool_in_hh_school2,coef_num_preschool_in_hh_work_and_school -util_num_preschool_in_hh_non_working_adult,Number of preschool children in household - Non-working adult interaction,(ptype == 4) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,, -util_num_preschool_in_hh_retired,Number of preschool children in household - Retired interaction,(ptype == 5) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,, -util_num_preschool_in_hh_driving_age_student,Number of preschool children in household - Driving-age student interaction,(ptype == 6) * (num_young_children),coef_num_preschool_in_hh_work1,,0,coef_num_preschool_in_hh_school2,coef_num_preschool_in_hh_work_and_school -util_num_preschool_in_hh_pre_driving_age_in_school,Number of preschool children in household - Pre-driving age child who is in school interaction,(ptype == 7) * (num_young_children),coef_num_preschool_in_hh_work1,,0,coef_num_preschool_in_hh_school2, -util_num_nonworkers_in_hh_ft,Number of non-workers in the household - Full-time worker interaction,(ptype == 1) * num_non_workers,,,coef_num_non_workers_in_hh_school1,, -util_num_nonworkers_in_hh_pt,Number of non-workers in the household - Part-time worker interaction,(ptype == 2) * num_non_workers,,,coef_num_non_workers_in_hh_school1,, -util_hh_income_gt_50k_ft,Household income higher than $50k - Full-time worker interaction,(ptype == 1) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,,coef_hh_income_gt_50k_worker_work_and_school -util_hh_income_gt_50k_pt,Household income higher than $50k - Part-time worker interaction,(ptype == 2) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,,coef_hh_income_gt_50k_worker_work_and_school -util_hh_income_gt_50k_univ,Household income higher than $50k - University student interaction,(ptype == 3) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,coef_hh_income_gt_50k_work,0,,coef_hh_income_gt_50k_student_work_and_school -util_hh_income_gt_50k_non_working_adult,Household income higher than $50k - Non-working adult interaction,(ptype == 4) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,, -util_hh_income_gt_50k_retired,Household income higher than $50k - Retired interaction,(ptype == 5) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,, -util_hh_income_gt_50k_driving_age_student,Household income higher than $50k - Driving-age student interaction,(ptype == 6) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,,0,,coef_hh_income_gt_50k_student_work_and_school -util_hh_income_gt_50k_pre_driving_age_student,Household income higher than $50k - Pre-driving age child who is in school interaction,(ptype == 7) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,,0,, -util_non_family_hh_ft,Non-family household - Full-time worker interaction,(ptype == 1) & non_family,0,,coef_non_family_hh_category1,,coef_non_family_hh_category1 -util_non_family_hh_pt,Non-family household - Part-time worker interaction,(ptype == 2) & non_family,0,,coef_non_family_hh_category1,,coef_non_family_hh_category1 -util_non_family_hh_univ,Non-family household - University student interaction,(ptype == 3) & non_family,coef_non_family_hh_category2,coef_non_family_hh_category2,0,,coef_non_family_hh_category2 -util_non_family_hh_non_working_adult,Non-family household - Non-working adult interaction,(ptype == 4) & non_family,0,,coef_non_family_hh_category1,, -util_non_family_hh_retired,Non-family household - Retired interaction,(ptype == 5) & non_family,0,,coef_non_family_hh_category1,, -util_non_family_hh_driving_age_student,Non-family household - Driving-age student interaction,(ptype == 6) & non_family,coef_non_family_hh_category2,,0,,coef_non_family_hh_category2 -util_non_family_hh_pre_driving_age_student,Non-family household - Pre-driving age child who is in school interaction,(ptype == 7) & non_family,coef_non_family_hh_category2,,0,, -util_num_under_16_not_at_school_ft,Number of children under 16 not at school - Full-time worker interaction,(ptype == 1) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,,coef_num_under_16_not_at_school_work_and_school -util_num_under_16_not_at_school_pt,Number of children under 16 not at school - Part-time worker interaction,(ptype == 2) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,,coef_num_under_16_not_at_school_work_and_school -util_num_under_16_not_at_school_univ,Number of children under 16 not at school - University student interaction,(ptype == 3) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,coef_num_under_16_not_at_school_school2,coef_num_under_16_not_at_school_work_and_school -util_num_under_16_not_at_school_non_working_adult,Number of children under 16 not at school - Non-working adult interaction,(ptype == 4) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,, -util_num_under_16_not_at_school_retired,Number of children under 16 not at school - Retired,(ptype == 5) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,, -util_num_under_16_not_at_school_driving_age_student,Number of children under 16 not at school - Driving-age student interaction,(ptype == 6) * num_under16_not_at_school,,,,coef_num_under_16_not_at_school_school2,coef_num_under_16_not_at_school_work_and_school -util_num_under_16_not_at_school_pre_driving_age,Number of children under 16 not at school - Pre-driving age child who is in school interaction,(ptype == 7) * num_under16_not_at_school,,,,coef_num_under_16_not_at_school_school2, -util_nome_urban_ft,Home is in urban area - Full-time worker interaction,(ptype == 1) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,,coef_home_urban_work_and_school -util_nome_urban_pt,Home is in urban area - Part-time worker interaction,(ptype == 2) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,,coef_home_urban_work_and_school -util_nome_urban_univ,Home is in urban area - University student interaction,(ptype == 3) & home_is_urban,coef_home_urban_work1,coef_home_urban_work2,0,coef_home_urban_school2,coef_home_urban_work_and_school -util_nome_urban_non_working_adult,Home is in urban area - Non-working adult interaction,(ptype == 4) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,, -util_nome_urban_retired,Home is in urban area - Retired interaction,(ptype == 5) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,, -util_nome_urban_driving_age_student,Home is in urban area - Driving-age student interaction,(ptype == 6) & home_is_urban,coef_home_urban_work1,,0,coef_home_urban_school2,coef_home_urban_work_and_school -util_nome_urban_pre_driving_age_student,Home is in urban area - Pre-driving age child who is in school interaction,(ptype == 7) & home_is_urban,coef_home_urban_work1,,0,coef_home_urban_school2, -util_availability_ft,Unavailable: Full-time worker,ptype == 1,,,,coef_unavailable, -util_availability_pt,Unavailable: Part-time worker,ptype == 2,,,,coef_unavailable, -util_availability_non_working_adult,Unavailable: Non-working adult,ptype == 4,,,,coef_unavailable,coef_unavailable -util_availability_retired,Unavailable: Retired,ptype == 5,,,,coef_unavailable,coef_unavailable -util_availability_driving_age_child,Unavailable: Driving-age child,ptype == 6,coef_unavailable,coef_unavailable,,, -util_availability_pre_driving_age_student,Unavailable: Pre-driving age child who is in school,ptype == 7,,coef_unavailable,,,coef_unavailable -util_availability_pre_driving_age_not_in_school,Unavailable: Pre-driving age child who is not in school,ptype == 8,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable -util_availability_work_tours_no_usual_work_location,Unavailable: Work tours for those with no usual work location,~(workplace_zone_id > -1),coef_unavailable,coef_unavailable,,,coef_unavailable -util_availability_school_tours_no_usual_school_location,Unavailable: School tours for those with no usual school location,~(school_zone_id > -1),,,coef_unavailable,coef_unavailable,coef_unavailable +Label,Description,Expression,work1,work2,school1,school2,work_and_school +util_ft_worker,Full-time worker alternative-specific constants,ptype == 1,0,coef_ft_worker_work2_asc,,, +util_pt_worker,Part-time worker alternative-specific constants,ptype == 2,0,coef_pt_worker_work2_asc,,, +util_univ,University student alternative-specific constants,ptype == 3,coef_univ_work1_asc,coef_univ_work2_asc,0,coef_univ_school2_asc,coef_univ_work_and_school_asc +util_non_working_adult,Non-working adult alternative-specific constants,ptype == 4,,,,, +util_retired,Retired alternative-specific constants,ptype == 5,,,,, +util_driving_age_child,Driving-age child alternative-specific constants,ptype == 6,,,0,coef_driving_age_child_school2_asc,coef_driving_age_child_work_and_school_asc +util_pre_driving_age_child,Pre-driving age child who is in school alternative-specific constants,ptype == 7,,,0,coef_pre_driving_age_child_school2_asc, +util_female_ft_worker,Female - Full-time worker interaction,(ptype == 1) & female,0,coef_female_work2,coef_female_school1,,coef_female_work_and_school +util_female_pt_worker,Female - Part-time worker interaction,(ptype == 2) & female,0,coef_female_work2,coef_female_school1,,coef_female_work_and_school +util_female_univ,Female - University student interaction,(ptype == 3) & female,coef_female_work1,coef_female_work2,coef_female_school1,coef_female_school2,coef_female_work_and_school +util_female_non_working_adult,Female - Non-working adult interaction,(ptype == 4) & female,0,coef_female_work2,coef_female_school1,, +util_female_retired,Female - Retired interaction,(ptype == 5) & female,0,coef_female_work2,coef_female_school1,, +util_female_driving_age_child,Female - Driving-age child interaction,(ptype == 6) & female,coef_female_work1,,0,coef_female_school2,coef_female_work_and_school +util_female_pre_driving,Female - Pre-driving age child who is in school interaction,(ptype == 7) & female,coef_female_work1,,0,coef_female_school2, +util_under_35_ft,Under 35 - Full-time worker interaction,(ptype == 1) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,,coef_under_35_work_and_school +util_under_35_pt,Under 35 - Part-time worker interaction,(ptype == 2) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,,coef_under_35_work_and_school +util_under_35_univ,Under 35 - University student interaction,(ptype == 3) & (age <= 35),coef_under_35_work1,coef_under_35_work2,0,coef_under_35_school2,coef_under_35_work_and_school +util_under_35_non_working,Under 35 - Non-working adult interaction,(ptype == 4) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,, +util_can_walk_to_work_ft,Can walk to work - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, +util_can_walk_to_work_pt,Can walk to work - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, +util_can_walk_to_work_univ,Can walk to work - University student interaction,(ptype == 3) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, +util_can_walk_to_work_non_working_adult,Can walk to work - Non-working adult interaction,(ptype == 4) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, +util_can_walk_to_work_retired,Can walk to work - Retired interaction,(ptype == 5) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, +util_can_walk_to_school_univ,Can walk to school - University student interaction,(ptype == 3) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, +util_can_walk_to_school_driving_age_child,Can walk to school - Driving-age child interaction,(ptype == 6) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, +util_can_walk_to_school_pre_driving_age_child,Can walk to school - Pre-driving age child who is in school interaction,(ptype == 7) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, +util_can_walk_to_work_or_school_ft,Can walk to work or school - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_pt,Can walk to work or school - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_univ,Can walk to work or school - University student interaction,(ptype == 3) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_driving_age_child,Can walk to work or school - Driving-age child interaction,(ptype == 6) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school +util_round_trip_auto_time_to_work_ft,Round trip auto time to work - Full-time worker interaction,(ptype == 1) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 +util_round_trip_auto_time_to_work_pt,Round trip auto time to work - Part-time worker interaction,(ptype == 2) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 +util_round_trip_auto_time_to_work_univ,Round trip auto time to work - University student interaction,(ptype == 3) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 +util_round_trip_auto_time_to_work_non_working_adult,Round trip auto time to work - Non-working adult interaction,(ptype == 4) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,, +util_round_trip_auto_time_to_work_retired,Round trip auto time to work - Retired,(ptype == 5) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,, +util_round_trip_auto_time_to_school_univ,Round trip auto time to school - University student interaction,(ptype == 3) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2,coef_round_trip_auto_time_to_work_work_and_school +util_round_trip_auto_time_to_school_driving_age_child,Round trip auto time to school - Driving-age child interaction,(ptype == 6) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2,coef_round_trip_auto_time_to_work_work_and_school +util_round_trip_auto_time_to_school_pre_driving_age_child,Round trip auto time to school - Pre-driving age child who is in school interaction,(ptype == 7) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2, +util_student_employted_univ,Student is employed - University student interaction,(ptype == 3) & student_is_employed,coef_student_employed,coef_student_employed,,,coef_student_employed +util_student_employted_driving_age_child,Student is employed - Driving-age child interaction,(ptype == 6) & student_is_employed,coef_student_employed,coef_student_employed,,,coef_student_employed +util_non_student_goes_to_school_ft,Non-student goes to school - Full-time worker interaction,(ptype == 1) & nonstudent_to_school,,,coef_non_student_goes_to_school,,coef_non_student_goes_to_school +util_non_student_goes_to_school_pt,Non-student goes to school - Part-time worker interaction,(ptype == 2) & nonstudent_to_school,,,coef_non_student_goes_to_school,,coef_non_student_goes_to_school +util_non_student_goes_to_school_non_working_adult,Non-student goes to school - Non-working adult interaction,(ptype == 4) & nonstudent_to_school,,,coef_non_student_goes_to_school,, +util_non_student_goes_to_school_retired,Non-student goes to school - Retired interaction,(ptype == 5) & nonstudent_to_school,,,coef_non_student_goes_to_school,, +util_no_cars_in_hh_ft,No cars in household - Full-time worker interaction,(ptype == 1) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,,coef_no_cars_in_hh_work_and_school +util_no_cars_in_hh_pt,No cars in household - Part-time worker interaction,(ptype == 2) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,,coef_no_cars_in_hh_work_and_school +util_no_cars_in_hh_unif,No cars in household - University student interaction,(ptype == 3) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,coef_no_cars_in_hh_school2,coef_no_cars_in_hh_work_and_school +util_no_cars_in_hh_non_working_adult,No cars in household - Non-working adult interaction,(ptype == 4) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,, +util_no_cars_in_hh_retired,No cars in household - Retired interaction,(ptype == 5) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,, +util_no_cars_in_hh_driving_age_student,No cars in household - Driving-age student interaction,(ptype == 6) & (auto_ownership == 0),,,,coef_no_cars_in_hh_school2,coef_no_cars_in_hh_work_and_school +util_no_cars_in_hh_pre_driving_age,No cars in household - Pre-driving age child who is in school interaction,(ptype == 7) & (auto_ownership == 0),,,,coef_no_cars_in_hh_school2, +util_fewer_cars_than_drivers_univ,Fewer cars than drivers in household - University student interaction,(ptype == 3) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2, +util_fewer_cars_than_drivers_driving_age_student,Fewer cars than drivers in household - Driving-age student interaction,(ptype == 6) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2, +util_fewer_cars_than_drivers_pre_driving_age,Fewer cars than drivers in household - Pre-driving age child who is in school interaction,(ptype == 7) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2, +util_num_preschool_in_hh_ft,Number of preschool children in household - Full-time worker interaction,(ptype == 1) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,,coef_num_preschool_in_hh_work_and_school +util_num_preschool_in_hh_pt,Number of preschool children in household - Part-time worker interaction,(ptype == 2) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,,coef_num_preschool_in_hh_work_and_school +util_num_preschool_in_hh_univ,Number of preschool children in household - University student interaction,(ptype == 3) * (num_young_children),coef_num_preschool_in_hh_work1,coef_num_preschool_in_hh_work2,0,coef_num_preschool_in_hh_school2,coef_num_preschool_in_hh_work_and_school +util_num_preschool_in_hh_non_working_adult,Number of preschool children in household - Non-working adult interaction,(ptype == 4) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,, +util_num_preschool_in_hh_retired,Number of preschool children in household - Retired interaction,(ptype == 5) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,, +util_num_preschool_in_hh_driving_age_student,Number of preschool children in household - Driving-age student interaction,(ptype == 6) * (num_young_children),coef_num_preschool_in_hh_work1,,0,coef_num_preschool_in_hh_school2,coef_num_preschool_in_hh_work_and_school +util_num_preschool_in_hh_pre_driving_age_in_school,Number of preschool children in household - Pre-driving age child who is in school interaction,(ptype == 7) * (num_young_children),coef_num_preschool_in_hh_work1,,0,coef_num_preschool_in_hh_school2, +util_num_nonworkers_in_hh_ft,Number of non-workers in the household - Full-time worker interaction,(ptype == 1) * num_non_workers,,,coef_num_non_workers_in_hh_school1,, +util_num_nonworkers_in_hh_pt,Number of non-workers in the household - Part-time worker interaction,(ptype == 2) * num_non_workers,,,coef_num_non_workers_in_hh_school1,, +util_hh_income_gt_50k_ft,Household income higher than $50k - Full-time worker interaction,(ptype == 1) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,,coef_hh_income_gt_50k_worker_work_and_school +util_hh_income_gt_50k_pt,Household income higher than $50k - Part-time worker interaction,(ptype == 2) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,,coef_hh_income_gt_50k_worker_work_and_school +util_hh_income_gt_50k_univ,Household income higher than $50k - University student interaction,(ptype == 3) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,coef_hh_income_gt_50k_work,0,,coef_hh_income_gt_50k_student_work_and_school +util_hh_income_gt_50k_non_working_adult,Household income higher than $50k - Non-working adult interaction,(ptype == 4) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,, +util_hh_income_gt_50k_retired,Household income higher than $50k - Retired interaction,(ptype == 5) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,, +util_hh_income_gt_50k_driving_age_student,Household income higher than $50k - Driving-age student interaction,(ptype == 6) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,,0,,coef_hh_income_gt_50k_student_work_and_school +util_hh_income_gt_50k_pre_driving_age_student,Household income higher than $50k - Pre-driving age child who is in school interaction,(ptype == 7) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,,0,, +util_non_family_hh_ft,Non-family household - Full-time worker interaction,(ptype == 1) & non_family,0,,coef_non_family_hh_category1,,coef_non_family_hh_category1 +util_non_family_hh_pt,Non-family household - Part-time worker interaction,(ptype == 2) & non_family,0,,coef_non_family_hh_category1,,coef_non_family_hh_category1 +util_non_family_hh_univ,Non-family household - University student interaction,(ptype == 3) & non_family,coef_non_family_hh_category2,coef_non_family_hh_category2,0,,coef_non_family_hh_category2 +util_non_family_hh_non_working_adult,Non-family household - Non-working adult interaction,(ptype == 4) & non_family,0,,coef_non_family_hh_category1,, +util_non_family_hh_retired,Non-family household - Retired interaction,(ptype == 5) & non_family,0,,coef_non_family_hh_category1,, +util_non_family_hh_driving_age_student,Non-family household - Driving-age student interaction,(ptype == 6) & non_family,coef_non_family_hh_category2,,0,,coef_non_family_hh_category2 +util_non_family_hh_pre_driving_age_student,Non-family household - Pre-driving age child who is in school interaction,(ptype == 7) & non_family,coef_non_family_hh_category2,,0,, +util_num_under_16_not_at_school_ft,Number of children under 16 not at school - Full-time worker interaction,(ptype == 1) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,,coef_num_under_16_not_at_school_work_and_school +util_num_under_16_not_at_school_pt,Number of children under 16 not at school - Part-time worker interaction,(ptype == 2) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,,coef_num_under_16_not_at_school_work_and_school +util_num_under_16_not_at_school_univ,Number of children under 16 not at school - University student interaction,(ptype == 3) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,coef_num_under_16_not_at_school_school2,coef_num_under_16_not_at_school_work_and_school +util_num_under_16_not_at_school_non_working_adult,Number of children under 16 not at school - Non-working adult interaction,(ptype == 4) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,, +util_num_under_16_not_at_school_retired,Number of children under 16 not at school - Retired,(ptype == 5) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,, +util_num_under_16_not_at_school_driving_age_student,Number of children under 16 not at school - Driving-age student interaction,(ptype == 6) * num_under16_not_at_school,,,,coef_num_under_16_not_at_school_school2,coef_num_under_16_not_at_school_work_and_school +util_num_under_16_not_at_school_pre_driving_age,Number of children under 16 not at school - Pre-driving age child who is in school interaction,(ptype == 7) * num_under16_not_at_school,,,,coef_num_under_16_not_at_school_school2, +util_nome_urban_ft,Home is in urban area - Full-time worker interaction,(ptype == 1) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,,coef_home_urban_work_and_school +util_nome_urban_pt,Home is in urban area - Part-time worker interaction,(ptype == 2) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,,coef_home_urban_work_and_school +util_nome_urban_univ,Home is in urban area - University student interaction,(ptype == 3) & home_is_urban,coef_home_urban_work1,coef_home_urban_work2,0,coef_home_urban_school2,coef_home_urban_work_and_school +util_nome_urban_non_working_adult,Home is in urban area - Non-working adult interaction,(ptype == 4) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,, +util_nome_urban_retired,Home is in urban area - Retired interaction,(ptype == 5) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,, +util_nome_urban_driving_age_student,Home is in urban area - Driving-age student interaction,(ptype == 6) & home_is_urban,coef_home_urban_work1,,0,coef_home_urban_school2,coef_home_urban_work_and_school +util_nome_urban_pre_driving_age_student,Home is in urban area - Pre-driving age child who is in school interaction,(ptype == 7) & home_is_urban,coef_home_urban_work1,,0,coef_home_urban_school2, +util_availability_ft,Unavailable: Full-time worker,ptype == 1,,,,coef_unavailable, +util_availability_pt,Unavailable: Part-time worker,ptype == 2,,,,coef_unavailable, +util_availability_non_working_adult,Unavailable: Non-working adult,ptype == 4,,,,coef_unavailable,coef_unavailable +util_availability_retired,Unavailable: Retired,ptype == 5,,,,coef_unavailable,coef_unavailable +util_availability_driving_age_child,Unavailable: Driving-age child,ptype == 6,coef_unavailable,coef_unavailable,,, +util_availability_pre_driving_age_student,Unavailable: Pre-driving age child who is in school,ptype == 7,,coef_unavailable,,,coef_unavailable +util_availability_pre_driving_age_not_in_school,Unavailable: Pre-driving age child who is not in school,ptype == 8,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable +util_availability_work_tours_no_usual_work_location,Unavailable: Work tours for those with no usual work location,~(workplace_zone_id > -1),coef_unavailable,coef_unavailable,,,coef_unavailable +util_availability_school_tours_no_usual_school_location,Unavailable: School tours for those with no usual school location,~(school_zone_id > -1),,,coef_unavailable,coef_unavailable,coef_unavailable diff --git a/activitysim/examples/example_mtc/configs/mandatory_tour_frequency.yaml b/activitysim/examples/prototype_mtc/configs/mandatory_tour_frequency.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/mandatory_tour_frequency.yaml rename to activitysim/examples/prototype_mtc/configs/mandatory_tour_frequency.yaml diff --git a/activitysim/examples/example_psrc/configs/mandatory_tour_frequency_alternatives.csv b/activitysim/examples/prototype_mtc/configs/mandatory_tour_frequency_alternatives.csv old mode 100755 new mode 100644 similarity index 96% rename from activitysim/examples/example_psrc/configs/mandatory_tour_frequency_alternatives.csv rename to activitysim/examples/prototype_mtc/configs/mandatory_tour_frequency_alternatives.csv index e4e04d48ef..025decbb1c --- a/activitysim/examples/example_psrc/configs/mandatory_tour_frequency_alternatives.csv +++ b/activitysim/examples/prototype_mtc/configs/mandatory_tour_frequency_alternatives.csv @@ -1,7 +1,7 @@ -#,,alt file for building tours even though simulation is simple_simulate not interaction_simulate -alt,work,school -work1,1,0 -work2,2,0 -school1,0,1 -school2,0,2 -work_and_school,1,1 +#,,alt file for building tours even though simulation is simple_simulate not interaction_simulate +alt,work,school +work1,1,0 +work2,2,0 +school1,0,1 +school2,0,2 +work_and_school,1,1 diff --git a/activitysim/examples/example_mtc/configs/mandatory_tour_frequency_coefficients.csv b/activitysim/examples/prototype_mtc/configs/mandatory_tour_frequency_coefficients.csv similarity index 97% rename from activitysim/examples/example_mtc/configs/mandatory_tour_frequency_coefficients.csv rename to activitysim/examples/prototype_mtc/configs/mandatory_tour_frequency_coefficients.csv index 8a4137324f..9bf04b2878 100644 --- a/activitysim/examples/example_mtc/configs/mandatory_tour_frequency_coefficients.csv +++ b/activitysim/examples/prototype_mtc/configs/mandatory_tour_frequency_coefficients.csv @@ -1,54 +1,54 @@ -coefficient_name,value,constrain -coef_unavailable,-999,T -coef_ft_worker_work2_asc,-3.3781,F -coef_pt_worker_work2_asc,-3.0476,F -coef_univ_work1_asc,2.166,F -coef_univ_work2_asc,-1.3965,F -coef_univ_school2_asc,-3.7429,F -coef_univ_work_and_school_asc,0.1073,F -coef_driving_age_child_school2_asc,-3.136,F -coef_driving_age_child_work_and_school_asc,-4.4362,F -coef_pre_driving_age_child_school2_asc,-3.9703,F -coef_female_work1,0.1737,F -coef_female_work2,-0.2255,F -coef_female_school1,0.1592,F -coef_female_school2,0.114,F -coef_female_work_and_school,-0.3442,F -coef_female_univ_work1,0.1737,F -coef_under_35_work1,-0.4629,F -coef_under_35_work2,-0.1375,F -coef_under_35_school1,0.7218,F -coef_under_35_school2,1.275,F -coef_under_35_work_and_school,0.9761,F -coef_can_walk_to_work_work2,0.5268,F -coef_can_walk_to_work_school2,0.7114,F -coef_can_walk_to_work_and_school,0.1391,F -coef_round_trip_auto_time_to_work_work2,-0.0035,F -coef_round_trip_auto_time_to_work_school2,-0.0034,F -coef_round_trip_auto_time_to_work_work_and_school,-0.0031,F -coef_student_employed,3.014,F -coef_non_student_goes_to_school,3.883,F -coef_no_cars_in_hh_work2,-1.306,F -coef_no_cars_in_hh_school2,-1.413,F -coef_no_cars_in_hh_work_and_school,-1.302,F -coef_few_cars_than_drivers_school2,-0.5759,F -coef_num_preschool_in_hh_work1,0.2191,F -coef_num_preschool_in_hh_work2,-0.1478,F -coef_num_preschool_in_hh_school1,-0.1335,F -coef_num_preschool_in_hh_school2,-0.5577,F -coef_num_preschool_in_hh_work_and_school,-0.1251,F -coef_num_non_workers_in_hh_school1,0.2574,F -coef_hh_income_gt_50k_work,-0.0528,F -coef_hh_income_gt_50k_school1,0.0347,F -coef_hh_income_gt_50k_worker_work_and_school,0.0347,F -coef_hh_income_gt_50k_student_work_and_school,-0.0528,F -coef_non_family_hh_category1,-0.25,F -coef_non_family_hh_category2,-0.1792,F -coef_num_under_16_not_at_school_work2,0.1804 -coef_num_under_16_not_at_school_school2,0.0866 -coef_num_under_16_not_at_school_work_and_school,-0.1955 -coef_home_urban_work1,-0.2831 -coef_home_urban_work2,0.2308 -coef_home_urban_school1,-0.1361 -coef_home_urban_school2,0.317 -coef_home_urban_work_and_school,-0.3509 +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_ft_worker_work2_asc,-3.3781,F +coef_pt_worker_work2_asc,-3.0476,F +coef_univ_work1_asc,2.166,F +coef_univ_work2_asc,-1.3965,F +coef_univ_school2_asc,-3.7429,F +coef_univ_work_and_school_asc,0.1073,F +coef_driving_age_child_school2_asc,-3.136,F +coef_driving_age_child_work_and_school_asc,-4.4362,F +coef_pre_driving_age_child_school2_asc,-3.9703,F +coef_female_work1,0.1737,F +coef_female_work2,-0.2255,F +coef_female_school1,0.1592,F +coef_female_school2,0.114,F +coef_female_work_and_school,-0.3442,F +coef_female_univ_work1,0.1737,F +coef_under_35_work1,-0.4629,F +coef_under_35_work2,-0.1375,F +coef_under_35_school1,0.7218,F +coef_under_35_school2,1.275,F +coef_under_35_work_and_school,0.9761,F +coef_can_walk_to_work_work2,0.5268,F +coef_can_walk_to_work_school2,0.7114,F +coef_can_walk_to_work_and_school,0.1391,F +coef_round_trip_auto_time_to_work_work2,-0.0035,F +coef_round_trip_auto_time_to_work_school2,-0.0034,F +coef_round_trip_auto_time_to_work_work_and_school,-0.0031,F +coef_student_employed,3.014,F +coef_non_student_goes_to_school,3.883,F +coef_no_cars_in_hh_work2,-1.306,F +coef_no_cars_in_hh_school2,-1.413,F +coef_no_cars_in_hh_work_and_school,-1.302,F +coef_few_cars_than_drivers_school2,-0.5759,F +coef_num_preschool_in_hh_work1,0.2191,F +coef_num_preschool_in_hh_work2,-0.1478,F +coef_num_preschool_in_hh_school1,-0.1335,F +coef_num_preschool_in_hh_school2,-0.5577,F +coef_num_preschool_in_hh_work_and_school,-0.1251,F +coef_num_non_workers_in_hh_school1,0.2574,F +coef_hh_income_gt_50k_work,-0.0528,F +coef_hh_income_gt_50k_school1,0.0347,F +coef_hh_income_gt_50k_worker_work_and_school,0.0347,F +coef_hh_income_gt_50k_student_work_and_school,-0.0528,F +coef_non_family_hh_category1,-0.25,F +coef_non_family_hh_category2,-0.1792,F +coef_num_under_16_not_at_school_work2,0.1804 +coef_num_under_16_not_at_school_school2,0.0866 +coef_num_under_16_not_at_school_work_and_school,-0.1955 +coef_home_urban_work1,-0.2831 +coef_home_urban_work2,0.2308 +coef_home_urban_school1,-0.1361 +coef_home_urban_school2,0.317 +coef_home_urban_work_and_school,-0.3509 diff --git a/activitysim/examples/example_mtc/configs/mandatory_tour_scheduling.yaml b/activitysim/examples/prototype_mtc/configs/mandatory_tour_scheduling.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/mandatory_tour_scheduling.yaml rename to activitysim/examples/prototype_mtc/configs/mandatory_tour_scheduling.yaml diff --git a/activitysim/examples/example_mtc/configs/network_los.yaml b/activitysim/examples/prototype_mtc/configs/network_los.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/network_los.yaml rename to activitysim/examples/prototype_mtc/configs/network_los.yaml diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_destination.csv b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_destination.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_destination.csv rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_destination.csv index 169becf1e3..18c6234ac4 --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_destination.csv +++ b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_destination.csv @@ -1,10 +1,10 @@ -Description,Expression,escort,escortkids,escortnokids,shopping,eatout,othmaint,social,othdiscr -"Distance, piecewise linear from 0 to 1 miles","@skims['DIST'].clip(0,1)",coef_escort_dist_0_2,coef_escort_dist_0_2,coef_escort_dist_0_2,0,coef_eatout_dist_0_2,0,coef_eatout_dist_0_2,coef_othdiscr_dist_0_2 -"Distance, piecewise linear from 1 to 2 miles","@(skims['DIST']-1).clip(0,1)",coef_escort_dist_0_2,coef_escort_dist_0_2,coef_escort_dist_0_2,0,coef_eatout_dist_0_2,0,coef_eatout_dist_0_2,coef_othdiscr_dist_0_2 -"Distance, piecewise linear from 2 to 5 miles","@(skims['DIST']-2).clip(0,3)",coef_escort_dist_2_5,coef_escort_dist_2_5,coef_escort_dist_2_5,coef_shopping_dist_2_5,coef_eatout_dist_2_5,coef_othmaint_dist_2_5,coef_social_dist_2_5,coef_othdiscr_dist_2_5 -"Distance, piecewise linear from 5 to 15 miles","@(skims['DIST']-5).clip(0,10)",coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_shopping_dist_5_plus,coef_eatout_dist_5_plus,coef_othmaint_dist_5_plus,coef_social_dist_5_plus,coef_othdiscr_dist_5_plus -"Distance, piecewise linear for 15+ miles",@(skims['DIST']-15.0).clip(0),coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_shopping_dist_5_plus,coef_eatout_dist_5_plus,coef_othmaint_dist_5_plus,coef_social_dist_5_plus,coef_othdiscr_dist_5_plus -Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1,1,1 -No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999,-999,-999 -Mode choice logsum,mode_choice_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum -Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1,1,1 +Description,Expression,escort,escortkids,escortnokids,shopping,eatout,othmaint,social,othdiscr +"Distance, piecewise linear from 0 to 1 miles","@skims['DIST'].clip(0,1)",coef_escort_dist_0_2,coef_escort_dist_0_2,coef_escort_dist_0_2,0,coef_eatout_dist_0_2,0,coef_eatout_dist_0_2,coef_othdiscr_dist_0_2 +"Distance, piecewise linear from 1 to 2 miles","@(skims['DIST']-1).clip(0,1)",coef_escort_dist_0_2,coef_escort_dist_0_2,coef_escort_dist_0_2,0,coef_eatout_dist_0_2,0,coef_eatout_dist_0_2,coef_othdiscr_dist_0_2 +"Distance, piecewise linear from 2 to 5 miles","@(skims['DIST']-2).clip(0,3)",coef_escort_dist_2_5,coef_escort_dist_2_5,coef_escort_dist_2_5,coef_shopping_dist_2_5,coef_eatout_dist_2_5,coef_othmaint_dist_2_5,coef_social_dist_2_5,coef_othdiscr_dist_2_5 +"Distance, piecewise linear from 5 to 15 miles","@(skims['DIST']-5).clip(0,10)",coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_shopping_dist_5_plus,coef_eatout_dist_5_plus,coef_othmaint_dist_5_plus,coef_social_dist_5_plus,coef_othdiscr_dist_5_plus +"Distance, piecewise linear for 15+ miles",@(skims['DIST']-15.0).clip(0),coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_shopping_dist_5_plus,coef_eatout_dist_5_plus,coef_othmaint_dist_5_plus,coef_social_dist_5_plus,coef_othdiscr_dist_5_plus +Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1,1,1 +No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999,-999,-999 +Mode choice logsum,mode_choice_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum +Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1,1,1 diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_destination.yaml b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_destination.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_destination.yaml rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_destination.yaml diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_destination_coefficients.csv b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_destination_coefficients.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_destination_coefficients.csv rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_destination_coefficients.csv diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_destination_sample.csv b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_destination_sample.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_destination_sample.csv rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_destination_sample.csv index e4bd1a116b..6d1cab933e --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_destination_sample.csv +++ b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_destination_sample.csv @@ -1,8 +1,8 @@ -Description,Expression,escort,escortkids,escortnokids,shopping,eatout,othmaint,social,othdiscr -"Distance, piecewise linear from 0 to 1 miles","@skims['DIST'].clip(0,1)",coef_escort_dist_0_2,coef_escort_dist_0_2,coef_escort_dist_0_2,0,coef_eatout_dist_0_2,0,coef_eatout_dist_0_2,coef_othdiscr_dist_0_2 -"Distance, piecewise linear from 1 to 2 miles","@(skims['DIST']-1).clip(0,1)",coef_escort_dist_0_2,coef_escort_dist_0_2,coef_escort_dist_0_2,0,coef_eatout_dist_0_2,0,coef_eatout_dist_0_2,coef_othdiscr_dist_0_2 -"Distance, piecewise linear from 2 to 5 miles","@(skims['DIST']-2).clip(0,3)",coef_escort_dist_2_5,coef_escort_dist_2_5,coef_escort_dist_2_5,coef_shopping_dist_2_5,coef_eatout_dist_2_5,coef_othmaint_dist_2_5,coef_social_dist_2_5,coef_othdiscr_dist_2_5 -"Distance, piecewise linear from 5 to 15 miles","@(skims['DIST']-5).clip(0,10)",coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_shopping_dist_5_plus,coef_eatout_dist_5_plus,coef_othmaint_dist_5_plus,coef_social_dist_5_plus,coef_othdiscr_dist_5_plus -"Distance, piecewise linear for 15+ miles",@(skims['DIST']-15.0).clip(0),coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_shopping_dist_5_plus,coef_eatout_dist_5_plus,coef_othmaint_dist_5_plus,coef_social_dist_5_plus,coef_othdiscr_dist_5_plus -Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1,1,1 -No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999,-999,-999 +Description,Expression,escort,escortkids,escortnokids,shopping,eatout,othmaint,social,othdiscr +"Distance, piecewise linear from 0 to 1 miles","@skims['DIST'].clip(0,1)",coef_escort_dist_0_2,coef_escort_dist_0_2,coef_escort_dist_0_2,0,coef_eatout_dist_0_2,0,coef_eatout_dist_0_2,coef_othdiscr_dist_0_2 +"Distance, piecewise linear from 1 to 2 miles","@(skims['DIST']-1).clip(0,1)",coef_escort_dist_0_2,coef_escort_dist_0_2,coef_escort_dist_0_2,0,coef_eatout_dist_0_2,0,coef_eatout_dist_0_2,coef_othdiscr_dist_0_2 +"Distance, piecewise linear from 2 to 5 miles","@(skims['DIST']-2).clip(0,3)",coef_escort_dist_2_5,coef_escort_dist_2_5,coef_escort_dist_2_5,coef_shopping_dist_2_5,coef_eatout_dist_2_5,coef_othmaint_dist_2_5,coef_social_dist_2_5,coef_othdiscr_dist_2_5 +"Distance, piecewise linear from 5 to 15 miles","@(skims['DIST']-5).clip(0,10)",coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_shopping_dist_5_plus,coef_eatout_dist_5_plus,coef_othmaint_dist_5_plus,coef_social_dist_5_plus,coef_othdiscr_dist_5_plus +"Distance, piecewise linear for 15+ miles",@(skims['DIST']-15.0).clip(0),coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_escort_dist_5_plus,coef_shopping_dist_5_plus,coef_eatout_dist_5_plus,coef_othmaint_dist_5_plus,coef_social_dist_5_plus,coef_othdiscr_dist_5_plus +Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1,1,1 +No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999,-999,-999 diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency.csv b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency.csv rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency.csv index c295ea5b07..76c60822e2 --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency.csv +++ b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency.csv @@ -1,211 +1,211 @@ -Label,Description,Expression,PTYPE_FULL,PTYPE_PART,PTYPE_UNIVERSITY,PTYPE_NONWORK,PTYPE_RETIRED,PTYPE_DRIVING,PTYPE_SCHOOL,PTYPE_PRESCHOOL -util_escorting_tour,Escorting Tour,escort,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour -util_discretionary_tour,Discretionary Tour,othdiscr,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour -util_shopping_tour,Shopping Tour,shopping,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour -util_maintenance_tour,Maintenance Tour,othmaint,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour -util_visiting_or_social_tour,Visiting/Social Tour,social,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour -util_eating_out_tour,Eating Out Tour,eatout,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour -util_total_number_of_tours_is_0_no_prior_tours,Total Number of Tours = 0 (No Prior Tours),(tot_tours == 0) & (num_mand == 0) & (num_hh_joint_tours == 0),coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours -util_total_number_of_tours_is_0_prior_tours,Total Number of Tours = 0 (1 or more Prior Tours),(tot_tours == 0) & ((num_mand > 0) | (num_hh_joint_tours > 0)),coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours -util_total_number_of_tours_is_1,Total Number of Tours = 1,tot_tours == 1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1 -util_total_number_of_tours_is_2,Total Number of Tours = 2,tot_tours == 2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2 -util_total_number_of_tours_is_3,Total Number of Tours = 3,tot_tours == 3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3 -util_total_number_of_tours_is_4,Total Number of Tours = 4,tot_tours == 4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4 -util_total_number_of_tours_is_5,Total Number of Tours = 5,tot_tours == 5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5 -util_total_number_of_tours_is_6_plus,Total Number of Tours = 6+,tot_tours > 5,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus -util_number_of_mandatory_tours_and_tour_frequency_is_0,Number of Mandatory tours & tour frequency =0,num_mand*(tot_tours == 0),coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0 -util_number_of_mandatory_tours_and_tour_frequency_is_1,Number of Mandatory tours & tour frequency =1,num_mand*(tot_tours == 1),coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1 -util_number_of_mandatory_tours_and_tour_frequency_is_2,Number of Mandatory tours & tour frequency =2,num_mand*(tot_tours == 2),coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2 -util_number_of_mandatory_tours_and_tour_frequency_is_3,Number of Mandatory tours & tour frequency =3,num_mand*(tot_tours == 3),coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3 -util_number_of_mandatory_tours_and_tour_frequency_is_4,Number of Mandatory tours & tour frequency =4,num_mand*(tot_tours == 4),coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4 -util_number_of_mandatory_tours_and_tour_frequency_is_5_plus,Number of Mandatory tours & tour frequency = 5+,num_mand*(tot_tours > 4),coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus -util_number_of_joint_tours_and_tour_frequency_is_0,Number of Joint tours & tour frequency =0,num_hh_joint_tours*(tot_tours == 0),coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0 -util_number_of_joint_tours_and_tour_frequency_is_1,Number of Joint tours & tour frequency =1,num_hh_joint_tours*(tot_tours == 1),coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1 -util_number_of_joint_tours_and_tour_frequency_is_2,Number of Joint tours & tour frequency =2,num_hh_joint_tours*(tot_tours == 2),coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2 -util_number_of_joint_tours_and_tour_frequency_is_3,Number of Joint tours & tour frequency =3,num_hh_joint_tours*(tot_tours == 3),coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3 -util_number_of_joint_tours_and_tour_frequency_is_4,Number of Joint tours & tour frequency =4,num_hh_joint_tours*(tot_tours == 4),coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4 -util_number_of_joint_tours_and_tour_frequency_is_5_plus,Number of Joint tours & tour frequency = 5+,num_hh_joint_tours*(tot_tours > 4),coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus -util_number_of_joint_shopping_tours,Number of Joint Shopping tours,shopping * num_hh_joint_shop_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours -util_number_of_joint_maintenance_tours,Number of Joint Maintenance tours,othmaint * num_hh_joint_maint_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours -util_number_of_joint_eating_out_tours,Number of Joint Eating Out tours,eatout * num_hh_joint_eatout_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours -util_number_of_joint_visit_tours,Number of Joint Visit tours,social * num_hh_joint_social_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours -util_number_of_joint_discretionary_tours,Number of Joint Discretionary tours,othdiscr * num_hh_joint_othdiscr_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours -util_logged_maximum_residual_window_tour_frequency_is_0,"Logged Maximum Residual Window, tour frequency =0",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 0),coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0 -util_logged_maximum_residual_window_tour_frequency_is_1,"Logged Maximum Residual Window, tour frequency =1",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 1),coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1 -util_logged_maximum_residual_window_tour_frequency_is_2,"Logged Maximum Residual Window, tour frequency =2",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 2),coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2 -util_logged_maximum_residual_window_tour_frequency_is_3,"Logged Maximum Residual Window, tour frequency =3",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 3),coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3 -util_logged_maximum_residual_window_tour_frequency_is_4,"Logged Maximum Residual Window, tour frequency =4",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 4),coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4 -util_logged_maximum_residual_window_tour_frequency_is_5_plus,"Logged Maximum Residual Window, tour frequency =5+",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours > 4),coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus -util_mediumlow_income_group_and_tour_frequency_is_1,Dummy for Mediumlow Income group (20K-50K) & tour frequency=1,medium_low_income & (tot_tours == 1),coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1 -util_mediumlow_income_group_and_tour_frequency_is_2,Dummy for Mediumlow Income group (20K-50K) & tour frequency=2,medium_low_income & (tot_tours == 2),coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2 -util_mediumlow_income_group_and_tour_frequency_is_3,Dummy for Mediumlow Income group (20K-50K) & tour frequency=3,medium_low_income & (tot_tours == 3),coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3 -util_mediumlow_income_group_and_tour_frequency_is_4,Dummy for Mediumlow Income group (20K-50K) & tour frequency=4,medium_low_income & (tot_tours == 4),coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4 -util_mediumlow_income_group_and_tour_frequency_is_5_plus,Dummy for Mediumlow Income group (20K-50K) & tour frequency=5+,medium_low_income & (tot_tours > 4),coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus -util_mediumhigh_income_group_and_tour_frequency_is_1,Dummy for MediumHigh Income group (50K-100K) & tour frequency=1,medium_high_income & (tot_tours == 1),coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1 -util_mediumhigh_income_group_and_tour_frequency_is_2,Dummy for MediumHigh Income group (50K-100K) & tour frequency=2,medium_high_income & (tot_tours == 2),coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2 -util_mediumhigh_income_group_and_tour_frequency_is_3,Dummy for MediumHigh Income group (50K-100K) & tour frequency=3,medium_high_income & (tot_tours == 3),coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3 -util_mediumhigh_income_group_and_tour_frequency_is_4,Dummy for MediumHigh Income group (50K-100K) & tour frequency=4,medium_high_income & (tot_tours == 4),coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4 -util_mediumhigh_income_group_and_tour_frequency_is_5_plus,Dummy for MediumHigh Income group (50K-100K) & tour frequency=5+,medium_high_income & (tot_tours > 4),coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus -util_high_income_group_and_tour_frequency_is_1,Dummy for High Income group (>100K) & tour frequency=1,high_income & (tot_tours == 1),coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1 -util_high_income_group_and_tour_frequency_is_2,Dummy for High Income group (>100K) & tour frequency=2,high_income & (tot_tours == 2),coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2 -util_high_income_group_and_tour_frequency_is_3,Dummy for High Income group (>100K) & tour frequency=3,high_income & (tot_tours == 3),coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3 -util_high_income_group_and_tour_frequency_is_4,Dummy for High Income group (>100K) & tour frequency=4,high_income & (tot_tours == 4),coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4 -util_high_income_group_and_tour_frequency_is_5_plus,Dummy for High Income group (>100K) & tour frequency=5+,high_income & (tot_tours > 4),coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus -util_mediumlow_income_group_and_shopping_tour,Dummy for Mediumlow Income group (20K-50K) & shopping tour,medium_low_income * shopping,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour -util_mediumhigh_income_group_and_shopping_tour,Dummy for Mediumhigh Income group (50K-100K) & shopping tour,medium_high_income * shopping,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour -util_high_income_group_and_shopping_tour,Dummy for High Income group (>100K) & shopping tour,high_income * shopping,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour -util_mediumlow_income_group_and_maintenance_tour,Dummy for Mediumlow Income group (20K-50K) & maintenance tour,medium_low_income * othmaint,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour -util_mediumhigh_income_group_and_maintenance_tour,Dummy for Mediumhigh Income group (50K-100K) & maintenance tour,medium_high_income * othmaint,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour -util_high_income_group_and_maintenance_tour,Dummy for High Income group (>100K) & maintenance tour,high_income * othmaint,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour -util_mediumlow_income_group_and_eating_out_tour,Dummy for Mediumlow Income group (20K-50K) & Eating out tour,medium_low_income * eatout,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour -util_mediumhigh_income_group_and_eating_out_tour,Dummy for Mediumhigh Income group (50K-100K) & Eating out tour,medium_high_income * eatout,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour -util_high_income_group_and_eating_out_tour,Dummy for High Income group (>100K) & Eating out tour,high_income * eatout,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour -util_mediumlow_income_group_and_discretionary_tour,Dummy for Mediumlow Income group (20K-50K) & Discretionary tour,medium_low_income * othdiscr,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour -util_mediumhigh_income_group_and_discretionary_tour,Dummy for Mediumhigh Income group (50K-100K) & Discretionary tour,medium_high_income * othdiscr,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour -util_high_income_group_and_discretionary_tour,Dummy for High Income group (>100K) & Discretionary tour,high_income * othdiscr,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour -util_mediumlow_income_group_and_visiting_tour,Dummy for Mediumlow Income group (20K-50K) & Visiting tour,medium_low_income * social,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour -util_mediumhigh_income_group_and_visiting_tour,Dummy for Mediumhigh Income group (50K-100K) & Visiting tour,medium_high_income * social,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour -util_high_income_group_and_visiting_tour,Dummy for High Income group (>100K) & Visiting tour,high_income * social,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour -util_female_and_tour_frequency_is_1,Dummy for Female & tour frequency =1,female & (tot_tours == 1),coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1 -util_female_and_tour_frequency_is_2,Dummy for Female & tour frequency =2,female & (tot_tours == 2),coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2 -util_female_and_tour_frequency_is_3,Dummy for Female & tour frequency =3,female & (tot_tours == 3),coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3 -util_female_and_tour_frequency_is_4,Dummy for Female & tour frequency =4,female & (tot_tours == 4),coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4 -util_female_and_tour_frequency_is_5,Dummy for Female & tour frequency =5,female & (tot_tours == 5),coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5 -util_female_and_escorting_tour,Dummy for Female & Escorting Tour,female * escort,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour -util_female_and_shopping_tour,Dummy for Female & Shopping Tour,female * shopping,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour -util_female_and_maintenance_tour,Dummy for Female & Maintenance Tour,female * othmaint,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour -util_female_and_eatingout_tour,Dummy for Female & EatingOut Tour,female * eatout,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour -util_female_and_discretionary_tour,Dummy for Female & Discretionary Tour,female * othdiscr,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour -util_zero_car_ownership_and_tour_frequency_is_1,Dummy for zero car ownership & tour frequency =1,no_cars & (tot_tours == 1),coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1 -util_zero_car_ownership_and_tour_frequency_is_2,Dummy for zero car ownership & tour frequency =2,no_cars & (tot_tours == 2),coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2 -util_zero_car_ownership_and_tour_frequency_is_3,Dummy for zero car ownership & tour frequency =3,no_cars & (tot_tours == 3),coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3 -util_zero_car_ownership_and_tour_frequency_is_4,Dummy for zero car ownership & tour frequency =4,no_cars & (tot_tours == 4),coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4 -util_zero_car_ownership_and_tour_frequency_is_5_plus,Dummy for zero car ownership & tour frequency =5+,no_cars & (tot_tours > 4),coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus -util_car_shortage_vs_workers_and_tour_frequency_is_1,Dummy for Car Shortage vs Workers & tour frequency =1,~no_cars & (car_sufficiency < 0) & (tot_tours == 1),coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1 -util_car_shortage_vs_workers_and_tour_frequency_is_2,Dummy for Car Shortage vs Workers & tour frequency =2,~no_cars & (car_sufficiency < 0) & (tot_tours == 2),coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2 -util_car_shortage_vs_workers_and_tour_frequency_is_3,Dummy for Car Shortage vs Workers & tour frequency =3,~no_cars & (car_sufficiency < 0) & (tot_tours == 3),coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3 -util_car_shortage_vs_workers_and_tour_frequency_is_4,Dummy for Car Shortage vs Workers & tour frequency =4,~no_cars & (car_sufficiency < 0) & (tot_tours == 4),coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4 -util_car_shortage_vs_workers_and_tour_frequency_is_5_plus,Dummy for Car Shortage vs Workers & tour frequency =5+,~no_cars & (car_sufficiency < 0) & (tot_tours > 4),coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus -util_car_surplus_vs_workers_and_tour_frequency_is_1,Dummy for Car Surplus vs Workers & tour frequency =1,~no_cars & (car_sufficiency > 0) & (tot_tours == 1),coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1 -util_car_surplus_vs_workers_and_tour_frequency_is_2,Dummy for Car Surplus vs Workers & tour frequency =2,~no_cars & (car_sufficiency > 0) & (tot_tours == 2),coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2 -util_car_surplus_vs_workers_and_tour_frequency_is_3,Dummy for Car Surplus vs Workers & tour frequency =3,~no_cars & (car_sufficiency > 0) & (tot_tours == 3),coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3 -util_car_surplus_vs_workers_and_tour_frequency_is_4,Dummy for Car Surplus vs Workers & tour frequency =4,~no_cars & (car_sufficiency > 0) & (tot_tours == 4),coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4 -util_car_surplus_vs_workers_and_tour_frequency_is_5_plus,Dummy for Car Surplus vs Workers & tour frequency =5+,~no_cars & (car_sufficiency > 0) & (tot_tours > 4),coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus -util_presence_of_non_worker_and_tour_frequency_is_1,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =1,has_non_worker & (tot_tours == 1),coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1 -util_presence_of_non_worker_and_tour_frequency_is_2,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =2,has_non_worker & (tot_tours == 2),coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2 -util_presence_of_non_worker_and_tour_frequency_is_3,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =3,has_non_worker & (tot_tours == 3),coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3 -util_presence_of_non_worker_and_tour_frequency_is_4,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =4,has_non_worker & (tot_tours == 4),coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4 -util_presence_of_non_worker_and_tour_frequency_is_5,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =5,has_non_worker & (tot_tours == 5),coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5 -util_presence_of_retiree_and_tour_frequency_is_1,Dummy for Presence of Retiree(other than modeled person) & tour frequency =1,has_retiree & (tot_tours == 1),coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1 -util_presence_of_retiree_and_tour_frequency_is_2,Dummy for Presence of Retiree(other than modeled person) & tour frequency =2,has_retiree & (tot_tours == 2),coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2 -util_presence_of_retiree_and_tour_frequency_is_3,Dummy for Presence of Retiree(other than modeled person) & tour frequency =3,has_retiree & (tot_tours == 3),coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3 -util_presence_of_retiree_and_tour_frequency_is_4,Dummy for Presence of Retiree(other than modeled person) & tour frequency =4,has_retiree & (tot_tours == 4),coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4 -util_presence_of_retiree_and_tour_frequency_is_5,Dummy for Presence of Retiree(other than modeled person) & tour frequency =5,has_retiree & (tot_tours == 5),coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5 -util_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =1,has_preschool_kid & (tot_tours == 1),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1 -util_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =2,has_preschool_kid & (tot_tours == 2),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2 -util_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =3,has_preschool_kid & (tot_tours == 3),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3 -util_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =4,has_preschool_kid & (tot_tours == 4),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4 -util_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =5,has_preschool_kid & (tot_tours == 5),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5 -util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =1,has_school_kid & (tot_tours == 1),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1 -util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =2,has_school_kid & (tot_tours == 2),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2 -util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =3,has_school_kid & (tot_tours == 3),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3 -util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =4,has_school_kid & (tot_tours == 4),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4 -util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =5,has_school_kid & (tot_tours == 5),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5 -util_presence_of_full_time_worker_and_escorting_tour,Dummy for Presence of Full time Worker (other than modeled person) & Escorting tour ,has_full_time * escort,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour -util_presence_of_part_time_worker_and_escorting_tour,Dummy for Presence of Part time Worker (other than modeled person) & Escorting tour ,has_part_time * escort,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour -util_presence_of_non_worker_and_escorting_tour,Dummy for Presence of Non-Worker (other than modeled person) & Escorting tour ,has_non_worker * escort,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour -util_presence_of_retiree_and_escorting_tour,Dummy for Presence of Retiree (other than modeled person) & Escorting tour ,has_retiree * escort,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour -util_presence_of_university_student_and_escorting_tour,Dummy for Presence of University Student (other than modeled person) & Escorting tour ,has_university * escort,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour -util_presence_of_driving_school_kid_and_escorting_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Escorting tour ,has_driving_kid * escort,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour -util_presence_of_pre_driving_school_kid_and_escorting_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Escorting tour ,has_school_kid * escort,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour -util_presence_of_pre_school_kid_and_escorting_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Escorting tour ,has_preschool_kid * escort,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour -util_at_home_pre_driving_school_kid_and_escorting_tour,Dummy for At home Pre-Driving School Kid & Escorting tour ,has_school_kid_at_home * escort,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour -util_at_home_pre_school_kid_and_escorting_tour,Dummy for At homef Pre-School Kid & Escorting tour ,has_preschool_kid_at_home * escort,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour -util_presence_of_full_time_worker_and_shopping_tour,Dummy for Presence of Full time Worker (other than modeled person) & Shopping tour ,has_full_time * shopping,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour -util_presence_of_part_time_worker_and_shopping_tour,Dummy for Presence of Part time Worker (other than modeled person) & Shopping tour ,has_part_time * shopping,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour -util_presence_of_non_worker_and_shopping_tour,Dummy for Presence of Non-Worker (other than modeled person) & Shopping tour ,has_non_worker * shopping,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour -util_presence_of_retiree_and_shopping_tour,Dummy for Presence of Retiree (other than modeled person) & Shopping tour ,has_retiree * shopping,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour -util_presence_of_university_student_and_shopping_tour,Dummy for Presence of University Student (other than modeled person) & Shopping tour ,has_university * shopping,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour -util_presence_of_driving_school_kid_and_shopping_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Shopping tour ,has_driving_kid * shopping,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour -util_presence_of_pre_driving_school_kid_and_shopping_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Shopping tour ,has_school_kid * shopping,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour -util_presence_of_pre_school_kid_and_shopping_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Shopping tour ,has_preschool_kid * shopping,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour -util_at_home_pre_driving_school_kid_and_shopping_tour,Dummy for At home Pre-Driving School Kid & Shopping tour ,has_school_kid_at_home * shopping,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour -util_at_home_pre_school_kid_and_shopping_tour,Dummy for At homef Pre-School Kid & Shopping tour ,has_preschool_kid_at_home * shopping,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour -util_presence_of_full_time_worker_and_maintenance_tour,Dummy for Presence of Full time Worker (other than modeled person) & Maintenance tour ,has_full_time * othmaint,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour -util_presence_of_part_time_worker_and_maintenance_tour,Dummy for Presence of Part time Worker (other than modeled person) & Maintenance tour ,has_part_time * othmaint,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour -util_presence_of_non_worker_and_maintenance_tour,Dummy for Presence of Non-Worker(other than modeled person) & Maintenance tour ,has_non_worker * othmaint,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour -util_presence_of_retiree_and_maintenance_tour,Dummy for Presence of Retiree (other than modeled person) & Maintenance tour ,has_retiree * othmaint,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour -util_presence_of_university_student_and_maintenance_tour,Dummy for Presence of University Student (other than modeled person) & Maintenance tour ,has_university * othmaint,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour -util_presence_of_driving_school_kid_and_maintenance_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Maintenance tour ,has_driving_kid * othmaint,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour -util_presence_of_pre_driving_school_kid_and_maintenance_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Maintenance tour ,has_school_kid * othmaint,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour -util_presence_of_pre_school_kid_and_maintenance_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Maintenance tour ,has_preschool_kid * othmaint,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour -util_at_home_pre_driving_school_kid_and_maintenance_tour,Dummy for At home Pre-Driving School Kid & Maintenance tour ,has_school_kid_at_home * othmaint,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour -util_at_home_pre_school_kid_and_maintenance_tour,Dummy for At homef Pre-School Kid & Maintenance tour ,has_preschool_kid_at_home * othmaint,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour -util_presence_of_full_time_worker_and_eating_out_tour,Dummy for Presence of Full time Worker (other than modeled person) & Eating Out tour ,has_full_time * eatout,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour -util_presence_of_part_time_worker_and_eating_out_tour,Dummy for Presence of Part time Worker (other than modeled person) & Eating Out tour ,has_part_time * eatout,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour -util_presence_of_non_worker_and_eating_out_tour,Dummy for Presence of Non-Worker (other than modeled person) & Eating Out tour ,has_non_worker * eatout,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour -util_presence_of_retiree_and_eating_out_tour,Dummy for Presence of Retiree (other than modeled person) & Eating Out tour ,has_retiree * eatout,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour -util_presence_of_university_student_and_eating_out_tour,Dummy for Presence of University Student (other than modeled person) & Eating Out tour ,has_university * eatout,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour -util_presence_of_driving_school_kid_and_eating_out_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Eating Out tour ,has_driving_kid * eatout,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour -util_presence_of_pre_driving_school_kid_and_eating_out_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Eating Out tour ,has_school_kid * eatout,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour -util_presence_of_pre_school_kid_and_eating_out_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Eating Out tour ,has_preschool_kid * eatout,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour -util_at_home_pre_driving_school_kid_and_eating_out_tour,Dummy for At home Pre-Driving School Kid & Eating Out tour ,has_school_kid_at_home * eatout,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour -util_at_home_pre_school_kid_and_eating_out_tour,Dummy for At homef Pre-School Kid & Eating Out tour ,has_preschool_kid_at_home * eatout,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour -util_presence_of_full_time_worker_and_discretionary_tour,Dummy for Presence of Full time Worker (other than modeled person) & Discretionary tour ,has_full_time * othdiscr,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour -util_presence_of_part_time_worker_and_discretionary_tour,Dummy for Presence of Part time Worker (other than modeled person) & Discretionary tour ,has_part_time * othdiscr,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour -util_presence_of_non_worker_and_discretionary_tour,Dummy for Presence of Non-Worker (other than modeled person) & Discretionary tour ,has_non_worker * othdiscr,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour -util_presence_of_retiree_and_discretionary_tour,Dummy for Presence of Retiree (other than modeled person) & Discretionary tour ,has_retiree * othdiscr,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour -util_presence_of_university_student_and_discretionary_tour,Dummy for Presence of University Student (other than modeled person) & Discretionary tour ,has_university * othdiscr,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour -util_presence_of_driving_school_kid_and_discretionary_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Discretionary tour ,has_driving_kid * othdiscr,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour -util_presence_of_pre_driving_school_kid_and_discretionary_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Discretionary tour ,has_school_kid * othdiscr,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour -util_presence_of_pre_school_kid_and_discretionary_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Discretionary tour ,has_preschool_kid * othdiscr,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour -util_at_home_pre_driving_school_kid_and_discretionary_tour,Dummy for At home Pre-Driving School Kid & Discretionary tour ,has_school_kid_at_home * othdiscr,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour -util_at_home_pre_school_kid_and_discretionary_tour,Dummy for At homef Pre-School Kid & Discretionary tour ,has_preschool_kid_at_home * othdiscr,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour -util_walk_access_to_retail_and_tour_frequency_is_1,Walk Access to Retail & Tour Frequency =1,nmRetail * (tot_tours == 1),coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1 -util_walk_access_to_retail_and_tour_frequency_is_2,Walk Access to Retail & Tour Frequency =2,nmRetail * (tot_tours == 2),coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2 -util_walk_access_to_retail_and_tour_frequency_is_3,Walk Access to Retail & Tour Frequency =3,nmRetail * (tot_tours == 3),coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3 -util_walk_access_to_retail_and_tour_frequency_is_4,Walk Access to Retail & Tour Frequency =4,nmRetail * (tot_tours == 4),coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4 -util_walk_access_to_retail_and_tour_frequency_is_5_plus,Walk Access to Retail & Tour Frequency =5+,nmRetail * (tot_tours > 4),coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus -util_transit_access_to_retail_and_tour_frequency_is_1,Transit Access to Retail & Tour Frequency =1,trOpRetail * (tot_tours == 1),coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1 -util_transit_access_to_retail_and_tour_frequency_is_2,Transit Access to Retail & Tour Frequency =2,trOpRetail * (tot_tours == 2),coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2 -util_transit_access_to_retail_and_tour_frequency_is_3,Transit Access to Retail & Tour Frequency =3,trOpRetail * (tot_tours == 3),coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3 -util_transit_access_to_retail_and_tour_frequency_is_4,Transit Access to Retail & Tour Frequency =4,trOpRetail * (tot_tours == 4),coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4 -util_transit_access_to_retail_and_tour_frequency_is_5_plus,Transit Access to Retail & Tour Frequency =5+,trOpRetail * (tot_tours > 4),coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus -util_auto_access_to_retail_and_tour_frequency_is_1,Auto Access to Retail & Tour Frequency =1,auOpRetail * (tot_tours == 1),coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1 -util_auto_access_to_retail_and_tour_frequency_is_2,Auto Access to Retail & Tour Frequency =2,auOpRetail * (tot_tours == 2),coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2 -util_auto_access_to_retail_and_tour_frequency_is_3,Auto Access to Retail & Tour Frequency =3,auOpRetail * (tot_tours == 3),coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3 -util_auto_access_to_retail_and_tour_frequency_is_4,Auto Access to Retail & Tour Frequency =4,auOpRetail * (tot_tours == 4),coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4 -util_auto_access_to_retail_and_tour_frequency_is_5_plus,Auto Access to Retail & Tour Frequency =5+,auOpRetail * (tot_tours > 4),coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus -util_walk_access_to_retail_and_escorting,Walk Access to Retail & Escorting ,nmRetail * escort,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting -util_transit_access_to_retail_and_escorting,Transit Access to Retail & Escorting ,trOpRetail * escort,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting -util_auto_access_to_retail_and_escorting,Auto Access to Retail & Escorting ,auOpRetail * escort,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting -util_walk_access_to_retail_and_shopping,Walk Access to Retail & Shopping ,nmRetail * shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping -util_transit_access_to_retail_and_shopping,Transit Access to Retail & Shopping ,trOpRetail * shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping -util_auto_access_to_retail_and_shopping,Auto Access to Retail & Shopping ,auOpRetail * shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping -util_walk_access_to_retail_and_maintenance,Walk Access to Retail & Maintenance ,nmRetail * othmaint,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance -util_transit_access_to_retail_and_maintenance,Transit Access to Retail & Maintenance ,trOpRetail * othmaint,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance -util_auto_access_to_retail_and_maintenance,Auto Access to Retail & Maintenance ,auOpRetail * othmaint,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance -util_walk_access_to_retail_and_eating_out,Walk Access to Retail & Eating Out ,nmRetail * eatout,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out -util_transit_access_to_retail_and_eating_out,Transit Access to Retail & Eating Out ,trOpRetail * eatout,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out -util_auto_access_to_retail_and_eating_out,Auto Access to Retail & Eating Out ,auOpRetail * eatout,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out -util_walk_access_to_retail_and_discretionary,Walk Access to Retail & Discretionary ,nmRetail * othdiscr,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary -util_transit_access_to_retail_and_discretionary,Transit Access to Retail & Discretionary ,trOpRetail * othdiscr,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary -util_auto_access_to_retail_and_discretionary,Auto Access to Retail & Discretionary ,auOpRetail * othdiscr,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary -util_urban_and_tour_frequency_is_1,Urban Areatype & Tour Frequency =1,home_is_urban & (tot_tours == 1),coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1 -util_urban_and_tour_frequency_is_2,Urban Areatype & Tour Frequency =2,home_is_urban & (tot_tours == 2),coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2 -util_urban_and_tour_frequency_is_3,Urban Areatype & Tour Frequency =3,home_is_urban & (tot_tours == 3),coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3 -util_urban_and_tour_frequency_is_4,Urban Areatype & Tour Frequency =4,home_is_urban & (tot_tours == 4),coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4 -util_urban_and_tour_frequency_is_5_plus,Urban Areatype & Tour Frequency =5+,home_is_urban & (tot_tours > 4),coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus -util_urban_and_escorting_tour,Urban Areatype & Escorting tour,home_is_urban * escort,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour -util_urban_and_shopping_tour,Urban Areatype &Shopping tour,home_is_urban * shopping,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour -util_urban_and_maintenance_tour,Urban Areatype & Maintenance tour,home_is_urban * othmaint,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour -util_urban_and_eatingout_tour,Urban Areatype & EatingOut tour,home_is_urban * eatout,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour -util_urban_and_discretionary_tour,Urban Areatype & Discretionary tour,home_is_urban * othdiscr,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour -util_1_escort_tour_constant,1 Escort Tour Constant,escort == 1,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant -util_2_plus_escort_tours_constant,2+ Escort Tours Constant,escort >= 2,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant -util_1_plus_shopping_tours_constant,1+ Shopping Tours Constant,shopping >= 1,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant -util_1_plus_maintenance_tours_constant,1+ Maintenance Tours Constant,othmaint >= 1,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant -util_1_plus_eating_out_tours_constant,1+ Eating Out Tours Constant,eatout >= 1,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant -util_1_plus_visting_tours_constant,1+ Visting Tours Constant,social >= 1,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant -util_1_plus_other_discretionary_tours_constant,1+ Other Discretionary Tours Constant,othdiscr >= 1,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant +Label,Description,Expression,PTYPE_FULL,PTYPE_PART,PTYPE_UNIVERSITY,PTYPE_NONWORK,PTYPE_RETIRED,PTYPE_DRIVING,PTYPE_SCHOOL,PTYPE_PRESCHOOL +util_escorting_tour,Escorting Tour,escort,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour +util_discretionary_tour,Discretionary Tour,othdiscr,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour +util_shopping_tour,Shopping Tour,shopping,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour +util_maintenance_tour,Maintenance Tour,othmaint,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour +util_visiting_or_social_tour,Visiting/Social Tour,social,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour +util_eating_out_tour,Eating Out Tour,eatout,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour +util_total_number_of_tours_is_0_no_prior_tours,Total Number of Tours = 0 (No Prior Tours),(tot_tours == 0) & (num_mand == 0) & (num_hh_joint_tours == 0),coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours +util_total_number_of_tours_is_0_prior_tours,Total Number of Tours = 0 (1 or more Prior Tours),(tot_tours == 0) & ((num_mand > 0) | (num_hh_joint_tours > 0)),coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours +util_total_number_of_tours_is_1,Total Number of Tours = 1,tot_tours == 1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1 +util_total_number_of_tours_is_2,Total Number of Tours = 2,tot_tours == 2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2 +util_total_number_of_tours_is_3,Total Number of Tours = 3,tot_tours == 3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3 +util_total_number_of_tours_is_4,Total Number of Tours = 4,tot_tours == 4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4 +util_total_number_of_tours_is_5,Total Number of Tours = 5,tot_tours == 5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5 +util_total_number_of_tours_is_6_plus,Total Number of Tours = 6+,tot_tours > 5,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus +util_number_of_mandatory_tours_and_tour_frequency_is_0,Number of Mandatory tours & tour frequency =0,num_mand*(tot_tours == 0),coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0 +util_number_of_mandatory_tours_and_tour_frequency_is_1,Number of Mandatory tours & tour frequency =1,num_mand*(tot_tours == 1),coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1 +util_number_of_mandatory_tours_and_tour_frequency_is_2,Number of Mandatory tours & tour frequency =2,num_mand*(tot_tours == 2),coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2 +util_number_of_mandatory_tours_and_tour_frequency_is_3,Number of Mandatory tours & tour frequency =3,num_mand*(tot_tours == 3),coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3 +util_number_of_mandatory_tours_and_tour_frequency_is_4,Number of Mandatory tours & tour frequency =4,num_mand*(tot_tours == 4),coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4 +util_number_of_mandatory_tours_and_tour_frequency_is_5_plus,Number of Mandatory tours & tour frequency = 5+,num_mand*(tot_tours > 4),coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus +util_number_of_joint_tours_and_tour_frequency_is_0,Number of Joint tours & tour frequency =0,num_hh_joint_tours*(tot_tours == 0),coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0 +util_number_of_joint_tours_and_tour_frequency_is_1,Number of Joint tours & tour frequency =1,num_hh_joint_tours*(tot_tours == 1),coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1 +util_number_of_joint_tours_and_tour_frequency_is_2,Number of Joint tours & tour frequency =2,num_hh_joint_tours*(tot_tours == 2),coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2 +util_number_of_joint_tours_and_tour_frequency_is_3,Number of Joint tours & tour frequency =3,num_hh_joint_tours*(tot_tours == 3),coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3 +util_number_of_joint_tours_and_tour_frequency_is_4,Number of Joint tours & tour frequency =4,num_hh_joint_tours*(tot_tours == 4),coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4 +util_number_of_joint_tours_and_tour_frequency_is_5_plus,Number of Joint tours & tour frequency = 5+,num_hh_joint_tours*(tot_tours > 4),coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus +util_number_of_joint_shopping_tours,Number of Joint Shopping tours,shopping * num_hh_joint_shop_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours +util_number_of_joint_maintenance_tours,Number of Joint Maintenance tours,othmaint * num_hh_joint_maint_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours +util_number_of_joint_eating_out_tours,Number of Joint Eating Out tours,eatout * num_hh_joint_eatout_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours +util_number_of_joint_visit_tours,Number of Joint Visit tours,social * num_hh_joint_social_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours +util_number_of_joint_discretionary_tours,Number of Joint Discretionary tours,othdiscr * num_hh_joint_othdiscr_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours +util_logged_maximum_residual_window_tour_frequency_is_0,"Logged Maximum Residual Window, tour frequency =0",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 0),coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0 +util_logged_maximum_residual_window_tour_frequency_is_1,"Logged Maximum Residual Window, tour frequency =1",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 1),coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1 +util_logged_maximum_residual_window_tour_frequency_is_2,"Logged Maximum Residual Window, tour frequency =2",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 2),coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2 +util_logged_maximum_residual_window_tour_frequency_is_3,"Logged Maximum Residual Window, tour frequency =3",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 3),coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3 +util_logged_maximum_residual_window_tour_frequency_is_4,"Logged Maximum Residual Window, tour frequency =4",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 4),coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4 +util_logged_maximum_residual_window_tour_frequency_is_5_plus,"Logged Maximum Residual Window, tour frequency =5+",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours > 4),coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus +util_mediumlow_income_group_and_tour_frequency_is_1,Dummy for Mediumlow Income group (20K-50K) & tour frequency=1,medium_low_income & (tot_tours == 1),coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1 +util_mediumlow_income_group_and_tour_frequency_is_2,Dummy for Mediumlow Income group (20K-50K) & tour frequency=2,medium_low_income & (tot_tours == 2),coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2 +util_mediumlow_income_group_and_tour_frequency_is_3,Dummy for Mediumlow Income group (20K-50K) & tour frequency=3,medium_low_income & (tot_tours == 3),coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3 +util_mediumlow_income_group_and_tour_frequency_is_4,Dummy for Mediumlow Income group (20K-50K) & tour frequency=4,medium_low_income & (tot_tours == 4),coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4 +util_mediumlow_income_group_and_tour_frequency_is_5_plus,Dummy for Mediumlow Income group (20K-50K) & tour frequency=5+,medium_low_income & (tot_tours > 4),coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus +util_mediumhigh_income_group_and_tour_frequency_is_1,Dummy for MediumHigh Income group (50K-100K) & tour frequency=1,medium_high_income & (tot_tours == 1),coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1 +util_mediumhigh_income_group_and_tour_frequency_is_2,Dummy for MediumHigh Income group (50K-100K) & tour frequency=2,medium_high_income & (tot_tours == 2),coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2 +util_mediumhigh_income_group_and_tour_frequency_is_3,Dummy for MediumHigh Income group (50K-100K) & tour frequency=3,medium_high_income & (tot_tours == 3),coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3 +util_mediumhigh_income_group_and_tour_frequency_is_4,Dummy for MediumHigh Income group (50K-100K) & tour frequency=4,medium_high_income & (tot_tours == 4),coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4 +util_mediumhigh_income_group_and_tour_frequency_is_5_plus,Dummy for MediumHigh Income group (50K-100K) & tour frequency=5+,medium_high_income & (tot_tours > 4),coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus +util_high_income_group_and_tour_frequency_is_1,Dummy for High Income group (>100K) & tour frequency=1,high_income & (tot_tours == 1),coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1 +util_high_income_group_and_tour_frequency_is_2,Dummy for High Income group (>100K) & tour frequency=2,high_income & (tot_tours == 2),coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2 +util_high_income_group_and_tour_frequency_is_3,Dummy for High Income group (>100K) & tour frequency=3,high_income & (tot_tours == 3),coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3 +util_high_income_group_and_tour_frequency_is_4,Dummy for High Income group (>100K) & tour frequency=4,high_income & (tot_tours == 4),coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4 +util_high_income_group_and_tour_frequency_is_5_plus,Dummy for High Income group (>100K) & tour frequency=5+,high_income & (tot_tours > 4),coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus +util_mediumlow_income_group_and_shopping_tour,Dummy for Mediumlow Income group (20K-50K) & shopping tour,medium_low_income * shopping,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour +util_mediumhigh_income_group_and_shopping_tour,Dummy for Mediumhigh Income group (50K-100K) & shopping tour,medium_high_income * shopping,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour +util_high_income_group_and_shopping_tour,Dummy for High Income group (>100K) & shopping tour,high_income * shopping,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour +util_mediumlow_income_group_and_maintenance_tour,Dummy for Mediumlow Income group (20K-50K) & maintenance tour,medium_low_income * othmaint,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour +util_mediumhigh_income_group_and_maintenance_tour,Dummy for Mediumhigh Income group (50K-100K) & maintenance tour,medium_high_income * othmaint,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour +util_high_income_group_and_maintenance_tour,Dummy for High Income group (>100K) & maintenance tour,high_income * othmaint,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour +util_mediumlow_income_group_and_eating_out_tour,Dummy for Mediumlow Income group (20K-50K) & Eating out tour,medium_low_income * eatout,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour +util_mediumhigh_income_group_and_eating_out_tour,Dummy for Mediumhigh Income group (50K-100K) & Eating out tour,medium_high_income * eatout,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour +util_high_income_group_and_eating_out_tour,Dummy for High Income group (>100K) & Eating out tour,high_income * eatout,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour +util_mediumlow_income_group_and_discretionary_tour,Dummy for Mediumlow Income group (20K-50K) & Discretionary tour,medium_low_income * othdiscr,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour +util_mediumhigh_income_group_and_discretionary_tour,Dummy for Mediumhigh Income group (50K-100K) & Discretionary tour,medium_high_income * othdiscr,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour +util_high_income_group_and_discretionary_tour,Dummy for High Income group (>100K) & Discretionary tour,high_income * othdiscr,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour +util_mediumlow_income_group_and_visiting_tour,Dummy for Mediumlow Income group (20K-50K) & Visiting tour,medium_low_income * social,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour +util_mediumhigh_income_group_and_visiting_tour,Dummy for Mediumhigh Income group (50K-100K) & Visiting tour,medium_high_income * social,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour +util_high_income_group_and_visiting_tour,Dummy for High Income group (>100K) & Visiting tour,high_income * social,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour +util_female_and_tour_frequency_is_1,Dummy for Female & tour frequency =1,female & (tot_tours == 1),coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1 +util_female_and_tour_frequency_is_2,Dummy for Female & tour frequency =2,female & (tot_tours == 2),coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2 +util_female_and_tour_frequency_is_3,Dummy for Female & tour frequency =3,female & (tot_tours == 3),coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3 +util_female_and_tour_frequency_is_4,Dummy for Female & tour frequency =4,female & (tot_tours == 4),coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4 +util_female_and_tour_frequency_is_5,Dummy for Female & tour frequency =5,female & (tot_tours == 5),coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5 +util_female_and_escorting_tour,Dummy for Female & Escorting Tour,female * escort,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour +util_female_and_shopping_tour,Dummy for Female & Shopping Tour,female * shopping,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour +util_female_and_maintenance_tour,Dummy for Female & Maintenance Tour,female * othmaint,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour +util_female_and_eatingout_tour,Dummy for Female & EatingOut Tour,female * eatout,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour +util_female_and_discretionary_tour,Dummy for Female & Discretionary Tour,female * othdiscr,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour +util_zero_car_ownership_and_tour_frequency_is_1,Dummy for zero car ownership & tour frequency =1,no_cars & (tot_tours == 1),coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1 +util_zero_car_ownership_and_tour_frequency_is_2,Dummy for zero car ownership & tour frequency =2,no_cars & (tot_tours == 2),coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2 +util_zero_car_ownership_and_tour_frequency_is_3,Dummy for zero car ownership & tour frequency =3,no_cars & (tot_tours == 3),coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3 +util_zero_car_ownership_and_tour_frequency_is_4,Dummy for zero car ownership & tour frequency =4,no_cars & (tot_tours == 4),coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4 +util_zero_car_ownership_and_tour_frequency_is_5_plus,Dummy for zero car ownership & tour frequency =5+,no_cars & (tot_tours > 4),coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus +util_car_shortage_vs_workers_and_tour_frequency_is_1,Dummy for Car Shortage vs Workers & tour frequency =1,~no_cars & (car_sufficiency < 0) & (tot_tours == 1),coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1 +util_car_shortage_vs_workers_and_tour_frequency_is_2,Dummy for Car Shortage vs Workers & tour frequency =2,~no_cars & (car_sufficiency < 0) & (tot_tours == 2),coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2 +util_car_shortage_vs_workers_and_tour_frequency_is_3,Dummy for Car Shortage vs Workers & tour frequency =3,~no_cars & (car_sufficiency < 0) & (tot_tours == 3),coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3 +util_car_shortage_vs_workers_and_tour_frequency_is_4,Dummy for Car Shortage vs Workers & tour frequency =4,~no_cars & (car_sufficiency < 0) & (tot_tours == 4),coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4 +util_car_shortage_vs_workers_and_tour_frequency_is_5_plus,Dummy for Car Shortage vs Workers & tour frequency =5+,~no_cars & (car_sufficiency < 0) & (tot_tours > 4),coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus +util_car_surplus_vs_workers_and_tour_frequency_is_1,Dummy for Car Surplus vs Workers & tour frequency =1,~no_cars & (car_sufficiency > 0) & (tot_tours == 1),coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1 +util_car_surplus_vs_workers_and_tour_frequency_is_2,Dummy for Car Surplus vs Workers & tour frequency =2,~no_cars & (car_sufficiency > 0) & (tot_tours == 2),coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2 +util_car_surplus_vs_workers_and_tour_frequency_is_3,Dummy for Car Surplus vs Workers & tour frequency =3,~no_cars & (car_sufficiency > 0) & (tot_tours == 3),coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3 +util_car_surplus_vs_workers_and_tour_frequency_is_4,Dummy for Car Surplus vs Workers & tour frequency =4,~no_cars & (car_sufficiency > 0) & (tot_tours == 4),coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4 +util_car_surplus_vs_workers_and_tour_frequency_is_5_plus,Dummy for Car Surplus vs Workers & tour frequency =5+,~no_cars & (car_sufficiency > 0) & (tot_tours > 4),coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus +util_presence_of_non_worker_and_tour_frequency_is_1,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =1,has_non_worker & (tot_tours == 1),coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1 +util_presence_of_non_worker_and_tour_frequency_is_2,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =2,has_non_worker & (tot_tours == 2),coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2 +util_presence_of_non_worker_and_tour_frequency_is_3,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =3,has_non_worker & (tot_tours == 3),coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3 +util_presence_of_non_worker_and_tour_frequency_is_4,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =4,has_non_worker & (tot_tours == 4),coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4 +util_presence_of_non_worker_and_tour_frequency_is_5,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =5,has_non_worker & (tot_tours == 5),coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5 +util_presence_of_retiree_and_tour_frequency_is_1,Dummy for Presence of Retiree(other than modeled person) & tour frequency =1,has_retiree & (tot_tours == 1),coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1 +util_presence_of_retiree_and_tour_frequency_is_2,Dummy for Presence of Retiree(other than modeled person) & tour frequency =2,has_retiree & (tot_tours == 2),coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2 +util_presence_of_retiree_and_tour_frequency_is_3,Dummy for Presence of Retiree(other than modeled person) & tour frequency =3,has_retiree & (tot_tours == 3),coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3 +util_presence_of_retiree_and_tour_frequency_is_4,Dummy for Presence of Retiree(other than modeled person) & tour frequency =4,has_retiree & (tot_tours == 4),coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4 +util_presence_of_retiree_and_tour_frequency_is_5,Dummy for Presence of Retiree(other than modeled person) & tour frequency =5,has_retiree & (tot_tours == 5),coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5 +util_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =1,has_preschool_kid & (tot_tours == 1),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1 +util_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =2,has_preschool_kid & (tot_tours == 2),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2 +util_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =3,has_preschool_kid & (tot_tours == 3),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3 +util_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =4,has_preschool_kid & (tot_tours == 4),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4 +util_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =5,has_preschool_kid & (tot_tours == 5),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5 +util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =1,has_school_kid & (tot_tours == 1),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1 +util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =2,has_school_kid & (tot_tours == 2),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2 +util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =3,has_school_kid & (tot_tours == 3),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3 +util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =4,has_school_kid & (tot_tours == 4),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4 +util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =5,has_school_kid & (tot_tours == 5),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5 +util_presence_of_full_time_worker_and_escorting_tour,Dummy for Presence of Full time Worker (other than modeled person) & Escorting tour ,has_full_time * escort,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour +util_presence_of_part_time_worker_and_escorting_tour,Dummy for Presence of Part time Worker (other than modeled person) & Escorting tour ,has_part_time * escort,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour +util_presence_of_non_worker_and_escorting_tour,Dummy for Presence of Non-Worker (other than modeled person) & Escorting tour ,has_non_worker * escort,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour +util_presence_of_retiree_and_escorting_tour,Dummy for Presence of Retiree (other than modeled person) & Escorting tour ,has_retiree * escort,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour +util_presence_of_university_student_and_escorting_tour,Dummy for Presence of University Student (other than modeled person) & Escorting tour ,has_university * escort,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour +util_presence_of_driving_school_kid_and_escorting_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Escorting tour ,has_driving_kid * escort,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour +util_presence_of_pre_driving_school_kid_and_escorting_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Escorting tour ,has_school_kid * escort,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour +util_presence_of_pre_school_kid_and_escorting_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Escorting tour ,has_preschool_kid * escort,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour +util_at_home_pre_driving_school_kid_and_escorting_tour,Dummy for At home Pre-Driving School Kid & Escorting tour ,has_school_kid_at_home * escort,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour +util_at_home_pre_school_kid_and_escorting_tour,Dummy for At homef Pre-School Kid & Escorting tour ,has_preschool_kid_at_home * escort,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour +util_presence_of_full_time_worker_and_shopping_tour,Dummy for Presence of Full time Worker (other than modeled person) & Shopping tour ,has_full_time * shopping,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour +util_presence_of_part_time_worker_and_shopping_tour,Dummy for Presence of Part time Worker (other than modeled person) & Shopping tour ,has_part_time * shopping,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour +util_presence_of_non_worker_and_shopping_tour,Dummy for Presence of Non-Worker (other than modeled person) & Shopping tour ,has_non_worker * shopping,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour +util_presence_of_retiree_and_shopping_tour,Dummy for Presence of Retiree (other than modeled person) & Shopping tour ,has_retiree * shopping,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour +util_presence_of_university_student_and_shopping_tour,Dummy for Presence of University Student (other than modeled person) & Shopping tour ,has_university * shopping,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour +util_presence_of_driving_school_kid_and_shopping_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Shopping tour ,has_driving_kid * shopping,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour +util_presence_of_pre_driving_school_kid_and_shopping_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Shopping tour ,has_school_kid * shopping,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour +util_presence_of_pre_school_kid_and_shopping_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Shopping tour ,has_preschool_kid * shopping,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour +util_at_home_pre_driving_school_kid_and_shopping_tour,Dummy for At home Pre-Driving School Kid & Shopping tour ,has_school_kid_at_home * shopping,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour +util_at_home_pre_school_kid_and_shopping_tour,Dummy for At homef Pre-School Kid & Shopping tour ,has_preschool_kid_at_home * shopping,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour +util_presence_of_full_time_worker_and_maintenance_tour,Dummy for Presence of Full time Worker (other than modeled person) & Maintenance tour ,has_full_time * othmaint,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour +util_presence_of_part_time_worker_and_maintenance_tour,Dummy for Presence of Part time Worker (other than modeled person) & Maintenance tour ,has_part_time * othmaint,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour +util_presence_of_non_worker_and_maintenance_tour,Dummy for Presence of Non-Worker(other than modeled person) & Maintenance tour ,has_non_worker * othmaint,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour +util_presence_of_retiree_and_maintenance_tour,Dummy for Presence of Retiree (other than modeled person) & Maintenance tour ,has_retiree * othmaint,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour +util_presence_of_university_student_and_maintenance_tour,Dummy for Presence of University Student (other than modeled person) & Maintenance tour ,has_university * othmaint,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour +util_presence_of_driving_school_kid_and_maintenance_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Maintenance tour ,has_driving_kid * othmaint,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour +util_presence_of_pre_driving_school_kid_and_maintenance_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Maintenance tour ,has_school_kid * othmaint,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour +util_presence_of_pre_school_kid_and_maintenance_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Maintenance tour ,has_preschool_kid * othmaint,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour +util_at_home_pre_driving_school_kid_and_maintenance_tour,Dummy for At home Pre-Driving School Kid & Maintenance tour ,has_school_kid_at_home * othmaint,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour +util_at_home_pre_school_kid_and_maintenance_tour,Dummy for At homef Pre-School Kid & Maintenance tour ,has_preschool_kid_at_home * othmaint,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour +util_presence_of_full_time_worker_and_eating_out_tour,Dummy for Presence of Full time Worker (other than modeled person) & Eating Out tour ,has_full_time * eatout,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour +util_presence_of_part_time_worker_and_eating_out_tour,Dummy for Presence of Part time Worker (other than modeled person) & Eating Out tour ,has_part_time * eatout,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour +util_presence_of_non_worker_and_eating_out_tour,Dummy for Presence of Non-Worker (other than modeled person) & Eating Out tour ,has_non_worker * eatout,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour +util_presence_of_retiree_and_eating_out_tour,Dummy for Presence of Retiree (other than modeled person) & Eating Out tour ,has_retiree * eatout,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour +util_presence_of_university_student_and_eating_out_tour,Dummy for Presence of University Student (other than modeled person) & Eating Out tour ,has_university * eatout,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour +util_presence_of_driving_school_kid_and_eating_out_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Eating Out tour ,has_driving_kid * eatout,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour +util_presence_of_pre_driving_school_kid_and_eating_out_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Eating Out tour ,has_school_kid * eatout,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour +util_presence_of_pre_school_kid_and_eating_out_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Eating Out tour ,has_preschool_kid * eatout,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour +util_at_home_pre_driving_school_kid_and_eating_out_tour,Dummy for At home Pre-Driving School Kid & Eating Out tour ,has_school_kid_at_home * eatout,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour +util_at_home_pre_school_kid_and_eating_out_tour,Dummy for At homef Pre-School Kid & Eating Out tour ,has_preschool_kid_at_home * eatout,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour +util_presence_of_full_time_worker_and_discretionary_tour,Dummy for Presence of Full time Worker (other than modeled person) & Discretionary tour ,has_full_time * othdiscr,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour +util_presence_of_part_time_worker_and_discretionary_tour,Dummy for Presence of Part time Worker (other than modeled person) & Discretionary tour ,has_part_time * othdiscr,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour +util_presence_of_non_worker_and_discretionary_tour,Dummy for Presence of Non-Worker (other than modeled person) & Discretionary tour ,has_non_worker * othdiscr,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour +util_presence_of_retiree_and_discretionary_tour,Dummy for Presence of Retiree (other than modeled person) & Discretionary tour ,has_retiree * othdiscr,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour +util_presence_of_university_student_and_discretionary_tour,Dummy for Presence of University Student (other than modeled person) & Discretionary tour ,has_university * othdiscr,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour +util_presence_of_driving_school_kid_and_discretionary_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Discretionary tour ,has_driving_kid * othdiscr,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour +util_presence_of_pre_driving_school_kid_and_discretionary_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Discretionary tour ,has_school_kid * othdiscr,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour +util_presence_of_pre_school_kid_and_discretionary_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Discretionary tour ,has_preschool_kid * othdiscr,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour +util_at_home_pre_driving_school_kid_and_discretionary_tour,Dummy for At home Pre-Driving School Kid & Discretionary tour ,has_school_kid_at_home * othdiscr,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour +util_at_home_pre_school_kid_and_discretionary_tour,Dummy for At homef Pre-School Kid & Discretionary tour ,has_preschool_kid_at_home * othdiscr,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour +util_walk_access_to_retail_and_tour_frequency_is_1,Walk Access to Retail & Tour Frequency =1,nmRetail * (tot_tours == 1),coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1 +util_walk_access_to_retail_and_tour_frequency_is_2,Walk Access to Retail & Tour Frequency =2,nmRetail * (tot_tours == 2),coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2 +util_walk_access_to_retail_and_tour_frequency_is_3,Walk Access to Retail & Tour Frequency =3,nmRetail * (tot_tours == 3),coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3 +util_walk_access_to_retail_and_tour_frequency_is_4,Walk Access to Retail & Tour Frequency =4,nmRetail * (tot_tours == 4),coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4 +util_walk_access_to_retail_and_tour_frequency_is_5_plus,Walk Access to Retail & Tour Frequency =5+,nmRetail * (tot_tours > 4),coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus +util_transit_access_to_retail_and_tour_frequency_is_1,Transit Access to Retail & Tour Frequency =1,trOpRetail * (tot_tours == 1),coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1 +util_transit_access_to_retail_and_tour_frequency_is_2,Transit Access to Retail & Tour Frequency =2,trOpRetail * (tot_tours == 2),coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2 +util_transit_access_to_retail_and_tour_frequency_is_3,Transit Access to Retail & Tour Frequency =3,trOpRetail * (tot_tours == 3),coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3 +util_transit_access_to_retail_and_tour_frequency_is_4,Transit Access to Retail & Tour Frequency =4,trOpRetail * (tot_tours == 4),coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4 +util_transit_access_to_retail_and_tour_frequency_is_5_plus,Transit Access to Retail & Tour Frequency =5+,trOpRetail * (tot_tours > 4),coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus +util_auto_access_to_retail_and_tour_frequency_is_1,Auto Access to Retail & Tour Frequency =1,auOpRetail * (tot_tours == 1),coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1 +util_auto_access_to_retail_and_tour_frequency_is_2,Auto Access to Retail & Tour Frequency =2,auOpRetail * (tot_tours == 2),coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2 +util_auto_access_to_retail_and_tour_frequency_is_3,Auto Access to Retail & Tour Frequency =3,auOpRetail * (tot_tours == 3),coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3 +util_auto_access_to_retail_and_tour_frequency_is_4,Auto Access to Retail & Tour Frequency =4,auOpRetail * (tot_tours == 4),coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4 +util_auto_access_to_retail_and_tour_frequency_is_5_plus,Auto Access to Retail & Tour Frequency =5+,auOpRetail * (tot_tours > 4),coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus +util_walk_access_to_retail_and_escorting,Walk Access to Retail & Escorting ,nmRetail * escort,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting +util_transit_access_to_retail_and_escorting,Transit Access to Retail & Escorting ,trOpRetail * escort,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting +util_auto_access_to_retail_and_escorting,Auto Access to Retail & Escorting ,auOpRetail * escort,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting +util_walk_access_to_retail_and_shopping,Walk Access to Retail & Shopping ,nmRetail * shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping +util_transit_access_to_retail_and_shopping,Transit Access to Retail & Shopping ,trOpRetail * shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping +util_auto_access_to_retail_and_shopping,Auto Access to Retail & Shopping ,auOpRetail * shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping +util_walk_access_to_retail_and_maintenance,Walk Access to Retail & Maintenance ,nmRetail * othmaint,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance +util_transit_access_to_retail_and_maintenance,Transit Access to Retail & Maintenance ,trOpRetail * othmaint,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance +util_auto_access_to_retail_and_maintenance,Auto Access to Retail & Maintenance ,auOpRetail * othmaint,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance +util_walk_access_to_retail_and_eating_out,Walk Access to Retail & Eating Out ,nmRetail * eatout,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out +util_transit_access_to_retail_and_eating_out,Transit Access to Retail & Eating Out ,trOpRetail * eatout,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out +util_auto_access_to_retail_and_eating_out,Auto Access to Retail & Eating Out ,auOpRetail * eatout,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out +util_walk_access_to_retail_and_discretionary,Walk Access to Retail & Discretionary ,nmRetail * othdiscr,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary +util_transit_access_to_retail_and_discretionary,Transit Access to Retail & Discretionary ,trOpRetail * othdiscr,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary +util_auto_access_to_retail_and_discretionary,Auto Access to Retail & Discretionary ,auOpRetail * othdiscr,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary +util_urban_and_tour_frequency_is_1,Urban Areatype & Tour Frequency =1,home_is_urban & (tot_tours == 1),coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1 +util_urban_and_tour_frequency_is_2,Urban Areatype & Tour Frequency =2,home_is_urban & (tot_tours == 2),coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2 +util_urban_and_tour_frequency_is_3,Urban Areatype & Tour Frequency =3,home_is_urban & (tot_tours == 3),coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3 +util_urban_and_tour_frequency_is_4,Urban Areatype & Tour Frequency =4,home_is_urban & (tot_tours == 4),coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4 +util_urban_and_tour_frequency_is_5_plus,Urban Areatype & Tour Frequency =5+,home_is_urban & (tot_tours > 4),coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus +util_urban_and_escorting_tour,Urban Areatype & Escorting tour,home_is_urban * escort,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour +util_urban_and_shopping_tour,Urban Areatype &Shopping tour,home_is_urban * shopping,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour +util_urban_and_maintenance_tour,Urban Areatype & Maintenance tour,home_is_urban * othmaint,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour +util_urban_and_eatingout_tour,Urban Areatype & EatingOut tour,home_is_urban * eatout,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour +util_urban_and_discretionary_tour,Urban Areatype & Discretionary tour,home_is_urban * othdiscr,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour +util_1_escort_tour_constant,1 Escort Tour Constant,escort == 1,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant +util_2_plus_escort_tours_constant,2+ Escort Tours Constant,escort >= 2,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant +util_1_plus_shopping_tours_constant,1+ Shopping Tours Constant,shopping >= 1,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant +util_1_plus_maintenance_tours_constant,1+ Maintenance Tours Constant,othmaint >= 1,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant +util_1_plus_eating_out_tours_constant,1+ Eating Out Tours Constant,eatout >= 1,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant +util_1_plus_visting_tours_constant,1+ Visting Tours Constant,social >= 1,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant +util_1_plus_other_discretionary_tours_constant,1+ Other Discretionary Tours Constant,othdiscr >= 1,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant util_0_auto_household_and_escorting_tour,Dummy for 0-auto household & Escorting Tour,escort * no_cars,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour \ No newline at end of file diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency.yaml b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_frequency.yaml rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency.yaml diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_alternatives.csv b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_alternatives.csv old mode 100755 new mode 100644 similarity index 92% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_alternatives.csv rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_alternatives.csv index 1c0052f963..b9765aa75a --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_alternatives.csv +++ b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_alternatives.csv @@ -1,97 +1,97 @@ -escort,shopping,othmaint,othdiscr,eatout,social -0,0,0,0,0,0 -0,0,0,1,0,0 -0,0,0,0,0,1 -0,0,0,1,0,1 -0,0,0,0,1,0 -0,0,0,1,1,0 -0,0,0,0,1,1 -0,0,0,1,1,1 -0,0,1,0,0,0 -0,0,1,1,0,0 -0,0,1,0,0,1 -0,0,1,1,0,1 -0,0,1,0,1,0 -0,0,1,1,1,0 -0,0,1,0,1,1 -0,0,1,1,1,1 -0,1,0,0,0,0 -0,1,0,1,0,0 -0,1,0,0,0,1 -0,1,0,1,0,1 -0,1,0,0,1,0 -0,1,0,1,1,0 -0,1,0,0,1,1 -0,1,0,1,1,1 -0,1,1,0,0,0 -0,1,1,1,0,0 -0,1,1,0,0,1 -0,1,1,1,0,1 -0,1,1,0,1,0 -0,1,1,1,1,0 -0,1,1,0,1,1 -0,1,1,1,1,1 -1,0,0,0,0,0 -1,0,0,1,0,0 -1,0,0,0,0,1 -1,0,0,1,0,1 -1,0,0,0,1,0 -1,0,0,1,1,0 -1,0,0,0,1,1 -1,0,0,1,1,1 -1,0,1,0,0,0 -1,0,1,1,0,0 -1,0,1,0,0,1 -1,0,1,1,0,1 -1,0,1,0,1,0 -1,0,1,1,1,0 -1,0,1,0,1,1 -1,0,1,1,1,1 -1,1,0,0,0,0 -1,1,0,1,0,0 -1,1,0,0,0,1 -1,1,0,1,0,1 -1,1,0,0,1,0 -1,1,0,1,1,0 -1,1,0,0,1,1 -1,1,0,1,1,1 -1,1,1,0,0,0 -1,1,1,1,0,0 -1,1,1,0,0,1 -1,1,1,1,0,1 -1,1,1,0,1,0 -1,1,1,1,1,0 -1,1,1,0,1,1 -1,1,1,1,1,1 -2,0,0,0,0,0 -2,0,0,1,0,0 -2,0,0,0,0,1 -2,0,0,1,0,1 -2,0,0,0,1,0 -2,0,0,1,1,0 -2,0,0,0,1,1 -2,0,0,1,1,1 -2,0,1,0,0,0 -2,0,1,1,0,0 -2,0,1,0,0,1 -2,0,1,1,0,1 -2,0,1,0,1,0 -2,0,1,1,1,0 -2,0,1,0,1,1 -2,0,1,1,1,1 -2,1,0,0,0,0 -2,1,0,1,0,0 -2,1,0,0,0,1 -2,1,0,1,0,1 -2,1,0,0,1,0 -2,1,0,1,1,0 -2,1,0,0,1,1 -2,1,0,1,1,1 -2,1,1,0,0,0 -2,1,1,1,0,0 -2,1,1,0,0,1 -2,1,1,1,0,1 -2,1,1,0,1,0 -2,1,1,1,1,0 -2,1,1,0,1,1 -2,1,1,1,1,1 +escort,shopping,othmaint,othdiscr,eatout,social +0,0,0,0,0,0 +0,0,0,1,0,0 +0,0,0,0,0,1 +0,0,0,1,0,1 +0,0,0,0,1,0 +0,0,0,1,1,0 +0,0,0,0,1,1 +0,0,0,1,1,1 +0,0,1,0,0,0 +0,0,1,1,0,0 +0,0,1,0,0,1 +0,0,1,1,0,1 +0,0,1,0,1,0 +0,0,1,1,1,0 +0,0,1,0,1,1 +0,0,1,1,1,1 +0,1,0,0,0,0 +0,1,0,1,0,0 +0,1,0,0,0,1 +0,1,0,1,0,1 +0,1,0,0,1,0 +0,1,0,1,1,0 +0,1,0,0,1,1 +0,1,0,1,1,1 +0,1,1,0,0,0 +0,1,1,1,0,0 +0,1,1,0,0,1 +0,1,1,1,0,1 +0,1,1,0,1,0 +0,1,1,1,1,0 +0,1,1,0,1,1 +0,1,1,1,1,1 +1,0,0,0,0,0 +1,0,0,1,0,0 +1,0,0,0,0,1 +1,0,0,1,0,1 +1,0,0,0,1,0 +1,0,0,1,1,0 +1,0,0,0,1,1 +1,0,0,1,1,1 +1,0,1,0,0,0 +1,0,1,1,0,0 +1,0,1,0,0,1 +1,0,1,1,0,1 +1,0,1,0,1,0 +1,0,1,1,1,0 +1,0,1,0,1,1 +1,0,1,1,1,1 +1,1,0,0,0,0 +1,1,0,1,0,0 +1,1,0,0,0,1 +1,1,0,1,0,1 +1,1,0,0,1,0 +1,1,0,1,1,0 +1,1,0,0,1,1 +1,1,0,1,1,1 +1,1,1,0,0,0 +1,1,1,1,0,0 +1,1,1,0,0,1 +1,1,1,1,0,1 +1,1,1,0,1,0 +1,1,1,1,1,0 +1,1,1,0,1,1 +1,1,1,1,1,1 +2,0,0,0,0,0 +2,0,0,1,0,0 +2,0,0,0,0,1 +2,0,0,1,0,1 +2,0,0,0,1,0 +2,0,0,1,1,0 +2,0,0,0,1,1 +2,0,0,1,1,1 +2,0,1,0,0,0 +2,0,1,1,0,0 +2,0,1,0,0,1 +2,0,1,1,0,1 +2,0,1,0,1,0 +2,0,1,1,1,0 +2,0,1,0,1,1 +2,0,1,1,1,1 +2,1,0,0,0,0 +2,1,0,1,0,0 +2,1,0,0,0,1 +2,1,0,1,0,1 +2,1,0,0,1,0 +2,1,0,1,1,0 +2,1,0,0,1,1 +2,1,0,1,1,1 +2,1,1,0,0,0 +2,1,1,1,0,0 +2,1,1,0,0,1 +2,1,1,1,0,1 +2,1,1,0,1,0 +2,1,1,1,1,0 +2,1,1,0,1,1 +2,1,1,1,1,1 diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv index af29a842b4..3b4cfe7326 --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv +++ b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv @@ -1,21 +1,21 @@ -Description,Target,Expression -#,, -,max_window,person_max_window(persons) -,log_max_window,np.log1p(max_window) -,medium_low_income,(persons.income_in_thousands > 20) & (persons.income_in_thousands <= 50) -,medium_high_income,(persons.income_in_thousands > 50) & (persons.income_in_thousands <= 100) -,high_income,(persons.income_in_thousands > 100) -,no_cars,(persons.auto_ownership == 0) -,car_sufficiency,persons.auto_ownership-persons.num_workers -#,, -# UEC file comments says these are joint tour counts per persons but code is for household counts,, -,_JOINT_TOURS,tours[tours.tour_category=='joint'] -,num_hh_joint_tours,"reindex_i(_JOINT_TOURS.groupby('household_id').size(), persons.household_id)" -,num_hh_joint_shop_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='shopping'].groupby('household_id').size(), persons.household_id)" -,num_hh_joint_eatout_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='eatout'].groupby('household_id').size(), persons.household_id)" -,num_hh_joint_maint_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='maint'].groupby('household_id').size(), persons.household_id)" -,num_hh_joint_social_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='social'].groupby('household_id').size(), persons.household_id)" -,num_hh_joint_othdiscr_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='othdiscr'].groupby('household_id').size(), persons.household_id)" -# non_mandatory tour frequency extension,, -,has_mandatory_tour,(persons.num_mand > 0) * 1 -,has_joint_tour,(persons.num_joint_tours > 0) * 1 +Description,Target,Expression +#,, +,max_window,person_max_window(persons) +,log_max_window,np.log1p(max_window) +,medium_low_income,(persons.income_in_thousands > 20) & (persons.income_in_thousands <= 50) +,medium_high_income,(persons.income_in_thousands > 50) & (persons.income_in_thousands <= 100) +,high_income,(persons.income_in_thousands > 100) +,no_cars,(persons.auto_ownership == 0) +,car_sufficiency,persons.auto_ownership-persons.num_workers +#,, +# UEC file comments says these are joint tour counts per persons but code is for household counts,, +,_JOINT_TOURS,tours[tours.tour_category=='joint'] +,num_hh_joint_tours,"reindex_i(_JOINT_TOURS.groupby('household_id').size(), persons.household_id)" +,num_hh_joint_shop_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='shopping'].groupby('household_id').size(), persons.household_id)" +,num_hh_joint_eatout_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='eatout'].groupby('household_id').size(), persons.household_id)" +,num_hh_joint_maint_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='maint'].groupby('household_id').size(), persons.household_id)" +,num_hh_joint_social_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='social'].groupby('household_id').size(), persons.household_id)" +,num_hh_joint_othdiscr_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='othdiscr'].groupby('household_id').size(), persons.household_id)" +# non_mandatory tour frequency extension,, +,has_mandatory_tour,(persons.num_mand > 0) * 1 +,has_joint_tour,(persons.num_joint_tours > 0) * 1 diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv index 68b0882011..d24ffa777e --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv +++ b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_DRIVING.csv @@ -1,211 +1,211 @@ -coefficient_name,value,constrain -coef_escorting_tour,0,T -coef_discretionary_tour,0,T -coef_shopping_tour,0,T -coef_maintenance_tour,0,T -coef_visiting_or_social_tour,0,T -coef_eating_out_tour,0,T -coef_total_number_of_tours_is_0_no_prior_tours,-999,T -coef_total_number_of_tours_is_0_prior_tours,0,T -coef_total_number_of_tours_is_1,-7.1506,F -coef_total_number_of_tours_is_2,-11.1214,F -coef_total_number_of_tours_is_3,-13.175,F -coef_total_number_of_tours_is_4,-999,T -coef_total_number_of_tours_is_5,-999,T -coef_total_number_of_tours_is_6_plus,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.234,F -coef_number_of_mandatory_tours_and_tour_frequency_is_2,-0.9231,F -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-6.5835,F -coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_0,0,T -coef_number_of_joint_tours_and_tour_frequency_is_1,-0.2162,F -coef_number_of_joint_tours_and_tour_frequency_is_2,-0.3587,F -coef_number_of_joint_tours_and_tour_frequency_is_3,-4.2701,F -coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_shopping_tours,0,T -coef_number_of_joint_maintenance_tours,0,T -coef_number_of_joint_eating_out_tours,0,T -coef_number_of_joint_visit_tours,0,T -coef_number_of_joint_discretionary_tours,0,T -coef_logged_maximum_residual_window_tour_frequency_is_0,0,T -coef_logged_maximum_residual_window_tour_frequency_is_1,1.3298,F -coef_logged_maximum_residual_window_tour_frequency_is_2,1.3759,F -coef_logged_maximum_residual_window_tour_frequency_is_3,3.2808,F -coef_logged_maximum_residual_window_tour_frequency_is_4,3.2808,F -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,3.2808,F -coef_mediumlow_income_group_and_tour_frequency_is_1,0,T -coef_mediumlow_income_group_and_tour_frequency_is_2,0,T -coef_mediumlow_income_group_and_tour_frequency_is_3,0,T -coef_mediumlow_income_group_and_tour_frequency_is_4,0,T -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_1,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_2,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_3,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_4,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0,T -coef_high_income_group_and_tour_frequency_is_1,0,T -coef_high_income_group_and_tour_frequency_is_2,0,T -coef_high_income_group_and_tour_frequency_is_3,0,T -coef_high_income_group_and_tour_frequency_is_4,0,T -coef_high_income_group_and_tour_frequency_is_5_plus,0,T -coef_mediumlow_income_group_and_shopping_tour,0,T -coef_mediumhigh_income_group_and_shopping_tour,0.2443,F -coef_high_income_group_and_shopping_tour,0.2443,F -coef_mediumlow_income_group_and_maintenance_tour,0,T -coef_mediumhigh_income_group_and_maintenance_tour,0.3982,F -coef_high_income_group_and_maintenance_tour,0.3982,F -coef_mediumlow_income_group_and_eating_out_tour,0,T -coef_mediumhigh_income_group_and_eating_out_tour,0.4916,F -coef_high_income_group_and_eating_out_tour,0.4916,F -coef_mediumlow_income_group_and_discretionary_tour,0.9169,F -coef_mediumhigh_income_group_and_discretionary_tour,1.405,F -coef_high_income_group_and_discretionary_tour,2.327,F -coef_mediumlow_income_group_and_visiting_tour,0,T -coef_mediumhigh_income_group_and_visiting_tour,0.2858,F -coef_high_income_group_and_visiting_tour,0.2858,F -coef_female_and_tour_frequency_is_1,0,T -coef_female_and_tour_frequency_is_2,0,T -coef_female_and_tour_frequency_is_3,0,T -coef_female_and_tour_frequency_is_4,0,T -coef_female_and_tour_frequency_is_5,0,T -coef_female_and_escorting_tour,0,T -coef_female_and_shopping_tour,0,T -coef_female_and_maintenance_tour,0,T -coef_female_and_eatingout_tour,0,T -coef_female_and_discretionary_tour,0,T -coef_zero_car_ownership_and_tour_frequency_is_1,-0.6369,F -coef_zero_car_ownership_and_tour_frequency_is_2,-0.6369,F -coef_zero_car_ownership_and_tour_frequency_is_3,-0.6369,F -coef_zero_car_ownership_and_tour_frequency_is_4,-0.6369,F -coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.6369,F -coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.6369,F -coef_car_shortage_vs_workers_and_tour_frequency_is_2,-0.6369,F -coef_car_shortage_vs_workers_and_tour_frequency_is_3,-0.6369,F -coef_car_shortage_vs_workers_and_tour_frequency_is_4,-0.6369,F -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-0.6369,F -coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.2902,F -coef_car_surplus_vs_workers_and_tour_frequency_is_2,2.0352,F -coef_car_surplus_vs_workers_and_tour_frequency_is_3,2.0352,F -coef_car_surplus_vs_workers_and_tour_frequency_is_4,2.0352,F -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,2.0352,F -coef_presence_of_non_worker_and_tour_frequency_is_1,0,T -coef_presence_of_non_worker_and_tour_frequency_is_2,-0.6571,F -coef_presence_of_non_worker_and_tour_frequency_is_3,-1.4044,F -coef_presence_of_non_worker_and_tour_frequency_is_4,-1.4044,F -coef_presence_of_non_worker_and_tour_frequency_is_5,-1.4044,F -coef_presence_of_retiree_and_tour_frequency_is_1,0,T -coef_presence_of_retiree_and_tour_frequency_is_2,0,T -coef_presence_of_retiree_and_tour_frequency_is_3,0,T -coef_presence_of_retiree_and_tour_frequency_is_4,0,T -coef_presence_of_retiree_and_tour_frequency_is_5,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,-0.3219,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,-1.0874,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,-1.0874,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,-1.0874,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,-1.0874,F -coef_presence_of_full_time_worker_and_escorting_tour,0,T -coef_presence_of_part_time_worker_and_escorting_tour,0,T -coef_presence_of_non_worker_and_escorting_tour,0,T -coef_presence_of_retiree_and_escorting_tour,0,T -coef_presence_of_university_student_and_escorting_tour,0,T -coef_presence_of_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_school_kid_and_escorting_tour,0,T -coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T -coef_at_home_pre_school_kid_and_escorting_tour,0,T -coef_presence_of_full_time_worker_and_shopping_tour,0,T -coef_presence_of_part_time_worker_and_shopping_tour,0,T -coef_presence_of_non_worker_and_shopping_tour,0,T -coef_presence_of_retiree_and_shopping_tour,0,T -coef_presence_of_university_student_and_shopping_tour,0,T -coef_presence_of_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_school_kid_and_shopping_tour,0,T -coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T -coef_at_home_pre_school_kid_and_shopping_tour,0,T -coef_presence_of_full_time_worker_and_maintenance_tour,0,T -coef_presence_of_part_time_worker_and_maintenance_tour,0,T -coef_presence_of_non_worker_and_maintenance_tour,0,T -coef_presence_of_retiree_and_maintenance_tour,0,T -coef_presence_of_university_student_and_maintenance_tour,0,T -coef_presence_of_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_school_kid_and_maintenance_tour,0,T -coef_presence_of_full_time_worker_and_eating_out_tour,0,T -coef_presence_of_part_time_worker_and_eating_out_tour,0,T -coef_presence_of_non_worker_and_eating_out_tour,0,T -coef_presence_of_retiree_and_eating_out_tour,0,T -coef_presence_of_university_student_and_eating_out_tour,0,T -coef_presence_of_driving_school_kid_and_eating_out_tour,-0.6377,F -coef_presence_of_pre_driving_school_kid_and_eating_out_tour,-1.5698,F -coef_presence_of_pre_school_kid_and_eating_out_tour,-0.2987,F -coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_school_kid_and_eating_out_tour,0,T -coef_presence_of_full_time_worker_and_discretionary_tour,0,T -coef_presence_of_part_time_worker_and_discretionary_tour,0,T -coef_presence_of_non_worker_and_discretionary_tour,0,T -coef_presence_of_retiree_and_discretionary_tour,0,T -coef_presence_of_university_student_and_discretionary_tour,-1.2834,F -coef_presence_of_driving_school_kid_and_discretionary_tour,-0.9202,F -coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_school_kid_and_discretionary_tour,0,T -coef_walk_access_to_retail_and_tour_frequency_is_1,0,T -coef_walk_access_to_retail_and_tour_frequency_is_2,0,T -coef_walk_access_to_retail_and_tour_frequency_is_3,0,T -coef_walk_access_to_retail_and_tour_frequency_is_4,0,T -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_transit_access_to_retail_and_tour_frequency_is_1,0,T -coef_transit_access_to_retail_and_tour_frequency_is_2,0,T -coef_transit_access_to_retail_and_tour_frequency_is_3,0,T -coef_transit_access_to_retail_and_tour_frequency_is_4,0,T -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_auto_access_to_retail_and_tour_frequency_is_1,0.1004,F -coef_auto_access_to_retail_and_tour_frequency_is_2,0.1004,F -coef_auto_access_to_retail_and_tour_frequency_is_3,0.1004,F -coef_auto_access_to_retail_and_tour_frequency_is_4,0.1004,F -coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0.1004,F -coef_walk_access_to_retail_and_escorting,0,T -coef_transit_access_to_retail_and_escorting,0,T -coef_auto_access_to_retail_and_escorting,0,T -coef_walk_access_to_retail_and_shopping,0,T -coef_transit_access_to_retail_and_shopping,0,T -coef_auto_access_to_retail_and_shopping,0,T -coef_walk_access_to_retail_and_maintenance,0,T -coef_transit_access_to_retail_and_maintenance,0,T -coef_auto_access_to_retail_and_maintenance,0,T -coef_walk_access_to_retail_and_eating_out,0,T -coef_transit_access_to_retail_and_eating_out,0,T -coef_auto_access_to_retail_and_eating_out,0,T -coef_walk_access_to_retail_and_discretionary,0,T -coef_transit_access_to_retail_and_discretionary,0,T -coef_auto_access_to_retail_and_discretionary,0,T -coef_urban_and_tour_frequency_is_1,0,T -coef_urban_and_tour_frequency_is_2,0,T -coef_urban_and_tour_frequency_is_3,0,T -coef_urban_and_tour_frequency_is_4,0,T -coef_urban_and_tour_frequency_is_5_plus,0,T -coef_urban_and_escorting_tour,0,T -coef_urban_and_shopping_tour,0,T -coef_urban_and_maintenance_tour,1.0394,F -coef_urban_and_eatingout_tour,0,T -coef_urban_and_discretionary_tour,0,T -coef_1_escort_tour_constant,-0.4934,F -coef_2_plus_escort_tours_constant,1.4155,F -coef_1_plus_shopping_tours_constant,0.532,F -coef_1_plus_maintenance_tours_constant,-0.4344,F -coef_1_plus_eating_out_tours_constant,-0.0242,F -coef_1_plus_visting_tours_constant,0.2367,F -coef_1_plus_other_discretionary_tours_constant,-0.2602,F +coefficient_name,value,constrain +coef_escorting_tour,0,T +coef_discretionary_tour,0,T +coef_shopping_tour,0,T +coef_maintenance_tour,0,T +coef_visiting_or_social_tour,0,T +coef_eating_out_tour,0,T +coef_total_number_of_tours_is_0_no_prior_tours,-999,T +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,-7.1506,F +coef_total_number_of_tours_is_2,-11.1214,F +coef_total_number_of_tours_is_3,-13.175,F +coef_total_number_of_tours_is_4,-999,T +coef_total_number_of_tours_is_5,-999,T +coef_total_number_of_tours_is_6_plus,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.234,F +coef_number_of_mandatory_tours_and_tour_frequency_is_2,-0.9231,F +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-6.5835,F +coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_0,0,T +coef_number_of_joint_tours_and_tour_frequency_is_1,-0.2162,F +coef_number_of_joint_tours_and_tour_frequency_is_2,-0.3587,F +coef_number_of_joint_tours_and_tour_frequency_is_3,-4.2701,F +coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_shopping_tours,0,T +coef_number_of_joint_maintenance_tours,0,T +coef_number_of_joint_eating_out_tours,0,T +coef_number_of_joint_visit_tours,0,T +coef_number_of_joint_discretionary_tours,0,T +coef_logged_maximum_residual_window_tour_frequency_is_0,0,T +coef_logged_maximum_residual_window_tour_frequency_is_1,1.3298,F +coef_logged_maximum_residual_window_tour_frequency_is_2,1.3759,F +coef_logged_maximum_residual_window_tour_frequency_is_3,3.2808,F +coef_logged_maximum_residual_window_tour_frequency_is_4,3.2808,F +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,3.2808,F +coef_mediumlow_income_group_and_tour_frequency_is_1,0,T +coef_mediumlow_income_group_and_tour_frequency_is_2,0,T +coef_mediumlow_income_group_and_tour_frequency_is_3,0,T +coef_mediumlow_income_group_and_tour_frequency_is_4,0,T +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_1,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_2,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_3,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_4,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0,T +coef_high_income_group_and_tour_frequency_is_1,0,T +coef_high_income_group_and_tour_frequency_is_2,0,T +coef_high_income_group_and_tour_frequency_is_3,0,T +coef_high_income_group_and_tour_frequency_is_4,0,T +coef_high_income_group_and_tour_frequency_is_5_plus,0,T +coef_mediumlow_income_group_and_shopping_tour,0,T +coef_mediumhigh_income_group_and_shopping_tour,0.2443,F +coef_high_income_group_and_shopping_tour,0.2443,F +coef_mediumlow_income_group_and_maintenance_tour,0,T +coef_mediumhigh_income_group_and_maintenance_tour,0.3982,F +coef_high_income_group_and_maintenance_tour,0.3982,F +coef_mediumlow_income_group_and_eating_out_tour,0,T +coef_mediumhigh_income_group_and_eating_out_tour,0.4916,F +coef_high_income_group_and_eating_out_tour,0.4916,F +coef_mediumlow_income_group_and_discretionary_tour,0.9169,F +coef_mediumhigh_income_group_and_discretionary_tour,1.405,F +coef_high_income_group_and_discretionary_tour,2.327,F +coef_mediumlow_income_group_and_visiting_tour,0,T +coef_mediumhigh_income_group_and_visiting_tour,0.2858,F +coef_high_income_group_and_visiting_tour,0.2858,F +coef_female_and_tour_frequency_is_1,0,T +coef_female_and_tour_frequency_is_2,0,T +coef_female_and_tour_frequency_is_3,0,T +coef_female_and_tour_frequency_is_4,0,T +coef_female_and_tour_frequency_is_5,0,T +coef_female_and_escorting_tour,0,T +coef_female_and_shopping_tour,0,T +coef_female_and_maintenance_tour,0,T +coef_female_and_eatingout_tour,0,T +coef_female_and_discretionary_tour,0,T +coef_zero_car_ownership_and_tour_frequency_is_1,-0.6369,F +coef_zero_car_ownership_and_tour_frequency_is_2,-0.6369,F +coef_zero_car_ownership_and_tour_frequency_is_3,-0.6369,F +coef_zero_car_ownership_and_tour_frequency_is_4,-0.6369,F +coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.6369,F +coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.6369,F +coef_car_shortage_vs_workers_and_tour_frequency_is_2,-0.6369,F +coef_car_shortage_vs_workers_and_tour_frequency_is_3,-0.6369,F +coef_car_shortage_vs_workers_and_tour_frequency_is_4,-0.6369,F +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-0.6369,F +coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.2902,F +coef_car_surplus_vs_workers_and_tour_frequency_is_2,2.0352,F +coef_car_surplus_vs_workers_and_tour_frequency_is_3,2.0352,F +coef_car_surplus_vs_workers_and_tour_frequency_is_4,2.0352,F +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,2.0352,F +coef_presence_of_non_worker_and_tour_frequency_is_1,0,T +coef_presence_of_non_worker_and_tour_frequency_is_2,-0.6571,F +coef_presence_of_non_worker_and_tour_frequency_is_3,-1.4044,F +coef_presence_of_non_worker_and_tour_frequency_is_4,-1.4044,F +coef_presence_of_non_worker_and_tour_frequency_is_5,-1.4044,F +coef_presence_of_retiree_and_tour_frequency_is_1,0,T +coef_presence_of_retiree_and_tour_frequency_is_2,0,T +coef_presence_of_retiree_and_tour_frequency_is_3,0,T +coef_presence_of_retiree_and_tour_frequency_is_4,0,T +coef_presence_of_retiree_and_tour_frequency_is_5,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,-0.3219,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,-1.0874,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,-1.0874,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,-1.0874,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,-1.0874,F +coef_presence_of_full_time_worker_and_escorting_tour,0,T +coef_presence_of_part_time_worker_and_escorting_tour,0,T +coef_presence_of_non_worker_and_escorting_tour,0,T +coef_presence_of_retiree_and_escorting_tour,0,T +coef_presence_of_university_student_and_escorting_tour,0,T +coef_presence_of_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_school_kid_and_escorting_tour,0,T +coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T +coef_at_home_pre_school_kid_and_escorting_tour,0,T +coef_presence_of_full_time_worker_and_shopping_tour,0,T +coef_presence_of_part_time_worker_and_shopping_tour,0,T +coef_presence_of_non_worker_and_shopping_tour,0,T +coef_presence_of_retiree_and_shopping_tour,0,T +coef_presence_of_university_student_and_shopping_tour,0,T +coef_presence_of_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_school_kid_and_shopping_tour,0,T +coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T +coef_at_home_pre_school_kid_and_shopping_tour,0,T +coef_presence_of_full_time_worker_and_maintenance_tour,0,T +coef_presence_of_part_time_worker_and_maintenance_tour,0,T +coef_presence_of_non_worker_and_maintenance_tour,0,T +coef_presence_of_retiree_and_maintenance_tour,0,T +coef_presence_of_university_student_and_maintenance_tour,0,T +coef_presence_of_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_school_kid_and_maintenance_tour,0,T +coef_presence_of_full_time_worker_and_eating_out_tour,0,T +coef_presence_of_part_time_worker_and_eating_out_tour,0,T +coef_presence_of_non_worker_and_eating_out_tour,0,T +coef_presence_of_retiree_and_eating_out_tour,0,T +coef_presence_of_university_student_and_eating_out_tour,0,T +coef_presence_of_driving_school_kid_and_eating_out_tour,-0.6377,F +coef_presence_of_pre_driving_school_kid_and_eating_out_tour,-1.5698,F +coef_presence_of_pre_school_kid_and_eating_out_tour,-0.2987,F +coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_school_kid_and_eating_out_tour,0,T +coef_presence_of_full_time_worker_and_discretionary_tour,0,T +coef_presence_of_part_time_worker_and_discretionary_tour,0,T +coef_presence_of_non_worker_and_discretionary_tour,0,T +coef_presence_of_retiree_and_discretionary_tour,0,T +coef_presence_of_university_student_and_discretionary_tour,-1.2834,F +coef_presence_of_driving_school_kid_and_discretionary_tour,-0.9202,F +coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_school_kid_and_discretionary_tour,0,T +coef_walk_access_to_retail_and_tour_frequency_is_1,0,T +coef_walk_access_to_retail_and_tour_frequency_is_2,0,T +coef_walk_access_to_retail_and_tour_frequency_is_3,0,T +coef_walk_access_to_retail_and_tour_frequency_is_4,0,T +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_transit_access_to_retail_and_tour_frequency_is_1,0,T +coef_transit_access_to_retail_and_tour_frequency_is_2,0,T +coef_transit_access_to_retail_and_tour_frequency_is_3,0,T +coef_transit_access_to_retail_and_tour_frequency_is_4,0,T +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_auto_access_to_retail_and_tour_frequency_is_1,0.1004,F +coef_auto_access_to_retail_and_tour_frequency_is_2,0.1004,F +coef_auto_access_to_retail_and_tour_frequency_is_3,0.1004,F +coef_auto_access_to_retail_and_tour_frequency_is_4,0.1004,F +coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0.1004,F +coef_walk_access_to_retail_and_escorting,0,T +coef_transit_access_to_retail_and_escorting,0,T +coef_auto_access_to_retail_and_escorting,0,T +coef_walk_access_to_retail_and_shopping,0,T +coef_transit_access_to_retail_and_shopping,0,T +coef_auto_access_to_retail_and_shopping,0,T +coef_walk_access_to_retail_and_maintenance,0,T +coef_transit_access_to_retail_and_maintenance,0,T +coef_auto_access_to_retail_and_maintenance,0,T +coef_walk_access_to_retail_and_eating_out,0,T +coef_transit_access_to_retail_and_eating_out,0,T +coef_auto_access_to_retail_and_eating_out,0,T +coef_walk_access_to_retail_and_discretionary,0,T +coef_transit_access_to_retail_and_discretionary,0,T +coef_auto_access_to_retail_and_discretionary,0,T +coef_urban_and_tour_frequency_is_1,0,T +coef_urban_and_tour_frequency_is_2,0,T +coef_urban_and_tour_frequency_is_3,0,T +coef_urban_and_tour_frequency_is_4,0,T +coef_urban_and_tour_frequency_is_5_plus,0,T +coef_urban_and_escorting_tour,0,T +coef_urban_and_shopping_tour,0,T +coef_urban_and_maintenance_tour,1.0394,F +coef_urban_and_eatingout_tour,0,T +coef_urban_and_discretionary_tour,0,T +coef_1_escort_tour_constant,-0.4934,F +coef_2_plus_escort_tours_constant,1.4155,F +coef_1_plus_shopping_tours_constant,0.532,F +coef_1_plus_maintenance_tours_constant,-0.4344,F +coef_1_plus_eating_out_tours_constant,-0.0242,F +coef_1_plus_visting_tours_constant,0.2367,F +coef_1_plus_other_discretionary_tours_constant,-0.2602,F coef_0_auto_household_and_escorting_tour,-2, \ No newline at end of file diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv index 6b9d8d7622..1c1d5221bd --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv +++ b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_FULL.csv @@ -1,211 +1,211 @@ -coefficient_name,value,constrain -coef_escorting_tour,0,T -coef_discretionary_tour,0,T -coef_shopping_tour,0,T -coef_maintenance_tour,0,T -coef_visiting_or_social_tour,0,T -coef_eating_out_tour,0,T -coef_total_number_of_tours_is_0_no_prior_tours,-999,T -coef_total_number_of_tours_is_0_prior_tours,0,T -coef_total_number_of_tours_is_1,-7.3572,F -coef_total_number_of_tours_is_2,-10.647,F -coef_total_number_of_tours_is_3,-13.5005,F -coef_total_number_of_tours_is_4,-16.3965,F -coef_total_number_of_tours_is_5,-19.6843,F -coef_total_number_of_tours_is_6_plus,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_1,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_2,-0.8887,F -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-2.3343,F -coef_number_of_mandatory_tours_and_tour_frequency_is_4,-2.3343,F -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-2.3343,F -coef_number_of_joint_tours_and_tour_frequency_is_0,0,T -coef_number_of_joint_tours_and_tour_frequency_is_1,0,T -coef_number_of_joint_tours_and_tour_frequency_is_2,0,T -coef_number_of_joint_tours_and_tour_frequency_is_3,0,T -coef_number_of_joint_tours_and_tour_frequency_is_4,0,T -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,0,T -coef_number_of_joint_shopping_tours,0,T -coef_number_of_joint_maintenance_tours,0,T -coef_number_of_joint_eating_out_tours,-0.5866,F -coef_number_of_joint_visit_tours,0,T -coef_number_of_joint_discretionary_tours,0,T -coef_logged_maximum_residual_window_tour_frequency_is_0,0,T -coef_logged_maximum_residual_window_tour_frequency_is_1,1.2562,F -coef_logged_maximum_residual_window_tour_frequency_is_2,1.2868,F -coef_logged_maximum_residual_window_tour_frequency_is_3,1.3993,F -coef_logged_maximum_residual_window_tour_frequency_is_4,1.3993,F -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.3993,F -coef_mediumlow_income_group_and_tour_frequency_is_1,0.4981,F -coef_mediumlow_income_group_and_tour_frequency_is_2,0.8345,F -coef_mediumlow_income_group_and_tour_frequency_is_3,1.0213,F -coef_mediumlow_income_group_and_tour_frequency_is_4,1.0213,F -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,1.0213,F -coef_mediumhigh_income_group_and_tour_frequency_is_1,0.4981,F -coef_mediumhigh_income_group_and_tour_frequency_is_2,0.8345,F -coef_mediumhigh_income_group_and_tour_frequency_is_3,1.0213,F -coef_mediumhigh_income_group_and_tour_frequency_is_4,1.0213,F -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.0213,F -coef_high_income_group_and_tour_frequency_is_1,0.5189,F -coef_high_income_group_and_tour_frequency_is_2,1.1336,F -coef_high_income_group_and_tour_frequency_is_3,1.3899,F -coef_high_income_group_and_tour_frequency_is_4,1.3899,F -coef_high_income_group_and_tour_frequency_is_5_plus,1.3899,F -coef_mediumlow_income_group_and_shopping_tour,0,T -coef_mediumhigh_income_group_and_shopping_tour,0,T -coef_high_income_group_and_shopping_tour,0,T -coef_mediumlow_income_group_and_maintenance_tour,0,T -coef_mediumhigh_income_group_and_maintenance_tour,0,T -coef_high_income_group_and_maintenance_tour,0,T -coef_mediumlow_income_group_and_eating_out_tour,0,T -coef_mediumhigh_income_group_and_eating_out_tour,0.5581,F -coef_high_income_group_and_eating_out_tour,0.5581,F -coef_mediumlow_income_group_and_discretionary_tour,0,T -coef_mediumhigh_income_group_and_discretionary_tour,0.2565,F -coef_high_income_group_and_discretionary_tour,0.2565,F -coef_mediumlow_income_group_and_visiting_tour,0,T -coef_mediumhigh_income_group_and_visiting_tour,-0.2423,F -coef_high_income_group_and_visiting_tour,-0.2423,F -coef_female_and_tour_frequency_is_1,-0.0766,F -coef_female_and_tour_frequency_is_2,-0.1062,F -coef_female_and_tour_frequency_is_3,-0.3274,F -coef_female_and_tour_frequency_is_4,-0.3274,F -coef_female_and_tour_frequency_is_5,-0.3274,F -coef_female_and_escorting_tour,0.1824,F -coef_female_and_shopping_tour,0,T -coef_female_and_maintenance_tour,0,T -coef_female_and_eatingout_tour,0,T -coef_female_and_discretionary_tour,0,T -coef_zero_car_ownership_and_tour_frequency_is_1,-0.3486,F -coef_zero_car_ownership_and_tour_frequency_is_2,-0.3486,F -coef_zero_car_ownership_and_tour_frequency_is_3,-0.3486,F -coef_zero_car_ownership_and_tour_frequency_is_4,-0.3486,F -coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.3486,F -coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.1304,F -coef_car_surplus_vs_workers_and_tour_frequency_is_2,0.1304,F -coef_car_surplus_vs_workers_and_tour_frequency_is_3,0.1304,F -coef_car_surplus_vs_workers_and_tour_frequency_is_4,0.1304,F -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0.1304,F -coef_presence_of_non_worker_and_tour_frequency_is_1,0,T -coef_presence_of_non_worker_and_tour_frequency_is_2,0,T -coef_presence_of_non_worker_and_tour_frequency_is_3,0,T -coef_presence_of_non_worker_and_tour_frequency_is_4,0,T -coef_presence_of_non_worker_and_tour_frequency_is_5,0,T -coef_presence_of_retiree_and_tour_frequency_is_1,0,T -coef_presence_of_retiree_and_tour_frequency_is_2,0,T -coef_presence_of_retiree_and_tour_frequency_is_3,0,T -coef_presence_of_retiree_and_tour_frequency_is_4,0,T -coef_presence_of_retiree_and_tour_frequency_is_5,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_full_time_worker_and_escorting_tour,0,T -coef_presence_of_part_time_worker_and_escorting_tour,0,T -coef_presence_of_non_worker_and_escorting_tour,-0.4815,F -coef_presence_of_retiree_and_escorting_tour,-0.808,F -coef_presence_of_university_student_and_escorting_tour,0,T -coef_presence_of_driving_school_kid_and_escorting_tour,0.3601,F -coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.3974,F -coef_presence_of_pre_school_kid_and_escorting_tour,0.6842,F -coef_at_home_pre_driving_school_kid_and_escorting_tour,-0.2746,F -coef_at_home_pre_school_kid_and_escorting_tour,-1.5675,F -coef_presence_of_full_time_worker_and_shopping_tour,-0.3059,F -coef_presence_of_part_time_worker_and_shopping_tour,-0.1541,F -coef_presence_of_non_worker_and_shopping_tour,-0.416,F -coef_presence_of_retiree_and_shopping_tour,0,T -coef_presence_of_university_student_and_shopping_tour,0,T -coef_presence_of_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_school_kid_and_shopping_tour,-0.208,F -coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T -coef_at_home_pre_school_kid_and_shopping_tour,0,T -coef_presence_of_full_time_worker_and_maintenance_tour,-0.1685,F -coef_presence_of_part_time_worker_and_maintenance_tour,-0.1584,F -coef_presence_of_non_worker_and_maintenance_tour,-0.3237,F -coef_presence_of_retiree_and_maintenance_tour,0,T -coef_presence_of_university_student_and_maintenance_tour,0,T -coef_presence_of_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_school_kid_and_maintenance_tour,0,T -coef_presence_of_full_time_worker_and_eating_out_tour,-0.3571,F -coef_presence_of_part_time_worker_and_eating_out_tour,0,T -coef_presence_of_non_worker_and_eating_out_tour,-0.2014,F -coef_presence_of_retiree_and_eating_out_tour,-0.5708,F -coef_presence_of_university_student_and_eating_out_tour,0,T -coef_presence_of_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_school_kid_and_eating_out_tour,-0.4225,F -coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_school_kid_and_eating_out_tour,0,T -coef_presence_of_full_time_worker_and_discretionary_tour,-0.667,F -coef_presence_of_part_time_worker_and_discretionary_tour,-0.2102,F -coef_presence_of_non_worker_and_discretionary_tour,-0.4281,F -coef_presence_of_retiree_and_discretionary_tour,-0.9104,F -coef_presence_of_university_student_and_discretionary_tour,-0.8551,F -coef_presence_of_driving_school_kid_and_discretionary_tour,-0.3963,F -coef_presence_of_pre_driving_school_kid_and_discretionary_tour,-0.3959,F -coef_presence_of_pre_school_kid_and_discretionary_tour,-0.5081,F -coef_at_home_pre_driving_school_kid_and_discretionary_tour,-0.4703,F -coef_at_home_pre_school_kid_and_discretionary_tour,-0.4703,F -coef_walk_access_to_retail_and_tour_frequency_is_1,0,T -coef_walk_access_to_retail_and_tour_frequency_is_2,0,T -coef_walk_access_to_retail_and_tour_frequency_is_3,0,T -coef_walk_access_to_retail_and_tour_frequency_is_4,0,T -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_transit_access_to_retail_and_tour_frequency_is_1,0.0226,F -coef_transit_access_to_retail_and_tour_frequency_is_2,0.0226,F -coef_transit_access_to_retail_and_tour_frequency_is_3,0.0226,F -coef_transit_access_to_retail_and_tour_frequency_is_4,0.0226,F -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0.0226,F -coef_auto_access_to_retail_and_tour_frequency_is_1,0,T -coef_auto_access_to_retail_and_tour_frequency_is_2,0,T -coef_auto_access_to_retail_and_tour_frequency_is_3,0,T -coef_auto_access_to_retail_and_tour_frequency_is_4,0,T -coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_walk_access_to_retail_and_escorting,0.0451,F -coef_transit_access_to_retail_and_escorting,0,T -coef_auto_access_to_retail_and_escorting,0,T -coef_walk_access_to_retail_and_shopping,0.033,F -coef_transit_access_to_retail_and_shopping,0,T -coef_auto_access_to_retail_and_shopping,0.1067,F -coef_walk_access_to_retail_and_maintenance,0,T -coef_transit_access_to_retail_and_maintenance,0,T -coef_auto_access_to_retail_and_maintenance,0.0749,F -coef_walk_access_to_retail_and_eating_out,0.145,F -coef_transit_access_to_retail_and_eating_out,0,T -coef_auto_access_to_retail_and_eating_out,0,T -coef_walk_access_to_retail_and_discretionary,0.0567,F -coef_transit_access_to_retail_and_discretionary,0,T -coef_auto_access_to_retail_and_discretionary,0.0844,F -coef_urban_and_tour_frequency_is_1,0,T -coef_urban_and_tour_frequency_is_2,0,T -coef_urban_and_tour_frequency_is_3,0,T -coef_urban_and_tour_frequency_is_4,0,T -coef_urban_and_tour_frequency_is_5_plus,0,T -coef_urban_and_escorting_tour,-0.4316,F -coef_urban_and_shopping_tour,0,T -coef_urban_and_maintenance_tour,0,T -coef_urban_and_eatingout_tour,0,T -coef_urban_and_discretionary_tour,0,T -coef_1_escort_tour_constant,0.0298,F -coef_2_plus_escort_tours_constant,0.7402,F -coef_1_plus_shopping_tours_constant,0.4774,F -coef_1_plus_maintenance_tours_constant,0.1202,F -coef_1_plus_eating_out_tours_constant,0.0097,F -coef_1_plus_visting_tours_constant,0.0522,F -coef_1_plus_other_discretionary_tours_constant,0.7412,F +coefficient_name,value,constrain +coef_escorting_tour,0,T +coef_discretionary_tour,0,T +coef_shopping_tour,0,T +coef_maintenance_tour,0,T +coef_visiting_or_social_tour,0,T +coef_eating_out_tour,0,T +coef_total_number_of_tours_is_0_no_prior_tours,-999,T +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,-7.3572,F +coef_total_number_of_tours_is_2,-10.647,F +coef_total_number_of_tours_is_3,-13.5005,F +coef_total_number_of_tours_is_4,-16.3965,F +coef_total_number_of_tours_is_5,-19.6843,F +coef_total_number_of_tours_is_6_plus,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_1,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_2,-0.8887,F +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-2.3343,F +coef_number_of_mandatory_tours_and_tour_frequency_is_4,-2.3343,F +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-2.3343,F +coef_number_of_joint_tours_and_tour_frequency_is_0,0,T +coef_number_of_joint_tours_and_tour_frequency_is_1,0,T +coef_number_of_joint_tours_and_tour_frequency_is_2,0,T +coef_number_of_joint_tours_and_tour_frequency_is_3,0,T +coef_number_of_joint_tours_and_tour_frequency_is_4,0,T +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,0,T +coef_number_of_joint_shopping_tours,0,T +coef_number_of_joint_maintenance_tours,0,T +coef_number_of_joint_eating_out_tours,-0.5866,F +coef_number_of_joint_visit_tours,0,T +coef_number_of_joint_discretionary_tours,0,T +coef_logged_maximum_residual_window_tour_frequency_is_0,0,T +coef_logged_maximum_residual_window_tour_frequency_is_1,1.2562,F +coef_logged_maximum_residual_window_tour_frequency_is_2,1.2868,F +coef_logged_maximum_residual_window_tour_frequency_is_3,1.3993,F +coef_logged_maximum_residual_window_tour_frequency_is_4,1.3993,F +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.3993,F +coef_mediumlow_income_group_and_tour_frequency_is_1,0.4981,F +coef_mediumlow_income_group_and_tour_frequency_is_2,0.8345,F +coef_mediumlow_income_group_and_tour_frequency_is_3,1.0213,F +coef_mediumlow_income_group_and_tour_frequency_is_4,1.0213,F +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,1.0213,F +coef_mediumhigh_income_group_and_tour_frequency_is_1,0.4981,F +coef_mediumhigh_income_group_and_tour_frequency_is_2,0.8345,F +coef_mediumhigh_income_group_and_tour_frequency_is_3,1.0213,F +coef_mediumhigh_income_group_and_tour_frequency_is_4,1.0213,F +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.0213,F +coef_high_income_group_and_tour_frequency_is_1,0.5189,F +coef_high_income_group_and_tour_frequency_is_2,1.1336,F +coef_high_income_group_and_tour_frequency_is_3,1.3899,F +coef_high_income_group_and_tour_frequency_is_4,1.3899,F +coef_high_income_group_and_tour_frequency_is_5_plus,1.3899,F +coef_mediumlow_income_group_and_shopping_tour,0,T +coef_mediumhigh_income_group_and_shopping_tour,0,T +coef_high_income_group_and_shopping_tour,0,T +coef_mediumlow_income_group_and_maintenance_tour,0,T +coef_mediumhigh_income_group_and_maintenance_tour,0,T +coef_high_income_group_and_maintenance_tour,0,T +coef_mediumlow_income_group_and_eating_out_tour,0,T +coef_mediumhigh_income_group_and_eating_out_tour,0.5581,F +coef_high_income_group_and_eating_out_tour,0.5581,F +coef_mediumlow_income_group_and_discretionary_tour,0,T +coef_mediumhigh_income_group_and_discretionary_tour,0.2565,F +coef_high_income_group_and_discretionary_tour,0.2565,F +coef_mediumlow_income_group_and_visiting_tour,0,T +coef_mediumhigh_income_group_and_visiting_tour,-0.2423,F +coef_high_income_group_and_visiting_tour,-0.2423,F +coef_female_and_tour_frequency_is_1,-0.0766,F +coef_female_and_tour_frequency_is_2,-0.1062,F +coef_female_and_tour_frequency_is_3,-0.3274,F +coef_female_and_tour_frequency_is_4,-0.3274,F +coef_female_and_tour_frequency_is_5,-0.3274,F +coef_female_and_escorting_tour,0.1824,F +coef_female_and_shopping_tour,0,T +coef_female_and_maintenance_tour,0,T +coef_female_and_eatingout_tour,0,T +coef_female_and_discretionary_tour,0,T +coef_zero_car_ownership_and_tour_frequency_is_1,-0.3486,F +coef_zero_car_ownership_and_tour_frequency_is_2,-0.3486,F +coef_zero_car_ownership_and_tour_frequency_is_3,-0.3486,F +coef_zero_car_ownership_and_tour_frequency_is_4,-0.3486,F +coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.3486,F +coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.1304,F +coef_car_surplus_vs_workers_and_tour_frequency_is_2,0.1304,F +coef_car_surplus_vs_workers_and_tour_frequency_is_3,0.1304,F +coef_car_surplus_vs_workers_and_tour_frequency_is_4,0.1304,F +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0.1304,F +coef_presence_of_non_worker_and_tour_frequency_is_1,0,T +coef_presence_of_non_worker_and_tour_frequency_is_2,0,T +coef_presence_of_non_worker_and_tour_frequency_is_3,0,T +coef_presence_of_non_worker_and_tour_frequency_is_4,0,T +coef_presence_of_non_worker_and_tour_frequency_is_5,0,T +coef_presence_of_retiree_and_tour_frequency_is_1,0,T +coef_presence_of_retiree_and_tour_frequency_is_2,0,T +coef_presence_of_retiree_and_tour_frequency_is_3,0,T +coef_presence_of_retiree_and_tour_frequency_is_4,0,T +coef_presence_of_retiree_and_tour_frequency_is_5,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_full_time_worker_and_escorting_tour,0,T +coef_presence_of_part_time_worker_and_escorting_tour,0,T +coef_presence_of_non_worker_and_escorting_tour,-0.4815,F +coef_presence_of_retiree_and_escorting_tour,-0.808,F +coef_presence_of_university_student_and_escorting_tour,0,T +coef_presence_of_driving_school_kid_and_escorting_tour,0.3601,F +coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.3974,F +coef_presence_of_pre_school_kid_and_escorting_tour,0.6842,F +coef_at_home_pre_driving_school_kid_and_escorting_tour,-0.2746,F +coef_at_home_pre_school_kid_and_escorting_tour,-1.5675,F +coef_presence_of_full_time_worker_and_shopping_tour,-0.3059,F +coef_presence_of_part_time_worker_and_shopping_tour,-0.1541,F +coef_presence_of_non_worker_and_shopping_tour,-0.416,F +coef_presence_of_retiree_and_shopping_tour,0,T +coef_presence_of_university_student_and_shopping_tour,0,T +coef_presence_of_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_school_kid_and_shopping_tour,-0.208,F +coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T +coef_at_home_pre_school_kid_and_shopping_tour,0,T +coef_presence_of_full_time_worker_and_maintenance_tour,-0.1685,F +coef_presence_of_part_time_worker_and_maintenance_tour,-0.1584,F +coef_presence_of_non_worker_and_maintenance_tour,-0.3237,F +coef_presence_of_retiree_and_maintenance_tour,0,T +coef_presence_of_university_student_and_maintenance_tour,0,T +coef_presence_of_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_school_kid_and_maintenance_tour,0,T +coef_presence_of_full_time_worker_and_eating_out_tour,-0.3571,F +coef_presence_of_part_time_worker_and_eating_out_tour,0,T +coef_presence_of_non_worker_and_eating_out_tour,-0.2014,F +coef_presence_of_retiree_and_eating_out_tour,-0.5708,F +coef_presence_of_university_student_and_eating_out_tour,0,T +coef_presence_of_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_school_kid_and_eating_out_tour,-0.4225,F +coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_school_kid_and_eating_out_tour,0,T +coef_presence_of_full_time_worker_and_discretionary_tour,-0.667,F +coef_presence_of_part_time_worker_and_discretionary_tour,-0.2102,F +coef_presence_of_non_worker_and_discretionary_tour,-0.4281,F +coef_presence_of_retiree_and_discretionary_tour,-0.9104,F +coef_presence_of_university_student_and_discretionary_tour,-0.8551,F +coef_presence_of_driving_school_kid_and_discretionary_tour,-0.3963,F +coef_presence_of_pre_driving_school_kid_and_discretionary_tour,-0.3959,F +coef_presence_of_pre_school_kid_and_discretionary_tour,-0.5081,F +coef_at_home_pre_driving_school_kid_and_discretionary_tour,-0.4703,F +coef_at_home_pre_school_kid_and_discretionary_tour,-0.4703,F +coef_walk_access_to_retail_and_tour_frequency_is_1,0,T +coef_walk_access_to_retail_and_tour_frequency_is_2,0,T +coef_walk_access_to_retail_and_tour_frequency_is_3,0,T +coef_walk_access_to_retail_and_tour_frequency_is_4,0,T +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_transit_access_to_retail_and_tour_frequency_is_1,0.0226,F +coef_transit_access_to_retail_and_tour_frequency_is_2,0.0226,F +coef_transit_access_to_retail_and_tour_frequency_is_3,0.0226,F +coef_transit_access_to_retail_and_tour_frequency_is_4,0.0226,F +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0.0226,F +coef_auto_access_to_retail_and_tour_frequency_is_1,0,T +coef_auto_access_to_retail_and_tour_frequency_is_2,0,T +coef_auto_access_to_retail_and_tour_frequency_is_3,0,T +coef_auto_access_to_retail_and_tour_frequency_is_4,0,T +coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_walk_access_to_retail_and_escorting,0.0451,F +coef_transit_access_to_retail_and_escorting,0,T +coef_auto_access_to_retail_and_escorting,0,T +coef_walk_access_to_retail_and_shopping,0.033,F +coef_transit_access_to_retail_and_shopping,0,T +coef_auto_access_to_retail_and_shopping,0.1067,F +coef_walk_access_to_retail_and_maintenance,0,T +coef_transit_access_to_retail_and_maintenance,0,T +coef_auto_access_to_retail_and_maintenance,0.0749,F +coef_walk_access_to_retail_and_eating_out,0.145,F +coef_transit_access_to_retail_and_eating_out,0,T +coef_auto_access_to_retail_and_eating_out,0,T +coef_walk_access_to_retail_and_discretionary,0.0567,F +coef_transit_access_to_retail_and_discretionary,0,T +coef_auto_access_to_retail_and_discretionary,0.0844,F +coef_urban_and_tour_frequency_is_1,0,T +coef_urban_and_tour_frequency_is_2,0,T +coef_urban_and_tour_frequency_is_3,0,T +coef_urban_and_tour_frequency_is_4,0,T +coef_urban_and_tour_frequency_is_5_plus,0,T +coef_urban_and_escorting_tour,-0.4316,F +coef_urban_and_shopping_tour,0,T +coef_urban_and_maintenance_tour,0,T +coef_urban_and_eatingout_tour,0,T +coef_urban_and_discretionary_tour,0,T +coef_1_escort_tour_constant,0.0298,F +coef_2_plus_escort_tours_constant,0.7402,F +coef_1_plus_shopping_tours_constant,0.4774,F +coef_1_plus_maintenance_tours_constant,0.1202,F +coef_1_plus_eating_out_tours_constant,0.0097,F +coef_1_plus_visting_tours_constant,0.0522,F +coef_1_plus_other_discretionary_tours_constant,0.7412,F coef_0_auto_household_and_escorting_tour,-2,T \ No newline at end of file diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv index 86b81420d1..3fe9f45ae5 --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv +++ b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_NONWORK.csv @@ -1,211 +1,211 @@ -coefficient_name,value,constrain -coef_escorting_tour,0,T -coef_discretionary_tour,0,T -coef_shopping_tour,0,T -coef_maintenance_tour,0,T -coef_visiting_or_social_tour,0,T -coef_eating_out_tour,0,T -coef_total_number_of_tours_is_0_no_prior_tours,-999,T -coef_total_number_of_tours_is_0_prior_tours,0,T -coef_total_number_of_tours_is_1,-8.9791,F -coef_total_number_of_tours_is_2,-12.0248,F -coef_total_number_of_tours_is_3,-14.8516,F -coef_total_number_of_tours_is_4,-17.7037,F -coef_total_number_of_tours_is_5,-999,T -coef_total_number_of_tours_is_6_plus,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.6766,F -coef_number_of_mandatory_tours_and_tour_frequency_is_2,-1.0518,F -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-1.0518,F -coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_0,0,T -coef_number_of_joint_tours_and_tour_frequency_is_1,-0.1699,F -coef_number_of_joint_tours_and_tour_frequency_is_2,-0.4285,F -coef_number_of_joint_tours_and_tour_frequency_is_3,-0.6551,F -coef_number_of_joint_tours_and_tour_frequency_is_4,-1.0411,F -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-1.0411,F -coef_number_of_joint_shopping_tours,-0.2391,F -coef_number_of_joint_maintenance_tours,0,T -coef_number_of_joint_eating_out_tours,-0.7727,F -coef_number_of_joint_visit_tours,0,T -coef_number_of_joint_discretionary_tours,0,T -coef_logged_maximum_residual_window_tour_frequency_is_0,0,T -coef_logged_maximum_residual_window_tour_frequency_is_1,1.7637,F -coef_logged_maximum_residual_window_tour_frequency_is_2,1.7928,F -coef_logged_maximum_residual_window_tour_frequency_is_3,1.7928,F -coef_logged_maximum_residual_window_tour_frequency_is_4,1.7928,F -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.7928,F -coef_mediumlow_income_group_and_tour_frequency_is_1,0.5709,F -coef_mediumlow_income_group_and_tour_frequency_is_2,0.8315,F -coef_mediumlow_income_group_and_tour_frequency_is_3,0.8315,F -coef_mediumlow_income_group_and_tour_frequency_is_4,0.8315,F -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0.8315,F -coef_mediumhigh_income_group_and_tour_frequency_is_1,0.7426,F -coef_mediumhigh_income_group_and_tour_frequency_is_2,0.8546,F -coef_mediumhigh_income_group_and_tour_frequency_is_3,1.0792,F -coef_mediumhigh_income_group_and_tour_frequency_is_4,1.0792,F -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.0792,F -coef_high_income_group_and_tour_frequency_is_1,1.0633,F -coef_high_income_group_and_tour_frequency_is_2,1.0633,F -coef_high_income_group_and_tour_frequency_is_3,1.7742,F -coef_high_income_group_and_tour_frequency_is_4,2.3941,F -coef_high_income_group_and_tour_frequency_is_5_plus,2.3941,F -coef_mediumlow_income_group_and_shopping_tour,0.7734,F -coef_mediumhigh_income_group_and_shopping_tour,0.8906,F -coef_high_income_group_and_shopping_tour,0.9776,F -coef_mediumlow_income_group_and_maintenance_tour,0,T -coef_mediumhigh_income_group_and_maintenance_tour,0,T -coef_high_income_group_and_maintenance_tour,0,T -coef_mediumlow_income_group_and_eating_out_tour,0.2766,F -coef_mediumhigh_income_group_and_eating_out_tour,0.4631,F -coef_high_income_group_and_eating_out_tour,0.7086,F -coef_mediumlow_income_group_and_discretionary_tour,0.1707,F -coef_mediumhigh_income_group_and_discretionary_tour,0.5009,F -coef_high_income_group_and_discretionary_tour,0.8846,F -coef_mediumlow_income_group_and_visiting_tour,-0.267,F -coef_mediumhigh_income_group_and_visiting_tour,-0.267,F -coef_high_income_group_and_visiting_tour,-0.9449,F -coef_female_and_tour_frequency_is_1,0.3902,F -coef_female_and_tour_frequency_is_2,0.5323,F -coef_female_and_tour_frequency_is_3,0.7452,F -coef_female_and_tour_frequency_is_4,1.1294,F -coef_female_and_tour_frequency_is_5,1.1294,F -coef_female_and_escorting_tour,0,T -coef_female_and_shopping_tour,0,T -coef_female_and_maintenance_tour,-0.2464,F -coef_female_and_eatingout_tour,0,T -coef_female_and_discretionary_tour,0,T -coef_zero_car_ownership_and_tour_frequency_is_1,-0.3623,F -coef_zero_car_ownership_and_tour_frequency_is_2,-1.272,F -coef_zero_car_ownership_and_tour_frequency_is_3,-1.9307,F -coef_zero_car_ownership_and_tour_frequency_is_4,-1.9307,F -coef_zero_car_ownership_and_tour_frequency_is_5_plus,-1.9307,F -coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.3623,F -coef_car_shortage_vs_workers_and_tour_frequency_is_2,-1.272,F -coef_car_shortage_vs_workers_and_tour_frequency_is_3,-1.9307,F -coef_car_shortage_vs_workers_and_tour_frequency_is_4,-1.9307,F -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-1.9307,F -coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.7738,F -coef_car_surplus_vs_workers_and_tour_frequency_is_2,0.7738,F -coef_car_surplus_vs_workers_and_tour_frequency_is_3,0.7738,F -coef_car_surplus_vs_workers_and_tour_frequency_is_4,0.7738,F -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0.7738,F -coef_presence_of_non_worker_and_tour_frequency_is_1,-0.3763,F -coef_presence_of_non_worker_and_tour_frequency_is_2,-0.719,F -coef_presence_of_non_worker_and_tour_frequency_is_3,-1.0229,F -coef_presence_of_non_worker_and_tour_frequency_is_4,-1.0229,F -coef_presence_of_non_worker_and_tour_frequency_is_5,-1.0229,F -coef_presence_of_retiree_and_tour_frequency_is_1,-0.464,F -coef_presence_of_retiree_and_tour_frequency_is_2,-0.4795,F -coef_presence_of_retiree_and_tour_frequency_is_3,-0.4795,F -coef_presence_of_retiree_and_tour_frequency_is_4,-0.4795,F -coef_presence_of_retiree_and_tour_frequency_is_5,-0.4795,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.7161,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-0.7161,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-0.7161,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-0.7161,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.7161,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0.1486,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0.484,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0.484,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0.484,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0.484,F -coef_presence_of_full_time_worker_and_escorting_tour,0.3947,F -coef_presence_of_part_time_worker_and_escorting_tour,-0.5861,F -coef_presence_of_non_worker_and_escorting_tour,0,T -coef_presence_of_retiree_and_escorting_tour,0,T -coef_presence_of_university_student_and_escorting_tour,0,T -coef_presence_of_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.3773,F -coef_presence_of_pre_school_kid_and_escorting_tour,0.7194,F -coef_at_home_pre_driving_school_kid_and_escorting_tour,-1.148,F -coef_at_home_pre_school_kid_and_escorting_tour,-0.1373,F -coef_presence_of_full_time_worker_and_shopping_tour,0,T -coef_presence_of_part_time_worker_and_shopping_tour,0,T -coef_presence_of_non_worker_and_shopping_tour,0,T -coef_presence_of_retiree_and_shopping_tour,0,T -coef_presence_of_university_student_and_shopping_tour,0,T -coef_presence_of_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_school_kid_and_shopping_tour,0,T -coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T -coef_at_home_pre_school_kid_and_shopping_tour,0,T -coef_presence_of_full_time_worker_and_maintenance_tour,0,T -coef_presence_of_part_time_worker_and_maintenance_tour,0,T -coef_presence_of_non_worker_and_maintenance_tour,0,T -coef_presence_of_retiree_and_maintenance_tour,0,T -coef_presence_of_university_student_and_maintenance_tour,0,T -coef_presence_of_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_school_kid_and_maintenance_tour,0,T -coef_presence_of_full_time_worker_and_eating_out_tour,-0.4667,F -coef_presence_of_part_time_worker_and_eating_out_tour,0,T -coef_presence_of_non_worker_and_eating_out_tour,-0.4976,F -coef_presence_of_retiree_and_eating_out_tour,-0.6911,F -coef_presence_of_university_student_and_eating_out_tour,0,T -coef_presence_of_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_driving_school_kid_and_eating_out_tour,-0.3926,F -coef_at_home_pre_school_kid_and_eating_out_tour,-0.3926,F -coef_presence_of_full_time_worker_and_discretionary_tour,-0.3545,F -coef_presence_of_part_time_worker_and_discretionary_tour,-0.3545,F -coef_presence_of_non_worker_and_discretionary_tour,0,T -coef_presence_of_retiree_and_discretionary_tour,0,T -coef_presence_of_university_student_and_discretionary_tour,0,T -coef_presence_of_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_school_kid_and_discretionary_tour,0,T -coef_walk_access_to_retail_and_tour_frequency_is_1,0.0713,F -coef_walk_access_to_retail_and_tour_frequency_is_2,0.1256,F -coef_walk_access_to_retail_and_tour_frequency_is_3,0.1508,F -coef_walk_access_to_retail_and_tour_frequency_is_4,0.1508,F -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.1508,F -coef_transit_access_to_retail_and_tour_frequency_is_1,0,T -coef_transit_access_to_retail_and_tour_frequency_is_2,0,T -coef_transit_access_to_retail_and_tour_frequency_is_3,0,T -coef_transit_access_to_retail_and_tour_frequency_is_4,0,T -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_auto_access_to_retail_and_tour_frequency_is_1,0,T -coef_auto_access_to_retail_and_tour_frequency_is_2,0,T -coef_auto_access_to_retail_and_tour_frequency_is_3,0,T -coef_auto_access_to_retail_and_tour_frequency_is_4,0,T -coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_walk_access_to_retail_and_escorting,0,T -coef_transit_access_to_retail_and_escorting,0,T -coef_auto_access_to_retail_and_escorting,0,T -coef_walk_access_to_retail_and_shopping,0.0598,F -coef_transit_access_to_retail_and_shopping,0,T -coef_auto_access_to_retail_and_shopping,0,T -coef_walk_access_to_retail_and_maintenance,0,T -coef_transit_access_to_retail_and_maintenance,0,T -coef_auto_access_to_retail_and_maintenance,0.0956,F -coef_walk_access_to_retail_and_eating_out,0,T -coef_transit_access_to_retail_and_eating_out,0,T -coef_auto_access_to_retail_and_eating_out,0,T -coef_walk_access_to_retail_and_discretionary,0.0772,F -coef_transit_access_to_retail_and_discretionary,0,T -coef_auto_access_to_retail_and_discretionary,0,T -coef_urban_and_tour_frequency_is_1,0,T -coef_urban_and_tour_frequency_is_2,0,T -coef_urban_and_tour_frequency_is_3,0,T -coef_urban_and_tour_frequency_is_4,0,T -coef_urban_and_tour_frequency_is_5_plus,0,T -coef_urban_and_escorting_tour,0,T -coef_urban_and_shopping_tour,0,T -coef_urban_and_maintenance_tour,0,T -coef_urban_and_eatingout_tour,0,T -coef_urban_and_discretionary_tour,0,T -coef_1_escort_tour_constant,-0.0629,F -coef_2_plus_escort_tours_constant,0.9273,F -coef_1_plus_shopping_tours_constant,0.4683,F -coef_1_plus_maintenance_tours_constant,-0.0653,F -coef_1_plus_eating_out_tours_constant,-0.1429,F -coef_1_plus_visting_tours_constant,-0.1272,F -coef_1_plus_other_discretionary_tours_constant,0.3334,F +coefficient_name,value,constrain +coef_escorting_tour,0,T +coef_discretionary_tour,0,T +coef_shopping_tour,0,T +coef_maintenance_tour,0,T +coef_visiting_or_social_tour,0,T +coef_eating_out_tour,0,T +coef_total_number_of_tours_is_0_no_prior_tours,-999,T +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,-8.9791,F +coef_total_number_of_tours_is_2,-12.0248,F +coef_total_number_of_tours_is_3,-14.8516,F +coef_total_number_of_tours_is_4,-17.7037,F +coef_total_number_of_tours_is_5,-999,T +coef_total_number_of_tours_is_6_plus,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.6766,F +coef_number_of_mandatory_tours_and_tour_frequency_is_2,-1.0518,F +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-1.0518,F +coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_0,0,T +coef_number_of_joint_tours_and_tour_frequency_is_1,-0.1699,F +coef_number_of_joint_tours_and_tour_frequency_is_2,-0.4285,F +coef_number_of_joint_tours_and_tour_frequency_is_3,-0.6551,F +coef_number_of_joint_tours_and_tour_frequency_is_4,-1.0411,F +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-1.0411,F +coef_number_of_joint_shopping_tours,-0.2391,F +coef_number_of_joint_maintenance_tours,0,T +coef_number_of_joint_eating_out_tours,-0.7727,F +coef_number_of_joint_visit_tours,0,T +coef_number_of_joint_discretionary_tours,0,T +coef_logged_maximum_residual_window_tour_frequency_is_0,0,T +coef_logged_maximum_residual_window_tour_frequency_is_1,1.7637,F +coef_logged_maximum_residual_window_tour_frequency_is_2,1.7928,F +coef_logged_maximum_residual_window_tour_frequency_is_3,1.7928,F +coef_logged_maximum_residual_window_tour_frequency_is_4,1.7928,F +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.7928,F +coef_mediumlow_income_group_and_tour_frequency_is_1,0.5709,F +coef_mediumlow_income_group_and_tour_frequency_is_2,0.8315,F +coef_mediumlow_income_group_and_tour_frequency_is_3,0.8315,F +coef_mediumlow_income_group_and_tour_frequency_is_4,0.8315,F +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0.8315,F +coef_mediumhigh_income_group_and_tour_frequency_is_1,0.7426,F +coef_mediumhigh_income_group_and_tour_frequency_is_2,0.8546,F +coef_mediumhigh_income_group_and_tour_frequency_is_3,1.0792,F +coef_mediumhigh_income_group_and_tour_frequency_is_4,1.0792,F +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.0792,F +coef_high_income_group_and_tour_frequency_is_1,1.0633,F +coef_high_income_group_and_tour_frequency_is_2,1.0633,F +coef_high_income_group_and_tour_frequency_is_3,1.7742,F +coef_high_income_group_and_tour_frequency_is_4,2.3941,F +coef_high_income_group_and_tour_frequency_is_5_plus,2.3941,F +coef_mediumlow_income_group_and_shopping_tour,0.7734,F +coef_mediumhigh_income_group_and_shopping_tour,0.8906,F +coef_high_income_group_and_shopping_tour,0.9776,F +coef_mediumlow_income_group_and_maintenance_tour,0,T +coef_mediumhigh_income_group_and_maintenance_tour,0,T +coef_high_income_group_and_maintenance_tour,0,T +coef_mediumlow_income_group_and_eating_out_tour,0.2766,F +coef_mediumhigh_income_group_and_eating_out_tour,0.4631,F +coef_high_income_group_and_eating_out_tour,0.7086,F +coef_mediumlow_income_group_and_discretionary_tour,0.1707,F +coef_mediumhigh_income_group_and_discretionary_tour,0.5009,F +coef_high_income_group_and_discretionary_tour,0.8846,F +coef_mediumlow_income_group_and_visiting_tour,-0.267,F +coef_mediumhigh_income_group_and_visiting_tour,-0.267,F +coef_high_income_group_and_visiting_tour,-0.9449,F +coef_female_and_tour_frequency_is_1,0.3902,F +coef_female_and_tour_frequency_is_2,0.5323,F +coef_female_and_tour_frequency_is_3,0.7452,F +coef_female_and_tour_frequency_is_4,1.1294,F +coef_female_and_tour_frequency_is_5,1.1294,F +coef_female_and_escorting_tour,0,T +coef_female_and_shopping_tour,0,T +coef_female_and_maintenance_tour,-0.2464,F +coef_female_and_eatingout_tour,0,T +coef_female_and_discretionary_tour,0,T +coef_zero_car_ownership_and_tour_frequency_is_1,-0.3623,F +coef_zero_car_ownership_and_tour_frequency_is_2,-1.272,F +coef_zero_car_ownership_and_tour_frequency_is_3,-1.9307,F +coef_zero_car_ownership_and_tour_frequency_is_4,-1.9307,F +coef_zero_car_ownership_and_tour_frequency_is_5_plus,-1.9307,F +coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.3623,F +coef_car_shortage_vs_workers_and_tour_frequency_is_2,-1.272,F +coef_car_shortage_vs_workers_and_tour_frequency_is_3,-1.9307,F +coef_car_shortage_vs_workers_and_tour_frequency_is_4,-1.9307,F +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-1.9307,F +coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.7738,F +coef_car_surplus_vs_workers_and_tour_frequency_is_2,0.7738,F +coef_car_surplus_vs_workers_and_tour_frequency_is_3,0.7738,F +coef_car_surplus_vs_workers_and_tour_frequency_is_4,0.7738,F +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0.7738,F +coef_presence_of_non_worker_and_tour_frequency_is_1,-0.3763,F +coef_presence_of_non_worker_and_tour_frequency_is_2,-0.719,F +coef_presence_of_non_worker_and_tour_frequency_is_3,-1.0229,F +coef_presence_of_non_worker_and_tour_frequency_is_4,-1.0229,F +coef_presence_of_non_worker_and_tour_frequency_is_5,-1.0229,F +coef_presence_of_retiree_and_tour_frequency_is_1,-0.464,F +coef_presence_of_retiree_and_tour_frequency_is_2,-0.4795,F +coef_presence_of_retiree_and_tour_frequency_is_3,-0.4795,F +coef_presence_of_retiree_and_tour_frequency_is_4,-0.4795,F +coef_presence_of_retiree_and_tour_frequency_is_5,-0.4795,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.7161,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-0.7161,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-0.7161,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-0.7161,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.7161,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0.1486,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0.484,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0.484,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0.484,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0.484,F +coef_presence_of_full_time_worker_and_escorting_tour,0.3947,F +coef_presence_of_part_time_worker_and_escorting_tour,-0.5861,F +coef_presence_of_non_worker_and_escorting_tour,0,T +coef_presence_of_retiree_and_escorting_tour,0,T +coef_presence_of_university_student_and_escorting_tour,0,T +coef_presence_of_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.3773,F +coef_presence_of_pre_school_kid_and_escorting_tour,0.7194,F +coef_at_home_pre_driving_school_kid_and_escorting_tour,-1.148,F +coef_at_home_pre_school_kid_and_escorting_tour,-0.1373,F +coef_presence_of_full_time_worker_and_shopping_tour,0,T +coef_presence_of_part_time_worker_and_shopping_tour,0,T +coef_presence_of_non_worker_and_shopping_tour,0,T +coef_presence_of_retiree_and_shopping_tour,0,T +coef_presence_of_university_student_and_shopping_tour,0,T +coef_presence_of_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_school_kid_and_shopping_tour,0,T +coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T +coef_at_home_pre_school_kid_and_shopping_tour,0,T +coef_presence_of_full_time_worker_and_maintenance_tour,0,T +coef_presence_of_part_time_worker_and_maintenance_tour,0,T +coef_presence_of_non_worker_and_maintenance_tour,0,T +coef_presence_of_retiree_and_maintenance_tour,0,T +coef_presence_of_university_student_and_maintenance_tour,0,T +coef_presence_of_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_school_kid_and_maintenance_tour,0,T +coef_presence_of_full_time_worker_and_eating_out_tour,-0.4667,F +coef_presence_of_part_time_worker_and_eating_out_tour,0,T +coef_presence_of_non_worker_and_eating_out_tour,-0.4976,F +coef_presence_of_retiree_and_eating_out_tour,-0.6911,F +coef_presence_of_university_student_and_eating_out_tour,0,T +coef_presence_of_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_driving_school_kid_and_eating_out_tour,-0.3926,F +coef_at_home_pre_school_kid_and_eating_out_tour,-0.3926,F +coef_presence_of_full_time_worker_and_discretionary_tour,-0.3545,F +coef_presence_of_part_time_worker_and_discretionary_tour,-0.3545,F +coef_presence_of_non_worker_and_discretionary_tour,0,T +coef_presence_of_retiree_and_discretionary_tour,0,T +coef_presence_of_university_student_and_discretionary_tour,0,T +coef_presence_of_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_school_kid_and_discretionary_tour,0,T +coef_walk_access_to_retail_and_tour_frequency_is_1,0.0713,F +coef_walk_access_to_retail_and_tour_frequency_is_2,0.1256,F +coef_walk_access_to_retail_and_tour_frequency_is_3,0.1508,F +coef_walk_access_to_retail_and_tour_frequency_is_4,0.1508,F +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.1508,F +coef_transit_access_to_retail_and_tour_frequency_is_1,0,T +coef_transit_access_to_retail_and_tour_frequency_is_2,0,T +coef_transit_access_to_retail_and_tour_frequency_is_3,0,T +coef_transit_access_to_retail_and_tour_frequency_is_4,0,T +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_auto_access_to_retail_and_tour_frequency_is_1,0,T +coef_auto_access_to_retail_and_tour_frequency_is_2,0,T +coef_auto_access_to_retail_and_tour_frequency_is_3,0,T +coef_auto_access_to_retail_and_tour_frequency_is_4,0,T +coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_walk_access_to_retail_and_escorting,0,T +coef_transit_access_to_retail_and_escorting,0,T +coef_auto_access_to_retail_and_escorting,0,T +coef_walk_access_to_retail_and_shopping,0.0598,F +coef_transit_access_to_retail_and_shopping,0,T +coef_auto_access_to_retail_and_shopping,0,T +coef_walk_access_to_retail_and_maintenance,0,T +coef_transit_access_to_retail_and_maintenance,0,T +coef_auto_access_to_retail_and_maintenance,0.0956,F +coef_walk_access_to_retail_and_eating_out,0,T +coef_transit_access_to_retail_and_eating_out,0,T +coef_auto_access_to_retail_and_eating_out,0,T +coef_walk_access_to_retail_and_discretionary,0.0772,F +coef_transit_access_to_retail_and_discretionary,0,T +coef_auto_access_to_retail_and_discretionary,0,T +coef_urban_and_tour_frequency_is_1,0,T +coef_urban_and_tour_frequency_is_2,0,T +coef_urban_and_tour_frequency_is_3,0,T +coef_urban_and_tour_frequency_is_4,0,T +coef_urban_and_tour_frequency_is_5_plus,0,T +coef_urban_and_escorting_tour,0,T +coef_urban_and_shopping_tour,0,T +coef_urban_and_maintenance_tour,0,T +coef_urban_and_eatingout_tour,0,T +coef_urban_and_discretionary_tour,0,T +coef_1_escort_tour_constant,-0.0629,F +coef_2_plus_escort_tours_constant,0.9273,F +coef_1_plus_shopping_tours_constant,0.4683,F +coef_1_plus_maintenance_tours_constant,-0.0653,F +coef_1_plus_eating_out_tours_constant,-0.1429,F +coef_1_plus_visting_tours_constant,-0.1272,F +coef_1_plus_other_discretionary_tours_constant,0.3334,F coef_0_auto_household_and_escorting_tour,-2,T \ No newline at end of file diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv index c015b2108f..18f2e3b888 --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv +++ b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PART.csv @@ -1,211 +1,211 @@ -coefficient_name,value,constrain -coef_escorting_tour,0,T -coef_discretionary_tour,0,T -coef_shopping_tour,0,T -coef_maintenance_tour,0,T -coef_visiting_or_social_tour,0,T -coef_eating_out_tour,0,T -coef_total_number_of_tours_is_0_no_prior_tours,-999,T -coef_total_number_of_tours_is_0_prior_tours,0,T -coef_total_number_of_tours_is_1,-7.6391,F -coef_total_number_of_tours_is_2,-10.4557,F -coef_total_number_of_tours_is_3,-14.0176,F -coef_total_number_of_tours_is_4,-16.9717,F -coef_total_number_of_tours_is_5,-999,T -coef_total_number_of_tours_is_6_plus,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.239,F -coef_number_of_mandatory_tours_and_tour_frequency_is_2,-1.8208,F -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-2.5923,F -coef_number_of_mandatory_tours_and_tour_frequency_is_4,-2.5923,F -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-2.5923,F -coef_number_of_joint_tours_and_tour_frequency_is_0,0,T -coef_number_of_joint_tours_and_tour_frequency_is_1,0,T -coef_number_of_joint_tours_and_tour_frequency_is_2,-1.1986,F -coef_number_of_joint_tours_and_tour_frequency_is_3,-1.1986,F -coef_number_of_joint_tours_and_tour_frequency_is_4,-1.1986,F -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_shopping_tours,0,T -coef_number_of_joint_maintenance_tours,0,T -coef_number_of_joint_eating_out_tours,0,T -coef_number_of_joint_visit_tours,0,T -coef_number_of_joint_discretionary_tours,0,T -coef_logged_maximum_residual_window_tour_frequency_is_0,0,T -coef_logged_maximum_residual_window_tour_frequency_is_1,1.5748,F -coef_logged_maximum_residual_window_tour_frequency_is_2,2.0026,F -coef_logged_maximum_residual_window_tour_frequency_is_3,2.0026,F -coef_logged_maximum_residual_window_tour_frequency_is_4,2.0026,F -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,2.0026,F -coef_mediumlow_income_group_and_tour_frequency_is_1,0.5981,F -coef_mediumlow_income_group_and_tour_frequency_is_2,0.9178,F -coef_mediumlow_income_group_and_tour_frequency_is_3,1.7539,F -coef_mediumlow_income_group_and_tour_frequency_is_4,1.7539,F -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,1.7539,F -coef_mediumhigh_income_group_and_tour_frequency_is_1,0.8682,F -coef_mediumhigh_income_group_and_tour_frequency_is_2,1.5362,F -coef_mediumhigh_income_group_and_tour_frequency_is_3,1.9331,F -coef_mediumhigh_income_group_and_tour_frequency_is_4,1.9331,F -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.9331,F -coef_high_income_group_and_tour_frequency_is_1,0.8682,F -coef_high_income_group_and_tour_frequency_is_2,1.5362,F -coef_high_income_group_and_tour_frequency_is_3,1.9331,F -coef_high_income_group_and_tour_frequency_is_4,1.9331,F -coef_high_income_group_and_tour_frequency_is_5_plus,1.9331,F -coef_mediumlow_income_group_and_shopping_tour,0.4421,F -coef_mediumhigh_income_group_and_shopping_tour,0.4421,F -coef_high_income_group_and_shopping_tour,0.7066,F -coef_mediumlow_income_group_and_maintenance_tour,0.6763,F -coef_mediumhigh_income_group_and_maintenance_tour,0.6763,F -coef_high_income_group_and_maintenance_tour,0.6763,F -coef_mediumlow_income_group_and_eating_out_tour,0,T -coef_mediumhigh_income_group_and_eating_out_tour,0,T -coef_high_income_group_and_eating_out_tour,0,T -coef_mediumlow_income_group_and_discretionary_tour,0.296,F -coef_mediumhigh_income_group_and_discretionary_tour,0.296,F -coef_high_income_group_and_discretionary_tour,0.296,F -coef_mediumlow_income_group_and_visiting_tour,-0.6868,F -coef_mediumhigh_income_group_and_visiting_tour,-0.6868,F -coef_high_income_group_and_visiting_tour,-0.6868,F -coef_female_and_tour_frequency_is_1,0,T -coef_female_and_tour_frequency_is_2,0,T -coef_female_and_tour_frequency_is_3,0,T -coef_female_and_tour_frequency_is_4,0,T -coef_female_and_tour_frequency_is_5,0,T -coef_female_and_escorting_tour,0,T -coef_female_and_shopping_tour,0.4524,F -coef_female_and_maintenance_tour,0,T -coef_female_and_eatingout_tour,0,T -coef_female_and_discretionary_tour,0.3072,F -coef_zero_car_ownership_and_tour_frequency_is_1,-0.5498,F -coef_zero_car_ownership_and_tour_frequency_is_2,-0.5498,F -coef_zero_car_ownership_and_tour_frequency_is_3,-0.5498,F -coef_zero_car_ownership_and_tour_frequency_is_4,-0.5498,F -coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.5498,F -coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.5498,F -coef_car_shortage_vs_workers_and_tour_frequency_is_2,-0.5498,F -coef_car_shortage_vs_workers_and_tour_frequency_is_3,-0.5498,F -coef_car_shortage_vs_workers_and_tour_frequency_is_4,-0.5498,F -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-0.5498,F -coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T -coef_presence_of_non_worker_and_tour_frequency_is_1,0,T -coef_presence_of_non_worker_and_tour_frequency_is_2,0,T -coef_presence_of_non_worker_and_tour_frequency_is_3,0,T -coef_presence_of_non_worker_and_tour_frequency_is_4,0,T -coef_presence_of_non_worker_and_tour_frequency_is_5,0,T -coef_presence_of_retiree_and_tour_frequency_is_1,0,T -coef_presence_of_retiree_and_tour_frequency_is_2,0,T -coef_presence_of_retiree_and_tour_frequency_is_3,0,T -coef_presence_of_retiree_and_tour_frequency_is_4,0,T -coef_presence_of_retiree_and_tour_frequency_is_5,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.1559,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-0.5681,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-0.5681,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-0.5681,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.5681,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_full_time_worker_and_escorting_tour,0,T -coef_presence_of_part_time_worker_and_escorting_tour,0,T -coef_presence_of_non_worker_and_escorting_tour,-0.5263,F -coef_presence_of_retiree_and_escorting_tour,-0.7516,F -coef_presence_of_university_student_and_escorting_tour,0,T -coef_presence_of_driving_school_kid_and_escorting_tour,0.4164,F -coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.5795,F -coef_presence_of_pre_school_kid_and_escorting_tour,0.5414,F -coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T -coef_at_home_pre_school_kid_and_escorting_tour,0,T -coef_presence_of_full_time_worker_and_shopping_tour,0,T -coef_presence_of_part_time_worker_and_shopping_tour,0,T -coef_presence_of_non_worker_and_shopping_tour,0,T -coef_presence_of_retiree_and_shopping_tour,0,T -coef_presence_of_university_student_and_shopping_tour,0,T -coef_presence_of_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_school_kid_and_shopping_tour,0,T -coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T -coef_at_home_pre_school_kid_and_shopping_tour,0,T -coef_presence_of_full_time_worker_and_maintenance_tour,-0.3131,F -coef_presence_of_part_time_worker_and_maintenance_tour,-0.5621,F -coef_presence_of_non_worker_and_maintenance_tour,0,T -coef_presence_of_retiree_and_maintenance_tour,0,T -coef_presence_of_university_student_and_maintenance_tour,0,T -coef_presence_of_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_school_kid_and_maintenance_tour,0,T -coef_presence_of_full_time_worker_and_eating_out_tour,0,T -coef_presence_of_part_time_worker_and_eating_out_tour,0,T -coef_presence_of_non_worker_and_eating_out_tour,-0.6545,F -coef_presence_of_retiree_and_eating_out_tour,-1.389,F -coef_presence_of_university_student_and_eating_out_tour,-1.4318,F -coef_presence_of_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_school_kid_and_eating_out_tour,0,T -coef_presence_of_full_time_worker_and_discretionary_tour,0,T -coef_presence_of_part_time_worker_and_discretionary_tour,0,T -coef_presence_of_non_worker_and_discretionary_tour,-1.0371,F -coef_presence_of_retiree_and_discretionary_tour,0,T -coef_presence_of_university_student_and_discretionary_tour,0,T -coef_presence_of_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_school_kid_and_discretionary_tour,0,T -coef_walk_access_to_retail_and_tour_frequency_is_1,0.0899,F -coef_walk_access_to_retail_and_tour_frequency_is_2,0.1447,F -coef_walk_access_to_retail_and_tour_frequency_is_3,0.3479,F -coef_walk_access_to_retail_and_tour_frequency_is_4,0.3479,F -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.3479,F -coef_transit_access_to_retail_and_tour_frequency_is_1,0,T -coef_transit_access_to_retail_and_tour_frequency_is_2,0,T -coef_transit_access_to_retail_and_tour_frequency_is_3,0,T -coef_transit_access_to_retail_and_tour_frequency_is_4,0,T -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_auto_access_to_retail_and_tour_frequency_is_1,0,T -coef_auto_access_to_retail_and_tour_frequency_is_2,0,T -coef_auto_access_to_retail_and_tour_frequency_is_3,0,T -coef_auto_access_to_retail_and_tour_frequency_is_4,0,T -coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_walk_access_to_retail_and_escorting,0,T -coef_transit_access_to_retail_and_escorting,0,T -coef_auto_access_to_retail_and_escorting,0,T -coef_walk_access_to_retail_and_shopping,0,T -coef_transit_access_to_retail_and_shopping,0,T -coef_auto_access_to_retail_and_shopping,0,T -coef_walk_access_to_retail_and_maintenance,0,T -coef_transit_access_to_retail_and_maintenance,0,T -coef_auto_access_to_retail_and_maintenance,0,T -coef_walk_access_to_retail_and_eating_out,0,T -coef_transit_access_to_retail_and_eating_out,0,T -coef_auto_access_to_retail_and_eating_out,0,T -coef_walk_access_to_retail_and_discretionary,0,T -coef_transit_access_to_retail_and_discretionary,0,T -coef_auto_access_to_retail_and_discretionary,0,T -coef_urban_and_tour_frequency_is_1,0,T -coef_urban_and_tour_frequency_is_2,0,T -coef_urban_and_tour_frequency_is_3,0,T -coef_urban_and_tour_frequency_is_4,0,T -coef_urban_and_tour_frequency_is_5_plus,0,T -coef_urban_and_escorting_tour,-0.3929,F -coef_urban_and_shopping_tour,0,T -coef_urban_and_maintenance_tour,0,T -coef_urban_and_eatingout_tour,0,T -coef_urban_and_discretionary_tour,0,T -coef_1_escort_tour_constant,0.5272,F -coef_2_plus_escort_tours_constant,1.5987,F -coef_1_plus_shopping_tours_constant,0.7569,F -coef_1_plus_maintenance_tours_constant,0.5533,F -coef_1_plus_eating_out_tours_constant,0.6914,F -coef_1_plus_visting_tours_constant,0.1405,F -coef_1_plus_other_discretionary_tours_constant,0.7989,F +coefficient_name,value,constrain +coef_escorting_tour,0,T +coef_discretionary_tour,0,T +coef_shopping_tour,0,T +coef_maintenance_tour,0,T +coef_visiting_or_social_tour,0,T +coef_eating_out_tour,0,T +coef_total_number_of_tours_is_0_no_prior_tours,-999,T +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,-7.6391,F +coef_total_number_of_tours_is_2,-10.4557,F +coef_total_number_of_tours_is_3,-14.0176,F +coef_total_number_of_tours_is_4,-16.9717,F +coef_total_number_of_tours_is_5,-999,T +coef_total_number_of_tours_is_6_plus,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.239,F +coef_number_of_mandatory_tours_and_tour_frequency_is_2,-1.8208,F +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-2.5923,F +coef_number_of_mandatory_tours_and_tour_frequency_is_4,-2.5923,F +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-2.5923,F +coef_number_of_joint_tours_and_tour_frequency_is_0,0,T +coef_number_of_joint_tours_and_tour_frequency_is_1,0,T +coef_number_of_joint_tours_and_tour_frequency_is_2,-1.1986,F +coef_number_of_joint_tours_and_tour_frequency_is_3,-1.1986,F +coef_number_of_joint_tours_and_tour_frequency_is_4,-1.1986,F +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_shopping_tours,0,T +coef_number_of_joint_maintenance_tours,0,T +coef_number_of_joint_eating_out_tours,0,T +coef_number_of_joint_visit_tours,0,T +coef_number_of_joint_discretionary_tours,0,T +coef_logged_maximum_residual_window_tour_frequency_is_0,0,T +coef_logged_maximum_residual_window_tour_frequency_is_1,1.5748,F +coef_logged_maximum_residual_window_tour_frequency_is_2,2.0026,F +coef_logged_maximum_residual_window_tour_frequency_is_3,2.0026,F +coef_logged_maximum_residual_window_tour_frequency_is_4,2.0026,F +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,2.0026,F +coef_mediumlow_income_group_and_tour_frequency_is_1,0.5981,F +coef_mediumlow_income_group_and_tour_frequency_is_2,0.9178,F +coef_mediumlow_income_group_and_tour_frequency_is_3,1.7539,F +coef_mediumlow_income_group_and_tour_frequency_is_4,1.7539,F +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,1.7539,F +coef_mediumhigh_income_group_and_tour_frequency_is_1,0.8682,F +coef_mediumhigh_income_group_and_tour_frequency_is_2,1.5362,F +coef_mediumhigh_income_group_and_tour_frequency_is_3,1.9331,F +coef_mediumhigh_income_group_and_tour_frequency_is_4,1.9331,F +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.9331,F +coef_high_income_group_and_tour_frequency_is_1,0.8682,F +coef_high_income_group_and_tour_frequency_is_2,1.5362,F +coef_high_income_group_and_tour_frequency_is_3,1.9331,F +coef_high_income_group_and_tour_frequency_is_4,1.9331,F +coef_high_income_group_and_tour_frequency_is_5_plus,1.9331,F +coef_mediumlow_income_group_and_shopping_tour,0.4421,F +coef_mediumhigh_income_group_and_shopping_tour,0.4421,F +coef_high_income_group_and_shopping_tour,0.7066,F +coef_mediumlow_income_group_and_maintenance_tour,0.6763,F +coef_mediumhigh_income_group_and_maintenance_tour,0.6763,F +coef_high_income_group_and_maintenance_tour,0.6763,F +coef_mediumlow_income_group_and_eating_out_tour,0,T +coef_mediumhigh_income_group_and_eating_out_tour,0,T +coef_high_income_group_and_eating_out_tour,0,T +coef_mediumlow_income_group_and_discretionary_tour,0.296,F +coef_mediumhigh_income_group_and_discretionary_tour,0.296,F +coef_high_income_group_and_discretionary_tour,0.296,F +coef_mediumlow_income_group_and_visiting_tour,-0.6868,F +coef_mediumhigh_income_group_and_visiting_tour,-0.6868,F +coef_high_income_group_and_visiting_tour,-0.6868,F +coef_female_and_tour_frequency_is_1,0,T +coef_female_and_tour_frequency_is_2,0,T +coef_female_and_tour_frequency_is_3,0,T +coef_female_and_tour_frequency_is_4,0,T +coef_female_and_tour_frequency_is_5,0,T +coef_female_and_escorting_tour,0,T +coef_female_and_shopping_tour,0.4524,F +coef_female_and_maintenance_tour,0,T +coef_female_and_eatingout_tour,0,T +coef_female_and_discretionary_tour,0.3072,F +coef_zero_car_ownership_and_tour_frequency_is_1,-0.5498,F +coef_zero_car_ownership_and_tour_frequency_is_2,-0.5498,F +coef_zero_car_ownership_and_tour_frequency_is_3,-0.5498,F +coef_zero_car_ownership_and_tour_frequency_is_4,-0.5498,F +coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.5498,F +coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.5498,F +coef_car_shortage_vs_workers_and_tour_frequency_is_2,-0.5498,F +coef_car_shortage_vs_workers_and_tour_frequency_is_3,-0.5498,F +coef_car_shortage_vs_workers_and_tour_frequency_is_4,-0.5498,F +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-0.5498,F +coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T +coef_presence_of_non_worker_and_tour_frequency_is_1,0,T +coef_presence_of_non_worker_and_tour_frequency_is_2,0,T +coef_presence_of_non_worker_and_tour_frequency_is_3,0,T +coef_presence_of_non_worker_and_tour_frequency_is_4,0,T +coef_presence_of_non_worker_and_tour_frequency_is_5,0,T +coef_presence_of_retiree_and_tour_frequency_is_1,0,T +coef_presence_of_retiree_and_tour_frequency_is_2,0,T +coef_presence_of_retiree_and_tour_frequency_is_3,0,T +coef_presence_of_retiree_and_tour_frequency_is_4,0,T +coef_presence_of_retiree_and_tour_frequency_is_5,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.1559,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-0.5681,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-0.5681,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-0.5681,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.5681,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_full_time_worker_and_escorting_tour,0,T +coef_presence_of_part_time_worker_and_escorting_tour,0,T +coef_presence_of_non_worker_and_escorting_tour,-0.5263,F +coef_presence_of_retiree_and_escorting_tour,-0.7516,F +coef_presence_of_university_student_and_escorting_tour,0,T +coef_presence_of_driving_school_kid_and_escorting_tour,0.4164,F +coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.5795,F +coef_presence_of_pre_school_kid_and_escorting_tour,0.5414,F +coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T +coef_at_home_pre_school_kid_and_escorting_tour,0,T +coef_presence_of_full_time_worker_and_shopping_tour,0,T +coef_presence_of_part_time_worker_and_shopping_tour,0,T +coef_presence_of_non_worker_and_shopping_tour,0,T +coef_presence_of_retiree_and_shopping_tour,0,T +coef_presence_of_university_student_and_shopping_tour,0,T +coef_presence_of_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_school_kid_and_shopping_tour,0,T +coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T +coef_at_home_pre_school_kid_and_shopping_tour,0,T +coef_presence_of_full_time_worker_and_maintenance_tour,-0.3131,F +coef_presence_of_part_time_worker_and_maintenance_tour,-0.5621,F +coef_presence_of_non_worker_and_maintenance_tour,0,T +coef_presence_of_retiree_and_maintenance_tour,0,T +coef_presence_of_university_student_and_maintenance_tour,0,T +coef_presence_of_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_school_kid_and_maintenance_tour,0,T +coef_presence_of_full_time_worker_and_eating_out_tour,0,T +coef_presence_of_part_time_worker_and_eating_out_tour,0,T +coef_presence_of_non_worker_and_eating_out_tour,-0.6545,F +coef_presence_of_retiree_and_eating_out_tour,-1.389,F +coef_presence_of_university_student_and_eating_out_tour,-1.4318,F +coef_presence_of_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_school_kid_and_eating_out_tour,0,T +coef_presence_of_full_time_worker_and_discretionary_tour,0,T +coef_presence_of_part_time_worker_and_discretionary_tour,0,T +coef_presence_of_non_worker_and_discretionary_tour,-1.0371,F +coef_presence_of_retiree_and_discretionary_tour,0,T +coef_presence_of_university_student_and_discretionary_tour,0,T +coef_presence_of_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_school_kid_and_discretionary_tour,0,T +coef_walk_access_to_retail_and_tour_frequency_is_1,0.0899,F +coef_walk_access_to_retail_and_tour_frequency_is_2,0.1447,F +coef_walk_access_to_retail_and_tour_frequency_is_3,0.3479,F +coef_walk_access_to_retail_and_tour_frequency_is_4,0.3479,F +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.3479,F +coef_transit_access_to_retail_and_tour_frequency_is_1,0,T +coef_transit_access_to_retail_and_tour_frequency_is_2,0,T +coef_transit_access_to_retail_and_tour_frequency_is_3,0,T +coef_transit_access_to_retail_and_tour_frequency_is_4,0,T +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_auto_access_to_retail_and_tour_frequency_is_1,0,T +coef_auto_access_to_retail_and_tour_frequency_is_2,0,T +coef_auto_access_to_retail_and_tour_frequency_is_3,0,T +coef_auto_access_to_retail_and_tour_frequency_is_4,0,T +coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_walk_access_to_retail_and_escorting,0,T +coef_transit_access_to_retail_and_escorting,0,T +coef_auto_access_to_retail_and_escorting,0,T +coef_walk_access_to_retail_and_shopping,0,T +coef_transit_access_to_retail_and_shopping,0,T +coef_auto_access_to_retail_and_shopping,0,T +coef_walk_access_to_retail_and_maintenance,0,T +coef_transit_access_to_retail_and_maintenance,0,T +coef_auto_access_to_retail_and_maintenance,0,T +coef_walk_access_to_retail_and_eating_out,0,T +coef_transit_access_to_retail_and_eating_out,0,T +coef_auto_access_to_retail_and_eating_out,0,T +coef_walk_access_to_retail_and_discretionary,0,T +coef_transit_access_to_retail_and_discretionary,0,T +coef_auto_access_to_retail_and_discretionary,0,T +coef_urban_and_tour_frequency_is_1,0,T +coef_urban_and_tour_frequency_is_2,0,T +coef_urban_and_tour_frequency_is_3,0,T +coef_urban_and_tour_frequency_is_4,0,T +coef_urban_and_tour_frequency_is_5_plus,0,T +coef_urban_and_escorting_tour,-0.3929,F +coef_urban_and_shopping_tour,0,T +coef_urban_and_maintenance_tour,0,T +coef_urban_and_eatingout_tour,0,T +coef_urban_and_discretionary_tour,0,T +coef_1_escort_tour_constant,0.5272,F +coef_2_plus_escort_tours_constant,1.5987,F +coef_1_plus_shopping_tours_constant,0.7569,F +coef_1_plus_maintenance_tours_constant,0.5533,F +coef_1_plus_eating_out_tours_constant,0.6914,F +coef_1_plus_visting_tours_constant,0.1405,F +coef_1_plus_other_discretionary_tours_constant,0.7989,F coef_0_auto_household_and_escorting_tour,-2,T \ No newline at end of file diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv index 3085ba2cb0..7514ed50a1 --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv +++ b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_PRESCHOOL.csv @@ -1,211 +1,211 @@ -coefficient_name,value,constrain -coef_escorting_tour,2.491,F -coef_discretionary_tour,0.903,F -coef_shopping_tour,0,T -coef_maintenance_tour,1.022,F -coef_visiting_or_social_tour,0.769,F -coef_eating_out_tour,0,T -coef_total_number_of_tours_is_0_no_prior_tours,-999,T -coef_total_number_of_tours_is_0_prior_tours,0,T -coef_total_number_of_tours_is_1,-5.759,F -coef_total_number_of_tours_is_2,-11.517,F -coef_total_number_of_tours_is_3,-17.276,F -coef_total_number_of_tours_is_4,-23.035,F -coef_total_number_of_tours_is_5,-999,T -coef_total_number_of_tours_is_6_plus,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_1,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_2,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_3,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_4,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,0,T -coef_number_of_joint_tours_and_tour_frequency_is_0,0,T -coef_number_of_joint_tours_and_tour_frequency_is_1,0,T -coef_number_of_joint_tours_and_tour_frequency_is_2,0,T -coef_number_of_joint_tours_and_tour_frequency_is_3,0,T -coef_number_of_joint_tours_and_tour_frequency_is_4,0,T -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,0,T -coef_number_of_joint_shopping_tours,0,T -coef_number_of_joint_maintenance_tours,0,T -coef_number_of_joint_eating_out_tours,0,T -coef_number_of_joint_visit_tours,0,T -coef_number_of_joint_discretionary_tours,0,T -coef_logged_maximum_residual_window_tour_frequency_is_0,0,T -coef_logged_maximum_residual_window_tour_frequency_is_1,0,T -coef_logged_maximum_residual_window_tour_frequency_is_2,0,T -coef_logged_maximum_residual_window_tour_frequency_is_3,0,T -coef_logged_maximum_residual_window_tour_frequency_is_4,0,T -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,0,T -coef_mediumlow_income_group_and_tour_frequency_is_1,0,T -coef_mediumlow_income_group_and_tour_frequency_is_2,0,T -coef_mediumlow_income_group_and_tour_frequency_is_3,0,T -coef_mediumlow_income_group_and_tour_frequency_is_4,0,T -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_1,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_2,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_3,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_4,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0,T -coef_high_income_group_and_tour_frequency_is_1,0,T -coef_high_income_group_and_tour_frequency_is_2,0,T -coef_high_income_group_and_tour_frequency_is_3,0,T -coef_high_income_group_and_tour_frequency_is_4,0,T -coef_high_income_group_and_tour_frequency_is_5_plus,0,T -coef_mediumlow_income_group_and_shopping_tour,0,T -coef_mediumhigh_income_group_and_shopping_tour,0,T -coef_high_income_group_and_shopping_tour,0,T -coef_mediumlow_income_group_and_maintenance_tour,0,T -coef_mediumhigh_income_group_and_maintenance_tour,0,T -coef_high_income_group_and_maintenance_tour,0,T -coef_mediumlow_income_group_and_eating_out_tour,0,T -coef_mediumhigh_income_group_and_eating_out_tour,0,T -coef_high_income_group_and_eating_out_tour,0,T -coef_mediumlow_income_group_and_discretionary_tour,0,T -coef_mediumhigh_income_group_and_discretionary_tour,0,T -coef_high_income_group_and_discretionary_tour,0,T -coef_mediumlow_income_group_and_visiting_tour,0,T -coef_mediumhigh_income_group_and_visiting_tour,0,T -coef_high_income_group_and_visiting_tour,0,T -coef_female_and_tour_frequency_is_1,0,T -coef_female_and_tour_frequency_is_2,0,T -coef_female_and_tour_frequency_is_3,0,T -coef_female_and_tour_frequency_is_4,0,T -coef_female_and_tour_frequency_is_5,0,T -coef_female_and_escorting_tour,0,T -coef_female_and_shopping_tour,0,T -coef_female_and_maintenance_tour,0,T -coef_female_and_eatingout_tour,0,T -coef_female_and_discretionary_tour,0,T -coef_zero_car_ownership_and_tour_frequency_is_1,0,T -coef_zero_car_ownership_and_tour_frequency_is_2,0,T -coef_zero_car_ownership_and_tour_frequency_is_3,0,T -coef_zero_car_ownership_and_tour_frequency_is_4,0,T -coef_zero_car_ownership_and_tour_frequency_is_5_plus,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T -coef_presence_of_non_worker_and_tour_frequency_is_1,0,T -coef_presence_of_non_worker_and_tour_frequency_is_2,0,T -coef_presence_of_non_worker_and_tour_frequency_is_3,0,T -coef_presence_of_non_worker_and_tour_frequency_is_4,0,T -coef_presence_of_non_worker_and_tour_frequency_is_5,0,T -coef_presence_of_retiree_and_tour_frequency_is_1,0,T -coef_presence_of_retiree_and_tour_frequency_is_2,0,T -coef_presence_of_retiree_and_tour_frequency_is_3,0,T -coef_presence_of_retiree_and_tour_frequency_is_4,0,T -coef_presence_of_retiree_and_tour_frequency_is_5,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_full_time_worker_and_escorting_tour,-0.893,F -coef_presence_of_part_time_worker_and_escorting_tour,0,T -coef_presence_of_non_worker_and_escorting_tour,0.89,F -coef_presence_of_retiree_and_escorting_tour,0,T -coef_presence_of_university_student_and_escorting_tour,0,T -coef_presence_of_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_school_kid_and_escorting_tour,0,T -coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T -coef_at_home_pre_school_kid_and_escorting_tour,0,T -coef_presence_of_full_time_worker_and_shopping_tour,0,T -coef_presence_of_part_time_worker_and_shopping_tour,1.155,F -coef_presence_of_non_worker_and_shopping_tour,0.808,F -coef_presence_of_retiree_and_shopping_tour,0,T -coef_presence_of_university_student_and_shopping_tour,0,T -coef_presence_of_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_school_kid_and_shopping_tour,0,T -coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T -coef_at_home_pre_school_kid_and_shopping_tour,0,T -coef_presence_of_full_time_worker_and_maintenance_tour,0,T -coef_presence_of_part_time_worker_and_maintenance_tour,0,T -coef_presence_of_non_worker_and_maintenance_tour,0,T -coef_presence_of_retiree_and_maintenance_tour,0,T -coef_presence_of_university_student_and_maintenance_tour,0,T -coef_presence_of_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_school_kid_and_maintenance_tour,0,T -coef_presence_of_full_time_worker_and_eating_out_tour,0,T -coef_presence_of_part_time_worker_and_eating_out_tour,1.037,F -coef_presence_of_non_worker_and_eating_out_tour,1.157,F -coef_presence_of_retiree_and_eating_out_tour,0,T -coef_presence_of_university_student_and_eating_out_tour,0,T -coef_presence_of_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_school_kid_and_eating_out_tour,0,T -coef_presence_of_full_time_worker_and_discretionary_tour,0,T -coef_presence_of_part_time_worker_and_discretionary_tour,0,T -coef_presence_of_non_worker_and_discretionary_tour,0.791,F -coef_presence_of_retiree_and_discretionary_tour,0,T -coef_presence_of_university_student_and_discretionary_tour,0,T -coef_presence_of_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_school_kid_and_discretionary_tour,0,T -coef_walk_access_to_retail_and_tour_frequency_is_1,0,T -coef_walk_access_to_retail_and_tour_frequency_is_2,0,T -coef_walk_access_to_retail_and_tour_frequency_is_3,0,T -coef_walk_access_to_retail_and_tour_frequency_is_4,0,T -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_transit_access_to_retail_and_tour_frequency_is_1,0,T -coef_transit_access_to_retail_and_tour_frequency_is_2,0,T -coef_transit_access_to_retail_and_tour_frequency_is_3,0,T -coef_transit_access_to_retail_and_tour_frequency_is_4,0,T -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_auto_access_to_retail_and_tour_frequency_is_1,0,T -coef_auto_access_to_retail_and_tour_frequency_is_2,0,T -coef_auto_access_to_retail_and_tour_frequency_is_3,0,T -coef_auto_access_to_retail_and_tour_frequency_is_4,0,T -coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_walk_access_to_retail_and_escorting,0,T -coef_transit_access_to_retail_and_escorting,0,T -coef_auto_access_to_retail_and_escorting,0,T -coef_walk_access_to_retail_and_shopping,0,T -coef_transit_access_to_retail_and_shopping,0,T -coef_auto_access_to_retail_and_shopping,0,T -coef_walk_access_to_retail_and_maintenance,0,T -coef_transit_access_to_retail_and_maintenance,0,T -coef_auto_access_to_retail_and_maintenance,0,T -coef_walk_access_to_retail_and_eating_out,0,T -coef_transit_access_to_retail_and_eating_out,0,T -coef_auto_access_to_retail_and_eating_out,0,T -coef_walk_access_to_retail_and_discretionary,0,T -coef_transit_access_to_retail_and_discretionary,0,T -coef_auto_access_to_retail_and_discretionary,0,T -coef_urban_and_tour_frequency_is_1,0,T -coef_urban_and_tour_frequency_is_2,0,T -coef_urban_and_tour_frequency_is_3,0,T -coef_urban_and_tour_frequency_is_4,0,T -coef_urban_and_tour_frequency_is_5_plus,0,T -coef_urban_and_escorting_tour,0,T -coef_urban_and_shopping_tour,0,T -coef_urban_and_maintenance_tour,0,T -coef_urban_and_eatingout_tour,0,T -coef_urban_and_discretionary_tour,0,T -coef_1_escort_tour_constant,0.3622,F -coef_2_plus_escort_tours_constant,2.2219,F -coef_1_plus_shopping_tours_constant,1.6919,F -coef_1_plus_maintenance_tours_constant,0.6788,F -coef_1_plus_eating_out_tours_constant,0.9612,F -coef_1_plus_visting_tours_constant,0.4424,F -coef_1_plus_other_discretionary_tours_constant,1.4935,F +coefficient_name,value,constrain +coef_escorting_tour,2.491,F +coef_discretionary_tour,0.903,F +coef_shopping_tour,0,T +coef_maintenance_tour,1.022,F +coef_visiting_or_social_tour,0.769,F +coef_eating_out_tour,0,T +coef_total_number_of_tours_is_0_no_prior_tours,-999,T +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,-5.759,F +coef_total_number_of_tours_is_2,-11.517,F +coef_total_number_of_tours_is_3,-17.276,F +coef_total_number_of_tours_is_4,-23.035,F +coef_total_number_of_tours_is_5,-999,T +coef_total_number_of_tours_is_6_plus,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_1,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_2,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_3,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_4,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,0,T +coef_number_of_joint_tours_and_tour_frequency_is_0,0,T +coef_number_of_joint_tours_and_tour_frequency_is_1,0,T +coef_number_of_joint_tours_and_tour_frequency_is_2,0,T +coef_number_of_joint_tours_and_tour_frequency_is_3,0,T +coef_number_of_joint_tours_and_tour_frequency_is_4,0,T +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,0,T +coef_number_of_joint_shopping_tours,0,T +coef_number_of_joint_maintenance_tours,0,T +coef_number_of_joint_eating_out_tours,0,T +coef_number_of_joint_visit_tours,0,T +coef_number_of_joint_discretionary_tours,0,T +coef_logged_maximum_residual_window_tour_frequency_is_0,0,T +coef_logged_maximum_residual_window_tour_frequency_is_1,0,T +coef_logged_maximum_residual_window_tour_frequency_is_2,0,T +coef_logged_maximum_residual_window_tour_frequency_is_3,0,T +coef_logged_maximum_residual_window_tour_frequency_is_4,0,T +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,0,T +coef_mediumlow_income_group_and_tour_frequency_is_1,0,T +coef_mediumlow_income_group_and_tour_frequency_is_2,0,T +coef_mediumlow_income_group_and_tour_frequency_is_3,0,T +coef_mediumlow_income_group_and_tour_frequency_is_4,0,T +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_1,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_2,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_3,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_4,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0,T +coef_high_income_group_and_tour_frequency_is_1,0,T +coef_high_income_group_and_tour_frequency_is_2,0,T +coef_high_income_group_and_tour_frequency_is_3,0,T +coef_high_income_group_and_tour_frequency_is_4,0,T +coef_high_income_group_and_tour_frequency_is_5_plus,0,T +coef_mediumlow_income_group_and_shopping_tour,0,T +coef_mediumhigh_income_group_and_shopping_tour,0,T +coef_high_income_group_and_shopping_tour,0,T +coef_mediumlow_income_group_and_maintenance_tour,0,T +coef_mediumhigh_income_group_and_maintenance_tour,0,T +coef_high_income_group_and_maintenance_tour,0,T +coef_mediumlow_income_group_and_eating_out_tour,0,T +coef_mediumhigh_income_group_and_eating_out_tour,0,T +coef_high_income_group_and_eating_out_tour,0,T +coef_mediumlow_income_group_and_discretionary_tour,0,T +coef_mediumhigh_income_group_and_discretionary_tour,0,T +coef_high_income_group_and_discretionary_tour,0,T +coef_mediumlow_income_group_and_visiting_tour,0,T +coef_mediumhigh_income_group_and_visiting_tour,0,T +coef_high_income_group_and_visiting_tour,0,T +coef_female_and_tour_frequency_is_1,0,T +coef_female_and_tour_frequency_is_2,0,T +coef_female_and_tour_frequency_is_3,0,T +coef_female_and_tour_frequency_is_4,0,T +coef_female_and_tour_frequency_is_5,0,T +coef_female_and_escorting_tour,0,T +coef_female_and_shopping_tour,0,T +coef_female_and_maintenance_tour,0,T +coef_female_and_eatingout_tour,0,T +coef_female_and_discretionary_tour,0,T +coef_zero_car_ownership_and_tour_frequency_is_1,0,T +coef_zero_car_ownership_and_tour_frequency_is_2,0,T +coef_zero_car_ownership_and_tour_frequency_is_3,0,T +coef_zero_car_ownership_and_tour_frequency_is_4,0,T +coef_zero_car_ownership_and_tour_frequency_is_5_plus,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T +coef_presence_of_non_worker_and_tour_frequency_is_1,0,T +coef_presence_of_non_worker_and_tour_frequency_is_2,0,T +coef_presence_of_non_worker_and_tour_frequency_is_3,0,T +coef_presence_of_non_worker_and_tour_frequency_is_4,0,T +coef_presence_of_non_worker_and_tour_frequency_is_5,0,T +coef_presence_of_retiree_and_tour_frequency_is_1,0,T +coef_presence_of_retiree_and_tour_frequency_is_2,0,T +coef_presence_of_retiree_and_tour_frequency_is_3,0,T +coef_presence_of_retiree_and_tour_frequency_is_4,0,T +coef_presence_of_retiree_and_tour_frequency_is_5,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_full_time_worker_and_escorting_tour,-0.893,F +coef_presence_of_part_time_worker_and_escorting_tour,0,T +coef_presence_of_non_worker_and_escorting_tour,0.89,F +coef_presence_of_retiree_and_escorting_tour,0,T +coef_presence_of_university_student_and_escorting_tour,0,T +coef_presence_of_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_school_kid_and_escorting_tour,0,T +coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T +coef_at_home_pre_school_kid_and_escorting_tour,0,T +coef_presence_of_full_time_worker_and_shopping_tour,0,T +coef_presence_of_part_time_worker_and_shopping_tour,1.155,F +coef_presence_of_non_worker_and_shopping_tour,0.808,F +coef_presence_of_retiree_and_shopping_tour,0,T +coef_presence_of_university_student_and_shopping_tour,0,T +coef_presence_of_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_school_kid_and_shopping_tour,0,T +coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T +coef_at_home_pre_school_kid_and_shopping_tour,0,T +coef_presence_of_full_time_worker_and_maintenance_tour,0,T +coef_presence_of_part_time_worker_and_maintenance_tour,0,T +coef_presence_of_non_worker_and_maintenance_tour,0,T +coef_presence_of_retiree_and_maintenance_tour,0,T +coef_presence_of_university_student_and_maintenance_tour,0,T +coef_presence_of_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_school_kid_and_maintenance_tour,0,T +coef_presence_of_full_time_worker_and_eating_out_tour,0,T +coef_presence_of_part_time_worker_and_eating_out_tour,1.037,F +coef_presence_of_non_worker_and_eating_out_tour,1.157,F +coef_presence_of_retiree_and_eating_out_tour,0,T +coef_presence_of_university_student_and_eating_out_tour,0,T +coef_presence_of_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_school_kid_and_eating_out_tour,0,T +coef_presence_of_full_time_worker_and_discretionary_tour,0,T +coef_presence_of_part_time_worker_and_discretionary_tour,0,T +coef_presence_of_non_worker_and_discretionary_tour,0.791,F +coef_presence_of_retiree_and_discretionary_tour,0,T +coef_presence_of_university_student_and_discretionary_tour,0,T +coef_presence_of_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_school_kid_and_discretionary_tour,0,T +coef_walk_access_to_retail_and_tour_frequency_is_1,0,T +coef_walk_access_to_retail_and_tour_frequency_is_2,0,T +coef_walk_access_to_retail_and_tour_frequency_is_3,0,T +coef_walk_access_to_retail_and_tour_frequency_is_4,0,T +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_transit_access_to_retail_and_tour_frequency_is_1,0,T +coef_transit_access_to_retail_and_tour_frequency_is_2,0,T +coef_transit_access_to_retail_and_tour_frequency_is_3,0,T +coef_transit_access_to_retail_and_tour_frequency_is_4,0,T +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_auto_access_to_retail_and_tour_frequency_is_1,0,T +coef_auto_access_to_retail_and_tour_frequency_is_2,0,T +coef_auto_access_to_retail_and_tour_frequency_is_3,0,T +coef_auto_access_to_retail_and_tour_frequency_is_4,0,T +coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_walk_access_to_retail_and_escorting,0,T +coef_transit_access_to_retail_and_escorting,0,T +coef_auto_access_to_retail_and_escorting,0,T +coef_walk_access_to_retail_and_shopping,0,T +coef_transit_access_to_retail_and_shopping,0,T +coef_auto_access_to_retail_and_shopping,0,T +coef_walk_access_to_retail_and_maintenance,0,T +coef_transit_access_to_retail_and_maintenance,0,T +coef_auto_access_to_retail_and_maintenance,0,T +coef_walk_access_to_retail_and_eating_out,0,T +coef_transit_access_to_retail_and_eating_out,0,T +coef_auto_access_to_retail_and_eating_out,0,T +coef_walk_access_to_retail_and_discretionary,0,T +coef_transit_access_to_retail_and_discretionary,0,T +coef_auto_access_to_retail_and_discretionary,0,T +coef_urban_and_tour_frequency_is_1,0,T +coef_urban_and_tour_frequency_is_2,0,T +coef_urban_and_tour_frequency_is_3,0,T +coef_urban_and_tour_frequency_is_4,0,T +coef_urban_and_tour_frequency_is_5_plus,0,T +coef_urban_and_escorting_tour,0,T +coef_urban_and_shopping_tour,0,T +coef_urban_and_maintenance_tour,0,T +coef_urban_and_eatingout_tour,0,T +coef_urban_and_discretionary_tour,0,T +coef_1_escort_tour_constant,0.3622,F +coef_2_plus_escort_tours_constant,2.2219,F +coef_1_plus_shopping_tours_constant,1.6919,F +coef_1_plus_maintenance_tours_constant,0.6788,F +coef_1_plus_eating_out_tours_constant,0.9612,F +coef_1_plus_visting_tours_constant,0.4424,F +coef_1_plus_other_discretionary_tours_constant,1.4935,F coef_0_auto_household_and_escorting_tour,-2,T \ No newline at end of file diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv index ea64f3ca34..dc76652a0b --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv +++ b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_RETIRED.csv @@ -1,211 +1,211 @@ -coefficient_name,value,constrain -coef_escorting_tour,0,T -coef_discretionary_tour,0,T -coef_shopping_tour,0,T -coef_maintenance_tour,0,T -coef_visiting_or_social_tour,0,T -coef_eating_out_tour,0,T -coef_total_number_of_tours_is_0_no_prior_tours,-999,T -coef_total_number_of_tours_is_0_prior_tours,0,T -coef_total_number_of_tours_is_1,-8.5684,F -coef_total_number_of_tours_is_2,-12.7416,F -coef_total_number_of_tours_is_3,-15.0978,F -coef_total_number_of_tours_is_4,-19.5439,F -coef_total_number_of_tours_is_5,-20.7897,F -coef_total_number_of_tours_is_6_plus,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_1,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_2,-5.0196,F -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-5.0196,F -coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_0,0,T -coef_number_of_joint_tours_and_tour_frequency_is_1,0,T -coef_number_of_joint_tours_and_tour_frequency_is_2,-0.95,F -coef_number_of_joint_tours_and_tour_frequency_is_3,-7.143,F -coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_shopping_tours,-0.8072,F -coef_number_of_joint_maintenance_tours,0,T -coef_number_of_joint_eating_out_tours,0,T -coef_number_of_joint_visit_tours,0,T -coef_number_of_joint_discretionary_tours,0,T -coef_logged_maximum_residual_window_tour_frequency_is_0,0,T -coef_logged_maximum_residual_window_tour_frequency_is_1,1.8357,F -coef_logged_maximum_residual_window_tour_frequency_is_2,2.2707,F -coef_logged_maximum_residual_window_tour_frequency_is_3,4.4023,F -coef_logged_maximum_residual_window_tour_frequency_is_4,4.4023,F -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,4.4023,F -coef_mediumlow_income_group_and_tour_frequency_is_1,0,T -coef_mediumlow_income_group_and_tour_frequency_is_2,0,T -coef_mediumlow_income_group_and_tour_frequency_is_3,0,T -coef_mediumlow_income_group_and_tour_frequency_is_4,0,T -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_1,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_2,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_3,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_4,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0,T -coef_high_income_group_and_tour_frequency_is_1,0,T -coef_high_income_group_and_tour_frequency_is_2,0,T -coef_high_income_group_and_tour_frequency_is_3,0,T -coef_high_income_group_and_tour_frequency_is_4,0,T -coef_high_income_group_and_tour_frequency_is_5_plus,0,T -coef_mediumlow_income_group_and_shopping_tour,1.0949,F -coef_mediumhigh_income_group_and_shopping_tour,1.0949,F -coef_high_income_group_and_shopping_tour,1.0949,F -coef_mediumlow_income_group_and_maintenance_tour,0.7648,F -coef_mediumhigh_income_group_and_maintenance_tour,0.7648,F -coef_high_income_group_and_maintenance_tour,1.3795,F -coef_mediumlow_income_group_and_eating_out_tour,0.9769,F -coef_mediumhigh_income_group_and_eating_out_tour,1.181,F -coef_high_income_group_and_eating_out_tour,1.4842,F -coef_mediumlow_income_group_and_discretionary_tour,1.0095,F -coef_mediumhigh_income_group_and_discretionary_tour,1.0095,F -coef_high_income_group_and_discretionary_tour,1.0095,F -coef_mediumlow_income_group_and_visiting_tour,0,T -coef_mediumhigh_income_group_and_visiting_tour,-0.4368,F -coef_high_income_group_and_visiting_tour,-0.5137,F -coef_female_and_tour_frequency_is_1,-0.9348,F -coef_female_and_tour_frequency_is_2,-1.3028,F -coef_female_and_tour_frequency_is_3,-2.266,F -coef_female_and_tour_frequency_is_4,-2.266,F -coef_female_and_tour_frequency_is_5,-2.266,F -coef_female_and_escorting_tour,0,T -coef_female_and_shopping_tour,0.9688,F -coef_female_and_maintenance_tour,0.7424,F -coef_female_and_eatingout_tour,0,T -coef_female_and_discretionary_tour,0.4954,F -coef_zero_car_ownership_and_tour_frequency_is_1,0,T -coef_zero_car_ownership_and_tour_frequency_is_2,0,T -coef_zero_car_ownership_and_tour_frequency_is_3,0,T -coef_zero_car_ownership_and_tour_frequency_is_4,0,T -coef_zero_car_ownership_and_tour_frequency_is_5_plus,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.7965,F -coef_car_surplus_vs_workers_and_tour_frequency_is_2,2.1302,F -coef_car_surplus_vs_workers_and_tour_frequency_is_3,2.1302,F -coef_car_surplus_vs_workers_and_tour_frequency_is_4,2.1302,F -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,2.1302,F -coef_presence_of_non_worker_and_tour_frequency_is_1,0.224,F -coef_presence_of_non_worker_and_tour_frequency_is_2,0.2436,F -coef_presence_of_non_worker_and_tour_frequency_is_3,0.62,F -coef_presence_of_non_worker_and_tour_frequency_is_4,3.3742,F -coef_presence_of_non_worker_and_tour_frequency_is_5,3.3742,F -coef_presence_of_retiree_and_tour_frequency_is_1,-0.4458,F -coef_presence_of_retiree_and_tour_frequency_is_2,-0.5315,F -coef_presence_of_retiree_and_tour_frequency_is_3,-0.5315,F -coef_presence_of_retiree_and_tour_frequency_is_4,-0.5315,F -coef_presence_of_retiree_and_tour_frequency_is_5,-0.5315,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_full_time_worker_and_escorting_tour,0,T -coef_presence_of_part_time_worker_and_escorting_tour,0,T -coef_presence_of_non_worker_and_escorting_tour,0,T -coef_presence_of_retiree_and_escorting_tour,0,T -coef_presence_of_university_student_and_escorting_tour,0,T -coef_presence_of_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.4903,F -coef_presence_of_pre_school_kid_and_escorting_tour,0.5027,F -coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T -coef_at_home_pre_school_kid_and_escorting_tour,0,T -coef_presence_of_full_time_worker_and_shopping_tour,-0.3609,F -coef_presence_of_part_time_worker_and_shopping_tour,0,T -coef_presence_of_non_worker_and_shopping_tour,0,T -coef_presence_of_retiree_and_shopping_tour,0,T -coef_presence_of_university_student_and_shopping_tour,0,T -coef_presence_of_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_school_kid_and_shopping_tour,0,T -coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T -coef_at_home_pre_school_kid_and_shopping_tour,0,T -coef_presence_of_full_time_worker_and_maintenance_tour,0,T -coef_presence_of_part_time_worker_and_maintenance_tour,0,T -coef_presence_of_non_worker_and_maintenance_tour,0,T -coef_presence_of_retiree_and_maintenance_tour,0,T -coef_presence_of_university_student_and_maintenance_tour,0,T -coef_presence_of_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_school_kid_and_maintenance_tour,0,T -coef_presence_of_full_time_worker_and_eating_out_tour,-0.788,F -coef_presence_of_part_time_worker_and_eating_out_tour,-0.788,F -coef_presence_of_non_worker_and_eating_out_tour,-0.788,F -coef_presence_of_retiree_and_eating_out_tour,-0.9282,F -coef_presence_of_university_student_and_eating_out_tour,0,T -coef_presence_of_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_school_kid_and_eating_out_tour,0,T -coef_presence_of_full_time_worker_and_discretionary_tour,-0.4835,F -coef_presence_of_part_time_worker_and_discretionary_tour,0,T -coef_presence_of_non_worker_and_discretionary_tour,-0.5603,F -coef_presence_of_retiree_and_discretionary_tour,0,T -coef_presence_of_university_student_and_discretionary_tour,0,T -coef_presence_of_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_school_kid_and_discretionary_tour,0,T -coef_walk_access_to_retail_and_tour_frequency_is_1,0.0616,F -coef_walk_access_to_retail_and_tour_frequency_is_2,0.0616,F -coef_walk_access_to_retail_and_tour_frequency_is_3,0.0616,F -coef_walk_access_to_retail_and_tour_frequency_is_4,0.0616,F -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.0616,F -coef_transit_access_to_retail_and_tour_frequency_is_1,0,T -coef_transit_access_to_retail_and_tour_frequency_is_2,0,T -coef_transit_access_to_retail_and_tour_frequency_is_3,0,T -coef_transit_access_to_retail_and_tour_frequency_is_4,0,T -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_auto_access_to_retail_and_tour_frequency_is_1,0,T -coef_auto_access_to_retail_and_tour_frequency_is_2,0,T -coef_auto_access_to_retail_and_tour_frequency_is_3,0,T -coef_auto_access_to_retail_and_tour_frequency_is_4,0,T -coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_walk_access_to_retail_and_escorting,0,T -coef_transit_access_to_retail_and_escorting,0,T -coef_auto_access_to_retail_and_escorting,0,T -coef_walk_access_to_retail_and_shopping,0,T -coef_transit_access_to_retail_and_shopping,0,T -coef_auto_access_to_retail_and_shopping,0,T -coef_walk_access_to_retail_and_maintenance,0,T -coef_transit_access_to_retail_and_maintenance,0,T -coef_auto_access_to_retail_and_maintenance,0,T -coef_walk_access_to_retail_and_eating_out,0,T -coef_transit_access_to_retail_and_eating_out,0,T -coef_auto_access_to_retail_and_eating_out,0,T -coef_walk_access_to_retail_and_discretionary,0,T -coef_transit_access_to_retail_and_discretionary,0,T -coef_auto_access_to_retail_and_discretionary,0,T -coef_urban_and_tour_frequency_is_1,0,T -coef_urban_and_tour_frequency_is_2,0,T -coef_urban_and_tour_frequency_is_3,0,T -coef_urban_and_tour_frequency_is_4,0,T -coef_urban_and_tour_frequency_is_5_plus,0,T -coef_urban_and_escorting_tour,0,T -coef_urban_and_shopping_tour,0,T -coef_urban_and_maintenance_tour,0,T -coef_urban_and_eatingout_tour,0,T -coef_urban_and_discretionary_tour,0,T -coef_1_escort_tour_constant,-0.3992,F -coef_2_plus_escort_tours_constant,0.5175,F -coef_1_plus_shopping_tours_constant,0.5947,F -coef_1_plus_maintenance_tours_constant,0.1046,F -coef_1_plus_eating_out_tours_constant,0.0245,F -coef_1_plus_visting_tours_constant,0.2789,F -coef_1_plus_other_discretionary_tours_constant,0.4282,F +coefficient_name,value,constrain +coef_escorting_tour,0,T +coef_discretionary_tour,0,T +coef_shopping_tour,0,T +coef_maintenance_tour,0,T +coef_visiting_or_social_tour,0,T +coef_eating_out_tour,0,T +coef_total_number_of_tours_is_0_no_prior_tours,-999,T +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,-8.5684,F +coef_total_number_of_tours_is_2,-12.7416,F +coef_total_number_of_tours_is_3,-15.0978,F +coef_total_number_of_tours_is_4,-19.5439,F +coef_total_number_of_tours_is_5,-20.7897,F +coef_total_number_of_tours_is_6_plus,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_1,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_2,-5.0196,F +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-5.0196,F +coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_0,0,T +coef_number_of_joint_tours_and_tour_frequency_is_1,0,T +coef_number_of_joint_tours_and_tour_frequency_is_2,-0.95,F +coef_number_of_joint_tours_and_tour_frequency_is_3,-7.143,F +coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_shopping_tours,-0.8072,F +coef_number_of_joint_maintenance_tours,0,T +coef_number_of_joint_eating_out_tours,0,T +coef_number_of_joint_visit_tours,0,T +coef_number_of_joint_discretionary_tours,0,T +coef_logged_maximum_residual_window_tour_frequency_is_0,0,T +coef_logged_maximum_residual_window_tour_frequency_is_1,1.8357,F +coef_logged_maximum_residual_window_tour_frequency_is_2,2.2707,F +coef_logged_maximum_residual_window_tour_frequency_is_3,4.4023,F +coef_logged_maximum_residual_window_tour_frequency_is_4,4.4023,F +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,4.4023,F +coef_mediumlow_income_group_and_tour_frequency_is_1,0,T +coef_mediumlow_income_group_and_tour_frequency_is_2,0,T +coef_mediumlow_income_group_and_tour_frequency_is_3,0,T +coef_mediumlow_income_group_and_tour_frequency_is_4,0,T +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_1,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_2,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_3,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_4,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0,T +coef_high_income_group_and_tour_frequency_is_1,0,T +coef_high_income_group_and_tour_frequency_is_2,0,T +coef_high_income_group_and_tour_frequency_is_3,0,T +coef_high_income_group_and_tour_frequency_is_4,0,T +coef_high_income_group_and_tour_frequency_is_5_plus,0,T +coef_mediumlow_income_group_and_shopping_tour,1.0949,F +coef_mediumhigh_income_group_and_shopping_tour,1.0949,F +coef_high_income_group_and_shopping_tour,1.0949,F +coef_mediumlow_income_group_and_maintenance_tour,0.7648,F +coef_mediumhigh_income_group_and_maintenance_tour,0.7648,F +coef_high_income_group_and_maintenance_tour,1.3795,F +coef_mediumlow_income_group_and_eating_out_tour,0.9769,F +coef_mediumhigh_income_group_and_eating_out_tour,1.181,F +coef_high_income_group_and_eating_out_tour,1.4842,F +coef_mediumlow_income_group_and_discretionary_tour,1.0095,F +coef_mediumhigh_income_group_and_discretionary_tour,1.0095,F +coef_high_income_group_and_discretionary_tour,1.0095,F +coef_mediumlow_income_group_and_visiting_tour,0,T +coef_mediumhigh_income_group_and_visiting_tour,-0.4368,F +coef_high_income_group_and_visiting_tour,-0.5137,F +coef_female_and_tour_frequency_is_1,-0.9348,F +coef_female_and_tour_frequency_is_2,-1.3028,F +coef_female_and_tour_frequency_is_3,-2.266,F +coef_female_and_tour_frequency_is_4,-2.266,F +coef_female_and_tour_frequency_is_5,-2.266,F +coef_female_and_escorting_tour,0,T +coef_female_and_shopping_tour,0.9688,F +coef_female_and_maintenance_tour,0.7424,F +coef_female_and_eatingout_tour,0,T +coef_female_and_discretionary_tour,0.4954,F +coef_zero_car_ownership_and_tour_frequency_is_1,0,T +coef_zero_car_ownership_and_tour_frequency_is_2,0,T +coef_zero_car_ownership_and_tour_frequency_is_3,0,T +coef_zero_car_ownership_and_tour_frequency_is_4,0,T +coef_zero_car_ownership_and_tour_frequency_is_5_plus,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.7965,F +coef_car_surplus_vs_workers_and_tour_frequency_is_2,2.1302,F +coef_car_surplus_vs_workers_and_tour_frequency_is_3,2.1302,F +coef_car_surplus_vs_workers_and_tour_frequency_is_4,2.1302,F +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,2.1302,F +coef_presence_of_non_worker_and_tour_frequency_is_1,0.224,F +coef_presence_of_non_worker_and_tour_frequency_is_2,0.2436,F +coef_presence_of_non_worker_and_tour_frequency_is_3,0.62,F +coef_presence_of_non_worker_and_tour_frequency_is_4,3.3742,F +coef_presence_of_non_worker_and_tour_frequency_is_5,3.3742,F +coef_presence_of_retiree_and_tour_frequency_is_1,-0.4458,F +coef_presence_of_retiree_and_tour_frequency_is_2,-0.5315,F +coef_presence_of_retiree_and_tour_frequency_is_3,-0.5315,F +coef_presence_of_retiree_and_tour_frequency_is_4,-0.5315,F +coef_presence_of_retiree_and_tour_frequency_is_5,-0.5315,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_full_time_worker_and_escorting_tour,0,T +coef_presence_of_part_time_worker_and_escorting_tour,0,T +coef_presence_of_non_worker_and_escorting_tour,0,T +coef_presence_of_retiree_and_escorting_tour,0,T +coef_presence_of_university_student_and_escorting_tour,0,T +coef_presence_of_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.4903,F +coef_presence_of_pre_school_kid_and_escorting_tour,0.5027,F +coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T +coef_at_home_pre_school_kid_and_escorting_tour,0,T +coef_presence_of_full_time_worker_and_shopping_tour,-0.3609,F +coef_presence_of_part_time_worker_and_shopping_tour,0,T +coef_presence_of_non_worker_and_shopping_tour,0,T +coef_presence_of_retiree_and_shopping_tour,0,T +coef_presence_of_university_student_and_shopping_tour,0,T +coef_presence_of_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_school_kid_and_shopping_tour,0,T +coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T +coef_at_home_pre_school_kid_and_shopping_tour,0,T +coef_presence_of_full_time_worker_and_maintenance_tour,0,T +coef_presence_of_part_time_worker_and_maintenance_tour,0,T +coef_presence_of_non_worker_and_maintenance_tour,0,T +coef_presence_of_retiree_and_maintenance_tour,0,T +coef_presence_of_university_student_and_maintenance_tour,0,T +coef_presence_of_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_school_kid_and_maintenance_tour,0,T +coef_presence_of_full_time_worker_and_eating_out_tour,-0.788,F +coef_presence_of_part_time_worker_and_eating_out_tour,-0.788,F +coef_presence_of_non_worker_and_eating_out_tour,-0.788,F +coef_presence_of_retiree_and_eating_out_tour,-0.9282,F +coef_presence_of_university_student_and_eating_out_tour,0,T +coef_presence_of_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_school_kid_and_eating_out_tour,0,T +coef_presence_of_full_time_worker_and_discretionary_tour,-0.4835,F +coef_presence_of_part_time_worker_and_discretionary_tour,0,T +coef_presence_of_non_worker_and_discretionary_tour,-0.5603,F +coef_presence_of_retiree_and_discretionary_tour,0,T +coef_presence_of_university_student_and_discretionary_tour,0,T +coef_presence_of_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_school_kid_and_discretionary_tour,0,T +coef_walk_access_to_retail_and_tour_frequency_is_1,0.0616,F +coef_walk_access_to_retail_and_tour_frequency_is_2,0.0616,F +coef_walk_access_to_retail_and_tour_frequency_is_3,0.0616,F +coef_walk_access_to_retail_and_tour_frequency_is_4,0.0616,F +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.0616,F +coef_transit_access_to_retail_and_tour_frequency_is_1,0,T +coef_transit_access_to_retail_and_tour_frequency_is_2,0,T +coef_transit_access_to_retail_and_tour_frequency_is_3,0,T +coef_transit_access_to_retail_and_tour_frequency_is_4,0,T +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_auto_access_to_retail_and_tour_frequency_is_1,0,T +coef_auto_access_to_retail_and_tour_frequency_is_2,0,T +coef_auto_access_to_retail_and_tour_frequency_is_3,0,T +coef_auto_access_to_retail_and_tour_frequency_is_4,0,T +coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_walk_access_to_retail_and_escorting,0,T +coef_transit_access_to_retail_and_escorting,0,T +coef_auto_access_to_retail_and_escorting,0,T +coef_walk_access_to_retail_and_shopping,0,T +coef_transit_access_to_retail_and_shopping,0,T +coef_auto_access_to_retail_and_shopping,0,T +coef_walk_access_to_retail_and_maintenance,0,T +coef_transit_access_to_retail_and_maintenance,0,T +coef_auto_access_to_retail_and_maintenance,0,T +coef_walk_access_to_retail_and_eating_out,0,T +coef_transit_access_to_retail_and_eating_out,0,T +coef_auto_access_to_retail_and_eating_out,0,T +coef_walk_access_to_retail_and_discretionary,0,T +coef_transit_access_to_retail_and_discretionary,0,T +coef_auto_access_to_retail_and_discretionary,0,T +coef_urban_and_tour_frequency_is_1,0,T +coef_urban_and_tour_frequency_is_2,0,T +coef_urban_and_tour_frequency_is_3,0,T +coef_urban_and_tour_frequency_is_4,0,T +coef_urban_and_tour_frequency_is_5_plus,0,T +coef_urban_and_escorting_tour,0,T +coef_urban_and_shopping_tour,0,T +coef_urban_and_maintenance_tour,0,T +coef_urban_and_eatingout_tour,0,T +coef_urban_and_discretionary_tour,0,T +coef_1_escort_tour_constant,-0.3992,F +coef_2_plus_escort_tours_constant,0.5175,F +coef_1_plus_shopping_tours_constant,0.5947,F +coef_1_plus_maintenance_tours_constant,0.1046,F +coef_1_plus_eating_out_tours_constant,0.0245,F +coef_1_plus_visting_tours_constant,0.2789,F +coef_1_plus_other_discretionary_tours_constant,0.4282,F coef_0_auto_household_and_escorting_tour,-2,T \ No newline at end of file diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv index 8f392d20ea..fae10e51ff --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv +++ b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_SCHOOL.csv @@ -1,211 +1,211 @@ -coefficient_name,value,constrain -coef_escorting_tour,0,T -coef_discretionary_tour,0,T -coef_shopping_tour,0,T -coef_maintenance_tour,0,T -coef_visiting_or_social_tour,0,T -coef_eating_out_tour,0,T -coef_total_number_of_tours_is_0_no_prior_tours,-999,T -coef_total_number_of_tours_is_0_prior_tours,0,T -coef_total_number_of_tours_is_1,-7.4863,F -coef_total_number_of_tours_is_2,-10.718,F -coef_total_number_of_tours_is_3,-13.7884,F -coef_total_number_of_tours_is_4,-999,T -coef_total_number_of_tours_is_5,-999,T -coef_total_number_of_tours_is_6_plus,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_1,-1.0331,F -coef_number_of_mandatory_tours_and_tour_frequency_is_2,-2.7445,F -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-2.7445,F -coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_0,0,T -coef_number_of_joint_tours_and_tour_frequency_is_1,-0.6149,F -coef_number_of_joint_tours_and_tour_frequency_is_2,-0.6149,F -coef_number_of_joint_tours_and_tour_frequency_is_3,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_shopping_tours,0,T -coef_number_of_joint_maintenance_tours,-1.3476,F -coef_number_of_joint_eating_out_tours,0,T -coef_number_of_joint_visit_tours,0,T -coef_number_of_joint_discretionary_tours,0,T -coef_logged_maximum_residual_window_tour_frequency_is_0,0,T -coef_logged_maximum_residual_window_tour_frequency_is_1,1.5603,F -coef_logged_maximum_residual_window_tour_frequency_is_2,1.5603,F -coef_logged_maximum_residual_window_tour_frequency_is_3,1.5603,F -coef_logged_maximum_residual_window_tour_frequency_is_4,1.5603,F -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.5603,F -coef_mediumlow_income_group_and_tour_frequency_is_1,1.0873,F -coef_mediumlow_income_group_and_tour_frequency_is_2,1.0873,F -coef_mediumlow_income_group_and_tour_frequency_is_3,1.0873,F -coef_mediumlow_income_group_and_tour_frequency_is_4,1.0873,F -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,1.0873,F -coef_mediumhigh_income_group_and_tour_frequency_is_1,1.5197,F -coef_mediumhigh_income_group_and_tour_frequency_is_2,1.5197,F -coef_mediumhigh_income_group_and_tour_frequency_is_3,1.5197,F -coef_mediumhigh_income_group_and_tour_frequency_is_4,1.5197,F -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.5197,F -coef_high_income_group_and_tour_frequency_is_1,2.0175,F -coef_high_income_group_and_tour_frequency_is_2,2.0175,F -coef_high_income_group_and_tour_frequency_is_3,2.0175,F -coef_high_income_group_and_tour_frequency_is_4,2.0175,F -coef_high_income_group_and_tour_frequency_is_5_plus,2.0175,F -coef_mediumlow_income_group_and_shopping_tour,-0.6506,F -coef_mediumhigh_income_group_and_shopping_tour,-0.6506,F -coef_high_income_group_and_shopping_tour,-0.6506,F -coef_mediumlow_income_group_and_maintenance_tour,0,T -coef_mediumhigh_income_group_and_maintenance_tour,0,T -coef_high_income_group_and_maintenance_tour,0,T -coef_mediumlow_income_group_and_eating_out_tour,-0.701,F -coef_mediumhigh_income_group_and_eating_out_tour,-0.701,F -coef_high_income_group_and_eating_out_tour,-0.701,F -coef_mediumlow_income_group_and_discretionary_tour,0,T -coef_mediumhigh_income_group_and_discretionary_tour,0,T -coef_high_income_group_and_discretionary_tour,0,T -coef_mediumlow_income_group_and_visiting_tour,0,T -coef_mediumhigh_income_group_and_visiting_tour,0,T -coef_high_income_group_and_visiting_tour,0,T -coef_female_and_tour_frequency_is_1,0,T -coef_female_and_tour_frequency_is_2,0,T -coef_female_and_tour_frequency_is_3,0,T -coef_female_and_tour_frequency_is_4,0,T -coef_female_and_tour_frequency_is_5,0,T -coef_female_and_escorting_tour,0,T -coef_female_and_shopping_tour,0,T -coef_female_and_maintenance_tour,0,T -coef_female_and_eatingout_tour,0,T -coef_female_and_discretionary_tour,0,T -coef_zero_car_ownership_and_tour_frequency_is_1,0,T -coef_zero_car_ownership_and_tour_frequency_is_2,0,T -coef_zero_car_ownership_and_tour_frequency_is_3,0,T -coef_zero_car_ownership_and_tour_frequency_is_4,0,T -coef_zero_car_ownership_and_tour_frequency_is_5_plus,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T -coef_presence_of_non_worker_and_tour_frequency_is_1,0.2177,F -coef_presence_of_non_worker_and_tour_frequency_is_2,0.2177,F -coef_presence_of_non_worker_and_tour_frequency_is_3,0.2177,F -coef_presence_of_non_worker_and_tour_frequency_is_4,0.2177,F -coef_presence_of_non_worker_and_tour_frequency_is_5,0.2177,F -coef_presence_of_retiree_and_tour_frequency_is_1,0,T -coef_presence_of_retiree_and_tour_frequency_is_2,0,T -coef_presence_of_retiree_and_tour_frequency_is_3,0,T -coef_presence_of_retiree_and_tour_frequency_is_4,0,T -coef_presence_of_retiree_and_tour_frequency_is_5,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.4439,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-0.4439,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-0.4439,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-0.4439,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.4439,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,-0.2264,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,-0.2264,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,-0.2264,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,-0.2264,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,-0.2264,F -coef_presence_of_full_time_worker_and_escorting_tour,0,T -coef_presence_of_part_time_worker_and_escorting_tour,0,T -coef_presence_of_non_worker_and_escorting_tour,0,T -coef_presence_of_retiree_and_escorting_tour,0,T -coef_presence_of_university_student_and_escorting_tour,0,T -coef_presence_of_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_school_kid_and_escorting_tour,0,T -coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T -coef_at_home_pre_school_kid_and_escorting_tour,0,T -coef_presence_of_full_time_worker_and_shopping_tour,0,T -coef_presence_of_part_time_worker_and_shopping_tour,0,T -coef_presence_of_non_worker_and_shopping_tour,-0.645,F -coef_presence_of_retiree_and_shopping_tour,0,T -coef_presence_of_university_student_and_shopping_tour,0,T -coef_presence_of_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_driving_school_kid_and_shopping_tour,0.9365,F -coef_presence_of_pre_school_kid_and_shopping_tour,0,T -coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T -coef_at_home_pre_school_kid_and_shopping_tour,0,T -coef_presence_of_full_time_worker_and_maintenance_tour,0,T -coef_presence_of_part_time_worker_and_maintenance_tour,0,T -coef_presence_of_non_worker_and_maintenance_tour,0,T -coef_presence_of_retiree_and_maintenance_tour,0,T -coef_presence_of_university_student_and_maintenance_tour,0,T -coef_presence_of_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_school_kid_and_maintenance_tour,0,T -coef_presence_of_full_time_worker_and_eating_out_tour,0,T -coef_presence_of_part_time_worker_and_eating_out_tour,0,T -coef_presence_of_non_worker_and_eating_out_tour,-1.3074,F -coef_presence_of_retiree_and_eating_out_tour,0,T -coef_presence_of_university_student_and_eating_out_tour,0,T -coef_presence_of_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_school_kid_and_eating_out_tour,0,T -coef_presence_of_full_time_worker_and_discretionary_tour,0.7526,F -coef_presence_of_part_time_worker_and_discretionary_tour,0.3721,F -coef_presence_of_non_worker_and_discretionary_tour,0,T -coef_presence_of_retiree_and_discretionary_tour,0,T -coef_presence_of_university_student_and_discretionary_tour,0,T -coef_presence_of_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_school_kid_and_discretionary_tour,0,T -coef_walk_access_to_retail_and_tour_frequency_is_1,0,T -coef_walk_access_to_retail_and_tour_frequency_is_2,0,T -coef_walk_access_to_retail_and_tour_frequency_is_3,0,T -coef_walk_access_to_retail_and_tour_frequency_is_4,0,T -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_transit_access_to_retail_and_tour_frequency_is_1,0,T -coef_transit_access_to_retail_and_tour_frequency_is_2,0,T -coef_transit_access_to_retail_and_tour_frequency_is_3,0,T -coef_transit_access_to_retail_and_tour_frequency_is_4,0,T -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_auto_access_to_retail_and_tour_frequency_is_1,0,T -coef_auto_access_to_retail_and_tour_frequency_is_2,0,T -coef_auto_access_to_retail_and_tour_frequency_is_3,0,T -coef_auto_access_to_retail_and_tour_frequency_is_4,0,T -coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_walk_access_to_retail_and_escorting,0,T -coef_transit_access_to_retail_and_escorting,0,T -coef_auto_access_to_retail_and_escorting,0.0629,F -coef_walk_access_to_retail_and_shopping,0,T -coef_transit_access_to_retail_and_shopping,0,T -coef_auto_access_to_retail_and_shopping,0,T -coef_walk_access_to_retail_and_maintenance,0,T -coef_transit_access_to_retail_and_maintenance,0,T -coef_auto_access_to_retail_and_maintenance,0,T -coef_walk_access_to_retail_and_eating_out,0.0738,F -coef_transit_access_to_retail_and_eating_out,0,T -coef_auto_access_to_retail_and_eating_out,0,T -coef_walk_access_to_retail_and_discretionary,0,T -coef_transit_access_to_retail_and_discretionary,0,T -coef_auto_access_to_retail_and_discretionary,0,T -coef_urban_and_tour_frequency_is_1,0,T -coef_urban_and_tour_frequency_is_2,0,T -coef_urban_and_tour_frequency_is_3,0,T -coef_urban_and_tour_frequency_is_4,0,T -coef_urban_and_tour_frequency_is_5_plus,0,T -coef_urban_and_escorting_tour,0.4352,F -coef_urban_and_shopping_tour,0,T -coef_urban_and_maintenance_tour,0,T -coef_urban_and_eatingout_tour,0,T -coef_urban_and_discretionary_tour,0,T -coef_1_escort_tour_constant,-0.7551,F -coef_2_plus_escort_tours_constant,-0.0086,F -coef_1_plus_shopping_tours_constant,0.4783,F -coef_1_plus_maintenance_tours_constant,-0.506,F -coef_1_plus_eating_out_tours_constant,1.1145,F -coef_1_plus_visting_tours_constant,-0.4006,F -coef_1_plus_other_discretionary_tours_constant,0.4634,F +coefficient_name,value,constrain +coef_escorting_tour,0,T +coef_discretionary_tour,0,T +coef_shopping_tour,0,T +coef_maintenance_tour,0,T +coef_visiting_or_social_tour,0,T +coef_eating_out_tour,0,T +coef_total_number_of_tours_is_0_no_prior_tours,-999,T +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,-7.4863,F +coef_total_number_of_tours_is_2,-10.718,F +coef_total_number_of_tours_is_3,-13.7884,F +coef_total_number_of_tours_is_4,-999,T +coef_total_number_of_tours_is_5,-999,T +coef_total_number_of_tours_is_6_plus,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_1,-1.0331,F +coef_number_of_mandatory_tours_and_tour_frequency_is_2,-2.7445,F +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-2.7445,F +coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_0,0,T +coef_number_of_joint_tours_and_tour_frequency_is_1,-0.6149,F +coef_number_of_joint_tours_and_tour_frequency_is_2,-0.6149,F +coef_number_of_joint_tours_and_tour_frequency_is_3,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_shopping_tours,0,T +coef_number_of_joint_maintenance_tours,-1.3476,F +coef_number_of_joint_eating_out_tours,0,T +coef_number_of_joint_visit_tours,0,T +coef_number_of_joint_discretionary_tours,0,T +coef_logged_maximum_residual_window_tour_frequency_is_0,0,T +coef_logged_maximum_residual_window_tour_frequency_is_1,1.5603,F +coef_logged_maximum_residual_window_tour_frequency_is_2,1.5603,F +coef_logged_maximum_residual_window_tour_frequency_is_3,1.5603,F +coef_logged_maximum_residual_window_tour_frequency_is_4,1.5603,F +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.5603,F +coef_mediumlow_income_group_and_tour_frequency_is_1,1.0873,F +coef_mediumlow_income_group_and_tour_frequency_is_2,1.0873,F +coef_mediumlow_income_group_and_tour_frequency_is_3,1.0873,F +coef_mediumlow_income_group_and_tour_frequency_is_4,1.0873,F +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,1.0873,F +coef_mediumhigh_income_group_and_tour_frequency_is_1,1.5197,F +coef_mediumhigh_income_group_and_tour_frequency_is_2,1.5197,F +coef_mediumhigh_income_group_and_tour_frequency_is_3,1.5197,F +coef_mediumhigh_income_group_and_tour_frequency_is_4,1.5197,F +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.5197,F +coef_high_income_group_and_tour_frequency_is_1,2.0175,F +coef_high_income_group_and_tour_frequency_is_2,2.0175,F +coef_high_income_group_and_tour_frequency_is_3,2.0175,F +coef_high_income_group_and_tour_frequency_is_4,2.0175,F +coef_high_income_group_and_tour_frequency_is_5_plus,2.0175,F +coef_mediumlow_income_group_and_shopping_tour,-0.6506,F +coef_mediumhigh_income_group_and_shopping_tour,-0.6506,F +coef_high_income_group_and_shopping_tour,-0.6506,F +coef_mediumlow_income_group_and_maintenance_tour,0,T +coef_mediumhigh_income_group_and_maintenance_tour,0,T +coef_high_income_group_and_maintenance_tour,0,T +coef_mediumlow_income_group_and_eating_out_tour,-0.701,F +coef_mediumhigh_income_group_and_eating_out_tour,-0.701,F +coef_high_income_group_and_eating_out_tour,-0.701,F +coef_mediumlow_income_group_and_discretionary_tour,0,T +coef_mediumhigh_income_group_and_discretionary_tour,0,T +coef_high_income_group_and_discretionary_tour,0,T +coef_mediumlow_income_group_and_visiting_tour,0,T +coef_mediumhigh_income_group_and_visiting_tour,0,T +coef_high_income_group_and_visiting_tour,0,T +coef_female_and_tour_frequency_is_1,0,T +coef_female_and_tour_frequency_is_2,0,T +coef_female_and_tour_frequency_is_3,0,T +coef_female_and_tour_frequency_is_4,0,T +coef_female_and_tour_frequency_is_5,0,T +coef_female_and_escorting_tour,0,T +coef_female_and_shopping_tour,0,T +coef_female_and_maintenance_tour,0,T +coef_female_and_eatingout_tour,0,T +coef_female_and_discretionary_tour,0,T +coef_zero_car_ownership_and_tour_frequency_is_1,0,T +coef_zero_car_ownership_and_tour_frequency_is_2,0,T +coef_zero_car_ownership_and_tour_frequency_is_3,0,T +coef_zero_car_ownership_and_tour_frequency_is_4,0,T +coef_zero_car_ownership_and_tour_frequency_is_5_plus,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T +coef_presence_of_non_worker_and_tour_frequency_is_1,0.2177,F +coef_presence_of_non_worker_and_tour_frequency_is_2,0.2177,F +coef_presence_of_non_worker_and_tour_frequency_is_3,0.2177,F +coef_presence_of_non_worker_and_tour_frequency_is_4,0.2177,F +coef_presence_of_non_worker_and_tour_frequency_is_5,0.2177,F +coef_presence_of_retiree_and_tour_frequency_is_1,0,T +coef_presence_of_retiree_and_tour_frequency_is_2,0,T +coef_presence_of_retiree_and_tour_frequency_is_3,0,T +coef_presence_of_retiree_and_tour_frequency_is_4,0,T +coef_presence_of_retiree_and_tour_frequency_is_5,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.4439,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-0.4439,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-0.4439,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-0.4439,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.4439,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,-0.2264,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,-0.2264,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,-0.2264,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,-0.2264,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,-0.2264,F +coef_presence_of_full_time_worker_and_escorting_tour,0,T +coef_presence_of_part_time_worker_and_escorting_tour,0,T +coef_presence_of_non_worker_and_escorting_tour,0,T +coef_presence_of_retiree_and_escorting_tour,0,T +coef_presence_of_university_student_and_escorting_tour,0,T +coef_presence_of_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_school_kid_and_escorting_tour,0,T +coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T +coef_at_home_pre_school_kid_and_escorting_tour,0,T +coef_presence_of_full_time_worker_and_shopping_tour,0,T +coef_presence_of_part_time_worker_and_shopping_tour,0,T +coef_presence_of_non_worker_and_shopping_tour,-0.645,F +coef_presence_of_retiree_and_shopping_tour,0,T +coef_presence_of_university_student_and_shopping_tour,0,T +coef_presence_of_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_driving_school_kid_and_shopping_tour,0.9365,F +coef_presence_of_pre_school_kid_and_shopping_tour,0,T +coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T +coef_at_home_pre_school_kid_and_shopping_tour,0,T +coef_presence_of_full_time_worker_and_maintenance_tour,0,T +coef_presence_of_part_time_worker_and_maintenance_tour,0,T +coef_presence_of_non_worker_and_maintenance_tour,0,T +coef_presence_of_retiree_and_maintenance_tour,0,T +coef_presence_of_university_student_and_maintenance_tour,0,T +coef_presence_of_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_school_kid_and_maintenance_tour,0,T +coef_presence_of_full_time_worker_and_eating_out_tour,0,T +coef_presence_of_part_time_worker_and_eating_out_tour,0,T +coef_presence_of_non_worker_and_eating_out_tour,-1.3074,F +coef_presence_of_retiree_and_eating_out_tour,0,T +coef_presence_of_university_student_and_eating_out_tour,0,T +coef_presence_of_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_school_kid_and_eating_out_tour,0,T +coef_presence_of_full_time_worker_and_discretionary_tour,0.7526,F +coef_presence_of_part_time_worker_and_discretionary_tour,0.3721,F +coef_presence_of_non_worker_and_discretionary_tour,0,T +coef_presence_of_retiree_and_discretionary_tour,0,T +coef_presence_of_university_student_and_discretionary_tour,0,T +coef_presence_of_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_school_kid_and_discretionary_tour,0,T +coef_walk_access_to_retail_and_tour_frequency_is_1,0,T +coef_walk_access_to_retail_and_tour_frequency_is_2,0,T +coef_walk_access_to_retail_and_tour_frequency_is_3,0,T +coef_walk_access_to_retail_and_tour_frequency_is_4,0,T +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_transit_access_to_retail_and_tour_frequency_is_1,0,T +coef_transit_access_to_retail_and_tour_frequency_is_2,0,T +coef_transit_access_to_retail_and_tour_frequency_is_3,0,T +coef_transit_access_to_retail_and_tour_frequency_is_4,0,T +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_auto_access_to_retail_and_tour_frequency_is_1,0,T +coef_auto_access_to_retail_and_tour_frequency_is_2,0,T +coef_auto_access_to_retail_and_tour_frequency_is_3,0,T +coef_auto_access_to_retail_and_tour_frequency_is_4,0,T +coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_walk_access_to_retail_and_escorting,0,T +coef_transit_access_to_retail_and_escorting,0,T +coef_auto_access_to_retail_and_escorting,0.0629,F +coef_walk_access_to_retail_and_shopping,0,T +coef_transit_access_to_retail_and_shopping,0,T +coef_auto_access_to_retail_and_shopping,0,T +coef_walk_access_to_retail_and_maintenance,0,T +coef_transit_access_to_retail_and_maintenance,0,T +coef_auto_access_to_retail_and_maintenance,0,T +coef_walk_access_to_retail_and_eating_out,0.0738,F +coef_transit_access_to_retail_and_eating_out,0,T +coef_auto_access_to_retail_and_eating_out,0,T +coef_walk_access_to_retail_and_discretionary,0,T +coef_transit_access_to_retail_and_discretionary,0,T +coef_auto_access_to_retail_and_discretionary,0,T +coef_urban_and_tour_frequency_is_1,0,T +coef_urban_and_tour_frequency_is_2,0,T +coef_urban_and_tour_frequency_is_3,0,T +coef_urban_and_tour_frequency_is_4,0,T +coef_urban_and_tour_frequency_is_5_plus,0,T +coef_urban_and_escorting_tour,0.4352,F +coef_urban_and_shopping_tour,0,T +coef_urban_and_maintenance_tour,0,T +coef_urban_and_eatingout_tour,0,T +coef_urban_and_discretionary_tour,0,T +coef_1_escort_tour_constant,-0.7551,F +coef_2_plus_escort_tours_constant,-0.0086,F +coef_1_plus_shopping_tours_constant,0.4783,F +coef_1_plus_maintenance_tours_constant,-0.506,F +coef_1_plus_eating_out_tours_constant,1.1145,F +coef_1_plus_visting_tours_constant,-0.4006,F +coef_1_plus_other_discretionary_tours_constant,0.4634,F coef_0_auto_household_and_escorting_tour,-2,T \ No newline at end of file diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv index 1080f36b4d..7799e3e5d2 --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv +++ b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_coefficients_PTYPE_UNIVERSITY.csv @@ -1,211 +1,211 @@ -coefficient_name,value,constrain -coef_escorting_tour,0,T -coef_discretionary_tour,0,T -coef_shopping_tour,0,T -coef_maintenance_tour,0,T -coef_visiting_or_social_tour,0,T -coef_eating_out_tour,0,T -coef_total_number_of_tours_is_0_no_prior_tours,-999,T -coef_total_number_of_tours_is_0_prior_tours,0,T -coef_total_number_of_tours_is_1,-6.2138,F -coef_total_number_of_tours_is_2,-8.908,F -coef_total_number_of_tours_is_3,-12.3261,F -coef_total_number_of_tours_is_4,-15.8114,F -coef_total_number_of_tours_is_5,-999,T -coef_total_number_of_tours_is_6_plus,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.1852,F -coef_number_of_mandatory_tours_and_tour_frequency_is_2,-0.8753,F -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-1.6158,F -coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_0,0,T -coef_number_of_joint_tours_and_tour_frequency_is_1,0,T -coef_number_of_joint_tours_and_tour_frequency_is_2,-0.3153,F -coef_number_of_joint_tours_and_tour_frequency_is_3,-0.7351,F -coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_shopping_tours,-0.713,F -coef_number_of_joint_maintenance_tours,0,T -coef_number_of_joint_eating_out_tours,0,T -coef_number_of_joint_visit_tours,0,T -coef_number_of_joint_discretionary_tours,0.6713,F -coef_logged_maximum_residual_window_tour_frequency_is_0,1.1858,F -coef_logged_maximum_residual_window_tour_frequency_is_1,1.4842,F -coef_logged_maximum_residual_window_tour_frequency_is_2,1.4842,F -coef_logged_maximum_residual_window_tour_frequency_is_3,1.4842,F -coef_logged_maximum_residual_window_tour_frequency_is_4,1.4842,F -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.4842,F -coef_mediumlow_income_group_and_tour_frequency_is_1,0,T -coef_mediumlow_income_group_and_tour_frequency_is_2,0,T -coef_mediumlow_income_group_and_tour_frequency_is_3,0,T -coef_mediumlow_income_group_and_tour_frequency_is_4,0,T -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_1,0.1109,F -coef_mediumhigh_income_group_and_tour_frequency_is_2,0.3914,F -coef_mediumhigh_income_group_and_tour_frequency_is_3,0.6137,F -coef_mediumhigh_income_group_and_tour_frequency_is_4,0.6137,F -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0.6137,F -coef_high_income_group_and_tour_frequency_is_1,0.3986,F -coef_high_income_group_and_tour_frequency_is_2,0.8009,F -coef_high_income_group_and_tour_frequency_is_3,0.8254,F -coef_high_income_group_and_tour_frequency_is_4,0.8254,F -coef_high_income_group_and_tour_frequency_is_5_plus,0.8254,F -coef_mediumlow_income_group_and_shopping_tour,0.5693,F -coef_mediumhigh_income_group_and_shopping_tour,0.5693,F -coef_high_income_group_and_shopping_tour,0.5693,F -coef_mediumlow_income_group_and_maintenance_tour,0,T -coef_mediumhigh_income_group_and_maintenance_tour,0,T -coef_high_income_group_and_maintenance_tour,0,T -coef_mediumlow_income_group_and_eating_out_tour,0,T -coef_mediumhigh_income_group_and_eating_out_tour,-0.7207,F -coef_high_income_group_and_eating_out_tour,-0.7207,F -coef_mediumlow_income_group_and_discretionary_tour,0,T -coef_mediumhigh_income_group_and_discretionary_tour,0,T -coef_high_income_group_and_discretionary_tour,0,T -coef_mediumlow_income_group_and_visiting_tour,0,T -coef_mediumhigh_income_group_and_visiting_tour,-0.3694,F -coef_high_income_group_and_visiting_tour,-0.3694,F -coef_female_and_tour_frequency_is_1,0.0973,F -coef_female_and_tour_frequency_is_2,0.2361,F -coef_female_and_tour_frequency_is_3,1.9002,F -coef_female_and_tour_frequency_is_4,1.9002,F -coef_female_and_tour_frequency_is_5,1.9002,F -coef_female_and_escorting_tour,0,T -coef_female_and_shopping_tour,0,T -coef_female_and_maintenance_tour,0,T -coef_female_and_eatingout_tour,-0.6568,F -coef_female_and_discretionary_tour,-0.3266,F -coef_zero_car_ownership_and_tour_frequency_is_1,-0.581,F -coef_zero_car_ownership_and_tour_frequency_is_2,-0.581,F -coef_zero_car_ownership_and_tour_frequency_is_3,-0.581,F -coef_zero_car_ownership_and_tour_frequency_is_4,-0.581,F -coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.581,F -coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.581,F -coef_car_shortage_vs_workers_and_tour_frequency_is_2,-0.581,F -coef_car_shortage_vs_workers_and_tour_frequency_is_3,-0.581,F -coef_car_shortage_vs_workers_and_tour_frequency_is_4,-0.581,F -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-0.581,F -coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T -coef_presence_of_non_worker_and_tour_frequency_is_1,-0.8506,F -coef_presence_of_non_worker_and_tour_frequency_is_2,-1.1804,F -coef_presence_of_non_worker_and_tour_frequency_is_3,-1.1804,F -coef_presence_of_non_worker_and_tour_frequency_is_4,-1.1804,F -coef_presence_of_non_worker_and_tour_frequency_is_5,-1.1804,F -coef_presence_of_retiree_and_tour_frequency_is_1,0,T -coef_presence_of_retiree_and_tour_frequency_is_2,0,T -coef_presence_of_retiree_and_tour_frequency_is_3,0,T -coef_presence_of_retiree_and_tour_frequency_is_4,0,T -coef_presence_of_retiree_and_tour_frequency_is_5,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.9961,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-1.9096,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-2.8469,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-2.8469,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-2.8469,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_full_time_worker_and_escorting_tour,0,T -coef_presence_of_part_time_worker_and_escorting_tour,-1.8213,F -coef_presence_of_non_worker_and_escorting_tour,0,T -coef_presence_of_retiree_and_escorting_tour,0,T -coef_presence_of_university_student_and_escorting_tour,0,T -coef_presence_of_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_driving_school_kid_and_escorting_tour,0.9489,F -coef_presence_of_pre_school_kid_and_escorting_tour,2.1465,F -coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T -coef_at_home_pre_school_kid_and_escorting_tour,0,T -coef_presence_of_full_time_worker_and_shopping_tour,-0.7728,F -coef_presence_of_part_time_worker_and_shopping_tour,-0.5199,F -coef_presence_of_non_worker_and_shopping_tour,0,T -coef_presence_of_retiree_and_shopping_tour,0,T -coef_presence_of_university_student_and_shopping_tour,0,T -coef_presence_of_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_school_kid_and_shopping_tour,1.3135,F -coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T -coef_at_home_pre_school_kid_and_shopping_tour,0,T -coef_presence_of_full_time_worker_and_maintenance_tour,0,T -coef_presence_of_part_time_worker_and_maintenance_tour,0,T -coef_presence_of_non_worker_and_maintenance_tour,0,T -coef_presence_of_retiree_and_maintenance_tour,0,T -coef_presence_of_university_student_and_maintenance_tour,0,T -coef_presence_of_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0.3863,F -coef_presence_of_pre_school_kid_and_maintenance_tour,0.9694,F -coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_school_kid_and_maintenance_tour,0,T -coef_presence_of_full_time_worker_and_eating_out_tour,-0.5251,F -coef_presence_of_part_time_worker_and_eating_out_tour,-1.9795,F -coef_presence_of_non_worker_and_eating_out_tour,0,T -coef_presence_of_retiree_and_eating_out_tour,0,T -coef_presence_of_university_student_and_eating_out_tour,-0.6529,F -coef_presence_of_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_school_kid_and_eating_out_tour,0,T -coef_presence_of_full_time_worker_and_discretionary_tour,-0.4833,F -coef_presence_of_part_time_worker_and_discretionary_tour,0,T -coef_presence_of_non_worker_and_discretionary_tour,0.9781,F -coef_presence_of_retiree_and_discretionary_tour,0,T -coef_presence_of_university_student_and_discretionary_tour,-0.6542,F -coef_presence_of_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_school_kid_and_discretionary_tour,0,T -coef_walk_access_to_retail_and_tour_frequency_is_1,0,T -coef_walk_access_to_retail_and_tour_frequency_is_2,0,T -coef_walk_access_to_retail_and_tour_frequency_is_3,0,T -coef_walk_access_to_retail_and_tour_frequency_is_4,0,T -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_transit_access_to_retail_and_tour_frequency_is_1,0.0664,F -coef_transit_access_to_retail_and_tour_frequency_is_2,0.0664,F -coef_transit_access_to_retail_and_tour_frequency_is_3,0.0664,F -coef_transit_access_to_retail_and_tour_frequency_is_4,0.0664,F -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0.0664,F -coef_auto_access_to_retail_and_tour_frequency_is_1,0,T -coef_auto_access_to_retail_and_tour_frequency_is_2,0,T -coef_auto_access_to_retail_and_tour_frequency_is_3,0,T -coef_auto_access_to_retail_and_tour_frequency_is_4,0,T -coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_walk_access_to_retail_and_escorting,0,T -coef_transit_access_to_retail_and_escorting,0,T -coef_auto_access_to_retail_and_escorting,0,T -coef_walk_access_to_retail_and_shopping,0.0972,F -coef_transit_access_to_retail_and_shopping,0,T -coef_auto_access_to_retail_and_shopping,0,T -coef_walk_access_to_retail_and_maintenance,0,T -coef_transit_access_to_retail_and_maintenance,0.0314,F -coef_auto_access_to_retail_and_maintenance,0,T -coef_walk_access_to_retail_and_eating_out,0,T -coef_transit_access_to_retail_and_eating_out,0,T -coef_auto_access_to_retail_and_eating_out,0.1018,F -coef_walk_access_to_retail_and_discretionary,0,T -coef_transit_access_to_retail_and_discretionary,0,T -coef_auto_access_to_retail_and_discretionary,0.094,F -coef_urban_and_tour_frequency_is_1,-1.1648,F -coef_urban_and_tour_frequency_is_2,-2.3177,F -coef_urban_and_tour_frequency_is_3,-2.5027,F -coef_urban_and_tour_frequency_is_4,-2.5027,F -coef_urban_and_tour_frequency_is_5_plus,-2.5027,F -coef_urban_and_escorting_tour,0.8516,F -coef_urban_and_shopping_tour,0.533,F -coef_urban_and_maintenance_tour,1.0316,F -coef_urban_and_eatingout_tour,0.68,F -coef_urban_and_discretionary_tour,0.9563,F -coef_1_escort_tour_constant,1.7028,F -coef_2_plus_escort_tours_constant,2.8379,F -coef_1_plus_shopping_tours_constant,1.8403,F -coef_1_plus_maintenance_tours_constant,0.3348,F -coef_1_plus_eating_out_tours_constant,2.0723,F -coef_1_plus_visting_tours_constant,1.2172,F -coef_1_plus_other_discretionary_tours_constant,1.3389,F +coefficient_name,value,constrain +coef_escorting_tour,0,T +coef_discretionary_tour,0,T +coef_shopping_tour,0,T +coef_maintenance_tour,0,T +coef_visiting_or_social_tour,0,T +coef_eating_out_tour,0,T +coef_total_number_of_tours_is_0_no_prior_tours,-999,T +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,-6.2138,F +coef_total_number_of_tours_is_2,-8.908,F +coef_total_number_of_tours_is_3,-12.3261,F +coef_total_number_of_tours_is_4,-15.8114,F +coef_total_number_of_tours_is_5,-999,T +coef_total_number_of_tours_is_6_plus,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.1852,F +coef_number_of_mandatory_tours_and_tour_frequency_is_2,-0.8753,F +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-1.6158,F +coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_0,0,T +coef_number_of_joint_tours_and_tour_frequency_is_1,0,T +coef_number_of_joint_tours_and_tour_frequency_is_2,-0.3153,F +coef_number_of_joint_tours_and_tour_frequency_is_3,-0.7351,F +coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_shopping_tours,-0.713,F +coef_number_of_joint_maintenance_tours,0,T +coef_number_of_joint_eating_out_tours,0,T +coef_number_of_joint_visit_tours,0,T +coef_number_of_joint_discretionary_tours,0.6713,F +coef_logged_maximum_residual_window_tour_frequency_is_0,1.1858,F +coef_logged_maximum_residual_window_tour_frequency_is_1,1.4842,F +coef_logged_maximum_residual_window_tour_frequency_is_2,1.4842,F +coef_logged_maximum_residual_window_tour_frequency_is_3,1.4842,F +coef_logged_maximum_residual_window_tour_frequency_is_4,1.4842,F +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.4842,F +coef_mediumlow_income_group_and_tour_frequency_is_1,0,T +coef_mediumlow_income_group_and_tour_frequency_is_2,0,T +coef_mediumlow_income_group_and_tour_frequency_is_3,0,T +coef_mediumlow_income_group_and_tour_frequency_is_4,0,T +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_1,0.1109,F +coef_mediumhigh_income_group_and_tour_frequency_is_2,0.3914,F +coef_mediumhigh_income_group_and_tour_frequency_is_3,0.6137,F +coef_mediumhigh_income_group_and_tour_frequency_is_4,0.6137,F +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0.6137,F +coef_high_income_group_and_tour_frequency_is_1,0.3986,F +coef_high_income_group_and_tour_frequency_is_2,0.8009,F +coef_high_income_group_and_tour_frequency_is_3,0.8254,F +coef_high_income_group_and_tour_frequency_is_4,0.8254,F +coef_high_income_group_and_tour_frequency_is_5_plus,0.8254,F +coef_mediumlow_income_group_and_shopping_tour,0.5693,F +coef_mediumhigh_income_group_and_shopping_tour,0.5693,F +coef_high_income_group_and_shopping_tour,0.5693,F +coef_mediumlow_income_group_and_maintenance_tour,0,T +coef_mediumhigh_income_group_and_maintenance_tour,0,T +coef_high_income_group_and_maintenance_tour,0,T +coef_mediumlow_income_group_and_eating_out_tour,0,T +coef_mediumhigh_income_group_and_eating_out_tour,-0.7207,F +coef_high_income_group_and_eating_out_tour,-0.7207,F +coef_mediumlow_income_group_and_discretionary_tour,0,T +coef_mediumhigh_income_group_and_discretionary_tour,0,T +coef_high_income_group_and_discretionary_tour,0,T +coef_mediumlow_income_group_and_visiting_tour,0,T +coef_mediumhigh_income_group_and_visiting_tour,-0.3694,F +coef_high_income_group_and_visiting_tour,-0.3694,F +coef_female_and_tour_frequency_is_1,0.0973,F +coef_female_and_tour_frequency_is_2,0.2361,F +coef_female_and_tour_frequency_is_3,1.9002,F +coef_female_and_tour_frequency_is_4,1.9002,F +coef_female_and_tour_frequency_is_5,1.9002,F +coef_female_and_escorting_tour,0,T +coef_female_and_shopping_tour,0,T +coef_female_and_maintenance_tour,0,T +coef_female_and_eatingout_tour,-0.6568,F +coef_female_and_discretionary_tour,-0.3266,F +coef_zero_car_ownership_and_tour_frequency_is_1,-0.581,F +coef_zero_car_ownership_and_tour_frequency_is_2,-0.581,F +coef_zero_car_ownership_and_tour_frequency_is_3,-0.581,F +coef_zero_car_ownership_and_tour_frequency_is_4,-0.581,F +coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.581,F +coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.581,F +coef_car_shortage_vs_workers_and_tour_frequency_is_2,-0.581,F +coef_car_shortage_vs_workers_and_tour_frequency_is_3,-0.581,F +coef_car_shortage_vs_workers_and_tour_frequency_is_4,-0.581,F +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-0.581,F +coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T +coef_presence_of_non_worker_and_tour_frequency_is_1,-0.8506,F +coef_presence_of_non_worker_and_tour_frequency_is_2,-1.1804,F +coef_presence_of_non_worker_and_tour_frequency_is_3,-1.1804,F +coef_presence_of_non_worker_and_tour_frequency_is_4,-1.1804,F +coef_presence_of_non_worker_and_tour_frequency_is_5,-1.1804,F +coef_presence_of_retiree_and_tour_frequency_is_1,0,T +coef_presence_of_retiree_and_tour_frequency_is_2,0,T +coef_presence_of_retiree_and_tour_frequency_is_3,0,T +coef_presence_of_retiree_and_tour_frequency_is_4,0,T +coef_presence_of_retiree_and_tour_frequency_is_5,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.9961,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-1.9096,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-2.8469,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-2.8469,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-2.8469,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_full_time_worker_and_escorting_tour,0,T +coef_presence_of_part_time_worker_and_escorting_tour,-1.8213,F +coef_presence_of_non_worker_and_escorting_tour,0,T +coef_presence_of_retiree_and_escorting_tour,0,T +coef_presence_of_university_student_and_escorting_tour,0,T +coef_presence_of_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_driving_school_kid_and_escorting_tour,0.9489,F +coef_presence_of_pre_school_kid_and_escorting_tour,2.1465,F +coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T +coef_at_home_pre_school_kid_and_escorting_tour,0,T +coef_presence_of_full_time_worker_and_shopping_tour,-0.7728,F +coef_presence_of_part_time_worker_and_shopping_tour,-0.5199,F +coef_presence_of_non_worker_and_shopping_tour,0,T +coef_presence_of_retiree_and_shopping_tour,0,T +coef_presence_of_university_student_and_shopping_tour,0,T +coef_presence_of_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_school_kid_and_shopping_tour,1.3135,F +coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T +coef_at_home_pre_school_kid_and_shopping_tour,0,T +coef_presence_of_full_time_worker_and_maintenance_tour,0,T +coef_presence_of_part_time_worker_and_maintenance_tour,0,T +coef_presence_of_non_worker_and_maintenance_tour,0,T +coef_presence_of_retiree_and_maintenance_tour,0,T +coef_presence_of_university_student_and_maintenance_tour,0,T +coef_presence_of_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0.3863,F +coef_presence_of_pre_school_kid_and_maintenance_tour,0.9694,F +coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_school_kid_and_maintenance_tour,0,T +coef_presence_of_full_time_worker_and_eating_out_tour,-0.5251,F +coef_presence_of_part_time_worker_and_eating_out_tour,-1.9795,F +coef_presence_of_non_worker_and_eating_out_tour,0,T +coef_presence_of_retiree_and_eating_out_tour,0,T +coef_presence_of_university_student_and_eating_out_tour,-0.6529,F +coef_presence_of_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_school_kid_and_eating_out_tour,0,T +coef_presence_of_full_time_worker_and_discretionary_tour,-0.4833,F +coef_presence_of_part_time_worker_and_discretionary_tour,0,T +coef_presence_of_non_worker_and_discretionary_tour,0.9781,F +coef_presence_of_retiree_and_discretionary_tour,0,T +coef_presence_of_university_student_and_discretionary_tour,-0.6542,F +coef_presence_of_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_school_kid_and_discretionary_tour,0,T +coef_walk_access_to_retail_and_tour_frequency_is_1,0,T +coef_walk_access_to_retail_and_tour_frequency_is_2,0,T +coef_walk_access_to_retail_and_tour_frequency_is_3,0,T +coef_walk_access_to_retail_and_tour_frequency_is_4,0,T +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_transit_access_to_retail_and_tour_frequency_is_1,0.0664,F +coef_transit_access_to_retail_and_tour_frequency_is_2,0.0664,F +coef_transit_access_to_retail_and_tour_frequency_is_3,0.0664,F +coef_transit_access_to_retail_and_tour_frequency_is_4,0.0664,F +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0.0664,F +coef_auto_access_to_retail_and_tour_frequency_is_1,0,T +coef_auto_access_to_retail_and_tour_frequency_is_2,0,T +coef_auto_access_to_retail_and_tour_frequency_is_3,0,T +coef_auto_access_to_retail_and_tour_frequency_is_4,0,T +coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_walk_access_to_retail_and_escorting,0,T +coef_transit_access_to_retail_and_escorting,0,T +coef_auto_access_to_retail_and_escorting,0,T +coef_walk_access_to_retail_and_shopping,0.0972,F +coef_transit_access_to_retail_and_shopping,0,T +coef_auto_access_to_retail_and_shopping,0,T +coef_walk_access_to_retail_and_maintenance,0,T +coef_transit_access_to_retail_and_maintenance,0.0314,F +coef_auto_access_to_retail_and_maintenance,0,T +coef_walk_access_to_retail_and_eating_out,0,T +coef_transit_access_to_retail_and_eating_out,0,T +coef_auto_access_to_retail_and_eating_out,0.1018,F +coef_walk_access_to_retail_and_discretionary,0,T +coef_transit_access_to_retail_and_discretionary,0,T +coef_auto_access_to_retail_and_discretionary,0.094,F +coef_urban_and_tour_frequency_is_1,-1.1648,F +coef_urban_and_tour_frequency_is_2,-2.3177,F +coef_urban_and_tour_frequency_is_3,-2.5027,F +coef_urban_and_tour_frequency_is_4,-2.5027,F +coef_urban_and_tour_frequency_is_5_plus,-2.5027,F +coef_urban_and_escorting_tour,0.8516,F +coef_urban_and_shopping_tour,0.533,F +coef_urban_and_maintenance_tour,1.0316,F +coef_urban_and_eatingout_tour,0.68,F +coef_urban_and_discretionary_tour,0.9563,F +coef_1_escort_tour_constant,1.7028,F +coef_2_plus_escort_tours_constant,2.8379,F +coef_1_plus_shopping_tours_constant,1.8403,F +coef_1_plus_maintenance_tours_constant,0.3348,F +coef_1_plus_eating_out_tours_constant,2.0723,F +coef_1_plus_visting_tours_constant,1.2172,F +coef_1_plus_other_discretionary_tours_constant,1.3389,F coef_0_auto_household_and_escorting_tour,-2,T \ No newline at end of file diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_extension_probs.csv b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_extension_probs.csv old mode 100755 new mode 100644 similarity index 95% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_extension_probs.csv rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_extension_probs.csv index 632f453ce0..ec78c4c8e7 --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_extension_probs.csv +++ b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_frequency_extension_probs.csv @@ -1,193 +1,193 @@ -ptype,has_mandatory_tour,has_joint_tour,nonmandatory_tour_type,0_tours,1_tours,2_tours -1,0,0,1,0.829545455,1,1 -2,0,0,1,0.769230769,1,1 -3,0,0,1,0.893939394,1,1 -4,0,0,1,0.75,1,1 -5,0,0,1,0.842105263,1,1 -6,0,0,1,0.714285714,1,1 -7,0,0,1,0.814814815,1,1 -8,0,0,1,0.75,1,1 -1,1,0,1,0.789473684,1,1 -2,1,0,1,0.6,1,1 -3,1,0,1,1,1,1 -4,1,0,1,1,1,1 -5,1,0,1,0.825910931,1,1 -6,1,0,1,0.837209302,1,1 -7,1,0,1,0.6,1,1 -8,1,0,1,1,1,1 -1,0,1,1,0.842105263,1,1 -2,0,1,1,1,1,1 -3,0,1,1,1,1,1 -4,0,1,1,1,1,1 -5,0,1,1,1,1,1 -6,0,1,1,1,1,1 -7,0,1,1,1,1,1 -8,0,1,1,1,1,1 -1,1,1,1,1,1,1 -2,1,1,1,1,1,1 -3,1,1,1,1,1,1 -4,1,1,1,1,1,1 -5,1,1,1,0.777777778,1,1 -6,1,1,1,1,1,1 -7,1,1,1,1,1,1 -8,1,1,1,1,1,1 -1,0,0,2,0.892694064,0.99086758,1 -2,0,0,2,0.84057971,0.992753623,1 -3,0,0,2,0.971014493,1,1 -4,0,0,2,0.96969697,1,1 -5,0,0,2,0.870056497,0.994350282,1 -6,0,0,2,0.866666667,1,1 -7,0,0,2,0.971014493,1,1 -8,0,0,2,0.931034483,1,1 -1,1,0,2,0.885057471,1,1 -2,1,0,2,0.727272727,1,1 -3,1,0,2,0.971428571,1,1 -4,1,0,2,1,1,1 -5,1,0,2,0.895977809,0.993065187,1 -6,1,0,2,0.885185185,1,1 -7,1,0,2,1,1,1 -8,1,0,2,1,1,1 -1,0,1,2,0.910087719,0.993421053,1 -2,0,1,2,0.88,1,1 -3,0,1,2,0.8,1,1 -4,0,1,2,1,1,1 -5,0,1,2,1,1,1 -6,0,1,2,1,1,1 -7,0,1,2,1,1,1 -8,0,1,2,1,1,1 -1,1,1,2,1,1,1 -2,1,1,2,1,1,1 -3,1,1,2,1,1,1 -4,1,1,2,1,1,1 -5,1,1,2,1,1,1 -6,1,1,2,0.964912281,1,1 -7,1,1,2,1,1,1 -8,1,1,2,0.888888889,1,1 -1,0,0,3,0.935643564,0.997524752,1 -2,0,0,3,0.905660377,1,1 -3,0,0,3,0.978813559,1,1 -4,0,0,3,0.928571429,1,1 -5,0,0,3,0.901515152,0.992424242,1 -6,0,0,3,0.863636364,1,1 -7,0,0,3,0.947368421,1,1 -8,0,0,3,0.913043478,1,1 -1,1,0,3,0.893333333,0.986666667,1 -2,1,0,3,1,1,1 -3,1,0,3,1,1,1 -4,1,0,3,0.857142857,1,1 -5,1,0,3,0.916071429,0.996428571,1 -6,1,0,3,0.856382979,0.984042553,1 -7,1,0,3,1,1,1 -8,1,0,3,1,1,1 -1,0,1,3,0.916201117,0.991620112,1 -2,0,1,3,0.912280702,0.98245614,1 -3,0,1,3,1,1,1 -4,0,1,3,1,1,1 -5,0,1,3,1,1,1 -6,0,1,3,0.833333333,1,1 -7,0,1,3,0.961538462,1,1 -8,0,1,3,1,1,1 -1,1,1,3,0.97826087,0.989130435,1 -2,1,1,3,0.97260274,1,1 -3,1,1,3,1,1,1 -4,1,1,3,1,1,1 -5,1,1,3,0.995762712,1,1 -6,1,1,3,0.921568627,0.980392157,1 -7,1,1,3,1,1,1 -8,1,1,3,1,1,1 -1,0,0,4,0.9218107,0.995884774,1 -2,0,0,4,0.900900901,1,1 -3,0,0,4,0.997354497,1,1 -4,0,0,4,0.991176471,1,1 -5,0,0,4,0.921568627,0.980392157,1 -6,0,0,4,0.954545455,1,1 -7,0,0,4,1,1,1 -8,0,0,4,0.954545455,1,1 -1,1,0,4,0.941176471,0.970588235,1 -2,1,0,4,0.925925926,1,1 -3,1,0,4,1,1,1 -4,1,0,4,0.875,1,1 -5,1,0,4,0.915322581,1,1 -6,1,0,4,0.947674419,0.994186047,1 -7,1,0,4,0.666666667,1,1 -8,1,0,4,1,1,1 -1,0,1,4,0.925925926,0.987654321,1 -2,0,1,4,0.903703704,1,1 -3,0,1,4,1,1,1 -4,0,1,4,1,1,1 -5,0,1,4,1,1,1 -6,0,1,4,1,1,1 -7,0,1,4,1,1,1 -8,0,1,4,1,1,1 -1,1,1,4,1,1,1 -2,1,1,4,0.911111111,1,1 -3,1,1,4,1,1,1 -4,1,1,4,1,1,1 -5,1,1,4,1,1,1 -6,1,1,4,0.962962963,1,1 -7,1,1,4,1,1,1 -8,1,1,4,1,1,1 -1,0,0,5,0.976744186,1,1 -2,0,0,5,0.981818182,1,1 -3,0,0,5,0.985915493,1,1 -4,0,0,5,1,1,1 -5,0,0,5,1,1,1 -6,0,0,5,1,1,1 -7,0,0,5,1,1,1 -8,0,0,5,0.875,1,1 -1,1,0,5,1,1,1 -2,1,0,5,1,1,1 -3,1,0,5,0.964285714,1,1 -4,1,0,5,1,1,1 -5,1,0,5,0.985714286,1,1 -6,1,0,5,0.951807229,1,1 -7,1,0,5,1,1,1 -8,1,0,5,1,1,1 -1,0,1,5,0.926605505,1,1 -2,0,1,5,0.941176471,1,1 -3,0,1,5,1,1,1 -4,0,1,5,1,1,1 -5,0,1,5,1,1,1 -6,0,1,5,1,1,1 -7,0,1,5,1,1,1 -8,0,1,5,1,1,1 -1,1,1,5,1,1,1 -2,1,1,5,1,1,1 -3,1,1,5,0.972972973,1,1 -4,1,1,5,1,1,1 -5,1,1,5,1,1,1 -6,1,1,5,0.933333333,1,1 -7,1,1,5,1,1,1 -8,1,1,5,1,1,1 -1,0,0,6,0.93837535,0.988795518,1 -2,0,0,6,0.888888889,1,1 -3,0,0,6,0.966832504,0.998341625,1 -4,0,0,6,0.942028986,1,1 -5,0,0,6,0.88034188,1,1 -6,0,0,6,0.925925926,1,1 -7,0,0,6,0.967741935,1,1 -8,0,0,6,0.90625,1,1 -1,1,0,6,0.85915493,1,1 -2,1,0,6,0.818181818,0.96969697,1 -3,1,0,6,1,1,1 -4,1,0,6,0.952380952,1,1 -5,1,0,6,0.879237288,0.997881356,1 -6,1,0,6,0.862944162,0.984771574,1 -7,1,0,6,0.9,1,1 -8,1,0,6,1,1,1 -1,0,1,6,0.927835052,0.996563574,1 -2,0,1,6,0.859375,0.9921875,1 -3,0,1,6,1,1,1 -4,0,1,6,1,1,1 -5,0,1,6,0.92,1,1 -6,0,1,6,1,1,1 -7,0,1,6,0.904761905,1,1 -8,0,1,6,1,1,1 -1,1,1,6,0.982758621,1,1 -2,1,1,6,0.927710843,0.987951807,1 -3,1,1,6,0.982954545,1,1 -4,1,1,6,0.938679245,1,1 -5,1,1,6,1,1,1 -6,1,1,6,0.9375,1,1 -7,1,1,6,1,1,1 -8,1,1,6,1,1,1 +ptype,has_mandatory_tour,has_joint_tour,nonmandatory_tour_type,0_tours,1_tours,2_tours +1,0,0,1,0.829545455,1,1 +2,0,0,1,0.769230769,1,1 +3,0,0,1,0.893939394,1,1 +4,0,0,1,0.75,1,1 +5,0,0,1,0.842105263,1,1 +6,0,0,1,0.714285714,1,1 +7,0,0,1,0.814814815,1,1 +8,0,0,1,0.75,1,1 +1,1,0,1,0.789473684,1,1 +2,1,0,1,0.6,1,1 +3,1,0,1,1,1,1 +4,1,0,1,1,1,1 +5,1,0,1,0.825910931,1,1 +6,1,0,1,0.837209302,1,1 +7,1,0,1,0.6,1,1 +8,1,0,1,1,1,1 +1,0,1,1,0.842105263,1,1 +2,0,1,1,1,1,1 +3,0,1,1,1,1,1 +4,0,1,1,1,1,1 +5,0,1,1,1,1,1 +6,0,1,1,1,1,1 +7,0,1,1,1,1,1 +8,0,1,1,1,1,1 +1,1,1,1,1,1,1 +2,1,1,1,1,1,1 +3,1,1,1,1,1,1 +4,1,1,1,1,1,1 +5,1,1,1,0.777777778,1,1 +6,1,1,1,1,1,1 +7,1,1,1,1,1,1 +8,1,1,1,1,1,1 +1,0,0,2,0.892694064,0.99086758,1 +2,0,0,2,0.84057971,0.992753623,1 +3,0,0,2,0.971014493,1,1 +4,0,0,2,0.96969697,1,1 +5,0,0,2,0.870056497,0.994350282,1 +6,0,0,2,0.866666667,1,1 +7,0,0,2,0.971014493,1,1 +8,0,0,2,0.931034483,1,1 +1,1,0,2,0.885057471,1,1 +2,1,0,2,0.727272727,1,1 +3,1,0,2,0.971428571,1,1 +4,1,0,2,1,1,1 +5,1,0,2,0.895977809,0.993065187,1 +6,1,0,2,0.885185185,1,1 +7,1,0,2,1,1,1 +8,1,0,2,1,1,1 +1,0,1,2,0.910087719,0.993421053,1 +2,0,1,2,0.88,1,1 +3,0,1,2,0.8,1,1 +4,0,1,2,1,1,1 +5,0,1,2,1,1,1 +6,0,1,2,1,1,1 +7,0,1,2,1,1,1 +8,0,1,2,1,1,1 +1,1,1,2,1,1,1 +2,1,1,2,1,1,1 +3,1,1,2,1,1,1 +4,1,1,2,1,1,1 +5,1,1,2,1,1,1 +6,1,1,2,0.964912281,1,1 +7,1,1,2,1,1,1 +8,1,1,2,0.888888889,1,1 +1,0,0,3,0.935643564,0.997524752,1 +2,0,0,3,0.905660377,1,1 +3,0,0,3,0.978813559,1,1 +4,0,0,3,0.928571429,1,1 +5,0,0,3,0.901515152,0.992424242,1 +6,0,0,3,0.863636364,1,1 +7,0,0,3,0.947368421,1,1 +8,0,0,3,0.913043478,1,1 +1,1,0,3,0.893333333,0.986666667,1 +2,1,0,3,1,1,1 +3,1,0,3,1,1,1 +4,1,0,3,0.857142857,1,1 +5,1,0,3,0.916071429,0.996428571,1 +6,1,0,3,0.856382979,0.984042553,1 +7,1,0,3,1,1,1 +8,1,0,3,1,1,1 +1,0,1,3,0.916201117,0.991620112,1 +2,0,1,3,0.912280702,0.98245614,1 +3,0,1,3,1,1,1 +4,0,1,3,1,1,1 +5,0,1,3,1,1,1 +6,0,1,3,0.833333333,1,1 +7,0,1,3,0.961538462,1,1 +8,0,1,3,1,1,1 +1,1,1,3,0.97826087,0.989130435,1 +2,1,1,3,0.97260274,1,1 +3,1,1,3,1,1,1 +4,1,1,3,1,1,1 +5,1,1,3,0.995762712,1,1 +6,1,1,3,0.921568627,0.980392157,1 +7,1,1,3,1,1,1 +8,1,1,3,1,1,1 +1,0,0,4,0.9218107,0.995884774,1 +2,0,0,4,0.900900901,1,1 +3,0,0,4,0.997354497,1,1 +4,0,0,4,0.991176471,1,1 +5,0,0,4,0.921568627,0.980392157,1 +6,0,0,4,0.954545455,1,1 +7,0,0,4,1,1,1 +8,0,0,4,0.954545455,1,1 +1,1,0,4,0.941176471,0.970588235,1 +2,1,0,4,0.925925926,1,1 +3,1,0,4,1,1,1 +4,1,0,4,0.875,1,1 +5,1,0,4,0.915322581,1,1 +6,1,0,4,0.947674419,0.994186047,1 +7,1,0,4,0.666666667,1,1 +8,1,0,4,1,1,1 +1,0,1,4,0.925925926,0.987654321,1 +2,0,1,4,0.903703704,1,1 +3,0,1,4,1,1,1 +4,0,1,4,1,1,1 +5,0,1,4,1,1,1 +6,0,1,4,1,1,1 +7,0,1,4,1,1,1 +8,0,1,4,1,1,1 +1,1,1,4,1,1,1 +2,1,1,4,0.911111111,1,1 +3,1,1,4,1,1,1 +4,1,1,4,1,1,1 +5,1,1,4,1,1,1 +6,1,1,4,0.962962963,1,1 +7,1,1,4,1,1,1 +8,1,1,4,1,1,1 +1,0,0,5,0.976744186,1,1 +2,0,0,5,0.981818182,1,1 +3,0,0,5,0.985915493,1,1 +4,0,0,5,1,1,1 +5,0,0,5,1,1,1 +6,0,0,5,1,1,1 +7,0,0,5,1,1,1 +8,0,0,5,0.875,1,1 +1,1,0,5,1,1,1 +2,1,0,5,1,1,1 +3,1,0,5,0.964285714,1,1 +4,1,0,5,1,1,1 +5,1,0,5,0.985714286,1,1 +6,1,0,5,0.951807229,1,1 +7,1,0,5,1,1,1 +8,1,0,5,1,1,1 +1,0,1,5,0.926605505,1,1 +2,0,1,5,0.941176471,1,1 +3,0,1,5,1,1,1 +4,0,1,5,1,1,1 +5,0,1,5,1,1,1 +6,0,1,5,1,1,1 +7,0,1,5,1,1,1 +8,0,1,5,1,1,1 +1,1,1,5,1,1,1 +2,1,1,5,1,1,1 +3,1,1,5,0.972972973,1,1 +4,1,1,5,1,1,1 +5,1,1,5,1,1,1 +6,1,1,5,0.933333333,1,1 +7,1,1,5,1,1,1 +8,1,1,5,1,1,1 +1,0,0,6,0.93837535,0.988795518,1 +2,0,0,6,0.888888889,1,1 +3,0,0,6,0.966832504,0.998341625,1 +4,0,0,6,0.942028986,1,1 +5,0,0,6,0.88034188,1,1 +6,0,0,6,0.925925926,1,1 +7,0,0,6,0.967741935,1,1 +8,0,0,6,0.90625,1,1 +1,1,0,6,0.85915493,1,1 +2,1,0,6,0.818181818,0.96969697,1 +3,1,0,6,1,1,1 +4,1,0,6,0.952380952,1,1 +5,1,0,6,0.879237288,0.997881356,1 +6,1,0,6,0.862944162,0.984771574,1 +7,1,0,6,0.9,1,1 +8,1,0,6,1,1,1 +1,0,1,6,0.927835052,0.996563574,1 +2,0,1,6,0.859375,0.9921875,1 +3,0,1,6,1,1,1 +4,0,1,6,1,1,1 +5,0,1,6,0.92,1,1 +6,0,1,6,1,1,1 +7,0,1,6,0.904761905,1,1 +8,0,1,6,1,1,1 +1,1,1,6,0.982758621,1,1 +2,1,1,6,0.927710843,0.987951807,1 +3,1,1,6,0.982954545,1,1 +4,1,1,6,0.938679245,1,1 +5,1,1,6,1,1,1 +6,1,1,6,0.9375,1,1 +7,1,1,6,1,1,1 +8,1,1,6,1,1,1 diff --git a/activitysim/examples/example_mtc/configs/non_mandatory_tour_scheduling.yaml b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_scheduling.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/non_mandatory_tour_scheduling.yaml rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_scheduling.yaml diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv rename to activitysim/examples/prototype_mtc/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv index e9f9f2f38e..7a2b90d1c9 --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv +++ b/activitysim/examples/prototype_mtc/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv @@ -1,4 +1,4 @@ -Description,Target,Expression -destination in central business district,destination_in_cbd,"(reindex(land_use.area_type, non_mandatory_tours.destination) < setting('cbd_threshold')) * 1" -#,, -,num_person_joint_tours,"reindex_i(joint_tour_participants.groupby('person_id').size(), non_mandatory_tours.person_id)" +Description,Target,Expression +destination in central business district,destination_in_cbd,"(reindex(land_use.area_type, non_mandatory_tours.destination) < setting('cbd_threshold')) * 1" +#,, +,num_person_joint_tours,"reindex_i(joint_tour_participants.groupby('person_id').size(), non_mandatory_tours.person_id)" diff --git a/activitysim/examples/example_psrc/configs/school_location.csv b/activitysim/examples/prototype_mtc/configs/school_location.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_psrc/configs/school_location.csv rename to activitysim/examples/prototype_mtc/configs/school_location.csv index 04d4647262..9448d12af2 --- a/activitysim/examples/example_psrc/configs/school_location.csv +++ b/activitysim/examples/prototype_mtc/configs/school_location.csv @@ -1,12 +1,12 @@ -Label,Description,Expression,university,highschool,gradeschool -local_dist,,_DIST@skims['DIST'],1,1,1 -util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_univ_dist_0_1,coef_high_dist_0_1,coef_grade_dist_0_1 -util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_univ_dist_1_2,coef_high_grade_dist_1_2,coef_high_grade_dist_1_2 -util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_univ_dist_2_5,coef_high_grade_dist_2_5,coef_high_grade_dist_2_5 -util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_univ_dist_5_15,coef_high_dist_5_15,coef_grade_dist_5_15 -util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_univ_dist_15_up,coef_high_dist_15_up,coef_grade_dist_15_up -util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1,1,1 -util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1 -util_no_attractions,No attractions,@df['size_term']==0,-999,-999,-999 -util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum -util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1 +Label,Description,Expression,university,highschool,gradeschool +local_dist,,_DIST@skims['DIST'],1,1,1 +util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_univ_dist_0_1,coef_high_dist_0_1,coef_grade_dist_0_1 +util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_univ_dist_1_2,coef_high_grade_dist_1_2,coef_high_grade_dist_1_2 +util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_univ_dist_2_5,coef_high_grade_dist_2_5,coef_high_grade_dist_2_5 +util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_univ_dist_5_15,coef_high_dist_5_15,coef_grade_dist_5_15 +util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_univ_dist_15_up,coef_high_dist_15_up,coef_grade_dist_15_up +util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1,1,1 +util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1 +util_no_attractions,No attractions,@df['size_term']==0,-999,-999,-999 +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum,coef_mode_logsum,coef_mode_logsum +util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1 diff --git a/activitysim/examples/example_mtc/configs/school_location.yaml b/activitysim/examples/prototype_mtc/configs/school_location.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/school_location.yaml rename to activitysim/examples/prototype_mtc/configs/school_location.yaml diff --git a/activitysim/examples/example_mtc/configs/school_location_coefficients.csv b/activitysim/examples/prototype_mtc/configs/school_location_coefficients.csv similarity index 96% rename from activitysim/examples/example_mtc/configs/school_location_coefficients.csv rename to activitysim/examples/prototype_mtc/configs/school_location_coefficients.csv index 4e4d638772..b9ef59c83f 100644 --- a/activitysim/examples/example_mtc/configs/school_location_coefficients.csv +++ b/activitysim/examples/prototype_mtc/configs/school_location_coefficients.csv @@ -1,17 +1,17 @@ -coefficient_name,value,constrain -coef_univ_dist_0_1,-3.2451,F -coef_univ_dist_1_2,-2.7011,F -coef_univ_dist_2_5,-0.5707,F -coef_univ_dist_5_15,-0.5002,F -coef_univ_dist_15_up,-0.073,F -coef_high_dist_0_1,-0.9523,F -coef_high_grade_dist_1_2,-0.57,F -coef_high_grade_dist_2_5,-0.57,F -coef_high_dist_5_15,-0.193,F -coef_high_dist_15_up,-0.1882,F -coef_grade_dist_0_1,-1.6419,F -#coef_high_grade_dist_1_2,-0.57,F -#coef_high_grade_dist_2_5,-0.57,F -coef_grade_dist_5_15,-0.2031,F -coef_grade_dist_15_up,-0.046,F -coef_mode_logsum,0.5358,F +coefficient_name,value,constrain +coef_univ_dist_0_1,-3.2451,F +coef_univ_dist_1_2,-2.7011,F +coef_univ_dist_2_5,-0.5707,F +coef_univ_dist_5_15,-0.5002,F +coef_univ_dist_15_up,-0.073,F +coef_high_dist_0_1,-0.9523,F +coef_high_grade_dist_1_2,-0.57,F +coef_high_grade_dist_2_5,-0.57,F +coef_high_dist_5_15,-0.193,F +coef_high_dist_15_up,-0.1882,F +coef_grade_dist_0_1,-1.6419,F +#coef_high_grade_dist_1_2,-0.57,F +#coef_high_grade_dist_2_5,-0.57,F +coef_grade_dist_5_15,-0.2031,F +coef_grade_dist_15_up,-0.046,F +coef_mode_logsum,0.5358,F diff --git a/activitysim/examples/example_psrc/configs/school_location_sample.csv b/activitysim/examples/prototype_mtc/configs/school_location_sample.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_psrc/configs/school_location_sample.csv rename to activitysim/examples/prototype_mtc/configs/school_location_sample.csv index f785f26749..d4f980e909 --- a/activitysim/examples/example_psrc/configs/school_location_sample.csv +++ b/activitysim/examples/prototype_mtc/configs/school_location_sample.csv @@ -1,10 +1,10 @@ -Label,Description,Expression,university,highschool,gradeschool -local_dist,,_DIST@skims['DIST'],1,1,1 -util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_univ_dist_0_1,coef_high_dist_0_1,coef_grade_dist_0_1 -util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_univ_dist_1_2,coef_high_grade_dist_1_2,coef_high_grade_dist_1_2 -util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_univ_dist_2_5,coef_high_grade_dist_2_5,coef_high_grade_dist_2_5 -util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_univ_dist_5_15,coef_high_dist_5_15,coef_grade_dist_5_15 -util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_univ_dist_15_up,coef_high_dist_15_up,coef_grade_dist_15_up -util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1,1,1 -util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1 -util_no_attractions,No attractions,@df['size_term']==0,-999,-999,-999 +Label,Description,Expression,university,highschool,gradeschool +local_dist,,_DIST@skims['DIST'],1,1,1 +util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_univ_dist_0_1,coef_high_dist_0_1,coef_grade_dist_0_1 +util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_univ_dist_1_2,coef_high_grade_dist_1_2,coef_high_grade_dist_1_2 +util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_univ_dist_2_5,coef_high_grade_dist_2_5,coef_high_grade_dist_2_5 +util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_univ_dist_5_15,coef_high_dist_5_15,coef_grade_dist_5_15 +util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_univ_dist_15_up,coef_high_dist_15_up,coef_grade_dist_15_up +util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1,1,1 +util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1 +util_no_attractions,No attractions,@df['size_term']==0,-999,-999,-999 diff --git a/activitysim/examples/example_mtc/configs/settings.yaml b/activitysim/examples/prototype_mtc/configs/settings.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/settings.yaml rename to activitysim/examples/prototype_mtc/configs/settings.yaml diff --git a/activitysim/examples/example_psrc/configs/shadow_pricing.yaml b/activitysim/examples/prototype_mtc/configs/shadow_pricing.yaml old mode 100755 new mode 100644 similarity index 96% rename from activitysim/examples/example_psrc/configs/shadow_pricing.yaml rename to activitysim/examples/prototype_mtc/configs/shadow_pricing.yaml index b8ee4e9afd..b61ec41928 --- a/activitysim/examples/example_psrc/configs/shadow_pricing.yaml +++ b/activitysim/examples/prototype_mtc/configs/shadow_pricing.yaml @@ -1,34 +1,34 @@ -shadow_pricing_models: - school: school_location - workplace: workplace_location - -# global switch to enable/disable loading of saved shadow prices -# (ignored if global use_shadow_pricing switch is False) -LOAD_SAVED_SHADOW_PRICES: True - -# number of shadow price iterations for cold start -MAX_ITERATIONS: 10 - -# number of shadow price iterations for warm start (after loading saved shadow_prices) -MAX_ITERATIONS_SAVED: 1 - -# ignore criteria for zones smaller than size_threshold -SIZE_THRESHOLD: 10 - -# zone passes if modeled is within percent_tolerance of predicted_size -PERCENT_TOLERANCE: 5 - -# max percentage of zones allowed to fail -FAIL_THRESHOLD: 10 - -# CTRAMP or daysim -SHADOW_PRICE_METHOD: ctramp -#SHADOW_PRICE_METHOD: daysim - -# ctramp-style shadow_pricing_method parameters -DAMPING_FACTOR: 1 - -# daysim-style shadow_pricing_method parameters -# FIXME should these be the same as PERCENT_TOLERANCE and FAIL_THRESHOLD above? -DAYSIM_ABSOLUTE_TOLERANCE: 50 -DAYSIM_PERCENT_TOLERANCE: 10 +shadow_pricing_models: + school: school_location + workplace: workplace_location + +# global switch to enable/disable loading of saved shadow prices +# (ignored if global use_shadow_pricing switch is False) +LOAD_SAVED_SHADOW_PRICES: True + +# number of shadow price iterations for cold start +MAX_ITERATIONS: 10 + +# number of shadow price iterations for warm start (after loading saved shadow_prices) +MAX_ITERATIONS_SAVED: 1 + +# ignore criteria for zones smaller than size_threshold +SIZE_THRESHOLD: 10 + +# zone passes if modeled is within percent_tolerance of predicted_size +PERCENT_TOLERANCE: 5 + +# max percentage of zones allowed to fail +FAIL_THRESHOLD: 10 + +# CTRAMP or daysim +SHADOW_PRICE_METHOD: ctramp +#SHADOW_PRICE_METHOD: daysim + +# ctramp-style shadow_pricing_method parameters +DAMPING_FACTOR: 1 + +# daysim-style shadow_pricing_method parameters +# FIXME should these be the same as PERCENT_TOLERANCE and FAIL_THRESHOLD above? +DAYSIM_ABSOLUTE_TOLERANCE: 50 +DAYSIM_PERCENT_TOLERANCE: 10 diff --git a/activitysim/examples/example_psrc/configs/stop_frequency.yaml b/activitysim/examples/prototype_mtc/configs/stop_frequency.yaml old mode 100755 new mode 100644 similarity index 96% rename from activitysim/examples/example_psrc/configs/stop_frequency.yaml rename to activitysim/examples/prototype_mtc/configs/stop_frequency.yaml index 66fcdd78f5..b12ff8f4b5 --- a/activitysim/examples/example_psrc/configs/stop_frequency.yaml +++ b/activitysim/examples/prototype_mtc/configs/stop_frequency.yaml @@ -1,77 +1,77 @@ -LOGIT_TYPE: MNL - -preprocessor: - SPEC: stop_frequency_annotate_tours_preprocessor - DF: tours_merged - TABLES: - - persons - - land_use - - accessibility - -SEGMENT_COL: primary_purpose - -SPEC_SEGMENTS: - - primary_purpose: work - SPEC: stop_frequency_work.csv - COEFFICIENTS: stop_frequency_coefficients_work.csv - - primary_purpose: school - SPEC: stop_frequency_school.csv - COEFFICIENTS: stop_frequency_coefficients_school.csv - - primary_purpose: univ - SPEC: stop_frequency_univ.csv - COEFFICIENTS: stop_frequency_coefficients_univ.csv - - primary_purpose: social - SPEC: stop_frequency_social.csv - COEFFICIENTS: stop_frequency_coefficients_social.csv - - primary_purpose: shopping - SPEC: stop_frequency_shopping.csv - COEFFICIENTS: stop_frequency_coefficients_shopping.csv - - primary_purpose: eatout - SPEC: stop_frequency_eatout.csv - COEFFICIENTS: stop_frequency_coefficients_eatout.csv - - primary_purpose: escort - SPEC: stop_frequency_escort.csv - COEFFICIENTS: stop_frequency_coefficients_escort.csv - - primary_purpose: othmaint - SPEC: stop_frequency_othmaint.csv - COEFFICIENTS: stop_frequency_coefficients_othmaint.csv - - primary_purpose: othdiscr - SPEC: stop_frequency_othdiscr.csv - COEFFICIENTS: stop_frequency_coefficients_othdiscr.csv - - primary_purpose: atwork - SPEC: stop_frequency_atwork.csv - COEFFICIENTS: stop_frequency_coefficients_atwork.csv - -CONSTANTS: - TRANSIT_MODES: - - WALK_LOC - - WALK_LRF - - WALK_EXP - - WALK_HVY - - WALK_COM - - DRIVE_LOC - - DRIVE_LRF - - DRIVE_EXP - - DRIVE_HVY - - DRIVE_COM - DRIVE_TO_TRANSIT_MODES: - - DRIVE_LOC - - DRIVE_LRF - - DRIVE_EXP - - DRIVE_HVY - - DRIVE_COM - NONMOTORIZED_MODES: - - WALK - - BIKE - SHOP_TOUR: shopping - MAINT_TOUR: othmaint - SCHOOL_TOUR: school - EATOUT_TOUR: eatout - SOCIAL_TOUR: social - num_atwork_subtours_map: - no_subtours: 0 - eat: 1 - business1: 1 - maint: 1 - business2: 2 - eat_business: 2 +LOGIT_TYPE: MNL + +preprocessor: + SPEC: stop_frequency_annotate_tours_preprocessor + DF: tours_merged + TABLES: + - persons + - land_use + - accessibility + +SEGMENT_COL: primary_purpose + +SPEC_SEGMENTS: + - primary_purpose: work + SPEC: stop_frequency_work.csv + COEFFICIENTS: stop_frequency_coefficients_work.csv + - primary_purpose: school + SPEC: stop_frequency_school.csv + COEFFICIENTS: stop_frequency_coefficients_school.csv + - primary_purpose: univ + SPEC: stop_frequency_univ.csv + COEFFICIENTS: stop_frequency_coefficients_univ.csv + - primary_purpose: social + SPEC: stop_frequency_social.csv + COEFFICIENTS: stop_frequency_coefficients_social.csv + - primary_purpose: shopping + SPEC: stop_frequency_shopping.csv + COEFFICIENTS: stop_frequency_coefficients_shopping.csv + - primary_purpose: eatout + SPEC: stop_frequency_eatout.csv + COEFFICIENTS: stop_frequency_coefficients_eatout.csv + - primary_purpose: escort + SPEC: stop_frequency_escort.csv + COEFFICIENTS: stop_frequency_coefficients_escort.csv + - primary_purpose: othmaint + SPEC: stop_frequency_othmaint.csv + COEFFICIENTS: stop_frequency_coefficients_othmaint.csv + - primary_purpose: othdiscr + SPEC: stop_frequency_othdiscr.csv + COEFFICIENTS: stop_frequency_coefficients_othdiscr.csv + - primary_purpose: atwork + SPEC: stop_frequency_atwork.csv + COEFFICIENTS: stop_frequency_coefficients_atwork.csv + +CONSTANTS: + TRANSIT_MODES: + - WALK_LOC + - WALK_LRF + - WALK_EXP + - WALK_HVY + - WALK_COM + - DRIVE_LOC + - DRIVE_LRF + - DRIVE_EXP + - DRIVE_HVY + - DRIVE_COM + DRIVE_TO_TRANSIT_MODES: + - DRIVE_LOC + - DRIVE_LRF + - DRIVE_EXP + - DRIVE_HVY + - DRIVE_COM + NONMOTORIZED_MODES: + - WALK + - BIKE + SHOP_TOUR: shopping + MAINT_TOUR: othmaint + SCHOOL_TOUR: school + EATOUT_TOUR: eatout + SOCIAL_TOUR: social + num_atwork_subtours_map: + no_subtours: 0 + eat: 1 + business1: 1 + maint: 1 + business2: 2 + eat_business: 2 diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_alternatives.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_alternatives.csv old mode 100755 new mode 100644 similarity index 94% rename from activitysim/examples/example_psrc/configs/stop_frequency_alternatives.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_alternatives.csv index 0153cb399b..72f49a7c77 --- a/activitysim/examples/example_psrc/configs/stop_frequency_alternatives.csv +++ b/activitysim/examples/prototype_mtc/configs/stop_frequency_alternatives.csv @@ -1,18 +1,18 @@ -#,,alt file for building tours even though simulation is simple_simulate not interaction_simulate -alt,out,in -0out_0in,0,0 -0out_1in,0,1 -0out_2in,0,2 -0out_3in,0,3 -1out_0in,1,0 -1out_1in,1,1 -1out_2in,1,2 -1out_3in,1,3 -2out_0in,2,0 -2out_1in,2,1 -2out_2in,2,2 -2out_3in,2,3 -3out_0in,3,0 -3out_1in,3,1 -3out_2in,3,2 -3out_3in,3,3 +#,,alt file for building tours even though simulation is simple_simulate not interaction_simulate +alt,out,in +0out_0in,0,0 +0out_1in,0,1 +0out_2in,0,2 +0out_3in,0,3 +1out_0in,1,0 +1out_1in,1,1 +1out_2in,1,2 +1out_3in,1,3 +2out_0in,2,0 +2out_1in,2,1 +2out_2in,2,2 +2out_3in,2,3 +3out_0in,3,0 +3out_1in,3,1 +3out_2in,3,2 +3out_3in,3,3 diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_annotate_tours_preprocessor.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/stop_frequency_annotate_tours_preprocessor.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_annotate_tours_preprocessor.csv index 982eeabb97..d7b2f1d61f --- a/activitysim/examples/example_psrc/configs/stop_frequency_annotate_tours_preprocessor.csv +++ b/activitysim/examples/prototype_mtc/configs/stop_frequency_annotate_tours_preprocessor.csv @@ -1,47 +1,47 @@ -Description,Target,Expression -#,, -# define primary_purpose to use for slicing choosers with a value that identifies the spec to be used ,, -# e.g. univ segment means there will be a spec called stop_frequency_univ.csv,, -# so the 'school' tour_type can treat univ and non-univ school tours differently,, -,primary_purpose,"df.tour_type.where((df.tour_type != 'school') | ~df.is_university, 'univ')" -,primary_purpose,"primary_purpose.where(df.tour_category!='atwork', 'atwork')" -#,, -,distance_in_miles,od_skims['DIST'] -#,, -,is_joint,df.tour_category=='joint' -,_HH_PERSON_COUNT,"lambda exp, persons: persons.query(exp).groupby('household_id').size()" -,num_full,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_FULL, persons), df.household_id)" -,num_part,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_PART, persons), df.household_id)" -,num_student,"reindex_i(_HH_PERSON_COUNT('pstudent != %s' % PSTUDENT_NOT, persons), df.household_id)" -Num Kids between 0 and 4 (including) years old,num_age_0_4,"reindex_i(_HH_PERSON_COUNT('age < 5', persons), df.household_id)" -Num kids between 4 and 15 (including) years old,num_age_5_15,"reindex_i(_HH_PERSON_COUNT('(age >= 5) & (age <16)', persons), df.household_id)" -Number of Adults (>= 16 years old),num_adult,"reindex_i(_HH_PERSON_COUNT('age >= 16', persons), df.household_id)" -,more_cars_than_workers,df.auto_ownership >= (num_full + num_part) -,tour_mode_is_transit,df.tour_mode.isin(TRANSIT_MODES) -,tour_mode_is_drive_transit,df.tour_mode.isin(DRIVE_TO_TRANSIT_MODES) -,tour_mode_is_non_motorized,df.tour_mode.isin(NONMOTORIZED_MODES) -#,, -#num_work_tours already defined,, -school but not university,num_school_tours,"reindex_i(df[primary_purpose==SCHOOL_TOUR].groupby('person_id').size(), df.person_id)" -,num_univ_tours,(df.is_university) * num_school_tours -#num_escort_tours already defined,, -# indiv tour counts should not include joint tours by point_person,, -,num_shop_tours,"reindex_i(df[~is_joint & (df.tour_type==SHOP_TOUR)].groupby('person_id').size(), df.person_id)" -,num_maint_tours,"reindex_i(df[~is_joint & (df.tour_type==MAINT_TOUR)].groupby('person_id').size(), df.person_id)" -,num_eatout_tours,"reindex_i(df[~is_joint & (df.tour_type==EATOUT_TOUR)].groupby('person_id').size(), df.person_id)" -,num_social_tours,"reindex_i(df[~is_joint & (df.tour_type==SOCIAL_TOUR)].groupby('person_id').size(), df.person_id)" -#,, -Number of subtours in the tour,num_atwork_subtours,"df.atwork_subtour_frequency.map(num_atwork_subtours_map, na_action='ignore').fillna(0).astype(np.int8)" -#,, -Number of hh shop tours including joint,num_hh_shop_tours,"reindex_i(df[df.tour_type==SHOP_TOUR].groupby('household_id').size(), df.person_id)" -Number of hh maint tours including joint,num_hh_maint_tours,"reindex_i(df[df.tour_type==MAINT_TOUR].groupby('household_id').size(), df.person_id)" -tourStartsInPeakPeriod,_tour_starts_in_peak,(network_los.skim_time_period_label(df.start) == 'AM') | (network_los.skim_time_period_label(df.start) == 'PM') -AccesibilityAtOrigin fallback,hhacc,0 -AccesibilityAtOrigin if transit,hhacc,"hhacc.where(~tour_mode_is_transit, df.trPkRetail.where(_tour_starts_in_peak, df.trOpRetail))" -AccesibilityAtOrigin if non_motorized,hhacc,"hhacc.where(~tour_mode_is_non_motorized, df.nmRetail)" -AccesibilityADestination fallback,pracc,0 -AccesibilityADestination peak transit,_dest_trPkRetail,"reindex(accessibility.trPkRetail, df.destination)" -AccesibilityADestination off-peak transit,_dest_trOpRetail,"reindex(accessibility.trOpRetail, df.destination)" -AccesibilityAtDestination if transit,pracc,"pracc.where(~tour_mode_is_transit, _dest_trPkRetail.where(_tour_starts_in_peak, _dest_trOpRetail))" -AccesibilityAtDestination if non_motorized,pracc,"pracc.where(~tour_mode_is_non_motorized, reindex(accessibility.nmRetail, df.destination))" -,destination_area_type,"reindex(land_use.area_type, df.destination)" +Description,Target,Expression +#,, +# define primary_purpose to use for slicing choosers with a value that identifies the spec to be used ,, +# e.g. univ segment means there will be a spec called stop_frequency_univ.csv,, +# so the 'school' tour_type can treat univ and non-univ school tours differently,, +,primary_purpose,"df.tour_type.where((df.tour_type != 'school') | ~df.is_university, 'univ')" +,primary_purpose,"primary_purpose.where(df.tour_category!='atwork', 'atwork')" +#,, +,distance_in_miles,od_skims['DIST'] +#,, +,is_joint,df.tour_category=='joint' +,_HH_PERSON_COUNT,"lambda exp, persons: persons.query(exp).groupby('household_id').size()" +,num_full,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_FULL, persons), df.household_id)" +,num_part,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_PART, persons), df.household_id)" +,num_student,"reindex_i(_HH_PERSON_COUNT('pstudent != %s' % PSTUDENT_NOT, persons), df.household_id)" +Num Kids between 0 and 4 (including) years old,num_age_0_4,"reindex_i(_HH_PERSON_COUNT('age < 5', persons), df.household_id)" +Num kids between 4 and 15 (including) years old,num_age_5_15,"reindex_i(_HH_PERSON_COUNT('(age >= 5) & (age <16)', persons), df.household_id)" +Number of Adults (>= 16 years old),num_adult,"reindex_i(_HH_PERSON_COUNT('age >= 16', persons), df.household_id)" +,more_cars_than_workers,df.auto_ownership >= (num_full + num_part) +,tour_mode_is_transit,df.tour_mode.isin(TRANSIT_MODES) +,tour_mode_is_drive_transit,df.tour_mode.isin(DRIVE_TO_TRANSIT_MODES) +,tour_mode_is_non_motorized,df.tour_mode.isin(NONMOTORIZED_MODES) +#,, +#num_work_tours already defined,, +school but not university,num_school_tours,"reindex_i(df[primary_purpose==SCHOOL_TOUR].groupby('person_id').size(), df.person_id)" +,num_univ_tours,(df.is_university) * num_school_tours +#num_escort_tours already defined,, +# indiv tour counts should not include joint tours by point_person,, +,num_shop_tours,"reindex_i(df[~is_joint & (df.tour_type==SHOP_TOUR)].groupby('person_id').size(), df.person_id)" +,num_maint_tours,"reindex_i(df[~is_joint & (df.tour_type==MAINT_TOUR)].groupby('person_id').size(), df.person_id)" +,num_eatout_tours,"reindex_i(df[~is_joint & (df.tour_type==EATOUT_TOUR)].groupby('person_id').size(), df.person_id)" +,num_social_tours,"reindex_i(df[~is_joint & (df.tour_type==SOCIAL_TOUR)].groupby('person_id').size(), df.person_id)" +#,, +Number of subtours in the tour,num_atwork_subtours,"df.atwork_subtour_frequency.map(num_atwork_subtours_map, na_action='ignore').fillna(0).astype(np.int8)" +#,, +Number of hh shop tours including joint,num_hh_shop_tours,"reindex_i(df[df.tour_type==SHOP_TOUR].groupby('household_id').size(), df.person_id)" +Number of hh maint tours including joint,num_hh_maint_tours,"reindex_i(df[df.tour_type==MAINT_TOUR].groupby('household_id').size(), df.person_id)" +tourStartsInPeakPeriod,_tour_starts_in_peak,(network_los.skim_time_period_label(df.start) == 'AM') | (network_los.skim_time_period_label(df.start) == 'PM') +AccesibilityAtOrigin fallback,hhacc,0 +AccesibilityAtOrigin if transit,hhacc,"hhacc.where(~tour_mode_is_transit, df.trPkRetail.where(_tour_starts_in_peak, df.trOpRetail))" +AccesibilityAtOrigin if non_motorized,hhacc,"hhacc.where(~tour_mode_is_non_motorized, df.nmRetail)" +AccesibilityADestination fallback,pracc,0 +AccesibilityADestination peak transit,_dest_trPkRetail,"reindex(accessibility.trPkRetail, df.destination)" +AccesibilityADestination off-peak transit,_dest_trOpRetail,"reindex(accessibility.trOpRetail, df.destination)" +AccesibilityAtDestination if transit,pracc,"pracc.where(~tour_mode_is_transit, _dest_trPkRetail.where(_tour_starts_in_peak, _dest_trOpRetail))" +AccesibilityAtDestination if non_motorized,pracc,"pracc.where(~tour_mode_is_non_motorized, reindex(accessibility.nmRetail, df.destination))" +,destination_area_type,"reindex(land_use.area_type, df.destination)" diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_atwork.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_atwork.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_atwork.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_atwork.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_coefficients_atwork.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_atwork.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_coefficients_atwork.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_atwork.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_coefficients_eatout.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_eatout.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_coefficients_eatout.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_eatout.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_coefficients_escort.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_escort.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_coefficients_escort.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_escort.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_coefficients_othdiscr.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_othdiscr.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_coefficients_othdiscr.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_othdiscr.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_coefficients_othmaint.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_othmaint.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_coefficients_othmaint.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_othmaint.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_coefficients_school.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_school.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_coefficients_school.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_school.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_coefficients_shopping.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_shopping.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_coefficients_shopping.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_shopping.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_coefficients_social.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_social.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_coefficients_social.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_social.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_coefficients_univ.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_univ.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_coefficients_univ.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_univ.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_coefficients_work.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_work.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_coefficients_work.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_coefficients_work.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_eatout.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_eatout.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_eatout.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_eatout.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_escort.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_escort.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_escort.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_escort.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_othdiscr.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_othdiscr.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_othdiscr.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_othdiscr.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_othmaint.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_othmaint.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_othmaint.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_othmaint.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_school.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_school.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_school.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_school.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_shopping.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_shopping.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_shopping.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_shopping.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_social.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_social.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_social.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_social.csv diff --git a/activitysim/examples/example_psrc/configs/stop_frequency_univ.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_univ.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/stop_frequency_univ.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_univ.csv diff --git a/activitysim/examples/example_mtc/configs/stop_frequency_work.csv b/activitysim/examples/prototype_mtc/configs/stop_frequency_work.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/stop_frequency_work.csv rename to activitysim/examples/prototype_mtc/configs/stop_frequency_work.csv diff --git a/activitysim/examples/example_mtc/configs/summarize.csv b/activitysim/examples/prototype_mtc/configs/summarize.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/summarize.csv rename to activitysim/examples/prototype_mtc/configs/summarize.csv diff --git a/activitysim/examples/example_mtc/configs/summarize.yaml b/activitysim/examples/prototype_mtc/configs/summarize.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/summarize.yaml rename to activitysim/examples/prototype_mtc/configs/summarize.yaml diff --git a/activitysim/examples/example_mtc/configs/summarize_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/summarize_preprocessor.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/summarize_preprocessor.csv rename to activitysim/examples/prototype_mtc/configs/summarize_preprocessor.csv diff --git a/activitysim/examples/example_psrc/configs/tour_departure_and_duration_alternatives.csv b/activitysim/examples/prototype_mtc/configs/tour_departure_and_duration_alternatives.csv old mode 100755 new mode 100644 similarity index 84% rename from activitysim/examples/example_psrc/configs/tour_departure_and_duration_alternatives.csv rename to activitysim/examples/prototype_mtc/configs/tour_departure_and_duration_alternatives.csv index 05f02b7964..bddab06b9d --- a/activitysim/examples/example_psrc/configs/tour_departure_and_duration_alternatives.csv +++ b/activitysim/examples/prototype_mtc/configs/tour_departure_and_duration_alternatives.csv @@ -1,191 +1,191 @@ -start,end -5,5 -5,6 -5,7 -5,8 -5,9 -5,10 -5,11 -5,12 -5,13 -5,14 -5,15 -5,16 -5,17 -5,18 -5,19 -5,20 -5,21 -5,22 -5,23 -6,6 -6,7 -6,8 -6,9 -6,10 -6,11 -6,12 -6,13 -6,14 -6,15 -6,16 -6,17 -6,18 -6,19 -6,20 -6,21 -6,22 -6,23 -7,7 -7,8 -7,9 -7,10 -7,11 -7,12 -7,13 -7,14 -7,15 -7,16 -7,17 -7,18 -7,19 -7,20 -7,21 -7,22 -7,23 -8,8 -8,9 -8,10 -8,11 -8,12 -8,13 -8,14 -8,15 -8,16 -8,17 -8,18 -8,19 -8,20 -8,21 -8,22 -8,23 -9,9 -9,10 -9,11 -9,12 -9,13 -9,14 -9,15 -9,16 -9,17 -9,18 -9,19 -9,20 -9,21 -9,22 -9,23 -10,10 -10,11 -10,12 -10,13 -10,14 -10,15 -10,16 -10,17 -10,18 -10,19 -10,20 -10,21 -10,22 -10,23 -11,11 -11,12 -11,13 -11,14 -11,15 -11,16 -11,17 -11,18 -11,19 -11,20 -11,21 -11,22 -11,23 -12,12 -12,13 -12,14 -12,15 -12,16 -12,17 -12,18 -12,19 -12,20 -12,21 -12,22 -12,23 -13,13 -13,14 -13,15 -13,16 -13,17 -13,18 -13,19 -13,20 -13,21 -13,22 -13,23 -14,14 -14,15 -14,16 -14,17 -14,18 -14,19 -14,20 -14,21 -14,22 -14,23 -15,15 -15,16 -15,17 -15,18 -15,19 -15,20 -15,21 -15,22 -15,23 -16,16 -16,17 -16,18 -16,19 -16,20 -16,21 -16,22 -16,23 -17,17 -17,18 -17,19 -17,20 -17,21 -17,22 -17,23 -18,18 -18,19 -18,20 -18,21 -18,22 -18,23 -19,19 -19,20 -19,21 -19,22 -19,23 -20,20 -20,21 -20,22 -20,23 -21,21 -21,22 -21,23 -22,22 -22,23 +start,end +5,5 +5,6 +5,7 +5,8 +5,9 +5,10 +5,11 +5,12 +5,13 +5,14 +5,15 +5,16 +5,17 +5,18 +5,19 +5,20 +5,21 +5,22 +5,23 +6,6 +6,7 +6,8 +6,9 +6,10 +6,11 +6,12 +6,13 +6,14 +6,15 +6,16 +6,17 +6,18 +6,19 +6,20 +6,21 +6,22 +6,23 +7,7 +7,8 +7,9 +7,10 +7,11 +7,12 +7,13 +7,14 +7,15 +7,16 +7,17 +7,18 +7,19 +7,20 +7,21 +7,22 +7,23 +8,8 +8,9 +8,10 +8,11 +8,12 +8,13 +8,14 +8,15 +8,16 +8,17 +8,18 +8,19 +8,20 +8,21 +8,22 +8,23 +9,9 +9,10 +9,11 +9,12 +9,13 +9,14 +9,15 +9,16 +9,17 +9,18 +9,19 +9,20 +9,21 +9,22 +9,23 +10,10 +10,11 +10,12 +10,13 +10,14 +10,15 +10,16 +10,17 +10,18 +10,19 +10,20 +10,21 +10,22 +10,23 +11,11 +11,12 +11,13 +11,14 +11,15 +11,16 +11,17 +11,18 +11,19 +11,20 +11,21 +11,22 +11,23 +12,12 +12,13 +12,14 +12,15 +12,16 +12,17 +12,18 +12,19 +12,20 +12,21 +12,22 +12,23 +13,13 +13,14 +13,15 +13,16 +13,17 +13,18 +13,19 +13,20 +13,21 +13,22 +13,23 +14,14 +14,15 +14,16 +14,17 +14,18 +14,19 +14,20 +14,21 +14,22 +14,23 +15,15 +15,16 +15,17 +15,18 +15,19 +15,20 +15,21 +15,22 +15,23 +16,16 +16,17 +16,18 +16,19 +16,20 +16,21 +16,22 +16,23 +17,17 +17,18 +17,19 +17,20 +17,21 +17,22 +17,23 +18,18 +18,19 +18,20 +18,21 +18,22 +18,23 +19,19 +19,20 +19,21 +19,22 +19,23 +20,20 +20,21 +20,22 +20,23 +21,21 +21,22 +21,23 +22,22 +22,23 23,23 \ No newline at end of file diff --git a/activitysim/examples/example_sandag/configs_2_zone/tour_departure_and_duration_segments.csv b/activitysim/examples/prototype_mtc/configs/tour_departure_and_duration_segments.csv similarity index 100% rename from activitysim/examples/example_sandag/configs_2_zone/tour_departure_and_duration_segments.csv rename to activitysim/examples/prototype_mtc/configs/tour_departure_and_duration_segments.csv diff --git a/activitysim/examples/example_mtc/configs/tour_mode_choice.csv b/activitysim/examples/prototype_mtc/configs/tour_mode_choice.csv similarity index 99% rename from activitysim/examples/example_mtc/configs/tour_mode_choice.csv rename to activitysim/examples/prototype_mtc/configs/tour_mode_choice.csv index 8f6e7aadb9..5bd4898e03 100644 --- a/activitysim/examples/example_mtc/configs/tour_mode_choice.csv +++ b/activitysim/examples/prototype_mtc/configs/tour_mode_choice.csv @@ -1,344 +1,344 @@ -Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_LOC,WALK_LRF,WALK_EXP,WALK_HVY,WALK_COM,DRIVE_LOC,DRIVE_LRF,DRIVE_EXP,DRIVE_HVY,DRIVE_COM,TAXI,TNC_SINGLE,TNC_SHARED -#,Drive alone no toll,,,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,auto_ownership == 0,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['SOV_TIME'] + dot_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Terminal_time,DRIVEALONEFREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']),coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOV_BTOLL'] + dot_skims['SOV_BTOLL']),coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Person_is_between_16_and_19_years_old,DRIVEALONEFREE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da_multiplier,,,,,,,,,,,,,,,,,,,, -#,Drive alone toll,,,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,auto_ownership == 0,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,age < 16,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['SOVTOLL_TIME'] + dot_skims['SOVTOLL_TIME'],,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Terminal_time,DRIVEALONEPAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOVTOLL_DIST'] + dot_skims['SOVTOLL_DIST']),,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOVTOLL_BTOLL'] + dot_skims['SOVTOLL_BTOLL']),,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOVTOLL_VTOLL'] + dot_skims['SOVTOLL_VTOLL']),,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Person_is_between_16_and_19_years_old,DRIVEALONEPAY - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),,coef_age1619_da_multiplier,,,,,,,,,,,,,,,,,,, -#,Shared ride 2,,,,,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Terminal_time,SHARED2FREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']),,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']) / costShareSr2,,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_One_person_household,SHARED2FREE - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Person_is_16_years_old_or_older,SHARED2FREE - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,,,,, -#,Shared ride 2 toll,,,,,,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,,,,,,,,,, -util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Terminal_time,SHARED2PAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']),,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']) / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']) / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_One_person_household,SHARED2PAY - One person household,@(df.hhsize == 1),,,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.hhsize == 2),,,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Person_is_16_years_old_or_older,SHARED2PAY - Person is 16 years old or older,@(df.age >= 16),,,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,,,, -#,Shared ride 3+,,,,,,,,,,,,,,,,,,,,,, -util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,,,,,,,,,, -util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@(odt_skims['HOV3_TIME'] + dot_skims['HOV3_TIME']),,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_Terminal_time,SHARED3FREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3_DIST'] + dot_skims['HOV3_DIST']),,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3_BTOLL'] + dot_skims['HOV3_BTOLL']) / costShareSr3,,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_One_person_household,SHARED3FREE - One person household,@(df.hhsize == 1),,,,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,,, -util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.hhsize == 2),,,,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,,, -util_SHARED3FREE_Person_is_16_years_old_or_older,SHARED3FREE - Person is 16 years old or older,@(df.age >= 16),,,,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,,, -#,Shared ride 3+ toll,,,,,,,,,,,,,,,,,,,,,, -util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,,,,,,,,,, -util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@(odt_skims['HOV3TOLL_TIME'] + dot_skims['HOV3TOLL_TIME']),,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Terminal_time,SHARED3PAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3TOLL_DIST'] + dot_skims['HOV3TOLL_DIST']),,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3TOLL_BTOLL'] + dot_skims['HOV3TOLL_BTOLL']) / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3TOLL_VTOLL'] + dot_skims['HOV3TOLL_VTOLL']) / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_One_person_household,SHARED3PAY - One person household,@(df.hhsize == 1),,,,,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,, -util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.hhsize == 2),,,,,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,, -util_SHARED3PAY_Person_is_16_years_old_or_older,SHARED3PAY - Person is 16 years old or older,@(df.age >= 16),,,,,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,, -#,Walk,,,,,,,,,,,,,,,,,,,,,, -#,FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,,,,,,,,,, -util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@walktimeshort_multiplier * (od_skims['DISTWALK'].clip(upper=walkThresh) + od_skims.reverse('DISTWALK').clip(upper=walkThresh))*60/walkSpeed,,,,,,,coef_ivt,,,,,,,,,,,,,, -util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * ((od_skims['DISTWALK'] - walkThresh).clip(lower=0) + (od_skims.reverse('DISTWALK') - walkThresh).clip(lower=0))*60/walkSpeed,,,,,,,coef_ivt,,,,,,,,,,,,,, -util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,coef_ivt,,,,,,,,,,,,,, -util_WALK_Topology,WALK - Topology,@coef_topology_walk_multiplier * df.dest_topology,,,,,,,coef_ivt,,,,,,,,,,,,,, -#,Bike,,,,,,,,,,,,,,,,,,,,,, -#,FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,,,,,,,,,, -util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,,,,,,,,,, -util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@biketimeshort_multiplier * (od_skims['DISTBIKE'].clip(upper=bikeThresh) + od_skims.reverse('DISTBIKE').clip(upper=bikeThresh))*60/bikeSpeed,,,,,,,,coef_ivt,,,,,,,,,,,,, -util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@biketimelong_multiplier * ((od_skims['DISTBIKE']-bikeThresh).clip(lower=0) + (od_skims.reverse('DISTBIKE')-bikeThresh).clip(lower=0))*60/bikeSpeed,,,,,,,,coef_ivt,,,,,,,,,,,,, -util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,coef_ivt,,,,,,,,,,,,, -util_BIKE_Topology,BIKE - Topology,@coef_topology_bike_multiplier * df.dest_topology,,,,,,,,coef_ivt,,,,,,,,,,,,, -#,Walk to Local,,,,,,,,,,,,,,,,,,,,,, -util_WALK_LOC_Unavailable,WALK_LOC - Unavailable,walk_local_available == False,,,,,,,,,-999,,,,,,,,,,,, -util_WALK_LOC_In_vehicle_time,WALK_LOC - In-vehicle time,@(odt_skims['WLK_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Short_iwait_time,WALK_LOC - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Long_iwait_time,WALK_LOC - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_transfer_wait_time,WALK_LOC - transfer wait time,@xwait_multiplier * (odt_skims['WLK_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_number_of_transfers,WALK_LOC - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_LOC_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_LOC_WLK_BOARDS']-1).clip(0)),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Walk_egress_time,WALK_LOC - Walk egress time,@2 * wegr_multiplier * df.destination_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Walk_other_time,WALK_LOC - Walk other time,@waux_multiplier * (odt_skims['WLK_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Fare,WALK_LOC - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_LOC_WLK_FAR'] + dot_skims['WLK_LOC_WLK_FAR']),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Destination_zone_densityIndex,WALK_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Topology,WALK_LOC - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Person_is_less_than_10_years_old,WALK_LOC - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,coef_age010_trn_multiplier,,,,,,,,,,,, -#,Walk to Light rail/Ferry,,,,,,,,,,,,,,,,,,,,,, -util_WALK_LRF_Unavailable,WALK_LRF - Unavailable,walk_lrf_available == False,,,,,,,,,,-999,,,,,,,,,,, -util_WALK_LRF_In_vehicle_time,WALK_LRF - In-vehicle time,@(odt_skims['WLK_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, -#, FIXME coefficients below are wrong or needlessly complex? could be re-expressed to avoid subtract?,,,,,,,,,,,,,,,,,,,,,, -util_WALK_LRF_In_vehicle_time_on_Light_Rail,WALK_LRF - In-vehicle time on Light Rail (incremental w/ ivt),@(ivt_lrt_multiplier-1)*(odt_skims['WLK_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_In_vehicle_time_on_Ferry,WALK_LRF - In-vehicle time on Ferry (incremental w/keyivt),@(ivt_ferry_multiplier-ivt_lrt_multiplier)*(odt_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Short_iwait_time,WALK_LRF - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Long_iwait_time,WALK_LRF - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_transfer_wait_time,WALK_LRF - transfer wait time,@xwait_multiplier * (odt_skims['WLK_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_number_of_transfers,WALK_LRF - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_LRF_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_LRF_WLK_BOARDS']-1).clip(0)),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Walk_access_time,WALK_LRF - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Walk_egress_time,WALK_LRF - Walk egress time,@2 * wegr_multiplier * df.destination_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Walk_other_time,WALK_LRF - Walk other time,@waux_multiplier * (odt_skims['WLK_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Fare,WALK_LRF - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_LRF_WLK_FAR'] + dot_skims['WLK_LRF_WLK_FAR']),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Destination_zone_densityIndex,WALK_LRF - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Topology,WALK_LRF - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Person_is_less_than_10_years_old,WALK_LRF - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,coef_age010_trn_multiplier,,,,,,,,,,, -#,Walk to Express bus,,,,,,,,,,,,,,,,,,,,,, -util_WALK_EXP_Unavailable,WALK_EXP - Unavailable,walk_express_available == False,,,,,,,,,,,-999,,,,,,,,,, -util_WALK_EXP_In_vehicle_time,WALK_EXP - In-vehicle time,@(odt_skims['WLK_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_In_vehicle_time_on_Express_bus,WALK_EXP - In-vehicle time on Express bus (incremental w/ ivt),@(ivt_exp_multiplier - 1)*(odt_skims['WLK_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Short_iwait_time,WALK_EXP - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Long_iwait_time,WALK_EXP - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_transfer_wait_time,WALK_EXP - transfer wait time,@xwait_multiplier * (odt_skims['WLK_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_number_of_transfers,WALK_EXP - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_EXP_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_EXP_WLK_BOARDS']-1).clip(0)),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Walk_access_time,WALK_EXP - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Walk_egress_time,WALK_EXP - Walk egress time,@2 * wegr_multiplier * df.destination_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Walk_other_time,WALK_EXP - Walk other time,@waux_multiplier * (odt_skims['WLK_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Fare,WALK_EXP - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_EXP_WLK_FAR'] + dot_skims['WLK_EXP_WLK_FAR']),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Destination_zone_densityIndex,WALK_EXP - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Topology,WALK_EXP - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Person_is_less_than_10_years_old,WALK_EXP - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,,coef_age010_trn_multiplier,,,,,,,,,, -#,Walk to Heavy Rail,,,,,,,,,,,,,,,,,,,,,, -util_WALK_HVY_Unavailable,WALK_HVY - Unavailable,walk_heavyrail_available == False,,,,,,,,,,,,-999,,,,,,,,, -util_WALK_HVY_In_vehicle_time,WALK_HVY - In-vehicle time,@(odt_skims['WLK_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_In_vehicle_time_on_heavy_rail,WALK_HVY - In-vehicle time on heavy rail (incremental w/ ivt),@(ivt_hvy_multiplier-1) * (odt_skims['WLK_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Short_iwait_time,WALK_HVY - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Long_iwait_time,WALK_HVY - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_transfer_wait_time,WALK_HVY - transfer wait time,@xwait_multiplier * (odt_skims['WLK_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_number_of_transfers,WALK_HVY - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_HVY_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_HVY_WLK_BOARDS']-1).clip(0)),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Walk_access_time,WALK_HVY - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Walk_egress_time,WALK_HVY - Walk egress time,@wegr_multiplier * 2 *df.destination_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Walk_other_time,WALK_HVY - Walk other time,@waux_multiplier * (odt_skims['WLK_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Fare,WALK_HVY - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_HVY_WLK_FAR'] + dot_skims['WLK_HVY_WLK_FAR']),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Destination_zone_densityIndex,WALK_HVY - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Topology,WALK_HVY - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Person_is_less_than_10_years_old,WALK_HVY - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,,,coef_age010_trn_multiplier,,,,,,,,, -#,Walk to Commuter rail,,,,,,,,,,,,,,,,,,,,,, -util_WALK_COM_Unavailable,WALK_COM - Unavailable,walk_commuter_available == False,,,,,,,,,,,,,-999,,,,,,,, -util_WALK_COM_In_vehicle_time,WALK_COM - In-vehicle time,@(odt_skims['WLK_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_In_vehicle_time_on_commuter_rail,WALK_COM - In-vehicle time on commuter rail (incremental w/ ivt),@(ivt_com_multiplier - 1) * (odt_skims['WLK_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Short_iwait_time,WALK_COM - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Long_iwait_time,WALK_COM - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_transfer_wait_time,WALK_COM - transfer wait time,@xwait_multiplier * (odt_skims['WLK_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_number_of_transfers,WALK_COM - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_COM_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_COM_WLK_BOARDS']-1).clip(0)),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Walk_access_time,WALK_COM - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Walk_egress_time,WALK_COM - Walk egress time,@2 * wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Walk_other_time,WALK_COM - Walk other time,@waux_multiplier * (odt_skims['WLK_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Fare,WALK_COM - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_COM_WLK_FAR'] + dot_skims['WLK_COM_WLK_FAR']),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Destination_zone_densityIndex,WALK_COM - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Topology,WALK_COM - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Person_is_less_than_10_years_old,WALK_COM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,coef_age010_trn_multiplier,,,,,,,, -#,Drive to Local,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_LOC_Unavailable,DRIVE_LOC - Unavailable,drive_local_available == False,,,,,,,,,,,,,,-999,,,,,,, -util_DRIVE_LOC_Unavailable_for_zero_auto_households,DRIVE_LOC - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,-999,,,,,,, -util_DRIVE_LOC_Unavailable_for_persons_less_than_16,DRIVE_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,-999,,,,,,, -util_DRIVE_LOC_In_vehicle_time,DRIVE_LOC - In-vehicle time,@(odt_skims['DRV_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Short_iwait_time,DRIVE_LOC - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_LOC_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Long_iwait_time,DRIVE_LOC - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_LOC_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_transfer_wait_time,DRIVE_LOC - transfer wait time,@xwait_multiplier * (odt_skims['DRV_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_number_of_transfers,DRIVE_LOC - number of transfers,@xfers_wlk_multiplier * ((odt_skims['DRV_LOC_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_LOC_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Drive_time,DRIVE_LOC - Drive time,@dtim_multiplier * (odt_skims['DRV_LOC_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Walk_access_time,DRIVE_LOC - Walk access time,@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Walk_egress_time,DRIVE_LOC - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Walk_other_time,DRIVE_LOC - Walk other time,@waux_multiplier * (odt_skims['DRV_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Fare_and_operating_cost,DRIVE_LOC - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_LOC_WLK_FAR'] + dot_skims['WLK_LOC_DRV_FAR']) + ((odt_skims['DRV_LOC_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_LOC_DRV_DDIST']/TRANSIT_SCALE_FACTOR) * costPerMile)),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LOC - Ratio of drive access distance to OD distance,@dacc_ratio * ((odt_skims['DRV_LOC_WLK_DDIST']/TRANSIT_SCALE_FACTOR+ dot_skims['WLK_LOC_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']*2)),,,,,,,,,,,,,,1,,,,,,, -util_DRIVE_LOC_Destination_zone_densityIndex,DRIVE_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Topology,DRIVE_LOC - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Person_is_less_than_10_years_old,DRIVE_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,coef_age010_trn_multiplier,,,,,,, -#,Drive to Light Rail/Ferry,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_LRF_Unavailable,DRIVE_LRF - Unavailable,drive_lrf_available == False,,,,,,,,,,,,,,,-999,,,,,, -util_DRIVE_LRF_Unavailable_for_zero_auto_households,DRIVE_LRF - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,-999,,,,,, -util_DRIVE_LRF_Unavailable_for_persons_less_than_16,DRIVE_LRF - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,-999,,,,,, -util_DRIVE_LRF_In_vehicle_time,DRIVE_LRF - In-vehicle time,@(odt_skims['DRV_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_In_vehicle_time_on_Light_Rail,DRIVE_LRF - In-vehicle time on Light Rail (incremental w/ ivt),@(ivt_lrt_multiplier-1) * (odt_skims['DRV_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_In_vehicle_time_on_Ferry,DRIVE_LRF - In-vehicle time on Ferry (incremental w/ keyivt),@(ivt_ferry_multiplier-ivt_lrt_multiplier)*(odt_skims['DRV_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_FERRYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Short_iwait_time,DRIVE_LRF - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_LRF_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Long_iwait_time,DRIVE_LRF - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_LRF_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_transfer_wait_time,DRIVE_LRF - transfer wait time,@xwait_multiplier * (odt_skims['DRV_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_number_of_transfers,DRIVE_LRF - number of transfers,@xfers_drv_multiplier * ((odt_skims['DRV_LRF_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_LRF_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Drive_time,DRIVE_LRF - Drive time,@dtim_multiplier * (odt_skims['DRV_LRF_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Walk_access_time,DRIVE_LRF - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Walk_egress_time,DRIVE_LRF - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Walk_other_time,DRIVE_LRF - Walk other time,@waux_multiplier * (odt_skims['DRV_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Fare_and_operating_cost,DRIVE_LRF - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_LRF_WLK_FAR']+dot_skims['WLK_LRF_DRV_FAR']) + ((odt_skims['DRV_LRF_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_LRF_DRV_DDIST']/TRANSIT_SCALE_FACTOR) *costPerMile)),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LRF - Ratio of drive access distance to OD distance,@dacc_ratio * ((odt_skims['DRV_LRF_WLK_DDIST']/TRANSIT_SCALE_FACTOR+ dot_skims['WLK_LRF_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']*2)),,,,,,,,,,,,,,,1,,,,,, -util_DRIVE_LRF_Destination_zone_densityIndex,DRIVE_LRF - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Topology,DRIVE_LRF - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Person_is_less_than_10_years_old,DRIVE_LRF - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,coef_age010_trn_multiplier,,,,,, -#,Drive to Express bus,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_EXP_Unavailable,DRIVE_EXP - Unavailable,drive_express_available == False,,,,,,,,,,,,,,,,-999,,,,, -util_DRIVE_EXP_Unavailable_for_zero_auto_households,DRIVE_EXP - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,-999,,,,, -util_DRIVE_EXP_Unavailable_for_persons_less_than_16,DRIVE_EXP - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,-999,,,,, -util_DRIVE_EXP_In_vehicle_time,DRIVE_EXP - In-vehicle time,@(odt_skims['DRV_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_In_vehicle_time_on_Express_bus,DRIVE_EXP - In-vehicle time on Express bus (incremental w/ ivt),@(ivt_exp_multiplier-1) * (odt_skims['DRV_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Short_iwait_time,DRIVE_EXP - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_EXP_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Long_iwait_time,DRIVE_EXP - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_EXP_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_transfer_wait_time,DRIVE_EXP - transfer wait time,@xwait_multiplier * (odt_skims['DRV_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_number_of_transfers,DRIVE_EXP - number of transfers,@xfers_drv_multiplier * ((odt_skims['DRV_EXP_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_EXP_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Drive_time,DRIVE_EXP - Drive time,@dtim_multiplier * (odt_skims['DRV_EXP_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Walk_access_time,DRIVE_EXP - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Walk_egress_ime,DRIVE_EXP - Walk egress ime (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Walk_other_time,DRIVE_EXP - Walk other time,@waux_multiplier * (odt_skims['DRV_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Fare_and_operating_cost,DRIVE_EXP - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_EXP_WLK_FAR']+dot_skims['WLK_EXP_DRV_FAR']) + ((odt_skims['DRV_EXP_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_EXP_DRV_DDIST']/TRANSIT_SCALE_FACTOR) *costPerMile)),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_EXP - Ratio of drive access distance to OD distance,@dacc_ratio * ((odt_skims['DRV_EXP_WLK_DDIST']/TRANSIT_SCALE_FACTOR+ dot_skims['WLK_EXP_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']*2)),,,,,,,,,,,,,,,,1,,,,, -util_DRIVE_EXP_Destination_zone_densityIndex,DRIVE_EXP - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Topology,DRIVE_EXP - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Person_is_less_than_10_years_old,DRIVE_EXP - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,coef_age010_trn_multiplier,,,,, -#,Drive to Heavy Rail,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_HVY_Unavailable,DRIVE_HVY - Unavailable,drive_heavyrail_available == False,,,,,,,,,,,,,,,,,-999,,,, -util_DRIVE_HVY_Unavailable_for_zero_auto_households,DRIVE_HVY - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,,-999,,,, -util_DRIVE_HVY_Unavailable_for_persons_less_than_16,DRIVE_HVY - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,-999,,,, -util_DRIVE_HVY_In_vehicle_time,DRIVE_HVY - In-vehicle time,@(odt_skims['DRV_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_In_vehicle_time_on_heavy_rail,DRIVE_HVY - In-vehicle time on heavy rail (incremental w/ ivt),@(ivt_hvy_multiplier-1) * (odt_skims['DRV_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Short_iwait_time,DRIVE_HVY - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_HVY_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Long_iwait_time,DRIVE_HVY - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_HVY_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_transfer_wait_time,DRIVE_HVY - transfer wait time,@xwait_multiplier * (odt_skims['DRV_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_number_of_transfers,DRIVE_HVY - number of transfers,@xfers_drv_multiplier * ((odt_skims['DRV_HVY_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_HVY_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Drive_time,DRIVE_HVY - Drive time,@dtim_multiplier * (odt_skims['DRV_HVY_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Walk_access_time,DRIVE_HVY - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Walk_egress_time,DRIVE_HVY - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Walk_other_time,DRIVE_HVY - Walk other time,@waux_multiplier * (odt_skims['DRV_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Fare_and_operating_cost,DRIVE_HVY - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_HVY_WLK_FAR']+dot_skims['WLK_HVY_DRV_FAR']) + ((odt_skims['DRV_HVY_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_HVY_DRV_DDIST']/TRANSIT_SCALE_FACTOR) *costPerMile)),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_HVY - Ratio of drive access distance to OD distance,@dacc_ratio * (odt_skims['DRV_HVY_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,,1,,,, -util_DRIVE_HVY_Destination_zone_densityIndex,DRIVE_HVY - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Topology,DRIVE_HVY - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Person_is_less_than_10_years_old,DRIVE_HVY - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,,coef_age010_trn_multiplier,,,, -#,Drive to Commuter Rail,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_COM_Unavailable,DRIVE_COM - Unavailable,drive_commuter_available == False,,,,,,,,,,,,,,,,,,-999,,, -util_DRIVE_COM_Unavailable_for_zero_auto_households,DRIVE_COM - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,,,-999,,, -util_DRIVE_COM_Unavailable_for_persons_less_than_16,DRIVE_COM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,,-999,,, -util_DRIVE_COM_In_vehicle_time,DRIVE_COM - In-vehicle time,@(odt_skims['DRV_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_In_vehicle_time_on_commuter_rail,DRIVE_COM - In-vehicle time on commuter rail (incremental w/ ivt),@(ivt_com_multiplier - 1) * (odt_skims['DRV_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Short_iwait_time,DRIVE_COM - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_COM_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Long_iwait_time,DRIVE_COM - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_COM_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_transfer_wait_time,DRIVE_COM - transfer wait time,@xwait_multiplier * (odt_skims['DRV_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_number_of_transfers,DRIVE_COM - number of transfers,@xfers_drv_multiplier * ((odt_skims['DRV_COM_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_COM_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Drive_time,DRIVE_COM - Drive time,@dtim_multiplier * (odt_skims['DRV_COM_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Walk_access_time,DRIVE_COM - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Walk_egress_time,DRIVE_COM - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Walk_other_time,DRIVE_COM - Walk other time,@waux_multiplier * (odt_skims['DRV_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Fare_and_operating_cost,DRIVE_COM - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_COM_WLK_FAR']+dot_skims['WLK_COM_DRV_FAR']) + ((odt_skims['DRV_COM_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_COM_DRV_DDIST']/TRANSIT_SCALE_FACTOR) *costPerMile)),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_COM - Ratio of drive access distance to OD distance,@dacc_ratio * ((odt_skims['DRV_COM_WLK_DDIST']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']*2)),,,,,,,,,,,,,,,,,,1,,, -util_DRIVE_COM_Destination_zone_densityIndex,DRIVE_COM - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Topology,DRIVE_COM - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Person_is_less_than_10_years_old,DRIVE_COM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,,,coef_age010_trn_multiplier,,, -#,Taxi,,,,,,,,,,,,,,,,,,,,,, -util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,,,,,,,,,,,,,,,,coef_ivt,, - util_Taxi_Wait_time,Taxi - Wait time,@ridehail_wait_time_multiplier * df.totalWaitTaxi,,,,,,,,,,,,,,,,,,,coef_ivt,, -util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,,,,coef_ivt,, -util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,coef_ivt,, -util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * Taxi_costPerMile +(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * Taxi_costPerMinute)*100,,,,,,,,,,,,,,,,,,,coef_ivt,, -#,TNC Single,,,,,,,,,,,,,,,,,,,,,, -util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Wait_time,TNC Single - Wait time,@ridehail_wait_time_multiplier * df.totalWaitSingleTNC,,,,,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * TNC_single_costPerMile + (odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,coef_ivt, -#,TNC Shared,,,,,,,,,,,,,,,,,,,,,, -util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_shared_IVTFactor,,,,,,,,,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Wait_time,TNC Shared - Wait time,@ridehail_wait_time_multiplier * df.totalWaitSharedTNC,,,,,,,,,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * TNC_shared_costPerMile + (odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,,coef_ivt -#,indiv tour ASCs,,,,,,,,,,,,,,,,,,,,,, -util_Walk_ASC_Zero_auto,Walk ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,walk_ASC_no_auto,,,,,,,,,,,,,, -util_Walk_ASC_Auto_deficient,Walk ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,walk_ASC_auto_deficient,,,,,,,,,,,,,, -util_Walk_ASC_Auto_sufficient,Walk ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,walk_ASC_auto_sufficient,,,,,,,,,,,,,, -util_Bike_ASC_Zero_auto,Bike ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,bike_ASC_no_auto,,,,,,,,,,,,, -util_Bike_ASC_Auto_deficient,Bike ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,bike_ASC_auto_deficient,,,,,,,,,,,,, -util_Bike_ASC_Auto_sufficient,Bike ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,bike_ASC_auto_sufficient,,,,,,,,,,,,, -util_Shared_ride_2_ASC_Zero_auto,Shared ride 2 ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,sr2_ASC_no_auto,sr2_ASC_no_auto,,,,,,,,,,,,,,,,, -util_Shared_ride_2_ASC_Auto_deficient,Shared ride 2 ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,sr2_ASC_auto_deficient,sr2_ASC_auto_deficient,,,,,,,,,,,,,,,,, -util_Shared_ride_2_ASC_Auto_sufficient,Shared ride 2 ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient,,,,,,,,,,,,,,,,, -util_Shared_ride_3p_Zero_auto,Shared ride 3+ - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,sr3p_ASC_no_auto,sr3p_ASC_no_auto,,,,,,,,,,,,,,, -util_Shared_ride_3p_Auto_deficient,Shared ride 3+ - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient,,,,,,,,,,,,,,, -util_Shared_ride_3p_Auto_sufficient,Shared ride 3+ - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient,,,,,,,,,,,,,,, -util_Walk_to_Transit_Zero_auto,Walk to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,,,,,,,, -util_Walk_to_Transit_Auto_deficient,Walk to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,,,,,,,, -util_Walk_to_Transit_Auto_sufficient,Walk to Transit - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,,,,,,,, -util_Drive_to_Transit_Zero_auto,Drive to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,drive_transit_ASC_no_auto,drive_transit_ASC_no_auto,drive_transit_ASC_no_auto,drive_transit_ASC_no_auto,drive_transit_ASC_no_auto,,, -util_Drive_to_Transit_Auto_deficient,Drive to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient,,, -util_Drive_to_Transit_Auto_sufficient,Drive to Transit - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient,,, -util_Taxi_Zero_auto,Taxi - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,taxi_ASC_no_auto,, -util_Taxi_Auto_deficient,Taxi - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,taxi_ASC_auto_deficient,, -util_Taxi_Auto_sufficient,Taxi - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,taxi_ASC_auto_sufficient,, -util_TNC_Single_Zero_auto,TNC Single - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,tnc_single_ASC_no_auto, -util_TNC_Single_Auto_deficient,TNC Single - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,tnc_single_ASC_auto_deficient, -util_TNC_Single_Auto_sufficient,TNC Single - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,tnc_single_ASC_auto_sufficient, -util_TNC_Shared_Zero_auto,TNC Shared - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,,tnc_shared_ASC_no_auto -util_TNC_Shared_Auto_deficient,TNC Shared - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,,tnc_shared_ASC_auto_deficient -util_TNC_Shared_Auto_sufficient,TNC Shared - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,,tnc_shared_ASC_auto_sufficient -#,joint tour ASCs,,,,,,,,,,,,,,,,,,,,,, -util_Joint_Walk_ASC_Zero_auto,Joint - Walk ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,joint_walk_ASC_no_auto,,,,,,,,,,,,,, -util_Joint_Walk_ASC_Auto_deficient,Joint - Walk ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,joint_walk_ASC_auto_deficient,,,,,,,,,,,,,, -util_Joint_Walk_ASC_Auto_sufficient,Joint - Walk ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,joint_walk_ASC_auto_sufficient,,,,,,,,,,,,,, -util_Joint_Bike_ASC_Zero_auto,Joint - Bike ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,joint_bike_ASC_no_auto,,,,,,,,,,,,, -util_Joint_Bike_ASC_Auto_deficient,Joint - Bike ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,joint_bike_ASC_auto_deficient,,,,,,,,,,,,, -util_Joint_Bike_ASC_Auto_sufficient,Joint - Bike ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,joint_bike_ASC_auto_sufficient,,,,,,,,,,,,, -util_Joint_Shared_ride_2_ASC_Zero_auto,Joint - Shared ride 2 ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto,,,,,,,,,,,,,,,,, -util_Joint_Shared_ride_2_ASC_Auto_deficient,Joint - Shared ride 2 ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient,,,,,,,,,,,,,,,,, -util_Joint_Shared_ride_2_ASC_Auto_sufficient,Joint - Shared ride 2 ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient,,,,,,,,,,,,,,,,, -util_Joint_Shared_ride_3p_Zero_auto,Joint - Shared ride 3+ - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto,,,,,,,,,,,,,,, -util_Joint_Shared_ride_3p_Auto_deficient,Joint - Shared ride 3+ - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient,,,,,,,,,,,,,,, -util_Joint_Shared_ride_3p_Auto_sufficient,Joint - Shared ride 3+ - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient,,,,,,,,,,,,,,, -util_Joint_Walk_to_Transit_Zero_auto,Joint - Walk to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,,,,,,,, -util_Joint_Walk_to_Transit_Auto_deficient,Joint - Walk to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,,,,,,,, -util_Joint_Walk_to_Transit_Auto_sufficient,Joint - Walk to Transit - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,,,,,,,, -util_Joint_Drive_to_Transit_Zero_auto,Joint - Drive to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto,,, -util_Joint_Drive_to_Transit_Auto_deficient,Joint - Drive to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient,,, -util_Joint_Drive_to_Transit_Auto_sufficient,Joint - Drive to Transit - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient,,, -util_Joint_Taxi_Zero_auto,Joint - Taxi - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,joint_taxi_ASC_no_auto,, -util_Joint_Taxi_Auto_deficient,Joint - Taxi - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,joint_taxi_ASC_auto_deficient,, -util_Joint_Taxi_Auto_sufficient,Joint - Taxi - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,joint_taxi_ASC_auto_sufficient,, -util_Joint_TNC_Single_Zero_auto,Joint - TNC Single - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,joint_tnc_single_ASC_no_auto, -util_Joint_TNC_Single_Auto_deficient,Joint - TNC Single - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,joint_tnc_single_ASC_auto_deficient, -util_Joint_TNC_Single_Auto_sufficient,Joint - TNC Single - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,joint_tnc_single_ASC_auto_sufficient, -util_Joint_TNC_Shared_Zero_auto,Joint - TNC Shared - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_no_auto -util_Joint_TNC_Shared_Auto_deficient,Joint - TNC Shared - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_auto_deficient -util_Joint_TNC_Shared_Auto_sufficient,Joint - TNC Shared - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_auto_sufficient -util_Local_bus_ASC,Local bus ASC,1,,,,,,,,,local_bus_ASC,,,,,local_bus_ASC,,,,,,, -util_Walk_to_Light_Rail_ASC,Walk to Light Rail ASC,@(df.walk_ferry_available == False),,,,,,,,,,walk_light_rail_ASC,,,,,,,,,,, -util_Drive_to_Light_Rail_ASC,Drive to Light Rail ASC,@(df.drive_ferry_available == False),,,,,,,,,,,,,,,drive_light_rail_ASC,,,,,, -util_Walk_to_Ferry_ASC,Walk to Ferry ASC,@df.walk_ferry_available,,,,,,,,,,walk_ferry_ASC,,,,,,,,,,, -util_Drive_to_Ferry_ASC,Drive to Ferry ASC,@df.drive_ferry_available,,,,,,,,,,,,,,,drive_ferry_ASC,,,,,, -util_Express_Bus_ASC,Express Bus ASC,1,,,,,,,,,,,express_bus_ASC,,,,,express_bus_ASC,,,,, -util_Heavy_Rail_ASC,Heavy Rail ASC,1,,,,,,,,,,,,heavy_rail_ASC,,,,,heavy_rail_ASC,,,, -util_Commuter_Rail,Commuter Rail,1,,,,,,,,,,,,,commuter_rail_ASC,,,,,commuter_rail_ASC,,, -util_Walk_to_Transit_dest_CBD,Walk to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,walk_transit_CBD_ASC,walk_transit_CBD_ASC,walk_transit_CBD_ASC,walk_transit_CBD_ASC,walk_transit_CBD_ASC,,,,,,,, -util_Drive_to_Transit_dest_CBD,Drive to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,,,,,,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,,, -util_Drive_to_Transit_distance_penalty,Drive to Transit - distance penalty,@drvtrn_distpen_0_multiplier * (1-od_skims['DIST']/drvtrn_distpen_max).clip(lower=0),,,,,,,,,,,,,,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,,, -#, FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,,,,,,,,,, -util_Walk_not_available_for_long_distances,Walk not available for long distances,@od_skims.max('DISTWALK') > 3,,,,,,,-999,,,,,,,,,,,,,, -util_Bike_not_available_for_long_distances,Bike not available for long distances,@od_skims.max('DISTBIKE') > 8,,,,,,,,-999,,,,,,,,,,,,, -util_Drive_alone_not_available_for_escort_tours,Drive alone not available for escort tours,is_escort,-999,-999,,,,,,,,,,,,,,,,,,, -#, max(c_densityIndexOrigin*originDensityIndex,originDensityIndexMax),,,,,,,,,1,1,1,1,1,1,1,,,,,, +Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_LOC,WALK_LRF,WALK_EXP,WALK_HVY,WALK_COM,DRIVE_LOC,DRIVE_LRF,DRIVE_EXP,DRIVE_HVY,DRIVE_COM,TAXI,TNC_SINGLE,TNC_SHARED +#,Drive alone no toll,,,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,auto_ownership == 0,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['SOV_TIME'] + dot_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Terminal_time,DRIVEALONEFREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']),coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOV_BTOLL'] + dot_skims['SOV_BTOLL']),coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Person_is_between_16_and_19_years_old,DRIVEALONEFREE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da_multiplier,,,,,,,,,,,,,,,,,,,, +#,Drive alone toll,,,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,auto_ownership == 0,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,age < 16,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['SOVTOLL_TIME'] + dot_skims['SOVTOLL_TIME'],,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Terminal_time,DRIVEALONEPAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOVTOLL_DIST'] + dot_skims['SOVTOLL_DIST']),,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOVTOLL_BTOLL'] + dot_skims['SOVTOLL_BTOLL']),,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['SOVTOLL_VTOLL'] + dot_skims['SOVTOLL_VTOLL']),,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Person_is_between_16_and_19_years_old,DRIVEALONEPAY - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),,coef_age1619_da_multiplier,,,,,,,,,,,,,,,,,,, +#,Shared ride 2,,,,,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Terminal_time,SHARED2FREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']),,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']) / costShareSr2,,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_One_person_household,SHARED2FREE - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Person_is_16_years_old_or_older,SHARED2FREE - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,,,,, +#,Shared ride 2 toll,,,,,,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,,,,,,,,,, +util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Terminal_time,SHARED2PAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']),,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']) / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']) / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_One_person_household,SHARED2PAY - One person household,@(df.hhsize == 1),,,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.hhsize == 2),,,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Person_is_16_years_old_or_older,SHARED2PAY - Person is 16 years old or older,@(df.age >= 16),,,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,,,, +#,Shared ride 3+,,,,,,,,,,,,,,,,,,,,,, +util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,,,,,,,,,, +util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@(odt_skims['HOV3_TIME'] + dot_skims['HOV3_TIME']),,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_Terminal_time,SHARED3FREE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3_DIST'] + dot_skims['HOV3_DIST']),,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3_BTOLL'] + dot_skims['HOV3_BTOLL']) / costShareSr3,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_One_person_household,SHARED3FREE - One person household,@(df.hhsize == 1),,,,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,,, +util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.hhsize == 2),,,,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,,, +util_SHARED3FREE_Person_is_16_years_old_or_older,SHARED3FREE - Person is 16 years old or older,@(df.age >= 16),,,,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,,, +#,Shared ride 3+ toll,,,,,,,,,,,,,,,,,,,,,, +util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,,,,,,,,,, +util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@(odt_skims['HOV3TOLL_TIME'] + dot_skims['HOV3TOLL_TIME']),,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Terminal_time,SHARED3PAY - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3TOLL_DIST'] + dot_skims['HOV3TOLL_DIST']),,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3TOLL_BTOLL'] + dot_skims['HOV3TOLL_BTOLL']) / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3TOLL_VTOLL'] + dot_skims['HOV3TOLL_VTOLL']) / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_One_person_household,SHARED3PAY - One person household,@(df.hhsize == 1),,,,,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,, +util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.hhsize == 2),,,,,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,, +util_SHARED3PAY_Person_is_16_years_old_or_older,SHARED3PAY - Person is 16 years old or older,@(df.age >= 16),,,,,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,, +#,Walk,,,,,,,,,,,,,,,,,,,,,, +#,FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,,,,,,,,,, +util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@walktimeshort_multiplier * (od_skims['DISTWALK'].clip(upper=walkThresh) + od_skims.reverse('DISTWALK').clip(upper=walkThresh))*60/walkSpeed,,,,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * ((od_skims['DISTWALK'] - walkThresh).clip(lower=0) + (od_skims.reverse('DISTWALK') - walkThresh).clip(lower=0))*60/walkSpeed,,,,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Topology,WALK - Topology,@coef_topology_walk_multiplier * df.dest_topology,,,,,,,coef_ivt,,,,,,,,,,,,,, +#,Bike,,,,,,,,,,,,,,,,,,,,,, +#,FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,,,,,,,,,, +util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,,,,,,,,,, +util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@biketimeshort_multiplier * (od_skims['DISTBIKE'].clip(upper=bikeThresh) + od_skims.reverse('DISTBIKE').clip(upper=bikeThresh))*60/bikeSpeed,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@biketimelong_multiplier * ((od_skims['DISTBIKE']-bikeThresh).clip(lower=0) + (od_skims.reverse('DISTBIKE')-bikeThresh).clip(lower=0))*60/bikeSpeed,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Topology,BIKE - Topology,@coef_topology_bike_multiplier * df.dest_topology,,,,,,,,coef_ivt,,,,,,,,,,,,, +#,Walk to Local,,,,,,,,,,,,,,,,,,,,,, +util_WALK_LOC_Unavailable,WALK_LOC - Unavailable,walk_local_available == False,,,,,,,,,-999,,,,,,,,,,,, +util_WALK_LOC_In_vehicle_time,WALK_LOC - In-vehicle time,@(odt_skims['WLK_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Short_iwait_time,WALK_LOC - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Long_iwait_time,WALK_LOC - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_transfer_wait_time,WALK_LOC - transfer wait time,@xwait_multiplier * (odt_skims['WLK_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_number_of_transfers,WALK_LOC - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_LOC_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_LOC_WLK_BOARDS']-1).clip(0)),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_egress_time,WALK_LOC - Walk egress time,@2 * wegr_multiplier * df.destination_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_other_time,WALK_LOC - Walk other time,@waux_multiplier * (odt_skims['WLK_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Fare,WALK_LOC - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_LOC_WLK_FAR'] + dot_skims['WLK_LOC_WLK_FAR']),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Destination_zone_densityIndex,WALK_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Topology,WALK_LOC - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Person_is_less_than_10_years_old,WALK_LOC - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,coef_age010_trn_multiplier,,,,,,,,,,,, +#,Walk to Light rail/Ferry,,,,,,,,,,,,,,,,,,,,,, +util_WALK_LRF_Unavailable,WALK_LRF - Unavailable,walk_lrf_available == False,,,,,,,,,,-999,,,,,,,,,,, +util_WALK_LRF_In_vehicle_time,WALK_LRF - In-vehicle time,@(odt_skims['WLK_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, +#, FIXME coefficients below are wrong or needlessly complex? could be re-expressed to avoid subtract?,,,,,,,,,,,,,,,,,,,,,, +util_WALK_LRF_In_vehicle_time_on_Light_Rail,WALK_LRF - In-vehicle time on Light Rail (incremental w/ ivt),@(ivt_lrt_multiplier-1)*(odt_skims['WLK_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_In_vehicle_time_on_Ferry,WALK_LRF - In-vehicle time on Ferry (incremental w/keyivt),@(ivt_ferry_multiplier-ivt_lrt_multiplier)*(odt_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Short_iwait_time,WALK_LRF - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Long_iwait_time,WALK_LRF - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_transfer_wait_time,WALK_LRF - transfer wait time,@xwait_multiplier * (odt_skims['WLK_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_number_of_transfers,WALK_LRF - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_LRF_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_LRF_WLK_BOARDS']-1).clip(0)),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Walk_access_time,WALK_LRF - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Walk_egress_time,WALK_LRF - Walk egress time,@2 * wegr_multiplier * df.destination_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Walk_other_time,WALK_LRF - Walk other time,@waux_multiplier * (odt_skims['WLK_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Fare,WALK_LRF - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_LRF_WLK_FAR'] + dot_skims['WLK_LRF_WLK_FAR']),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Destination_zone_densityIndex,WALK_LRF - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Topology,WALK_LRF - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Person_is_less_than_10_years_old,WALK_LRF - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,coef_age010_trn_multiplier,,,,,,,,,,, +#,Walk to Express bus,,,,,,,,,,,,,,,,,,,,,, +util_WALK_EXP_Unavailable,WALK_EXP - Unavailable,walk_express_available == False,,,,,,,,,,,-999,,,,,,,,,, +util_WALK_EXP_In_vehicle_time,WALK_EXP - In-vehicle time,@(odt_skims['WLK_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_In_vehicle_time_on_Express_bus,WALK_EXP - In-vehicle time on Express bus (incremental w/ ivt),@(ivt_exp_multiplier - 1)*(odt_skims['WLK_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Short_iwait_time,WALK_EXP - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Long_iwait_time,WALK_EXP - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_transfer_wait_time,WALK_EXP - transfer wait time,@xwait_multiplier * (odt_skims['WLK_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_number_of_transfers,WALK_EXP - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_EXP_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_EXP_WLK_BOARDS']-1).clip(0)),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Walk_access_time,WALK_EXP - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Walk_egress_time,WALK_EXP - Walk egress time,@2 * wegr_multiplier * df.destination_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Walk_other_time,WALK_EXP - Walk other time,@waux_multiplier * (odt_skims['WLK_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Fare,WALK_EXP - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_EXP_WLK_FAR'] + dot_skims['WLK_EXP_WLK_FAR']),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Destination_zone_densityIndex,WALK_EXP - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Topology,WALK_EXP - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Person_is_less_than_10_years_old,WALK_EXP - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,,coef_age010_trn_multiplier,,,,,,,,,, +#,Walk to Heavy Rail,,,,,,,,,,,,,,,,,,,,,, +util_WALK_HVY_Unavailable,WALK_HVY - Unavailable,walk_heavyrail_available == False,,,,,,,,,,,,-999,,,,,,,,, +util_WALK_HVY_In_vehicle_time,WALK_HVY - In-vehicle time,@(odt_skims['WLK_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_In_vehicle_time_on_heavy_rail,WALK_HVY - In-vehicle time on heavy rail (incremental w/ ivt),@(ivt_hvy_multiplier-1) * (odt_skims['WLK_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Short_iwait_time,WALK_HVY - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Long_iwait_time,WALK_HVY - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_transfer_wait_time,WALK_HVY - transfer wait time,@xwait_multiplier * (odt_skims['WLK_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_number_of_transfers,WALK_HVY - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_HVY_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_HVY_WLK_BOARDS']-1).clip(0)),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Walk_access_time,WALK_HVY - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Walk_egress_time,WALK_HVY - Walk egress time,@wegr_multiplier * 2 *df.destination_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Walk_other_time,WALK_HVY - Walk other time,@waux_multiplier * (odt_skims['WLK_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Fare,WALK_HVY - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_HVY_WLK_FAR'] + dot_skims['WLK_HVY_WLK_FAR']),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Destination_zone_densityIndex,WALK_HVY - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Topology,WALK_HVY - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Person_is_less_than_10_years_old,WALK_HVY - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,,,coef_age010_trn_multiplier,,,,,,,,, +#,Walk to Commuter rail,,,,,,,,,,,,,,,,,,,,,, +util_WALK_COM_Unavailable,WALK_COM - Unavailable,walk_commuter_available == False,,,,,,,,,,,,,-999,,,,,,,, +util_WALK_COM_In_vehicle_time,WALK_COM - In-vehicle time,@(odt_skims['WLK_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_In_vehicle_time_on_commuter_rail,WALK_COM - In-vehicle time on commuter rail (incremental w/ ivt),@(ivt_com_multiplier - 1) * (odt_skims['WLK_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Short_iwait_time,WALK_COM - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Long_iwait_time,WALK_COM - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_transfer_wait_time,WALK_COM - transfer wait time,@xwait_multiplier * (odt_skims['WLK_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_number_of_transfers,WALK_COM - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_COM_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_COM_WLK_BOARDS']-1).clip(0)),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Walk_access_time,WALK_COM - Walk access time,@2 * wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Walk_egress_time,WALK_COM - Walk egress time,@2 * wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Walk_other_time,WALK_COM - Walk other time,@waux_multiplier * (odt_skims['WLK_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Fare,WALK_COM - Fare,@ivt_cost_multiplier * df.ivot * (odt_skims['WLK_COM_WLK_FAR'] + dot_skims['WLK_COM_WLK_FAR']),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Destination_zone_densityIndex,WALK_COM - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Topology,WALK_COM - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Person_is_less_than_10_years_old,WALK_COM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,coef_age010_trn_multiplier,,,,,,,, +#,Drive to Local,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_LOC_Unavailable,DRIVE_LOC - Unavailable,drive_local_available == False,,,,,,,,,,,,,,-999,,,,,,, +util_DRIVE_LOC_Unavailable_for_zero_auto_households,DRIVE_LOC - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,-999,,,,,,, +util_DRIVE_LOC_Unavailable_for_persons_less_than_16,DRIVE_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,-999,,,,,,, +util_DRIVE_LOC_In_vehicle_time,DRIVE_LOC - In-vehicle time,@(odt_skims['DRV_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Short_iwait_time,DRIVE_LOC - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_LOC_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Long_iwait_time,DRIVE_LOC - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_LOC_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_transfer_wait_time,DRIVE_LOC - transfer wait time,@xwait_multiplier * (odt_skims['DRV_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_number_of_transfers,DRIVE_LOC - number of transfers,@xfers_wlk_multiplier * ((odt_skims['DRV_LOC_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_LOC_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Drive_time,DRIVE_LOC - Drive time,@dtim_multiplier * (odt_skims['DRV_LOC_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Walk_access_time,DRIVE_LOC - Walk access time,@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Walk_egress_time,DRIVE_LOC - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Walk_other_time,DRIVE_LOC - Walk other time,@waux_multiplier * (odt_skims['DRV_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LOC_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Fare_and_operating_cost,DRIVE_LOC - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_LOC_WLK_FAR'] + dot_skims['WLK_LOC_DRV_FAR']) + ((odt_skims['DRV_LOC_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_LOC_DRV_DDIST']/TRANSIT_SCALE_FACTOR) * costPerMile)),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LOC - Ratio of drive access distance to OD distance,@dacc_ratio * ((odt_skims['DRV_LOC_WLK_DDIST']/TRANSIT_SCALE_FACTOR+ dot_skims['WLK_LOC_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']*2)),,,,,,,,,,,,,,1,,,,,,, +util_DRIVE_LOC_Destination_zone_densityIndex,DRIVE_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Topology,DRIVE_LOC - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Person_is_less_than_10_years_old,DRIVE_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,coef_age010_trn_multiplier,,,,,,, +#,Drive to Light Rail/Ferry,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_LRF_Unavailable,DRIVE_LRF - Unavailable,drive_lrf_available == False,,,,,,,,,,,,,,,-999,,,,,, +util_DRIVE_LRF_Unavailable_for_zero_auto_households,DRIVE_LRF - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,-999,,,,,, +util_DRIVE_LRF_Unavailable_for_persons_less_than_16,DRIVE_LRF - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,-999,,,,,, +util_DRIVE_LRF_In_vehicle_time,DRIVE_LRF - In-vehicle time,@(odt_skims['DRV_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_In_vehicle_time_on_Light_Rail,DRIVE_LRF - In-vehicle time on Light Rail (incremental w/ ivt),@(ivt_lrt_multiplier-1) * (odt_skims['DRV_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_In_vehicle_time_on_Ferry,DRIVE_LRF - In-vehicle time on Ferry (incremental w/ keyivt),@(ivt_ferry_multiplier-ivt_lrt_multiplier)*(odt_skims['DRV_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_FERRYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Short_iwait_time,DRIVE_LRF - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_LRF_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Long_iwait_time,DRIVE_LRF - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_LRF_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_transfer_wait_time,DRIVE_LRF - transfer wait time,@xwait_multiplier * (odt_skims['DRV_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_number_of_transfers,DRIVE_LRF - number of transfers,@xfers_drv_multiplier * ((odt_skims['DRV_LRF_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_LRF_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Drive_time,DRIVE_LRF - Drive time,@dtim_multiplier * (odt_skims['DRV_LRF_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Walk_access_time,DRIVE_LRF - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Walk_egress_time,DRIVE_LRF - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Walk_other_time,DRIVE_LRF - Walk other time,@waux_multiplier * (odt_skims['DRV_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_LRF_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Fare_and_operating_cost,DRIVE_LRF - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_LRF_WLK_FAR']+dot_skims['WLK_LRF_DRV_FAR']) + ((odt_skims['DRV_LRF_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_LRF_DRV_DDIST']/TRANSIT_SCALE_FACTOR) *costPerMile)),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LRF - Ratio of drive access distance to OD distance,@dacc_ratio * ((odt_skims['DRV_LRF_WLK_DDIST']/TRANSIT_SCALE_FACTOR+ dot_skims['WLK_LRF_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']*2)),,,,,,,,,,,,,,,1,,,,,, +util_DRIVE_LRF_Destination_zone_densityIndex,DRIVE_LRF - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Topology,DRIVE_LRF - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Person_is_less_than_10_years_old,DRIVE_LRF - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,coef_age010_trn_multiplier,,,,,, +#,Drive to Express bus,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_EXP_Unavailable,DRIVE_EXP - Unavailable,drive_express_available == False,,,,,,,,,,,,,,,,-999,,,,, +util_DRIVE_EXP_Unavailable_for_zero_auto_households,DRIVE_EXP - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,-999,,,,, +util_DRIVE_EXP_Unavailable_for_persons_less_than_16,DRIVE_EXP - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,-999,,,,, +util_DRIVE_EXP_In_vehicle_time,DRIVE_EXP - In-vehicle time,@(odt_skims['DRV_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_In_vehicle_time_on_Express_bus,DRIVE_EXP - In-vehicle time on Express bus (incremental w/ ivt),@(ivt_exp_multiplier-1) * (odt_skims['DRV_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Short_iwait_time,DRIVE_EXP - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_EXP_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Long_iwait_time,DRIVE_EXP - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_EXP_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_transfer_wait_time,DRIVE_EXP - transfer wait time,@xwait_multiplier * (odt_skims['DRV_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_number_of_transfers,DRIVE_EXP - number of transfers,@xfers_drv_multiplier * ((odt_skims['DRV_EXP_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_EXP_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Drive_time,DRIVE_EXP - Drive time,@dtim_multiplier * (odt_skims['DRV_EXP_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Walk_access_time,DRIVE_EXP - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Walk_egress_ime,DRIVE_EXP - Walk egress ime (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Walk_other_time,DRIVE_EXP - Walk other time,@waux_multiplier * (odt_skims['DRV_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_EXP_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Fare_and_operating_cost,DRIVE_EXP - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_EXP_WLK_FAR']+dot_skims['WLK_EXP_DRV_FAR']) + ((odt_skims['DRV_EXP_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_EXP_DRV_DDIST']/TRANSIT_SCALE_FACTOR) *costPerMile)),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_EXP - Ratio of drive access distance to OD distance,@dacc_ratio * ((odt_skims['DRV_EXP_WLK_DDIST']/TRANSIT_SCALE_FACTOR+ dot_skims['WLK_EXP_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']*2)),,,,,,,,,,,,,,,,1,,,,, +util_DRIVE_EXP_Destination_zone_densityIndex,DRIVE_EXP - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Topology,DRIVE_EXP - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Person_is_less_than_10_years_old,DRIVE_EXP - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,coef_age010_trn_multiplier,,,,, +#,Drive to Heavy Rail,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_HVY_Unavailable,DRIVE_HVY - Unavailable,drive_heavyrail_available == False,,,,,,,,,,,,,,,,,-999,,,, +util_DRIVE_HVY_Unavailable_for_zero_auto_households,DRIVE_HVY - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,,-999,,,, +util_DRIVE_HVY_Unavailable_for_persons_less_than_16,DRIVE_HVY - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,-999,,,, +util_DRIVE_HVY_In_vehicle_time,DRIVE_HVY - In-vehicle time,@(odt_skims['DRV_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_In_vehicle_time_on_heavy_rail,DRIVE_HVY - In-vehicle time on heavy rail (incremental w/ ivt),@(ivt_hvy_multiplier-1) * (odt_skims['DRV_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Short_iwait_time,DRIVE_HVY - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_HVY_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Long_iwait_time,DRIVE_HVY - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_HVY_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_transfer_wait_time,DRIVE_HVY - transfer wait time,@xwait_multiplier * (odt_skims['DRV_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_number_of_transfers,DRIVE_HVY - number of transfers,@xfers_drv_multiplier * ((odt_skims['DRV_HVY_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_HVY_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Drive_time,DRIVE_HVY - Drive time,@dtim_multiplier * (odt_skims['DRV_HVY_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Walk_access_time,DRIVE_HVY - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Walk_egress_time,DRIVE_HVY - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Walk_other_time,DRIVE_HVY - Walk other time,@waux_multiplier * (odt_skims['DRV_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_HVY_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Fare_and_operating_cost,DRIVE_HVY - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_HVY_WLK_FAR']+dot_skims['WLK_HVY_DRV_FAR']) + ((odt_skims['DRV_HVY_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_HVY_DRV_DDIST']/TRANSIT_SCALE_FACTOR) *costPerMile)),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_HVY - Ratio of drive access distance to OD distance,@dacc_ratio * (odt_skims['DRV_HVY_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,,1,,,, +util_DRIVE_HVY_Destination_zone_densityIndex,DRIVE_HVY - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Topology,DRIVE_HVY - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Person_is_less_than_10_years_old,DRIVE_HVY - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,,coef_age010_trn_multiplier,,,, +#,Drive to Commuter Rail,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_COM_Unavailable,DRIVE_COM - Unavailable,drive_commuter_available == False,,,,,,,,,,,,,,,,,,-999,,, +util_DRIVE_COM_Unavailable_for_zero_auto_households,DRIVE_COM - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,,,-999,,, +util_DRIVE_COM_Unavailable_for_persons_less_than_16,DRIVE_COM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,,-999,,, +util_DRIVE_COM_In_vehicle_time,DRIVE_COM - In-vehicle time,@(odt_skims['DRV_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_In_vehicle_time_on_commuter_rail,DRIVE_COM - In-vehicle time on commuter rail (incremental w/ ivt),@(ivt_com_multiplier - 1) * (odt_skims['DRV_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Short_iwait_time,DRIVE_COM - Short iwait time,@short_i_wait_multiplier * ((odt_skims['DRV_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh) + (dot_skims['WLK_COM_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh)),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Long_iwait_time,DRIVE_COM - Long iwait time,@long_i_wait_multiplier * ((odt_skims['DRV_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0) + (dot_skims['WLK_COM_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0)),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_transfer_wait_time,DRIVE_COM - transfer wait time,@xwait_multiplier * (odt_skims['DRV_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_XWAIT']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_number_of_transfers,DRIVE_COM - number of transfers,@xfers_drv_multiplier * ((odt_skims['DRV_COM_WLK_BOARDS']-1).clip(0) + (dot_skims['WLK_COM_DRV_BOARDS']-1).clip(0)),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Drive_time,DRIVE_COM - Drive time,@dtim_multiplier * (odt_skims['DRV_COM_WLK_DTIM']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_DTIM']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Walk_access_time,DRIVE_COM - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Walk_egress_time,DRIVE_COM - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Walk_other_time,DRIVE_COM - Walk other time,@waux_multiplier * (odt_skims['DRV_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_WAUX']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Fare_and_operating_cost,DRIVE_COM - Fare and operating cost,@ivt_cost_multiplier * df.ivot * ((odt_skims['DRV_COM_WLK_FAR']+dot_skims['WLK_COM_DRV_FAR']) + ((odt_skims['DRV_COM_WLK_DDIST']/TRANSIT_SCALE_FACTOR+dot_skims['WLK_COM_DRV_DDIST']/TRANSIT_SCALE_FACTOR) *costPerMile)),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_COM - Ratio of drive access distance to OD distance,@dacc_ratio * ((odt_skims['DRV_COM_WLK_DDIST']/TRANSIT_SCALE_FACTOR + dot_skims['WLK_COM_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']*2)),,,,,,,,,,,,,,,,,,1,,, +util_DRIVE_COM_Destination_zone_densityIndex,DRIVE_COM - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Topology,DRIVE_COM - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Person_is_less_than_10_years_old,DRIVE_COM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,,,coef_age010_trn_multiplier,,, +#,Taxi,,,,,,,,,,,,,,,,,,,,,, +util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,,,,,,,,,,,,,,,,coef_ivt,, + util_Taxi_Wait_time,Taxi - Wait time,@ridehail_wait_time_multiplier * df.totalWaitTaxi,,,,,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * Taxi_costPerMile +(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * Taxi_costPerMinute)*100,,,,,,,,,,,,,,,,,,,coef_ivt,, +#,TNC Single,,,,,,,,,,,,,,,,,,,,,, +util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']),,,,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Wait_time,TNC Single - Wait time,@ridehail_wait_time_multiplier * df.totalWaitSingleTNC,,,,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * TNC_single_costPerMile + (odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,coef_ivt, +#,TNC Shared,,,,,,,,,,,,,,,,,,,,,, +util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@(odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_shared_IVTFactor,,,,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Wait_time,TNC Shared - Wait time,@ridehail_wait_time_multiplier * df.totalWaitSharedTNC,,,,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare * 2 + (odt_skims['HOV2TOLL_DIST'] + dot_skims['HOV2TOLL_DIST']) * TNC_shared_costPerMile + (odt_skims['HOV2TOLL_TIME'] + dot_skims['HOV2TOLL_TIME']) * TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,,coef_ivt +#,indiv tour ASCs,,,,,,,,,,,,,,,,,,,,,, +util_Walk_ASC_Zero_auto,Walk ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,walk_ASC_no_auto,,,,,,,,,,,,,, +util_Walk_ASC_Auto_deficient,Walk ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,walk_ASC_auto_deficient,,,,,,,,,,,,,, +util_Walk_ASC_Auto_sufficient,Walk ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,walk_ASC_auto_sufficient,,,,,,,,,,,,,, +util_Bike_ASC_Zero_auto,Bike ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,bike_ASC_no_auto,,,,,,,,,,,,, +util_Bike_ASC_Auto_deficient,Bike ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,bike_ASC_auto_deficient,,,,,,,,,,,,, +util_Bike_ASC_Auto_sufficient,Bike ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,bike_ASC_auto_sufficient,,,,,,,,,,,,, +util_Shared_ride_2_ASC_Zero_auto,Shared ride 2 ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,sr2_ASC_no_auto,sr2_ASC_no_auto,,,,,,,,,,,,,,,,, +util_Shared_ride_2_ASC_Auto_deficient,Shared ride 2 ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,sr2_ASC_auto_deficient,sr2_ASC_auto_deficient,,,,,,,,,,,,,,,,, +util_Shared_ride_2_ASC_Auto_sufficient,Shared ride 2 ASC - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient,,,,,,,,,,,,,,,,, +util_Shared_ride_3p_Zero_auto,Shared ride 3+ - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,sr3p_ASC_no_auto,sr3p_ASC_no_auto,,,,,,,,,,,,,,, +util_Shared_ride_3p_Auto_deficient,Shared ride 3+ - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient,,,,,,,,,,,,,,, +util_Shared_ride_3p_Auto_sufficient,Shared ride 3+ - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient,,,,,,,,,,,,,,, +util_Walk_to_Transit_Zero_auto,Walk to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,,,,,,,, +util_Walk_to_Transit_Auto_deficient,Walk to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,,,,,,,, +util_Walk_to_Transit_Auto_sufficient,Walk to Transit - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,,,,,,,, +util_Drive_to_Transit_Zero_auto,Drive to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,drive_transit_ASC_no_auto,drive_transit_ASC_no_auto,drive_transit_ASC_no_auto,drive_transit_ASC_no_auto,drive_transit_ASC_no_auto,,, +util_Drive_to_Transit_Auto_deficient,Drive to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient,,, +util_Drive_to_Transit_Auto_sufficient,Drive to Transit - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient,,, +util_Taxi_Zero_auto,Taxi - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,taxi_ASC_no_auto,, +util_Taxi_Auto_deficient,Taxi - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,taxi_ASC_auto_deficient,, +util_Taxi_Auto_sufficient,Taxi - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,taxi_ASC_auto_sufficient,, +util_TNC_Single_Zero_auto,TNC Single - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,tnc_single_ASC_no_auto, +util_TNC_Single_Auto_deficient,TNC Single - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,tnc_single_ASC_auto_deficient, +util_TNC_Single_Auto_sufficient,TNC Single - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,tnc_single_ASC_auto_sufficient, +util_TNC_Shared_Zero_auto,TNC Shared - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,,tnc_shared_ASC_no_auto +util_TNC_Shared_Auto_deficient,TNC Shared - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,,tnc_shared_ASC_auto_deficient +util_TNC_Shared_Auto_sufficient,TNC Shared - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,,tnc_shared_ASC_auto_sufficient +#,joint tour ASCs,,,,,,,,,,,,,,,,,,,,,, +util_Joint_Walk_ASC_Zero_auto,Joint - Walk ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,joint_walk_ASC_no_auto,,,,,,,,,,,,,, +util_Joint_Walk_ASC_Auto_deficient,Joint - Walk ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,joint_walk_ASC_auto_deficient,,,,,,,,,,,,,, +util_Joint_Walk_ASC_Auto_sufficient,Joint - Walk ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,joint_walk_ASC_auto_sufficient,,,,,,,,,,,,,, +util_Joint_Bike_ASC_Zero_auto,Joint - Bike ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,joint_bike_ASC_no_auto,,,,,,,,,,,,, +util_Joint_Bike_ASC_Auto_deficient,Joint - Bike ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,joint_bike_ASC_auto_deficient,,,,,,,,,,,,, +util_Joint_Bike_ASC_Auto_sufficient,Joint - Bike ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,joint_bike_ASC_auto_sufficient,,,,,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Zero_auto,Joint - Shared ride 2 ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto,,,,,,,,,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Auto_deficient,Joint - Shared ride 2 ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient,,,,,,,,,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Auto_sufficient,Joint - Shared ride 2 ASC - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient,,,,,,,,,,,,,,,,, +util_Joint_Shared_ride_3p_Zero_auto,Joint - Shared ride 3+ - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto,,,,,,,,,,,,,,, +util_Joint_Shared_ride_3p_Auto_deficient,Joint - Shared ride 3+ - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient,,,,,,,,,,,,,,, +util_Joint_Shared_ride_3p_Auto_sufficient,Joint - Shared ride 3+ - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient,,,,,,,,,,,,,,, +util_Joint_Walk_to_Transit_Zero_auto,Joint - Walk to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,,,,,,,, +util_Joint_Walk_to_Transit_Auto_deficient,Joint - Walk to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,,,,,,,, +util_Joint_Walk_to_Transit_Auto_sufficient,Joint - Walk to Transit - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,,,,,,,, +util_Joint_Drive_to_Transit_Zero_auto,Joint - Drive to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto,,, +util_Joint_Drive_to_Transit_Auto_deficient,Joint - Drive to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient,,, +util_Joint_Drive_to_Transit_Auto_sufficient,Joint - Drive to Transit - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient,,, +util_Joint_Taxi_Zero_auto,Joint - Taxi - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,joint_taxi_ASC_no_auto,, +util_Joint_Taxi_Auto_deficient,Joint - Taxi - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,joint_taxi_ASC_auto_deficient,, +util_Joint_Taxi_Auto_sufficient,Joint - Taxi - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,joint_taxi_ASC_auto_sufficient,, +util_Joint_TNC_Single_Zero_auto,Joint - TNC Single - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,joint_tnc_single_ASC_no_auto, +util_Joint_TNC_Single_Auto_deficient,Joint - TNC Single - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,joint_tnc_single_ASC_auto_deficient, +util_Joint_TNC_Single_Auto_sufficient,Joint - TNC Single - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,joint_tnc_single_ASC_auto_sufficient, +util_Joint_TNC_Shared_Zero_auto,Joint - TNC Shared - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_no_auto +util_Joint_TNC_Shared_Auto_deficient,Joint - TNC Shared - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_auto_deficient +util_Joint_TNC_Shared_Auto_sufficient,Joint - TNC Shared - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_auto_sufficient +util_Local_bus_ASC,Local bus ASC,1,,,,,,,,,local_bus_ASC,,,,,local_bus_ASC,,,,,,, +util_Walk_to_Light_Rail_ASC,Walk to Light Rail ASC,@(df.walk_ferry_available == False),,,,,,,,,,walk_light_rail_ASC,,,,,,,,,,, +util_Drive_to_Light_Rail_ASC,Drive to Light Rail ASC,@(df.drive_ferry_available == False),,,,,,,,,,,,,,,drive_light_rail_ASC,,,,,, +util_Walk_to_Ferry_ASC,Walk to Ferry ASC,@df.walk_ferry_available,,,,,,,,,,walk_ferry_ASC,,,,,,,,,,, +util_Drive_to_Ferry_ASC,Drive to Ferry ASC,@df.drive_ferry_available,,,,,,,,,,,,,,,drive_ferry_ASC,,,,,, +util_Express_Bus_ASC,Express Bus ASC,1,,,,,,,,,,,express_bus_ASC,,,,,express_bus_ASC,,,,, +util_Heavy_Rail_ASC,Heavy Rail ASC,1,,,,,,,,,,,,heavy_rail_ASC,,,,,heavy_rail_ASC,,,, +util_Commuter_Rail,Commuter Rail,1,,,,,,,,,,,,,commuter_rail_ASC,,,,,commuter_rail_ASC,,, +util_Walk_to_Transit_dest_CBD,Walk to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,walk_transit_CBD_ASC,walk_transit_CBD_ASC,walk_transit_CBD_ASC,walk_transit_CBD_ASC,walk_transit_CBD_ASC,,,,,,,, +util_Drive_to_Transit_dest_CBD,Drive to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,,,,,,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,,, +util_Drive_to_Transit_distance_penalty,Drive to Transit - distance penalty,@drvtrn_distpen_0_multiplier * (1-od_skims['DIST']/drvtrn_distpen_max).clip(lower=0),,,,,,,,,,,,,,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,,, +#, FIXME - skims aren't symmetrical,so we have to make sure they can get back,,,,,,,,,,,,,,,,,,,,, +util_Walk_not_available_for_long_distances,Walk not available for long distances,@od_skims.max('DISTWALK') > 3,,,,,,,-999,,,,,,,,,,,,,, +util_Bike_not_available_for_long_distances,Bike not available for long distances,@od_skims.max('DISTBIKE') > 8,,,,,,,,-999,,,,,,,,,,,,, +util_Drive_alone_not_available_for_escort_tours,Drive alone not available for escort tours,is_escort,-999,-999,,,,,,,,,,,,,,,,,,, +#, max(c_densityIndexOrigin*originDensityIndex,originDensityIndexMax),,,,,,,,,1,1,1,1,1,1,1,,,,,, diff --git a/activitysim/examples/example_mtc/configs/tour_mode_choice.yaml b/activitysim/examples/prototype_mtc/configs/tour_mode_choice.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/tour_mode_choice.yaml rename to activitysim/examples/prototype_mtc/configs/tour_mode_choice.yaml diff --git a/activitysim/examples/example_mtc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv rename to activitysim/examples/prototype_mtc/configs/tour_mode_choice_annotate_choosers_preprocessor.csv diff --git a/activitysim/examples/example_mtc/configs/tour_mode_choice_coefficients.csv b/activitysim/examples/prototype_mtc/configs/tour_mode_choice_coefficients.csv similarity index 97% rename from activitysim/examples/example_mtc/configs/tour_mode_choice_coefficients.csv rename to activitysim/examples/prototype_mtc/configs/tour_mode_choice_coefficients.csv index c5d9a264a2..9693953808 100644 --- a/activitysim/examples/example_mtc/configs/tour_mode_choice_coefficients.csv +++ b/activitysim/examples/prototype_mtc/configs/tour_mode_choice_coefficients.csv @@ -1,308 +1,308 @@ -coefficient_name,value,constrain -coef_one,1,T -coef_nest_root,1.00,T -coef_nest_AUTO,0.72,T -coef_nest_AUTO_DRIVEALONE,0.35,T -coef_nest_AUTO_SHAREDRIDE2,0.35,T -coef_nest_AUTO_SHAREDRIDE3,0.35,T -coef_nest_NONMOTORIZED,0.72,T -coef_nest_TRANSIT,0.72,T -coef_nest_TRANSIT_WALKACCESS,0.5,T -coef_nest_TRANSIT_DRIVEACCESS,0.5,T -coef_nest_RIDEHAIL,0.36,T -coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,-0.0175,F -coef_ivt_school_univ,-0.0224,F -coef_ivt_work,-0.0134,F -coef_ivt_atwork,-0.0188,F -coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,15,F -coef_topology_walk_multiplier_atwork,7.5,F -coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,20,F -coef_topology_bike_multiplier_atwork,10,F -coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,2.2,F -coef_topology_trn_multiplier_atwork,2,F -coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F -coef_age1619_da_multiplier_school_univ,-1.3813,F -coef_age1619_da_multiplier_atwork,0.0032336,F -coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F -coef_age010_trn_multiplier_school_univ,-1.5548,F -coef_age010_trn_multiplier_atwork,0.000722,F -coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,-1.366,F -coef_age16p_sr_multiplier_school_univ_work_atwork,0,F -coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,0,F -coef_hhsize1_sr_multiplier_work,-0.734588,F -coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,0,F -coef_hhsize2_sr_multiplier_school_univ,-0.6359,F -walk_ASC_no_auto_eatout,5.1251173,F -walk_ASC_no_auto_escort,2.8012068,F -walk_ASC_no_auto_othdiscr,3.2665946,F -walk_ASC_no_auto_othmaint,1.287299,F -walk_ASC_no_auto_school,18.414557,F -walk_ASC_no_auto_shopping,2.3768773,F -walk_ASC_no_auto_social,1.8680915,F -walk_ASC_no_auto_univ,6.408967,F -walk_ASC_no_auto_work,5.7672157,F -walk_ASC_no_auto_atwork,6.669213,F -walk_ASC_auto_deficient_eatout,3.274605,F -walk_ASC_auto_deficient_escort,-0.90204656,F -walk_ASC_auto_deficient_othdiscr,2.2494075,F -walk_ASC_auto_deficient_othmaint,1.3690404,F -walk_ASC_auto_deficient_school,3.2573624,F -walk_ASC_auto_deficient_shopping,2.2701733,F -walk_ASC_auto_deficient_social,2.870184,F -walk_ASC_auto_deficient_univ,4.50591,F -walk_ASC_auto_deficient_work,2.4010417,F -walk_ASC_auto_deficient_atwork,0.92546093,F -walk_ASC_auto_sufficient_eatout,1.5516903,F -walk_ASC_auto_sufficient_escort,-0.8116066,F -walk_ASC_auto_sufficient_othdiscr,1.2633476,F -walk_ASC_auto_sufficient_othmaint,0.7999634,F -walk_ASC_auto_sufficient_school,0.6476856,F -walk_ASC_auto_sufficient_shopping,0.7312663,F -walk_ASC_auto_sufficient_social,1.7072186,F -walk_ASC_auto_sufficient_univ,1.0607665,F -walk_ASC_auto_sufficient_work,0.053265337,F -walk_ASC_auto_sufficient_atwork,0.677216,F -bike_ASC_no_auto_eatout,0.86807096,F -bike_ASC_no_auto_escort,-0.716212,F -bike_ASC_no_auto_othdiscr,-0.3764232,F -bike_ASC_no_auto_othmaint,1.5394334,F -bike_ASC_no_auto_school,12.098735,F -bike_ASC_no_auto_shopping,0.8341555,F -bike_ASC_no_auto_social,0.02058321,F -bike_ASC_no_auto_univ,4.2945156,F -bike_ASC_no_auto_work,3.1940088,F -bike_ASC_no_auto_atwork,-0.90725845,F -bike_ASC_auto_deficient_eatout,-1.5691106,F -bike_ASC_auto_deficient_escort,-4.527928,F -bike_ASC_auto_deficient_othdiscr,-0.09246834,F -bike_ASC_auto_deficient_othmaint,-1.5184649,F -bike_ASC_auto_deficient_school,-0.5280678,F -bike_ASC_auto_deficient_shopping,-0.87584466,F -bike_ASC_auto_deficient_social,0.6345214,F -bike_ASC_auto_deficient_univ,-0.669235,F -bike_ASC_auto_deficient_work,0.25318968,F -bike_ASC_auto_deficient_atwork,-0.8074083,F -bike_ASC_auto_sufficient_eatout,-1.2003471,F -bike_ASC_auto_sufficient_escort,-5.0631084,F -bike_ASC_auto_sufficient_othdiscr,-1.0714597,F -bike_ASC_auto_sufficient_othmaint,-2.8083024,F -bike_ASC_auto_sufficient_school,-2.1134686,F -bike_ASC_auto_sufficient_shopping,-2.5662103,F -bike_ASC_auto_sufficient_social,-1.368071,F -bike_ASC_auto_sufficient_univ,-1.9397832,F -bike_ASC_auto_sufficient_work,-1.5800232,F -bike_ASC_auto_sufficient_atwork,15.72017,F -sr2_ASC_no_auto_all,0,F -sr2_ASC_auto_deficient_eatout,0.5882345,F -sr2_ASC_auto_deficient_escort,0,F -sr2_ASC_auto_deficient_othdiscr,0.6601513,F -sr2_ASC_auto_deficient_othmaint,0.2621527,F -sr2_ASC_auto_deficient_school,0.12474365,F -sr2_ASC_auto_deficient_shopping,0.24409756,F -sr2_ASC_auto_deficient_social,1.8558528,F -sr2_ASC_auto_deficient_univ,-1.6922346,F -sr2_ASC_auto_deficient_work,-0.33803123,F -sr2_ASC_auto_deficient_atwork,-2.1102421,F -sr2_ASC_auto_sufficient_eatout,0.86280555,F -sr2_ASC_auto_sufficient_escort,0,F -sr2_ASC_auto_sufficient_othdiscr,0.49684617,F -sr2_ASC_auto_sufficient_othmaint,0.25817883,F -sr2_ASC_auto_sufficient_school,-1.6062657,F -sr2_ASC_auto_sufficient_shopping,0.19770707,F -sr2_ASC_auto_sufficient_social,0.5236025,F -sr2_ASC_auto_sufficient_univ,-1.859427,F -sr2_ASC_auto_sufficient_work,-1.0857458,F -sr2_ASC_auto_sufficient_atwork,-1.4450618,F -sr3p_ASC_no_auto_eatout,0.3219998,F -sr3p_ASC_no_auto_escort,-1.8129267,F -sr3p_ASC_no_auto_othdiscr,0.27216902,F -sr3p_ASC_no_auto_othmaint,-0.8031854,F -sr3p_ASC_no_auto_school,-6.0240827,F -sr3p_ASC_no_auto_shopping,-0.27978948,F -sr3p_ASC_no_auto_social,-1.4036902,F -sr3p_ASC_no_auto_univ,-6.056001,F -sr3p_ASC_no_auto_work,-0.5831269,F -sr3p_ASC_no_auto_atwork,0.5826626,F -sr3p_ASC_auto_deficient_eatout,0.04605236,F -sr3p_ASC_auto_deficient_escort,-0.40818766,F -sr3p_ASC_auto_deficient_othdiscr,1.0470966,F -sr3p_ASC_auto_deficient_othmaint,-1.3493925,F -sr3p_ASC_auto_deficient_school,0.7149571,F -sr3p_ASC_auto_deficient_shopping,-0.073370166,F -sr3p_ASC_auto_deficient_social,1.5007243,F -sr3p_ASC_auto_deficient_univ,-1.7277422,F -sr3p_ASC_auto_deficient_work,-0.8527042,F -sr3p_ASC_auto_deficient_atwork,-2.514658,F -sr3p_ASC_auto_sufficient_eatout,0.8468596,F -sr3p_ASC_auto_sufficient_escort,-0.05741253,F -sr3p_ASC_auto_sufficient_othdiscr,0.58850205,F -sr3p_ASC_auto_sufficient_othmaint,-0.07549867,F -sr3p_ASC_auto_sufficient_school,-1.0201935,F -sr3p_ASC_auto_sufficient_shopping,-0.077571295,F -sr3p_ASC_auto_sufficient_social,0.50617886,F -sr3p_ASC_auto_sufficient_univ,-1.9047098,F -sr3p_ASC_auto_sufficient_work,-1.4699702,F -sr3p_ASC_auto_sufficient_atwork,-1.652174,F -walk_transit_ASC_no_auto_eatout,2.5936368,F -walk_transit_ASC_no_auto_escort,-2.2172081,F -walk_transit_ASC_no_auto_othdiscr,2.2437785,F -walk_transit_ASC_no_auto_othmaint,2.5643456,F -walk_transit_ASC_no_auto_school,21.383749,F -walk_transit_ASC_no_auto_shopping,2.1067476,F -walk_transit_ASC_no_auto_social,1.3814651,F -walk_transit_ASC_no_auto_univ,8.786037,F -walk_transit_ASC_no_auto_work,5.0354166,F -walk_transit_ASC_no_auto_atwork,2.7041876,F -walk_transit_ASC_auto_deficient_eatout,-0.03896324,F -walk_transit_ASC_auto_deficient_escort,-4.960704,F -walk_transit_ASC_auto_deficient_othdiscr,0.9530884,F -walk_transit_ASC_auto_deficient_othmaint,-3.0597258,F -walk_transit_ASC_auto_deficient_school,4.120708,F -walk_transit_ASC_auto_deficient_shopping,-0.8476569,F -walk_transit_ASC_auto_deficient_social,0.97444487,F -walk_transit_ASC_auto_deficient_univ,3.1362555,F -walk_transit_ASC_auto_deficient_work,0.65302855,F -walk_transit_ASC_auto_deficient_atwork,-2.9988291,F -walk_transit_ASC_auto_sufficient_eatout,-1.1126906,F -walk_transit_ASC_auto_sufficient_escort,-4.934847,F -walk_transit_ASC_auto_sufficient_othdiscr,-0.80636793,F -walk_transit_ASC_auto_sufficient_othmaint,-1.5471172,F -walk_transit_ASC_auto_sufficient_school,0.74590874,F -walk_transit_ASC_auto_sufficient_shopping,-2.2036798,F -walk_transit_ASC_auto_sufficient_social,-0.3453759,F -walk_transit_ASC_auto_sufficient_univ,0.4731163,F -walk_transit_ASC_auto_sufficient_work,-0.8916507,F -walk_transit_ASC_auto_sufficient_atwork,-3.401027,F -drive_transit_ASC_no_auto_all,0,F -drive_transit_ASC_auto_deficient_eatout,0.5998061,F -drive_transit_ASC_auto_deficient_escort,-1.1537067,F -drive_transit_ASC_auto_deficient_othdiscr,0.3199308,F -drive_transit_ASC_auto_deficient_othmaint,-0.29943228,F -drive_transit_ASC_auto_deficient_school,5.3252654,F -drive_transit_ASC_auto_deficient_shopping,-0.41849178,F -drive_transit_ASC_auto_deficient_social,1.5627195,F -drive_transit_ASC_auto_deficient_univ,1.8501176,F -drive_transit_ASC_auto_deficient_work,0.10081567,F -drive_transit_ASC_auto_deficient_atwork,-998.8196,F -drive_transit_ASC_auto_sufficient_eatout,-0.96951586,F -drive_transit_ASC_auto_sufficient_escort,-4.6014247,F -drive_transit_ASC_auto_sufficient_othdiscr,-0.3785917,F -drive_transit_ASC_auto_sufficient_othmaint,-2.6249478,F -drive_transit_ASC_auto_sufficient_school,1.40135,F -drive_transit_ASC_auto_sufficient_shopping,-2.1718938,F -drive_transit_ASC_auto_sufficient_social,-0.61585575,F -drive_transit_ASC_auto_sufficient_univ,1.3587753,F -drive_transit_ASC_auto_sufficient_work,-1.0045459,F -drive_transit_ASC_auto_sufficient_atwork,-999.21466,F -taxi_ASC_no_auto_eatout_othdiscr_social,0.9923,F -taxi_ASC_no_auto_escort_othmaint_shopping,1.8939,F -taxi_ASC_no_auto_school_univ,-7,T -taxi_ASC_no_auto_work,4.7291,F -taxi_ASC_no_auto_atwork,4.1021,F -taxi_ASC_auto_deficient_eatout_othdiscr_social,-3.1317,F -taxi_ASC_auto_deficient_escort_othmaint_shopping,0.1766,F -taxi_ASC_auto_deficient_school,-0.3338,F -taxi_ASC_auto_deficient_univ,4.2492,F -taxi_ASC_auto_deficient_work,-1.4766,F -taxi_ASC_auto_deficient_atwork,-4.4046,F -taxi_ASC_auto_sufficient_eatout_othdiscr_social,-3.0374,F -taxi_ASC_auto_sufficient_escort_othmaint_shopping,-1.8055,F -taxi_ASC_auto_sufficient_school,-2.4294,F -taxi_ASC_auto_sufficient_univ,-0.3131,F -taxi_ASC_auto_sufficient_work,-4.8509,F -taxi_ASC_auto_sufficient_atwork,-2.8804,F -tnc_single_ASC_no_auto_eatout_othdiscr_social,1.6852,F -tnc_single_ASC_no_auto_escort_othmaint_shopping,1.8605,F -tnc_single_ASC_no_auto_school,-7,T -tnc_single_ASC_no_auto_univ,-2.519,F -tnc_single_ASC_no_auto_work,5.7855,F -tnc_single_ASC_no_auto_atwork,4.4982,F -tnc_single_ASC_auto_deficient_eatout_othdiscr_social,-2.9623,F -tnc_single_ASC_auto_deficient_escort_othmaint_shopping,0.6748,F -tnc_single_ASC_auto_deficient_school,-0.5524,F -tnc_single_ASC_auto_deficient_univ,1.0221,F -tnc_single_ASC_auto_deficient_work,-0.8013,F -tnc_single_ASC_auto_deficient_atwork,-3.7626,F -tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,-2.3239,F -tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,-1.45,F -tnc_single_ASC_auto_sufficient_school,-2.8375,F -tnc_single_ASC_auto_sufficient_univ,0.2088,F -tnc_single_ASC_auto_sufficient_work,-4.1946,F -tnc_single_ASC_auto_sufficient_atwork,-2.7988,F -tnc_shared_ASC_no_auto_eatout_othdiscr_social,0.6464,F -tnc_shared_ASC_no_auto_escort_othmaint_shopping,0.9361,F -tnc_shared_ASC_no_auto_school,-7,T -tnc_shared_ASC_no_auto_univ,-5.8116,F -tnc_shared_ASC_no_auto_work,3.2429,F -tnc_shared_ASC_no_auto_atwork,3.3672,F -tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,-4.3576,F -tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,-0.3863,F -tnc_shared_ASC_auto_deficient_school,-1.4746,F -tnc_shared_ASC_auto_deficient_univ,3.25,F -tnc_shared_ASC_auto_deficient_work,-2.1435,F -tnc_shared_ASC_auto_deficient_atwork,-4.5089,F -tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,-3.6638,F -tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,-2.4365,F -tnc_shared_ASC_auto_sufficient_school,-3.7219,F -tnc_shared_ASC_auto_sufficient_univ,-0.9068,F -tnc_shared_ASC_auto_sufficient_work,-5.3575,F -tnc_shared_ASC_auto_sufficient_atwork,-3.5397,F -joint_walk_ASC_no_auto_all,-0.21274701,F -joint_walk_ASC_auto_deficient_all,-1.9607706,F -joint_walk_ASC_auto_sufficient_all,-3.2352157,F -joint_bike_ASC_no_auto_all,-2.8671598,F -joint_bike_ASC_auto_deficient_all,-6.076415,F -joint_bike_ASC_auto_sufficient_all,-6.3760657,F -joint_sr2_ASC_no_auto_all,0,T -joint_sr2_ASC_auto_deficient_all,0,T -joint_sr2_ASC_auto_sufficient_all,0,T -joint_sr3p_ASC_no_auto_all,0.5630671,F -joint_sr3p_ASC_auto_deficient_all,-1.8841692,F -joint_sr3p_ASC_auto_sufficient_all,-2.234826,F -joint_walk_transit_ASC_no_auto_all,0.62292415,F -joint_walk_transit_ASC_auto_deficient_all,-5.1634483,F -joint_walk_transit_ASC_auto_sufficient_all,-18.264534,F -joint_drive_transit_ASC_no_auto_all,0,T -joint_drive_transit_ASC_auto_deficient_all,-5.9632215,F -joint_drive_transit_ASC_auto_sufficient_all,-8.045285,F -joint_taxi_ASC_no_auto_all,-4.5792,F -joint_taxi_ASC_auto_deficient_all,-9.8157,F -joint_taxi_ASC_auto_sufficient_all,-11.7099,T -joint_tnc_single_ASC_no_auto_all,-4.4917,F -joint_tnc_single_ASC_auto_deficient_all,-9.8961,F -joint_tnc_single_ASC_auto_sufficient_all,-14.0159,T -joint_tnc_shared_ASC_no_auto_all,-4.3002,F -joint_tnc_shared_ASC_auto_deficient_all,-11.1572,F -joint_tnc_shared_ASC_auto_sufficient_all,-13.205,T -local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,-0.090703264,F -local_bus_ASC_school_univ,-0.06508621,F -local_bus_ASC_work,0.06689507,F -walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F -walk_light_rail_ASC_school_univ,1.6814003,F -walk_light_rail_ASC_work,0.8255567,F -drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F -drive_light_rail_ASC_school_univ,1.6814003,F -drive_light_rail_ASC_work,0.8255567,F -walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F -walk_ferry_ASC_school_univ,2.0202317,F -walk_ferry_ASC_work,0.93322605,F -drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F -drive_ferry_ASC_school_univ,2.0202317,F -drive_ferry_ASC_work,0.93322605,F -express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9692316,F -express_bus_ASC_school_univ,0.32496938,F -express_bus_ASC_work,-0.5165474,F -heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7706121,F -heavy_rail_ASC_school_univ,0.96200377,F -heavy_rail_ASC_work,0.64772975,F -commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7270185,F -commuter_rail_ASC_school_univ,1.0336206,F -commuter_rail_ASC_work,0.725503,F -walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F -walk_transit_CBD_ASC_school_univ,0.672,F -walk_transit_CBD_ASC_work,0.804,F -walk_transit_CBD_ASC_atwork,0.564,F -drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F -drive_transit_CBD_ASC_school_univ,0.672,F -drive_transit_CBD_ASC_work,1.1,F -drive_transit_CBD_ASC_atwork,0.564,F +coefficient_name,value,constrain +coef_one,1,T +coef_nest_root,1.00,T +coef_nest_AUTO,0.72,T +coef_nest_AUTO_DRIVEALONE,0.35,T +coef_nest_AUTO_SHAREDRIDE2,0.35,T +coef_nest_AUTO_SHAREDRIDE3,0.35,T +coef_nest_NONMOTORIZED,0.72,T +coef_nest_TRANSIT,0.72,T +coef_nest_TRANSIT_WALKACCESS,0.5,T +coef_nest_TRANSIT_DRIVEACCESS,0.5,T +coef_nest_RIDEHAIL,0.36,T +coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,-0.0175,F +coef_ivt_school_univ,-0.0224,F +coef_ivt_work,-0.0134,F +coef_ivt_atwork,-0.0188,F +coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,15,F +coef_topology_walk_multiplier_atwork,7.5,F +coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,20,F +coef_topology_bike_multiplier_atwork,10,F +coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,2.2,F +coef_topology_trn_multiplier_atwork,2,F +coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F +coef_age1619_da_multiplier_school_univ,-1.3813,F +coef_age1619_da_multiplier_atwork,0.0032336,F +coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F +coef_age010_trn_multiplier_school_univ,-1.5548,F +coef_age010_trn_multiplier_atwork,0.000722,F +coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,-1.366,F +coef_age16p_sr_multiplier_school_univ_work_atwork,0,F +coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,0,F +coef_hhsize1_sr_multiplier_work,-0.734588,F +coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,0,F +coef_hhsize2_sr_multiplier_school_univ,-0.6359,F +walk_ASC_no_auto_eatout,5.1251173,F +walk_ASC_no_auto_escort,2.8012068,F +walk_ASC_no_auto_othdiscr,3.2665946,F +walk_ASC_no_auto_othmaint,1.287299,F +walk_ASC_no_auto_school,18.414557,F +walk_ASC_no_auto_shopping,2.3768773,F +walk_ASC_no_auto_social,1.8680915,F +walk_ASC_no_auto_univ,6.408967,F +walk_ASC_no_auto_work,5.7672157,F +walk_ASC_no_auto_atwork,6.669213,F +walk_ASC_auto_deficient_eatout,3.274605,F +walk_ASC_auto_deficient_escort,-0.90204656,F +walk_ASC_auto_deficient_othdiscr,2.2494075,F +walk_ASC_auto_deficient_othmaint,1.3690404,F +walk_ASC_auto_deficient_school,3.2573624,F +walk_ASC_auto_deficient_shopping,2.2701733,F +walk_ASC_auto_deficient_social,2.870184,F +walk_ASC_auto_deficient_univ,4.50591,F +walk_ASC_auto_deficient_work,2.4010417,F +walk_ASC_auto_deficient_atwork,0.92546093,F +walk_ASC_auto_sufficient_eatout,1.5516903,F +walk_ASC_auto_sufficient_escort,-0.8116066,F +walk_ASC_auto_sufficient_othdiscr,1.2633476,F +walk_ASC_auto_sufficient_othmaint,0.7999634,F +walk_ASC_auto_sufficient_school,0.6476856,F +walk_ASC_auto_sufficient_shopping,0.7312663,F +walk_ASC_auto_sufficient_social,1.7072186,F +walk_ASC_auto_sufficient_univ,1.0607665,F +walk_ASC_auto_sufficient_work,0.053265337,F +walk_ASC_auto_sufficient_atwork,0.677216,F +bike_ASC_no_auto_eatout,0.86807096,F +bike_ASC_no_auto_escort,-0.716212,F +bike_ASC_no_auto_othdiscr,-0.3764232,F +bike_ASC_no_auto_othmaint,1.5394334,F +bike_ASC_no_auto_school,12.098735,F +bike_ASC_no_auto_shopping,0.8341555,F +bike_ASC_no_auto_social,0.02058321,F +bike_ASC_no_auto_univ,4.2945156,F +bike_ASC_no_auto_work,3.1940088,F +bike_ASC_no_auto_atwork,-0.90725845,F +bike_ASC_auto_deficient_eatout,-1.5691106,F +bike_ASC_auto_deficient_escort,-4.527928,F +bike_ASC_auto_deficient_othdiscr,-0.09246834,F +bike_ASC_auto_deficient_othmaint,-1.5184649,F +bike_ASC_auto_deficient_school,-0.5280678,F +bike_ASC_auto_deficient_shopping,-0.87584466,F +bike_ASC_auto_deficient_social,0.6345214,F +bike_ASC_auto_deficient_univ,-0.669235,F +bike_ASC_auto_deficient_work,0.25318968,F +bike_ASC_auto_deficient_atwork,-0.8074083,F +bike_ASC_auto_sufficient_eatout,-1.2003471,F +bike_ASC_auto_sufficient_escort,-5.0631084,F +bike_ASC_auto_sufficient_othdiscr,-1.0714597,F +bike_ASC_auto_sufficient_othmaint,-2.8083024,F +bike_ASC_auto_sufficient_school,-2.1134686,F +bike_ASC_auto_sufficient_shopping,-2.5662103,F +bike_ASC_auto_sufficient_social,-1.368071,F +bike_ASC_auto_sufficient_univ,-1.9397832,F +bike_ASC_auto_sufficient_work,-1.5800232,F +bike_ASC_auto_sufficient_atwork,15.72017,F +sr2_ASC_no_auto_all,0,F +sr2_ASC_auto_deficient_eatout,0.5882345,F +sr2_ASC_auto_deficient_escort,0,F +sr2_ASC_auto_deficient_othdiscr,0.6601513,F +sr2_ASC_auto_deficient_othmaint,0.2621527,F +sr2_ASC_auto_deficient_school,0.12474365,F +sr2_ASC_auto_deficient_shopping,0.24409756,F +sr2_ASC_auto_deficient_social,1.8558528,F +sr2_ASC_auto_deficient_univ,-1.6922346,F +sr2_ASC_auto_deficient_work,-0.33803123,F +sr2_ASC_auto_deficient_atwork,-2.1102421,F +sr2_ASC_auto_sufficient_eatout,0.86280555,F +sr2_ASC_auto_sufficient_escort,0,F +sr2_ASC_auto_sufficient_othdiscr,0.49684617,F +sr2_ASC_auto_sufficient_othmaint,0.25817883,F +sr2_ASC_auto_sufficient_school,-1.6062657,F +sr2_ASC_auto_sufficient_shopping,0.19770707,F +sr2_ASC_auto_sufficient_social,0.5236025,F +sr2_ASC_auto_sufficient_univ,-1.859427,F +sr2_ASC_auto_sufficient_work,-1.0857458,F +sr2_ASC_auto_sufficient_atwork,-1.4450618,F +sr3p_ASC_no_auto_eatout,0.3219998,F +sr3p_ASC_no_auto_escort,-1.8129267,F +sr3p_ASC_no_auto_othdiscr,0.27216902,F +sr3p_ASC_no_auto_othmaint,-0.8031854,F +sr3p_ASC_no_auto_school,-6.0240827,F +sr3p_ASC_no_auto_shopping,-0.27978948,F +sr3p_ASC_no_auto_social,-1.4036902,F +sr3p_ASC_no_auto_univ,-6.056001,F +sr3p_ASC_no_auto_work,-0.5831269,F +sr3p_ASC_no_auto_atwork,0.5826626,F +sr3p_ASC_auto_deficient_eatout,0.04605236,F +sr3p_ASC_auto_deficient_escort,-0.40818766,F +sr3p_ASC_auto_deficient_othdiscr,1.0470966,F +sr3p_ASC_auto_deficient_othmaint,-1.3493925,F +sr3p_ASC_auto_deficient_school,0.7149571,F +sr3p_ASC_auto_deficient_shopping,-0.073370166,F +sr3p_ASC_auto_deficient_social,1.5007243,F +sr3p_ASC_auto_deficient_univ,-1.7277422,F +sr3p_ASC_auto_deficient_work,-0.8527042,F +sr3p_ASC_auto_deficient_atwork,-2.514658,F +sr3p_ASC_auto_sufficient_eatout,0.8468596,F +sr3p_ASC_auto_sufficient_escort,-0.05741253,F +sr3p_ASC_auto_sufficient_othdiscr,0.58850205,F +sr3p_ASC_auto_sufficient_othmaint,-0.07549867,F +sr3p_ASC_auto_sufficient_school,-1.0201935,F +sr3p_ASC_auto_sufficient_shopping,-0.077571295,F +sr3p_ASC_auto_sufficient_social,0.50617886,F +sr3p_ASC_auto_sufficient_univ,-1.9047098,F +sr3p_ASC_auto_sufficient_work,-1.4699702,F +sr3p_ASC_auto_sufficient_atwork,-1.652174,F +walk_transit_ASC_no_auto_eatout,2.5936368,F +walk_transit_ASC_no_auto_escort,-2.2172081,F +walk_transit_ASC_no_auto_othdiscr,2.2437785,F +walk_transit_ASC_no_auto_othmaint,2.5643456,F +walk_transit_ASC_no_auto_school,21.383749,F +walk_transit_ASC_no_auto_shopping,2.1067476,F +walk_transit_ASC_no_auto_social,1.3814651,F +walk_transit_ASC_no_auto_univ,8.786037,F +walk_transit_ASC_no_auto_work,5.0354166,F +walk_transit_ASC_no_auto_atwork,2.7041876,F +walk_transit_ASC_auto_deficient_eatout,-0.03896324,F +walk_transit_ASC_auto_deficient_escort,-4.960704,F +walk_transit_ASC_auto_deficient_othdiscr,0.9530884,F +walk_transit_ASC_auto_deficient_othmaint,-3.0597258,F +walk_transit_ASC_auto_deficient_school,4.120708,F +walk_transit_ASC_auto_deficient_shopping,-0.8476569,F +walk_transit_ASC_auto_deficient_social,0.97444487,F +walk_transit_ASC_auto_deficient_univ,3.1362555,F +walk_transit_ASC_auto_deficient_work,0.65302855,F +walk_transit_ASC_auto_deficient_atwork,-2.9988291,F +walk_transit_ASC_auto_sufficient_eatout,-1.1126906,F +walk_transit_ASC_auto_sufficient_escort,-4.934847,F +walk_transit_ASC_auto_sufficient_othdiscr,-0.80636793,F +walk_transit_ASC_auto_sufficient_othmaint,-1.5471172,F +walk_transit_ASC_auto_sufficient_school,0.74590874,F +walk_transit_ASC_auto_sufficient_shopping,-2.2036798,F +walk_transit_ASC_auto_sufficient_social,-0.3453759,F +walk_transit_ASC_auto_sufficient_univ,0.4731163,F +walk_transit_ASC_auto_sufficient_work,-0.8916507,F +walk_transit_ASC_auto_sufficient_atwork,-3.401027,F +drive_transit_ASC_no_auto_all,0,F +drive_transit_ASC_auto_deficient_eatout,0.5998061,F +drive_transit_ASC_auto_deficient_escort,-1.1537067,F +drive_transit_ASC_auto_deficient_othdiscr,0.3199308,F +drive_transit_ASC_auto_deficient_othmaint,-0.29943228,F +drive_transit_ASC_auto_deficient_school,5.3252654,F +drive_transit_ASC_auto_deficient_shopping,-0.41849178,F +drive_transit_ASC_auto_deficient_social,1.5627195,F +drive_transit_ASC_auto_deficient_univ,1.8501176,F +drive_transit_ASC_auto_deficient_work,0.10081567,F +drive_transit_ASC_auto_deficient_atwork,-998.8196,F +drive_transit_ASC_auto_sufficient_eatout,-0.96951586,F +drive_transit_ASC_auto_sufficient_escort,-4.6014247,F +drive_transit_ASC_auto_sufficient_othdiscr,-0.3785917,F +drive_transit_ASC_auto_sufficient_othmaint,-2.6249478,F +drive_transit_ASC_auto_sufficient_school,1.40135,F +drive_transit_ASC_auto_sufficient_shopping,-2.1718938,F +drive_transit_ASC_auto_sufficient_social,-0.61585575,F +drive_transit_ASC_auto_sufficient_univ,1.3587753,F +drive_transit_ASC_auto_sufficient_work,-1.0045459,F +drive_transit_ASC_auto_sufficient_atwork,-999.21466,F +taxi_ASC_no_auto_eatout_othdiscr_social,0.9923,F +taxi_ASC_no_auto_escort_othmaint_shopping,1.8939,F +taxi_ASC_no_auto_school_univ,-7,T +taxi_ASC_no_auto_work,4.7291,F +taxi_ASC_no_auto_atwork,4.1021,F +taxi_ASC_auto_deficient_eatout_othdiscr_social,-3.1317,F +taxi_ASC_auto_deficient_escort_othmaint_shopping,0.1766,F +taxi_ASC_auto_deficient_school,-0.3338,F +taxi_ASC_auto_deficient_univ,4.2492,F +taxi_ASC_auto_deficient_work,-1.4766,F +taxi_ASC_auto_deficient_atwork,-4.4046,F +taxi_ASC_auto_sufficient_eatout_othdiscr_social,-3.0374,F +taxi_ASC_auto_sufficient_escort_othmaint_shopping,-1.8055,F +taxi_ASC_auto_sufficient_school,-2.4294,F +taxi_ASC_auto_sufficient_univ,-0.3131,F +taxi_ASC_auto_sufficient_work,-4.8509,F +taxi_ASC_auto_sufficient_atwork,-2.8804,F +tnc_single_ASC_no_auto_eatout_othdiscr_social,1.6852,F +tnc_single_ASC_no_auto_escort_othmaint_shopping,1.8605,F +tnc_single_ASC_no_auto_school,-7,T +tnc_single_ASC_no_auto_univ,-2.519,F +tnc_single_ASC_no_auto_work,5.7855,F +tnc_single_ASC_no_auto_atwork,4.4982,F +tnc_single_ASC_auto_deficient_eatout_othdiscr_social,-2.9623,F +tnc_single_ASC_auto_deficient_escort_othmaint_shopping,0.6748,F +tnc_single_ASC_auto_deficient_school,-0.5524,F +tnc_single_ASC_auto_deficient_univ,1.0221,F +tnc_single_ASC_auto_deficient_work,-0.8013,F +tnc_single_ASC_auto_deficient_atwork,-3.7626,F +tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,-2.3239,F +tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,-1.45,F +tnc_single_ASC_auto_sufficient_school,-2.8375,F +tnc_single_ASC_auto_sufficient_univ,0.2088,F +tnc_single_ASC_auto_sufficient_work,-4.1946,F +tnc_single_ASC_auto_sufficient_atwork,-2.7988,F +tnc_shared_ASC_no_auto_eatout_othdiscr_social,0.6464,F +tnc_shared_ASC_no_auto_escort_othmaint_shopping,0.9361,F +tnc_shared_ASC_no_auto_school,-7,T +tnc_shared_ASC_no_auto_univ,-5.8116,F +tnc_shared_ASC_no_auto_work,3.2429,F +tnc_shared_ASC_no_auto_atwork,3.3672,F +tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,-4.3576,F +tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,-0.3863,F +tnc_shared_ASC_auto_deficient_school,-1.4746,F +tnc_shared_ASC_auto_deficient_univ,3.25,F +tnc_shared_ASC_auto_deficient_work,-2.1435,F +tnc_shared_ASC_auto_deficient_atwork,-4.5089,F +tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,-3.6638,F +tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,-2.4365,F +tnc_shared_ASC_auto_sufficient_school,-3.7219,F +tnc_shared_ASC_auto_sufficient_univ,-0.9068,F +tnc_shared_ASC_auto_sufficient_work,-5.3575,F +tnc_shared_ASC_auto_sufficient_atwork,-3.5397,F +joint_walk_ASC_no_auto_all,-0.21274701,F +joint_walk_ASC_auto_deficient_all,-1.9607706,F +joint_walk_ASC_auto_sufficient_all,-3.2352157,F +joint_bike_ASC_no_auto_all,-2.8671598,F +joint_bike_ASC_auto_deficient_all,-6.076415,F +joint_bike_ASC_auto_sufficient_all,-6.3760657,F +joint_sr2_ASC_no_auto_all,0,T +joint_sr2_ASC_auto_deficient_all,0,T +joint_sr2_ASC_auto_sufficient_all,0,T +joint_sr3p_ASC_no_auto_all,0.5630671,F +joint_sr3p_ASC_auto_deficient_all,-1.8841692,F +joint_sr3p_ASC_auto_sufficient_all,-2.234826,F +joint_walk_transit_ASC_no_auto_all,0.62292415,F +joint_walk_transit_ASC_auto_deficient_all,-5.1634483,F +joint_walk_transit_ASC_auto_sufficient_all,-18.264534,F +joint_drive_transit_ASC_no_auto_all,0,T +joint_drive_transit_ASC_auto_deficient_all,-5.9632215,F +joint_drive_transit_ASC_auto_sufficient_all,-8.045285,F +joint_taxi_ASC_no_auto_all,-4.5792,F +joint_taxi_ASC_auto_deficient_all,-9.8157,F +joint_taxi_ASC_auto_sufficient_all,-11.7099,T +joint_tnc_single_ASC_no_auto_all,-4.4917,F +joint_tnc_single_ASC_auto_deficient_all,-9.8961,F +joint_tnc_single_ASC_auto_sufficient_all,-14.0159,T +joint_tnc_shared_ASC_no_auto_all,-4.3002,F +joint_tnc_shared_ASC_auto_deficient_all,-11.1572,F +joint_tnc_shared_ASC_auto_sufficient_all,-13.205,T +local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,-0.090703264,F +local_bus_ASC_school_univ,-0.06508621,F +local_bus_ASC_work,0.06689507,F +walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F +walk_light_rail_ASC_school_univ,1.6814003,F +walk_light_rail_ASC_work,0.8255567,F +drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.76895475,F +drive_light_rail_ASC_school_univ,1.6814003,F +drive_light_rail_ASC_work,0.8255567,F +walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F +walk_ferry_ASC_school_univ,2.0202317,F +walk_ferry_ASC_work,0.93322605,F +drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9401238,F +drive_ferry_ASC_school_univ,2.0202317,F +drive_ferry_ASC_work,0.93322605,F +express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.9692316,F +express_bus_ASC_school_univ,0.32496938,F +express_bus_ASC_work,-0.5165474,F +heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7706121,F +heavy_rail_ASC_school_univ,0.96200377,F +heavy_rail_ASC_work,0.64772975,F +commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0.7270185,F +commuter_rail_ASC_school_univ,1.0336206,F +commuter_rail_ASC_work,0.725503,F +walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F +walk_transit_CBD_ASC_school_univ,0.672,F +walk_transit_CBD_ASC_work,0.804,F +walk_transit_CBD_ASC_atwork,0.564,F +drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F +drive_transit_CBD_ASC_school_univ,0.672,F +drive_transit_CBD_ASC_work,1.1,F +drive_transit_CBD_ASC_atwork,0.564,F diff --git a/activitysim/examples/example_psrc/configs/tour_mode_choice_coeffs_template.csv b/activitysim/examples/prototype_mtc/configs/tour_mode_choice_coefficients_template.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_psrc/configs/tour_mode_choice_coeffs_template.csv rename to activitysim/examples/prototype_mtc/configs/tour_mode_choice_coefficients_template.csv index 2e97238f2c..b1b009a3f0 --- a/activitysim/examples/example_psrc/configs/tour_mode_choice_coeffs_template.csv +++ b/activitysim/examples/prototype_mtc/configs/tour_mode_choice_coefficients_template.csv @@ -1,87 +1,87 @@ -coefficient_name,eatout,escort,othdiscr,othmaint,school,shopping,social,univ,work,atwork -#same for all segments,,,,,,,,,, -coef_one,,,,,,,,,, -coef_nest_root,,,,,,,,,, -coef_nest_AUTO,,,,,,,,,, -coef_nest_AUTO_DRIVEALONE,,,,,,,,,, -coef_nest_AUTO_SHAREDRIDE2,,,,,,,,,, -coef_nest_AUTO_SHAREDRIDE3,,,,,,,,,, -coef_nest_NONMOTORIZED,,,,,,,,,, -coef_nest_TRANSIT,,,,,,,,,, -coef_nest_TRANSIT_WALKACCESS,,,,,,,,,, -coef_nest_TRANSIT_DRIVEACCESS,,,,,,,,,, -coef_nest_RIDEHAIL,,,,,,,,,, -#,,,,,,,,,, -coef_ivt,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_work,coef_ivt_atwork -coef_topology_walk_multiplier,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_atwork -coef_topology_bike_multiplier,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_atwork -coef_topology_trn_multiplier,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_atwork -coef_age1619_da_multiplier,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_atwork -coef_age010_trn_multiplier,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_atwork -coef_age16p_sr_multiplier,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork -coef_hhsize1_sr_multiplier,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_work,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork -coef_hhsize2_sr_multiplier,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork -walk_ASC_no_auto,walk_ASC_no_auto_eatout,walk_ASC_no_auto_escort,walk_ASC_no_auto_othdiscr,walk_ASC_no_auto_othmaint,walk_ASC_no_auto_school,walk_ASC_no_auto_shopping,walk_ASC_no_auto_social,walk_ASC_no_auto_univ,walk_ASC_no_auto_work,walk_ASC_no_auto_atwork -walk_ASC_auto_deficient,walk_ASC_auto_deficient_eatout,walk_ASC_auto_deficient_escort,walk_ASC_auto_deficient_othdiscr,walk_ASC_auto_deficient_othmaint,walk_ASC_auto_deficient_school,walk_ASC_auto_deficient_shopping,walk_ASC_auto_deficient_social,walk_ASC_auto_deficient_univ,walk_ASC_auto_deficient_work,walk_ASC_auto_deficient_atwork -walk_ASC_auto_sufficient,walk_ASC_auto_sufficient_eatout,walk_ASC_auto_sufficient_escort,walk_ASC_auto_sufficient_othdiscr,walk_ASC_auto_sufficient_othmaint,walk_ASC_auto_sufficient_school,walk_ASC_auto_sufficient_shopping,walk_ASC_auto_sufficient_social,walk_ASC_auto_sufficient_univ,walk_ASC_auto_sufficient_work,walk_ASC_auto_sufficient_atwork -bike_ASC_no_auto,bike_ASC_no_auto_eatout,bike_ASC_no_auto_escort,bike_ASC_no_auto_othdiscr,bike_ASC_no_auto_othmaint,bike_ASC_no_auto_school,bike_ASC_no_auto_shopping,bike_ASC_no_auto_social,bike_ASC_no_auto_univ,bike_ASC_no_auto_work,bike_ASC_no_auto_atwork -bike_ASC_auto_deficient,bike_ASC_auto_deficient_eatout,bike_ASC_auto_deficient_escort,bike_ASC_auto_deficient_othdiscr,bike_ASC_auto_deficient_othmaint,bike_ASC_auto_deficient_school,bike_ASC_auto_deficient_shopping,bike_ASC_auto_deficient_social,bike_ASC_auto_deficient_univ,bike_ASC_auto_deficient_work,bike_ASC_auto_deficient_atwork -bike_ASC_auto_sufficient,bike_ASC_auto_sufficient_eatout,bike_ASC_auto_sufficient_escort,bike_ASC_auto_sufficient_othdiscr,bike_ASC_auto_sufficient_othmaint,bike_ASC_auto_sufficient_school,bike_ASC_auto_sufficient_shopping,bike_ASC_auto_sufficient_social,bike_ASC_auto_sufficient_univ,bike_ASC_auto_sufficient_work,bike_ASC_auto_sufficient_atwork -sr2_ASC_no_auto,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all -sr2_ASC_auto_deficient,sr2_ASC_auto_deficient_eatout,sr2_ASC_auto_deficient_escort,sr2_ASC_auto_deficient_othdiscr,sr2_ASC_auto_deficient_othmaint,sr2_ASC_auto_deficient_school,sr2_ASC_auto_deficient_shopping,sr2_ASC_auto_deficient_social,sr2_ASC_auto_deficient_univ,sr2_ASC_auto_deficient_work,sr2_ASC_auto_deficient_atwork -sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient_eatout,sr2_ASC_auto_sufficient_escort,sr2_ASC_auto_sufficient_othdiscr,sr2_ASC_auto_sufficient_othmaint,sr2_ASC_auto_sufficient_school,sr2_ASC_auto_sufficient_shopping,sr2_ASC_auto_sufficient_social,sr2_ASC_auto_sufficient_univ,sr2_ASC_auto_sufficient_work,sr2_ASC_auto_sufficient_atwork -sr3p_ASC_no_auto,sr3p_ASC_no_auto_eatout,sr3p_ASC_no_auto_escort,sr3p_ASC_no_auto_othdiscr,sr3p_ASC_no_auto_othmaint,sr3p_ASC_no_auto_school,sr3p_ASC_no_auto_shopping,sr3p_ASC_no_auto_social,sr3p_ASC_no_auto_univ,sr3p_ASC_no_auto_work,sr3p_ASC_no_auto_atwork -sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient_eatout,sr3p_ASC_auto_deficient_escort,sr3p_ASC_auto_deficient_othdiscr,sr3p_ASC_auto_deficient_othmaint,sr3p_ASC_auto_deficient_school,sr3p_ASC_auto_deficient_shopping,sr3p_ASC_auto_deficient_social,sr3p_ASC_auto_deficient_univ,sr3p_ASC_auto_deficient_work,sr3p_ASC_auto_deficient_atwork -sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient_eatout,sr3p_ASC_auto_sufficient_escort,sr3p_ASC_auto_sufficient_othdiscr,sr3p_ASC_auto_sufficient_othmaint,sr3p_ASC_auto_sufficient_school,sr3p_ASC_auto_sufficient_shopping,sr3p_ASC_auto_sufficient_social,sr3p_ASC_auto_sufficient_univ,sr3p_ASC_auto_sufficient_work,sr3p_ASC_auto_sufficient_atwork -walk_transit_ASC_no_auto,walk_transit_ASC_no_auto_eatout,walk_transit_ASC_no_auto_escort,walk_transit_ASC_no_auto_othdiscr,walk_transit_ASC_no_auto_othmaint,walk_transit_ASC_no_auto_school,walk_transit_ASC_no_auto_shopping,walk_transit_ASC_no_auto_social,walk_transit_ASC_no_auto_univ,walk_transit_ASC_no_auto_work,walk_transit_ASC_no_auto_atwork -walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient_eatout,walk_transit_ASC_auto_deficient_escort,walk_transit_ASC_auto_deficient_othdiscr,walk_transit_ASC_auto_deficient_othmaint,walk_transit_ASC_auto_deficient_school,walk_transit_ASC_auto_deficient_shopping,walk_transit_ASC_auto_deficient_social,walk_transit_ASC_auto_deficient_univ,walk_transit_ASC_auto_deficient_work,walk_transit_ASC_auto_deficient_atwork -walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient_eatout,walk_transit_ASC_auto_sufficient_escort,walk_transit_ASC_auto_sufficient_othdiscr,walk_transit_ASC_auto_sufficient_othmaint,walk_transit_ASC_auto_sufficient_school,walk_transit_ASC_auto_sufficient_shopping,walk_transit_ASC_auto_sufficient_social,walk_transit_ASC_auto_sufficient_univ,walk_transit_ASC_auto_sufficient_work,walk_transit_ASC_auto_sufficient_atwork -drive_transit_ASC_no_auto,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all -drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient_eatout,drive_transit_ASC_auto_deficient_escort,drive_transit_ASC_auto_deficient_othdiscr,drive_transit_ASC_auto_deficient_othmaint,drive_transit_ASC_auto_deficient_school,drive_transit_ASC_auto_deficient_shopping,drive_transit_ASC_auto_deficient_social,drive_transit_ASC_auto_deficient_univ,drive_transit_ASC_auto_deficient_work,drive_transit_ASC_auto_deficient_atwork -drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient_eatout,drive_transit_ASC_auto_sufficient_escort,drive_transit_ASC_auto_sufficient_othdiscr,drive_transit_ASC_auto_sufficient_othmaint,drive_transit_ASC_auto_sufficient_school,drive_transit_ASC_auto_sufficient_shopping,drive_transit_ASC_auto_sufficient_social,drive_transit_ASC_auto_sufficient_univ,drive_transit_ASC_auto_sufficient_work,drive_transit_ASC_auto_sufficient_atwork -taxi_ASC_no_auto,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_work,taxi_ASC_no_auto_atwork -taxi_ASC_auto_deficient,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_school,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_univ,taxi_ASC_auto_deficient_work,taxi_ASC_auto_deficient_atwork -taxi_ASC_auto_sufficient,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_school,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_univ,taxi_ASC_auto_sufficient_work,taxi_ASC_auto_sufficient_atwork -tnc_single_ASC_no_auto,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_school,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_univ,tnc_single_ASC_no_auto_work,tnc_single_ASC_no_auto_atwork -tnc_single_ASC_auto_deficient,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_school,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_univ,tnc_single_ASC_auto_deficient_work,tnc_single_ASC_auto_deficient_atwork -tnc_single_ASC_auto_sufficient,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_school,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_univ,tnc_single_ASC_auto_sufficient_work,tnc_single_ASC_auto_sufficient_atwork -tnc_shared_ASC_no_auto,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_school,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_univ,tnc_shared_ASC_no_auto_work,tnc_shared_ASC_no_auto_atwork -tnc_shared_ASC_auto_deficient,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_school,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_univ,tnc_shared_ASC_auto_deficient_work,tnc_shared_ASC_auto_deficient_atwork -tnc_shared_ASC_auto_sufficient,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_school,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_univ,tnc_shared_ASC_auto_sufficient_work,tnc_shared_ASC_auto_sufficient_atwork -joint_walk_ASC_no_auto,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all -joint_walk_ASC_auto_deficient,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all -joint_walk_ASC_auto_sufficient,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all -joint_bike_ASC_no_auto,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all -joint_bike_ASC_auto_deficient,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all -joint_bike_ASC_auto_sufficient,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all -joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all -joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all -joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all -joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all -joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all -joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all -joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all -joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all -joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all -joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all -joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all -joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all -joint_taxi_ASC_no_auto,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all -joint_taxi_ASC_auto_deficient,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all -joint_taxi_ASC_auto_sufficient,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all -joint_tnc_single_ASC_no_auto,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all -joint_tnc_single_ASC_auto_deficient,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all -joint_tnc_single_ASC_auto_sufficient,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all -joint_tnc_shared_ASC_no_auto,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all -joint_tnc_shared_ASC_auto_deficient,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all -joint_tnc_shared_ASC_auto_sufficient,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all -local_bus_ASC,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_school_univ,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_school_univ,local_bus_ASC_work,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -walk_light_rail_ASC,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_work,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -drive_light_rail_ASC,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_work,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -walk_ferry_ASC,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_work,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -drive_ferry_ASC,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_work,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -express_bus_ASC,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_work,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -heavy_rail_ASC,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_work,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -commuter_rail_ASC,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_work,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -walk_transit_CBD_ASC,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_work,walk_transit_CBD_ASC_atwork -drive_transit_CBD_ASC,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_work,drive_transit_CBD_ASC_atwork +coefficient_name,eatout,escort,othdiscr,othmaint,school,shopping,social,univ,work,atwork +#same for all segments,,,,,,,,,, +coef_one,,,,,,,,,, +coef_nest_root,,,,,,,,,, +coef_nest_AUTO,,,,,,,,,, +coef_nest_AUTO_DRIVEALONE,,,,,,,,,, +coef_nest_AUTO_SHAREDRIDE2,,,,,,,,,, +coef_nest_AUTO_SHAREDRIDE3,,,,,,,,,, +coef_nest_NONMOTORIZED,,,,,,,,,, +coef_nest_TRANSIT,,,,,,,,,, +coef_nest_TRANSIT_WALKACCESS,,,,,,,,,, +coef_nest_TRANSIT_DRIVEACCESS,,,,,,,,,, +coef_nest_RIDEHAIL,,,,,,,,,, +#,,,,,,,,,, +coef_ivt,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_work,coef_ivt_atwork +coef_topology_walk_multiplier,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_atwork +coef_topology_bike_multiplier,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_atwork +coef_topology_trn_multiplier,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_atwork +coef_age1619_da_multiplier,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_atwork +coef_age010_trn_multiplier,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_atwork +coef_age16p_sr_multiplier,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork +coef_hhsize1_sr_multiplier,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_work,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork +coef_hhsize2_sr_multiplier,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork +walk_ASC_no_auto,walk_ASC_no_auto_eatout,walk_ASC_no_auto_escort,walk_ASC_no_auto_othdiscr,walk_ASC_no_auto_othmaint,walk_ASC_no_auto_school,walk_ASC_no_auto_shopping,walk_ASC_no_auto_social,walk_ASC_no_auto_univ,walk_ASC_no_auto_work,walk_ASC_no_auto_atwork +walk_ASC_auto_deficient,walk_ASC_auto_deficient_eatout,walk_ASC_auto_deficient_escort,walk_ASC_auto_deficient_othdiscr,walk_ASC_auto_deficient_othmaint,walk_ASC_auto_deficient_school,walk_ASC_auto_deficient_shopping,walk_ASC_auto_deficient_social,walk_ASC_auto_deficient_univ,walk_ASC_auto_deficient_work,walk_ASC_auto_deficient_atwork +walk_ASC_auto_sufficient,walk_ASC_auto_sufficient_eatout,walk_ASC_auto_sufficient_escort,walk_ASC_auto_sufficient_othdiscr,walk_ASC_auto_sufficient_othmaint,walk_ASC_auto_sufficient_school,walk_ASC_auto_sufficient_shopping,walk_ASC_auto_sufficient_social,walk_ASC_auto_sufficient_univ,walk_ASC_auto_sufficient_work,walk_ASC_auto_sufficient_atwork +bike_ASC_no_auto,bike_ASC_no_auto_eatout,bike_ASC_no_auto_escort,bike_ASC_no_auto_othdiscr,bike_ASC_no_auto_othmaint,bike_ASC_no_auto_school,bike_ASC_no_auto_shopping,bike_ASC_no_auto_social,bike_ASC_no_auto_univ,bike_ASC_no_auto_work,bike_ASC_no_auto_atwork +bike_ASC_auto_deficient,bike_ASC_auto_deficient_eatout,bike_ASC_auto_deficient_escort,bike_ASC_auto_deficient_othdiscr,bike_ASC_auto_deficient_othmaint,bike_ASC_auto_deficient_school,bike_ASC_auto_deficient_shopping,bike_ASC_auto_deficient_social,bike_ASC_auto_deficient_univ,bike_ASC_auto_deficient_work,bike_ASC_auto_deficient_atwork +bike_ASC_auto_sufficient,bike_ASC_auto_sufficient_eatout,bike_ASC_auto_sufficient_escort,bike_ASC_auto_sufficient_othdiscr,bike_ASC_auto_sufficient_othmaint,bike_ASC_auto_sufficient_school,bike_ASC_auto_sufficient_shopping,bike_ASC_auto_sufficient_social,bike_ASC_auto_sufficient_univ,bike_ASC_auto_sufficient_work,bike_ASC_auto_sufficient_atwork +sr2_ASC_no_auto,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all,sr2_ASC_no_auto_all +sr2_ASC_auto_deficient,sr2_ASC_auto_deficient_eatout,sr2_ASC_auto_deficient_escort,sr2_ASC_auto_deficient_othdiscr,sr2_ASC_auto_deficient_othmaint,sr2_ASC_auto_deficient_school,sr2_ASC_auto_deficient_shopping,sr2_ASC_auto_deficient_social,sr2_ASC_auto_deficient_univ,sr2_ASC_auto_deficient_work,sr2_ASC_auto_deficient_atwork +sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient_eatout,sr2_ASC_auto_sufficient_escort,sr2_ASC_auto_sufficient_othdiscr,sr2_ASC_auto_sufficient_othmaint,sr2_ASC_auto_sufficient_school,sr2_ASC_auto_sufficient_shopping,sr2_ASC_auto_sufficient_social,sr2_ASC_auto_sufficient_univ,sr2_ASC_auto_sufficient_work,sr2_ASC_auto_sufficient_atwork +sr3p_ASC_no_auto,sr3p_ASC_no_auto_eatout,sr3p_ASC_no_auto_escort,sr3p_ASC_no_auto_othdiscr,sr3p_ASC_no_auto_othmaint,sr3p_ASC_no_auto_school,sr3p_ASC_no_auto_shopping,sr3p_ASC_no_auto_social,sr3p_ASC_no_auto_univ,sr3p_ASC_no_auto_work,sr3p_ASC_no_auto_atwork +sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient_eatout,sr3p_ASC_auto_deficient_escort,sr3p_ASC_auto_deficient_othdiscr,sr3p_ASC_auto_deficient_othmaint,sr3p_ASC_auto_deficient_school,sr3p_ASC_auto_deficient_shopping,sr3p_ASC_auto_deficient_social,sr3p_ASC_auto_deficient_univ,sr3p_ASC_auto_deficient_work,sr3p_ASC_auto_deficient_atwork +sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient_eatout,sr3p_ASC_auto_sufficient_escort,sr3p_ASC_auto_sufficient_othdiscr,sr3p_ASC_auto_sufficient_othmaint,sr3p_ASC_auto_sufficient_school,sr3p_ASC_auto_sufficient_shopping,sr3p_ASC_auto_sufficient_social,sr3p_ASC_auto_sufficient_univ,sr3p_ASC_auto_sufficient_work,sr3p_ASC_auto_sufficient_atwork +walk_transit_ASC_no_auto,walk_transit_ASC_no_auto_eatout,walk_transit_ASC_no_auto_escort,walk_transit_ASC_no_auto_othdiscr,walk_transit_ASC_no_auto_othmaint,walk_transit_ASC_no_auto_school,walk_transit_ASC_no_auto_shopping,walk_transit_ASC_no_auto_social,walk_transit_ASC_no_auto_univ,walk_transit_ASC_no_auto_work,walk_transit_ASC_no_auto_atwork +walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient_eatout,walk_transit_ASC_auto_deficient_escort,walk_transit_ASC_auto_deficient_othdiscr,walk_transit_ASC_auto_deficient_othmaint,walk_transit_ASC_auto_deficient_school,walk_transit_ASC_auto_deficient_shopping,walk_transit_ASC_auto_deficient_social,walk_transit_ASC_auto_deficient_univ,walk_transit_ASC_auto_deficient_work,walk_transit_ASC_auto_deficient_atwork +walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient_eatout,walk_transit_ASC_auto_sufficient_escort,walk_transit_ASC_auto_sufficient_othdiscr,walk_transit_ASC_auto_sufficient_othmaint,walk_transit_ASC_auto_sufficient_school,walk_transit_ASC_auto_sufficient_shopping,walk_transit_ASC_auto_sufficient_social,walk_transit_ASC_auto_sufficient_univ,walk_transit_ASC_auto_sufficient_work,walk_transit_ASC_auto_sufficient_atwork +drive_transit_ASC_no_auto,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all,drive_transit_ASC_no_auto_all +drive_transit_ASC_auto_deficient,drive_transit_ASC_auto_deficient_eatout,drive_transit_ASC_auto_deficient_escort,drive_transit_ASC_auto_deficient_othdiscr,drive_transit_ASC_auto_deficient_othmaint,drive_transit_ASC_auto_deficient_school,drive_transit_ASC_auto_deficient_shopping,drive_transit_ASC_auto_deficient_social,drive_transit_ASC_auto_deficient_univ,drive_transit_ASC_auto_deficient_work,drive_transit_ASC_auto_deficient_atwork +drive_transit_ASC_auto_sufficient,drive_transit_ASC_auto_sufficient_eatout,drive_transit_ASC_auto_sufficient_escort,drive_transit_ASC_auto_sufficient_othdiscr,drive_transit_ASC_auto_sufficient_othmaint,drive_transit_ASC_auto_sufficient_school,drive_transit_ASC_auto_sufficient_shopping,drive_transit_ASC_auto_sufficient_social,drive_transit_ASC_auto_sufficient_univ,drive_transit_ASC_auto_sufficient_work,drive_transit_ASC_auto_sufficient_atwork +taxi_ASC_no_auto,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_work,taxi_ASC_no_auto_atwork +taxi_ASC_auto_deficient,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_school,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_univ,taxi_ASC_auto_deficient_work,taxi_ASC_auto_deficient_atwork +taxi_ASC_auto_sufficient,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_school,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_univ,taxi_ASC_auto_sufficient_work,taxi_ASC_auto_sufficient_atwork +tnc_single_ASC_no_auto,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_school,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_univ,tnc_single_ASC_no_auto_work,tnc_single_ASC_no_auto_atwork +tnc_single_ASC_auto_deficient,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_school,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_univ,tnc_single_ASC_auto_deficient_work,tnc_single_ASC_auto_deficient_atwork +tnc_single_ASC_auto_sufficient,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_school,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_univ,tnc_single_ASC_auto_sufficient_work,tnc_single_ASC_auto_sufficient_atwork +tnc_shared_ASC_no_auto,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_school,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_univ,tnc_shared_ASC_no_auto_work,tnc_shared_ASC_no_auto_atwork +tnc_shared_ASC_auto_deficient,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_school,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_univ,tnc_shared_ASC_auto_deficient_work,tnc_shared_ASC_auto_deficient_atwork +tnc_shared_ASC_auto_sufficient,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_school,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_univ,tnc_shared_ASC_auto_sufficient_work,tnc_shared_ASC_auto_sufficient_atwork +joint_walk_ASC_no_auto,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all +joint_walk_ASC_auto_deficient,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all +joint_walk_ASC_auto_sufficient,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all +joint_bike_ASC_no_auto,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all +joint_bike_ASC_auto_deficient,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all +joint_bike_ASC_auto_sufficient,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all +joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all +joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all +joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all +joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all +joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all +joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all +joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all +joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all +joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all +joint_drive_transit_ASC_no_auto,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all,joint_drive_transit_ASC_no_auto_all +joint_drive_transit_ASC_auto_deficient,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all,joint_drive_transit_ASC_auto_deficient_all +joint_drive_transit_ASC_auto_sufficient,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all,joint_drive_transit_ASC_auto_sufficient_all +joint_taxi_ASC_no_auto,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all +joint_taxi_ASC_auto_deficient,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all +joint_taxi_ASC_auto_sufficient,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all +joint_tnc_single_ASC_no_auto,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all +joint_tnc_single_ASC_auto_deficient,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all +joint_tnc_single_ASC_auto_sufficient,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all +joint_tnc_shared_ASC_no_auto,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all +joint_tnc_shared_ASC_auto_deficient,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all +joint_tnc_shared_ASC_auto_sufficient,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all +local_bus_ASC,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_school_univ,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_bus_ASC_school_univ,local_bus_ASC_work,local_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +walk_light_rail_ASC,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_work,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +drive_light_rail_ASC,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_work,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +walk_ferry_ASC,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_work,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +drive_ferry_ASC,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_work,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +express_bus_ASC,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_work,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +heavy_rail_ASC,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_work,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +commuter_rail_ASC,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_work,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +walk_transit_CBD_ASC,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_work,walk_transit_CBD_ASC_atwork +drive_transit_CBD_ASC,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_work,drive_transit_CBD_ASC_atwork diff --git a/activitysim/examples/example_mtc/configs/tour_scheduling_atwork.csv b/activitysim/examples/prototype_mtc/configs/tour_scheduling_atwork.csv similarity index 99% rename from activitysim/examples/example_mtc/configs/tour_scheduling_atwork.csv rename to activitysim/examples/prototype_mtc/configs/tour_scheduling_atwork.csv index 93846ae3a2..22abaf2fd3 100644 --- a/activitysim/examples/example_mtc/configs/tour_scheduling_atwork.csv +++ b/activitysim/examples/prototype_mtc/configs/tour_scheduling_atwork.csv @@ -1,59 +1,59 @@ -Label,Description,Expression,Coefficient -#,Departure Constants,, -util_early_start_at_5,Early start at 5,start < 6,coef_early_start_at_5 -util_am_peak_start_at_6,AM peak start at 6,start == 6,coef_am_peak_start_at_6 -util_am_peak_start_at_7,AM peak start at 7,start == 7,coef_am_peak_start_at_7 -util_am_peak_start_at_8,AM peak start at 8,start == 8,coef_am_peak_start_at_8 -util_am_peak_start_at_9,AM peak start at 9,start == 9,coef_am_peak_start_at_9 -util_midday_start_at_10_11_12,Midday start at 10/11/12,(start > 9) & (start < 13),coef_midday_start_at_10_11_12 -util_midday_start_at_13_14_15,Midday start at 13/14/15,(start > 12) & (start < 16),coef_midday_start_at_13_14_15 -util_pm_peak_start_at_16_17_18,PM peak start at 16/17/18,(start > 15) & (start < 19),coef_pm_peak_start_at_16_17_18 -util_evening_start_at_19_20_21,Evening start at 19/20/21,(start > 18) & (start < 22),coef_evening_start_at_19_20_21 -util_late_start_at_22_23,Late start at 22/23,start > 21,coef_late_start_at_22_23 -#,Arrival Constants,, -util_early_end_at_5_6,Early end at 5/6 ,end < 7,coef_early_end_at_5_6 -util_am_peak_end,AM peak end,(end > 6) & (end < 10),coef_am_peak_end -util_midday_end_at_10_11_12,Midday end at 10/11/12,(end > 9) & (end < 13),coef_midday_end_at_10_11_12 -util_midday_end_at_13_14,Midday end at 13/14,(end > 12) & (end < 15),coef_midday_end_at_13_14 -util_pm_peak_end_at_15,PM peak end at 15,end == 15,coef_pm_peak_end_at_15 -util_pm_peak_end_at_16,PM peak end at 16,end == 16,coef_pm_peak_end_at_16 -util_pm_peak_end_at_17,PM peak end at 17,end == 17,coef_pm_peak_end_at_17 -util_pm_peak_end_at_18,PM peak end at 18,end == 18,coef_pm_peak_end_at_18 -util_evening_end_at_19_20_21,Evening end at 19/20/21,(end > 18) & (end < 22),coef_evening_end_at_19_20_21 -util_late_end_at_22_23,Late end at 22/23,end > 21,coef_late_end_at_22_23 -#,,, -util_duration_of_0_hours,Duration of 0 hours,duration==0,coef_duration_of_0_hours -util_duration_of_1_hour,Duration of 1 hour,duration==1,coef_duration_of_1_hour -util_duration_of_2_to_3_hours,Duration of 2 to 3 hours,(duration >=1) and (duration <= 4),coef_duration_of_2_to_3_hours -util_duration_of_4_to_5_hours,Duration of 4 to 5 hours,(duration >=4) and (duration <=5),coef_duration_of_4_to_5_hours -util_duration_of_6_to_7_hours,Duration of 6 to 7 hours,(duration >=6) and (duration <=7),coef_duration_of_6_to_7_hours -util_duration_of_8_to_10_hours,Duration of 8 to 10 hours,(duration >=8) and (duration <=10),coef_duration_of_8_to_10_hours -util_duration_of_11_to_13_hours,Duration of 11 to 13 hours,(duration >=11) and (duration <=13),coef_duration_of_11_to_13_hours -util_duration_of_14_to_18_hours,Duration of 14 to 18 hours,(duration >=14) and (duration <=18),coef_duration_of_14_to_18_hours -#,,, -util_start_shift_for_outbound_auto_travel_time_off_peak,Start shift for outbound auto travel time for off-peak,"@df.start * np.minimum(df.sovtimemd, time_cap)",coef_start_shift_for_outbound_auto_travel_time_off_peak -util_start_shift_for_inbound_auto_travel_time_off_peak,Start shift for inbound auto travel time for off-peak,"@df.start * np.minimum(df.sovtimemd_t, time_cap)",coef_start_shift_for_inbound_auto_travel_time_off_peak -util_duration_shift_for_outbound_auto_travel_time_off_peak,Duration shift for outbound auto travel time for off-peak,"@df.duration * np.minimum(df.sovtimemd, time_cap)",coef_duration_shift_for_outbound_auto_travel_time_off_peak -util_duration_shift_for_inbound_auto_travel_time_off_peak,Duration shift for inbound auto travel time for off-peak,"@df.duration * np.minimum(df.sovtimemd_t, time_cap)",coef_duration_shift_for_inbound_auto_travel_time_off_peak -#,,, -util_start_shift_for_business_related_,Start shift for business-related sub-tour purpose,(tour_type == 'business') * start,coef_start_shift_for_business_related_ -util_duration_shift_for_business_related_,Duration shift for business-related sub-tour purpose,(tour_type == 'business') * duration,coef_duration_shift_for_business_related_ -util_start_shift_for_first_sub_tour_of_same_work_tour,Start shift for first sub-tour of the same work tour,(tour_type_num == 1) * start,coef_start_shift_for_first_sub_tour_of_same_work_tour -util_duration_shift_for_first_sub_tour_of_same_work_tour,Duration shift for first sub-tour of the same work tour,(tour_type_num == 1) * duration,coef_duration_shift_for_first_sub_tour_of_same_work_tour -util_start_shift_for_subsequent_sub_tour_of_same_work_tour,Start shift for subsequent sub-tour of the same work tour,(tour_type_num == 2) * start,coef_start_shift_for_subsequent_sub_tour_of_same_work_tour -util_duration_shift_for_subsequent_sub_tour_of_same_work_tour,Duration shift for subsequent sub-tour of the same work tour,(tour_type_num == 2) * duration,coef_duration_shift_for_subsequent_sub_tour_of_same_work_tour -util_start_shift_for_number_of_mandatory_tours,Start shift for number of mandatory tours made by the person,start * num_mand,coef_start_shift_for_number_of_mandatory_tours -util_duration_shift_for_number_of_mandatory_tours,Duration shift for number of mandatory tours made by the person,duration * num_mand,coef_duration_shift_for_number_of_mandatory_tours -util_start_shift_for_number_of_joint_tours,Start shift for number of joint tours in which the person participated,start * num_joint_tours,coef_start_shift_for_number_of_joint_tours -util_duration_shift_for_number_of_joint_tours,Duration shift for number of joint tours in which the person participated,duration * num_joint_tours,coef_duration_shift_for_number_of_joint_tours -util_start_shift_for_number_of_individual_nonmandatory_tours,Start shift for number of individual nonm tours (including escort) made by the person,start * num_non_mand,coef_start_shift_for_number_of_individual_nonmandatory_tours -util_duration_shift_for_number_of_individual_nonmandatory_tours,Duration shift for number of individual nonm tours (including escort) made by the person,duration * num_non_mand,coef_duration_shift_for_number_of_individual_nonmandatory_tours -#,,, -util_dummy_for_business_related_purpose_and_duration_from_0_to_1,Dummy for business-related purpose and duration from 0 to 1,(tour_type == 'business') & (duration <=1),coef_dummy_for_business_related_purpose_and_duration_from_0_to_1 -util_dummy_for_eating_out_purpose_and_duration_of_1_hour,Dummy for eating-out purpose and duration of 1 hour,(tour_type == 'business') & (duration ==1),coef_dummy_for_eating_out_purpose_and_duration_of_1_hour -util_dummy_for_eating_out_purpose_and_departure_at_11,Dummy for eating-out purpose and departure at 11,(tour_type == 'business') & (start == 11),coef_dummy_for_eating_out_purpose_and_departure_at_11 -util_dummy_for_eating_out_purpose_and_departure_at_12,Dummy for eating-out purpose and departure at 12,(tour_type == 'business') & (start == 12),coef_dummy_for_eating_out_purpose_and_departure_at_12 -util_dummy_for_eating_out_purpose_and_departure_at_13,Dummy for eating-out purpose and departure at 13,(tour_type == 'business') & (start == 13),coef_dummy_for_eating_out_purpose_and_departure_at_13 -#,,, -#,Mode Choice Logsum,mode_choice_logsum, +Label,Description,Expression,Coefficient +#,Departure Constants,, +util_early_start_at_5,Early start at 5,start < 6,coef_early_start_at_5 +util_am_peak_start_at_6,AM peak start at 6,start == 6,coef_am_peak_start_at_6 +util_am_peak_start_at_7,AM peak start at 7,start == 7,coef_am_peak_start_at_7 +util_am_peak_start_at_8,AM peak start at 8,start == 8,coef_am_peak_start_at_8 +util_am_peak_start_at_9,AM peak start at 9,start == 9,coef_am_peak_start_at_9 +util_midday_start_at_10_11_12,Midday start at 10/11/12,(start > 9) & (start < 13),coef_midday_start_at_10_11_12 +util_midday_start_at_13_14_15,Midday start at 13/14/15,(start > 12) & (start < 16),coef_midday_start_at_13_14_15 +util_pm_peak_start_at_16_17_18,PM peak start at 16/17/18,(start > 15) & (start < 19),coef_pm_peak_start_at_16_17_18 +util_evening_start_at_19_20_21,Evening start at 19/20/21,(start > 18) & (start < 22),coef_evening_start_at_19_20_21 +util_late_start_at_22_23,Late start at 22/23,start > 21,coef_late_start_at_22_23 +#,Arrival Constants,, +util_early_end_at_5_6,Early end at 5/6 ,end < 7,coef_early_end_at_5_6 +util_am_peak_end,AM peak end,(end > 6) & (end < 10),coef_am_peak_end +util_midday_end_at_10_11_12,Midday end at 10/11/12,(end > 9) & (end < 13),coef_midday_end_at_10_11_12 +util_midday_end_at_13_14,Midday end at 13/14,(end > 12) & (end < 15),coef_midday_end_at_13_14 +util_pm_peak_end_at_15,PM peak end at 15,end == 15,coef_pm_peak_end_at_15 +util_pm_peak_end_at_16,PM peak end at 16,end == 16,coef_pm_peak_end_at_16 +util_pm_peak_end_at_17,PM peak end at 17,end == 17,coef_pm_peak_end_at_17 +util_pm_peak_end_at_18,PM peak end at 18,end == 18,coef_pm_peak_end_at_18 +util_evening_end_at_19_20_21,Evening end at 19/20/21,(end > 18) & (end < 22),coef_evening_end_at_19_20_21 +util_late_end_at_22_23,Late end at 22/23,end > 21,coef_late_end_at_22_23 +#,,, +util_duration_of_0_hours,Duration of 0 hours,duration==0,coef_duration_of_0_hours +util_duration_of_1_hour,Duration of 1 hour,duration==1,coef_duration_of_1_hour +util_duration_of_2_to_3_hours,Duration of 2 to 3 hours,(duration >=1) and (duration <= 4),coef_duration_of_2_to_3_hours +util_duration_of_4_to_5_hours,Duration of 4 to 5 hours,(duration >=4) and (duration <=5),coef_duration_of_4_to_5_hours +util_duration_of_6_to_7_hours,Duration of 6 to 7 hours,(duration >=6) and (duration <=7),coef_duration_of_6_to_7_hours +util_duration_of_8_to_10_hours,Duration of 8 to 10 hours,(duration >=8) and (duration <=10),coef_duration_of_8_to_10_hours +util_duration_of_11_to_13_hours,Duration of 11 to 13 hours,(duration >=11) and (duration <=13),coef_duration_of_11_to_13_hours +util_duration_of_14_to_18_hours,Duration of 14 to 18 hours,(duration >=14) and (duration <=18),coef_duration_of_14_to_18_hours +#,,, +util_start_shift_for_outbound_auto_travel_time_off_peak,Start shift for outbound auto travel time for off-peak,"@df.start * np.minimum(df.sovtimemd, time_cap)",coef_start_shift_for_outbound_auto_travel_time_off_peak +util_start_shift_for_inbound_auto_travel_time_off_peak,Start shift for inbound auto travel time for off-peak,"@df.start * np.minimum(df.sovtimemd_t, time_cap)",coef_start_shift_for_inbound_auto_travel_time_off_peak +util_duration_shift_for_outbound_auto_travel_time_off_peak,Duration shift for outbound auto travel time for off-peak,"@df.duration * np.minimum(df.sovtimemd, time_cap)",coef_duration_shift_for_outbound_auto_travel_time_off_peak +util_duration_shift_for_inbound_auto_travel_time_off_peak,Duration shift for inbound auto travel time for off-peak,"@df.duration * np.minimum(df.sovtimemd_t, time_cap)",coef_duration_shift_for_inbound_auto_travel_time_off_peak +#,,, +util_start_shift_for_business_related_,Start shift for business-related sub-tour purpose,(tour_type == 'business') * start,coef_start_shift_for_business_related_ +util_duration_shift_for_business_related_,Duration shift for business-related sub-tour purpose,(tour_type == 'business') * duration,coef_duration_shift_for_business_related_ +util_start_shift_for_first_sub_tour_of_same_work_tour,Start shift for first sub-tour of the same work tour,(tour_type_num == 1) * start,coef_start_shift_for_first_sub_tour_of_same_work_tour +util_duration_shift_for_first_sub_tour_of_same_work_tour,Duration shift for first sub-tour of the same work tour,(tour_type_num == 1) * duration,coef_duration_shift_for_first_sub_tour_of_same_work_tour +util_start_shift_for_subsequent_sub_tour_of_same_work_tour,Start shift for subsequent sub-tour of the same work tour,(tour_type_num == 2) * start,coef_start_shift_for_subsequent_sub_tour_of_same_work_tour +util_duration_shift_for_subsequent_sub_tour_of_same_work_tour,Duration shift for subsequent sub-tour of the same work tour,(tour_type_num == 2) * duration,coef_duration_shift_for_subsequent_sub_tour_of_same_work_tour +util_start_shift_for_number_of_mandatory_tours,Start shift for number of mandatory tours made by the person,start * num_mand,coef_start_shift_for_number_of_mandatory_tours +util_duration_shift_for_number_of_mandatory_tours,Duration shift for number of mandatory tours made by the person,duration * num_mand,coef_duration_shift_for_number_of_mandatory_tours +util_start_shift_for_number_of_joint_tours,Start shift for number of joint tours in which the person participated,start * num_joint_tours,coef_start_shift_for_number_of_joint_tours +util_duration_shift_for_number_of_joint_tours,Duration shift for number of joint tours in which the person participated,duration * num_joint_tours,coef_duration_shift_for_number_of_joint_tours +util_start_shift_for_number_of_individual_nonmandatory_tours,Start shift for number of individual nonm tours (including escort) made by the person,start * num_non_mand,coef_start_shift_for_number_of_individual_nonmandatory_tours +util_duration_shift_for_number_of_individual_nonmandatory_tours,Duration shift for number of individual nonm tours (including escort) made by the person,duration * num_non_mand,coef_duration_shift_for_number_of_individual_nonmandatory_tours +#,,, +util_dummy_for_business_related_purpose_and_duration_from_0_to_1,Dummy for business-related purpose and duration from 0 to 1,(tour_type == 'business') & (duration <=1),coef_dummy_for_business_related_purpose_and_duration_from_0_to_1 +util_dummy_for_eating_out_purpose_and_duration_of_1_hour,Dummy for eating-out purpose and duration of 1 hour,(tour_type == 'business') & (duration ==1),coef_dummy_for_eating_out_purpose_and_duration_of_1_hour +util_dummy_for_eating_out_purpose_and_departure_at_11,Dummy for eating-out purpose and departure at 11,(tour_type == 'business') & (start == 11),coef_dummy_for_eating_out_purpose_and_departure_at_11 +util_dummy_for_eating_out_purpose_and_departure_at_12,Dummy for eating-out purpose and departure at 12,(tour_type == 'business') & (start == 12),coef_dummy_for_eating_out_purpose_and_departure_at_12 +util_dummy_for_eating_out_purpose_and_departure_at_13,Dummy for eating-out purpose and departure at 13,(tour_type == 'business') & (start == 13),coef_dummy_for_eating_out_purpose_and_departure_at_13 +#,,, +#,Mode Choice Logsum,mode_choice_logsum, #,,, \ No newline at end of file diff --git a/activitysim/examples/example_mtc/configs/tour_scheduling_atwork.yaml b/activitysim/examples/prototype_mtc/configs/tour_scheduling_atwork.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/tour_scheduling_atwork.yaml rename to activitysim/examples/prototype_mtc/configs/tour_scheduling_atwork.yaml diff --git a/activitysim/examples/example_psrc/configs/tour_scheduling_atwork_coeffs.csv b/activitysim/examples/prototype_mtc/configs/tour_scheduling_atwork_coefficients.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/tour_scheduling_atwork_coeffs.csv rename to activitysim/examples/prototype_mtc/configs/tour_scheduling_atwork_coefficients.csv index cdbd1457d3..dd09d225d4 --- a/activitysim/examples/example_psrc/configs/tour_scheduling_atwork_coeffs.csv +++ b/activitysim/examples/prototype_mtc/configs/tour_scheduling_atwork_coefficients.csv @@ -1,50 +1,50 @@ -coefficient_name,value,constrain -coef_early_start_at_5,-7.765548476,F -coef_am_peak_start_at_6,-6.156717827,F -coef_am_peak_start_at_7,-4.061708142,F -coef_am_peak_start_at_8,-2.330535201,F -coef_am_peak_start_at_9,-1.881593386,F -coef_midday_start_at_10_11_12,0,T -coef_midday_start_at_13_14_15,-0.77502158,F -coef_pm_peak_start_at_16_17_18,-0.227528489,F -coef_evening_start_at_19_20_21,-1.015090023,F -coef_late_start_at_22_23,-0.737570054,F -coef_early_end_at_5_6,-2.928312295,F -coef_am_peak_end,-2.928312295,F -coef_midday_end_at_10_11_12,-2.297264374,F -coef_midday_end_at_13_14,0,T -coef_pm_peak_end_at_15,-0.578344457,F -coef_pm_peak_end_at_16,-1.09408722,F -coef_pm_peak_end_at_17,-1.1658466,F -coef_pm_peak_end_at_18,-1.496131081,F -coef_evening_end_at_19_20_21,-2.31998226,F -coef_late_end_at_22_23,-2.31998226,F -coef_duration_of_0_hours,-0.906681512,F -coef_duration_of_1_hour,0,T -coef_duration_of_2_to_3_hours,-1.362175802,F -coef_duration_of_4_to_5_hours,-0.819617616,F -coef_duration_of_6_to_7_hours,1.088111072,F -coef_duration_of_8_to_10_hours,1.734038505,F -coef_duration_of_11_to_13_hours,0.3,F -coef_duration_of_14_to_18_hours,0,T -coef_start_shift_for_outbound_auto_travel_time_off_peak,0.00065,F -coef_start_shift_for_inbound_auto_travel_time_off_peak,0.00065,F -coef_duration_shift_for_outbound_auto_travel_time_off_peak,0.00981,F -coef_duration_shift_for_inbound_auto_travel_time_off_peak,0.00981,F -coef_start_shift_for_business_related_,-0.1113,F -coef_duration_shift_for_business_related_,0.2646,F -coef_start_shift_for_first_sub_tour_of_same_work_tour,-0.5433,F -coef_duration_shift_for_first_sub_tour_of_same_work_tour,-0.3992,F -coef_start_shift_for_subsequent_sub_tour_of_same_work_tour,-0.1844,F -coef_duration_shift_for_subsequent_sub_tour_of_same_work_tour,-0.1844,F -coef_start_shift_for_number_of_mandatory_tours,-0.0193,F -coef_duration_shift_for_number_of_mandatory_tours,-0.7702,F -coef_start_shift_for_number_of_joint_tours,-0.0206,F -coef_duration_shift_for_number_of_joint_tours,-0.2497,F -coef_start_shift_for_number_of_individual_nonmandatory_tours,-0.0128,F -coef_duration_shift_for_number_of_individual_nonmandatory_tours,-0.0422,F -coef_dummy_for_business_related_purpose_and_duration_from_0_to_1,-1.543,F -coef_dummy_for_eating_out_purpose_and_duration_of_1_hour,0.3999,F -coef_dummy_for_eating_out_purpose_and_departure_at_11,1.511,F -coef_dummy_for_eating_out_purpose_and_departure_at_12,2.721,F -coef_dummy_for_eating_out_purpose_and_departure_at_13,2.122,F +coefficient_name,value,constrain +coef_early_start_at_5,-7.765548476,F +coef_am_peak_start_at_6,-6.156717827,F +coef_am_peak_start_at_7,-4.061708142,F +coef_am_peak_start_at_8,-2.330535201,F +coef_am_peak_start_at_9,-1.881593386,F +coef_midday_start_at_10_11_12,0,T +coef_midday_start_at_13_14_15,-0.77502158,F +coef_pm_peak_start_at_16_17_18,-0.227528489,F +coef_evening_start_at_19_20_21,-1.015090023,F +coef_late_start_at_22_23,-0.737570054,F +coef_early_end_at_5_6,-2.928312295,F +coef_am_peak_end,-2.928312295,F +coef_midday_end_at_10_11_12,-2.297264374,F +coef_midday_end_at_13_14,0,T +coef_pm_peak_end_at_15,-0.578344457,F +coef_pm_peak_end_at_16,-1.09408722,F +coef_pm_peak_end_at_17,-1.1658466,F +coef_pm_peak_end_at_18,-1.496131081,F +coef_evening_end_at_19_20_21,-2.31998226,F +coef_late_end_at_22_23,-2.31998226,F +coef_duration_of_0_hours,-0.906681512,F +coef_duration_of_1_hour,0,T +coef_duration_of_2_to_3_hours,-1.362175802,F +coef_duration_of_4_to_5_hours,-0.819617616,F +coef_duration_of_6_to_7_hours,1.088111072,F +coef_duration_of_8_to_10_hours,1.734038505,F +coef_duration_of_11_to_13_hours,0.3,F +coef_duration_of_14_to_18_hours,0,T +coef_start_shift_for_outbound_auto_travel_time_off_peak,0.00065,F +coef_start_shift_for_inbound_auto_travel_time_off_peak,0.00065,F +coef_duration_shift_for_outbound_auto_travel_time_off_peak,0.00981,F +coef_duration_shift_for_inbound_auto_travel_time_off_peak,0.00981,F +coef_start_shift_for_business_related_,-0.1113,F +coef_duration_shift_for_business_related_,0.2646,F +coef_start_shift_for_first_sub_tour_of_same_work_tour,-0.5433,F +coef_duration_shift_for_first_sub_tour_of_same_work_tour,-0.3992,F +coef_start_shift_for_subsequent_sub_tour_of_same_work_tour,-0.1844,F +coef_duration_shift_for_subsequent_sub_tour_of_same_work_tour,-0.1844,F +coef_start_shift_for_number_of_mandatory_tours,-0.0193,F +coef_duration_shift_for_number_of_mandatory_tours,-0.7702,F +coef_start_shift_for_number_of_joint_tours,-0.0206,F +coef_duration_shift_for_number_of_joint_tours,-0.2497,F +coef_start_shift_for_number_of_individual_nonmandatory_tours,-0.0128,F +coef_duration_shift_for_number_of_individual_nonmandatory_tours,-0.0422,F +coef_dummy_for_business_related_purpose_and_duration_from_0_to_1,-1.543,F +coef_dummy_for_eating_out_purpose_and_duration_of_1_hour,0.3999,F +coef_dummy_for_eating_out_purpose_and_departure_at_11,1.511,F +coef_dummy_for_eating_out_purpose_and_departure_at_12,2.721,F +coef_dummy_for_eating_out_purpose_and_departure_at_13,2.122,F diff --git a/activitysim/examples/example_psrc/configs/tour_scheduling_atwork_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/tour_scheduling_atwork_preprocessor.csv old mode 100755 new mode 100644 similarity index 97% rename from activitysim/examples/example_psrc/configs/tour_scheduling_atwork_preprocessor.csv rename to activitysim/examples/prototype_mtc/configs/tour_scheduling_atwork_preprocessor.csv index 8409f2ed17..5c9c77403c --- a/activitysim/examples/example_psrc/configs/tour_scheduling_atwork_preprocessor.csv +++ b/activitysim/examples/prototype_mtc/configs/tour_scheduling_atwork_preprocessor.csv @@ -1,3 +1,3 @@ -Description,Target,Expression -,sovtimemd,"od_skims[('SOV_TIME', 'MD')]" -,sovtimemd_t,"od_skims.reverse(('SOV_TIME', 'MD'))" +Description,Target,Expression +,sovtimemd,"od_skims[('SOV_TIME', 'MD')]" +,sovtimemd_t,"od_skims.reverse(('SOV_TIME', 'MD'))" diff --git a/activitysim/examples/example_mtc/configs/tour_scheduling_joint.csv b/activitysim/examples/prototype_mtc/configs/tour_scheduling_joint.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/tour_scheduling_joint.csv rename to activitysim/examples/prototype_mtc/configs/tour_scheduling_joint.csv diff --git a/activitysim/examples/example_mtc/configs/tour_scheduling_joint_coefficients.csv b/activitysim/examples/prototype_mtc/configs/tour_scheduling_joint_coefficients.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/tour_scheduling_joint_coefficients.csv rename to activitysim/examples/prototype_mtc/configs/tour_scheduling_joint_coefficients.csv diff --git a/activitysim/examples/example_mtc/configs/tour_scheduling_nonmandatory.csv b/activitysim/examples/prototype_mtc/configs/tour_scheduling_nonmandatory.csv similarity index 99% rename from activitysim/examples/example_mtc/configs/tour_scheduling_nonmandatory.csv rename to activitysim/examples/prototype_mtc/configs/tour_scheduling_nonmandatory.csv index d8bf36b455..e81bddda6e 100644 --- a/activitysim/examples/example_mtc/configs/tour_scheduling_nonmandatory.csv +++ b/activitysim/examples/prototype_mtc/configs/tour_scheduling_nonmandatory.csv @@ -1,96 +1,96 @@ -Label,Description,Expression,Coefficient -util_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends,Subsequent tour must start after previous tour for this purpose ends,(start < end_previous) & (tour_type_num > 1),coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends -util_free_flow_round_trip_auto_time_shift_effects_duration,Free-flow round trip auto time shift effects - duration,roundtrip_auto_time_to_work * duration,coef_free_flow_round_trip_auto_time_shift_effects_duration -util_shopping_tour_departure_shift_effects,Shopping tour - departure shift effects,(tour_type == 'shopping') * start,coef_shopping_tour_departure_shift_effects -util_shopping_tour_duration_shift_effects,Shopping tour - duration shift effects,(tour_type == 'shopping') * duration,coef_shopping_tour_duration_shift_effects -util_maintenance_tour_departure_shift_effects,Maintenance tour - departure shift effects,(tour_type == 'othmaint') * start,coef_maintenance_tour_departure_shift_effects -util_maintenance_tour_duration_shift_effects,Maintenance tour - departure shift effects,(tour_type == 'othmaint') * duration,coef_maintenance_tour_duration_shift_effects -util_visit_tour_departure_shift_effects_start,Visit tour - departure shift effects,(tour_type == 'social') * start,coef_visit_tour_departure_shift_effects -util_visit_tour_duration_shift_effects_duration,Visit tour - departure shift effects,(tour_type == 'social') * duration,coef_visit_tour_duration_shift_effects -util_eat_out_tour_departure_shift_effects,Eat Out tour - departure shift effects,(tour_type == 'eatout') * start,coef_eat_out_tour_departure_shift_effects -util_school_child_age_16_plus_departure_shift_effects,School child age 16+ - departure shift effects,(ptype == 6) * start,coef_school_child_age_16_plus_departure_shift_effects -util_school_child_age_16_plus_duration_shift_effects,School child age 16+ - duration shift effects,(ptype == 6) * duration,coef_school_child_age_16_plus_duration_shift_effects -util_school_child_age_under_16_departure_shift_effects,School child age under 16 - departure shift effects,(ptype == 7) * start,coef_school_child_age_under_16_departure_shift_effects -util_school_child_age_under_16_duration_shift_effects,School child age under 16 - duration shift effects,(ptype == 7) * duration,coef_school_child_age_under_16_duration_shift_effects -util_destination_in_cbd_duration_shift_effects,Destination in CBD - duration shift effects,destination_in_cbd * duration,coef_destination_in_cbd_duration_shift_effects -util_number_of_mandatory_tours_departure_shift_effects,Number of mandatory tours - departure shift effects,num_mand * start,coef_number_of_mandatory_tours_departure_shift_effects -util_number_of_joint_tours_departure_shift_effects,Number of joint tours - departure shift effects,num_person_joint_tours * start,coef_number_of_joint_tours_departure_shift_effects -util_number_of_escort_tours_departure_shift_effects,Number of escort tours - departure shift effects,num_escort_tours * start,coef_number_of_escort_tours_departure_shift_effects -util_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects,Number of idividual non-mandatory tours (excluding escort) - departure shift effects,num_non_escort_tours * start,coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects -util_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,First of 2+ tours for same purpose - departure shift effect,((tour_type_count>1) & (tour_type_num == 1)) * start,coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect -util_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,subsequent of 2+ tours for same purpose - duration shift effect,(tour_type_num > 1) * duration,coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect -util_maintenance_tour_depart_before_7,Maintenance tour - depart before 7,(tour_type == 'othmaint') & (start < 7),coef_maintenance_tour_depart_before_7 -util_shopping_tour_depart_before_8,Shopping tour - depart before 8,(tour_type == 'shopping') & (start < 8),coef_shopping_tour_depart_before_8 -util_shopping_tour_arrive_after_22,Shopping tour - arrive after 22,(tour_type == 'shopping') & (end > 22),coef_shopping_tour_arrive_after_22 -util_school_child_under_16_arrive_after_22,School child under 16 - arrive after 22,(ptype == 7) & (end > 22),coef_school_child_under_16_arrive_after_22 -util_university_student_arrive_after_22,University student - arrive after 22,(ptype == 3) & (end > 22),coef_university_student_arrive_after_22 -util_shopping_tour_duration_lt_2_hours,Shopping tour - duration < 2 hours,(tour_type == 'shopping') & (duration < 2),coef_shopping_tour_duration_lt_2_hours -util_discretionary_tour_duration_lt_2_hours,Discretionary tour - duration < 2 hours,(tour_type == 'othdiscr') & (duration < 2),coef_discretionary_tour_duration_lt_2_hours -util_adult_with_children_in_hh_arrive_19_21,Adult with children in HH - arrive 19 - 21,adult & (num_children > 0) & ( end > 18 ) & ( end < 22 ),coef_adult_with_children_in_hh_arrive_19_21 -#,,, -#,Mode Choice Logsum,mode_choice_logsum,#mode_choice_logsum -#,,,# -util_dummy_adjacent_before,,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)",coef_dummy -util_dummy_adjacent_after,,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)",coef_dummy -util_some_previously_scheduled_tour_ends_in_this_departure_hour,Some previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.person_id, df.start)",coef_some_previously_scheduled_tour_ends_in_this_departure_hour -util_some_previously_scheduled_tour_begins_in_this_arrival_hour,Some previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.person_id, df.end)",coef_some_previously_scheduled_tour_begins_in_this_arrival_hour -util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_type_num > 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_type_num > 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction -util_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours,Remaining individual non-mandatory tours to be scheduled / number of unscheduled hours,"@((1.0 + df.tour_count - df.tour_num)) / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours -#,#,,# -util_departure_constants_early,Departure Constants -- Early (up to 5),(tour_type != 'escort') & (start < 6),coef_departure_constants_early -util_departure_constants_am_peak_1,Departure Constants -- AM peak 1 (6),(tour_type != 'escort') & (start == 6),coef_departure_constants_am_peak_1 -util_departure_constants_am_peak_2,Departure Constants -- AM peak 2 (7),(tour_type != 'escort') & (start == 7),coef_departure_constants_am_peak_2 -util_departure_constants_am_peak_3,Departure Constants -- AM peak 3 (8),(tour_type != 'escort') & (start == 8),coef_departure_constants_am_peak_3 -util_departure_constants_am_peak_4,Departure Constants -- AM peak 4 (9),(tour_type != 'escort') & (start == 9),coef_departure_constants_am_peak_4 -util_departure_constants_midday_1,Departure Constants -- Midday 1 (10 to 12),(tour_type != 'escort') & (start > 9) & (start < 13),coef_departure_constants_midday_1 -util_departure_constants_midday_2,Departure Constants -- Midday 2 (13 to 15),(tour_type != 'escort') & (start > 12) & (start < 16),coef_departure_constants_midday_2 -util_departure_constants_pm_peak,Departure Constants -- PM peak (16 to 18),(tour_type != 'escort') & (start > 15) & (start < 19),coef_departure_constants_pm_peak -util_departure_constants_evening,Departure Constants -- Evening (19 to 21),(tour_type != 'escort') & (start > 18) & (start < 22),coef_departure_constants_evening -util_departure_constants_late,Departure Constants -- Late (22 and later),(tour_type != 'escort') & (start > 21),coef_departure_constants_late -util_arrival_constants_early,Arrival Constants -- Early (up to 6),(tour_type != 'escort') & (end < 7),coef_arrival_constants_early -util_arrival_constants_am_peak,Arrival Constants -- AM peak (7 to 9),(tour_type != 'escort') & (end > 6) & (end < 10),coef_arrival_constants_am_peak -util_arrival_constants_midday_1,Arrival Constants -- Midday 1 (10 to 12),(tour_type != 'escort') & (end > 9) & (end < 13),coef_arrival_constants_midday_1 -util_arrival_constants_midday_2,Arrival Constants -- Midday 2 (13 to 14),(tour_type != 'escort') & (end > 12) & (end < 15),coef_arrival_constants_midday_2 -util_arrival_constants_pm_peak_1,Arrival Constants -- PM peak 1 (15),(tour_type != 'escort') & (end == 15),coef_arrival_constants_pm_peak_1 -util_arrival_constants_pm_peak_2,Arrival Constants -- PM peak 2 (16),(tour_type != 'escort') & (end == 16),coef_arrival_constants_pm_peak_2 -util_arrival_constants_pm_peak_3,Arrival Constants -- PM peak 3 (17),(tour_type != 'escort') & (end == 17),coef_arrival_constants_pm_peak_3 -util_arrival_constants_pm_peak_4,Arrival Constants -- PM peak 4 (18),(tour_type != 'escort') & (end == 18),coef_arrival_constants_pm_peak_4 -util_arrival_constants_evening,Arrival Constants -- Evening (19 to 21),(tour_type != 'escort') & (end > 18) & (end < 22),coef_arrival_constants_evening -util_arrival_constants_late,Arrival Constants -- Late (22 and later),(tour_type != 'escort') & (end > 21),coef_arrival_constants_late -util_duration_constants_0_to_1_hours,Duration Constants -- 0 to 1 hours,(tour_type != 'escort') & (duration < 2),coef_duration_constants_0_to_1_hours -util_duration_constants_2_to_3_hours,Duration Constants -- 2 to 3 hours,(tour_type != 'escort') & (duration > 1) & (duration < 4),coef_duration_constants_2_to_3_hours -util_duration_constants_4_to_5_hours,Duration Constants -- 4 to 5 hours,(tour_type != 'escort') & (duration > 3) & (duration < 6),coef_duration_constants_4_to_5_hours -util_duration_constants_6_to_7_hours,Duration Constants -- 6 to 7 hours,(tour_type != 'escort') & (duration > 5) & (duration < 8),coef_duration_constants_6_to_7_hours -util_duration_constants_8_to_10_hours,Duration Constants -- 8 to 10 hours,(tour_type != 'escort') & (duration > 7) & (duration < 11),coef_duration_constants_8_to_10_hours -util_duration_constants_11_to_13_hours,Duration Constants -- 11 to 13 hours,(tour_type != 'escort') & (duration > 10) & (duration < 14),coef_duration_constants_11_to_13_hours -util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,(tour_type != 'escort') & (duration > 13) & (duration < 19),coef_duration_constants_14_to_18_hours -util_escort_tour_departure_constants_early,Escort Tour Departure Constants -- Early (up to 5),(tour_type == 'escort') & (start < 6),coef_escort_tour_departure_constants_early -util_escort_tour_departure_constants_am_peak_1,Escort Tour Departure Constants -- AM peak 1 (6),(tour_type == 'escort') & (start == 6),coef_escort_tour_departure_constants_am_peak_1 -util_escort_tour_departure_constants_am_peak_2,Escort Tour Departure Constants -- AM peak 2 (7),(tour_type == 'escort') & (start == 7),coef_escort_tour_departure_constants_am_peak_2 -util_escort_tour_departure_constants_am_peak_3,Escort Tour Departure Constants -- AM peak 3 (8),(tour_type == 'escort') & (start == 8),coef_escort_tour_departure_constants_am_peak_3 -util_escort_tour_departure_constants_am_peak_4,Escort Tour Departure Constants -- AM peak 4 (9),(tour_type == 'escort') & (start == 9),coef_escort_tour_departure_constants_am_peak_4 -util_escort_tour_departure_constants_midday_1,Escort Tour Departure Constants -- Midday 1 (10 to 12),(tour_type == 'escort') & (start > 9) & (start < 13),coef_escort_tour_departure_constants_midday_1 -util_escort_tour_departure_constants_midday_2,Escort Tour Departure Constants -- Midday 2 (13 to 15),(tour_type == 'escort') & (start > 12) & (start < 16),coef_escort_tour_departure_constants_midday_2 -util_escort_tour_departure_constants_pm_peak,Escort Tour Departure Constants -- PM peak (16 to 18),(tour_type == 'escort') & (start > 15) & (start < 19),coef_escort_tour_departure_constants_pm_peak -util_escort_tour_departure_constants_evening,Escort Tour Departure Constants -- Evening (19 to 21),(tour_type == 'escort') & (start > 18) & (start < 22),coef_escort_tour_departure_constants_evening -util_escort_tour_departure_constants_late,Escort Tour Departure Constants -- Late (22 and later),(tour_type == 'escort') & (start > 21),coef_escort_tour_departure_constants_late -util_escort_tour_arrival_constants_early,Escort Tour Arrival Constants -- Early (up to 6),(tour_type == 'escort') & (end < 7),coef_escort_tour_arrival_constants_early -util_escort_tour_arrival_constants_am_peak,Escort Tour Arrival Constants -- AM peak (7 to 9),(tour_type == 'escort') & (end > 6) & (end < 10),coef_escort_tour_arrival_constants_am_peak -util_escort_tour_arrival_constants_midday_1,Escort Tour Arrival Constants -- Midday 1 (10 to 12),(tour_type == 'escort') & (end > 9) & (end < 13),coef_escort_tour_arrival_constants_midday_1 -util_escort_tour_arrival_constants_midday_2,Escort Tour Arrival Constants -- Midday 2 (13 to 14),(tour_type == 'escort') & (end > 12) & (end < 15),coef_escort_tour_arrival_constants_midday_2 -util_escort_tour_arrival_constants_pm_peak_1,Escort Tour Arrival Constants -- PM peak 1 (15),(tour_type == 'escort') & (end == 15),coef_escort_tour_arrival_constants_pm_peak_1 -util_escort_tour_arrival_constants_pm_peak_2,Escort Tour Arrival Constants -- PM peak 2 (16),(tour_type == 'escort') & (end == 16),coef_escort_tour_arrival_constants_pm_peak_2 -util_escort_tour_arrival_constants_pm_peak_3,Escort Tour Arrival Constants -- PM peak 3 (17),(tour_type == 'escort') & (end == 17),coef_escort_tour_arrival_constants_pm_peak_3 -util_escort_tour_arrival_constants_pm_peak_4,Escort Tour Arrival Constants -- PM peak 4 (18),(tour_type == 'escort') & (end == 18),coef_escort_tour_arrival_constants_pm_peak_4 -util_escort_tour_arrival_constants_evening,Escort Tour Arrival Constants -- Evening (19 to 21),(tour_type == 'escort') & (end > 18) & (end < 22),coef_escort_tour_arrival_constants_evening -util_escort_tour_arrival_constants_late,Escort Tour Arrival Constants -- Late (22 and later),(tour_type == 'escort') & (end > 21),coef_escort_tour_arrival_constants_late -util_escort_tour_duration_constants_0_to_1_hours,Escort Tour Duration Constants -- 0 to 1 hours,(tour_type == 'escort') & (duration < 2),coef_escort_tour_duration_constants_0_to_1_hours -util_escort_tour_duration_constants_2_to_3_hours,Escort Tour Duration Constants -- 2 to 3 hours,(tour_type == 'escort') & (duration > 1) & (duration < 4),coef_escort_tour_duration_constants_2_to_3_hours -util_escort_tour_duration_constants_4_to_5_hours,Escort Tour Duration Constants -- 4 to 5 hours,(tour_type == 'escort') & (duration > 3) & (duration < 6),coef_escort_tour_duration_constants_4_to_5_hours -util_escort_tour_duration_constants_6_to_7_hours,Escort Tour Duration Constants -- 6 to 7 hours,(tour_type == 'escort') & (duration > 5) & (duration < 8),coef_escort_tour_duration_constants_6_to_7_hours -util_escort_tour_duration_constants_8_to_10_hours,Escort Tour Duration Constants -- 8 to 10 hours,(tour_type == 'escort') & (duration > 7) & (duration < 11),coef_escort_tour_duration_constants_8_to_10_hours -util_escort_tour_duration_constants_11_to_13_hours,Escort Tour Duration Constants -- 11 to 13 hours,(tour_type == 'escort') & (duration > 10) & (duration < 14),coef_escort_tour_duration_constants_11_to_13_hours -util_escort_tour_duration_constants_14_to_18_hours,Escort Tour Duration Constants -- 14 to 18 hours,(tour_type == 'escort') & (duration > 13) & (duration < 19),coef_escort_tour_duration_constants_14_to_18_hours +Label,Description,Expression,Coefficient +util_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends,Subsequent tour must start after previous tour for this purpose ends,(start < end_previous) & (tour_type_num > 1),coef_subsequent_tour_must_start_after_previous_tour_for_this_purpose_ends +util_free_flow_round_trip_auto_time_shift_effects_duration,Free-flow round trip auto time shift effects - duration,roundtrip_auto_time_to_work * duration,coef_free_flow_round_trip_auto_time_shift_effects_duration +util_shopping_tour_departure_shift_effects,Shopping tour - departure shift effects,(tour_type == 'shopping') * start,coef_shopping_tour_departure_shift_effects +util_shopping_tour_duration_shift_effects,Shopping tour - duration shift effects,(tour_type == 'shopping') * duration,coef_shopping_tour_duration_shift_effects +util_maintenance_tour_departure_shift_effects,Maintenance tour - departure shift effects,(tour_type == 'othmaint') * start,coef_maintenance_tour_departure_shift_effects +util_maintenance_tour_duration_shift_effects,Maintenance tour - departure shift effects,(tour_type == 'othmaint') * duration,coef_maintenance_tour_duration_shift_effects +util_visit_tour_departure_shift_effects_start,Visit tour - departure shift effects,(tour_type == 'social') * start,coef_visit_tour_departure_shift_effects +util_visit_tour_duration_shift_effects_duration,Visit tour - departure shift effects,(tour_type == 'social') * duration,coef_visit_tour_duration_shift_effects +util_eat_out_tour_departure_shift_effects,Eat Out tour - departure shift effects,(tour_type == 'eatout') * start,coef_eat_out_tour_departure_shift_effects +util_school_child_age_16_plus_departure_shift_effects,School child age 16+ - departure shift effects,(ptype == 6) * start,coef_school_child_age_16_plus_departure_shift_effects +util_school_child_age_16_plus_duration_shift_effects,School child age 16+ - duration shift effects,(ptype == 6) * duration,coef_school_child_age_16_plus_duration_shift_effects +util_school_child_age_under_16_departure_shift_effects,School child age under 16 - departure shift effects,(ptype == 7) * start,coef_school_child_age_under_16_departure_shift_effects +util_school_child_age_under_16_duration_shift_effects,School child age under 16 - duration shift effects,(ptype == 7) * duration,coef_school_child_age_under_16_duration_shift_effects +util_destination_in_cbd_duration_shift_effects,Destination in CBD - duration shift effects,destination_in_cbd * duration,coef_destination_in_cbd_duration_shift_effects +util_number_of_mandatory_tours_departure_shift_effects,Number of mandatory tours - departure shift effects,num_mand * start,coef_number_of_mandatory_tours_departure_shift_effects +util_number_of_joint_tours_departure_shift_effects,Number of joint tours - departure shift effects,num_person_joint_tours * start,coef_number_of_joint_tours_departure_shift_effects +util_number_of_escort_tours_departure_shift_effects,Number of escort tours - departure shift effects,num_escort_tours * start,coef_number_of_escort_tours_departure_shift_effects +util_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects,Number of idividual non-mandatory tours (excluding escort) - departure shift effects,num_non_escort_tours * start,coef_number_of_individual_non_mandatory_tours_excluding_escort_departure_shift_effects +util_first_of_2_plus_tours_for_same_purpose_departure_shift_effect,First of 2+ tours for same purpose - departure shift effect,((tour_type_count>1) & (tour_type_num == 1)) * start,coef_first_of_2_plus_tours_for_same_purpose_departure_shift_effect +util_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect,subsequent of 2+ tours for same purpose - duration shift effect,(tour_type_num > 1) * duration,coef_subsequent_of_2_plus_tours_for_same_purpose_duration_shift_effect +util_maintenance_tour_depart_before_7,Maintenance tour - depart before 7,(tour_type == 'othmaint') & (start < 7),coef_maintenance_tour_depart_before_7 +util_shopping_tour_depart_before_8,Shopping tour - depart before 8,(tour_type == 'shopping') & (start < 8),coef_shopping_tour_depart_before_8 +util_shopping_tour_arrive_after_22,Shopping tour - arrive after 22,(tour_type == 'shopping') & (end > 22),coef_shopping_tour_arrive_after_22 +util_school_child_under_16_arrive_after_22,School child under 16 - arrive after 22,(ptype == 7) & (end > 22),coef_school_child_under_16_arrive_after_22 +util_university_student_arrive_after_22,University student - arrive after 22,(ptype == 3) & (end > 22),coef_university_student_arrive_after_22 +util_shopping_tour_duration_lt_2_hours,Shopping tour - duration < 2 hours,(tour_type == 'shopping') & (duration < 2),coef_shopping_tour_duration_lt_2_hours +util_discretionary_tour_duration_lt_2_hours,Discretionary tour - duration < 2 hours,(tour_type == 'othdiscr') & (duration < 2),coef_discretionary_tour_duration_lt_2_hours +util_adult_with_children_in_hh_arrive_19_21,Adult with children in HH - arrive 19 - 21,adult & (num_children > 0) & ( end > 18 ) & ( end < 22 ),coef_adult_with_children_in_hh_arrive_19_21 +#,,, +#,Mode Choice Logsum,mode_choice_logsum,#mode_choice_logsum +#,,,# +util_dummy_adjacent_before,,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)",coef_dummy +util_dummy_adjacent_after,,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)",coef_dummy +util_some_previously_scheduled_tour_ends_in_this_departure_hour,Some previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.person_id, df.start)",coef_some_previously_scheduled_tour_ends_in_this_departure_hour +util_some_previously_scheduled_tour_begins_in_this_arrival_hour,Some previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.person_id, df.end)",coef_some_previously_scheduled_tour_begins_in_this_arrival_hour +util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_type_count>1) & (df.tour_type_num == 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_type_num > 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_type_num > 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction +util_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours,Remaining individual non-mandatory tours to be scheduled / number of unscheduled hours,"@((1.0 + df.tour_count - df.tour_num)) / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_ratio_of_individual_non_mandatory_tours_to_be_scheduled_to_number_of_unscheduled_hours +#,#,,# +util_departure_constants_early,Departure Constants -- Early (up to 5),(tour_type != 'escort') & (start < 6),coef_departure_constants_early +util_departure_constants_am_peak_1,Departure Constants -- AM peak 1 (6),(tour_type != 'escort') & (start == 6),coef_departure_constants_am_peak_1 +util_departure_constants_am_peak_2,Departure Constants -- AM peak 2 (7),(tour_type != 'escort') & (start == 7),coef_departure_constants_am_peak_2 +util_departure_constants_am_peak_3,Departure Constants -- AM peak 3 (8),(tour_type != 'escort') & (start == 8),coef_departure_constants_am_peak_3 +util_departure_constants_am_peak_4,Departure Constants -- AM peak 4 (9),(tour_type != 'escort') & (start == 9),coef_departure_constants_am_peak_4 +util_departure_constants_midday_1,Departure Constants -- Midday 1 (10 to 12),(tour_type != 'escort') & (start > 9) & (start < 13),coef_departure_constants_midday_1 +util_departure_constants_midday_2,Departure Constants -- Midday 2 (13 to 15),(tour_type != 'escort') & (start > 12) & (start < 16),coef_departure_constants_midday_2 +util_departure_constants_pm_peak,Departure Constants -- PM peak (16 to 18),(tour_type != 'escort') & (start > 15) & (start < 19),coef_departure_constants_pm_peak +util_departure_constants_evening,Departure Constants -- Evening (19 to 21),(tour_type != 'escort') & (start > 18) & (start < 22),coef_departure_constants_evening +util_departure_constants_late,Departure Constants -- Late (22 and later),(tour_type != 'escort') & (start > 21),coef_departure_constants_late +util_arrival_constants_early,Arrival Constants -- Early (up to 6),(tour_type != 'escort') & (end < 7),coef_arrival_constants_early +util_arrival_constants_am_peak,Arrival Constants -- AM peak (7 to 9),(tour_type != 'escort') & (end > 6) & (end < 10),coef_arrival_constants_am_peak +util_arrival_constants_midday_1,Arrival Constants -- Midday 1 (10 to 12),(tour_type != 'escort') & (end > 9) & (end < 13),coef_arrival_constants_midday_1 +util_arrival_constants_midday_2,Arrival Constants -- Midday 2 (13 to 14),(tour_type != 'escort') & (end > 12) & (end < 15),coef_arrival_constants_midday_2 +util_arrival_constants_pm_peak_1,Arrival Constants -- PM peak 1 (15),(tour_type != 'escort') & (end == 15),coef_arrival_constants_pm_peak_1 +util_arrival_constants_pm_peak_2,Arrival Constants -- PM peak 2 (16),(tour_type != 'escort') & (end == 16),coef_arrival_constants_pm_peak_2 +util_arrival_constants_pm_peak_3,Arrival Constants -- PM peak 3 (17),(tour_type != 'escort') & (end == 17),coef_arrival_constants_pm_peak_3 +util_arrival_constants_pm_peak_4,Arrival Constants -- PM peak 4 (18),(tour_type != 'escort') & (end == 18),coef_arrival_constants_pm_peak_4 +util_arrival_constants_evening,Arrival Constants -- Evening (19 to 21),(tour_type != 'escort') & (end > 18) & (end < 22),coef_arrival_constants_evening +util_arrival_constants_late,Arrival Constants -- Late (22 and later),(tour_type != 'escort') & (end > 21),coef_arrival_constants_late +util_duration_constants_0_to_1_hours,Duration Constants -- 0 to 1 hours,(tour_type != 'escort') & (duration < 2),coef_duration_constants_0_to_1_hours +util_duration_constants_2_to_3_hours,Duration Constants -- 2 to 3 hours,(tour_type != 'escort') & (duration > 1) & (duration < 4),coef_duration_constants_2_to_3_hours +util_duration_constants_4_to_5_hours,Duration Constants -- 4 to 5 hours,(tour_type != 'escort') & (duration > 3) & (duration < 6),coef_duration_constants_4_to_5_hours +util_duration_constants_6_to_7_hours,Duration Constants -- 6 to 7 hours,(tour_type != 'escort') & (duration > 5) & (duration < 8),coef_duration_constants_6_to_7_hours +util_duration_constants_8_to_10_hours,Duration Constants -- 8 to 10 hours,(tour_type != 'escort') & (duration > 7) & (duration < 11),coef_duration_constants_8_to_10_hours +util_duration_constants_11_to_13_hours,Duration Constants -- 11 to 13 hours,(tour_type != 'escort') & (duration > 10) & (duration < 14),coef_duration_constants_11_to_13_hours +util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,(tour_type != 'escort') & (duration > 13) & (duration < 19),coef_duration_constants_14_to_18_hours +util_escort_tour_departure_constants_early,Escort Tour Departure Constants -- Early (up to 5),(tour_type == 'escort') & (start < 6),coef_escort_tour_departure_constants_early +util_escort_tour_departure_constants_am_peak_1,Escort Tour Departure Constants -- AM peak 1 (6),(tour_type == 'escort') & (start == 6),coef_escort_tour_departure_constants_am_peak_1 +util_escort_tour_departure_constants_am_peak_2,Escort Tour Departure Constants -- AM peak 2 (7),(tour_type == 'escort') & (start == 7),coef_escort_tour_departure_constants_am_peak_2 +util_escort_tour_departure_constants_am_peak_3,Escort Tour Departure Constants -- AM peak 3 (8),(tour_type == 'escort') & (start == 8),coef_escort_tour_departure_constants_am_peak_3 +util_escort_tour_departure_constants_am_peak_4,Escort Tour Departure Constants -- AM peak 4 (9),(tour_type == 'escort') & (start == 9),coef_escort_tour_departure_constants_am_peak_4 +util_escort_tour_departure_constants_midday_1,Escort Tour Departure Constants -- Midday 1 (10 to 12),(tour_type == 'escort') & (start > 9) & (start < 13),coef_escort_tour_departure_constants_midday_1 +util_escort_tour_departure_constants_midday_2,Escort Tour Departure Constants -- Midday 2 (13 to 15),(tour_type == 'escort') & (start > 12) & (start < 16),coef_escort_tour_departure_constants_midday_2 +util_escort_tour_departure_constants_pm_peak,Escort Tour Departure Constants -- PM peak (16 to 18),(tour_type == 'escort') & (start > 15) & (start < 19),coef_escort_tour_departure_constants_pm_peak +util_escort_tour_departure_constants_evening,Escort Tour Departure Constants -- Evening (19 to 21),(tour_type == 'escort') & (start > 18) & (start < 22),coef_escort_tour_departure_constants_evening +util_escort_tour_departure_constants_late,Escort Tour Departure Constants -- Late (22 and later),(tour_type == 'escort') & (start > 21),coef_escort_tour_departure_constants_late +util_escort_tour_arrival_constants_early,Escort Tour Arrival Constants -- Early (up to 6),(tour_type == 'escort') & (end < 7),coef_escort_tour_arrival_constants_early +util_escort_tour_arrival_constants_am_peak,Escort Tour Arrival Constants -- AM peak (7 to 9),(tour_type == 'escort') & (end > 6) & (end < 10),coef_escort_tour_arrival_constants_am_peak +util_escort_tour_arrival_constants_midday_1,Escort Tour Arrival Constants -- Midday 1 (10 to 12),(tour_type == 'escort') & (end > 9) & (end < 13),coef_escort_tour_arrival_constants_midday_1 +util_escort_tour_arrival_constants_midday_2,Escort Tour Arrival Constants -- Midday 2 (13 to 14),(tour_type == 'escort') & (end > 12) & (end < 15),coef_escort_tour_arrival_constants_midday_2 +util_escort_tour_arrival_constants_pm_peak_1,Escort Tour Arrival Constants -- PM peak 1 (15),(tour_type == 'escort') & (end == 15),coef_escort_tour_arrival_constants_pm_peak_1 +util_escort_tour_arrival_constants_pm_peak_2,Escort Tour Arrival Constants -- PM peak 2 (16),(tour_type == 'escort') & (end == 16),coef_escort_tour_arrival_constants_pm_peak_2 +util_escort_tour_arrival_constants_pm_peak_3,Escort Tour Arrival Constants -- PM peak 3 (17),(tour_type == 'escort') & (end == 17),coef_escort_tour_arrival_constants_pm_peak_3 +util_escort_tour_arrival_constants_pm_peak_4,Escort Tour Arrival Constants -- PM peak 4 (18),(tour_type == 'escort') & (end == 18),coef_escort_tour_arrival_constants_pm_peak_4 +util_escort_tour_arrival_constants_evening,Escort Tour Arrival Constants -- Evening (19 to 21),(tour_type == 'escort') & (end > 18) & (end < 22),coef_escort_tour_arrival_constants_evening +util_escort_tour_arrival_constants_late,Escort Tour Arrival Constants -- Late (22 and later),(tour_type == 'escort') & (end > 21),coef_escort_tour_arrival_constants_late +util_escort_tour_duration_constants_0_to_1_hours,Escort Tour Duration Constants -- 0 to 1 hours,(tour_type == 'escort') & (duration < 2),coef_escort_tour_duration_constants_0_to_1_hours +util_escort_tour_duration_constants_2_to_3_hours,Escort Tour Duration Constants -- 2 to 3 hours,(tour_type == 'escort') & (duration > 1) & (duration < 4),coef_escort_tour_duration_constants_2_to_3_hours +util_escort_tour_duration_constants_4_to_5_hours,Escort Tour Duration Constants -- 4 to 5 hours,(tour_type == 'escort') & (duration > 3) & (duration < 6),coef_escort_tour_duration_constants_4_to_5_hours +util_escort_tour_duration_constants_6_to_7_hours,Escort Tour Duration Constants -- 6 to 7 hours,(tour_type == 'escort') & (duration > 5) & (duration < 8),coef_escort_tour_duration_constants_6_to_7_hours +util_escort_tour_duration_constants_8_to_10_hours,Escort Tour Duration Constants -- 8 to 10 hours,(tour_type == 'escort') & (duration > 7) & (duration < 11),coef_escort_tour_duration_constants_8_to_10_hours +util_escort_tour_duration_constants_11_to_13_hours,Escort Tour Duration Constants -- 11 to 13 hours,(tour_type == 'escort') & (duration > 10) & (duration < 14),coef_escort_tour_duration_constants_11_to_13_hours +util_escort_tour_duration_constants_14_to_18_hours,Escort Tour Duration Constants -- 14 to 18 hours,(tour_type == 'escort') & (duration > 13) & (duration < 19),coef_escort_tour_duration_constants_14_to_18_hours diff --git a/activitysim/examples/example_mtc/configs/tour_scheduling_nonmandatory_coefficients.csv b/activitysim/examples/prototype_mtc/configs/tour_scheduling_nonmandatory_coefficients.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/tour_scheduling_nonmandatory_coefficients.csv rename to activitysim/examples/prototype_mtc/configs/tour_scheduling_nonmandatory_coefficients.csv diff --git a/activitysim/examples/example_mtc/configs/tour_scheduling_school.csv b/activitysim/examples/prototype_mtc/configs/tour_scheduling_school.csv similarity index 99% rename from activitysim/examples/example_mtc/configs/tour_scheduling_school.csv rename to activitysim/examples/prototype_mtc/configs/tour_scheduling_school.csv index a5f8bcbe56..4c34c1edce 100644 --- a/activitysim/examples/example_mtc/configs/tour_scheduling_school.csv +++ b/activitysim/examples/prototype_mtc/configs/tour_scheduling_school.csv @@ -1,62 +1,62 @@ -Label,Description,Expression,Coefficient -util_roundtrip_auto_time_to_work,Free-flow round trip auto time shift effects - duration,roundtrip_auto_time_to_work * duration,coef_roundtrip_auto_time_to_work -util_ft_worker_departure,Full-time worker departure shift effects,(ptype == 1) * start,coef_ft_worker_departure -util_ft_worker_duration,Full-time worker duration shift effects,(ptype == 1) * duration,coef_ft_worker_duration -util_non_worker_departure,Non-working adult departure shift effects,(ptype == 4) * start,coef_non_worker_departure -util_univ_departure,University student departure shift effects,(ptype == 3) * start,coef_univ_departure -util_univ_duration,University student duration shift effects,(ptype == 3) * duration,coef_univ_duration -util_student_driver_duration,Student driving age duration shift effects,(ptype == 7) * duration,coef_student_driver_duration -util_all_adults_ft_worker_duration,All adults work full time- duration,(num_workers == hhsize) * duration,coef_all_adults_ft_worker_duration -util_subsequent_tour_must_start_after_previous_tour_ends,Subsequent tour must start after previous tour ends,(tour_num > 1) & (start < end_previous),coef_subsequent_tour_must_start_after_previous_tour_ends -util_first_of_2plus_school_tours_departure,First of 2+ school/univ. tours- departure,((tour_count>1) & (tour_num == 1)) * start,coef_first_of_2plus_school_tours_departure -util_first_of_2plus_school_tours_duration,First of 2+ school/univ. tours- duration,((tour_count>1) & (tour_num == 1)) * duration,coef_first_of_2plus_school_tours_duration -util_subsequent_2plus_school_tours_duration,Subsequent of 2+ school/univ. tours- duration,(tour_num > 1) * duration,coef_subsequent_2plus_school_tours_duration -util_hh_income_early_departure,Household income -- Early departure interaction,(income_in_thousands >= 100) & (start < 6),coef_hh_income_early_departure -util_hh_income_late_arrival,Household income -- Late arrival interaction,(income_in_thousands >= 100) & (end > 22),coef_hh_income_late_arrival -util_first_of_2plus_school_lt_6_hours,First of 2+ school/univ tours- duration<6 hrs,(tour_count>1) & (tour_num == 1) & (duration < 6),coef_first_of_2plus_school_lt_6_hours -util_subsequent_of_2plus_school_lt_6_hours,Subsequent of 2+ school/univ tours- duration<6 hrs,(tour_num > 1) & (duration < 6),coef_subsequent_of_2plus_school_lt_6_hours -util_school_plus_work_tours_by_student_lt_6_hours,School+work tours by student- duration<6 hrs,work_and_school_and_worker & (duration < 6),coef_school_plus_work_tours_by_student_lt_6_hours -util_school_plus_work_tours_by_worker_lt_6_hours,School+work tours by worker- duration<6 hrs,work_and_school_and_student & (duration < 6),coef_school_plus_work_tours_by_worker_lt_6_hours -#,,, -util_mode_choice_logsum,Mode Choice Logsum,mode_choice_logsum,coef_mode_choice_logsum -#,,, -#,,, FIXME - use temps as timetable ops can be very time-consuming -util_dummy_adjacent_before,,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)",coef_dummy -util_dummy_adjacent_after,,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)",coef_dummy -util_previous_tour_ends_this_departure_hour,Previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.person_id, df.start)",coef_previous_tour_ends_this_departure_hour -util_previous_tour_begins_this_arrival_hour,Previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.person_id, df.end)",coef_previous_tour_begins_this_arrival_hour -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction -util_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours,Remaining work/school tours to be scheduled / number of unscheduled hours,"@((df.tour_count>1) & (df.tour_num == 1)) * 1.0 / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours -#,,, -util_departure_constants_early_up_to_5,Departure Constants -- Early (up to 5),start < 6,coef_departure_constants_early -util_departure_constants_am_peak_1 _6,Departure Constants -- AM peak 1 (6),start == 6,coef_departure_constants_am_peak_1 -util_departure_constants_am_peak_2_7,Departure Constants -- AM peak 2 (7),start == 7,coef_departure_constants_am_peak_2 -util_departure_constants_am_peak_3_8,Departure Constants -- AM peak 3 (8),start == 8,coef_departure_constants_am_peak_3 -util_departure_constants_am_peak_4_9,Departure Constants -- AM peak 4 (9),start == 9,coef_departure_constants_am_peak_4 -util_departure_constants_midday_1_10_to_12,Departure Constants -- Midday 1 (10 to 12),(start > 9) & (start < 13),coef_departure_constants_midday_1 -util_departure_constants_midday_2_13_to_15,Departure Constants -- Midday 2 (13 to 15),(start > 12) & (start < 16),coef_departure_constants_midday_2 -util_departure_constants_pm_peak_16_to_18,Departure Constants -- PM peak (16 to 18),(start > 15) & (start < 19),coef_departure_constants_pm_peak -util_departure_constants_evening_19_to_21,Departure Constants -- Evening (19 to 21),(start > 18) & (start < 22),coef_departure_constants_evening -util_departure_constants_late_22_and_later,Departure Constants -- Late (22 and later),start > 21,coef_departure_constants_late -util_arrival_constants_early_up_to_6,Arrival Constants -- Early (up to 6),end < 7,coef_arrival_constants_early -util_arrival_constants_am_peak_7_to_9,Arrival Constants -- AM peak (7 to 9),(end > 6) & (end < 10),coef_arrival_constants_am_peak -util_arrival_constants_midday_1_10_to_12,Arrival Constants -- Midday 1 (10 to 12),(end > 9) & (end < 13),coef_arrival_constants_midday_1 -util_arrival_constants_midday_2_13_to_14,Arrival Constants -- Midday 2 (13 to 14),(end > 12) & (end < 15),coef_arrival_constants_midday_2 -util_arrival_constants_pm_peak_1_15,Arrival Constants -- PM peak 1 (15),end == 15,coef_arrival_constants_pm_peak_1 -util_arrival_constants_pm_peak_2_16,Arrival Constants -- PM peak 2 (16),end == 16,coef_arrival_constants_pm_peak_2 -util_arrival_constants_pm_peak_3_17,Arrival Constants -- PM peak 3 (17),end == 17,coef_arrival_constants_pm_peak_3 -util_arrival_constants_pm_peak_4_18,Arrival Constants -- PM peak 4 (18),end == 18,coef_arrival_constants_pm_peak_4 -util_arrival_constants_evening_19_to_21,Arrival Constants -- Evening (19 to 21),(end > 18) & (end < 22),coef_arrival_constants_evening -util_arrival_constants_late_22_and_later,Arrival Constants -- Late (22 and later),end > 21,coef_arrival_constants_late -util_duration_constants_0_to_2_hours,Duration Constants -- 0 to 2 hours,duration < 3,coef_duration_constants_0_to_2_hours -util_duration_constants_3_to_4_hours,Duration Constants -- 3 to 4 hours,(duration > 2) & (duration < 5),coef_duration_constants_3_to_4_hours -util_duration_constants_5_to_6_hours,Duration Constants -- 5 to 6 hours,(duration > 4) & (duration < 7),coef_duration_constants_5_to_6_hours -util_duration_constants_7_to_8_hours,Duration Constants -- 7 to 8 hours,(duration > 6) & (duration < 9),coef_duration_constants_7_to_8_hours -util_duration_constants_9_hours,Duration Constants -- 9 hours,duration == 9,coef_duration_constants_9_hours -util_duration_constants_10_hours,Duration Constants -- 10 hours,duration == 10,coef_duration_constants_10_hours -util_duration_constants_11_hours,Duration Constants -- 11 hours,duration == 11,coef_duration_constants_11_hours -util_duration_constants_12_to_13_hours,Duration Constants -- 12 to 13 hours,(duration > 11) & (duration < 14),coef_duration_constants_12_to_13_hours -util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,(duration > 13) & (duration < 19),coef_duration_constants_14_to_18_hours +Label,Description,Expression,Coefficient +util_roundtrip_auto_time_to_work,Free-flow round trip auto time shift effects - duration,roundtrip_auto_time_to_work * duration,coef_roundtrip_auto_time_to_work +util_ft_worker_departure,Full-time worker departure shift effects,(ptype == 1) * start,coef_ft_worker_departure +util_ft_worker_duration,Full-time worker duration shift effects,(ptype == 1) * duration,coef_ft_worker_duration +util_non_worker_departure,Non-working adult departure shift effects,(ptype == 4) * start,coef_non_worker_departure +util_univ_departure,University student departure shift effects,(ptype == 3) * start,coef_univ_departure +util_univ_duration,University student duration shift effects,(ptype == 3) * duration,coef_univ_duration +util_student_driver_duration,Student driving age duration shift effects,(ptype == 7) * duration,coef_student_driver_duration +util_all_adults_ft_worker_duration,All adults work full time- duration,(num_workers == hhsize) * duration,coef_all_adults_ft_worker_duration +util_subsequent_tour_must_start_after_previous_tour_ends,Subsequent tour must start after previous tour ends,(tour_num > 1) & (start < end_previous),coef_subsequent_tour_must_start_after_previous_tour_ends +util_first_of_2plus_school_tours_departure,First of 2+ school/univ. tours- departure,((tour_count>1) & (tour_num == 1)) * start,coef_first_of_2plus_school_tours_departure +util_first_of_2plus_school_tours_duration,First of 2+ school/univ. tours- duration,((tour_count>1) & (tour_num == 1)) * duration,coef_first_of_2plus_school_tours_duration +util_subsequent_2plus_school_tours_duration,Subsequent of 2+ school/univ. tours- duration,(tour_num > 1) * duration,coef_subsequent_2plus_school_tours_duration +util_hh_income_early_departure,Household income -- Early departure interaction,(income_in_thousands >= 100) & (start < 6),coef_hh_income_early_departure +util_hh_income_late_arrival,Household income -- Late arrival interaction,(income_in_thousands >= 100) & (end > 22),coef_hh_income_late_arrival +util_first_of_2plus_school_lt_6_hours,First of 2+ school/univ tours- duration<6 hrs,(tour_count>1) & (tour_num == 1) & (duration < 6),coef_first_of_2plus_school_lt_6_hours +util_subsequent_of_2plus_school_lt_6_hours,Subsequent of 2+ school/univ tours- duration<6 hrs,(tour_num > 1) & (duration < 6),coef_subsequent_of_2plus_school_lt_6_hours +util_school_plus_work_tours_by_student_lt_6_hours,School+work tours by student- duration<6 hrs,work_and_school_and_worker & (duration < 6),coef_school_plus_work_tours_by_student_lt_6_hours +util_school_plus_work_tours_by_worker_lt_6_hours,School+work tours by worker- duration<6 hrs,work_and_school_and_student & (duration < 6),coef_school_plus_work_tours_by_worker_lt_6_hours +#,,, +util_mode_choice_logsum,Mode Choice Logsum,mode_choice_logsum,coef_mode_choice_logsum +#,,, +#,,, FIXME - use temps as timetable ops can be very time-consuming +util_dummy_adjacent_before,,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)",coef_dummy +util_dummy_adjacent_after,,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)",coef_dummy +util_previous_tour_ends_this_departure_hour,Previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.person_id, df.start)",coef_previous_tour_ends_this_departure_hour +util_previous_tour_begins_this_arrival_hour,Previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.person_id, df.end)",coef_previous_tour_begins_this_arrival_hour +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction +util_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours,Remaining work/school tours to be scheduled / number of unscheduled hours,"@((df.tour_count>1) & (df.tour_num == 1)) * 1.0 / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours +#,,, +util_departure_constants_early_up_to_5,Departure Constants -- Early (up to 5),start < 6,coef_departure_constants_early +util_departure_constants_am_peak_1 _6,Departure Constants -- AM peak 1 (6),start == 6,coef_departure_constants_am_peak_1 +util_departure_constants_am_peak_2_7,Departure Constants -- AM peak 2 (7),start == 7,coef_departure_constants_am_peak_2 +util_departure_constants_am_peak_3_8,Departure Constants -- AM peak 3 (8),start == 8,coef_departure_constants_am_peak_3 +util_departure_constants_am_peak_4_9,Departure Constants -- AM peak 4 (9),start == 9,coef_departure_constants_am_peak_4 +util_departure_constants_midday_1_10_to_12,Departure Constants -- Midday 1 (10 to 12),(start > 9) & (start < 13),coef_departure_constants_midday_1 +util_departure_constants_midday_2_13_to_15,Departure Constants -- Midday 2 (13 to 15),(start > 12) & (start < 16),coef_departure_constants_midday_2 +util_departure_constants_pm_peak_16_to_18,Departure Constants -- PM peak (16 to 18),(start > 15) & (start < 19),coef_departure_constants_pm_peak +util_departure_constants_evening_19_to_21,Departure Constants -- Evening (19 to 21),(start > 18) & (start < 22),coef_departure_constants_evening +util_departure_constants_late_22_and_later,Departure Constants -- Late (22 and later),start > 21,coef_departure_constants_late +util_arrival_constants_early_up_to_6,Arrival Constants -- Early (up to 6),end < 7,coef_arrival_constants_early +util_arrival_constants_am_peak_7_to_9,Arrival Constants -- AM peak (7 to 9),(end > 6) & (end < 10),coef_arrival_constants_am_peak +util_arrival_constants_midday_1_10_to_12,Arrival Constants -- Midday 1 (10 to 12),(end > 9) & (end < 13),coef_arrival_constants_midday_1 +util_arrival_constants_midday_2_13_to_14,Arrival Constants -- Midday 2 (13 to 14),(end > 12) & (end < 15),coef_arrival_constants_midday_2 +util_arrival_constants_pm_peak_1_15,Arrival Constants -- PM peak 1 (15),end == 15,coef_arrival_constants_pm_peak_1 +util_arrival_constants_pm_peak_2_16,Arrival Constants -- PM peak 2 (16),end == 16,coef_arrival_constants_pm_peak_2 +util_arrival_constants_pm_peak_3_17,Arrival Constants -- PM peak 3 (17),end == 17,coef_arrival_constants_pm_peak_3 +util_arrival_constants_pm_peak_4_18,Arrival Constants -- PM peak 4 (18),end == 18,coef_arrival_constants_pm_peak_4 +util_arrival_constants_evening_19_to_21,Arrival Constants -- Evening (19 to 21),(end > 18) & (end < 22),coef_arrival_constants_evening +util_arrival_constants_late_22_and_later,Arrival Constants -- Late (22 and later),end > 21,coef_arrival_constants_late +util_duration_constants_0_to_2_hours,Duration Constants -- 0 to 2 hours,duration < 3,coef_duration_constants_0_to_2_hours +util_duration_constants_3_to_4_hours,Duration Constants -- 3 to 4 hours,(duration > 2) & (duration < 5),coef_duration_constants_3_to_4_hours +util_duration_constants_5_to_6_hours,Duration Constants -- 5 to 6 hours,(duration > 4) & (duration < 7),coef_duration_constants_5_to_6_hours +util_duration_constants_7_to_8_hours,Duration Constants -- 7 to 8 hours,(duration > 6) & (duration < 9),coef_duration_constants_7_to_8_hours +util_duration_constants_9_hours,Duration Constants -- 9 hours,duration == 9,coef_duration_constants_9_hours +util_duration_constants_10_hours,Duration Constants -- 10 hours,duration == 10,coef_duration_constants_10_hours +util_duration_constants_11_hours,Duration Constants -- 11 hours,duration == 11,coef_duration_constants_11_hours +util_duration_constants_12_to_13_hours,Duration Constants -- 12 to 13 hours,(duration > 11) & (duration < 14),coef_duration_constants_12_to_13_hours +util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,(duration > 13) & (duration < 19),coef_duration_constants_14_to_18_hours diff --git a/activitysim/examples/example_mtc/configs/tour_scheduling_school_coefficients.csv b/activitysim/examples/prototype_mtc/configs/tour_scheduling_school_coefficients.csv similarity index 98% rename from activitysim/examples/example_mtc/configs/tour_scheduling_school_coefficients.csv rename to activitysim/examples/prototype_mtc/configs/tour_scheduling_school_coefficients.csv index b5d8c8050b..6fd040134d 100644 --- a/activitysim/examples/example_mtc/configs/tour_scheduling_school_coefficients.csv +++ b/activitysim/examples/prototype_mtc/configs/tour_scheduling_school_coefficients.csv @@ -1,57 +1,57 @@ -coefficient_name,value,constrain -coef_dummy,1,T -coef_roundtrip_auto_time_to_work,0.003195,F -coef_ft_worker_departure,0.3971,F -coef_ft_worker_duration,-0.1908,F -coef_non_worker_departure,0.5539,F -coef_univ_departure,0.28,F -coef_univ_duration,-0.2907,F -coef_student_driver_duration,0.03464,F -coef_all_adults_ft_worker_duration,0.1093,F -coef_subsequent_tour_must_start_after_previous_tour_ends,-100,T -coef_first_of_2plus_school_tours_departure,-0.3002,F -coef_first_of_2plus_school_tours_duration,-0.1593,F -coef_subsequent_2plus_school_tours_duration,-0.2338,F -coef_hh_income_early_departure,-0.8837,F -coef_hh_income_late_arrival,-0.3533,F -coef_first_of_2plus_school_lt_6_hours,1.487,F -coef_subsequent_of_2plus_school_lt_6_hours,2.142,F -coef_school_plus_work_tours_by_student_lt_6_hours,1.73,F -coef_school_plus_work_tours_by_worker_lt_6_hours,2.142,F -coef_mode_choice_logsum,2.127,F -coef_previous_tour_ends_this_departure_hour,-0.5995,F -coef_previous_tour_begins_this_arrival_hour,-1.102,F -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.08975,F -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,-0.003049,F -coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-0.44,F -coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.5271,F -coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours,-16.67,F -coef_departure_constants_early,-3.820662404,F -coef_departure_constants_am_peak_1,-1.617644056,F -coef_departure_constants_am_peak_2,0,T -coef_departure_constants_am_peak_3,-0.073826841,F -coef_departure_constants_am_peak_4,-2.080570769,F -coef_departure_constants_midday_1,-2.985739457,F -coef_departure_constants_midday_2,-3.628434646,F -coef_departure_constants_pm_peak,-3.10250515,F -coef_departure_constants_evening,-5.230287836,F -coef_departure_constants_late,-11.88604728,F -coef_arrival_constants_early,-2.428718399,F -coef_arrival_constants_am_peak,-2.428718399,F -coef_arrival_constants_midday_1,-1.237908768,F -coef_arrival_constants_midday_2,-0.539768931,F -coef_arrival_constants_pm_peak_1,0,T -coef_arrival_constants_pm_peak_2,-0.389169248,F -coef_arrival_constants_pm_peak_3,-0.198120349,F -coef_arrival_constants_pm_peak_4,-0.253624684,F -coef_arrival_constants_evening,-0.870146904,F -coef_arrival_constants_late,-1.75200049,F -coef_duration_constants_0_to_2_hours,-1.409955689,F -coef_duration_constants_3_to_4_hours,-0.745893252,F -coef_duration_constants_5_to_6_hours,-0.567636622,F -coef_duration_constants_7_to_8_hours,0,T -coef_duration_constants_9_hours,-0.650806684,F -coef_duration_constants_10_hours,-0.904788983,F -coef_duration_constants_11_hours,-1.521162604,F -coef_duration_constants_12_to_13_hours,-2.418488917,F +coefficient_name,value,constrain +coef_dummy,1,T +coef_roundtrip_auto_time_to_work,0.003195,F +coef_ft_worker_departure,0.3971,F +coef_ft_worker_duration,-0.1908,F +coef_non_worker_departure,0.5539,F +coef_univ_departure,0.28,F +coef_univ_duration,-0.2907,F +coef_student_driver_duration,0.03464,F +coef_all_adults_ft_worker_duration,0.1093,F +coef_subsequent_tour_must_start_after_previous_tour_ends,-100,T +coef_first_of_2plus_school_tours_departure,-0.3002,F +coef_first_of_2plus_school_tours_duration,-0.1593,F +coef_subsequent_2plus_school_tours_duration,-0.2338,F +coef_hh_income_early_departure,-0.8837,F +coef_hh_income_late_arrival,-0.3533,F +coef_first_of_2plus_school_lt_6_hours,1.487,F +coef_subsequent_of_2plus_school_lt_6_hours,2.142,F +coef_school_plus_work_tours_by_student_lt_6_hours,1.73,F +coef_school_plus_work_tours_by_worker_lt_6_hours,2.142,F +coef_mode_choice_logsum,2.127,F +coef_previous_tour_ends_this_departure_hour,-0.5995,F +coef_previous_tour_begins_this_arrival_hour,-1.102,F +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.08975,F +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,-0.003049,F +coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-0.44,F +coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.5271,F +coef_remaining_work_school_tours_to_be_scheduled_div_number_of_unscheduled_hours,-16.67,F +coef_departure_constants_early,-3.820662404,F +coef_departure_constants_am_peak_1,-1.617644056,F +coef_departure_constants_am_peak_2,0,T +coef_departure_constants_am_peak_3,-0.073826841,F +coef_departure_constants_am_peak_4,-2.080570769,F +coef_departure_constants_midday_1,-2.985739457,F +coef_departure_constants_midday_2,-3.628434646,F +coef_departure_constants_pm_peak,-3.10250515,F +coef_departure_constants_evening,-5.230287836,F +coef_departure_constants_late,-11.88604728,F +coef_arrival_constants_early,-2.428718399,F +coef_arrival_constants_am_peak,-2.428718399,F +coef_arrival_constants_midday_1,-1.237908768,F +coef_arrival_constants_midday_2,-0.539768931,F +coef_arrival_constants_pm_peak_1,0,T +coef_arrival_constants_pm_peak_2,-0.389169248,F +coef_arrival_constants_pm_peak_3,-0.198120349,F +coef_arrival_constants_pm_peak_4,-0.253624684,F +coef_arrival_constants_evening,-0.870146904,F +coef_arrival_constants_late,-1.75200049,F +coef_duration_constants_0_to_2_hours,-1.409955689,F +coef_duration_constants_3_to_4_hours,-0.745893252,F +coef_duration_constants_5_to_6_hours,-0.567636622,F +coef_duration_constants_7_to_8_hours,0,T +coef_duration_constants_9_hours,-0.650806684,F +coef_duration_constants_10_hours,-0.904788983,F +coef_duration_constants_11_hours,-1.521162604,F +coef_duration_constants_12_to_13_hours,-2.418488917,F coef_duration_constants_14_to_18_hours,-2.503137295,F \ No newline at end of file diff --git a/activitysim/examples/example_mtc/configs/tour_scheduling_work.csv b/activitysim/examples/prototype_mtc/configs/tour_scheduling_work.csv similarity index 99% rename from activitysim/examples/example_mtc/configs/tour_scheduling_work.csv rename to activitysim/examples/prototype_mtc/configs/tour_scheduling_work.csv index d80fc39b35..7aa6a363a9 100644 --- a/activitysim/examples/example_mtc/configs/tour_scheduling_work.csv +++ b/activitysim/examples/prototype_mtc/configs/tour_scheduling_work.csv @@ -1,70 +1,70 @@ -Label,Description,Expression,Coefficient -util_free_flow_round_trip_auto_time_shift_effects_departure,Free-flow round trip auto time shift effects - departure,roundtrip_auto_time_to_work * start,coef_free_flow_round_trip_auto_time_shift_effects_departure -util_free_flow_round_trip_auto_time_shift_effects_duration,Free-flow round trip auto time shift effects - duration,roundtrip_auto_time_to_work * duration,coef_free_flow_round_trip_auto_time_shift_effects_duration -util_part_time_worker_departure_shift_effects,Part-time worker departure shift effects,(ptype == 2) * start,coef_part_time_worker_departure_shift_effects -util_non_working_adult_duration_shift_effects,Non-working adult duration shift effects,(ptype == 4) * duration,coef_non_working_adult_duration_shift_effects -util_university_student_departure_shift_effects,University student departure shift effects,(ptype == 3) * start,coef_university_student_departure_shift_effects -util_household_income_departure_shift_effects,Household income departure shift effects,income_in_thousands * start,coef_household_income_departure_shift_effects -util_destination_in_cbd_departure_shift_effects,Destination in CBD departure shift effects,workplace_in_cbd * start,coef_destination_in_cbd_departure_shift_effects -util_destination_in_cbd_duration_shift_effects,Destination in CBD duration shift effects,workplace_in_cbd * duration,coef_destination_in_cbd_duration_shift_effects -util_subsequent_tour_must_start_after_previous_tour_ends,Subsequent tour must start after previous tour ends,(tour_num > 1) & (start < end_previous),coef_subsequent_tour_must_start_after_previous_tour_ends -util_first_of_2plus_work_tours_departure_shift_effects,First of 2+ work tours departure shift effects,((tour_count>1) & (tour_num == 1)) * start,coef_first_of_2plus_work_tours_departure_shift_effects -util_first_of_2plus_work_tours_duration_shift_effects,First of 2+ work tours duration shift effects,((tour_count>1) & (tour_num == 1)) * duration,coef_first_of_2plus_work_tours_duration_shift_effects -util_subsequent_2plus_work_departure_tours_shift_effects,Subsequent 2+ work departure tours shift effects,(tour_num == 2) * start,coef_subsequent_2plus_work_departure_tours_shift_effects -util_subsequent_2plus_work_duration_tours_shift_effects,Subsequent 2+ work duration tours shift effects,(tour_num == 2) * duration,coef_subsequent_2plus_work_duration_tours_shift_effects -util_household_income_early_departure_interaction,Household income -- Early departure interaction,(income_in_thousands > 100) & (start < 6),coef_household_income_early_departure_interaction -util_household_income_late_arrival_interaction,Household income -- Late arrival interaction,(income_in_thousands > 100) & (end > 22),coef_household_income_late_arrival_interaction -util_destination_in_cbd_early_departure_interaction,Destination in CBD -- Early departure interaction,workplace_in_cbd & (start < 6),coef_destination_in_cbd_early_departure_interaction -util_destination_in_cbd_late_arrival_interaction,Destination in CBD -- Late arrival interaction,workplace_in_cbd & (end > 22),coef_destination_in_cbd_late_arrival_interaction -util_rural_household_early_departure_interaction,Rural household -- Early departure interaction,home_is_rural & (start < 6),coef_rural_household_early_departure_interaction -util_rural_household_late_arrival_interaction,Rural household -- Late arrival interaction,home_is_rural & (end > 22),coef_rural_household_late_arrival_interaction -util_full_time_worker_duration_lt_9_hours_interaction,Full-time worker -- duration < 9 hours interaction,(ptype == 1) & (duration < 9),coef_full_time_worker_duration_lt_9_hours_interaction -util_full_time_worker_10_to_12_departure_interaction,Full-time worker -- 10 to 12 departure interaction,(ptype == 1) & (start > 9) & (start < 13),coef_full_time_worker_10_to_12_departure_interaction -util_worker_13_to_15_arrival_interaction,Part-time worker -- 13 to 15 arrival interaction,(ptype == 2) & (end > 12) & (end < 16),coef_part_time_worker_13_to_15_arrival_interaction -util_first_of_2plus_work_tours_duration_lt_8_hrs,First of 2+ work tours- duration<8 hrs,((tour_count>1) & (tour_num == 1)) & (duration < 8),coef_first_of_2plus_work_tours_duration_lt_8_hrs -util_subsequent_of_2plus_work_tours_duration_lt_8_hrs,Subsequent of 2+ work tours- duration<8 hrs,(tour_num == 2) & (duration < 8),coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs -util_tours_by_worker_duration_lt_8_hrs,Work+school tours by worker- duration<8 hrs,(mandatory_tour_frequency == 'work_and_school') & is_worker & (duration < 8),coef_tours_by_worker_duration_lt_8_hrs -util_tours_by_student_duration_lt_8_hrs,School+work tours by student- duration<8 hrs,(mandatory_tour_frequency == 'work_and_school') & is_student & (duration < 8),coef_tours_by_student_duration_lt_8_hrs -#,,, -util_mode_choice_logsum,Mode Choice Logsum,mode_choice_logsum,coef_mode_choice_logsum -#,,, -#,FIXME - use temps _adjacent_window_before and _adjacent_window_after because timetable ops can be very time-consuming,, -util_dummy_adjacent_before,local temp variable,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)",coef_dummy -util_dummy_adjacent_after,local temp variable,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)",coef_dummy -util_previous_tour_ends_this_departure_hour,Previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.person_id, df.start)",coef_previously_scheduled_tour_ends_in_this_departure_hour -util_previously_scheduled_tour_begins_in_this_arrival_hour,Previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.person_id, df.end)",coef_previously_scheduled_tour_begins_in_this_arrival_hour -util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction -util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction -util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction -util_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours,Remaining work/school tours to be scheduled / number of unscheduled hours,"@((df.tour_count>1) & (df.tour_num == 1)) * 1.0 / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours -#,,, -util_departure_constants_early,Departure Constants -- Early (up to 5),start < 6,coef_departure_constants_early -util_departure_constants_am_peak_1,Departure Constants -- AM peak 1 (6),start == 6,coef_departure_constants_am_peak_1 -util_departure_constants_am_peak_2,Departure Constants -- AM peak 2 (7),start == 7,coef_departure_constants_am_peak_2 -util_departure_constants_am_peak_3,Departure Constants -- AM peak 3 (8),start == 8,coef_departure_constants_am_peak_3 -util_departure_constants_am_peak_4,Departure Constants -- AM peak 4 (9),start == 9,coef_departure_constants_am_peak_4 -util_departure_constants_midday_1,Departure Constants -- Midday 1 (10 to 12),(start > 9) & (start < 13),coef_departure_constants_midday_1 -util_departure_constants_midday_2,Departure Constants -- Midday 2 (13 to 15),(start > 12) & (start < 16),coef_departure_constants_midday_2 -util_departure_constants_pm_peak,Departure Constants -- PM peak (16 to 18),(start > 15) & (start < 19),coef_departure_constants_pm_peak -util_departure_constants_evening,Departure Constants -- Evening (19 to 21),(start > 18) & (start < 22),coef_departure_constants_evening -util_departure_constants_late,Departure Constants -- Late (22 and later),start > 21,coef_departure_constants_late -util_arrival_constants_early,Arrival Constants -- Early (up to 6),end < 7,coef_arrival_constants_early -util_arrival_constants_am_peak,Arrival Constants -- AM peak (7 to 9),(end > 6) & (end < 10),coef_arrival_constants_am_peak -util_arrival_constants_midday_1,Arrival Constants -- Midday 1 (10 to 12),(end > 9) & (end < 13),coef_arrival_constants_midday_1 -util_arrival_constants_midday_2,Arrival Constants -- Midday 2 (13 to 14),(end > 12) & (end < 15),coef_arrival_constants_midday_2 -util_arrival_constants_pm_peak_1,Arrival Constants -- PM peak 1 (15),end == 15,coef_arrival_constants_pm_peak_1 -util_arrival_constants_pm_peak_2,Arrival Constants -- PM peak 2 (16),end == 16,coef_arrival_constants_pm_peak_2 -util_arrival_constants_pm_peak_3,Arrival Constants -- PM peak 3 (17),end == 17,coef_arrival_constants_pm_peak_3 -util_arrival_constants_pm_peak_4,Arrival Constants -- PM peak 4 (18),end == 18,coef_arrival_constants_pm_peak_4 -util_arrival_constants_evening,Arrival Constants -- Evening (19 to 21),(end > 18) & (end < 22),coef_arrival_constants_evening -util_arrival_constants_late,Arrival Constants -- Late (22 and later),end > 21,coef_arrival_constants_late -util_duration_constants_0_to_2_hours,Duration Constants -- 0 to 2 hours,duration < 3,coef_duration_constants_0_to_2_hours -util_duration_constants_3_to_4_hours,Duration Constants -- 3 to 4 hours,(duration > 2) & (duration < 5),coef_duration_constants_3_to_4_hours -util_duration_constants_5_to_6_hours,Duration Constants -- 5 to 6 hours,(duration > 4) & (duration < 7),coef_duration_constants_5_to_6_hours -util_duration_constants_7_to_8_hours,Duration Constants -- 7 to 8 hours,(duration > 6) & (duration < 9),coef_duration_constants_7_to_8_hours -util_duration_constants_9_hours,Duration Constants -- 9 hours,duration == 9,coef_duration_constants_9_hours -util_duration_constants_10_hours,Duration Constants -- 10 hours,duration == 10,coef_duration_constants_10_hours -util_duration_constants_11_hours,Duration Constants -- 11 hours,duration == 11,coef_duration_constants_11_hours -util_duration_constants_12_to_13_hours,Duration Constants -- 12 to 13 hours,(duration > 11) & (duration < 14),coef_duration_constants_12_to_13_hours -util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,(duration > 13) & (duration < 19),coef_duration_constants_14_to_18_hours +Label,Description,Expression,Coefficient +util_free_flow_round_trip_auto_time_shift_effects_departure,Free-flow round trip auto time shift effects - departure,roundtrip_auto_time_to_work * start,coef_free_flow_round_trip_auto_time_shift_effects_departure +util_free_flow_round_trip_auto_time_shift_effects_duration,Free-flow round trip auto time shift effects - duration,roundtrip_auto_time_to_work * duration,coef_free_flow_round_trip_auto_time_shift_effects_duration +util_part_time_worker_departure_shift_effects,Part-time worker departure shift effects,(ptype == 2) * start,coef_part_time_worker_departure_shift_effects +util_non_working_adult_duration_shift_effects,Non-working adult duration shift effects,(ptype == 4) * duration,coef_non_working_adult_duration_shift_effects +util_university_student_departure_shift_effects,University student departure shift effects,(ptype == 3) * start,coef_university_student_departure_shift_effects +util_household_income_departure_shift_effects,Household income departure shift effects,income_in_thousands * start,coef_household_income_departure_shift_effects +util_destination_in_cbd_departure_shift_effects,Destination in CBD departure shift effects,workplace_in_cbd * start,coef_destination_in_cbd_departure_shift_effects +util_destination_in_cbd_duration_shift_effects,Destination in CBD duration shift effects,workplace_in_cbd * duration,coef_destination_in_cbd_duration_shift_effects +util_subsequent_tour_must_start_after_previous_tour_ends,Subsequent tour must start after previous tour ends,(tour_num > 1) & (start < end_previous),coef_subsequent_tour_must_start_after_previous_tour_ends +util_first_of_2plus_work_tours_departure_shift_effects,First of 2+ work tours departure shift effects,((tour_count>1) & (tour_num == 1)) * start,coef_first_of_2plus_work_tours_departure_shift_effects +util_first_of_2plus_work_tours_duration_shift_effects,First of 2+ work tours duration shift effects,((tour_count>1) & (tour_num == 1)) * duration,coef_first_of_2plus_work_tours_duration_shift_effects +util_subsequent_2plus_work_departure_tours_shift_effects,Subsequent 2+ work departure tours shift effects,(tour_num == 2) * start,coef_subsequent_2plus_work_departure_tours_shift_effects +util_subsequent_2plus_work_duration_tours_shift_effects,Subsequent 2+ work duration tours shift effects,(tour_num == 2) * duration,coef_subsequent_2plus_work_duration_tours_shift_effects +util_household_income_early_departure_interaction,Household income -- Early departure interaction,(income_in_thousands > 100) & (start < 6),coef_household_income_early_departure_interaction +util_household_income_late_arrival_interaction,Household income -- Late arrival interaction,(income_in_thousands > 100) & (end > 22),coef_household_income_late_arrival_interaction +util_destination_in_cbd_early_departure_interaction,Destination in CBD -- Early departure interaction,workplace_in_cbd & (start < 6),coef_destination_in_cbd_early_departure_interaction +util_destination_in_cbd_late_arrival_interaction,Destination in CBD -- Late arrival interaction,workplace_in_cbd & (end > 22),coef_destination_in_cbd_late_arrival_interaction +util_rural_household_early_departure_interaction,Rural household -- Early departure interaction,home_is_rural & (start < 6),coef_rural_household_early_departure_interaction +util_rural_household_late_arrival_interaction,Rural household -- Late arrival interaction,home_is_rural & (end > 22),coef_rural_household_late_arrival_interaction +util_full_time_worker_duration_lt_9_hours_interaction,Full-time worker -- duration < 9 hours interaction,(ptype == 1) & (duration < 9),coef_full_time_worker_duration_lt_9_hours_interaction +util_full_time_worker_10_to_12_departure_interaction,Full-time worker -- 10 to 12 departure interaction,(ptype == 1) & (start > 9) & (start < 13),coef_full_time_worker_10_to_12_departure_interaction +util_worker_13_to_15_arrival_interaction,Part-time worker -- 13 to 15 arrival interaction,(ptype == 2) & (end > 12) & (end < 16),coef_part_time_worker_13_to_15_arrival_interaction +util_first_of_2plus_work_tours_duration_lt_8_hrs,First of 2+ work tours- duration<8 hrs,((tour_count>1) & (tour_num == 1)) & (duration < 8),coef_first_of_2plus_work_tours_duration_lt_8_hrs +util_subsequent_of_2plus_work_tours_duration_lt_8_hrs,Subsequent of 2+ work tours- duration<8 hrs,(tour_num == 2) & (duration < 8),coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs +util_tours_by_worker_duration_lt_8_hrs,Work+school tours by worker- duration<8 hrs,(mandatory_tour_frequency == 'work_and_school') & is_worker & (duration < 8),coef_tours_by_worker_duration_lt_8_hrs +util_tours_by_student_duration_lt_8_hrs,School+work tours by student- duration<8 hrs,(mandatory_tour_frequency == 'work_and_school') & is_student & (duration < 8),coef_tours_by_student_duration_lt_8_hrs +#,,, +util_mode_choice_logsum,Mode Choice Logsum,mode_choice_logsum,coef_mode_choice_logsum +#,,, +#,FIXME - use temps _adjacent_window_before and _adjacent_window_after because timetable ops can be very time-consuming,, +util_dummy_adjacent_before,local temp variable,"_adjacent_window_before@tt.adjacent_window_before(df.person_id, df.start)",coef_dummy +util_dummy_adjacent_after,local temp variable,"_adjacent_window_after@tt.adjacent_window_after(df.person_id, df.end)",coef_dummy +util_previous_tour_ends_this_departure_hour,Previously-scheduled tour ends in this departure hour,"@tt.previous_tour_ends(df.person_id, df.start)",coef_previously_scheduled_tour_ends_in_this_departure_hour +util_previously_scheduled_tour_begins_in_this_arrival_hour,Previously-scheduled tour begins in this arrival hour,"@tt.previous_tour_begins(df.person_id, df.end)",coef_previously_scheduled_tour_begins_in_this_arrival_hour +util_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,Adjacent window exists before this departure hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,Adjacent window exists after this arrival hour - first tour interaction,"@(df.tour_count>1) & (df.tour_num == 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction +util_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,Adjacent window exists before this departure hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_before",coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction +util_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,Adjacent window exists after this arrival hour - second+ tour interaction,"@(df.tour_num > 1) & _adjacent_window_after",coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction +util_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours,Remaining work/school tours to be scheduled / number of unscheduled hours,"@((df.tour_count>1) & (df.tour_num == 1)) * 1.0 / tt.remaining_periods_available(df.person_id, df.start, df.end)",coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours +#,,, +util_departure_constants_early,Departure Constants -- Early (up to 5),start < 6,coef_departure_constants_early +util_departure_constants_am_peak_1,Departure Constants -- AM peak 1 (6),start == 6,coef_departure_constants_am_peak_1 +util_departure_constants_am_peak_2,Departure Constants -- AM peak 2 (7),start == 7,coef_departure_constants_am_peak_2 +util_departure_constants_am_peak_3,Departure Constants -- AM peak 3 (8),start == 8,coef_departure_constants_am_peak_3 +util_departure_constants_am_peak_4,Departure Constants -- AM peak 4 (9),start == 9,coef_departure_constants_am_peak_4 +util_departure_constants_midday_1,Departure Constants -- Midday 1 (10 to 12),(start > 9) & (start < 13),coef_departure_constants_midday_1 +util_departure_constants_midday_2,Departure Constants -- Midday 2 (13 to 15),(start > 12) & (start < 16),coef_departure_constants_midday_2 +util_departure_constants_pm_peak,Departure Constants -- PM peak (16 to 18),(start > 15) & (start < 19),coef_departure_constants_pm_peak +util_departure_constants_evening,Departure Constants -- Evening (19 to 21),(start > 18) & (start < 22),coef_departure_constants_evening +util_departure_constants_late,Departure Constants -- Late (22 and later),start > 21,coef_departure_constants_late +util_arrival_constants_early,Arrival Constants -- Early (up to 6),end < 7,coef_arrival_constants_early +util_arrival_constants_am_peak,Arrival Constants -- AM peak (7 to 9),(end > 6) & (end < 10),coef_arrival_constants_am_peak +util_arrival_constants_midday_1,Arrival Constants -- Midday 1 (10 to 12),(end > 9) & (end < 13),coef_arrival_constants_midday_1 +util_arrival_constants_midday_2,Arrival Constants -- Midday 2 (13 to 14),(end > 12) & (end < 15),coef_arrival_constants_midday_2 +util_arrival_constants_pm_peak_1,Arrival Constants -- PM peak 1 (15),end == 15,coef_arrival_constants_pm_peak_1 +util_arrival_constants_pm_peak_2,Arrival Constants -- PM peak 2 (16),end == 16,coef_arrival_constants_pm_peak_2 +util_arrival_constants_pm_peak_3,Arrival Constants -- PM peak 3 (17),end == 17,coef_arrival_constants_pm_peak_3 +util_arrival_constants_pm_peak_4,Arrival Constants -- PM peak 4 (18),end == 18,coef_arrival_constants_pm_peak_4 +util_arrival_constants_evening,Arrival Constants -- Evening (19 to 21),(end > 18) & (end < 22),coef_arrival_constants_evening +util_arrival_constants_late,Arrival Constants -- Late (22 and later),end > 21,coef_arrival_constants_late +util_duration_constants_0_to_2_hours,Duration Constants -- 0 to 2 hours,duration < 3,coef_duration_constants_0_to_2_hours +util_duration_constants_3_to_4_hours,Duration Constants -- 3 to 4 hours,(duration > 2) & (duration < 5),coef_duration_constants_3_to_4_hours +util_duration_constants_5_to_6_hours,Duration Constants -- 5 to 6 hours,(duration > 4) & (duration < 7),coef_duration_constants_5_to_6_hours +util_duration_constants_7_to_8_hours,Duration Constants -- 7 to 8 hours,(duration > 6) & (duration < 9),coef_duration_constants_7_to_8_hours +util_duration_constants_9_hours,Duration Constants -- 9 hours,duration == 9,coef_duration_constants_9_hours +util_duration_constants_10_hours,Duration Constants -- 10 hours,duration == 10,coef_duration_constants_10_hours +util_duration_constants_11_hours,Duration Constants -- 11 hours,duration == 11,coef_duration_constants_11_hours +util_duration_constants_12_to_13_hours,Duration Constants -- 12 to 13 hours,(duration > 11) & (duration < 14),coef_duration_constants_12_to_13_hours +util_duration_constants_14_to_18_hours,Duration Constants -- 14 to 18 hours,(duration > 13) & (duration < 19),coef_duration_constants_14_to_18_hours diff --git a/activitysim/examples/example_mtc/configs/tour_scheduling_work_coefficients.csv b/activitysim/examples/prototype_mtc/configs/tour_scheduling_work_coefficients.csv similarity index 98% rename from activitysim/examples/example_mtc/configs/tour_scheduling_work_coefficients.csv rename to activitysim/examples/prototype_mtc/configs/tour_scheduling_work_coefficients.csv index 7404512d1f..3b842e5351 100644 --- a/activitysim/examples/example_mtc/configs/tour_scheduling_work_coefficients.csv +++ b/activitysim/examples/prototype_mtc/configs/tour_scheduling_work_coefficients.csv @@ -1,65 +1,65 @@ -coefficient_name,value,constrain -coef_dummy,1,T -coef_free_flow_round_trip_auto_time_shift_effects_departure,-0.00114,F -coef_free_flow_round_trip_auto_time_shift_effects_duration,0.00221,F -coef_part_time_worker_departure_shift_effects,0.06736,F -coef_non_working_adult_duration_shift_effects,-0.1207,F -coef_university_student_departure_shift_effects,0.05747,F -coef_household_income_departure_shift_effects,0.000208,F -coef_destination_in_cbd_departure_shift_effects,0.04717,F -coef_destination_in_cbd_duration_shift_effects,0.08679,F -coef_subsequent_tour_must_start_after_previous_tour_ends,-100,T -coef_first_of_2plus_work_tours_departure_shift_effects,-0.3033,F -coef_first_of_2plus_work_tours_duration_shift_effects,-0.1861,F -coef_subsequent_2plus_work_departure_tours_shift_effects,-0.5381,F -coef_subsequent_2plus_work_duration_tours_shift_effects,-0.3174,F -coef_household_income_early_departure_interaction,-0.4854,F -coef_household_income_late_arrival_interaction,-0.3839,F -coef_destination_in_cbd_early_departure_interaction,-0.4566,F -coef_destination_in_cbd_late_arrival_interaction,-0.2334,F -coef_rural_household_early_departure_interaction,0.4039,F -coef_rural_household_late_arrival_interaction,-0.3451,F -coef_full_time_worker_duration_lt_9_hours_interaction,-1.257,F -coef_full_time_worker_10_to_12_departure_interaction,-0.5182,F -coef_part_time_worker_13_to_15_arrival_interaction,0.5433,F -coef_first_of_2plus_work_tours_duration_lt_8_hrs,1.98,F -coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs,2.582,F -coef_tours_by_worker_duration_lt_8_hrs,0.9126,F -coef_tours_by_student_duration_lt_8_hrs,2.582,F -coef_mode_choice_logsum,1.027,F -coef_previously_scheduled_tour_ends_in_this_departure_hour,-0.8935,F -coef_previously_scheduled_tour_begins_in_this_arrival_hour,-1.334,F -coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.1771,F -coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,0.3627,F -coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-0.2123,F -coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.1012,F -coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours,-18.68,F -coef_departure_constants_early,-0.95272527,F -coef_departure_constants_am_peak_1,-0.616180906,F -coef_departure_constants_am_peak_2,0,T -coef_departure_constants_am_peak_3,-0.254714726, -coef_departure_constants_am_peak_4,-1.251346024,F -coef_departure_constants_midday_1,-1.705868992,F -coef_departure_constants_midday_2,-1.693570583,F -coef_departure_constants_pm_peak,-1.439991962,F -coef_departure_constants_evening,-1.610513243,F -coef_departure_constants_late,-2.883415223,F -coef_arrival_constants_early,0,T -coef_arrival_constants_am_peak,-1.854520626,F -coef_arrival_constants_midday_1,-0.495972037,F -coef_arrival_constants_midday_2,-0.378554081,F -coef_arrival_constants_pm_peak_1,0,T -coef_arrival_constants_pm_peak_2,0.2760839,F -coef_arrival_constants_pm_peak_3,0.699587132,F -coef_arrival_constants_pm_peak_4,0.799289377,F -coef_arrival_constants_evening,0.103566251,F -coef_arrival_constants_late,-0.965957339,F -coef_duration_constants_0_to_2_hours,-2.52826639,F -coef_duration_constants_3_to_4_hours,-0.918974457,F -coef_duration_constants_5_to_6_hours,-0.718550288,F -coef_duration_constants_7_to_8_hours,-0.139623566,F -coef_duration_constants_9_hours,0.055706243,F -coef_duration_constants_10_hours,0,T -coef_duration_constants_11_hours,-0.347795391,F -coef_duration_constants_12_to_13_hours,-1.008222346,F +coefficient_name,value,constrain +coef_dummy,1,T +coef_free_flow_round_trip_auto_time_shift_effects_departure,-0.00114,F +coef_free_flow_round_trip_auto_time_shift_effects_duration,0.00221,F +coef_part_time_worker_departure_shift_effects,0.06736,F +coef_non_working_adult_duration_shift_effects,-0.1207,F +coef_university_student_departure_shift_effects,0.05747,F +coef_household_income_departure_shift_effects,0.000208,F +coef_destination_in_cbd_departure_shift_effects,0.04717,F +coef_destination_in_cbd_duration_shift_effects,0.08679,F +coef_subsequent_tour_must_start_after_previous_tour_ends,-100,T +coef_first_of_2plus_work_tours_departure_shift_effects,-0.3033,F +coef_first_of_2plus_work_tours_duration_shift_effects,-0.1861,F +coef_subsequent_2plus_work_departure_tours_shift_effects,-0.5381,F +coef_subsequent_2plus_work_duration_tours_shift_effects,-0.3174,F +coef_household_income_early_departure_interaction,-0.4854,F +coef_household_income_late_arrival_interaction,-0.3839,F +coef_destination_in_cbd_early_departure_interaction,-0.4566,F +coef_destination_in_cbd_late_arrival_interaction,-0.2334,F +coef_rural_household_early_departure_interaction,0.4039,F +coef_rural_household_late_arrival_interaction,-0.3451,F +coef_full_time_worker_duration_lt_9_hours_interaction,-1.257,F +coef_full_time_worker_10_to_12_departure_interaction,-0.5182,F +coef_part_time_worker_13_to_15_arrival_interaction,0.5433,F +coef_first_of_2plus_work_tours_duration_lt_8_hrs,1.98,F +coef_subsequent_of_2plus_work_tours_duration_lt_8_hrs,2.582,F +coef_tours_by_worker_duration_lt_8_hrs,0.9126,F +coef_tours_by_student_duration_lt_8_hrs,2.582,F +coef_mode_choice_logsum,1.027,F +coef_previously_scheduled_tour_ends_in_this_departure_hour,-0.8935,F +coef_previously_scheduled_tour_begins_in_this_arrival_hour,-1.334,F +coef_adjacent_window_exists_before_this_departure_hour_first_tour_interaction,0.1771,F +coef_adjacent_window_exists_after_this_arrival_hour_first_tour_interaction,0.3627,F +coef_adjacent_window_exists_before_this_departure_hour_second_plus_tour_interaction,-0.2123,F +coef_adjacent_window_exists_after_this_arrival_hour_second_plus_tour_interaction,-0.1012,F +coef_remaining_tours_to_be_scheduled_div_number_of_unscheduled_hours,-18.68,F +coef_departure_constants_early,-0.95272527,F +coef_departure_constants_am_peak_1,-0.616180906,F +coef_departure_constants_am_peak_2,0,T +coef_departure_constants_am_peak_3,-0.254714726, +coef_departure_constants_am_peak_4,-1.251346024,F +coef_departure_constants_midday_1,-1.705868992,F +coef_departure_constants_midday_2,-1.693570583,F +coef_departure_constants_pm_peak,-1.439991962,F +coef_departure_constants_evening,-1.610513243,F +coef_departure_constants_late,-2.883415223,F +coef_arrival_constants_early,0,T +coef_arrival_constants_am_peak,-1.854520626,F +coef_arrival_constants_midday_1,-0.495972037,F +coef_arrival_constants_midday_2,-0.378554081,F +coef_arrival_constants_pm_peak_1,0,T +coef_arrival_constants_pm_peak_2,0.2760839,F +coef_arrival_constants_pm_peak_3,0.699587132,F +coef_arrival_constants_pm_peak_4,0.799289377,F +coef_arrival_constants_evening,0.103566251,F +coef_arrival_constants_late,-0.965957339,F +coef_duration_constants_0_to_2_hours,-2.52826639,F +coef_duration_constants_3_to_4_hours,-0.918974457,F +coef_duration_constants_5_to_6_hours,-0.718550288,F +coef_duration_constants_7_to_8_hours,-0.139623566,F +coef_duration_constants_9_hours,0.055706243,F +coef_duration_constants_10_hours,0,T +coef_duration_constants_11_hours,-0.347795391,F +coef_duration_constants_12_to_13_hours,-1.008222346,F coef_duration_constants_14_to_18_hours,-1.701858847,F \ No newline at end of file diff --git a/activitysim/examples/example_mtc/configs/trip_destination.csv b/activitysim/examples/prototype_mtc/configs/trip_destination.csv similarity index 99% rename from activitysim/examples/example_mtc/configs/trip_destination.csv rename to activitysim/examples/prototype_mtc/configs/trip_destination.csv index f1cbdf0948..b12246c13f 100644 --- a/activitysim/examples/example_mtc/configs/trip_destination.csv +++ b/activitysim/examples/prototype_mtc/configs/trip_destination.csv @@ -1,19 +1,19 @@ -Label,Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork -local_dist_od,,_od_DIST@od_skims['DIST'],1,1,1,1,1,1,1,1,1,1 -local_dist_dp,,_dp_DIST@dp_skims['DIST'],1,1,1,1,1,1,1,1,1,1 -util_size_term,size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one -util_no_attractions,no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE -util_stop_zone_CDB_are_type,#stop zone CBD area type,"@reindex(land_use.area_type, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, -util_distance_inbound,distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (_od_DIST + _dp_DIST),coef_util_distance_work_outbound,coef_util_distance_univ,coef_util_distance_school,coef_util_distance_escort,coef_util_distance_shopping,coef_util_distance_eatout,coef_util_distance_othmaint,coef_util_distance_social,coef_util_distance_othdiscr,coef_util_distance_atwork -util_distance_outbound,distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (_od_DIST + _dp_DIST),coef_util_distance_work_inbound,coef_util_distance_univ,coef_util_distance_school,coef_util_distance_escort,coef_util_distance_shopping,coef_util_distance_eatout,coef_util_distance_othmaint,coef_util_distance_social,coef_util_distance_othdiscr,coef_util_distance_atwork -util_distance_joint,distance (calibration adjustment joint),@df.is_joint * (_od_DIST + _dp_DIST),,,,coef_distance_joint,coef_distance_joint,coef_distance_joint,coef_distance_joint,coef_distance_joint,coef_distance_joint, -util_prox_home_outbound,stop proximity to home (outbound),@df.outbound * _od_DIST,coef_prox_home_outbound_work,,,,,,,,, -util_prox_home_inbound,stop proximity to home (inbound),@~df.outbound * _dp_DIST,coef_prox_home_inbound_work,,,,,,,,, -util_prox_dest_outbound,stop proximity to main destination (outbound),@df.outbound * _dp_DIST,coef_prox_dest_outbound_work,,,,,,,,, -util_prox_dest_inbound,stop proximity to main destination (inbound),@~df.outbound * _od_DIST,,,,,,,,,, -#,,,,,,,,,,,, -util_sample_of_alternatives_correction_factor,"Sample of alternatives correction factor","@np.minimum(np.log(df.pick_count/df.prob), 60)",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one -util_mode_choice_logsum_os,Mode choice logsum from origin to stop,od_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum -util_stop_not_accessible_by_this_tour_mode,Can't access stop zone by this tour mode,(od_logsum < -100),coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE -util_mode_choice_logsum_sd,Mode choice logsum from stop to destination,dp_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum -util_dest_not_accessible_by_this_tour_mode,Can't access destination zone by this tour mode,(dp_logsum < -100),coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE +Label,Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork +local_dist_od,,_od_DIST@od_skims['DIST'],1,1,1,1,1,1,1,1,1,1 +local_dist_dp,,_dp_DIST@dp_skims['DIST'],1,1,1,1,1,1,1,1,1,1 +util_size_term,size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one +util_no_attractions,no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE +util_stop_zone_CDB_are_type,#stop zone CBD area type,"@reindex(land_use.area_type, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, +util_distance_inbound,distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (_od_DIST + _dp_DIST),coef_util_distance_work_outbound,coef_util_distance_univ,coef_util_distance_school,coef_util_distance_escort,coef_util_distance_shopping,coef_util_distance_eatout,coef_util_distance_othmaint,coef_util_distance_social,coef_util_distance_othdiscr,coef_util_distance_atwork +util_distance_outbound,distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (_od_DIST + _dp_DIST),coef_util_distance_work_inbound,coef_util_distance_univ,coef_util_distance_school,coef_util_distance_escort,coef_util_distance_shopping,coef_util_distance_eatout,coef_util_distance_othmaint,coef_util_distance_social,coef_util_distance_othdiscr,coef_util_distance_atwork +util_distance_joint,distance (calibration adjustment joint),@df.is_joint * (_od_DIST + _dp_DIST),,,,coef_distance_joint,coef_distance_joint,coef_distance_joint,coef_distance_joint,coef_distance_joint,coef_distance_joint, +util_prox_home_outbound,stop proximity to home (outbound),@df.outbound * _od_DIST,coef_prox_home_outbound_work,,,,,,,,, +util_prox_home_inbound,stop proximity to home (inbound),@~df.outbound * _dp_DIST,coef_prox_home_inbound_work,,,,,,,,, +util_prox_dest_outbound,stop proximity to main destination (outbound),@df.outbound * _dp_DIST,coef_prox_dest_outbound_work,,,,,,,,, +util_prox_dest_inbound,stop proximity to main destination (inbound),@~df.outbound * _od_DIST,,,,,,,,,, +#,,,,,,,,,,,, +util_sample_of_alternatives_correction_factor,"Sample of alternatives correction factor","@np.minimum(np.log(df.pick_count/df.prob), 60)",coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one,coef_one +util_mode_choice_logsum_os,Mode choice logsum from origin to stop,od_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum +util_stop_not_accessible_by_this_tour_mode,Can't access stop zone by this tour mode,(od_logsum < -100),coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE +util_mode_choice_logsum_sd,Mode choice logsum from stop to destination,dp_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum +util_dest_not_accessible_by_this_tour_mode,Can't access destination zone by this tour mode,(dp_logsum < -100),coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE,coef_UNAVAILABLE diff --git a/activitysim/examples/example_mtc/configs/trip_destination.yaml b/activitysim/examples/prototype_mtc/configs/trip_destination.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/trip_destination.yaml rename to activitysim/examples/prototype_mtc/configs/trip_destination.yaml diff --git a/activitysim/examples/example_semcog/configs/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/trip_destination_annotate_trips_preprocessor.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_semcog/configs/trip_destination_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_mtc/configs/trip_destination_annotate_trips_preprocessor.csv index e4317d8e82..1a1afb0748 --- a/activitysim/examples/example_semcog/configs/trip_destination_annotate_trips_preprocessor.csv +++ b/activitysim/examples/prototype_mtc/configs/trip_destination_annotate_trips_preprocessor.csv @@ -1,10 +1,10 @@ -Description,Target,Expression -#,, -,tour_mode,"reindex(tours.tour_mode, df.tour_id)" -,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id),reindex_i(tours.end, df.tour_id))" -,trip_period,network_los.skim_time_period_label(_tod) -,is_joint,"reindex(tours.tour_category, df.tour_id)=='joint'" -#,,not needed as school is not chosen as an intermediate trip destination -#,_grade_school,"(df.primary_purpose == 'school') & reindex(persons.is_gradeschool, df.person_id)" -#,size_segment,"df.primary_purpose.where(df.primary_purpose != 'school', np.where(_grade_school,'gradeschool', 'highschool'))" +Description,Target,Expression +#,, +,tour_mode,"reindex(tours.tour_mode, df.tour_id)" +,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id),reindex_i(tours.end, df.tour_id))" +,trip_period,network_los.skim_time_period_label(_tod) +,is_joint,"reindex(tours.tour_category, df.tour_id)=='joint'" +#,,not needed as school is not chosen as an intermediate trip destination +#,_grade_school,"(df.primary_purpose == 'school') & reindex(persons.is_gradeschool, df.person_id)" +#,size_segment,"df.primary_purpose.where(df.primary_purpose != 'school', np.where(_grade_school,'gradeschool', 'highschool'))" ,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id))" \ No newline at end of file diff --git a/activitysim/examples/example_mtc/configs/trip_destination_coefficients.csv b/activitysim/examples/prototype_mtc/configs/trip_destination_coefficients.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/trip_destination_coefficients.csv rename to activitysim/examples/prototype_mtc/configs/trip_destination_coefficients.csv diff --git a/activitysim/examples/example_mtc/configs/trip_destination_sample.csv b/activitysim/examples/prototype_mtc/configs/trip_destination_sample.csv similarity index 99% rename from activitysim/examples/example_mtc/configs/trip_destination_sample.csv rename to activitysim/examples/prototype_mtc/configs/trip_destination_sample.csv index 7a350c3d9c..857fb44c03 100644 --- a/activitysim/examples/example_mtc/configs/trip_destination_sample.csv +++ b/activitysim/examples/prototype_mtc/configs/trip_destination_sample.csv @@ -1,18 +1,18 @@ -Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork -,_od_DIST@od_skims['DIST'],1,1,1,1,1,1,1,1,1,1 -,_dp_DIST@dp_skims['DIST'],1,1,1,1,1,1,1,1,1,1 -Not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & (od_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & (dp_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (od_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (dp_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -#If transit tour is not in walk sub-zone it must be walkable,,,,,,,,,,, -size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",1,1,1,1,1,1,1,1,1,1 -no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -#stop zone CBD area type,"@reindex(land_use.area_type, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, -distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (_od_DIST + _dp_DIST),-0.04972591574229,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 -distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (_od_DIST + _dp_DIST),0.147813278663948,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 -distance (calibration adjustment joint),@df.is_joint * (_od_DIST + _dp_DIST),0,0,0,-0.1238,-0.1238,-0.1238,-0.1238,-0.1238,-0.123801985,0 -stop proximity to home (outbound),@df.outbound * _od_DIST,-0.3800,0,0,0,0,0,0,0,0,0 -stop proximity to home (inbound),@~df.outbound * _od_DIST,-0.1500,0,0,0,0,0,0,0,0,0 -stop proximity to main destination (outbound),@df.outbound * _dp_DIST,-0.26,,,,,,,,, -stop proximity to main destination (inbound),@~df.outbound * _od_DIST,0,,,,,,,,, +Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork +,_od_DIST@od_skims['DIST'],1,1,1,1,1,1,1,1,1,1 +,_dp_DIST@dp_skims['DIST'],1,1,1,1,1,1,1,1,1,1 +Not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & (od_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & (dp_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (od_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (dp_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +#If transit tour is not in walk sub-zone it must be walkable,,,,,,,,,,, +size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",1,1,1,1,1,1,1,1,1,1 +no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +#stop zone CBD area type,"@reindex(land_use.area_type, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, +distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (_od_DIST + _dp_DIST),-0.04972591574229,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 +distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (_od_DIST + _dp_DIST),0.147813278663948,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 +distance (calibration adjustment joint),@df.is_joint * (_od_DIST + _dp_DIST),0,0,0,-0.1238,-0.1238,-0.1238,-0.1238,-0.1238,-0.123801985,0 +stop proximity to home (outbound),@df.outbound * _od_DIST,-0.3800,0,0,0,0,0,0,0,0,0 +stop proximity to home (inbound),@~df.outbound * _od_DIST,-0.1500,0,0,0,0,0,0,0,0,0 +stop proximity to main destination (outbound),@df.outbound * _dp_DIST,-0.26,,,,,,,,, +stop proximity to main destination (inbound),@~df.outbound * _od_DIST,0,,,,,,,,, diff --git a/activitysim/examples/example_mtc/configs/trip_mode_choice.csv b/activitysim/examples/prototype_mtc/configs/trip_mode_choice.csv similarity index 99% rename from activitysim/examples/example_mtc/configs/trip_mode_choice.csv rename to activitysim/examples/prototype_mtc/configs/trip_mode_choice.csv index 2da242e956..3ac15254ba 100644 --- a/activitysim/examples/example_mtc/configs/trip_mode_choice.csv +++ b/activitysim/examples/prototype_mtc/configs/trip_mode_choice.csv @@ -1,405 +1,405 @@ -Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_LOC,WALK_LRF,WALK_EXP,WALK_HVY,WALK_COM,DRIVE_LOC,DRIVE_LRF,DRIVE_EXP,DRIVE_HVY,DRIVE_COM,TAXI,TNC_SINGLE,TNC_SHARED -#,Drive alone no toll,,,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,auto_ownership == 0,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Terminal_time,DRIVEALONEFREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['SOV_DIST'],coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost,coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOV_BTOLL'],coef_ivt,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEFREE_Person_is_between_16_and_19_years_old,DRIVEALONEFREE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da,,,,,,,,,,,,,,,,,,,, -#,Drive alone toll,,,,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,auto_ownership == 0,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,age < 16,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['SOVTOLL_TIME'],,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Terminal_time,DRIVEALONEPAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['SOVTOLL_DIST'],,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost,,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOVTOLL_BTOLL'],,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOVTOLL_VTOLL'],,coef_ivt,,,,,,,,,,,,,,,,,,, -util_DRIVEALONEPAY_Person_is_between_16_and_19_years_old,DRIVEALONEPAY - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),,coef_age1619_da,,,,,,,,,,,,,,,,,,, -#,Shared ride 2,,,,,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@odt_skims['HOV2_TIME'],,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Terminal_time,SHARED2FREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV2_DIST'],,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2_BTOLL'] / costShareSr2,,,coef_ivt,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_One_person_household,SHARED2FREE - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr,,,,,,,,,,,,,,,,,, -util_SHARED2FREE_Person_is_16_years_old_or_older,SHARED2FREE - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr,,,,,,,,,,,,,,,,,, -#,Shared ride 2 toll,,,,,,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,,,,,,,,,, -util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@odt_skims['HOV2TOLL_TIME'],,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Terminal_time,SHARED2PAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV2TOLL_DIST'],,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_BTOLL'] / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'] / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, -util_SHARED2PAY_One_person_household,SHARED2PAY - One person household,@(df.hhsize == 1),,,,coef_hhsize1_sr,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.hhsize == 2),,,,coef_hhsize2_sr,,,,,,,,,,,,,,,,, -util_SHARED2PAY_Person_is_16_years_old_or_older,SHARED2PAY - Person is 16 years old or older,@(df.age >= 16),,,,coef_age16p_sr,,,,,,,,,,,,,,,,, -#,Shared ride 3+,,,,,,,,,,,,,,,,,,,,,, -util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,,,,,,,,,, -util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@odt_skims['HOV3_TIME'],,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_Terminal_time,SHARED3FREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV3_DIST'],,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3_BTOLL'] / costShareSr3,,,,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED3FREE_One_person_household,SHARED3FREE - One person household,@(df.hhsize == 1),,,,,coef_hhsize1_sr,,,,,,,,,,,,,,,, -util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.hhsize == 2),,,,,coef_hhsize2_sr,,,,,,,,,,,,,,,, -util_SHARED3FREE_Person_is_16_years_old_or_older,SHARED3FREE - Person is 16 years old or older,@(df.age >= 16),,,,,coef_age16p_sr,,,,,,,,,,,,,,,, -#,Shared ride 3+ toll,,,,,,,,,,,,,,,,,,,,,, -util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,,,,,,,,,, -util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@odt_skims['HOV3TOLL_TIME'],,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Terminal_time,SHARED3PAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV3TOLL_DIST'],,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3TOLL_BTOLL'] / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3TOLL_VTOLL'] / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3PAY_One_person_household,SHARED3PAY - One person household,@(df.hhsize == 1),,,,,,coef_hhsize1_sr,,,,,,,,,,,,,,, -util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.hhsize == 2),,,,,,coef_hhsize2_sr,,,,,,,,,,,,,,, -util_SHARED3PAY_Person_is_16_years_old_or_older,SHARED3PAY - Person is 16 years old or older,@(df.age >= 16),,,,,,coef_age16p_sr,,,,,,,,,,,,,,, -#,Walk,,,,,,,,,,,,,,,,,,,,,, -util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@coef_walktimeshort_multiplier * od_skims['DISTWALK'].clip(upper=walkThresh) * 60/walkSpeed,,,,,,,coef_ivt,,,,,,,,,,,,,, -util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * (od_skims['DISTWALK'] - walkThresh).clip(lower=0) * 60/walkSpeed,,,,,,,coef_ivt,,,,,,,,,,,,,, -util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,coef_ivt,,,,,,,,,,,,,, -util_WALK_Topology,WALK - Topology,@topology_walk_multiplier * df.trip_topology,,,,,,,coef_ivt,,,,,,,,,,,,,, -#,Bike,,,,,,,,,,,,,,,,,,,,,, -util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,,,,,,,,,, -util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@coef_biketimeshort_multiplier * od_skims['DISTBIKE'].clip(upper=bikeThresh)*60/bikeSpeed,,,,,,,,coef_ivt,,,,,,,,,,,,, -util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@coef_biketimeshort_multiplier * biketimelong_multiplier * (od_skims['DISTBIKE']-bikeThresh).clip(lower=0)*60/bikeSpeed,,,,,,,,coef_ivt,,,,,,,,,,,,, -util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier*df.density_index,,,,,,,,coef_ivt,,,,,,,,,,,,, -util_BIKE_Topology,BIKE - Topology,@topology_bike_multiplier * df.trip_topology,,,,,,,,coef_ivt,,,,,,,,,,,,, -#,Walk to Local,,,,,,,,,,,,,,,,,,,,,, -util_WALK_LOC_Unavailable,WALK_LOC - Unavailable,walk_local_available == False,,,,,,,,,-999,,,,,,,,,,,, -util_WALK_LOC_In_vehicle_time,WALK_LOC - In-vehicle time,@odt_skims['WLK_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Short_iwait_time,WALK_LOC - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Long_iwait_time,WALK_LOC - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_transfer_wait_time,WALK_LOC - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_number_of_transfers,WALK_LOC - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_LOC_WLK_BOARDS']-1).clip(0),,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Walk_egress_time,WALK_LOC - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Walk_other_time,WALK_LOC - Walk other time,@coef_waux_multiplier * odt_skims['WLK_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Fare,WALK_LOC - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_LOC_WLK_FAR'],,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Destination_zone_densityIndex,WALK_LOC - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Topology,WALK_LOC - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Person_is_less_than_10_years_old,WALK_LOC - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,coef_age010_trn,,,,,,,,,,,, -#,Walk to Light rail/Ferry,,,,,,,,,,,,,,,,,,,,,, -util_WALK_LRF_Unavailable,WALK_LRF - Unavailable,walk_lrf_available == False,,,,,,,,,,-999,,,,,,,,,,, -util_WALK_LRF_In_vehicle_time,WALK_LRF - In-vehicle time,@odt_skims['WLK_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_In_vehicle_time_on_Light_Rail,WALK_LRF - In-vehicle time on Light Rail (incremental w/ ivt),@(coef_ivt_lrt_multiplier-1) * odt_skims['WLK_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_In_vehicle_time_on_Ferry,WALK_LRF - In-vehicle time on Ferry (incremental w/keyivt),@(coef_ivt_ferry_multiplier-coef_ivt_lrt_multiplier) * odt_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Short_iwait_time,WALK_LRF - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Long_iwait_time,WALK_LRF - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_transfer_wait_time,WALK_LRF - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_number_of_transfers,WALK_LRF - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_LRF_WLK_BOARDS']-1).clip(0),,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Walk_access_time,WALK_LRF - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Walk_egress_time,WALK_LRF - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Walk_other_time,WALK_LRF - Walk otherLight rail/Ferry time,@coef_waux_multiplier * odt_skims['WLK_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Fare,WALK_LRF - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_LRF_WLK_FAR'],,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Destination_zone_densityIndex,WALK_LRF - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Topology,WALK_LRF - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_LRF_Person_is_less_than_10_years_old,WALK_LRF - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,coef_age010_trn,,,,,,,,,,, -#,Walk to Express bus,,,,,,,,,,,,,,,,,,,,,, -util_WALK_EXP_Unavailable,WALK_EXP - Unavailable,walk_express_available == False,,,,,,,,,,,-999,,,,,,,,,, -util_WALK_EXP_In_vehicle_time,WALK_EXP - In-vehicle time,@odt_skims['WLK_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_In_vehicle_time_on_Express_bus,WALK_EXP - In-vehicle time on Express bus (incremental w/ ivt),@(ivt_exp_multiplier - 1) * odt_skims['WLK_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Short_iwait_time,WALK_EXP - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Long_iwait_time,WALK_EXP - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_transfer_wait_time,WALK_EXP - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_number_of_transfers,WALK_EXP - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_EXP_WLK_BOARDS']-1).clip(0),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Walk_access_time,WALK_EXP - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Walk_egress_time,WALK_EXP - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Walk_other_time,WALK_EXP - Walk other time,@coef_waux_multiplier * odt_skims['WLK_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Fare,WALK_EXP - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_EXP_WLK_FAR'],,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Destination_zone_densityIndex,WALK_EXP - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Topology,WALK_EXP - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_EXP_Person_is_less_than_10_years_old,WALK_EXP - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,,coef_age010_trn,,,,,,,,,, -#,Walk to Heavy Rail,,,,,,,,,,,,,,,,,,,,,, -util_WALK_HVY_Unavailable,WALK_HVY - Unavailable,walk_heavyrail_available == False,,,,,,,,,,,,-999,,,,,,,,, -util_WALK_HVY_In_vehicle_time,WALK_HVY - In-vehicle time,@odt_skims['WLK_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_In_vehicle_time_on_heavy_rail,WALK_HVY - In-vehicle time on heavy rail (incremental w/ ivt),@(ivt_hvy_multiplier-1) * odt_skims['WLK_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Short_iwait_time,WALK_HVY - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Long_iwait_time,WALK_HVY - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_transfer_wait_time,WALK_HVY - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_number_of_transfers,WALK_HVY - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_HVY_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Walk_access_time,WALK_HVY - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Walk_egress_time,WALK_HVY - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Walk_other_time,WALK_HVY - Walk other time,@coef_waux_multiplier * odt_skims['WLK_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Fare,WALK_HVY - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_HVY_WLK_FAR'],,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Destination_zone_densityIndex,WALK_HVY - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Topology,WALK_HVY - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,coef_ivt,,,,,,,,, -util_WALK_HVY_Person_is_less_than_10_years_old,WALK_HVY - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,,,coef_age010_trn,,,,,,,,, -#,Walk to Commuter rail,,,,,,,,,,,,,,,,,,,,,, -util_WALK_COM_Unavailable,WALK_COM - Unavailable,walk_commuter_available == False,,,,,,,,,,,,,-999,,,,,,,, -util_WALK_COM_In_vehicle_time,WALK_COM - In-vehicle time,@odt_skims['WLK_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_In_vehicle_time_on_commuter_rail,WALK_COM - In-vehicle time on commuter rail (incremental w/ ivt),@(ivt_com_multiplier - 1) * odt_skims['WLK_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Short_iwait_time,WALK_COM - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Long_iwait_time,WALK_COM - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_transfer_wait_time,WALK_COM - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_number_of_transfers,WALK_COM - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_COM_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Walk_access_time,WALK_COM - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Walk_egress_time,WALK_COM - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Walk_other_time,WALK_COM - Walk other time,@coef_waux_multiplier * odt_skims['WLK_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Fare,WALK_COM - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_COM_WLK_FAR'],,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Destination_zone_densityIndex,WALK_COM - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Topology,WALK_COM - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,coef_ivt,,,,,,,, -util_WALK_COM_Person_is_less_than_10_years_old,WALK_COM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,coef_age010_trn,,,,,,,, -#,Drive to Local,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_LOC_Unavailable_for_zero_auto_households,DRIVE_LOC - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,-999,,,,,,, -util_DRIVE_LOC_Unavailable_for_persons_less_than_16,DRIVE_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,-999,,,,,,, -util_DRIVE_LOC_Destination_zone_densityIndex,DRIVE_LOC - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Topology,DRIVE_LOC - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_Person_is_less_than_10_years_old,DRIVE_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,coef_age010_trn,,,,,,, -util_DRIVE_LOC_outbound_Unavailable,DRIVE_LOC outbound - Unavailable,outbound & ~drive_local_available_outbound,,,,,,,,,,,,,,-999,,,,,,, -util_DRIVE_LOC_outbound_In_vehicle_time,DRIVE_LOC outbound - In-vehicle time,@df.outbound * odt_skims['DRV_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_Short_iwait_time,DRIVE_LOC outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_Long_iwait_time,DRIVE_LOC outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_transfer_wait_time,DRIVE_LOC outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_number_of_transfers,DRIVE_LOC outbound - number of transfers,@df.outbound * xfers_wlk_multiplier * (odt_skims['DRV_LOC_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_Drive_time,DRIVE_LOC outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_LOC_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_Walk_egress_time,DRIVE_LOC outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_Walk_other_time,DRIVE_LOC outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_Fare_and_operating_cost,DRIVE_LOC outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_LOC_WLK_FAR'] + costPerMile*odt_skims['DRV_LOC_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LOC outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_LOC_WLK_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']),,,,,,,,,,,,,,1,,,,,,, -util_DRIVE_LOC_inbound_Unavailable,DRIVE_LOC inbound - Unavailable,inbound & ~drive_local_available_inbound,,,,,,,,,,,,,,-999,,,,,,, -util_DRIVE_LOC_inbound_In_vehicle_time,DRIVE_LOC inbound - In-vehicle time,@df.inbound * odt_skims['WLK_LOC_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_Short_iwait_time,DRIVE_LOC inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_LOC_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_Long_iwait_time,DRIVE_LOC inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_LOC_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_transfer_wait_time,DRIVE_LOC inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_LOC_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_number_of_transfers,DRIVE_LOC inbound - number of transfers,@df.inbound * xfers_wlk_multiplier * (odt_skims['WLK_LOC_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_Drive_time,DRIVE_LOC inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WLK_LOC_DRV_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_Walk_access_time,DRIVE_LOC inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_Walk_other_time,DRIVE_LOC inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WLK_LOC_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_Fare_and_operating_cost,DRIVE_LOC inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_LOC_DRV_FAR'] + costPerMile*odt_skims['WLK_LOC_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, -util_DRIVE_LOC_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LOC inbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['WLK_LOC_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']),,,,,,,,,,,,,,1,,,,,,, -#,Drive to Light Rail/Ferry,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_LRF_Unavailable_for_zero_auto_households,DRIVE_LRF - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,-999,,,,,, -util_DRIVE_LRF_Unavailable_for_persons_less_than_16,DRIVE_LRF - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,-999,,,,,, -util_DRIVE_LRF_Destination_zone_densityIndex,DRIVE_LRF - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Topology,DRIVE_LRF - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_Person_is_less_than_10_years_old,DRIVE_LRF - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,coef_age010_trn,,,,,, -util_DRIVE_LRF_outbound_Unavailable,DRIVE_LRF outbound - Unavailable,outbound & ~drive_lrf_available_outbound,,,,,,,,,,,,,,,-999,,,,,, -util_DRIVE_LRF_outbound_In_vehicle_time,DRIVE_LRF outbound - In-vehicle time,@df.outbound * odt_skims['DRV_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_In_vehicle_time_on_LR,DRIVE_LRF outbound - In-vehicle time on Light Rail (incremental w/ ivt),@df.outbound * (coef_ivt_lrt_multiplier - 1)*odt_skims['DRV_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_In_vehicle_time_on_Ferry,DRIVE_LRF outbound - In-vehicle time on Ferry (incremental w/ keyivt),@df.outbound * (coef_ivt_ferry_multiplier-coef_ivt_lrt_multiplier)*odt_skims['DRV_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_Short_iwait_time,DRIVE_LRF outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_Long_iwait_time,DRIVE_LRF outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_transfer_wait_time,DRIVE_LRF outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_number_of_transfers,DRIVE_LRF outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DRV_LRF_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_Drive_time,DRIVE_LRF outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_LRF_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_Walk_egress_time,DRIVE_LRF outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_Walk_other_time,DRIVE_LRF outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_Fare_and_operating_cost,DRIVE_LRF outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_LRF_WLK_FAR'] + costPerMile * odt_skims['DRV_LRF_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LRF outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_LRF_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,1,,,,,, -util_DRIVE_LRF_inbound_Unavailable,DRIVE_LRF inbound - Unavailable,inbound & ~drive_lrf_available_inbound,,,,,,,,,,,,,,,-999,,,,,, -util_DRIVE_LRF_inbound_In_vehicle_time,DRIVE_LRF inbound - In-vehicle time,@df.inbound * odt_skims['WLK_LRF_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_In_vehicle_time_on_LR,DRIVE_LRF inbound - In-vehicle time on Light Rail (incremental w/ ivt),@df.inbound * (coef_ivt_lrt_multiplier - 1)*odt_skims['WLK_LRF_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_In_vehicle_time_on_Ferry,DRIVE_LRF inbound - In-vehicle time on Ferry (incremental w/ keyivt),@df.inbound * (coef_ivt_ferry_multiplier-coef_ivt_lrt_multiplier)*odt_skims['WLK_LRF_DRV_FERRYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_Short_iwait_time,DRIVE_LRF inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_LRF_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_Long_iwait_time,DRIVE_LRF inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_LRF_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_transfer_wait_time,DRIVE_LRF inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_LRF_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_number_of_transfers,DRIVE_LRF inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WLK_LRF_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_Drive_time,DRIVE_LRF inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WLK_LRF_DRV_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_Walk_access_time,DRIVE_LRF inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_Walk_other_time,DRIVE_LRF inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WLK_LRF_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_Fare_and_operating_cost,DRIVE_LRF inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_LRF_DRV_FAR'] + costPerMile * odt_skims['WLK_LRF_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, -util_DRIVE_LRF_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LRF inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WLK_LRF_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ od_skims['DIST'],,,,,,,,,,,,,,,1,,,,,, -#,Drive to Express bus,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_EXP_Unavailable_for_zero_auto_households,DRIVE_EXP - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,-999,,,,, -util_DRIVE_EXP_Unavailable_for_persons_less_than_16,DRIVE_EXP - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,-999,,,,, -util_DRIVE_EXP_Destination_zone_densityIndex,DRIVE_EXP - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Topology,DRIVE_EXP - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_Person_is_less_than_10_years_old,DRIVE_EXP - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,coef_age010_trn,,,,, -util_DRIVE_EXP_outbound_Unavailable,DRIVE_EXP outbound - Unavailable,outbound & ~drive_express_available_outbound,,,,,,,,,,,,,,,,-999,,,,, -util_DRIVE_EXP_outbound_In_vehicle_time,DRIVE_EXP outbound - In-vehicle time,@df.outbound * odt_skims['DRV_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_In_vehicle_time_on_EXP,DRIVE_EXP outbound - In-vehicle time on Express bus (incremental w/ ivt),@df.outbound * (ivt_exp_multiplier - 1) * odt_skims['DRV_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_Short_iwait_time,DRIVE_EXP outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_Long_iwait_time,DRIVE_EXP outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_transfer_wait_time,DRIVE_EXP outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_number_of_transfers,DRIVE_EXP outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DRV_EXP_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_Drive_time,DRIVE_EXP outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_EXP_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_Walk_egress_time,DRIVE_EXP outbound - Walk egress ime,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_Walk_other_time,DRIVE_EXP outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_Fare_and_operating_cost,DRIVE_EXP outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_EXP_WLK_FAR'] + costPerMile * odt_skims['DRV_EXP_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_EXP outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_EXP_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_Unavailable,DRIVE_EXP inbound - Unavailable,inbound & ~drive_express_available_inbound,,,,,,,,,,,,,,,,-999,,,,, -util_DRIVE_EXP_inbound_In_vehicle_time,DRIVE_EXP inbound - In-vehicle time,@df.inbound * odt_skims['WLK_EXP_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_In_vehicle_time_on_EXP,DRIVE_EXP inbound - In-vehicle time on Express bus (incremental w/ ivt),@df.inbound * (ivt_exp_multiplier - 1) * odt_skims['WLK_EXP_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_Short_iwait_time,DRIVE_EXP inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_EXP_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_Long_iwait_time,DRIVE_EXP inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_EXP_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_transfer_wait_time,DRIVE_EXP inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_EXP_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_number_of_transfers,DRIVE_EXP inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WLK_EXP_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_Drive_time,DRIVE_EXP inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WLK_EXP_DRV_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_Walk_access_time,DRIVE_EXP inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_Walk_other_time,DRIVE_EXP inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WLK_EXP_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_Fare_and_operating_cost,DRIVE_EXP inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_EXP_DRV_FAR'] + costPerMile * odt_skims['WLK_EXP_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, -util_DRIVE_EXP_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_EXP inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WLK_EXP_DRV_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,1,,,,, -#,Drive to Heavy Rail,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_HVY_Unavailable_for_zero_auto_households,DRIVE_HVY - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,,-999,,,, -util_DRIVE_HVY_Unavailable_for_persons_less_than_16,DRIVE_HVY - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,-999,,,, -util_DRIVE_HVY_Destination_zone_densityIndex,DRIVE_HVY - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Topology,DRIVE_HVY - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_Person_is_less_than_10_years_old,DRIVE_HVY - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,,coef_age010_trn,,,, -util_DRIVE_HVY_outbound_Unavailable,DRIVE_HVY outbound - Unavailable,outbound & ~drive_heavyrail_available_outbound,,,,,,,,,,,,,,,,,-999,,,, -util_DRIVE_HVY_outbound_In_vehicle_time,DRIVE_HVY outbound - In-vehicle time,@df.outbound * odt_skims['DRV_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_In_vehicle_time_on_HVY,DRIVE_HVY outbound - In-vehicle time on heavy rail (incremental w/ ivt),@df.outbound * (ivt_hvy_multiplier - 1) * odt_skims['DRV_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_Short_iwait_time,DRIVE_HVY outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_Long_iwait_time,DRIVE_HVY outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_transfer_wait_time,DRIVE_HVY outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_number_of_transfers,DRIVE_HVY outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DRV_HVY_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_Drive_time,DRIVE_HVY outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_HVY_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_Walk_egress_time,DRIVE_HVY outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_Walk_other_time,DRIVE_HVY outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_Fare_and_operating_cost,DRIVE_HVY outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_HVY_WLK_FAR'] + costPerMile * odt_skims['DRV_HVY_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_HVY outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_HVY_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_Unavailable,DRIVE_HVY inbound - Unavailable,inbound & ~drive_heavyrail_available_inbound,,,,,,,,,,,,,,,,,-999,,,, -util_DRIVE_HVY_inbound_In_vehicle_time,DRIVE_HVY inbound - In-vehicle time,@df.inbound * odt_skims['WLK_HVY_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_In_vehicle_time_on_HVY,DRIVE_HVY inbound - In-vehicle time on heavy rail (incremental w/ ivt),@df.inbound * (ivt_hvy_multiplier - 1) * odt_skims['WLK_HVY_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_Short_iwait_time,DRIVE_HVY inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_HVY_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_Long_iwait_time,DRIVE_HVY inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_HVY_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_transfer_wait_time,DRIVE_HVY inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_HVY_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_number_of_transfers,DRIVE_HVY inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WLK_HVY_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_Drive_time,DRIVE_HVY inbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_HVY_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_Walk_access_time,DRIVE_HVY inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_Walk_other_time,DRIVE_HVY inbound - Walk other time,@coef_waux_multiplier * odt_skims['WLK_HVY_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_Fare_and_operating_cost,DRIVE_HVY inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_HVY_DRV_FAR'] + costPerMile * odt_skims['WLK_HVY_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, -util_DRIVE_HVY_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_HVY inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WLK_HVY_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ od_skims['DIST'],,,,,,,,,,,,,,,,,1,,,, -#,#Drive to Commuter Rail,,,,,,,,,,,,,,,,,,,,,, -util_DRIVE_COM_Unavailable_for_zero_auto_households,DRIVE_COM - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,,,-999,,, -util_DRIVE_COM_Unavailable_for_persons_less_than_16,DRIVE_COM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,,-999,,, -util_DRIVE_COM_Destination_zone_densityIndex,DRIVE_COM - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Topology,DRIVE_COM - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_Person_is_less_than_10_years_old,DRIVE_COM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,,,coef_age010_trn,,, -util_DRIVE_COM_outbound_Unavailable,DRIVE_COM outbound - Unavailable,outbound & ~drive_commuter_available_outbound,,,,,,,,,,,,,,,,,,-999,,, -util_DRIVE_COM_outbound_In_vehicle_time,DRIVE_COM outbound - In-vehicle time,@df.outbound * odt_skims['DRV_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_In_vehicle_time_on_COM,DRIVE_COM outbound - In-vehicle time on commuter rail (incremental w/ ivt),@df.outbound * (ivt_com_multiplier - 1) * odt_skims['DRV_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_Short_iwait_time,DRIVE_COM outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_Long_iwait_time,DRIVE_COM outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_transfer_wait_time,DRIVE_COM outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_number_of_transfers,DRIVE_COM outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DRV_COM_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_Drive_time,DRIVE_COM outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_COM_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_Walk_egress_time,DRIVE_COM outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_Walk_other_time,DRIVE_COM outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_Fare_and_operating_cost,DRIVE_COM outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_COM_WLK_FAR'] + costPerMile * odt_skims['DRV_COM_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_COM outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_COM_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,,,1,,, -util_DRIVE_COM_inbound_Unavailable,DRIVE_COM inbound - Unavailable,inbound & ~drive_commuter_available_inbound,,,,,,,,,,,,,,,,,,-999,,, -util_DRIVE_COM_inbound_In_vehicle_time,DRIVE_COM inbound - In-vehicle time,@df.inbound * odt_skims['WLK_COM_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_In_vehicle_time_on_COM,DRIVE_COM inbound - In-vehicle time on commuter rail (incremental w/ ivt),@df.inbound * (ivt_com_multiplier - 1) * odt_skims['WLK_COM_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_Short_iwait_time,DRIVE_COM inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_COM_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_Long_iwait_time,DRIVE_COM inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_COM_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_transfer_wait_time,DRIVE_COM inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_COM_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_number_of_transfers,DRIVE_COM inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WLK_COM_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_Drive_time,DRIVE_COM inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WLK_COM_DRV_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_Walk_access_time,DRIVE_COM inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_Walk_other_time,DRIVE_COM inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WLK_COM_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_Fare_and_operating_cost,DRIVE_COM inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_COM_DRV_FAR'] + costPerMile * odt_skims['WLK_COM_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, -util_DRIVE_COM_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_COM inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WLK_COM_DRV_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,,,1,,, -#,Taxi,,,,,,,,,,,,,,,,,,,,,, -util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@odt_skims['HOV2TOLL_TIME'],,,,,,,,,,,,,,,,,,,coef_ivt,, -util_Taxi_Wait_time,Taxi - Wait time,@ridehail_wait_time_multiplier * df.origTaxiWaitTime,,,,,,,,,,,,,,,,,,,coef_ivt,, -util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,,,,,,,,coef_ivt,, -util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_BTOLL'],,,,,,,,,,,,,,,,,,,coef_ivt,, -util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare + odt_skims['HOV2TOLL_DIST'] * Taxi_costPerMile + odt_skims['HOV2TOLL_TIME'] * Taxi_costPerMinute)*100,,,,,,,,,,,,,,,,,,,coef_ivt,, -#,TNC Single,,,,,,,,,,,,,,,,,,,,,, -util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@odt_skims['HOV2TOLL_TIME'] ,,,,,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Wait_time,TNC Single - Wait time,@ridehail_wait_time_multiplier * df.origSingleTNCWaitTime,,,,,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare + odt_skims['HOV2TOLL_DIST'] * TNC_single_costPerMile + odt_skims['HOV2TOLL_TIME'] * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,coef_ivt, -#,#TNC Shared,,,,,,,,,,,,,,,,,,,,,, -util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@odt_skims['HOV2TOLL_TIME'] * TNC_shared_IVTFactor,,,,,,,,,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Wait_time,TNC Shared - Wait time,@ridehail_wait_time_multiplier * df.origSharedTNCWaitTime,,,,,,,,,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare + odt_skims['HOV2TOLL_DIST'] * TNC_shared_costPerMile + odt_skims['HOV2TOLL_TIME']* TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,,coef_ivt -#,,,,,,,,,,,,,,,,,,,,,,, -util_tour_mode_is_auto,Auto tour mode availability,tour_mode_is_auto,,,,,,,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, -util_tour_mode_is_walk,Walk tour mode availability,tour_mode_is_walk,-999,-999,-999,-999,-999,-999,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, -util_tour_mode_is_bike,Bike tour mode availability,tour_mode_is_bike,-999,-999,-999,-999,-999,-999,,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, -util_tour_mode_is_walk_transit,Walk to Transit tour mode availability,tour_mode_is_walk_transit,-999,-999,,,,,,-999,,,,,,-999,-999,-999,-999,-999,,, -util_tour_mode_is_drive_transit,Drive to Transit tour modes availability,tour_mode_is_drive_transit,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,,,,,,, -util_tour_mode_is_ride_hail,Ride hail tour modes availability,tour_mode_is_ride_hail,-999,-999,,,,,,-999,,,,,,-999,-999,-999,-999,-999,,, -,#indiv tour ASCs,,,,,,,,,,,,,,,,,,,,,, -util_Drive_Alone_tour_mode_ASC_shared_ride_2_df_is_indiv,Drive Alone tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,sov_ASC_sr2,sov_ASC_sr2,,,,,,,,,,,,,,,,, -util_Drive_Alone_tour_mode_ASC_shared_ride_3_plus,Drive Alone tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,sov_ASC_sr3p,sov_ASC_sr3p,,,,,,,,,,,,,,, -util_Drive_Alone_tour_mode_ASC_walk,Drive Alone tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,sov_ASC_walk,,,,,,,,,,,,,, -util_Drive_Alone_tour_mode_ASC_ride_hail,Drive Alone tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,,,,,,,,,,,,,sov_ASC_rh,sov_ASC_rh,sov_ASC_rh -util_Shared_Ride_2_tour_mode_ASC_shared_ride_2,Shared Ride 2 tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,sr2_ASC_sr2,sr2_ASC_sr2,,,,,,,,,,,,,,,,, -util_Shared_Ride_2_tour_mode_ASC_shared_ride_3_plus,Shared Ride 2 tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,sr2_ASC_sr3p,sr2_ASC_sr3p,,,,,,,,,,,,,,, -util_Shared_Ride_2_tour_mode_ASC_walk,Shared Ride 2 tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,sr2_ASC_walk,,,,,,,,,,,,,, -util_Shared_Ride_2_tour_mode_ASC_ride_hail,Shared Ride 2 tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,,,,,,,,,,,,,sr2_ASC_rh,sr2_ASC_rh,sr2_ASC_rh -util_Shared_Ride_3_tour_mode_ASC_shared_ride_2,Shared Ride 3+ tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,sr3p_ASC_sr2,sr3p_ASC_sr2,,,,,,,,,,,,,,,,, -util_Shared_Ride_3_tour_mode_ASC_shared_ride_3_plus,Shared Ride 3+ tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,sr3p_ASC_sr3p,sr3p_ASC_sr3p,,,,,,,,,,,,,,, -util_Shared_Ride_3_tour_mode_ASC_walk,Shared Ride 3+ tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,sr3p_ASC_walk,,,,,,,,,,,,,, -util_Shared_Ride_3_tour_mode_ASC_ride_hail,Shared Ride 3+ tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,,,,,,,,,,,,,sr3p_ASC_rh,sr3p_ASC_rh,sr3p_ASC_rh -util_Walk_tour_mode_ASC_ride_hail,Walk tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_WALK_MODE),,,,,,,,,,,,,,,,,,,walk_ASC_rh,walk_ASC_rh,walk_ASC_rh -util_Bike_tour_mode_ASC_walk,Bike tour mode ASC -- walk,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,,,,bike_ASC_walk,,,,,,,,,,,,,, -util_Bike_tour_mode_ASC_ride_hail,Bike tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,,,,bike_ASC_rh,bike_ASC_rh,bike_ASC_rh -util_Walk_to_Transit_tour_mode_ASC_light_rail,Walk to Transit tour mode ASC -- light rail,@(df.is_indiv & df.tour_mode_is_walk_transit & ~df.walk_ferry_available),,,,,,,,,,walk_transit_ASC_lightrail,,,,,,,,,,, -util_Walk_to_Transit_tour_mode_ASC_ferry,Walk to Transit tour mode ASC -- ferry,@(df.is_indiv & df.tour_mode_is_walk_transit & df.walk_ferry_available),,,,,,,,,,walk_transit_ASC_ferry,,,,,,,,,,, -util_Walk_to_Transit_tour_mode_ASC_express_bus,Walk to Transit tour mode ASC -- express bus,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,walk_transit_ASC_express,,,,,,,,,, -util_Walk_to_Transit_tour_mode_ASC_heavy_rail,Walk to Transit tour mode ASC -- heavy rail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,walk_transit_ASC_heavyrail,,,,,,,,, -util_Walk_to_Transit_tour_mode_ASC_commuter_rail,Walk to Transit tour mode ASC -- commuter rail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,,walk_transit_ASC_commuter,,,,,,,, -util_Walk_to_Transit_tour_mode_ASC_shared_ride_2,Walk to Transit tour mode ASC -- shared ride 2,@(df.is_indiv & df.tour_mode_is_walk_transit),,,walk_transit_ASC_sr2,walk_transit_ASC_sr2,,,,,,,,,,,,,,,,, -util_Walk_to_Transit_tour_mode_ASC_shared_ride_3_plus,Walk to Transit tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,walk_transit_ASC_sr3p,walk_transit_ASC_sr3p,,,,,,,,,,,,,,, -util_Walk_to_Transit_tour_mode_ASC_walk,Walk to Transit tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,walk_transit_ASC_walk,,,,,,,,,,,,,, -util_Walk_to_Transit_tour_mode_ASC_ride_hail,Walk to Transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,,,,,,,,walk_transit_ASC_rh,walk_transit_ASC_rh,walk_transit_ASC_rh -util_Drive_to_Transit_tour_mode_ASC_light_rail_skims_differ,Drive to Transit tour mode ASC -- light rail (higher b/c loc d-trn skims differ),@(df.is_indiv & df.tour_mode_is_drive_transit & ~df.drive_ferry_available),,,,,,,,,,,,,,,drive_transit_ASC_lightrail,,,,,, -util_Drive_to_Transit_tour_mode_ASC_ferry,Drive to Transit tour mode ASC -- ferry,@(df.is_indiv & df.tour_mode_is_drive_transit & df.drive_ferry_available),,,,,,,,,,,,,,,drive_transit_ASC_ferry,,,,,, -util_Drive_to_Transit_tour_mode_ASC_express_bus,Drive to Transit tour mode ASC -- express bus,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,drive_transit_ASC_express,,,,, -util_Drive_to_Transit_tour_mode_ASC_heavy_rail,Drive to Transit tour mode ASC -- heavy rail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,drive_transit_ASC_heavyrail,,,, -util_Drive_to_Transit_tour_mode_ASC_commuter_rail,Drive to Transit tour mode ASC -- commuter rail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,drive_transit_ASC_commuter,,, -util_Drive_to_Transit_tour_mode_ASC_ride_hail,Drive to Transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,,drive_transit_ASC_rh,drive_transit_ASC_rh,drive_transit_ASC_rh -util_Ride_Hail_tour_mode_ASC_shared_ride_2,Ride Hail tour mode ASC -- shared ride 2,@(df.is_indiv & df.tour_mode_is_ride_hail),,,ride_hail_ASC_sr2,ride_hail_ASC_sr2,,,,,,,,,,,,,,,,, -util_Ride_Hail_tour_mode_ASC_shared_ride_3_plus,Ride Hail tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,ride_hail_ASC_sr3p,ride_hail_ASC_sr3p,,,,,,,,,,,,,,, -util_Ride_Hail_tour_mode_ASC_walk,Ride Hail tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,ride_hail_ASC_walk,,,,,,,,,,,,,, -util_Ride_Hail_tour_mode_ASC_walk_to_transit,Ride Hail tour mode ASC -- walk to transit,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,,,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,,,,,,,, -util_Ride_Hail_tour_mode_ASC_ride_hail_taxi,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,ride_hail_ASC_taxi,, -util_Ride_Hail_tour_mode_ASC_ride_hail_single,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,ride_hail_ASC_tnc_single, -util_Ride_Hail_tour_mode_ASC_ride_hail_shared,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,,ride_hail_ASC_tnc_shared -#,joint tour ASCs,,,,,,,,,,,,,,,,,,,,,, -util_joint_auto_tour_mode_ASC_shared_ride_2,joint - auto tour mode ASC -- shared ride 2,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,joint_auto_ASC_sr2,joint_auto_ASC_sr2,,,,,,,,,,,,,,,,, -util_joint_auto_tour_mode_ASC_shared_ride_3_,joint - auto tour mode ASC -- shared ride 3+,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,,joint_auto_ASC_sr3p,joint_auto_ASC_sr3p,,,,,,,,,,,,,,, -util_joint_auto_tour_mode_ASC_walk,joint - auto tour mode ASC -- walk,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,,,,joint_auto_ASC_walk,,,,,,,,,,,,,, -util_joint_auto_tour_mode_ASC_ride_hail,joint - auto tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,joint_auto_ASC_rh,joint_auto_ASC_rh,joint_auto_ASC_rh -util_joint_Walk_tour_mode_ASC_ride_hail,joint - Walk tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,joint_walk_ASC_rh,,,,,,,,,,,,,, -util_joint_Bike_tour_mode_ASC_walk,joint - Bike tour mode ASC -- walk,@df.is_joint & (df.i_tour_mode == I_BIKE_MODE),,,,,,,joint_bike_ASC_walk,,,,,,,,,,,,,, -util_joint_Bike_tour_mode_ASC_ride_hail,joint - Bike tour mode ASC -- ride hail,@df.is_joint & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,,,,joint_bike_ASC_rh,joint_bike_ASC_rh,joint_bike_ASC_rh -util_joint_Walk_to_Transit_tour_mode_ASC_light_rail,joint - Walk to Transit tour mode ASC -- light rail,@(df.is_joint & df.tour_mode_is_walk_transit & ~df.walk_ferry_available),,,,,,,,,,joint_walk_transit_ASC_lightrail,,,,,,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_ferry,joint - Walk to Transit tour mode ASC -- ferry,@(df.is_joint & df.tour_mode_is_walk_transit & df.walk_ferry_available),,,,,,,,,,joint_walk_transit_ASC_ferry,,,,,,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_express_bus,joint - Walk to Transit tour mode ASC -- express bus,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,joint_walk_transit_ASC_express,,,,,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_heavy_rail,joint - Walk to Transit tour mode ASC -- heavy rail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,joint_walk_transit_ASC_heavyrail,,,,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_commuter_rail,joint - Walk to Transit tour mode ASC -- commuter rail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,,joint_walk_transit_ASC_commuter,,,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_shared_ride_2,joint - Walk to Transit tour mode ASC -- shared ride 2,@(df.is_joint & df.tour_mode_is_walk_transit),,,joint_walk_transit_ASC_sr2,joint_walk_transit_ASC_sr2,,,,,,,,,,,,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_shared_ride_3_plus,joint - Walk to Transit tour mode ASC -- shared ride 3+,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,joint_walk_transit_ASC_sr3p,joint_walk_transit_ASC_sr3p,,,,,,,,,,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_walk,joint - Walk to Transit tour mode ASC -- walk,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,joint_walk_transit_ASC_walk,,,,,,,,,,,,,, -util_joint_Walk_to_Transit_tour_mode_ASC_ride_hail,joint - Walk to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,,,,,,,,joint_walk_transit_ASC_rh,joint_walk_transit_ASC_rh,joint_walk_transit_ASC_rh -util_joint_Drive_to_Transit_tour_mode_ASC_light_rail_skims_differ,joint - Drive to Transit tour mode ASC -- light rail (higher b/c loc d-trn skims differ),@(df.is_joint & df.tour_mode_is_drive_transit & ~df.drive_ferry_available),,,,,,,,,,,,,,,joint_drive_transit_ASC_lightrail,,,,,, -util_joint_Drive_to_Transit_tour_mode_ASC_ferry,joint - Drive to Transit tour mode ASC -- ferry,@(df.is_joint & df.tour_mode_is_drive_transit & df.drive_ferry_available),,,,,,,,,,,,,,,joint_drive_transit_ASC_ferry,,,,,, -util_joint_Drive_to_Transit_tour_mode_ASC_express_bus,joint - Drive to Transit tour mode ASC -- express bus,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,joint_drive_transit_ASC_express,,,,, -util_joint_Drive_to_Transit_tour_mode_ASC_heavy_rail,joint - Drive to Transit tour mode ASC -- heavy rail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,joint_drive_transit_ASC_heavyrail,,,, -util_joint_Drive_to_Transit_tour_mode_ASC_commuter_rail,joint - Drive to Transit tour mode ASC -- commuter rail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,joint_drive_transit_ASC_commuter,,, -util_joint_Drive_to_Transit_tour_mode_ASC_ride_hail,joint - Drive to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,,joint_drive_transit_ASC_rh,joint_drive_transit_ASC_rh,joint_drive_transit_ASC_rh -util_joint_Ride_Hail_tour_mode_ASC_shared_ride_2,joint - Ride Hail tour mode ASC -- shared ride 2,@(df.is_joint & df.tour_mode_is_ride_hail),,,joint_ride_hail_ASC_sr2,joint_ride_hail_ASC_sr2,,,,,,,,,,,,,,,,, -util_joint_Ride_Hail_tour_mode_ASC_shared_ride_3_plus,joint - Ride Hail tour mode ASC -- shared ride 3+,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,joint_ride_hail_ASC_sr3p,joint_ride_hail_ASC_sr3p,,,,,,,,,,,,,,, -util_joint_Ride_Hail_tour_mode_ASC_walk,joint - Ride Hail tour mode ASC -- walk,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,joint_ride_hail_ASC_walk,,,,,,,,,,,,,, -util_joint_Ride_Hail_tour_mode_ASC_walk_to_transit,joint - Ride Hail tour mode ASC -- walk to transit,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,,,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,,,,,,,, -util_joint_Ride_Hail_tour_mode_ASC_ride_hail_taxi,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_taxi,, -util_joint_Ride_Hail_tour_mode_ASC_ride_hail_single,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_tnc_single, -util_joint_Ride_Hail_tour_mode_ASC_ride_hail_shared,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_tnc_shared -#,#,,,,,,,,,,,,,,,,,,,,,, -util_Walk_not_available_for_long_distances,Walk not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTWALK'] > 3),,,,,,,-999,,,,,,,,,,,,,, -util_Bike_not_available_for_long_distances,Bike not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTBIKE'] > 8),,,,,,,,-999,,,,,,,,,,,,, -util_origin_density_index,Origin density index,@origin_density_applied*(origin_density_index_multiplier*df.origin_density_index).clip(origin_density_index_max),,,,,,,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,,,,,,,coef_ivt,coef_ivt -util_walk_express_penalty,Walk-express penalty for intermediate stops,@walk_express_penalty * ~(df.first_trip | df.first_trip),,,,,,,,,,,coef_ivt,,,,,,,,,, -util_adjust_tnc_shared,TNC shared adjustment,@adjust_tnc_shared,,,,,,,,,,,,,,,,,,,,,coef_ivt +Label,Description,Expression,DRIVEALONEFREE,DRIVEALONEPAY,SHARED2FREE,SHARED2PAY,SHARED3FREE,SHARED3PAY,WALK,BIKE,WALK_LOC,WALK_LRF,WALK_EXP,WALK_HVY,WALK_COM,DRIVE_LOC,DRIVE_LRF,DRIVE_EXP,DRIVE_HVY,DRIVE_COM,TAXI,TNC_SINGLE,TNC_SHARED +#,Drive alone no toll,,,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable,DRIVEALONEFREE - Unavailable,sov_available == False,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_zero_auto_households,DRIVEALONEFREE - Unavailable for zero auto households,auto_ownership == 0,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_persons_less_than_16,DRIVEALONEFREE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_for_joint_tours,DRIVEALONEFREE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Unavailable_if_didn't_drive_to_work,DRIVEALONEFREE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_In_vehicle_time,DRIVEALONEFREE - In-vehicle time,@odt_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Terminal_time,DRIVEALONEFREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Operating_cost,DRIVEALONEFREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['SOV_DIST'],coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Parking_cost,DRIVEALONEFREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost,coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Bridge_toll,DRIVEALONEFREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOV_BTOLL'],coef_ivt,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEFREE_Person_is_between_16_and_19_years_old,DRIVEALONEFREE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da,,,,,,,,,,,,,,,,,,,, +#,Drive alone toll,,,,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable,DRIVEALONEPAY - Unavailable,sovtoll_available == False,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_zero_auto_households,DRIVEALONEPAY - Unavailable for zero auto households,auto_ownership == 0,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_persons_less_than_16,DRIVEALONEPAY - Unavailable for persons less than 16,age < 16,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_for_joint_tours,DRIVEALONEPAY - Unavailable for joint tours,is_joint == True,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Unavailable_if_didn't_drive_to_work,DRIVEALONEPAY - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,,-999,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_In_vehicle_time,DRIVEALONEPAY - In-vehicle time,@odt_skims['SOVTOLL_TIME'],,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Terminal_time,DRIVEALONEPAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Operating_cost,DRIVEALONEPAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['SOVTOLL_DIST'],,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Parking_cost,DRIVEALONEPAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost,,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Bridge_toll,DRIVEALONEPAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOVTOLL_BTOLL'],,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Value_toll,DRIVEALONEPAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['SOVTOLL_VTOLL'],,coef_ivt,,,,,,,,,,,,,,,,,,, +util_DRIVEALONEPAY_Person_is_between_16_and_19_years_old,DRIVEALONEPAY - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),,coef_age1619_da,,,,,,,,,,,,,,,,,,, +#,Shared ride 2,,,,,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Unavailable,SHARED2FREE - Unavailable,hov2_available == False,,,-999,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Unavailable_based_on_party_size,SHARED2FREE - Unavailable based on party size,is_joint & (number_of_participants > 2),,,-999,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_In_vehicle_time,SHARED2FREE - In-vehicle time,@odt_skims['HOV2_TIME'],,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Terminal_time,SHARED2FREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Operating_cost,SHARED2FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV2_DIST'],,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Parking_cost,SHARED2FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr2,,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Bridge_toll,SHARED2FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2_BTOLL'] / costShareSr2,,,coef_ivt,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_One_person_household,SHARED2FREE - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Two_person_household,SHARED2FREE - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr,,,,,,,,,,,,,,,,,, +util_SHARED2FREE_Person_is_16_years_old_or_older,SHARED2FREE - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr,,,,,,,,,,,,,,,,,, +#,Shared ride 2 toll,,,,,,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Unavailable,SHARED2PAY - Unavailable,hov2toll_available == False,,,,-999,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Unavailable_based_on_party_size,SHARED2PAY - Unavailable based on party size,is_joint & (number_of_participants > 2),,,,-999,,,,,,,,,,,,,,,,, +util_SHARED2PAY_In_vehicle_time,SHARED2PAY - In-vehicle time,@odt_skims['HOV2TOLL_TIME'],,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Terminal_time,SHARED2PAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Operating_cost,SHARED2PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV2TOLL_DIST'],,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Parking_cost,SHARED2PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Bridge_toll,SHARED2PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_BTOLL'] / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Value_toll,SHARED2PAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'] / costShareSr2,,,,coef_ivt,,,,,,,,,,,,,,,,, +util_SHARED2PAY_One_person_household,SHARED2PAY - One person household,@(df.hhsize == 1),,,,coef_hhsize1_sr,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Two_person_household,SHARED2PAY - Two person household,@(df.hhsize == 2),,,,coef_hhsize2_sr,,,,,,,,,,,,,,,,, +util_SHARED2PAY_Person_is_16_years_old_or_older,SHARED2PAY - Person is 16 years old or older,@(df.age >= 16),,,,coef_age16p_sr,,,,,,,,,,,,,,,,, +#,Shared ride 3+,,,,,,,,,,,,,,,,,,,,,, +util_SHARED3FREE_Unavailable,SHARED3FREE - Unavailable,hov3_available == False,,,,,-999,,,,,,,,,,,,,,,, +util_SHARED3FREE_In_vehicle_time,SHARED3FREE - In-vehicle time,@odt_skims['HOV3_TIME'],,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_Terminal_time,SHARED3FREE - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_Operating_cost,SHARED3FREE - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV3_DIST'],,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_Parking_cost,SHARED3FREE - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr3,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_Bridge_toll,SHARED3FREE - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3_BTOLL'] / costShareSr3,,,,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED3FREE_One_person_household,SHARED3FREE - One person household,@(df.hhsize == 1),,,,,coef_hhsize1_sr,,,,,,,,,,,,,,,, +util_SHARED3FREE_Two_person_household,SHARED3FREE - Two person household,@(df.hhsize == 2),,,,,coef_hhsize2_sr,,,,,,,,,,,,,,,, +util_SHARED3FREE_Person_is_16_years_old_or_older,SHARED3FREE - Person is 16 years old or older,@(df.age >= 16),,,,,coef_age16p_sr,,,,,,,,,,,,,,,, +#,Shared ride 3+ toll,,,,,,,,,,,,,,,,,,,,,, +util_SHARED3PAY_Unavailable,SHARED3PAY - Unavailable,hov3toll_available == False,,,,,,-999,,,,,,,,,,,,,,, +util_SHARED3PAY_In_vehicle_time,SHARED3PAY - In-vehicle time,@odt_skims['HOV3TOLL_TIME'],,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Terminal_time,SHARED3PAY - Terminal time,@coef_walktimeshort_multiplier * df.total_terminal_time,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Operating_cost,SHARED3PAY - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * odt_skims['HOV3TOLL_DIST'],,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Parking_cost,SHARED3PAY - Parking cost,@ivt_cost_multiplier * df.ivot * df.total_parking_cost / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Bridge_toll,SHARED3PAY - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3TOLL_BTOLL'] / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_Value_toll,SHARED3PAY - Value toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV3TOLL_VTOLL'] / costShareSr3,,,,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3PAY_One_person_household,SHARED3PAY - One person household,@(df.hhsize == 1),,,,,,coef_hhsize1_sr,,,,,,,,,,,,,,, +util_SHARED3PAY_Two_person_household,SHARED3PAY - Two person household,@(df.hhsize == 2),,,,,,coef_hhsize2_sr,,,,,,,,,,,,,,, +util_SHARED3PAY_Person_is_16_years_old_or_older,SHARED3PAY - Person is 16 years old or older,@(df.age >= 16),,,,,,coef_age16p_sr,,,,,,,,,,,,,,, +#,Walk,,,,,,,,,,,,,,,,,,,,,, +util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@coef_walktimeshort_multiplier * od_skims['DISTWALK'].clip(upper=walkThresh) * 60/walkSpeed,,,,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * (od_skims['DISTWALK'] - walkThresh).clip(lower=0) * 60/walkSpeed,,,,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Topology,WALK - Topology,@topology_walk_multiplier * df.trip_topology,,,,,,,coef_ivt,,,,,,,,,,,,,, +#,Bike,,,,,,,,,,,,,,,,,,,,,, +util_BIKE_Unavailable_if_didn't_bike_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,,,,-999,,,,,,,,,,,,, +util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@coef_biketimeshort_multiplier * od_skims['DISTBIKE'].clip(upper=bikeThresh)*60/bikeSpeed,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@coef_biketimeshort_multiplier * biketimelong_multiplier * (od_skims['DISTBIKE']-bikeThresh).clip(lower=0)*60/bikeSpeed,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier*df.density_index,,,,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Topology,BIKE - Topology,@topology_bike_multiplier * df.trip_topology,,,,,,,,coef_ivt,,,,,,,,,,,,, +#,Walk to Local,,,,,,,,,,,,,,,,,,,,,, +util_WALK_LOC_Unavailable,WALK_LOC - Unavailable,walk_local_available == False,,,,,,,,,-999,,,,,,,,,,,, +util_WALK_LOC_In_vehicle_time,WALK_LOC - In-vehicle time,@odt_skims['WLK_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Short_iwait_time,WALK_LOC - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Long_iwait_time,WALK_LOC - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_transfer_wait_time,WALK_LOC - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_number_of_transfers,WALK_LOC - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_LOC_WLK_BOARDS']-1).clip(0),,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_egress_time,WALK_LOC - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_other_time,WALK_LOC - Walk other time,@coef_waux_multiplier * odt_skims['WLK_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Fare,WALK_LOC - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_LOC_WLK_FAR'],,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Destination_zone_densityIndex,WALK_LOC - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Topology,WALK_LOC - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Person_is_less_than_10_years_old,WALK_LOC - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,coef_age010_trn,,,,,,,,,,,, +#,Walk to Light rail/Ferry,,,,,,,,,,,,,,,,,,,,,, +util_WALK_LRF_Unavailable,WALK_LRF - Unavailable,walk_lrf_available == False,,,,,,,,,,-999,,,,,,,,,,, +util_WALK_LRF_In_vehicle_time,WALK_LRF - In-vehicle time,@odt_skims['WLK_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_In_vehicle_time_on_Light_Rail,WALK_LRF - In-vehicle time on Light Rail (incremental w/ ivt),@(coef_ivt_lrt_multiplier-1) * odt_skims['WLK_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_In_vehicle_time_on_Ferry,WALK_LRF - In-vehicle time on Ferry (incremental w/keyivt),@(coef_ivt_ferry_multiplier-coef_ivt_lrt_multiplier) * odt_skims['WLK_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Short_iwait_time,WALK_LRF - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Long_iwait_time,WALK_LRF - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_transfer_wait_time,WALK_LRF - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_number_of_transfers,WALK_LRF - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_LRF_WLK_BOARDS']-1).clip(0),,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Walk_access_time,WALK_LRF - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Walk_egress_time,WALK_LRF - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Walk_other_time,WALK_LRF - Walk otherLight rail/Ferry time,@coef_waux_multiplier * odt_skims['WLK_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Fare,WALK_LRF - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_LRF_WLK_FAR'],,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Destination_zone_densityIndex,WALK_LRF - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Topology,WALK_LRF - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_LRF_Person_is_less_than_10_years_old,WALK_LRF - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,coef_age010_trn,,,,,,,,,,, +#,Walk to Express bus,,,,,,,,,,,,,,,,,,,,,, +util_WALK_EXP_Unavailable,WALK_EXP - Unavailable,walk_express_available == False,,,,,,,,,,,-999,,,,,,,,,, +util_WALK_EXP_In_vehicle_time,WALK_EXP - In-vehicle time,@odt_skims['WLK_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_In_vehicle_time_on_Express_bus,WALK_EXP - In-vehicle time on Express bus (incremental w/ ivt),@(ivt_exp_multiplier - 1) * odt_skims['WLK_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Short_iwait_time,WALK_EXP - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Long_iwait_time,WALK_EXP - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_transfer_wait_time,WALK_EXP - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_number_of_transfers,WALK_EXP - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_EXP_WLK_BOARDS']-1).clip(0),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Walk_access_time,WALK_EXP - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Walk_egress_time,WALK_EXP - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Walk_other_time,WALK_EXP - Walk other time,@coef_waux_multiplier * odt_skims['WLK_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Fare,WALK_EXP - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_EXP_WLK_FAR'],,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Destination_zone_densityIndex,WALK_EXP - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Topology,WALK_EXP - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_EXP_Person_is_less_than_10_years_old,WALK_EXP - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,,coef_age010_trn,,,,,,,,,, +#,Walk to Heavy Rail,,,,,,,,,,,,,,,,,,,,,, +util_WALK_HVY_Unavailable,WALK_HVY - Unavailable,walk_heavyrail_available == False,,,,,,,,,,,,-999,,,,,,,,, +util_WALK_HVY_In_vehicle_time,WALK_HVY - In-vehicle time,@odt_skims['WLK_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_In_vehicle_time_on_heavy_rail,WALK_HVY - In-vehicle time on heavy rail (incremental w/ ivt),@(ivt_hvy_multiplier-1) * odt_skims['WLK_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Short_iwait_time,WALK_HVY - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Long_iwait_time,WALK_HVY - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_transfer_wait_time,WALK_HVY - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_number_of_transfers,WALK_HVY - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_HVY_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Walk_access_time,WALK_HVY - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Walk_egress_time,WALK_HVY - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Walk_other_time,WALK_HVY - Walk other time,@coef_waux_multiplier * odt_skims['WLK_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Fare,WALK_HVY - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_HVY_WLK_FAR'],,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Destination_zone_densityIndex,WALK_HVY - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Topology,WALK_HVY - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,coef_ivt,,,,,,,,, +util_WALK_HVY_Person_is_less_than_10_years_old,WALK_HVY - Person is less than 10 years old,@(df.age <= 10),,,,,,,,,,,,coef_age010_trn,,,,,,,,, +#,Walk to Commuter rail,,,,,,,,,,,,,,,,,,,,,, +util_WALK_COM_Unavailable,WALK_COM - Unavailable,walk_commuter_available == False,,,,,,,,,,,,,-999,,,,,,,, +util_WALK_COM_In_vehicle_time,WALK_COM - In-vehicle time,@odt_skims['WLK_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_In_vehicle_time_on_commuter_rail,WALK_COM - In-vehicle time on commuter rail (incremental w/ ivt),@(ivt_com_multiplier - 1) * odt_skims['WLK_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Short_iwait_time,WALK_COM - Short iwait time,@coef_short_iwait_multiplier * (odt_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Long_iwait_time,WALK_COM - Long iwait time,@coef_long_iwait_multiplier * (odt_skims['WLK_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_transfer_wait_time,WALK_COM - transfer wait time,@coef_xwait_multiplier * odt_skims['WLK_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_number_of_transfers,WALK_COM - number of transfers,@xfers_wlk_multiplier * (odt_skims['WLK_COM_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Walk_access_time,WALK_COM - Walk access time,@coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Walk_egress_time,WALK_COM - Walk egress time,@coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Walk_other_time,WALK_COM - Walk other time,@coef_waux_multiplier * odt_skims['WLK_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Fare,WALK_COM - Fare,@ivt_cost_multiplier * df.ivot * odt_skims['WLK_COM_WLK_FAR'],,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Destination_zone_densityIndex,WALK_COM - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Topology,WALK_COM - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,coef_ivt,,,,,,,, +util_WALK_COM_Person_is_less_than_10_years_old,WALK_COM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,coef_age010_trn,,,,,,,, +#,Drive to Local,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_LOC_Unavailable_for_zero_auto_households,DRIVE_LOC - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,-999,,,,,,, +util_DRIVE_LOC_Unavailable_for_persons_less_than_16,DRIVE_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,-999,,,,,,, +util_DRIVE_LOC_Destination_zone_densityIndex,DRIVE_LOC - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Topology,DRIVE_LOC - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_Person_is_less_than_10_years_old,DRIVE_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,coef_age010_trn,,,,,,, +util_DRIVE_LOC_outbound_Unavailable,DRIVE_LOC outbound - Unavailable,outbound & ~drive_local_available_outbound,,,,,,,,,,,,,,-999,,,,,,, +util_DRIVE_LOC_outbound_In_vehicle_time,DRIVE_LOC outbound - In-vehicle time,@df.outbound * odt_skims['DRV_LOC_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_Short_iwait_time,DRIVE_LOC outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_Long_iwait_time,DRIVE_LOC outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_LOC_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_transfer_wait_time,DRIVE_LOC outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_LOC_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_number_of_transfers,DRIVE_LOC outbound - number of transfers,@df.outbound * xfers_wlk_multiplier * (odt_skims['DRV_LOC_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_Drive_time,DRIVE_LOC outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_LOC_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_Walk_egress_time,DRIVE_LOC outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_Walk_other_time,DRIVE_LOC outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_LOC_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_Fare_and_operating_cost,DRIVE_LOC outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_LOC_WLK_FAR'] + costPerMile*odt_skims['DRV_LOC_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LOC outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_LOC_WLK_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']),,,,,,,,,,,,,,1,,,,,,, +util_DRIVE_LOC_inbound_Unavailable,DRIVE_LOC inbound - Unavailable,inbound & ~drive_local_available_inbound,,,,,,,,,,,,,,-999,,,,,,, +util_DRIVE_LOC_inbound_In_vehicle_time,DRIVE_LOC inbound - In-vehicle time,@df.inbound * odt_skims['WLK_LOC_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_Short_iwait_time,DRIVE_LOC inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_LOC_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_Long_iwait_time,DRIVE_LOC inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_LOC_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_transfer_wait_time,DRIVE_LOC inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_LOC_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_number_of_transfers,DRIVE_LOC inbound - number of transfers,@df.inbound * xfers_wlk_multiplier * (odt_skims['WLK_LOC_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_Drive_time,DRIVE_LOC inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WLK_LOC_DRV_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_Walk_access_time,DRIVE_LOC inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_Walk_other_time,DRIVE_LOC inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WLK_LOC_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_Fare_and_operating_cost,DRIVE_LOC inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_LOC_DRV_FAR'] + costPerMile*odt_skims['WLK_LOC_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,coef_ivt,,,,,,, +util_DRIVE_LOC_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LOC inbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['WLK_LOC_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ (od_skims['DIST']),,,,,,,,,,,,,,1,,,,,,, +#,Drive to Light Rail/Ferry,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_LRF_Unavailable_for_zero_auto_households,DRIVE_LRF - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,-999,,,,,, +util_DRIVE_LRF_Unavailable_for_persons_less_than_16,DRIVE_LRF - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,-999,,,,,, +util_DRIVE_LRF_Destination_zone_densityIndex,DRIVE_LRF - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Topology,DRIVE_LRF - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_Person_is_less_than_10_years_old,DRIVE_LRF - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,coef_age010_trn,,,,,, +util_DRIVE_LRF_outbound_Unavailable,DRIVE_LRF outbound - Unavailable,outbound & ~drive_lrf_available_outbound,,,,,,,,,,,,,,,-999,,,,,, +util_DRIVE_LRF_outbound_In_vehicle_time,DRIVE_LRF outbound - In-vehicle time,@df.outbound * odt_skims['DRV_LRF_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_In_vehicle_time_on_LR,DRIVE_LRF outbound - In-vehicle time on Light Rail (incremental w/ ivt),@df.outbound * (coef_ivt_lrt_multiplier - 1)*odt_skims['DRV_LRF_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_In_vehicle_time_on_Ferry,DRIVE_LRF outbound - In-vehicle time on Ferry (incremental w/ keyivt),@df.outbound * (coef_ivt_ferry_multiplier-coef_ivt_lrt_multiplier)*odt_skims['DRV_LRF_WLK_FERRYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_Short_iwait_time,DRIVE_LRF outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_Long_iwait_time,DRIVE_LRF outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_LRF_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_transfer_wait_time,DRIVE_LRF outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_LRF_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_number_of_transfers,DRIVE_LRF outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DRV_LRF_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_Drive_time,DRIVE_LRF outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_LRF_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_Walk_egress_time,DRIVE_LRF outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_Walk_other_time,DRIVE_LRF outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_LRF_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_Fare_and_operating_cost,DRIVE_LRF outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_LRF_WLK_FAR'] + costPerMile * odt_skims['DRV_LRF_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LRF outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_LRF_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,1,,,,,, +util_DRIVE_LRF_inbound_Unavailable,DRIVE_LRF inbound - Unavailable,inbound & ~drive_lrf_available_inbound,,,,,,,,,,,,,,,-999,,,,,, +util_DRIVE_LRF_inbound_In_vehicle_time,DRIVE_LRF inbound - In-vehicle time,@df.inbound * odt_skims['WLK_LRF_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_In_vehicle_time_on_LR,DRIVE_LRF inbound - In-vehicle time on Light Rail (incremental w/ ivt),@df.inbound * (coef_ivt_lrt_multiplier - 1)*odt_skims['WLK_LRF_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_In_vehicle_time_on_Ferry,DRIVE_LRF inbound - In-vehicle time on Ferry (incremental w/ keyivt),@df.inbound * (coef_ivt_ferry_multiplier-coef_ivt_lrt_multiplier)*odt_skims['WLK_LRF_DRV_FERRYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_Short_iwait_time,DRIVE_LRF inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_LRF_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_Long_iwait_time,DRIVE_LRF inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_LRF_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_transfer_wait_time,DRIVE_LRF inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_LRF_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_number_of_transfers,DRIVE_LRF inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WLK_LRF_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_Drive_time,DRIVE_LRF inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WLK_LRF_DRV_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_Walk_access_time,DRIVE_LRF inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_Walk_other_time,DRIVE_LRF inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WLK_LRF_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_Fare_and_operating_cost,DRIVE_LRF inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_LRF_DRV_FAR'] + costPerMile * odt_skims['WLK_LRF_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,coef_ivt,,,,,, +util_DRIVE_LRF_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_LRF inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WLK_LRF_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ od_skims['DIST'],,,,,,,,,,,,,,,1,,,,,, +#,Drive to Express bus,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_EXP_Unavailable_for_zero_auto_households,DRIVE_EXP - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,-999,,,,, +util_DRIVE_EXP_Unavailable_for_persons_less_than_16,DRIVE_EXP - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,-999,,,,, +util_DRIVE_EXP_Destination_zone_densityIndex,DRIVE_EXP - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Topology,DRIVE_EXP - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_Person_is_less_than_10_years_old,DRIVE_EXP - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,coef_age010_trn,,,,, +util_DRIVE_EXP_outbound_Unavailable,DRIVE_EXP outbound - Unavailable,outbound & ~drive_express_available_outbound,,,,,,,,,,,,,,,,-999,,,,, +util_DRIVE_EXP_outbound_In_vehicle_time,DRIVE_EXP outbound - In-vehicle time,@df.outbound * odt_skims['DRV_EXP_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_In_vehicle_time_on_EXP,DRIVE_EXP outbound - In-vehicle time on Express bus (incremental w/ ivt),@df.outbound * (ivt_exp_multiplier - 1) * odt_skims['DRV_EXP_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_Short_iwait_time,DRIVE_EXP outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_Long_iwait_time,DRIVE_EXP outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_EXP_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_transfer_wait_time,DRIVE_EXP outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_EXP_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_number_of_transfers,DRIVE_EXP outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DRV_EXP_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_Drive_time,DRIVE_EXP outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_EXP_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_Walk_egress_time,DRIVE_EXP outbound - Walk egress ime,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_Walk_other_time,DRIVE_EXP outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_EXP_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_Fare_and_operating_cost,DRIVE_EXP outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_EXP_WLK_FAR'] + costPerMile * odt_skims['DRV_EXP_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_EXP outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_EXP_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_Unavailable,DRIVE_EXP inbound - Unavailable,inbound & ~drive_express_available_inbound,,,,,,,,,,,,,,,,-999,,,,, +util_DRIVE_EXP_inbound_In_vehicle_time,DRIVE_EXP inbound - In-vehicle time,@df.inbound * odt_skims['WLK_EXP_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_In_vehicle_time_on_EXP,DRIVE_EXP inbound - In-vehicle time on Express bus (incremental w/ ivt),@df.inbound * (ivt_exp_multiplier - 1) * odt_skims['WLK_EXP_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_Short_iwait_time,DRIVE_EXP inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_EXP_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_Long_iwait_time,DRIVE_EXP inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_EXP_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_transfer_wait_time,DRIVE_EXP inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_EXP_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_number_of_transfers,DRIVE_EXP inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WLK_EXP_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_Drive_time,DRIVE_EXP inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WLK_EXP_DRV_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_Walk_access_time,DRIVE_EXP inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_Walk_other_time,DRIVE_EXP inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WLK_EXP_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_Fare_and_operating_cost,DRIVE_EXP inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_EXP_DRV_FAR'] + costPerMile * odt_skims['WLK_EXP_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,coef_ivt,,,,, +util_DRIVE_EXP_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_EXP inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WLK_EXP_DRV_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,1,,,,, +#,Drive to Heavy Rail,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_HVY_Unavailable_for_zero_auto_households,DRIVE_HVY - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,,-999,,,, +util_DRIVE_HVY_Unavailable_for_persons_less_than_16,DRIVE_HVY - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,-999,,,, +util_DRIVE_HVY_Destination_zone_densityIndex,DRIVE_HVY - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Topology,DRIVE_HVY - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_Person_is_less_than_10_years_old,DRIVE_HVY - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,,coef_age010_trn,,,, +util_DRIVE_HVY_outbound_Unavailable,DRIVE_HVY outbound - Unavailable,outbound & ~drive_heavyrail_available_outbound,,,,,,,,,,,,,,,,,-999,,,, +util_DRIVE_HVY_outbound_In_vehicle_time,DRIVE_HVY outbound - In-vehicle time,@df.outbound * odt_skims['DRV_HVY_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_In_vehicle_time_on_HVY,DRIVE_HVY outbound - In-vehicle time on heavy rail (incremental w/ ivt),@df.outbound * (ivt_hvy_multiplier - 1) * odt_skims['DRV_HVY_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_Short_iwait_time,DRIVE_HVY outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_Long_iwait_time,DRIVE_HVY outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_HVY_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_transfer_wait_time,DRIVE_HVY outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_HVY_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_number_of_transfers,DRIVE_HVY outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DRV_HVY_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_Drive_time,DRIVE_HVY outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_HVY_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_Walk_egress_time,DRIVE_HVY outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_Walk_other_time,DRIVE_HVY outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_HVY_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_Fare_and_operating_cost,DRIVE_HVY outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_HVY_WLK_FAR'] + costPerMile * odt_skims['DRV_HVY_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_HVY outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_HVY_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_Unavailable,DRIVE_HVY inbound - Unavailable,inbound & ~drive_heavyrail_available_inbound,,,,,,,,,,,,,,,,,-999,,,, +util_DRIVE_HVY_inbound_In_vehicle_time,DRIVE_HVY inbound - In-vehicle time,@df.inbound * odt_skims['WLK_HVY_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_In_vehicle_time_on_HVY,DRIVE_HVY inbound - In-vehicle time on heavy rail (incremental w/ ivt),@df.inbound * (ivt_hvy_multiplier - 1) * odt_skims['WLK_HVY_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_Short_iwait_time,DRIVE_HVY inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_HVY_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_Long_iwait_time,DRIVE_HVY inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_HVY_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_transfer_wait_time,DRIVE_HVY inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_HVY_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_number_of_transfers,DRIVE_HVY inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WLK_HVY_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_Drive_time,DRIVE_HVY inbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_HVY_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_Walk_access_time,DRIVE_HVY inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_Walk_other_time,DRIVE_HVY inbound - Walk other time,@coef_waux_multiplier * odt_skims['WLK_HVY_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_Fare_and_operating_cost,DRIVE_HVY inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_HVY_DRV_FAR'] + costPerMile * odt_skims['WLK_HVY_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,coef_ivt,,,, +util_DRIVE_HVY_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_HVY inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WLK_HVY_DRV_DDIST']/TRANSIT_SCALE_FACTOR)/ od_skims['DIST'],,,,,,,,,,,,,,,,,1,,,, +#,#Drive to Commuter Rail,,,,,,,,,,,,,,,,,,,,,, +util_DRIVE_COM_Unavailable_for_zero_auto_households,DRIVE_COM - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,,,,,,,,-999,,, +util_DRIVE_COM_Unavailable_for_persons_less_than_16,DRIVE_COM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,,,,,-999,,, +util_DRIVE_COM_Destination_zone_densityIndex,DRIVE_COM - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Topology,DRIVE_COM - Topology,@topology_trn_multiplier * df.trip_topology,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_Person_is_less_than_10_years_old,DRIVE_COM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,,,,,coef_age010_trn,,, +util_DRIVE_COM_outbound_Unavailable,DRIVE_COM outbound - Unavailable,outbound & ~drive_commuter_available_outbound,,,,,,,,,,,,,,,,,,-999,,, +util_DRIVE_COM_outbound_In_vehicle_time,DRIVE_COM outbound - In-vehicle time,@df.outbound * odt_skims['DRV_COM_WLK_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_In_vehicle_time_on_COM,DRIVE_COM outbound - In-vehicle time on commuter rail (incremental w/ ivt),@df.outbound * (ivt_com_multiplier - 1) * odt_skims['DRV_COM_WLK_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_Short_iwait_time,DRIVE_COM outbound - Short iwait time,@df.outbound * coef_short_iwait_multiplier * (odt_skims['DRV_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_Long_iwait_time,DRIVE_COM outbound - Long iwait time,@df.outbound * coef_long_iwait_multiplier * (odt_skims['DRV_COM_WLK_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_transfer_wait_time,DRIVE_COM outbound - transfer wait time,@df.outbound * coef_xwait_multiplier * odt_skims['DRV_COM_WLK_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_number_of_transfers,DRIVE_COM outbound - number of transfers,@df.outbound * xfers_drv_multiplier * (odt_skims['DRV_COM_WLK_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_Drive_time,DRIVE_COM outbound - Drive time,@df.outbound * coef_dtim_multiplier * odt_skims['DRV_COM_WLK_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_Walk_egress_time,DRIVE_COM outbound - Walk egress time,@df.outbound * coef_wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_Walk_other_time,DRIVE_COM outbound - Walk other time,@df.outbound * coef_waux_multiplier * odt_skims['DRV_COM_WLK_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_Fare_and_operating_cost,DRIVE_COM outbound - Fare and operating cost,@df.outbound * ivt_cost_multiplier * df.ivot * (odt_skims['DRV_COM_WLK_FAR'] + costPerMile * odt_skims['DRV_COM_WLK_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_outbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_COM outbound - Ratio of drive access distance to OD distance,@df.outbound * dacc_ratio_multiplier * (odt_skims['DRV_COM_WLK_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,,,1,,, +util_DRIVE_COM_inbound_Unavailable,DRIVE_COM inbound - Unavailable,inbound & ~drive_commuter_available_inbound,,,,,,,,,,,,,,,,,,-999,,, +util_DRIVE_COM_inbound_In_vehicle_time,DRIVE_COM inbound - In-vehicle time,@df.inbound * odt_skims['WLK_COM_DRV_TOTIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_In_vehicle_time_on_COM,DRIVE_COM inbound - In-vehicle time on commuter rail (incremental w/ ivt),@df.inbound * (ivt_com_multiplier - 1) * odt_skims['WLK_COM_DRV_KEYIVT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_Short_iwait_time,DRIVE_COM inbound - Short iwait time,@df.inbound * coef_short_iwait_multiplier * (odt_skims['WLK_COM_DRV_IWAIT']/TRANSIT_SCALE_FACTOR).clip(upper=waitThresh),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_Long_iwait_time,DRIVE_COM inbound - Long iwait time,@df.inbound * coef_long_iwait_multiplier * (odt_skims['WLK_COM_DRV_IWAIT']/TRANSIT_SCALE_FACTOR-waitThresh).clip(0),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_transfer_wait_time,DRIVE_COM inbound - transfer wait time,@df.inbound * coef_xwait_multiplier * odt_skims['WLK_COM_DRV_XWAIT']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_number_of_transfers,DRIVE_COM inbound - number of transfers,@df.inbound * xfers_drv_multiplier * (odt_skims['WLK_COM_DRV_BOARDS']-1).clip(0),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_Drive_time,DRIVE_COM inbound - Drive time,@df.inbound * coef_dtim_multiplier * odt_skims['WLK_COM_DRV_DTIM']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_Walk_access_time,DRIVE_COM inbound - Walk access time,@df.inbound * coef_wacc_multiplier * df.origin_walk_time,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_Walk_other_time,DRIVE_COM inbound - Walk other time,@df.inbound * coef_waux_multiplier * odt_skims['WLK_COM_DRV_WAUX']/TRANSIT_SCALE_FACTOR,,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_Fare_and_operating_cost,DRIVE_COM inbound - Fare and operating cost,@df.inbound * ivt_cost_multiplier * df.ivot * (odt_skims['WLK_COM_DRV_FAR'] + costPerMile * odt_skims['WLK_COM_DRV_DDIST']/TRANSIT_SCALE_FACTOR),,,,,,,,,,,,,,,,,,coef_ivt,,, +util_DRIVE_COM_inbound_Ratio_of_drive_access_distance_to_OD_distance,DRIVE_COM inbound - Ratio of drive access distance to OD distance,@df.inbound * dacc_ratio_multiplier * (odt_skims['WLK_COM_DRV_DDIST']/TRANSIT_SCALE_FACTOR) / od_skims['DIST'],,,,,,,,,,,,,,,,,,1,,, +#,Taxi,,,,,,,,,,,,,,,,,,,,,, +util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@odt_skims['HOV2TOLL_TIME'],,,,,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Wait_time,Taxi - Wait time,@ridehail_wait_time_multiplier * df.origTaxiWaitTime,,,,,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_BTOLL'],,,,,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare + odt_skims['HOV2TOLL_DIST'] * Taxi_costPerMile + odt_skims['HOV2TOLL_TIME'] * Taxi_costPerMinute)*100,,,,,,,,,,,,,,,,,,,coef_ivt,, +#,TNC Single,,,,,,,,,,,,,,,,,,,,,, +util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@odt_skims['HOV2TOLL_TIME'] ,,,,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Wait_time,TNC Single - Wait time,@ridehail_wait_time_multiplier * df.origSingleTNCWaitTime,,,,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare + odt_skims['HOV2TOLL_DIST'] * TNC_single_costPerMile + odt_skims['HOV2TOLL_TIME'] * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,coef_ivt, +#,#TNC Shared,,,,,,,,,,,,,,,,,,,,,, +util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@odt_skims['HOV2TOLL_TIME'] * TNC_shared_IVTFactor,,,,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Wait_time,TNC Shared - Wait time,@ridehail_wait_time_multiplier * df.origSharedTNCWaitTime,,,,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * odt_skims['HOV2TOLL_VTOLL'],,,,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare + odt_skims['HOV2TOLL_DIST'] * TNC_shared_costPerMile + odt_skims['HOV2TOLL_TIME']* TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,,,,,,,,,coef_ivt +#,,,,,,,,,,,,,,,,,,,,,,, +util_tour_mode_is_auto,Auto tour mode availability,tour_mode_is_auto,,,,,,,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, +util_tour_mode_is_walk,Walk tour mode availability,tour_mode_is_walk,-999,-999,-999,-999,-999,-999,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, +util_tour_mode_is_bike,Bike tour mode availability,tour_mode_is_bike,-999,-999,-999,-999,-999,-999,,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, +util_tour_mode_is_walk_transit,Walk to Transit tour mode availability,tour_mode_is_walk_transit,-999,-999,,,,,,-999,,,,,,-999,-999,-999,-999,-999,,, +util_tour_mode_is_drive_transit,Drive to Transit tour modes availability,tour_mode_is_drive_transit,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,,,,,,, +util_tour_mode_is_ride_hail,Ride hail tour modes availability,tour_mode_is_ride_hail,-999,-999,,,,,,-999,,,,,,-999,-999,-999,-999,-999,,, +,#indiv tour ASCs,,,,,,,,,,,,,,,,,,,,,, +util_Drive_Alone_tour_mode_ASC_shared_ride_2_df_is_indiv,Drive Alone tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,sov_ASC_sr2,sov_ASC_sr2,,,,,,,,,,,,,,,,, +util_Drive_Alone_tour_mode_ASC_shared_ride_3_plus,Drive Alone tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,sov_ASC_sr3p,sov_ASC_sr3p,,,,,,,,,,,,,,, +util_Drive_Alone_tour_mode_ASC_walk,Drive Alone tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,sov_ASC_walk,,,,,,,,,,,,,, +util_Drive_Alone_tour_mode_ASC_ride_hail,Drive Alone tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,,,,,,,,,,,,,sov_ASC_rh,sov_ASC_rh,sov_ASC_rh +util_Shared_Ride_2_tour_mode_ASC_shared_ride_2,Shared Ride 2 tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,sr2_ASC_sr2,sr2_ASC_sr2,,,,,,,,,,,,,,,,, +util_Shared_Ride_2_tour_mode_ASC_shared_ride_3_plus,Shared Ride 2 tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,sr2_ASC_sr3p,sr2_ASC_sr3p,,,,,,,,,,,,,,, +util_Shared_Ride_2_tour_mode_ASC_walk,Shared Ride 2 tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,sr2_ASC_walk,,,,,,,,,,,,,, +util_Shared_Ride_2_tour_mode_ASC_ride_hail,Shared Ride 2 tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,,,,,,,,,,,,,sr2_ASC_rh,sr2_ASC_rh,sr2_ASC_rh +util_Shared_Ride_3_tour_mode_ASC_shared_ride_2,Shared Ride 3+ tour mode ASC -- shared ride 2,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,sr3p_ASC_sr2,sr3p_ASC_sr2,,,,,,,,,,,,,,,,, +util_Shared_Ride_3_tour_mode_ASC_shared_ride_3_plus,Shared Ride 3+ tour mode ASC -- shared ride 3+,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,sr3p_ASC_sr3p,sr3p_ASC_sr3p,,,,,,,,,,,,,,, +util_Shared_Ride_3_tour_mode_ASC_walk,Shared Ride 3+ tour mode ASC -- walk,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,sr3p_ASC_walk,,,,,,,,,,,,,, +util_Shared_Ride_3_tour_mode_ASC_ride_hail,Shared Ride 3+ tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,,,,,,,,,,,,,sr3p_ASC_rh,sr3p_ASC_rh,sr3p_ASC_rh +util_Walk_tour_mode_ASC_ride_hail,Walk tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_WALK_MODE),,,,,,,,,,,,,,,,,,,walk_ASC_rh,walk_ASC_rh,walk_ASC_rh +util_Bike_tour_mode_ASC_walk,Bike tour mode ASC -- walk,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,,,,bike_ASC_walk,,,,,,,,,,,,,, +util_Bike_tour_mode_ASC_ride_hail,Bike tour mode ASC -- ride hail,@df.is_indiv & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,,,,bike_ASC_rh,bike_ASC_rh,bike_ASC_rh +util_Walk_to_Transit_tour_mode_ASC_light_rail,Walk to Transit tour mode ASC -- light rail,@(df.is_indiv & df.tour_mode_is_walk_transit & ~df.walk_ferry_available),,,,,,,,,,walk_transit_ASC_lightrail,,,,,,,,,,, +util_Walk_to_Transit_tour_mode_ASC_ferry,Walk to Transit tour mode ASC -- ferry,@(df.is_indiv & df.tour_mode_is_walk_transit & df.walk_ferry_available),,,,,,,,,,walk_transit_ASC_ferry,,,,,,,,,,, +util_Walk_to_Transit_tour_mode_ASC_express_bus,Walk to Transit tour mode ASC -- express bus,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,walk_transit_ASC_express,,,,,,,,,, +util_Walk_to_Transit_tour_mode_ASC_heavy_rail,Walk to Transit tour mode ASC -- heavy rail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,walk_transit_ASC_heavyrail,,,,,,,,, +util_Walk_to_Transit_tour_mode_ASC_commuter_rail,Walk to Transit tour mode ASC -- commuter rail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,,walk_transit_ASC_commuter,,,,,,,, +util_Walk_to_Transit_tour_mode_ASC_shared_ride_2,Walk to Transit tour mode ASC -- shared ride 2,@(df.is_indiv & df.tour_mode_is_walk_transit),,,walk_transit_ASC_sr2,walk_transit_ASC_sr2,,,,,,,,,,,,,,,,, +util_Walk_to_Transit_tour_mode_ASC_shared_ride_3_plus,Walk to Transit tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,walk_transit_ASC_sr3p,walk_transit_ASC_sr3p,,,,,,,,,,,,,,, +util_Walk_to_Transit_tour_mode_ASC_walk,Walk to Transit tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,walk_transit_ASC_walk,,,,,,,,,,,,,, +util_Walk_to_Transit_tour_mode_ASC_ride_hail,Walk to Transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,,,,,,,,walk_transit_ASC_rh,walk_transit_ASC_rh,walk_transit_ASC_rh +util_Drive_to_Transit_tour_mode_ASC_light_rail_skims_differ,Drive to Transit tour mode ASC -- light rail (higher b/c loc d-trn skims differ),@(df.is_indiv & df.tour_mode_is_drive_transit & ~df.drive_ferry_available),,,,,,,,,,,,,,,drive_transit_ASC_lightrail,,,,,, +util_Drive_to_Transit_tour_mode_ASC_ferry,Drive to Transit tour mode ASC -- ferry,@(df.is_indiv & df.tour_mode_is_drive_transit & df.drive_ferry_available),,,,,,,,,,,,,,,drive_transit_ASC_ferry,,,,,, +util_Drive_to_Transit_tour_mode_ASC_express_bus,Drive to Transit tour mode ASC -- express bus,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,drive_transit_ASC_express,,,,, +util_Drive_to_Transit_tour_mode_ASC_heavy_rail,Drive to Transit tour mode ASC -- heavy rail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,drive_transit_ASC_heavyrail,,,, +util_Drive_to_Transit_tour_mode_ASC_commuter_rail,Drive to Transit tour mode ASC -- commuter rail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,drive_transit_ASC_commuter,,, +util_Drive_to_Transit_tour_mode_ASC_ride_hail,Drive to Transit tour mode ASC -- ride hail,@(df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,,drive_transit_ASC_rh,drive_transit_ASC_rh,drive_transit_ASC_rh +util_Ride_Hail_tour_mode_ASC_shared_ride_2,Ride Hail tour mode ASC -- shared ride 2,@(df.is_indiv & df.tour_mode_is_ride_hail),,,ride_hail_ASC_sr2,ride_hail_ASC_sr2,,,,,,,,,,,,,,,,, +util_Ride_Hail_tour_mode_ASC_shared_ride_3_plus,Ride Hail tour mode ASC -- shared ride 3+,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,ride_hail_ASC_sr3p,ride_hail_ASC_sr3p,,,,,,,,,,,,,,, +util_Ride_Hail_tour_mode_ASC_walk,Ride Hail tour mode ASC -- walk,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,ride_hail_ASC_walk,,,,,,,,,,,,,, +util_Ride_Hail_tour_mode_ASC_walk_to_transit,Ride Hail tour mode ASC -- walk to transit,@(df.is_indiv & df.tour_mode_is_ride_hail),,,,,,,,,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,ride_hail_ASC_walk_transit,,,,,,,, +util_Ride_Hail_tour_mode_ASC_ride_hail_taxi,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,ride_hail_ASC_taxi,, +util_Ride_Hail_tour_mode_ASC_ride_hail_single,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,ride_hail_ASC_tnc_single, +util_Ride_Hail_tour_mode_ASC_ride_hail_shared,Ride Hail tour mode ASC -- ride hail,@(df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,,ride_hail_ASC_tnc_shared +#,joint tour ASCs,,,,,,,,,,,,,,,,,,,,,, +util_joint_auto_tour_mode_ASC_shared_ride_2,joint - auto tour mode ASC -- shared ride 2,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,joint_auto_ASC_sr2,joint_auto_ASC_sr2,,,,,,,,,,,,,,,,, +util_joint_auto_tour_mode_ASC_shared_ride_3_,joint - auto tour mode ASC -- shared ride 3+,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,,joint_auto_ASC_sr3p,joint_auto_ASC_sr3p,,,,,,,,,,,,,,, +util_joint_auto_tour_mode_ASC_walk,joint - auto tour mode ASC -- walk,@(df.is_joint & df.i_tour_mode.isin(I_AUTO_MODES)),,,,,,,joint_auto_ASC_walk,,,,,,,,,,,,,, +util_joint_auto_tour_mode_ASC_ride_hail,joint - auto tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,joint_auto_ASC_rh,joint_auto_ASC_rh,joint_auto_ASC_rh +util_joint_Walk_tour_mode_ASC_ride_hail,joint - Walk tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,joint_walk_ASC_rh,,,,,,,,,,,,,, +util_joint_Bike_tour_mode_ASC_walk,joint - Bike tour mode ASC -- walk,@df.is_joint & (df.i_tour_mode == I_BIKE_MODE),,,,,,,joint_bike_ASC_walk,,,,,,,,,,,,,, +util_joint_Bike_tour_mode_ASC_ride_hail,joint - Bike tour mode ASC -- ride hail,@df.is_joint & (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,,,,joint_bike_ASC_rh,joint_bike_ASC_rh,joint_bike_ASC_rh +util_joint_Walk_to_Transit_tour_mode_ASC_light_rail,joint - Walk to Transit tour mode ASC -- light rail,@(df.is_joint & df.tour_mode_is_walk_transit & ~df.walk_ferry_available),,,,,,,,,,joint_walk_transit_ASC_lightrail,,,,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_ferry,joint - Walk to Transit tour mode ASC -- ferry,@(df.is_joint & df.tour_mode_is_walk_transit & df.walk_ferry_available),,,,,,,,,,joint_walk_transit_ASC_ferry,,,,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_express_bus,joint - Walk to Transit tour mode ASC -- express bus,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,joint_walk_transit_ASC_express,,,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_heavy_rail,joint - Walk to Transit tour mode ASC -- heavy rail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,joint_walk_transit_ASC_heavyrail,,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_commuter_rail,joint - Walk to Transit tour mode ASC -- commuter rail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,,joint_walk_transit_ASC_commuter,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_shared_ride_2,joint - Walk to Transit tour mode ASC -- shared ride 2,@(df.is_joint & df.tour_mode_is_walk_transit),,,joint_walk_transit_ASC_sr2,joint_walk_transit_ASC_sr2,,,,,,,,,,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_shared_ride_3_plus,joint - Walk to Transit tour mode ASC -- shared ride 3+,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,joint_walk_transit_ASC_sr3p,joint_walk_transit_ASC_sr3p,,,,,,,,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_walk,joint - Walk to Transit tour mode ASC -- walk,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,joint_walk_transit_ASC_walk,,,,,,,,,,,,,, +util_joint_Walk_to_Transit_tour_mode_ASC_ride_hail,joint - Walk to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_walk_transit),,,,,,,,,,,,,,,,,,,joint_walk_transit_ASC_rh,joint_walk_transit_ASC_rh,joint_walk_transit_ASC_rh +util_joint_Drive_to_Transit_tour_mode_ASC_light_rail_skims_differ,joint - Drive to Transit tour mode ASC -- light rail (higher b/c loc d-trn skims differ),@(df.is_joint & df.tour_mode_is_drive_transit & ~df.drive_ferry_available),,,,,,,,,,,,,,,joint_drive_transit_ASC_lightrail,,,,,, +util_joint_Drive_to_Transit_tour_mode_ASC_ferry,joint - Drive to Transit tour mode ASC -- ferry,@(df.is_joint & df.tour_mode_is_drive_transit & df.drive_ferry_available),,,,,,,,,,,,,,,joint_drive_transit_ASC_ferry,,,,,, +util_joint_Drive_to_Transit_tour_mode_ASC_express_bus,joint - Drive to Transit tour mode ASC -- express bus,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,joint_drive_transit_ASC_express,,,,, +util_joint_Drive_to_Transit_tour_mode_ASC_heavy_rail,joint - Drive to Transit tour mode ASC -- heavy rail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,joint_drive_transit_ASC_heavyrail,,,, +util_joint_Drive_to_Transit_tour_mode_ASC_commuter_rail,joint - Drive to Transit tour mode ASC -- commuter rail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,joint_drive_transit_ASC_commuter,,, +util_joint_Drive_to_Transit_tour_mode_ASC_ride_hail,joint - Drive to Transit tour mode ASC -- ride hail,@(df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,,,,joint_drive_transit_ASC_rh,joint_drive_transit_ASC_rh,joint_drive_transit_ASC_rh +util_joint_Ride_Hail_tour_mode_ASC_shared_ride_2,joint - Ride Hail tour mode ASC -- shared ride 2,@(df.is_joint & df.tour_mode_is_ride_hail),,,joint_ride_hail_ASC_sr2,joint_ride_hail_ASC_sr2,,,,,,,,,,,,,,,,, +util_joint_Ride_Hail_tour_mode_ASC_shared_ride_3_plus,joint - Ride Hail tour mode ASC -- shared ride 3+,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,joint_ride_hail_ASC_sr3p,joint_ride_hail_ASC_sr3p,,,,,,,,,,,,,,, +util_joint_Ride_Hail_tour_mode_ASC_walk,joint - Ride Hail tour mode ASC -- walk,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,joint_ride_hail_ASC_walk,,,,,,,,,,,,,, +util_joint_Ride_Hail_tour_mode_ASC_walk_to_transit,joint - Ride Hail tour mode ASC -- walk to transit,@(df.is_joint & df.tour_mode_is_ride_hail),,,,,,,,,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,joint_ride_hail_ASC_walk_transit,,,,,,,, +util_joint_Ride_Hail_tour_mode_ASC_ride_hail_taxi,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_taxi,, +util_joint_Ride_Hail_tour_mode_ASC_ride_hail_single,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_tnc_single, +util_joint_Ride_Hail_tour_mode_ASC_ride_hail_shared,joint - Ride Hail tour mode ASC -- ride hail,@(df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,,,,joint_ride_hail_ASC_tnc_shared +#,#,,,,,,,,,,,,,,,,,,,,,, +util_Walk_not_available_for_long_distances,Walk not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTWALK'] > 3),,,,,,,-999,,,,,,,,,,,,,, +util_Bike_not_available_for_long_distances,Bike not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTBIKE'] > 8),,,,,,,,-999,,,,,,,,,,,,, +util_origin_density_index,Origin density index,@origin_density_applied*(origin_density_index_multiplier*df.origin_density_index).clip(origin_density_index_max),,,,,,,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,,,,,,,coef_ivt,coef_ivt +util_walk_express_penalty,Walk-express penalty for intermediate stops,@walk_express_penalty * ~(df.first_trip | df.first_trip),,,,,,,,,,,coef_ivt,,,,,,,,,, +util_adjust_tnc_shared,TNC shared adjustment,@adjust_tnc_shared,,,,,,,,,,,,,,,,,,,,,coef_ivt diff --git a/activitysim/examples/example_psrc/configs/trip_mode_choice.yaml b/activitysim/examples/prototype_mtc/configs/trip_mode_choice.yaml similarity index 100% rename from activitysim/examples/example_psrc/configs/trip_mode_choice.yaml rename to activitysim/examples/prototype_mtc/configs/trip_mode_choice.yaml diff --git a/activitysim/examples/example_mtc/configs/trip_mode_choice_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/trip_mode_choice_annotate_trips_preprocessor.csv similarity index 100% rename from activitysim/examples/example_mtc/configs/trip_mode_choice_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_mtc/configs/trip_mode_choice_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/example_psrc/configs/trip_mode_choice_coefficients.csv b/activitysim/examples/prototype_mtc/configs/trip_mode_choice_coefficients.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/trip_mode_choice_coefficients.csv rename to activitysim/examples/prototype_mtc/configs/trip_mode_choice_coefficients.csv diff --git a/activitysim/examples/example_psrc/configs/trip_mode_choice_coefficients_template.csv b/activitysim/examples/prototype_mtc/configs/trip_mode_choice_coefficients_template.csv similarity index 100% rename from activitysim/examples/example_psrc/configs/trip_mode_choice_coefficients_template.csv rename to activitysim/examples/prototype_mtc/configs/trip_mode_choice_coefficients_template.csv diff --git a/activitysim/examples/example_mtc/configs/trip_purpose.yaml b/activitysim/examples/prototype_mtc/configs/trip_purpose.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/trip_purpose.yaml rename to activitysim/examples/prototype_mtc/configs/trip_purpose.yaml diff --git a/activitysim/examples/example_psrc/configs/trip_purpose_and_destination.yaml b/activitysim/examples/prototype_mtc/configs/trip_purpose_and_destination.yaml old mode 100755 new mode 100644 similarity index 96% rename from activitysim/examples/example_psrc/configs/trip_purpose_and_destination.yaml rename to activitysim/examples/prototype_mtc/configs/trip_purpose_and_destination.yaml index 4895aa5268..76f5923489 --- a/activitysim/examples/example_psrc/configs/trip_purpose_and_destination.yaml +++ b/activitysim/examples/prototype_mtc/configs/trip_purpose_and_destination.yaml @@ -1,6 +1,6 @@ - -MAX_ITERATIONS: 5 - -# drop failed trips and cleanup failed trip leg_mates for consistency -# (i.e. adjust trip_count, trip_num, first for missing failed trips) -CLEANUP: True + +MAX_ITERATIONS: 5 + +# drop failed trips and cleanup failed trip leg_mates for consistency +# (i.e. adjust trip_count, trip_num, first for missing failed trips) +CLEANUP: True diff --git a/activitysim/examples/example_semcog/configs/trip_purpose_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/trip_purpose_annotate_trips_preprocessor.csv old mode 100755 new mode 100644 similarity index 96% rename from activitysim/examples/example_semcog/configs/trip_purpose_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_mtc/configs/trip_purpose_annotate_trips_preprocessor.csv index 0e20453303..782116aa99 --- a/activitysim/examples/example_semcog/configs/trip_purpose_annotate_trips_preprocessor.csv +++ b/activitysim/examples/prototype_mtc/configs/trip_purpose_annotate_trips_preprocessor.csv @@ -1,5 +1,5 @@ -Description,Target,Expression -#,, -,ptype,"reindex(persons.ptype, df.person_id)" -,person_type,ptype.map(PTYPE_NAME) -,start,"reindex_i(tours.start, df.tour_id)" +Description,Target,Expression +#,, +,ptype,"reindex(persons.ptype, df.person_id)" +,person_type,ptype.map(PTYPE_NAME) +,start,"reindex_i(tours.start, df.tour_id)" diff --git a/activitysim/examples/example_psrc/configs/trip_purpose_probs.csv b/activitysim/examples/prototype_mtc/configs/trip_purpose_probs.csv old mode 100755 new mode 100644 similarity index 98% rename from activitysim/examples/example_psrc/configs/trip_purpose_probs.csv rename to activitysim/examples/prototype_mtc/configs/trip_purpose_probs.csv index 342581d0eb..6827f511e2 --- a/activitysim/examples/example_psrc/configs/trip_purpose_probs.csv +++ b/activitysim/examples/prototype_mtc/configs/trip_purpose_probs.csv @@ -1,132 +1,132 @@ -primary_purpose,outbound,depart_range_start,depart_range_end,person_type,work,univ,school,escort,shopping,othmaint,eatout,social,othdiscr -work,True,5,8,PTYPE_FULL,0.198,0.004,0,0.466,0.083,0.086,0.093,0.004,0.066 -work,True,5,8,PTYPE_PART,0.094,0,0,0.657,0.076,0.07,0.067,0.009,0.027 -work,True,5,8,PTYPE_UNIVERSITY,0.067,0.081,0,0.433,0.005,0.038,0.153,0.108,0.115 -work,True,9,23,PTYPE_FULL,0.278,0.008,0,0.172,0.18,0.193,0.107,0.016,0.046 -work,True,9,23,PTYPE_PART,0.442,0,0,0.089,0.105,0.175,0.102,0.03,0.057 -work,True,9,23,PTYPE_UNIVERSITY,0.049,0.086,0,0.392,0.159,0.157,0.069,0.073,0.015 -work,True,5,23,PTYPE_DRIVING,0,0,0,0,0.2,0.2,0.2,0.2,0.2 -univ,True,5,23,PTYPE_FULL,0.526,0.178,0,0.016,0.16,0.035,0.028,0.057,0 -univ,True,5,23,PTYPE_PART,0.059,0.941,0,0,0,0,0,0,0 -univ,True,5,23,PTYPE_UNIVERSITY,0.109,0.034,0,0.382,0.136,0.147,0.094,0.048,0.05 -school,True,5,23,PTYPE_DRIVING,0,0,0,0.548,0.015,0.1,0.206,0.073,0.058 -school,True,5,23,PTYPE_SCHOOL,0,0,0,0.53,0.025,0.084,0.112,0.048,0.201 -school,True,5,23,PTYPE_PRESCHOOL,0,0,0,0.772,0.007,0.086,0.023,0.071,0.041 -escort,True,5,23,PTYPE_FULL,0,0,0,0.55,0.153,0.084,0.104,0.049,0.06 -escort,True,5,23,PTYPE_PART,0,0,0,0.449,0.194,0.07,0.167,0.059,0.061 -escort,True,5,23,PTYPE_UNIVERSITY,0,0,0,0.509,0.193,0.158,0.048,0.058,0.034 -escort,True,5,23,PTYPE_NONWORK,0,0,0,0.444,0.216,0.084,0.108,0.118,0.03 -escort,True,5,23,PTYPE_RETIRED,0,0,0,0.37,0.204,0.192,0.03,0.068,0.136 -escort,True,5,23,PTYPE_DRIVING,0,0,0,0.586,0.227,0,0.072,0.115,0 -escort,True,5,23,PTYPE_SCHOOL,0,0,0,0.37,0.183,0.29,0.064,0.013,0.08 -escort,True,5,23,PTYPE_PRESCHOOL,0,0,0,0.531,0.064,0,0.131,0.196,0.078 -shopping,True,5,23,PTYPE_FULL,0,0,0,0.102,0.456,0.226,0.11,0.06,0.046 -shopping,True,5,23,PTYPE_PART,0,0,0,0.182,0.291,0.311,0.108,0.031,0.077 -shopping,True,5,23,PTYPE_UNIVERSITY,0,0,0,0.13,0.262,0.36,0.124,0.06,0.064 -shopping,True,5,23,PTYPE_NONWORK,0,0,0,0.144,0.336,0.274,0.122,0.068,0.056 -shopping,True,5,23,PTYPE_RETIRED,0,0,0,0.058,0.357,0.418,0.05,0.047,0.07 -shopping,True,5,23,PTYPE_DRIVING,0,0,0,0.076,0.193,0.298,0.047,0.13,0.256 -shopping,True,5,23,PTYPE_SCHOOL,0,0,0,0.121,0.142,0.232,0.291,0.03,0.184 -shopping,True,5,23,PTYPE_PRESCHOOL,0,0,0,0.138,0.292,0.301,0.187,0.064,0.018 -othmaint,True,5,23,PTYPE_FULL,0,0,0,0.201,0.252,0.366,0.117,0.032,0.032 -othmaint,True,5,23,PTYPE_PART,0,0,0,0.27,0.259,0.325,0.109,0,0.037 -othmaint,True,5,23,PTYPE_UNIVERSITY,0,0,0,0.489,0.13,0.167,0.025,0.15,0.039 -othmaint,True,5,23,PTYPE_NONWORK,0,0,0,0.279,0.229,0.344,0.078,0.039,0.031 -othmaint,True,5,23,PTYPE_RETIRED,0,0,0,0.224,0.139,0.321,0.098,0.064,0.154 -othmaint,True,5,23,PTYPE_DRIVING,0,0,0,0.135,0,0.259,0.083,0.523,0 -othmaint,True,5,23,PTYPE_SCHOOL,0,0,0,0.191,0.408,0.344,0.041,0.008,0.008 -othmaint,True,5,23,PTYPE_PRESCHOOL,0,0,0,0.143,0.301,0.464,0.017,0.029,0.046 -eatout,True,5,23,PTYPE_FULL,0,0,0,0.144,0.283,0.202,0.036,0.129,0.206 -eatout,True,5,23,PTYPE_PART,0,0,0,0.169,0.374,0.179,0.013,0.135,0.13 -eatout,True,5,23,PTYPE_UNIVERSITY,0,0,0,0.32,0.085,0.111,0,0.153,0.331 -eatout,True,5,23,PTYPE_NONWORK,0,0,0,0.201,0.224,0.269,0.063,0.082,0.161 -eatout,True,5,23,PTYPE_RETIRED,0,0,0,0.142,0.237,0.237,0.034,0.123,0.227 -eatout,True,5,23,PTYPE_DRIVING,0,0,0,0.175,0.289,0.346,0,0.105,0.085 -eatout,True,5,23,PTYPE_SCHOOL,0,0,0,0.124,0.135,0.135,0.04,0.048,0.518 -eatout,True,5,23,PTYPE_PRESCHOOL,0,0,0,0.055,0.329,0.165,0.061,0,0.39 -social,True,5,23,PTYPE_FULL,0,0,0,0.186,0.382,0.144,0.122,0.126,0.04 -social,True,5,23,PTYPE_PART,0,0,0,0.175,0.153,0.167,0.147,0.183,0.175 -social,True,5,23,PTYPE_UNIVERSITY,0,0,0,0,0.212,0.091,0.432,0.234,0.031 -social,True,5,23,PTYPE_NONWORK,0,0,0,0.311,0.392,0.149,0.071,0.058,0.019 -social,True,5,23,PTYPE_RETIRED,0,0,0,0.12,0.407,0.203,0.151,0.102,0.017 -social,True,5,23,PTYPE_DRIVING,0,0,0,0,0,0,0,0.415,0.585 -social,True,5,23,PTYPE_SCHOOL,0,0,0,0.322,0.11,0.05,0,0.378,0.14 -social,True,5,23,PTYPE_PRESCHOOL,0,0,0,0.294,0,0.159,0,0.547,0 -othdiscr,True,5,23,PTYPE_FULL,0,0,0,0.236,0.169,0.143,0.19,0.093,0.169 -othdiscr,True,5,23,PTYPE_PART,0,0,0,0.223,0.208,0.181,0.193,0.129,0.066 -othdiscr,True,5,23,PTYPE_UNIVERSITY,0,0,0,0.135,0.123,0.061,0.342,0.123,0.216 -othdiscr,True,5,23,PTYPE_NONWORK,0,0,0,0.263,0.295,0.148,0.088,0.082,0.124 -othdiscr,True,5,23,PTYPE_RETIRED,0,0,0,0.225,0.056,0.389,0.16,0.091,0.079 -othdiscr,True,5,23,PTYPE_DRIVING,0,0,0,0.311,0.126,0.051,0.018,0.142,0.352 -othdiscr,True,5,23,PTYPE_SCHOOL,0,0,0,0.222,0.112,0.172,0.173,0.141,0.18 -othdiscr,True,5,23,PTYPE_PRESCHOOL,0,0,0,0.271,0.108,0.393,0.146,0.043,0.039 -atwork,True,5,23,PTYPE_FULL,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014 -atwork,True,5,23,PTYPE_PART,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014 -atwork,True,5,23,PTYPE_UNIVERSITY,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014 -atwork,True,5,23,PTYPE_DRIVING,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014 -#,,,,,,,,,,,,, -work,False,5,14,PTYPE_FULL,0.175,0,0,0.14,0.27,0.162,0.134,0.05,0.069 -work,False,5,14,PTYPE_PART,0.097,0,0,0.252,0.211,0.192,0.159,0.089,0 -work,False,5,14,PTYPE_UNIVERSITY,0.134,0,0,0.329,0.114,0.212,0.169,0.042,0 -work,False,15,23,PTYPE_FULL,0.151,0.011,0,0.201,0.28,0.127,0.103,0.035,0.092 -work,False,15,23,PTYPE_PART,0.11,0,0,0.243,0.281,0.13,0.119,0.036,0.081 -work,False,15,23,PTYPE_UNIVERSITY,0.058,0.127,0,0.224,0.269,0.079,0.072,0.108,0.063 -work,False,5,23,PTYPE_DRIVING,0,0,0,0,0.2,0.2,0.2,0.2,0.2 -univ,False,5,23,PTYPE_FULL,0.352,0.032,0,0.032,0.146,0.114,0.177,0.028,0.119 -univ,False,5,23,PTYPE_PART,0,0,0,0.822,0.178,0,0,0,0 -univ,False,5,23,PTYPE_UNIVERSITY,0.054,0.025,0,0.194,0.209,0.179,0.159,0.067,0.113 -school,False,5,23,PTYPE_DRIVING,0,0,0,0.301,0.117,0.098,0.169,0.186,0.129 -school,False,5,23,PTYPE_SCHOOL,0,0,0,0.166,0.158,0.147,0.122,0.133,0.274 -school,False,5,23,PTYPE_PRESCHOOL,0,0,0,0.38,0.148,0.089,0.146,0.102,0.135 -escort,False,5,23,PTYPE_FULL,0,0,0,0.343,0.235,0.114,0.222,0.039,0.047 -escort,False,5,23,PTYPE_PART,0,0,0,0.24,0.298,0.128,0.157,0.045,0.132 -escort,False,5,23,PTYPE_UNIVERSITY,0,0,0,0.195,0.319,0.287,0.02,0.027,0.152 -escort,False,5,23,PTYPE_NONWORK,0,0,0,0.28,0.325,0.169,0.103,0.05,0.073 -escort,False,5,23,PTYPE_RETIRED,0,0,0,0.31,0.317,0.073,0.111,0.112,0.077 -escort,False,5,23,PTYPE_DRIVING,0,0,0,0,0.489,0,0.148,0.363,0 -escort,False,5,23,PTYPE_SCHOOL,0,0,0,0.188,0.259,0.129,0.202,0.06,0.162 -escort,False,5,23,PTYPE_PRESCHOOL,0,0,0,0.413,0.215,0.118,0.211,0.019,0.024 -shopping,False,5,23,PTYPE_FULL,0,0,0,0.091,0.526,0.159,0.152,0.047,0.025 -shopping,False,5,23,PTYPE_PART,0,0,0,0.104,0.553,0.156,0.105,0.037,0.045 -shopping,False,5,23,PTYPE_UNIVERSITY,0,0,0,0.1,0.43,0.064,0.344,0.003,0.059 -shopping,False,5,23,PTYPE_NONWORK,0,0,0,0.11,0.528,0.158,0.122,0.059,0.023 -shopping,False,5,23,PTYPE_RETIRED,0,0,0,0.052,0.549,0.159,0.123,0.06,0.057 -shopping,False,5,23,PTYPE_DRIVING,0,0,0,0.118,0.707,0,0.041,0.134,0 -shopping,False,5,23,PTYPE_SCHOOL,0,0,0,0.015,0.19,0.256,0.157,0.179,0.203 -shopping,False,5,23,PTYPE_PRESCHOOL,0,0,0,0.206,0.172,0.22,0.202,0.158,0.042 -othmaint,False,5,23,PTYPE_FULL,0,0,0,0.171,0.364,0.215,0.159,0.029,0.062 -othmaint,False,5,23,PTYPE_PART,0,0,0,0.228,0.365,0.17,0.13,0.041,0.066 -othmaint,False,5,23,PTYPE_UNIVERSITY,0,0,0,0.046,0.345,0.192,0.298,0.06,0.059 -othmaint,False,5,23,PTYPE_NONWORK,0,0,0,0.17,0.423,0.158,0.171,0.064,0.014 -othmaint,False,5,23,PTYPE_RETIRED,0,0,0,0.099,0.391,0.213,0.241,0.036,0.02 -othmaint,False,5,23,PTYPE_DRIVING,0,0,0,0.031,0.356,0.075,0.458,0.031,0.049 -othmaint,False,5,23,PTYPE_SCHOOL,0,0,0,0.181,0.255,0.142,0.313,0,0.109 -othmaint,False,5,23,PTYPE_PRESCHOOL,0,0,0,0.164,0.249,0.338,0.053,0.006,0.19 -eatout,False,5,23,PTYPE_FULL,0,0,0,0.106,0.44,0.112,0.041,0.128,0.173 -eatout,False,5,23,PTYPE_PART,0,0,0,0.168,0.331,0.225,0.023,0.063,0.19 -eatout,False,5,23,PTYPE_UNIVERSITY,0,0,0,0.165,0.334,0.104,0.088,0.135,0.174 -eatout,False,5,23,PTYPE_NONWORK,0,0,0,0.148,0.547,0.092,0.056,0.055,0.102 -eatout,False,5,23,PTYPE_RETIRED,0,0,0,0.166,0.414,0.169,0.02,0.166,0.065 -eatout,False,5,23,PTYPE_DRIVING,0,0,0,0.195,0.332,0.114,0.114,0,0.245 -eatout,False,5,23,PTYPE_SCHOOL,0,0,0,0.072,0.356,0.053,0.019,0.169,0.331 -eatout,False,5,23,PTYPE_PRESCHOOL,0,0,0,0.01,0.286,0.045,0.117,0.064,0.478 -social,False,5,23,PTYPE_FULL,0,0,0,0.12,0.286,0.123,0.19,0.255,0.026 -social,False,5,23,PTYPE_PART,0,0,0,0.106,0.122,0.039,0.553,0.047,0.133 -social,False,5,23,PTYPE_UNIVERSITY,0,0,0,0.105,0.274,0.176,0,0.206,0.239 -social,False,5,23,PTYPE_NONWORK,0,0,0,0.313,0.326,0.13,0.062,0.075,0.094 -social,False,5,23,PTYPE_RETIRED,0,0,0,0.097,0.338,0.067,0.156,0.328,0.014 -social,False,5,23,PTYPE_DRIVING,0,0,0,0,0,0.368,0.15,0.482,0 -social,False,5,23,PTYPE_SCHOOL,0,0,0,0.058,0.162,0.085,0.281,0.125,0.289 -social,False,5,23,PTYPE_PRESCHOOL,0,0,0,0.23,0.028,0.072,0.23,0.44,0 -othdiscr,False,5,23,PTYPE_FULL,0,0,0,0.108,0.319,0.132,0.27,0.112,0.059 -othdiscr,False,5,23,PTYPE_PART,0,0,0,0.102,0.346,0.154,0.181,0.087,0.13 -othdiscr,False,5,23,PTYPE_UNIVERSITY,0,0,0,0.116,0.374,0.124,0.162,0.033,0.191 -othdiscr,False,5,23,PTYPE_NONWORK,0,0,0,0.11,0.389,0.19,0.19,0.067,0.054 -othdiscr,False,5,23,PTYPE_RETIRED,0,0,0,0.111,0.284,0.186,0.197,0.111,0.111 -othdiscr,False,5,23,PTYPE_DRIVING,0,0,0,0.277,0.304,0.057,0.205,0.157,0 -othdiscr,False,5,23,PTYPE_SCHOOL,0,0,0,0.114,0.204,0.148,0.291,0.089,0.154 -othdiscr,False,5,23,PTYPE_PRESCHOOL,0,0,0,0.335,0.133,0.111,0.282,0.052,0.087 -atwork,False,5,23,PTYPE_FULL,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018 -atwork,False,5,23,PTYPE_PART,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018 -atwork,False,5,23,PTYPE_UNIVERSITY,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018 -atwork,False,5,23,PTYPE_DRIVING,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018 +primary_purpose,outbound,depart_range_start,depart_range_end,person_type,work,univ,school,escort,shopping,othmaint,eatout,social,othdiscr +work,True,5,8,PTYPE_FULL,0.198,0.004,0,0.466,0.083,0.086,0.093,0.004,0.066 +work,True,5,8,PTYPE_PART,0.094,0,0,0.657,0.076,0.07,0.067,0.009,0.027 +work,True,5,8,PTYPE_UNIVERSITY,0.067,0.081,0,0.433,0.005,0.038,0.153,0.108,0.115 +work,True,9,23,PTYPE_FULL,0.278,0.008,0,0.172,0.18,0.193,0.107,0.016,0.046 +work,True,9,23,PTYPE_PART,0.442,0,0,0.089,0.105,0.175,0.102,0.03,0.057 +work,True,9,23,PTYPE_UNIVERSITY,0.049,0.086,0,0.392,0.159,0.157,0.069,0.073,0.015 +work,True,5,23,PTYPE_DRIVING,0,0,0,0,0.2,0.2,0.2,0.2,0.2 +univ,True,5,23,PTYPE_FULL,0.526,0.178,0,0.016,0.16,0.035,0.028,0.057,0 +univ,True,5,23,PTYPE_PART,0.059,0.941,0,0,0,0,0,0,0 +univ,True,5,23,PTYPE_UNIVERSITY,0.109,0.034,0,0.382,0.136,0.147,0.094,0.048,0.05 +school,True,5,23,PTYPE_DRIVING,0,0,0,0.548,0.015,0.1,0.206,0.073,0.058 +school,True,5,23,PTYPE_SCHOOL,0,0,0,0.53,0.025,0.084,0.112,0.048,0.201 +school,True,5,23,PTYPE_PRESCHOOL,0,0,0,0.772,0.007,0.086,0.023,0.071,0.041 +escort,True,5,23,PTYPE_FULL,0,0,0,0.55,0.153,0.084,0.104,0.049,0.06 +escort,True,5,23,PTYPE_PART,0,0,0,0.449,0.194,0.07,0.167,0.059,0.061 +escort,True,5,23,PTYPE_UNIVERSITY,0,0,0,0.509,0.193,0.158,0.048,0.058,0.034 +escort,True,5,23,PTYPE_NONWORK,0,0,0,0.444,0.216,0.084,0.108,0.118,0.03 +escort,True,5,23,PTYPE_RETIRED,0,0,0,0.37,0.204,0.192,0.03,0.068,0.136 +escort,True,5,23,PTYPE_DRIVING,0,0,0,0.586,0.227,0,0.072,0.115,0 +escort,True,5,23,PTYPE_SCHOOL,0,0,0,0.37,0.183,0.29,0.064,0.013,0.08 +escort,True,5,23,PTYPE_PRESCHOOL,0,0,0,0.531,0.064,0,0.131,0.196,0.078 +shopping,True,5,23,PTYPE_FULL,0,0,0,0.102,0.456,0.226,0.11,0.06,0.046 +shopping,True,5,23,PTYPE_PART,0,0,0,0.182,0.291,0.311,0.108,0.031,0.077 +shopping,True,5,23,PTYPE_UNIVERSITY,0,0,0,0.13,0.262,0.36,0.124,0.06,0.064 +shopping,True,5,23,PTYPE_NONWORK,0,0,0,0.144,0.336,0.274,0.122,0.068,0.056 +shopping,True,5,23,PTYPE_RETIRED,0,0,0,0.058,0.357,0.418,0.05,0.047,0.07 +shopping,True,5,23,PTYPE_DRIVING,0,0,0,0.076,0.193,0.298,0.047,0.13,0.256 +shopping,True,5,23,PTYPE_SCHOOL,0,0,0,0.121,0.142,0.232,0.291,0.03,0.184 +shopping,True,5,23,PTYPE_PRESCHOOL,0,0,0,0.138,0.292,0.301,0.187,0.064,0.018 +othmaint,True,5,23,PTYPE_FULL,0,0,0,0.201,0.252,0.366,0.117,0.032,0.032 +othmaint,True,5,23,PTYPE_PART,0,0,0,0.27,0.259,0.325,0.109,0,0.037 +othmaint,True,5,23,PTYPE_UNIVERSITY,0,0,0,0.489,0.13,0.167,0.025,0.15,0.039 +othmaint,True,5,23,PTYPE_NONWORK,0,0,0,0.279,0.229,0.344,0.078,0.039,0.031 +othmaint,True,5,23,PTYPE_RETIRED,0,0,0,0.224,0.139,0.321,0.098,0.064,0.154 +othmaint,True,5,23,PTYPE_DRIVING,0,0,0,0.135,0,0.259,0.083,0.523,0 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a/activitysim/examples/example_mtc/configs/trip_scheduling_probs.csv +++ b/activitysim/examples/prototype_mtc/configs/trip_scheduling_probs.csv @@ -1,1369 +1,1369 @@ -primary_purpose,outbound,tour_hour,trip_num,HR5,HR6,HR7,HR8,HR9,HR10,HR11,HR12,HR13,HR14,HR15,HR16,HR17,HR18,HR19,HR20,HR21,HR22,HR23 -work,TRUE,5,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -work,TRUE,5,2,0.249730906,0.477180111,0.215788882,0.02257625,0.009653299,0.001272067,0.002559828,0.005345297,0.012868196,0.000858457,0,0.00130551,0,0.000861198,0,0,0,0,0 -work,TRUE,5,3,0.269166724,0.331378773,0.290398422,0.047428828,0.032211326,0.003681738,0,0.00648104,0.007547054,0.006178507,0,0.005527589,0,0,0,0,0,0,0 -work,TRUE,5,4,0.087782501,0.257488508,0.384088251,0.077346978,0.060562922,0,0,0.049138541,0,0.014538525,0,0,0,0.041701151,0.018235082,0,0.009117541,0,0 -work,TRUE,6,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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activitysim/examples/example_psrc/configs/workplace_location.csv rename to activitysim/examples/prototype_mtc/configs/workplace_location.csv index ea56a39c9f..3ac8b59e96 --- a/activitysim/examples/example_psrc/configs/workplace_location.csv +++ b/activitysim/examples/prototype_mtc/configs/workplace_location.csv @@ -1,14 +1,14 @@ -Label,Description,Expression,coefficient -local_dist,,_DIST@skims['DIST'],1 -util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_dist_0_1 -util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_dist_1_2 -util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_dist_2_5 -util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_dist_5_15 -util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_dist_15_up -util_dist_0_5_high,"Distance 0 to 5 mi, high and very high income",@(df['income_segment']>=WORK_HIGH_SEGMENT_ID) * _DIST.clip(upper=5),coef_dist_0_5_high -util_dist_15_up_high,"Distance 5+ mi, high and very high income",@(df['income_segment']>=WORK_HIGH_SEGMENT_ID) * (_DIST-5).clip(0),coef_dist_5_up_high -util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1 -util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1 -util_no_attractions,No attractions,@df['size_term']==0,-999 -util_mode_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum +Label,Description,Expression,coefficient +local_dist,,_DIST@skims['DIST'],1 +util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_dist_0_1 +util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_dist_1_2 +util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_dist_2_5 +util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_dist_5_15 +util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_dist_15_up +util_dist_0_5_high,"Distance 0 to 5 mi, high and very high income",@(df['income_segment']>=WORK_HIGH_SEGMENT_ID) * _DIST.clip(upper=5),coef_dist_0_5_high +util_dist_15_up_high,"Distance 5+ mi, high and very high income",@(df['income_segment']>=WORK_HIGH_SEGMENT_ID) * (_DIST-5).clip(0),coef_dist_5_up_high +util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1 +util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1 +util_no_attractions,No attractions,@df['size_term']==0,-999 +util_mode_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1 \ No newline at end of file diff --git a/activitysim/examples/example_mtc/configs/workplace_location.yaml b/activitysim/examples/prototype_mtc/configs/workplace_location.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/workplace_location.yaml rename to activitysim/examples/prototype_mtc/configs/workplace_location.yaml diff --git a/activitysim/examples/example_psrc/configs/workplace_location_coeffs.csv b/activitysim/examples/prototype_mtc/configs/workplace_location_coefficients.csv old mode 100755 new mode 100644 similarity index 96% rename from activitysim/examples/example_psrc/configs/workplace_location_coeffs.csv rename to activitysim/examples/prototype_mtc/configs/workplace_location_coefficients.csv index 709b0b4102..b3ac834103 --- a/activitysim/examples/example_psrc/configs/workplace_location_coeffs.csv +++ b/activitysim/examples/prototype_mtc/configs/workplace_location_coefficients.csv @@ -1,9 +1,9 @@ -coefficient_name,value,constrain -coef_dist_0_1,-0.8428,F -coef_dist_1_2,-0.3104,F -coef_dist_2_5,-0.3783,F -coef_dist_5_15,-0.1285,F -coef_dist_15_up,-0.0917,F -coef_dist_0_5_high,0.15,F -coef_dist_5_up_high,0.02,F -coef_mode_logsum,0.3,F +coefficient_name,value,constrain +coef_dist_0_1,-0.8428,F +coef_dist_1_2,-0.3104,F +coef_dist_2_5,-0.3783,F +coef_dist_5_15,-0.1285,F +coef_dist_15_up,-0.0917,F +coef_dist_0_5_high,0.15,F +coef_dist_5_up_high,0.02,F +coef_mode_logsum,0.3,F diff --git a/activitysim/examples/example_psrc/configs/workplace_location_sample.csv b/activitysim/examples/prototype_mtc/configs/workplace_location_sample.csv old mode 100755 new mode 100644 similarity index 99% rename from activitysim/examples/example_psrc/configs/workplace_location_sample.csv rename to activitysim/examples/prototype_mtc/configs/workplace_location_sample.csv index e97161217d..0216bae26f --- a/activitysim/examples/example_psrc/configs/workplace_location_sample.csv +++ b/activitysim/examples/prototype_mtc/configs/workplace_location_sample.csv @@ -1,12 +1,12 @@ -Label,Description,Expression,coefficient -local_dist,,_DIST@skims['DIST'],1 -util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_dist_0_1 -util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_dist_1_2 -util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_dist_2_5 -util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_dist_5_15 -util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_dist_15_up -util_dist_0_5_high,"Distance 0 to 5 mi, high and very high income",@(df['income_segment']>=WORK_HIGH_SEGMENT_ID) * _DIST.clip(upper=5),coef_dist_0_5_high -util_dist_15_up_high,"Distance 5+ mi, high and very high income",@(df['income_segment']>=WORK_HIGH_SEGMENT_ID) * (_DIST-5).clip(0),coef_dist_5_up_high -util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1 -util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1 +Label,Description,Expression,coefficient +local_dist,,_DIST@skims['DIST'],1 +util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_dist_0_1 +util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_dist_1_2 +util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_dist_2_5 +util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_dist_5_15 +util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_dist_15_up +util_dist_0_5_high,"Distance 0 to 5 mi, high and very high income",@(df['income_segment']>=WORK_HIGH_SEGMENT_ID) * _DIST.clip(upper=5),coef_dist_0_5_high +util_dist_15_up_high,"Distance 5+ mi, high and very high income",@(df['income_segment']>=WORK_HIGH_SEGMENT_ID) * (_DIST-5).clip(0),coef_dist_5_up_high +util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1 +util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1 util_no_attractions,No attractions,@df['size_term']==0,-999 \ No newline at end of file diff --git a/activitysim/examples/example_mtc/configs/write_data_dictionary.yaml b/activitysim/examples/prototype_mtc/configs/write_data_dictionary.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs/write_data_dictionary.yaml rename to activitysim/examples/prototype_mtc/configs/write_data_dictionary.yaml diff --git a/activitysim/examples/example_mtc/configs/write_trip_matrices.yaml b/activitysim/examples/prototype_mtc/configs/write_trip_matrices.yaml similarity index 96% rename from activitysim/examples/example_mtc/configs/write_trip_matrices.yaml rename to activitysim/examples/prototype_mtc/configs/write_trip_matrices.yaml index a9f14a336d..095b3160cc 100644 --- a/activitysim/examples/example_mtc/configs/write_trip_matrices.yaml +++ b/activitysim/examples/prototype_mtc/configs/write_trip_matrices.yaml @@ -1,277 +1,277 @@ -# read trips table post preprocessor and run expressions to code -# additional data fields, with one data fields for each matrix specified below - -preprocessor: - SPEC: write_trip_matrices_annotate_trips_preprocessor - DF: trips - TABLES: - - tours - -# divide trip counts by household expansion factor -HH_EXPANSION_WEIGHT_COL: sample_rate # added when households read in - -# save preprocessed trips table to pipeline if desired -SAVE_TRIPS_TABLE: False - -MATRICES: - - file_name: trips_ea.omx - tables: - - name: DRIVEALONEFREE_EA - data_field: DRIVEALONEFREE_EA - - name: DRIVEALONEPAY_EA - data_field: DRIVEALONEPAY_EA - - name: SHARED2FREE_EA - data_field: SHARED2FREE_EA - - name: SHARED2PAY_EA - data_field: SHARED2PAY_EA - - name: SHARED3FREE_EA - data_field: SHARED3FREE_EA - - name: SHARED3PAY_EA - data_field: SHARED3PAY_EA - - name: WALK_EA - data_field: WALK_EA - - name: BIKE_EA - data_field: BIKE_EA - - name: WALK_LOC_WALK_EA - data_field: WALK_LOC_WALK_EA - - name: WALK_LRF_WALK_EA - data_field: WALK_LRF_WALK_EA - - name: WALK_EXP_WALK_EA - data_field: WALK_EXP_WALK_EA - - name: WALK_HVY_WALK_EA - data_field: WALK_HVY_WALK_EA - - name: WALK_COM_WALK_EA - data_field: WALK_COM_WALK_EA - - name: DRIVE_LOC_WALK_EA - data_field: DRIVE_LOC_WALK_EA - - name: DRIVE_LRF_WALK_EA - data_field: DRIVE_LRF_WALK_EA - - name: DRIVE_EXP_WALK_EA - data_field: DRIVE_EXP_WALK_EA - - name: DRIVE_HVY_WALK_EA - data_field: DRIVE_HVY_WALK_EA - - name: DRIVE_COM_WALK_EA - data_field: DRIVE_COM_WALK_EA - - name: WALK_LOC_DRIVE_EA - data_field: WALK_LOC_DRIVE_EA - - name: WALK_LRF_DRIVE_EA - data_field: WALK_LRF_DRIVE_EA - - name: WALK_EXP_DRIVE_EA - data_field: WALK_EXP_DRIVE_EA - - name: WALK_DRIVE_HVY_EA - data_field: WALK_DRIVE_HVY_EA - - name: WALK_COM_DRIVE_EA - data_field: WALK_COM_DRIVE_EA - - file_name: trips_am.omx - tables: - - name: DRIVEALONEFREE_AM - data_field: DRIVEALONEFREE_AM - - name: DRIVEALONEPAY_AM - data_field: DRIVEALONEPAY_AM - - name: SHARED2FREE_AM - data_field: SHARED2FREE_AM - - name: SHARED2PAY_AM - data_field: SHARED2PAY_AM - - name: SHARED3FREE_AM - data_field: SHARED3FREE_AM - - name: SHARED3PAY_AM - data_field: SHARED3PAY_AM - - name: WALK_AM - data_field: WALK_AM - - name: BIKE_AM - data_field: BIKE_AM - - name: WALK_LOC_WALK_AM - data_field: WALK_LOC_WALK_AM - - name: WALK_LRF_WALK_AM - data_field: WALK_LRF_WALK_AM - - name: WALK_EXP_WALK_AM - data_field: WALK_EXP_WALK_AM - - name: WALK_HVY_WALK_AM - data_field: WALK_HVY_WALK_AM - - name: WALK_COM_WALK_AM - data_field: WALK_COM_WALK_AM - - name: DRIVE_LOC_WALK_AM - data_field: DRIVE_LOC_WALK_AM - - name: DRIVE_LRF_WALK_AM - data_field: DRIVE_LRF_WALK_AM - - name: DRIVE_EXP_WALK_AM - data_field: DRIVE_EXP_WALK_AM - - name: DRIVE_HVY_WALK_AM - data_field: DRIVE_HVY_WALK_AM - - name: DRIVE_COM_WALK_AM - data_field: DRIVE_COM_WALK_AM - - name: WALK_LOC_DRIVE_AM - data_field: WALK_LOC_DRIVE_AM - - name: WALK_LRF_DRIVE_AM - data_field: WALK_LRF_DRIVE_AM - - name: WALK_EXP_DRIVE_AM - data_field: WALK_EXP_DRIVE_AM - - name: WALK_DRIVE_HVY_AM - data_field: WALK_DRIVE_HVY_AM - - name: WALK_COM_DRIVE_AM - data_field: WALK_COM_DRIVE_AM - - file_name: trips_md.omx - tables: - - name: DRIVEALONEFREE_MD - data_field: DRIVEALONEFREE_MD - - name: DRIVEALONEPAY_MD - data_field: DRIVEALONEPAY_MD - - name: SHARED2FREE_MD - data_field: SHARED2FREE_MD - - name: SHARED2PAY_MD - data_field: SHARED2PAY_MD - - name: SHARED3FREE_MD - data_field: SHARED3FREE_MD - - name: SHARED3PAY_MD - data_field: SHARED3PAY_MD - - name: WALK_MD - data_field: WALK_MD - - name: BIKE_MD - data_field: BIKE_MD - - name: WALK_LOC_WALK_MD - data_field: WALK_LOC_WALK_MD - - name: WALK_LRF_WALK_MD - data_field: WALK_LRF_WALK_MD - - name: WALK_EXP_WALK_MD - data_field: WALK_EXP_WALK_MD - - name: WALK_HVY_WALK_MD - data_field: WALK_HVY_WALK_MD - - name: WALK_COM_WALK_MD - data_field: WALK_COM_WALK_MD - - name: DRIVE_LOC_WALK_MD - data_field: DRIVE_LOC_WALK_MD - - name: DRIVE_LRF_WALK_MD - data_field: DRIVE_LRF_WALK_MD - - name: DRIVE_EXP_WALK_MD - data_field: DRIVE_EXP_WALK_MD - - name: DRIVE_HVY_WALK_MD - data_field: DRIVE_HVY_WALK_MD - - name: DRIVE_COM_WALK_MD - data_field: DRIVE_COM_WALK_MD - - name: WALK_LOC_DRIVE_MD - data_field: WALK_LOC_DRIVE_MD - - name: WALK_LRF_DRIVE_MD - data_field: WALK_LRF_DRIVE_MD - - name: WALK_EXP_DRIVE_MD - data_field: WALK_EXP_DRIVE_MD - - name: WALK_DRIVE_HVY_MD - data_field: WALK_DRIVE_HVY_MD - - name: WALK_COM_DRIVE_MD - data_field: WALK_COM_DRIVE_MD - - file_name: trips_pm.omx - tables: - - name: DRIVEALONEFREE_PM - data_field: DRIVEALONEFREE_PM - - name: DRIVEALONEPAY_PM - data_field: DRIVEALONEPAY_PM - - name: SHARED2FREE_PM - data_field: SHARED2FREE_PM - - name: SHARED2PAY_PM - data_field: SHARED2PAY_PM - - name: SHARED3FREE_PM - data_field: SHARED3FREE_PM - - name: SHARED3PAY_PM - data_field: SHARED3PAY_PM - - name: WALK_PM - data_field: WALK_PM - - name: BIKE_PM - data_field: BIKE_PM - - name: WALK_LOC_WALK_PM - data_field: WALK_LOC_WALK_PM - - name: WALK_LRF_WALK_PM - data_field: WALK_LRF_WALK_PM - - name: WALK_EXP_WALK_PM - data_field: WALK_EXP_WALK_PM - - name: WALK_HVY_WALK_PM - data_field: WALK_HVY_WALK_PM - - name: WALK_COM_WALK_PM - data_field: WALK_COM_WALK_PM - - name: DRIVE_LOC_WALK_PM - data_field: DRIVE_LOC_WALK_PM - - name: DRIVE_LRF_WALK_PM - data_field: DRIVE_LRF_WALK_PM - - name: DRIVE_EXP_WALK_PM - data_field: DRIVE_EXP_WALK_PM - - name: DRIVE_HVY_WALK_PM - data_field: DRIVE_HVY_WALK_PM - - name: DRIVE_COM_WALK_PM - data_field: DRIVE_COM_WALK_PM - - name: WALK_LOC_DRIVE_PM - data_field: WALK_LOC_DRIVE_PM - - name: WALK_LRF_DRIVE_PM - data_field: WALK_LRF_DRIVE_PM - - name: WALK_EXP_DRIVE_PM - data_field: WALK_EXP_DRIVE_PM - - name: WALK_DRIVE_HVY_PM - data_field: WALK_DRIVE_HVY_PM - - name: WALK_COM_DRIVE_PM - data_field: WALK_COM_DRIVE_PM - - file_name: trips_ev.omx - tables: - - name: DRIVEALONEFREE_EV - data_field: DRIVEALONEFREE_EV - - name: DRIVEALONEPAY_EV - data_field: DRIVEALONEPAY_EV - - name: SHARED2FREE_EV - data_field: SHARED2FREE_EV - - name: SHARED2PAY_EV - data_field: SHARED2PAY_EV - - name: SHARED3FREE_EV - data_field: SHARED3FREE_EV - - name: SHARED3PAY_EV - data_field: SHARED3PAY_EV - - name: WALK_EV - data_field: WALK_EV - - name: BIKE_EV - data_field: BIKE_EV - - name: WALK_LOC_WALK_EV - data_field: WALK_LOC_WALK_EV - - name: WALK_LRF_WALK_EV - data_field: WALK_LRF_WALK_EV - - name: WALK_EXP_WALK_EV - data_field: WALK_EXP_WALK_EV - - name: WALK_HVY_WALK_EV - data_field: WALK_HVY_WALK_EV - - name: WALK_COM_WALK_EV - data_field: WALK_COM_WALK_EV - - name: DRIVE_LOC_WALK_EV - data_field: DRIVE_LOC_WALK_EV - - name: DRIVE_LRF_WALK_EV - data_field: DRIVE_LRF_WALK_EV - - name: DRIVE_EXP_WALK_EV - data_field: DRIVE_EXP_WALK_EV - - name: DRIVE_HVY_WALK_EV - data_field: DRIVE_HVY_WALK_EV - - name: DRIVE_COM_WALK_EV - data_field: DRIVE_COM_WALK_EV - - name: WALK_LOC_DRIVE_EV - data_field: WALK_LOC_DRIVE_EV - - name: WALK_LRF_DRIVE_EV - data_field: WALK_LRF_DRIVE_EV - - name: WALK_EXP_DRIVE_EV - data_field: WALK_EXP_DRIVE_EV - - name: WALK_DRIVE_HVY_EV - data_field: WALK_DRIVE_HVY_EV - - name: WALK_COM_DRIVE_EV - data_field: WALK_COM_DRIVE_EV - -CONSTANTS: - time_periods: - EA: - first_hour: 3 - last_hour: 5 - AM: - first_hour: 6 - last_hour: 9 - MD: - first_hour: 10 - last_hour: 14 - PM: - first_hour: 15 - last_hour: 18 - EV: - first_hour: 19 - last_hour: 2 - # SHARED2 and SHARED3 Occupancies - OCC_SHARED2: 2.0 - OCC_SHARED3: 3.33 +# read trips table post preprocessor and run expressions to code +# additional data fields, with one data fields for each matrix specified below + +preprocessor: + SPEC: write_trip_matrices_annotate_trips_preprocessor + DF: trips + TABLES: + - tours + +# divide trip counts by household expansion factor +HH_EXPANSION_WEIGHT_COL: sample_rate # added when households read in + +# save preprocessed trips table to pipeline if desired +SAVE_TRIPS_TABLE: False + +MATRICES: + - file_name: trips_ea.omx + tables: + - name: DRIVEALONEFREE_EA + data_field: DRIVEALONEFREE_EA + - name: DRIVEALONEPAY_EA + data_field: DRIVEALONEPAY_EA + - name: SHARED2FREE_EA + data_field: SHARED2FREE_EA + - name: SHARED2PAY_EA + data_field: SHARED2PAY_EA + - name: SHARED3FREE_EA + data_field: SHARED3FREE_EA + - name: SHARED3PAY_EA + data_field: SHARED3PAY_EA + - name: WALK_EA + data_field: WALK_EA + - name: BIKE_EA + data_field: BIKE_EA + - name: WALK_LOC_WALK_EA + data_field: WALK_LOC_WALK_EA + - name: WALK_LRF_WALK_EA + data_field: WALK_LRF_WALK_EA + - name: WALK_EXP_WALK_EA + data_field: WALK_EXP_WALK_EA + - name: WALK_HVY_WALK_EA + data_field: WALK_HVY_WALK_EA + - name: WALK_COM_WALK_EA + data_field: WALK_COM_WALK_EA + - name: DRIVE_LOC_WALK_EA + data_field: DRIVE_LOC_WALK_EA + - name: DRIVE_LRF_WALK_EA + data_field: DRIVE_LRF_WALK_EA + - name: DRIVE_EXP_WALK_EA + data_field: DRIVE_EXP_WALK_EA + - name: DRIVE_HVY_WALK_EA + data_field: DRIVE_HVY_WALK_EA + - name: DRIVE_COM_WALK_EA + data_field: DRIVE_COM_WALK_EA + - name: WALK_LOC_DRIVE_EA + data_field: WALK_LOC_DRIVE_EA + - name: WALK_LRF_DRIVE_EA + data_field: WALK_LRF_DRIVE_EA + - name: WALK_EXP_DRIVE_EA + data_field: WALK_EXP_DRIVE_EA + - name: WALK_DRIVE_HVY_EA + data_field: WALK_DRIVE_HVY_EA + - name: WALK_COM_DRIVE_EA + data_field: WALK_COM_DRIVE_EA + - file_name: trips_am.omx + tables: + - name: DRIVEALONEFREE_AM + data_field: DRIVEALONEFREE_AM + - name: DRIVEALONEPAY_AM + data_field: DRIVEALONEPAY_AM + - name: SHARED2FREE_AM + data_field: SHARED2FREE_AM + - name: SHARED2PAY_AM + data_field: SHARED2PAY_AM + - name: SHARED3FREE_AM + data_field: SHARED3FREE_AM + - name: SHARED3PAY_AM + data_field: SHARED3PAY_AM + - name: WALK_AM + data_field: WALK_AM + - name: BIKE_AM + data_field: BIKE_AM + - name: WALK_LOC_WALK_AM + data_field: WALK_LOC_WALK_AM + - name: WALK_LRF_WALK_AM + data_field: WALK_LRF_WALK_AM + - name: WALK_EXP_WALK_AM + data_field: WALK_EXP_WALK_AM + - name: WALK_HVY_WALK_AM + data_field: WALK_HVY_WALK_AM + - name: WALK_COM_WALK_AM + data_field: WALK_COM_WALK_AM + - name: DRIVE_LOC_WALK_AM + data_field: DRIVE_LOC_WALK_AM + - name: DRIVE_LRF_WALK_AM + data_field: DRIVE_LRF_WALK_AM + - name: DRIVE_EXP_WALK_AM + data_field: DRIVE_EXP_WALK_AM + - name: DRIVE_HVY_WALK_AM + data_field: DRIVE_HVY_WALK_AM + - name: DRIVE_COM_WALK_AM + data_field: DRIVE_COM_WALK_AM + - name: WALK_LOC_DRIVE_AM + data_field: WALK_LOC_DRIVE_AM + - name: WALK_LRF_DRIVE_AM + data_field: WALK_LRF_DRIVE_AM + - name: WALK_EXP_DRIVE_AM + data_field: WALK_EXP_DRIVE_AM + - name: WALK_DRIVE_HVY_AM + data_field: WALK_DRIVE_HVY_AM + - name: WALK_COM_DRIVE_AM + data_field: WALK_COM_DRIVE_AM + - file_name: trips_md.omx + tables: + - name: DRIVEALONEFREE_MD + data_field: DRIVEALONEFREE_MD + - name: DRIVEALONEPAY_MD + data_field: DRIVEALONEPAY_MD + - name: SHARED2FREE_MD + data_field: SHARED2FREE_MD + - name: SHARED2PAY_MD + data_field: SHARED2PAY_MD + - name: SHARED3FREE_MD + data_field: SHARED3FREE_MD + - name: SHARED3PAY_MD + data_field: SHARED3PAY_MD + - name: WALK_MD + data_field: WALK_MD + - name: BIKE_MD + data_field: BIKE_MD + - name: WALK_LOC_WALK_MD + data_field: WALK_LOC_WALK_MD + - name: WALK_LRF_WALK_MD + data_field: WALK_LRF_WALK_MD + - name: WALK_EXP_WALK_MD + data_field: WALK_EXP_WALK_MD + - name: WALK_HVY_WALK_MD + data_field: WALK_HVY_WALK_MD + - name: WALK_COM_WALK_MD + data_field: WALK_COM_WALK_MD + - name: DRIVE_LOC_WALK_MD + data_field: DRIVE_LOC_WALK_MD + - name: DRIVE_LRF_WALK_MD + data_field: DRIVE_LRF_WALK_MD + - name: DRIVE_EXP_WALK_MD + data_field: DRIVE_EXP_WALK_MD + - name: DRIVE_HVY_WALK_MD + data_field: DRIVE_HVY_WALK_MD + - name: DRIVE_COM_WALK_MD + data_field: DRIVE_COM_WALK_MD + - name: WALK_LOC_DRIVE_MD + data_field: WALK_LOC_DRIVE_MD + - name: WALK_LRF_DRIVE_MD + data_field: WALK_LRF_DRIVE_MD + - name: WALK_EXP_DRIVE_MD + data_field: WALK_EXP_DRIVE_MD + - name: WALK_DRIVE_HVY_MD + data_field: WALK_DRIVE_HVY_MD + - name: WALK_COM_DRIVE_MD + data_field: WALK_COM_DRIVE_MD + - file_name: trips_pm.omx + tables: + - name: DRIVEALONEFREE_PM + data_field: DRIVEALONEFREE_PM + - name: DRIVEALONEPAY_PM + data_field: DRIVEALONEPAY_PM + - name: SHARED2FREE_PM + data_field: SHARED2FREE_PM + - name: SHARED2PAY_PM + data_field: SHARED2PAY_PM + - name: SHARED3FREE_PM + data_field: SHARED3FREE_PM + - name: SHARED3PAY_PM + data_field: SHARED3PAY_PM + - name: WALK_PM + data_field: WALK_PM + - name: BIKE_PM + data_field: BIKE_PM + - name: WALK_LOC_WALK_PM + data_field: WALK_LOC_WALK_PM + - name: WALK_LRF_WALK_PM + data_field: WALK_LRF_WALK_PM + - name: WALK_EXP_WALK_PM + data_field: WALK_EXP_WALK_PM + - name: WALK_HVY_WALK_PM + data_field: WALK_HVY_WALK_PM + - name: WALK_COM_WALK_PM + data_field: WALK_COM_WALK_PM + - name: DRIVE_LOC_WALK_PM + data_field: DRIVE_LOC_WALK_PM + - name: DRIVE_LRF_WALK_PM + data_field: DRIVE_LRF_WALK_PM + - name: DRIVE_EXP_WALK_PM + data_field: DRIVE_EXP_WALK_PM + - name: DRIVE_HVY_WALK_PM + data_field: DRIVE_HVY_WALK_PM + - name: DRIVE_COM_WALK_PM + data_field: DRIVE_COM_WALK_PM + - name: WALK_LOC_DRIVE_PM + data_field: WALK_LOC_DRIVE_PM + - name: WALK_LRF_DRIVE_PM + data_field: WALK_LRF_DRIVE_PM + - name: WALK_EXP_DRIVE_PM + data_field: WALK_EXP_DRIVE_PM + - name: WALK_DRIVE_HVY_PM + data_field: WALK_DRIVE_HVY_PM + - name: WALK_COM_DRIVE_PM + data_field: WALK_COM_DRIVE_PM + - file_name: trips_ev.omx + tables: + - name: DRIVEALONEFREE_EV + data_field: DRIVEALONEFREE_EV + - name: DRIVEALONEPAY_EV + data_field: DRIVEALONEPAY_EV + - name: SHARED2FREE_EV + data_field: SHARED2FREE_EV + - name: SHARED2PAY_EV + data_field: SHARED2PAY_EV + - name: SHARED3FREE_EV + data_field: SHARED3FREE_EV + - name: SHARED3PAY_EV + data_field: SHARED3PAY_EV + - name: WALK_EV + data_field: WALK_EV + - name: BIKE_EV + data_field: BIKE_EV + - name: WALK_LOC_WALK_EV + data_field: WALK_LOC_WALK_EV + - name: WALK_LRF_WALK_EV + data_field: WALK_LRF_WALK_EV + - name: WALK_EXP_WALK_EV + data_field: WALK_EXP_WALK_EV + - name: WALK_HVY_WALK_EV + data_field: WALK_HVY_WALK_EV + - name: WALK_COM_WALK_EV + data_field: WALK_COM_WALK_EV + - name: DRIVE_LOC_WALK_EV + data_field: DRIVE_LOC_WALK_EV + - name: DRIVE_LRF_WALK_EV + data_field: DRIVE_LRF_WALK_EV + - name: DRIVE_EXP_WALK_EV + data_field: DRIVE_EXP_WALK_EV + - name: DRIVE_HVY_WALK_EV + data_field: DRIVE_HVY_WALK_EV + - name: DRIVE_COM_WALK_EV + data_field: DRIVE_COM_WALK_EV + - name: WALK_LOC_DRIVE_EV + data_field: WALK_LOC_DRIVE_EV + - name: WALK_LRF_DRIVE_EV + data_field: WALK_LRF_DRIVE_EV + - name: WALK_EXP_DRIVE_EV + data_field: WALK_EXP_DRIVE_EV + - name: WALK_DRIVE_HVY_EV + data_field: WALK_DRIVE_HVY_EV + - name: WALK_COM_DRIVE_EV + data_field: WALK_COM_DRIVE_EV + +CONSTANTS: + time_periods: + EA: + first_hour: 3 + last_hour: 5 + AM: + first_hour: 6 + last_hour: 9 + MD: + first_hour: 10 + last_hour: 14 + PM: + first_hour: 15 + last_hour: 18 + EV: + first_hour: 19 + last_hour: 2 + # SHARED2 and SHARED3 Occupancies + OCC_SHARED2: 2.0 + OCC_SHARED3: 3.33 diff --git a/activitysim/examples/example_mtc/configs/write_trip_matrices_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_mtc/configs/write_trip_matrices_annotate_trips_preprocessor.csv similarity index 98% rename from activitysim/examples/example_mtc/configs/write_trip_matrices_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_mtc/configs/write_trip_matrices_annotate_trips_preprocessor.csv index 2f740b2da8..1c599d7567 100644 --- a/activitysim/examples/example_mtc/configs/write_trip_matrices_annotate_trips_preprocessor.csv +++ b/activitysim/examples/prototype_mtc/configs/write_trip_matrices_annotate_trips_preprocessor.csv @@ -1,130 +1,130 @@ -Description,Target,Expression -# add additional fields,, -,tour_participants,trips.tour_id.map(tours.number_of_participants) -,distance,od_skims['DIST'] -# code time periods,, -,is_ea,"trips.depart.between(time_periods['EA']['first_hour'], time_periods['EA']['last_hour'])" -,is_am,"trips.depart.between(time_periods['AM']['first_hour'], time_periods['AM']['last_hour'])" -,is_md,"trips.depart.between(time_periods['MD']['first_hour'], time_periods['MD']['last_hour'])" -,is_pm,"trips.depart.between(time_periods['PM']['first_hour'], time_periods['PM']['last_hour'])" -,is_ev,(trips.depart >= time_periods['EV']['first_hour']) | (trips.depart <= time_periods['EV']['last_hour']) -# ea trips,, -,DRIVEALONEFREE_EA,((trips.trip_mode == 'DRIVEALONEFREE') & is_ea) * tour_participants -,DRIVEALONEPAY_EA,((trips.trip_mode == 'DRIVEALONEPAY') & is_ea) * tour_participants -,SHARED2FREE_EA,((trips.trip_mode == 'SHARED2FREE') & is_ea) * tour_participants / OCC_SHARED2 -,SHARED2PAY_EA,((trips.trip_mode == 'SHARED2PAY') & is_ea) * tour_participants / OCC_SHARED2 -,SHARED3FREE_EA,((trips.trip_mode == 'SHARED3FREE') & is_ea) * tour_participants / OCC_SHARED3 -,SHARED3PAY_EA,((trips.trip_mode == 'SHARED3PAY') & is_ea) * tour_participants / OCC_SHARED3 -,WALK_EA,((trips.trip_mode == 'WALK') & is_ea) * tour_participants -,BIKE_EA,((trips.trip_mode == 'BIKE') & is_ea) * tour_participants -,WALK_LOC_WALK_EA,((trips.trip_mode == 'WALK_LOC') & is_ea) * tour_participants -,WALK_LRF_WALK_EA,((trips.trip_mode == 'WALK_LRF') & is_ea) * tour_participants -,WALK_EXP_WALK_EA,((trips.trip_mode == 'WALK_EXP') & is_ea) * tour_participants -,WALK_HVY_WALK_EA,((trips.trip_mode == 'WALK_HVY') & is_ea) * tour_participants -,WALK_COM_WALK_EA,((trips.trip_mode == 'WALK_COM') & is_ea) * tour_participants -,DRIVE_LOC_WALK_EA,((trips.trip_mode == 'DRIVE_LOC') & is_ea & trips.outbound) * tour_participants -,DRIVE_LRF_WALK_EA,((trips.trip_mode == 'DRIVE_LRF') & is_ea & trips.outbound) * tour_participants -,DRIVE_EXP_WALK_EA,((trips.trip_mode == 'DRIVE_EXP') & is_ea & trips.outbound) * tour_participants -,DRIVE_HVY_WALK_EA,((trips.trip_mode == 'DRIVE_HVY') & is_ea & trips.outbound) * tour_participants -,DRIVE_COM_WALK_EA,((trips.trip_mode == 'DRIVE_COM') & is_ea & trips.outbound) * tour_participants -,WALK_LOC_DRIVE_EA,((trips.trip_mode == 'DRIVE_LOC') & is_ea & ~trips.outbound) * tour_participants -,WALK_LRF_DRIVE_EA,((trips.trip_mode == 'DRIVE_LRF') & is_ea & ~trips.outbound) * tour_participants -,WALK_EXP_DRIVE_EA,((trips.trip_mode == 'DRIVE_EXP') & is_ea & ~trips.outbound) * tour_participants -,WALK_DRIVE_HVY_EA,((trips.trip_mode == 'DRIVE_HVY') & is_ea & ~trips.outbound) * tour_participants -,WALK_COM_DRIVE_EA,((trips.trip_mode == 'DRIVE_COM') & is_ea & ~trips.outbound) * tour_participants -# am trips,, -,DRIVEALONEFREE_AM,((trips.trip_mode == 'DRIVEALONEFREE') & is_am) * tour_participants -,DRIVEALONEPAY_AM,((trips.trip_mode == 'DRIVEALONEPAY') & is_am) * tour_participants -,SHARED2FREE_AM,((trips.trip_mode == 'SHARED2FREE') & is_am) * tour_participants / OCC_SHARED2 -,SHARED2PAY_AM,((trips.trip_mode == 'SHARED2PAY') & is_am) * tour_participants / OCC_SHARED2 -,SHARED3FREE_AM,((trips.trip_mode == 'SHARED3FREE') & is_am) * tour_participants / OCC_SHARED3 -,SHARED3PAY_AM,((trips.trip_mode == 'SHARED3PAY') & is_am) * tour_participants / OCC_SHARED3 -,WALK_AM,((trips.trip_mode == 'WALK') & is_am) * tour_participants -,BIKE_AM,((trips.trip_mode == 'BIKE') & is_am) * tour_participants -,WALK_LOC_WALK_AM,((trips.trip_mode == 'WALK_LOC') & is_am) * tour_participants -,WALK_LRF_WALK_AM,((trips.trip_mode == 'WALK_LRF') & is_am) * tour_participants -,WALK_EXP_WALK_AM,((trips.trip_mode == 'WALK_EXP') & is_am) * tour_participants -,WALK_HVY_WALK_AM,((trips.trip_mode == 'WALK_HVY') & is_am) * tour_participants -,WALK_COM_WALK_AM,((trips.trip_mode == 'WALK_COM') & is_am) * tour_participants -,DRIVE_LOC_WALK_AM,((trips.trip_mode == 'DRIVE_LOC') & is_am & trips.outbound) * tour_participants -,DRIVE_LRF_WALK_AM,((trips.trip_mode == 'DRIVE_LRF') & is_am & trips.outbound) * tour_participants -,DRIVE_EXP_WALK_AM,((trips.trip_mode == 'DRIVE_EXP') & is_am & trips.outbound) * tour_participants -,DRIVE_HVY_WALK_AM,((trips.trip_mode == 'DRIVE_HVY') & is_am & trips.outbound) * tour_participants -,DRIVE_COM_WALK_AM,((trips.trip_mode == 'DRIVE_COM') & is_am & trips.outbound) * tour_participants -,WALK_LOC_DRIVE_AM,((trips.trip_mode == 'DRIVE_LOC') & is_am & ~trips.outbound) * tour_participants -,WALK_LRF_DRIVE_AM,((trips.trip_mode == 'DRIVE_LRF') & is_am & ~trips.outbound) * tour_participants -,WALK_EXP_DRIVE_AM,((trips.trip_mode == 'DRIVE_EXP') & is_am & ~trips.outbound) * tour_participants -,WALK_DRIVE_HVY_AM,((trips.trip_mode == 'DRIVE_HVY') & is_am & ~trips.outbound) * tour_participants -,WALK_COM_DRIVE_AM,((trips.trip_mode == 'DRIVE_COM') & is_am & ~trips.outbound) * tour_participants -# md trips,, -,DRIVEALONEFREE_MD,((trips.trip_mode == 'DRIVEALONEFREE') & is_md) * tour_participants -,DRIVEALONEPAY_MD,((trips.trip_mode == 'DRIVEALONEPAY') & is_md) * tour_participants -,SHARED2FREE_MD,((trips.trip_mode == 'SHARED2FREE') & is_md) * tour_participants / OCC_SHARED2 -,SHARED2PAY_MD,((trips.trip_mode == 'SHARED2PAY') & is_md) * tour_participants / OCC_SHARED2 -,SHARED3FREE_MD,((trips.trip_mode == 'SHARED3FREE') & is_md) * tour_participants / OCC_SHARED3 -,SHARED3PAY_MD,((trips.trip_mode == 'SHARED3PAY') & is_md) * tour_participants / OCC_SHARED3 -,WALK_MD,((trips.trip_mode == 'WALK') & is_md) * tour_participants -,BIKE_MD,((trips.trip_mode == 'BIKE') & is_md) * tour_participants -,WALK_LOC_WALK_MD,((trips.trip_mode == 'WALK_LOC') & is_md) * tour_participants -,WALK_LRF_WALK_MD,((trips.trip_mode == 'WALK_LRF') & is_md) * tour_participants -,WALK_EXP_WALK_MD,((trips.trip_mode == 'WALK_EXP') & is_md) * tour_participants -,WALK_HVY_WALK_MD,((trips.trip_mode == 'WALK_HVY') & is_md) * tour_participants -,WALK_COM_WALK_MD,((trips.trip_mode == 'WALK_COM') & is_md) * tour_participants -,DRIVE_LOC_WALK_MD,((trips.trip_mode == 'DRIVE_LOC') & is_md & trips.outbound) * tour_participants -,DRIVE_LRF_WALK_MD,((trips.trip_mode == 'DRIVE_LRF') & is_md & trips.outbound) * tour_participants -,DRIVE_EXP_WALK_MD,((trips.trip_mode == 'DRIVE_EXP') & is_md & trips.outbound) * tour_participants -,DRIVE_HVY_WALK_MD,((trips.trip_mode == 'DRIVE_HVY') & is_md & trips.outbound) * tour_participants -,DRIVE_COM_WALK_MD,((trips.trip_mode == 'DRIVE_COM') & is_md & trips.outbound) * tour_participants -,WALK_LOC_DRIVE_MD,((trips.trip_mode == 'DRIVE_LOC') & is_md & ~trips.outbound) * tour_participants -,WALK_LRF_DRIVE_MD,((trips.trip_mode == 'DRIVE_LRF') & is_md & ~trips.outbound) * tour_participants -,WALK_EXP_DRIVE_MD,((trips.trip_mode == 'DRIVE_EXP') & is_md & ~trips.outbound) * tour_participants -,WALK_DRIVE_HVY_MD,((trips.trip_mode == 'DRIVE_HVY') & is_md & ~trips.outbound) * tour_participants -,WALK_COM_DRIVE_MD,((trips.trip_mode == 'DRIVE_COM') & is_md & ~trips.outbound) * tour_participants -# pm trips,, -,DRIVEALONEFREE_PM,((trips.trip_mode == 'DRIVEALONEFREE') & is_pm) * tour_participants -,DRIVEALONEPAY_PM,((trips.trip_mode == 'DRIVEALONEPAY') & is_pm) * tour_participants -,SHARED2FREE_PM,((trips.trip_mode == 'SHARED2FREE') & is_pm) * tour_participants / OCC_SHARED2 -,SHARED2PAY_PM,((trips.trip_mode == 'SHARED2PAY') & is_pm) * tour_participants / OCC_SHARED2 -,SHARED3FREE_PM,((trips.trip_mode == 'SHARED3FREE') & is_pm) * tour_participants / OCC_SHARED3 -,SHARED3PAY_PM,((trips.trip_mode == 'SHARED3PAY') & is_pm) * tour_participants / OCC_SHARED3 -,WALK_PM,((trips.trip_mode == 'WALK') & is_pm) * tour_participants -,BIKE_PM,((trips.trip_mode == 'BIKE') & is_pm) * tour_participants -,WALK_LOC_WALK_PM,((trips.trip_mode == 'WALK_LOC') & is_pm) * tour_participants -,WALK_LRF_WALK_PM,((trips.trip_mode == 'WALK_LRF') & is_pm) * tour_participants -,WALK_EXP_WALK_PM,((trips.trip_mode == 'WALK_EXP') & is_pm) * tour_participants -,WALK_HVY_WALK_PM,((trips.trip_mode == 'WALK_HVY') & is_pm) * tour_participants -,WALK_COM_WALK_PM,((trips.trip_mode == 'WALK_COM') & is_pm) * tour_participants -,DRIVE_LOC_WALK_PM,((trips.trip_mode == 'DRIVE_LOC') & is_pm & trips.outbound) * tour_participants -,DRIVE_LRF_WALK_PM,((trips.trip_mode == 'DRIVE_LRF') & is_pm & trips.outbound) * tour_participants -,DRIVE_EXP_WALK_PM,((trips.trip_mode == 'DRIVE_EXP') & is_pm & trips.outbound) * tour_participants -,DRIVE_HVY_WALK_PM,((trips.trip_mode == 'DRIVE_HVY') & is_pm & trips.outbound) * tour_participants -,DRIVE_COM_WALK_PM,((trips.trip_mode == 'DRIVE_COM') & is_pm & trips.outbound) * tour_participants -,WALK_LOC_DRIVE_PM,((trips.trip_mode == 'DRIVE_LOC') & is_pm & ~trips.outbound) * tour_participants -,WALK_LRF_DRIVE_PM,((trips.trip_mode == 'DRIVE_LRF') & is_pm & ~trips.outbound) * tour_participants -,WALK_EXP_DRIVE_PM,((trips.trip_mode == 'DRIVE_EXP') & is_pm & ~trips.outbound) * tour_participants -,WALK_DRIVE_HVY_PM,((trips.trip_mode == 'DRIVE_HVY') & is_pm & ~trips.outbound) * tour_participants -,WALK_COM_DRIVE_PM,((trips.trip_mode == 'DRIVE_COM') & is_pm & ~trips.outbound) * tour_participants -# ev trips,, -,DRIVEALONEFREE_EV,((trips.trip_mode == 'DRIVEALONEFREE') & is_ev) * tour_participants -,DRIVEALONEPAY_EV,((trips.trip_mode == 'DRIVEALONEPAY') & is_ev) * tour_participants -,SHARED2FREE_EV,((trips.trip_mode == 'SHARED2FREE') & is_ev) * tour_participants / OCC_SHARED2 -,SHARED2PAY_EV,((trips.trip_mode == 'SHARED2PAY') & is_ev) * tour_participants / OCC_SHARED2 -,SHARED3FREE_EV,((trips.trip_mode == 'SHARED3FREE') & is_ev) * tour_participants / OCC_SHARED3 -,SHARED3PAY_EV,((trips.trip_mode == 'SHARED3PAY') & is_ev) * tour_participants / OCC_SHARED3 -,WALK_EV,((trips.trip_mode == 'WALK') & is_ev) * tour_participants -,BIKE_EV,((trips.trip_mode == 'BIKE') & is_ev) * tour_participants -,WALK_LOC_WALK_EV,((trips.trip_mode == 'WALK_LOC') & is_ev) * tour_participants -,WALK_LRF_WALK_EV,((trips.trip_mode == 'WALK_LRF') & is_ev) * tour_participants -,WALK_EXP_WALK_EV,((trips.trip_mode == 'WALK_EXP') & is_ev) * tour_participants -,WALK_HVY_WALK_EV,((trips.trip_mode == 'WALK_HVY') & is_ev) * tour_participants -,WALK_COM_WALK_EV,((trips.trip_mode == 'WALK_COM') & is_ev) * tour_participants -,DRIVE_LOC_WALK_EV,((trips.trip_mode == 'DRIVE_LOC') & is_ev & trips.outbound) * tour_participants -,DRIVE_LRF_WALK_EV,((trips.trip_mode == 'DRIVE_LRF') & is_ev & trips.outbound) * tour_participants -,DRIVE_EXP_WALK_EV,((trips.trip_mode == 'DRIVE_EXP') & is_ev & trips.outbound) * tour_participants -,DRIVE_HVY_WALK_EV,((trips.trip_mode == 'DRIVE_HVY') & is_ev & trips.outbound) * tour_participants -,DRIVE_COM_WALK_EV,((trips.trip_mode == 'DRIVE_COM') & is_ev & trips.outbound) * tour_participants -,WALK_LOC_DRIVE_EV,((trips.trip_mode == 'DRIVE_LOC') & is_ev & ~trips.outbound) * tour_participants -,WALK_LRF_DRIVE_EV,((trips.trip_mode == 'DRIVE_LRF') & is_ev & ~trips.outbound) * tour_participants -,WALK_EXP_DRIVE_EV,((trips.trip_mode == 'DRIVE_EXP') & is_ev & ~trips.outbound) * tour_participants -,WALK_DRIVE_HVY_EV,((trips.trip_mode == 'DRIVE_HVY') & is_ev & ~trips.outbound) * tour_participants -,WALK_COM_DRIVE_EV,((trips.trip_mode == 'DRIVE_COM') & is_ev & ~trips.outbound) * tour_participants +Description,Target,Expression +# add additional fields,, +,tour_participants,trips.tour_id.map(tours.number_of_participants) +,distance,od_skims['DIST'] +# code time periods,, +,is_ea,"trips.depart.between(time_periods['EA']['first_hour'], time_periods['EA']['last_hour'])" +,is_am,"trips.depart.between(time_periods['AM']['first_hour'], time_periods['AM']['last_hour'])" +,is_md,"trips.depart.between(time_periods['MD']['first_hour'], time_periods['MD']['last_hour'])" +,is_pm,"trips.depart.between(time_periods['PM']['first_hour'], time_periods['PM']['last_hour'])" +,is_ev,(trips.depart >= time_periods['EV']['first_hour']) | (trips.depart <= time_periods['EV']['last_hour']) +# ea trips,, +,DRIVEALONEFREE_EA,((trips.trip_mode == 'DRIVEALONEFREE') & is_ea) * tour_participants +,DRIVEALONEPAY_EA,((trips.trip_mode == 'DRIVEALONEPAY') & is_ea) * tour_participants +,SHARED2FREE_EA,((trips.trip_mode == 'SHARED2FREE') & is_ea) * tour_participants / OCC_SHARED2 +,SHARED2PAY_EA,((trips.trip_mode == 'SHARED2PAY') & is_ea) * tour_participants / OCC_SHARED2 +,SHARED3FREE_EA,((trips.trip_mode == 'SHARED3FREE') & is_ea) * tour_participants / OCC_SHARED3 +,SHARED3PAY_EA,((trips.trip_mode == 'SHARED3PAY') & is_ea) * tour_participants / OCC_SHARED3 +,WALK_EA,((trips.trip_mode == 'WALK') & is_ea) * tour_participants +,BIKE_EA,((trips.trip_mode == 'BIKE') & is_ea) * tour_participants +,WALK_LOC_WALK_EA,((trips.trip_mode == 'WALK_LOC') & is_ea) * tour_participants +,WALK_LRF_WALK_EA,((trips.trip_mode == 'WALK_LRF') & is_ea) * tour_participants +,WALK_EXP_WALK_EA,((trips.trip_mode == 'WALK_EXP') & is_ea) * tour_participants +,WALK_HVY_WALK_EA,((trips.trip_mode == 'WALK_HVY') & is_ea) * tour_participants +,WALK_COM_WALK_EA,((trips.trip_mode == 'WALK_COM') & is_ea) * tour_participants +,DRIVE_LOC_WALK_EA,((trips.trip_mode == 'DRIVE_LOC') & is_ea & trips.outbound) * tour_participants +,DRIVE_LRF_WALK_EA,((trips.trip_mode == 'DRIVE_LRF') & is_ea & trips.outbound) * tour_participants +,DRIVE_EXP_WALK_EA,((trips.trip_mode == 'DRIVE_EXP') & is_ea & trips.outbound) * tour_participants +,DRIVE_HVY_WALK_EA,((trips.trip_mode == 'DRIVE_HVY') & is_ea & trips.outbound) * tour_participants +,DRIVE_COM_WALK_EA,((trips.trip_mode == 'DRIVE_COM') & is_ea & trips.outbound) * tour_participants +,WALK_LOC_DRIVE_EA,((trips.trip_mode == 'DRIVE_LOC') & is_ea & ~trips.outbound) * tour_participants +,WALK_LRF_DRIVE_EA,((trips.trip_mode == 'DRIVE_LRF') & is_ea & ~trips.outbound) * tour_participants +,WALK_EXP_DRIVE_EA,((trips.trip_mode == 'DRIVE_EXP') & is_ea & ~trips.outbound) * tour_participants +,WALK_DRIVE_HVY_EA,((trips.trip_mode == 'DRIVE_HVY') & is_ea & ~trips.outbound) * tour_participants +,WALK_COM_DRIVE_EA,((trips.trip_mode == 'DRIVE_COM') & is_ea & ~trips.outbound) * tour_participants +# am trips,, +,DRIVEALONEFREE_AM,((trips.trip_mode == 'DRIVEALONEFREE') & is_am) * tour_participants +,DRIVEALONEPAY_AM,((trips.trip_mode == 'DRIVEALONEPAY') & is_am) * tour_participants +,SHARED2FREE_AM,((trips.trip_mode == 'SHARED2FREE') & is_am) * tour_participants / OCC_SHARED2 +,SHARED2PAY_AM,((trips.trip_mode == 'SHARED2PAY') & is_am) * tour_participants / OCC_SHARED2 +,SHARED3FREE_AM,((trips.trip_mode == 'SHARED3FREE') & is_am) * tour_participants / OCC_SHARED3 +,SHARED3PAY_AM,((trips.trip_mode == 'SHARED3PAY') & is_am) * tour_participants / OCC_SHARED3 +,WALK_AM,((trips.trip_mode == 'WALK') & is_am) * tour_participants +,BIKE_AM,((trips.trip_mode == 'BIKE') & is_am) * tour_participants +,WALK_LOC_WALK_AM,((trips.trip_mode == 'WALK_LOC') & is_am) * tour_participants +,WALK_LRF_WALK_AM,((trips.trip_mode == 'WALK_LRF') & is_am) * tour_participants +,WALK_EXP_WALK_AM,((trips.trip_mode == 'WALK_EXP') & is_am) * tour_participants +,WALK_HVY_WALK_AM,((trips.trip_mode == 'WALK_HVY') & is_am) * tour_participants +,WALK_COM_WALK_AM,((trips.trip_mode == 'WALK_COM') & is_am) * tour_participants +,DRIVE_LOC_WALK_AM,((trips.trip_mode == 'DRIVE_LOC') & is_am & trips.outbound) * tour_participants +,DRIVE_LRF_WALK_AM,((trips.trip_mode == 'DRIVE_LRF') & is_am & trips.outbound) * tour_participants +,DRIVE_EXP_WALK_AM,((trips.trip_mode == 'DRIVE_EXP') & is_am & trips.outbound) * tour_participants +,DRIVE_HVY_WALK_AM,((trips.trip_mode == 'DRIVE_HVY') & is_am & trips.outbound) * tour_participants +,DRIVE_COM_WALK_AM,((trips.trip_mode == 'DRIVE_COM') & is_am & trips.outbound) * tour_participants +,WALK_LOC_DRIVE_AM,((trips.trip_mode == 'DRIVE_LOC') & is_am & ~trips.outbound) * tour_participants +,WALK_LRF_DRIVE_AM,((trips.trip_mode == 'DRIVE_LRF') & is_am & ~trips.outbound) * tour_participants +,WALK_EXP_DRIVE_AM,((trips.trip_mode == 'DRIVE_EXP') & is_am & ~trips.outbound) * tour_participants +,WALK_DRIVE_HVY_AM,((trips.trip_mode == 'DRIVE_HVY') & is_am & ~trips.outbound) * tour_participants +,WALK_COM_DRIVE_AM,((trips.trip_mode == 'DRIVE_COM') & is_am & ~trips.outbound) * tour_participants +# md trips,, +,DRIVEALONEFREE_MD,((trips.trip_mode == 'DRIVEALONEFREE') & is_md) * tour_participants +,DRIVEALONEPAY_MD,((trips.trip_mode == 'DRIVEALONEPAY') & is_md) * tour_participants +,SHARED2FREE_MD,((trips.trip_mode == 'SHARED2FREE') & is_md) * tour_participants / OCC_SHARED2 +,SHARED2PAY_MD,((trips.trip_mode == 'SHARED2PAY') & is_md) * tour_participants / OCC_SHARED2 +,SHARED3FREE_MD,((trips.trip_mode == 'SHARED3FREE') & is_md) * tour_participants / OCC_SHARED3 +,SHARED3PAY_MD,((trips.trip_mode == 'SHARED3PAY') & is_md) * tour_participants / OCC_SHARED3 +,WALK_MD,((trips.trip_mode == 'WALK') & is_md) * tour_participants +,BIKE_MD,((trips.trip_mode == 'BIKE') & is_md) * tour_participants +,WALK_LOC_WALK_MD,((trips.trip_mode == 'WALK_LOC') & is_md) * tour_participants +,WALK_LRF_WALK_MD,((trips.trip_mode == 'WALK_LRF') & is_md) * tour_participants +,WALK_EXP_WALK_MD,((trips.trip_mode == 'WALK_EXP') & is_md) * tour_participants +,WALK_HVY_WALK_MD,((trips.trip_mode == 'WALK_HVY') & is_md) * tour_participants +,WALK_COM_WALK_MD,((trips.trip_mode == 'WALK_COM') & is_md) * tour_participants +,DRIVE_LOC_WALK_MD,((trips.trip_mode == 'DRIVE_LOC') & is_md & trips.outbound) * tour_participants +,DRIVE_LRF_WALK_MD,((trips.trip_mode == 'DRIVE_LRF') & is_md & trips.outbound) * tour_participants +,DRIVE_EXP_WALK_MD,((trips.trip_mode == 'DRIVE_EXP') & is_md & trips.outbound) * tour_participants +,DRIVE_HVY_WALK_MD,((trips.trip_mode == 'DRIVE_HVY') & is_md & trips.outbound) * tour_participants +,DRIVE_COM_WALK_MD,((trips.trip_mode == 'DRIVE_COM') & is_md & trips.outbound) * tour_participants +,WALK_LOC_DRIVE_MD,((trips.trip_mode == 'DRIVE_LOC') & is_md & ~trips.outbound) * tour_participants +,WALK_LRF_DRIVE_MD,((trips.trip_mode == 'DRIVE_LRF') & is_md & ~trips.outbound) * tour_participants +,WALK_EXP_DRIVE_MD,((trips.trip_mode == 'DRIVE_EXP') & is_md & ~trips.outbound) * tour_participants +,WALK_DRIVE_HVY_MD,((trips.trip_mode == 'DRIVE_HVY') & is_md & ~trips.outbound) * tour_participants +,WALK_COM_DRIVE_MD,((trips.trip_mode == 'DRIVE_COM') & is_md & ~trips.outbound) * tour_participants +# pm trips,, +,DRIVEALONEFREE_PM,((trips.trip_mode == 'DRIVEALONEFREE') & is_pm) * tour_participants +,DRIVEALONEPAY_PM,((trips.trip_mode == 'DRIVEALONEPAY') & is_pm) * tour_participants +,SHARED2FREE_PM,((trips.trip_mode == 'SHARED2FREE') & is_pm) * tour_participants / OCC_SHARED2 +,SHARED2PAY_PM,((trips.trip_mode == 'SHARED2PAY') & is_pm) * tour_participants / OCC_SHARED2 +,SHARED3FREE_PM,((trips.trip_mode == 'SHARED3FREE') & is_pm) * tour_participants / OCC_SHARED3 +,SHARED3PAY_PM,((trips.trip_mode == 'SHARED3PAY') & is_pm) * tour_participants / OCC_SHARED3 +,WALK_PM,((trips.trip_mode == 'WALK') & is_pm) * tour_participants +,BIKE_PM,((trips.trip_mode == 'BIKE') & is_pm) * tour_participants +,WALK_LOC_WALK_PM,((trips.trip_mode == 'WALK_LOC') & is_pm) * tour_participants +,WALK_LRF_WALK_PM,((trips.trip_mode == 'WALK_LRF') & is_pm) * tour_participants +,WALK_EXP_WALK_PM,((trips.trip_mode == 'WALK_EXP') & is_pm) * tour_participants +,WALK_HVY_WALK_PM,((trips.trip_mode == 'WALK_HVY') & is_pm) * tour_participants +,WALK_COM_WALK_PM,((trips.trip_mode == 'WALK_COM') & is_pm) * tour_participants +,DRIVE_LOC_WALK_PM,((trips.trip_mode == 'DRIVE_LOC') & is_pm & trips.outbound) * tour_participants +,DRIVE_LRF_WALK_PM,((trips.trip_mode == 'DRIVE_LRF') & is_pm & trips.outbound) * tour_participants +,DRIVE_EXP_WALK_PM,((trips.trip_mode == 'DRIVE_EXP') & is_pm & trips.outbound) * tour_participants +,DRIVE_HVY_WALK_PM,((trips.trip_mode == 'DRIVE_HVY') & is_pm & trips.outbound) * tour_participants +,DRIVE_COM_WALK_PM,((trips.trip_mode == 'DRIVE_COM') & is_pm & trips.outbound) * tour_participants +,WALK_LOC_DRIVE_PM,((trips.trip_mode == 'DRIVE_LOC') & is_pm & ~trips.outbound) * tour_participants +,WALK_LRF_DRIVE_PM,((trips.trip_mode == 'DRIVE_LRF') & is_pm & ~trips.outbound) * tour_participants +,WALK_EXP_DRIVE_PM,((trips.trip_mode == 'DRIVE_EXP') & is_pm & ~trips.outbound) * tour_participants +,WALK_DRIVE_HVY_PM,((trips.trip_mode == 'DRIVE_HVY') & is_pm & ~trips.outbound) * tour_participants +,WALK_COM_DRIVE_PM,((trips.trip_mode == 'DRIVE_COM') & is_pm & ~trips.outbound) * tour_participants +# ev trips,, +,DRIVEALONEFREE_EV,((trips.trip_mode == 'DRIVEALONEFREE') & is_ev) * tour_participants +,DRIVEALONEPAY_EV,((trips.trip_mode == 'DRIVEALONEPAY') & is_ev) * tour_participants +,SHARED2FREE_EV,((trips.trip_mode == 'SHARED2FREE') & is_ev) * tour_participants / OCC_SHARED2 +,SHARED2PAY_EV,((trips.trip_mode == 'SHARED2PAY') & is_ev) * tour_participants / OCC_SHARED2 +,SHARED3FREE_EV,((trips.trip_mode == 'SHARED3FREE') & is_ev) * tour_participants / OCC_SHARED3 +,SHARED3PAY_EV,((trips.trip_mode == 'SHARED3PAY') & is_ev) * tour_participants / OCC_SHARED3 +,WALK_EV,((trips.trip_mode == 'WALK') & is_ev) * tour_participants +,BIKE_EV,((trips.trip_mode == 'BIKE') & is_ev) * tour_participants +,WALK_LOC_WALK_EV,((trips.trip_mode == 'WALK_LOC') & is_ev) * tour_participants +,WALK_LRF_WALK_EV,((trips.trip_mode == 'WALK_LRF') & is_ev) * tour_participants +,WALK_EXP_WALK_EV,((trips.trip_mode == 'WALK_EXP') & is_ev) * tour_participants +,WALK_HVY_WALK_EV,((trips.trip_mode == 'WALK_HVY') & is_ev) * tour_participants +,WALK_COM_WALK_EV,((trips.trip_mode == 'WALK_COM') & is_ev) * tour_participants +,DRIVE_LOC_WALK_EV,((trips.trip_mode == 'DRIVE_LOC') & is_ev & trips.outbound) * tour_participants +,DRIVE_LRF_WALK_EV,((trips.trip_mode == 'DRIVE_LRF') & is_ev & trips.outbound) * tour_participants +,DRIVE_EXP_WALK_EV,((trips.trip_mode == 'DRIVE_EXP') & is_ev & trips.outbound) * tour_participants +,DRIVE_HVY_WALK_EV,((trips.trip_mode == 'DRIVE_HVY') & is_ev & trips.outbound) * tour_participants +,DRIVE_COM_WALK_EV,((trips.trip_mode == 'DRIVE_COM') & is_ev & trips.outbound) * tour_participants +,WALK_LOC_DRIVE_EV,((trips.trip_mode == 'DRIVE_LOC') & is_ev & ~trips.outbound) * tour_participants +,WALK_LRF_DRIVE_EV,((trips.trip_mode == 'DRIVE_LRF') & is_ev & ~trips.outbound) * tour_participants +,WALK_EXP_DRIVE_EV,((trips.trip_mode == 'DRIVE_EXP') & is_ev & ~trips.outbound) * tour_participants +,WALK_DRIVE_HVY_EV,((trips.trip_mode == 'DRIVE_HVY') & is_ev & ~trips.outbound) * tour_participants +,WALK_COM_DRIVE_EV,((trips.trip_mode == 'DRIVE_COM') & is_ev & ~trips.outbound) * tour_participants diff --git a/activitysim/examples/example_mtc/configs_mp/logging.yaml b/activitysim/examples/prototype_mtc/configs_mp/logging.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs_mp/logging.yaml rename to activitysim/examples/prototype_mtc/configs_mp/logging.yaml diff --git a/activitysim/examples/example_mtc/configs_mp/settings.yaml b/activitysim/examples/prototype_mtc/configs_mp/settings.yaml similarity index 100% rename from activitysim/examples/example_mtc/configs_mp/settings.yaml rename to activitysim/examples/prototype_mtc/configs_mp/settings.yaml diff --git a/activitysim/examples/example_mtc/data/.gitignore b/activitysim/examples/prototype_mtc/data/.gitignore similarity index 100% rename from activitysim/examples/example_mtc/data/.gitignore rename to activitysim/examples/prototype_mtc/data/.gitignore diff --git a/activitysim/examples/example_multiple_zone/data_1/households.csv b/activitysim/examples/prototype_mtc/data/households.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/data_1/households.csv rename to activitysim/examples/prototype_mtc/data/households.csv diff --git a/activitysim/examples/example_multiple_zone/data_1/land_use.csv b/activitysim/examples/prototype_mtc/data/land_use.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/data_1/land_use.csv rename to activitysim/examples/prototype_mtc/data/land_use.csv diff --git a/activitysim/examples/example_multiple_zone/data/mtc_asim.h5 b/activitysim/examples/prototype_mtc/data/mtc_asim.h5 similarity index 100% rename from activitysim/examples/example_multiple_zone/data/mtc_asim.h5 rename to activitysim/examples/prototype_mtc/data/mtc_asim.h5 diff --git a/activitysim/examples/example_multiple_zone/data/override_hh_ids.csv b/activitysim/examples/prototype_mtc/data/override_hh_ids.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/data/override_hh_ids.csv rename to activitysim/examples/prototype_mtc/data/override_hh_ids.csv diff --git a/activitysim/examples/example_multiple_zone/data_1/persons.csv b/activitysim/examples/prototype_mtc/data/persons.csv similarity index 100% rename from activitysim/examples/example_multiple_zone/data_1/persons.csv rename to activitysim/examples/prototype_mtc/data/persons.csv diff --git a/activitysim/examples/example_multiple_zone/data_1/skims.omx b/activitysim/examples/prototype_mtc/data/skims.omx similarity index 100% rename from activitysim/examples/example_multiple_zone/data_1/skims.omx rename to activitysim/examples/prototype_mtc/data/skims.omx diff --git a/activitysim/examples/example_mtc/notebooks/README.md b/activitysim/examples/prototype_mtc/notebooks/README.md similarity index 100% rename from activitysim/examples/example_mtc/notebooks/README.md rename to activitysim/examples/prototype_mtc/notebooks/README.md diff --git a/activitysim/examples/example_mtc/notebooks/adding_tncs.ipynb b/activitysim/examples/prototype_mtc/notebooks/adding_tncs.ipynb similarity index 100% rename from activitysim/examples/example_mtc/notebooks/adding_tncs.ipynb rename to activitysim/examples/prototype_mtc/notebooks/adding_tncs.ipynb diff --git a/activitysim/examples/example_mtc/notebooks/change_in_auto_ownership.ipynb b/activitysim/examples/prototype_mtc/notebooks/change_in_auto_ownership.ipynb similarity index 99% rename from activitysim/examples/example_mtc/notebooks/change_in_auto_ownership.ipynb rename to activitysim/examples/prototype_mtc/notebooks/change_in_auto_ownership.ipynb index e093ecb92b..3220d27896 100644 --- a/activitysim/examples/example_mtc/notebooks/change_in_auto_ownership.ipynb +++ b/activitysim/examples/prototype_mtc/notebooks/change_in_auto_ownership.ipynb @@ -41,26 +41,26 @@ "copying configs_mp ...\n", "copying output ...\n", "copying README.MD ...\n", - "copied! new project files are in C:\\projects\\development\\activitysim_rsg\\notebooks\\example_base_auto_own\\example_mtc\n", + "copied! new project files are in C:\\projects\\development\\activitysim_rsg\\notebooks\\example_base_auto_own\\prototype_mtc\n", "the copied example can be run with\n", "\n", - " activitysim run -w example_base_auto_own\\example_mtc\n", + " activitysim run -w example_base_auto_own\\prototype_mtc\n", "copying data ...\n", "copying configs ...\n", "copying configs_mp ...\n", "copying output ...\n", "copying README.MD ...\n", - "copied! new project files are in C:\\projects\\development\\activitysim_rsg\\notebooks\\example_base_auto_own_alternative\\example_mtc\n", + "copied! new project files are in C:\\projects\\development\\activitysim_rsg\\notebooks\\example_base_auto_own_alternative\\prototype_mtc\n", "the copied example can be run with\n", "\n", - " activitysim run -w example_base_auto_own_alternative\\example_mtc\n" + " activitysim run -w example_base_auto_own_alternative\\prototype_mtc\n" ] } ], "source": [ - "!activitysim create -e example_mtc -d example_base_auto_own\n", + "!activitysim create -e prototype_mtc -d example_base_auto_own\n", "\n", - "!activitysim create -e example_mtc -d example_base_auto_own_alternative" + "!activitysim create -e prototype_mtc -d example_base_auto_own_alternative" ] }, { diff --git a/activitysim/examples/example_mtc/notebooks/getting_started.ipynb b/activitysim/examples/prototype_mtc/notebooks/getting_started.ipynb similarity index 99% rename from activitysim/examples/example_mtc/notebooks/getting_started.ipynb rename to activitysim/examples/prototype_mtc/notebooks/getting_started.ipynb index 2a03e452ab..501b2882fe 100644 --- a/activitysim/examples/example_mtc/notebooks/getting_started.ipynb +++ b/activitysim/examples/prototype_mtc/notebooks/getting_started.ipynb @@ -78,7 +78,7 @@ "source": [ "# Creating an Example Setup\n", "\n", - "The example is included in the package and can be copied to a user defined location using the package's command line interface. The example includes all model steps. The command below copies the example_mtc example to a new example folder. It also changes into the new example folder so we can run the model from there." + "The example is included in the package and can be copied to a user defined location using the package's command line interface. The example includes all model steps. The command below copies the prototype_mtc example to a new example folder. It also changes into the new example folder so we can run the model from there." ] }, { @@ -103,13 +103,13 @@ "copying configs_mp ...\n", "copying output ...\n", "copying README.MD ...\n", - "copied! new project files are in C:\\projects\\development\\activitysim\\activitysim\\examples\\example_mtc\\notebooks\\example\n", - "C:\\projects\\development\\activitysim\\activitysim\\examples\\example_mtc\\notebooks\\example\n" + "copied! new project files are in C:\\projects\\development\\activitysim\\activitysim\\examples\\prototype_mtc\\notebooks\\example\n", + "C:\\projects\\development\\activitysim\\activitysim\\examples\\prototype_mtc\\notebooks\\example\n" ] } ], "source": [ - "!activitysim create -e example_mtc -d example\n", + "!activitysim create -e prototype_mtc -d example\n", "%cd example" ] }, @@ -122,7 +122,7 @@ "source": [ "# Run the Example\n", "\n", - "The code below runs the example, which runs in a few minutes. The example consists of 100 synthetic households and the first 25 zones in the example model region. The full example (**example_mtc_full**) can be created and downloaded from the [activitysim resources](https://github.com/RSGInc/activitysim_resources) repository using activitysim's create command above. As the model runs, it logs information to the screen. \n", + "The code below runs the example, which runs in a few minutes. The example consists of 100 synthetic households and the first 25 zones in the example model region. The full example (**prototype_mtc_full**) can be created and downloaded from the [activitysim resources](https://github.com/ActivitySim/activitysim_resources) repository using activitysim's create command above. As the model runs, it logs information to the screen. \n", "\n", "To run the example, use activitysim's built-in run command. As shown in the script help, the default settings assume a configs, data, and output folder in the current directory." ] diff --git a/activitysim/examples/example_mtc/notebooks/memory_usage.ipynb b/activitysim/examples/prototype_mtc/notebooks/memory_usage.ipynb similarity index 100% rename from activitysim/examples/example_mtc/notebooks/memory_usage.ipynb rename to activitysim/examples/prototype_mtc/notebooks/memory_usage.ipynb diff --git a/activitysim/examples/example_mtc/notebooks/summarizing_results.ipynb b/activitysim/examples/prototype_mtc/notebooks/summarizing_results.ipynb similarity index 100% rename from activitysim/examples/example_mtc/notebooks/summarizing_results.ipynb rename to activitysim/examples/prototype_mtc/notebooks/summarizing_results.ipynb diff --git a/activitysim/examples/example_mtc/notebooks/trips_in_time_and_space.ipynb b/activitysim/examples/prototype_mtc/notebooks/trips_in_time_and_space.ipynb similarity index 100% rename from activitysim/examples/example_mtc/notebooks/trips_in_time_and_space.ipynb rename to activitysim/examples/prototype_mtc/notebooks/trips_in_time_and_space.ipynb diff --git a/activitysim/examples/example_mtc/notebooks/zone_shapefile/bayarea_rtaz1454_rev1.dbf b/activitysim/examples/prototype_mtc/notebooks/zone_shapefile/bayarea_rtaz1454_rev1.dbf similarity index 100% rename from activitysim/examples/example_mtc/notebooks/zone_shapefile/bayarea_rtaz1454_rev1.dbf rename to activitysim/examples/prototype_mtc/notebooks/zone_shapefile/bayarea_rtaz1454_rev1.dbf diff --git a/activitysim/examples/example_mtc/notebooks/zone_shapefile/bayarea_rtaz1454_rev1.prj b/activitysim/examples/prototype_mtc/notebooks/zone_shapefile/bayarea_rtaz1454_rev1.prj similarity index 100% rename from activitysim/examples/example_mtc/notebooks/zone_shapefile/bayarea_rtaz1454_rev1.prj rename to activitysim/examples/prototype_mtc/notebooks/zone_shapefile/bayarea_rtaz1454_rev1.prj diff --git a/activitysim/examples/example_mtc/notebooks/zone_shapefile/bayarea_rtaz1454_rev1.shp b/activitysim/examples/prototype_mtc/notebooks/zone_shapefile/bayarea_rtaz1454_rev1.shp similarity index 100% rename from activitysim/examples/example_mtc/notebooks/zone_shapefile/bayarea_rtaz1454_rev1.shp rename to activitysim/examples/prototype_mtc/notebooks/zone_shapefile/bayarea_rtaz1454_rev1.shp diff --git a/activitysim/examples/example_mtc/notebooks/zone_shapefile/bayarea_rtaz1454_rev1.shx b/activitysim/examples/prototype_mtc/notebooks/zone_shapefile/bayarea_rtaz1454_rev1.shx similarity index 100% rename from activitysim/examples/example_mtc/notebooks/zone_shapefile/bayarea_rtaz1454_rev1.shx rename to activitysim/examples/prototype_mtc/notebooks/zone_shapefile/bayarea_rtaz1454_rev1.shx diff --git a/activitysim/examples/example_mtc/output/.gitignore b/activitysim/examples/prototype_mtc/output/.gitignore similarity index 100% rename from activitysim/examples/example_mtc/output/.gitignore rename to activitysim/examples/prototype_mtc/output/.gitignore diff --git a/activitysim/examples/example_sandag/output_1/cache/.gitignore b/activitysim/examples/prototype_mtc/output/cache/.gitignore similarity index 100% rename from activitysim/examples/example_sandag/output_1/cache/.gitignore rename to activitysim/examples/prototype_mtc/output/cache/.gitignore diff --git a/activitysim/examples/example_sandag/output_1/trace/.gitignore b/activitysim/examples/prototype_mtc/output/log/.gitignore similarity index 100% rename from activitysim/examples/example_sandag/output_1/trace/.gitignore rename to activitysim/examples/prototype_mtc/output/log/.gitignore diff --git a/activitysim/examples/example_mtc/output/summarize/dashboard-1-summary.yaml b/activitysim/examples/prototype_mtc/output/summarize/dashboard-1-summary.yaml similarity index 100% rename from activitysim/examples/example_mtc/output/summarize/dashboard-1-summary.yaml rename to activitysim/examples/prototype_mtc/output/summarize/dashboard-1-summary.yaml diff --git a/activitysim/examples/example_mtc/output/summarize/taz1454.geojson b/activitysim/examples/prototype_mtc/output/summarize/taz1454.geojson similarity index 100% rename from activitysim/examples/example_mtc/output/summarize/taz1454.geojson rename to activitysim/examples/prototype_mtc/output/summarize/taz1454.geojson diff --git a/activitysim/examples/example_mtc/output/summarize/topsheet.yaml b/activitysim/examples/prototype_mtc/output/summarize/topsheet.yaml similarity index 100% rename from activitysim/examples/example_mtc/output/summarize/topsheet.yaml rename to activitysim/examples/prototype_mtc/output/summarize/topsheet.yaml diff --git a/activitysim/examples/example_sandag/output_2/log/.gitignore b/activitysim/examples/prototype_mtc/output/trace/.gitignore similarity index 100% rename from activitysim/examples/example_sandag/output_2/log/.gitignore rename to activitysim/examples/prototype_mtc/output/trace/.gitignore diff --git a/activitysim/examples/example_mtc/simulation.py b/activitysim/examples/prototype_mtc/simulation.py similarity index 100% rename from activitysim/examples/example_mtc/simulation.py rename to activitysim/examples/prototype_mtc/simulation.py diff --git a/activitysim/examples/example_multiple_zone/test/configs_2_zone/network_los.yaml b/activitysim/examples/prototype_mtc/test/configs/network_los.yaml similarity index 100% rename from activitysim/examples/example_multiple_zone/test/configs_2_zone/network_los.yaml rename to activitysim/examples/prototype_mtc/test/configs/network_los.yaml diff --git a/activitysim/examples/example_mtc/test/configs/settings.yaml b/activitysim/examples/prototype_mtc/test/configs/settings.yaml similarity index 100% rename from activitysim/examples/example_mtc/test/configs/settings.yaml rename to activitysim/examples/prototype_mtc/test/configs/settings.yaml diff --git a/activitysim/examples/example_sandag/test/configs_1_zone/network_los.yaml b/activitysim/examples/prototype_mtc/test/configs_chunkless/network_los.yaml similarity index 100% rename from activitysim/examples/example_sandag/test/configs_1_zone/network_los.yaml rename to activitysim/examples/prototype_mtc/test/configs_chunkless/network_los.yaml diff --git a/activitysim/examples/example_mtc/test/configs_chunkless/settings.yaml b/activitysim/examples/prototype_mtc/test/configs_chunkless/settings.yaml similarity index 100% rename from activitysim/examples/example_mtc/test/configs_chunkless/settings.yaml rename to activitysim/examples/prototype_mtc/test/configs_chunkless/settings.yaml diff --git a/activitysim/examples/example_sandag/test/configs_2_zone/network_los.yaml b/activitysim/examples/prototype_mtc/test/configs_mp/network_los.yaml similarity index 100% rename from activitysim/examples/example_sandag/test/configs_2_zone/network_los.yaml rename to activitysim/examples/prototype_mtc/test/configs_mp/network_los.yaml diff --git a/activitysim/examples/example_mtc/test/configs_mp/settings.yaml b/activitysim/examples/prototype_mtc/test/configs_mp/settings.yaml similarity index 100% rename from activitysim/examples/example_mtc/test/configs_mp/settings.yaml rename to activitysim/examples/prototype_mtc/test/configs_mp/settings.yaml diff --git a/activitysim/examples/example_sandag/output_2/.gitignore b/activitysim/examples/prototype_mtc/test/output/.gitignore similarity index 100% rename from activitysim/examples/example_sandag/output_2/.gitignore rename to activitysim/examples/prototype_mtc/test/output/.gitignore diff --git a/activitysim/examples/example_sandag/output_2/cache/.gitignore b/activitysim/examples/prototype_mtc/test/output/cache/.gitignore similarity index 100% rename from activitysim/examples/example_sandag/output_2/cache/.gitignore rename to activitysim/examples/prototype_mtc/test/output/cache/.gitignore diff --git a/activitysim/examples/example_sandag/output_2/trace/.gitignore b/activitysim/examples/prototype_mtc/test/output/trace/.gitignore similarity index 100% rename from activitysim/examples/example_sandag/output_2/trace/.gitignore rename to activitysim/examples/prototype_mtc/test/output/trace/.gitignore diff --git a/activitysim/examples/example_mtc/test/regress/final_trips.csv b/activitysim/examples/prototype_mtc/test/regress/final_trips.csv similarity index 100% rename from activitysim/examples/example_mtc/test/regress/final_trips.csv rename to activitysim/examples/prototype_mtc/test/regress/final_trips.csv diff --git a/activitysim/examples/prototype_mtc/test/simulation.py b/activitysim/examples/prototype_mtc/test/simulation.py new file mode 100755 index 0000000000..97ca6b6483 --- /dev/null +++ b/activitysim/examples/prototype_mtc/test/simulation.py @@ -0,0 +1,15 @@ +# ActivitySim +# See full license in LICENSE.txt. + +import sys +import argparse + +from activitysim.cli.run import add_run_args, run + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + add_run_args(parser) + args = parser.parse_args() + + sys.exit(run(args)) diff --git a/activitysim/examples/example_mtc/test/test_mtc.py b/activitysim/examples/prototype_mtc/test/test_mtc.py similarity index 96% rename from activitysim/examples/example_mtc/test/test_mtc.py rename to activitysim/examples/prototype_mtc/test/test_mtc.py index a81c439cbd..da8f63410d 100644 --- a/activitysim/examples/example_mtc/test/test_mtc.py +++ b/activitysim/examples/prototype_mtc/test/test_mtc.py @@ -18,7 +18,7 @@ def teardown_function(func): def run_test_mtc(multiprocess=False, chunkless=False): def example_path(dirname): - resource = os.path.join('examples', 'example_mtc', dirname) + resource = os.path.join('examples', 'prototype_mtc', dirname) return pkg_resources.resource_filename('activitysim', resource) def test_path(dirname): diff --git a/activitysim/examples/prototype_mtc_extended/README.MD b/activitysim/examples/prototype_mtc_extended/README.MD new file mode 100644 index 0000000000..9ee48c17f1 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/README.MD @@ -0,0 +1,6 @@ + +### Prototype MTC Extended Example + +The prototype MTC example with the following additional models: +* vehicle type model +* vehicle allocation model diff --git a/activitysim/examples/example_mtc_extended/configs/annotate_tours_tour_mode_choice.csv b/activitysim/examples/prototype_mtc_extended/configs/annotate_tours_tour_mode_choice.csv similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/annotate_tours_tour_mode_choice.csv rename to activitysim/examples/prototype_mtc_extended/configs/annotate_tours_tour_mode_choice.csv diff --git a/activitysim/examples/example_mtc_extended/configs/settings.yaml b/activitysim/examples/prototype_mtc_extended/configs/settings.yaml similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/settings.yaml rename to activitysim/examples/prototype_mtc_extended/configs/settings.yaml diff --git a/activitysim/examples/example_mtc_extended/configs/tour_mode_choice.csv b/activitysim/examples/prototype_mtc_extended/configs/tour_mode_choice.csv similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/tour_mode_choice.csv rename to activitysim/examples/prototype_mtc_extended/configs/tour_mode_choice.csv diff --git a/activitysim/examples/example_mtc_extended/configs/tour_mode_choice.yaml b/activitysim/examples/prototype_mtc_extended/configs/tour_mode_choice.yaml similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/tour_mode_choice.yaml rename to activitysim/examples/prototype_mtc_extended/configs/tour_mode_choice.yaml diff --git a/activitysim/examples/example_mtc_extended/configs/tour_mode_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/prototype_mtc_extended/configs/tour_mode_choice_annotate_choosers_preprocessor.csv similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/tour_mode_choice_annotate_choosers_preprocessor.csv rename to activitysim/examples/prototype_mtc_extended/configs/tour_mode_choice_annotate_choosers_preprocessor.csv diff --git a/activitysim/examples/example_mtc_extended/configs/trip_mode_choice.csv b/activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice.csv similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/trip_mode_choice.csv rename to activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice.csv diff --git a/activitysim/examples/example_mtc_extended/configs/trip_mode_choice.yaml b/activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice.yaml similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/trip_mode_choice.yaml rename to activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice.yaml diff --git a/activitysim/examples/example_mtc_extended/configs/trip_mode_choice_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice_annotate_trips_preprocessor.csv similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/trip_mode_choice_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_mtc_extended/configs/trip_mode_choice_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/example_mtc_extended/configs/vehicle_allocation.csv b/activitysim/examples/prototype_mtc_extended/configs/vehicle_allocation.csv similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/vehicle_allocation.csv rename to activitysim/examples/prototype_mtc_extended/configs/vehicle_allocation.csv diff --git a/activitysim/examples/example_mtc_extended/configs/vehicle_allocation.yaml b/activitysim/examples/prototype_mtc_extended/configs/vehicle_allocation.yaml similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/vehicle_allocation.yaml rename to activitysim/examples/prototype_mtc_extended/configs/vehicle_allocation.yaml diff --git a/activitysim/examples/example_mtc_extended/configs/vehicle_allocation_annotate_choosers_preprocessor.csv b/activitysim/examples/prototype_mtc_extended/configs/vehicle_allocation_annotate_choosers_preprocessor.csv similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/vehicle_allocation_annotate_choosers_preprocessor.csv rename to activitysim/examples/prototype_mtc_extended/configs/vehicle_allocation_annotate_choosers_preprocessor.csv diff --git a/activitysim/examples/example_mtc_extended/configs/vehicle_allocation_coefficients.csv b/activitysim/examples/prototype_mtc_extended/configs/vehicle_allocation_coefficients.csv similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/vehicle_allocation_coefficients.csv rename to activitysim/examples/prototype_mtc_extended/configs/vehicle_allocation_coefficients.csv diff --git a/activitysim/examples/example_mtc_extended/configs/vehicle_type_choice.yaml b/activitysim/examples/prototype_mtc_extended/configs/vehicle_type_choice.yaml similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/vehicle_type_choice.yaml rename to activitysim/examples/prototype_mtc_extended/configs/vehicle_type_choice.yaml diff --git a/activitysim/examples/example_mtc_extended/configs/vehicle_type_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/prototype_mtc_extended/configs/vehicle_type_choice_annotate_choosers_preprocessor.csv similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/vehicle_type_choice_annotate_choosers_preprocessor.csv rename to activitysim/examples/prototype_mtc_extended/configs/vehicle_type_choice_annotate_choosers_preprocessor.csv diff --git a/activitysim/examples/example_mtc_extended/configs/vehicle_type_choice_op2.csv b/activitysim/examples/prototype_mtc_extended/configs/vehicle_type_choice_op2.csv similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/vehicle_type_choice_op2.csv rename to activitysim/examples/prototype_mtc_extended/configs/vehicle_type_choice_op2.csv diff --git a/activitysim/examples/example_mtc_extended/configs/vehicle_type_choice_op2_coefficients.csv b/activitysim/examples/prototype_mtc_extended/configs/vehicle_type_choice_op2_coefficients.csv similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/vehicle_type_choice_op2_coefficients.csv rename to activitysim/examples/prototype_mtc_extended/configs/vehicle_type_choice_op2_coefficients.csv diff --git a/activitysim/examples/example_mtc_extended/configs/vehicle_type_choice_op2_fuel_type_probs.csv b/activitysim/examples/prototype_mtc_extended/configs/vehicle_type_choice_op2_fuel_type_probs.csv similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/vehicle_type_choice_op2_fuel_type_probs.csv rename to activitysim/examples/prototype_mtc_extended/configs/vehicle_type_choice_op2_fuel_type_probs.csv diff --git a/activitysim/examples/example_mtc_extended/configs/vehicle_type_choice_op4.csv b/activitysim/examples/prototype_mtc_extended/configs/vehicle_type_choice_op4.csv similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/vehicle_type_choice_op4.csv rename to activitysim/examples/prototype_mtc_extended/configs/vehicle_type_choice_op4.csv diff --git a/activitysim/examples/example_mtc_extended/configs/vehicle_type_choice_op4_coefficients.csv b/activitysim/examples/prototype_mtc_extended/configs/vehicle_type_choice_op4_coefficients.csv similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/vehicle_type_choice_op4_coefficients.csv rename to activitysim/examples/prototype_mtc_extended/configs/vehicle_type_choice_op4_coefficients.csv diff --git a/activitysim/examples/example_mtc_extended/configs/vehicle_type_data.csv b/activitysim/examples/prototype_mtc_extended/configs/vehicle_type_data.csv similarity index 100% rename from activitysim/examples/example_mtc_extended/configs/vehicle_type_data.csv rename to activitysim/examples/prototype_mtc_extended/configs/vehicle_type_data.csv diff --git a/activitysim/examples/example_mtc_extended/configs_mp/logging.yaml b/activitysim/examples/prototype_mtc_extended/configs_mp/logging.yaml similarity index 100% rename from activitysim/examples/example_mtc_extended/configs_mp/logging.yaml rename to activitysim/examples/prototype_mtc_extended/configs_mp/logging.yaml diff --git a/activitysim/examples/example_mtc_extended/configs_mp/settings.yaml b/activitysim/examples/prototype_mtc_extended/configs_mp/settings.yaml similarity index 100% rename from activitysim/examples/example_mtc_extended/configs_mp/settings.yaml rename to activitysim/examples/prototype_mtc_extended/configs_mp/settings.yaml diff --git a/activitysim/examples/example_sandag/output_3/.gitignore b/activitysim/examples/prototype_mtc_extended/output/.gitignore similarity index 100% rename from activitysim/examples/example_sandag/output_3/.gitignore rename to activitysim/examples/prototype_mtc_extended/output/.gitignore diff --git a/activitysim/examples/example_sandag/output_3/cache/.gitignore b/activitysim/examples/prototype_mtc_extended/output/cache/.gitignore similarity index 100% rename from activitysim/examples/example_sandag/output_3/cache/.gitignore rename to activitysim/examples/prototype_mtc_extended/output/cache/.gitignore diff --git a/activitysim/examples/example_sandag/output_3/log/.gitignore b/activitysim/examples/prototype_mtc_extended/output/log/.gitignore similarity index 100% rename from activitysim/examples/example_sandag/output_3/log/.gitignore rename to activitysim/examples/prototype_mtc_extended/output/log/.gitignore diff --git a/activitysim/examples/example_sandag/output_3/trace/.gitignore b/activitysim/examples/prototype_mtc_extended/output/trace/.gitignore similarity index 100% rename from activitysim/examples/example_sandag/output_3/trace/.gitignore rename to activitysim/examples/prototype_mtc_extended/output/trace/.gitignore diff --git a/activitysim/examples/example_mtc_extended/test/configs/settings.yaml b/activitysim/examples/prototype_mtc_extended/test/configs/settings.yaml similarity index 100% rename from activitysim/examples/example_mtc_extended/test/configs/settings.yaml rename to activitysim/examples/prototype_mtc_extended/test/configs/settings.yaml diff --git a/activitysim/examples/example_sandag/test/output/.gitignore b/activitysim/examples/prototype_mtc_extended/test/output/.gitignore similarity index 100% rename from activitysim/examples/example_sandag/test/output/.gitignore rename to activitysim/examples/prototype_mtc_extended/test/output/.gitignore diff --git a/activitysim/examples/example_sandag/test/output/cache/.gitignore b/activitysim/examples/prototype_mtc_extended/test/output/cache/.gitignore similarity index 100% rename from activitysim/examples/example_sandag/test/output/cache/.gitignore rename to activitysim/examples/prototype_mtc_extended/test/output/cache/.gitignore diff --git a/activitysim/examples/example_sandag/test/output/trace/.gitignore b/activitysim/examples/prototype_mtc_extended/test/output/trace/.gitignore similarity index 100% rename from activitysim/examples/example_sandag/test/output/trace/.gitignore rename to activitysim/examples/prototype_mtc_extended/test/output/trace/.gitignore diff --git a/activitysim/examples/example_mtc_extended/test/regress/final_trips.csv b/activitysim/examples/prototype_mtc_extended/test/regress/final_trips.csv similarity index 100% rename from activitysim/examples/example_mtc_extended/test/regress/final_trips.csv rename to activitysim/examples/prototype_mtc_extended/test/regress/final_trips.csv diff --git a/activitysim/examples/example_mtc_extended/test/regress/final_vehicles.csv b/activitysim/examples/prototype_mtc_extended/test/regress/final_vehicles.csv similarity index 100% rename from activitysim/examples/example_mtc_extended/test/regress/final_vehicles.csv rename to activitysim/examples/prototype_mtc_extended/test/regress/final_vehicles.csv diff --git a/activitysim/examples/prototype_mtc_extended/test/simulation.py b/activitysim/examples/prototype_mtc_extended/test/simulation.py new file mode 100644 index 0000000000..97ca6b6483 --- /dev/null +++ b/activitysim/examples/prototype_mtc_extended/test/simulation.py @@ -0,0 +1,15 @@ +# ActivitySim +# See full license in LICENSE.txt. + +import sys +import argparse + +from activitysim.cli.run import add_run_args, run + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + add_run_args(parser) + args = parser.parse_args() + + sys.exit(run(args)) diff --git a/activitysim/examples/example_mtc_extended/test/test_mtc_extended.py b/activitysim/examples/prototype_mtc_extended/test/test_mtc_extended.py similarity index 88% rename from activitysim/examples/example_mtc_extended/test/test_mtc_extended.py rename to activitysim/examples/prototype_mtc_extended/test/test_mtc_extended.py index acf60459f6..dfadfbcf29 100644 --- a/activitysim/examples/example_mtc_extended/test/test_mtc_extended.py +++ b/activitysim/examples/prototype_mtc_extended/test/test_mtc_extended.py @@ -15,14 +15,14 @@ def teardown_function(func): inject.reinject_decorated_tables() -def test_mtc_extended(): +def test_prototype_mtc_extended(): def example_path(dirname): - resource = os.path.join('examples', 'example_mtc_extended', dirname) + resource = os.path.join('examples', 'prototype_mtc_extended', dirname) return pkg_resources.resource_filename('activitysim', resource) def example_mtc_path(dirname): - resource = os.path.join('examples', 'example_mtc', dirname) + resource = os.path.join('examples', 'prototype_mtc', dirname) return pkg_resources.resource_filename('activitysim', resource) def test_path(dirname): @@ -54,4 +54,4 @@ def regress(): if __name__ == '__main__': - test_mtc_extended() + test_prototype_mtc_extended() diff --git a/activitysim/examples/example_sandag_xborder/README.md b/activitysim/examples/prototype_sandag_xborder/README.md similarity index 100% rename from activitysim/examples/example_sandag_xborder/README.md rename to activitysim/examples/prototype_sandag_xborder/README.md diff --git a/activitysim/examples/example_sandag_xborder/configs/annotate_households.csv b/activitysim/examples/prototype_sandag_xborder/configs/annotate_households.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/annotate_households.csv rename to activitysim/examples/prototype_sandag_xborder/configs/annotate_households.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/annotate_landuse.csv b/activitysim/examples/prototype_sandag_xborder/configs/annotate_landuse.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/annotate_landuse.csv rename to activitysim/examples/prototype_sandag_xborder/configs/annotate_landuse.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/annotate_persons.csv b/activitysim/examples/prototype_sandag_xborder/configs/annotate_persons.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/annotate_persons.csv rename to activitysim/examples/prototype_sandag_xborder/configs/annotate_persons.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/annotate_tours.csv b/activitysim/examples/prototype_sandag_xborder/configs/annotate_tours.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/annotate_tours.csv rename to activitysim/examples/prototype_sandag_xborder/configs/annotate_tours.csv diff --git a/activitysim/examples/example_marin/configs/constants.yaml b/activitysim/examples/prototype_sandag_xborder/configs/constants.yaml similarity index 95% rename from activitysim/examples/example_marin/configs/constants.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/constants.yaml index 626a0c415e..6199378b42 100755 --- a/activitysim/examples/example_marin/configs/constants.yaml +++ b/activitysim/examples/prototype_sandag_xborder/configs/constants.yaml @@ -1,64 +1,64 @@ -## ActivitySim -## See full license in LICENSE.txt. - -walk_speed: 3.00 - -#HHT_NONE: 0 -#HHT_FAMILY_MARRIED: 1 -#HHT_FAMILY_MALE: 2 -#HHT_FAMILY_FEMALE: 3 -#HHT_NONFAMILY_MALE_ALONE: 4 -#HHT_NONFAMILY_MALE_NOTALONE: 5 -#HHT_NONFAMILY_FEMALE_ALONE: 6 -#HHT_NONFAMILY_FEMALE_NOTALONE: 7 - -# convenience for expression files -HHT_NONFAMILY: [4, 5, 6, 7] -HHT_FAMILY: [1, 2, 3] - -PSTUDENT_GRADE_OR_HIGH: 1 -PSTUDENT_UNIVERSITY: 2 -PSTUDENT_NOT: 3 - -GRADE_SCHOOL_MAX_AGE: 14 -GRADE_SCHOOL_MIN_AGE: 5 - -SCHOOL_SEGMENT_NONE: 0 -SCHOOL_SEGMENT_GRADE: 1 -SCHOOL_SEGMENT_HIGH: 2 -SCHOOL_SEGMENT_UNIV: 3 - -#INCOME_SEGMENT_LOW: 1 -#INCOME_SEGMENT_MED: 2 -#INCOME_SEGMENT_HIGH: 3 -#INCOME_SEGMENT_VERYHIGH: 4 - -PEMPLOY_FULL: 1 -PEMPLOY_PART: 2 -PEMPLOY_NOT: 3 -PEMPLOY_CHILD: 4 - -PTYPE_FULL: &ptype_full 1 -PTYPE_PART: &ptype_part 2 -PTYPE_UNIVERSITY: &ptype_university 3 -PTYPE_NONWORK: &ptype_nonwork 4 -PTYPE_RETIRED: &ptype_retired 5 -PTYPE_DRIVING: &ptype_driving 6 -PTYPE_SCHOOL: &ptype_school 7 -PTYPE_PRESCHOOL: &ptype_preschool 8 - -# these appear as column headers in non_mandatory_tour_frequency.csv -PTYPE_NAME: - *ptype_full: PTYPE_FULL - *ptype_part: PTYPE_PART - *ptype_university: PTYPE_UNIVERSITY - *ptype_nonwork: PTYPE_NONWORK - *ptype_retired: PTYPE_RETIRED - *ptype_driving: PTYPE_DRIVING - *ptype_school: PTYPE_SCHOOL - *ptype_preschool: PTYPE_PRESCHOOL - - -CDAP_ACTIVITY_MANDATORY: M -CDAP_ACTIVITY_NONMANDATORY: N -CDAP_ACTIVITY_HOME: H +## ActivitySim +## See full license in LICENSE.txt. + +walk_speed: 3.00 + +#HHT_NONE: 0 +#HHT_FAMILY_MARRIED: 1 +#HHT_FAMILY_MALE: 2 +#HHT_FAMILY_FEMALE: 3 +#HHT_NONFAMILY_MALE_ALONE: 4 +#HHT_NONFAMILY_MALE_NOTALONE: 5 +#HHT_NONFAMILY_FEMALE_ALONE: 6 +#HHT_NONFAMILY_FEMALE_NOTALONE: 7 + +# convenience for expression files +HHT_NONFAMILY: [4, 5, 6, 7] +HHT_FAMILY: [1, 2, 3] + +PSTUDENT_GRADE_OR_HIGH: 1 +PSTUDENT_UNIVERSITY: 2 +PSTUDENT_NOT: 3 + +GRADE_SCHOOL_MAX_AGE: 14 +GRADE_SCHOOL_MIN_AGE: 5 + +SCHOOL_SEGMENT_NONE: 0 +SCHOOL_SEGMENT_GRADE: 1 +SCHOOL_SEGMENT_HIGH: 2 +SCHOOL_SEGMENT_UNIV: 3 + +#INCOME_SEGMENT_LOW: 1 +#INCOME_SEGMENT_MED: 2 +#INCOME_SEGMENT_HIGH: 3 +#INCOME_SEGMENT_VERYHIGH: 4 + +PEMPLOY_FULL: 1 +PEMPLOY_PART: 2 +PEMPLOY_NOT: 3 +PEMPLOY_CHILD: 4 + +PTYPE_FULL: &ptype_full 1 +PTYPE_PART: &ptype_part 2 +PTYPE_UNIVERSITY: &ptype_university 3 +PTYPE_NONWORK: &ptype_nonwork 4 +PTYPE_RETIRED: &ptype_retired 5 +PTYPE_DRIVING: &ptype_driving 6 +PTYPE_SCHOOL: &ptype_school 7 +PTYPE_PRESCHOOL: &ptype_preschool 8 + +# these appear as column headers in non_mandatory_tour_frequency.csv +PTYPE_NAME: + *ptype_full: PTYPE_FULL + *ptype_part: PTYPE_PART + *ptype_university: PTYPE_UNIVERSITY + *ptype_nonwork: PTYPE_NONWORK + *ptype_retired: PTYPE_RETIRED + *ptype_driving: PTYPE_DRIVING + *ptype_school: PTYPE_SCHOOL + *ptype_preschool: PTYPE_PRESCHOOL + + +CDAP_ACTIVITY_MANDATORY: M +CDAP_ACTIVITY_NONMANDATORY: N +CDAP_ACTIVITY_HOME: H diff --git a/activitysim/examples/example_sandag_xborder/configs/destination_choice_size_terms.csv b/activitysim/examples/prototype_sandag_xborder/configs/destination_choice_size_terms.csv similarity index 99% rename from activitysim/examples/example_sandag_xborder/configs/destination_choice_size_terms.csv rename to activitysim/examples/prototype_sandag_xborder/configs/destination_choice_size_terms.csv index 03228db8c9..3fc8caa0c9 100644 --- a/activitysim/examples/example_sandag_xborder/configs/destination_choice_size_terms.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/destination_choice_size_terms.csv @@ -1,11 +1,11 @@ -model_selector,segment,emp_ag,emp_const_non_bldg_prod,emp_const_non_bldg_office,emp_utilities_prod,emp_utilities_office,emp_const_bldg_prod,emp_const_bldg_office,emp_mfg_prod,emp_mfg_office,emp_whsle_whs,emp_trans,emp_retail,emp_prof_bus_svcs,emp_prof_bus_svcs_bldg_maint,emp_pvt_ed_k12,emp_pvt_ed_post_k12_oth,emp_health,emp_personal_svcs_office,emp_amusement,emp_hotel,emp_restaurant_bar,emp_personal_svcs_retail,emp_religious,emp_pvt_hh,emp_state_local_gov_ent,emp_fed_non_mil,emp_fed_mil,emp_state_local_gov_blue,emp_state_local_gov_white,emp_public_ed,emp_own_occ_dwell_mgmt,emp_fed_gov_accts,emp_st_lcl_gov_accts,emp_cap_accts,collegeenroll,othercollegeenroll,adultschenrl,hh -tour_od_choice,work,0.0000,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,1.0000,0.9860,0.9860,0.0000,0.0000,0.0000,0.9860,0.5620,0.5620,0.5620,0.9860,0.3630,0.3630,0.3630,0.3630,0.3630,0.3630,0.3630,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -tour_od_choice,school,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0870,0.0870,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0870,0.0000,0.0000,0.0000,0.0000,1.0000,1.0000,1.0000,0.0000 -tour_od_choice,shop,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,2.3060,2.3060,2.3060,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -tour_od_choice,visit,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,1.5760,0.0000,0.0000,0.0000,0.0000,0.0000,1.0380,1.0380,1.0380,1.0380,1.0380,1.0380,1.0380,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,1.0000 -tour_od_choice,other,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,1.0000,0.0000,0.0000,0.1670,0.1670,0.0280,0.0000,0.7100,0.7100,0.7100,0.0000,4.7070,4.7070,4.7070,4.7070,4.7070,4.7070,4.7070,0.1670,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -trip,work,0.0000,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,1.0000,0.9860,0.9860,0.0000,0.0000,0.0000,0.9860,0.5620,0.5620,0.5620,0.9860,0.3630,0.3630,0.3630,0.3630,0.3630,0.3630,0.3630,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -trip,school,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0870,0.0870,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0870,0.0000,0.0000,0.0000,0.0000,1.0000,1.0000,1.0000,0.0000 -trip,shop,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,2.3060,2.3060,2.3060,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 -trip,visit,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,1.5760,0.0000,0.0000,0.0000,0.0000,0.0000,1.0380,1.0380,1.0380,1.0380,1.0380,1.0380,1.0380,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,1.0000 +model_selector,segment,emp_ag,emp_const_non_bldg_prod,emp_const_non_bldg_office,emp_utilities_prod,emp_utilities_office,emp_const_bldg_prod,emp_const_bldg_office,emp_mfg_prod,emp_mfg_office,emp_whsle_whs,emp_trans,emp_retail,emp_prof_bus_svcs,emp_prof_bus_svcs_bldg_maint,emp_pvt_ed_k12,emp_pvt_ed_post_k12_oth,emp_health,emp_personal_svcs_office,emp_amusement,emp_hotel,emp_restaurant_bar,emp_personal_svcs_retail,emp_religious,emp_pvt_hh,emp_state_local_gov_ent,emp_fed_non_mil,emp_fed_mil,emp_state_local_gov_blue,emp_state_local_gov_white,emp_public_ed,emp_own_occ_dwell_mgmt,emp_fed_gov_accts,emp_st_lcl_gov_accts,emp_cap_accts,collegeenroll,othercollegeenroll,adultschenrl,hh +tour_od_choice,work,0.0000,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,1.0000,0.9860,0.9860,0.0000,0.0000,0.0000,0.9860,0.5620,0.5620,0.5620,0.9860,0.3630,0.3630,0.3630,0.3630,0.3630,0.3630,0.3630,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 +tour_od_choice,school,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0870,0.0870,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0870,0.0000,0.0000,0.0000,0.0000,1.0000,1.0000,1.0000,0.0000 +tour_od_choice,shop,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,2.3060,2.3060,2.3060,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 +tour_od_choice,visit,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,1.5760,0.0000,0.0000,0.0000,0.0000,0.0000,1.0380,1.0380,1.0380,1.0380,1.0380,1.0380,1.0380,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,1.0000 +tour_od_choice,other,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,1.0000,0.0000,0.0000,0.1670,0.1670,0.0280,0.0000,0.7100,0.7100,0.7100,0.0000,4.7070,4.7070,4.7070,4.7070,4.7070,4.7070,4.7070,0.1670,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 +trip,work,0.0000,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,0.1840,1.0000,0.9860,0.9860,0.0000,0.0000,0.0000,0.9860,0.5620,0.5620,0.5620,0.9860,0.3630,0.3630,0.3630,0.3630,0.3630,0.3630,0.3630,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 +trip,school,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0870,0.0870,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0870,0.0000,0.0000,0.0000,0.0000,1.0000,1.0000,1.0000,0.0000 +trip,shop,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,1.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,2.3060,2.3060,2.3060,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 +trip,visit,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,1.5760,0.0000,0.0000,0.0000,0.0000,0.0000,1.0380,1.0380,1.0380,1.0380,1.0380,1.0380,1.0380,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,1.0000 trip,other,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,1.0000,0.0000,0.0000,0.1670,0.1670,0.0280,0.0000,0.7100,0.7100,0.7100,0.0000,4.7070,4.7070,4.7070,4.7070,4.7070,4.7070,4.7070,0.1670,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000 \ No newline at end of file diff --git a/activitysim/examples/example_sandag_xborder/configs/estimation.yaml b/activitysim/examples/prototype_sandag_xborder/configs/estimation.yaml similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/estimation.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/estimation.yaml diff --git a/activitysim/examples/example_sandag_xborder/configs/initialize_households.yaml b/activitysim/examples/prototype_sandag_xborder/configs/initialize_households.yaml similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/initialize_households.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/initialize_households.yaml diff --git a/activitysim/examples/example_sandag_xborder/configs/initialize_landuse.yaml b/activitysim/examples/prototype_sandag_xborder/configs/initialize_landuse.yaml similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/initialize_landuse.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/initialize_landuse.yaml diff --git a/activitysim/examples/example_sandag_xborder/configs/initialize_tours.yaml b/activitysim/examples/prototype_sandag_xborder/configs/initialize_tours.yaml similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/initialize_tours.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/initialize_tours.yaml diff --git a/activitysim/examples/example_sandag_xborder/configs/logging.yaml b/activitysim/examples/prototype_sandag_xborder/configs/logging.yaml similarity index 95% rename from activitysim/examples/example_sandag_xborder/configs/logging.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/logging.yaml index 33b6a4b1cc..df20cf0c7e 100755 --- a/activitysim/examples/example_sandag_xborder/configs/logging.yaml +++ b/activitysim/examples/prototype_sandag_xborder/configs/logging.yaml @@ -1,54 +1,54 @@ -# Config for logging -# ------------------ -# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema - -logging: - version: 1 - disable_existing_loggers: true - - - # Configuring the default (root) logger is highly recommended - root: - level: NOTSET - handlers: [console, logfile] - - loggers: - - activitysim: - level: DEBUG - handlers: [console, logfile] - propagate: false - - orca: - level: WARN - handlers: [console, logfile] - propagate: false - - handlers: - - logfile: - class: logging.FileHandler - filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] - mode: w - formatter: fileFormatter - level: NOTSET - - console: - class: logging.StreamHandler - stream: ext://sys.stdout - formatter: simpleFormatter - level: NOTSET - - formatters: - - simpleFormatter: - class: logging.Formatter - # format: '%(levelname)s - %(name)s - %(message)s' - format: '%(levelname)s - %(message)s' - datefmt: '%d/%m/%Y %H:%M:%S' - - fileFormatter: - class: logging.Formatter - format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' - datefmt: '%d/%m/%Y %H:%M:%S' - +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console, logfile] + + loggers: + + activitysim: + level: DEBUG + handlers: [console, logfile] + propagate: false + + orca: + level: WARN + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + level: NOTSET + + formatters: + + simpleFormatter: + class: logging.Formatter + # format: '%(levelname)s - %(name)s - %(message)s' + format: '%(levelname)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + diff --git a/activitysim/examples/example_sandag_xborder/configs/network_los.yaml b/activitysim/examples/prototype_sandag_xborder/configs/network_los.yaml similarity index 96% rename from activitysim/examples/example_sandag_xborder/configs/network_los.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/network_los.yaml index ec6171eb75..de998a3546 100755 --- a/activitysim/examples/example_sandag_xborder/configs/network_los.yaml +++ b/activitysim/examples/prototype_sandag_xborder/configs/network_los.yaml @@ -1,156 +1,156 @@ -inherit_settings: True - -zone_system: 3 - -skim_dict_factory: NumpyArraySkimFactory -#skim_dict_factory: MemMapSkimFactory - -# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) -read_skim_cache: True -# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs -write_skim_cache: True - -# rebuild and overwrite existing tap_tap_utilities cache -rebuild_tvpb_cache: True - -# when checkpointing cache. also write a csv version of tvpb cache for tracing -# (writes csv file when writing/checkpointing cache (i.e. when cached changed) even if rebuild_tvpb_cache is False) -# (n.b. csv file could be quite large if cache is STATIC!) -trace_tvpb_cache_as_csv: False - -taz_skims: - - traffic_skims_xborder_EA.omx - - traffic_skims_xborder_AM.omx - - traffic_skims_xborder_MD.omx - - traffic_skims_xborder_PM.omx - - traffic_skims_xborder_EV.omx - -tap_skims: - # we require that skims for all tap_tap sets have unique names - # and can therefor share a single skim_dict without name collision - # e.g. TRN_XWAIT_FAST__AM, TRN_XWAIT_SHORT__AM, TRN_XWAIT_CHEAP__AM - - transit_skims_xborder.omx - - -# FIXME why no taz.csv? -# tas: taz.csv - -maz: mazs_xborder.csv # mgra.csv - -tap: taps.csv # this is just tap ids - -tap_lines: tap_lines.csv - -maz_to_maz: - tables: - - maz_maz_walk.csv # microMgraEquivMinutes.csv - -maz_to_tap: - walk: - table: maz_tap_walk.csv # microMgraTapEquivMinutes.csv - # if provided, this column will be used (together with tap_lines table) to trim the near tap set - # to only include the nearest tap to origin when more than one tap serves the same line - tap_line_distance_col: dist - -skim_time_periods: - time_window: 1440 - period_minutes: 30 - periods: [0, 11, 17, 30, 37, 48] # asim xborder - # periods: [0, 3, 9, 22, 29, 48] # ctramp xborder - labels: &skim_time_period_labels ['EA', 'AM', 'MD', 'PM', 'EV'] - -# transit virtual path builder settings -TVPB_SETTINGS: # find this in sandag bestPathTAP UEC - - tour_mode_choice: - units: utility - path_types: - WTW: - access: walk - egress: walk - max_paths_across_tap_sets: 4 - max_paths_per_tap_set: 1 - tap_tap_settings: - SPEC: tvpb_utility_tap_tap.csv - PREPROCESSOR: - SPEC: tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv - DF: df - # FIXME this has to be explicitly specified, since e.g. attribute columns are assigned in expression files - attribute_segments: - tod: *skim_time_period_labels - attributes_as_columns: - - tod - maz_tap_settings: - walk: - SPEC: tvpb_utility_walk_maz_tap.csv - CHOOSER_COLUMNS: - - walkTime - - dist - - CONSTANTS: - c_ivt: -0.028 - c_cost: -0.0011 - # transit "factors" from SANDAG - eb_fac: 1.0 - brt_fac: 0.9000 - lrt_fac: 0.6500 - cr_fac: 0.6500 - hyp_fac: 0.6500 - # coeffs used in maz_tap and tap_tap utility expressions - c_walk: 1.7 - c_fwt: 1.5 - c_waux: 2.5 - c_xwt: 2 - C_UNAVAILABLE: -9999 - # alternative-specific constants - exp_asc: 10 # express bus asc - brt_asc: -10 # BRT asc - lrt_asc: -20 # LRT alternative-specific constant - cr_asc: 0.00 # Commuter Rail alternative-specific constant - - trip_mode_choice: - units: utility - path_types: - WTW: - access: walk - egress: walk - max_paths_across_tap_sets: 4 - max_paths_per_tap_set: 1 - tap_tap_settings: - SPEC: tvpb_utility_tap_tap.csv - PREPROCESSOR: - SPEC: tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv - DF: df - # FIXME this has to be explicitly specified, since e.g. attribute columns are assigned in expression files - attribute_segments: - tod: *skim_time_period_labels - attributes_as_columns: - - tod - maz_tap_settings: - walk: - SPEC: tvpb_utility_walk_maz_tap.csv - CHOOSER_COLUMNS: - - walkTime - - dist - - CONSTANTS: - c_ivt: -0.03 - c_cost: -0.0003 - # transit "factors" from SANDAG - eb_fac: 1.0 - brt_fac: 0.9000 - lrt_fac: 0.6500 - cr_fac: 0.6500 - # coeffs used in maz_tap and tap_tap utility expressions - c_walk: 1.7 - c_fwt: 1.5 - c_waux: 2.5 - c_xwt: 2 - C_UNAVAILABLE: -9999 - # alternative-specific constants - exp_asc: 10 # express bus asc - brt_asc: -10 # BRT asc - lrt_asc: -20 # LRT alternative-specific constant - cr_asc: 0.00 # Commuter Rail alternative-specific constant - - +inherit_settings: True + +zone_system: 3 + +skim_dict_factory: NumpyArraySkimFactory +#skim_dict_factory: MemMapSkimFactory + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: True +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: True + +# rebuild and overwrite existing tap_tap_utilities cache +rebuild_tvpb_cache: True + +# when checkpointing cache. also write a csv version of tvpb cache for tracing +# (writes csv file when writing/checkpointing cache (i.e. when cached changed) even if rebuild_tvpb_cache is False) +# (n.b. csv file could be quite large if cache is STATIC!) +trace_tvpb_cache_as_csv: False + +taz_skims: + - traffic_skims_xborder_EA.omx + - traffic_skims_xborder_AM.omx + - traffic_skims_xborder_MD.omx + - traffic_skims_xborder_PM.omx + - traffic_skims_xborder_EV.omx + +tap_skims: + # we require that skims for all tap_tap sets have unique names + # and can therefor share a single skim_dict without name collision + # e.g. TRN_XWAIT_FAST__AM, TRN_XWAIT_SHORT__AM, TRN_XWAIT_CHEAP__AM + - transit_skims_xborder.omx + + +# FIXME why no taz.csv? +# tas: taz.csv + +maz: mazs_xborder.csv # mgra.csv + +tap: taps.csv # this is just tap ids + +tap_lines: tap_lines.csv + +maz_to_maz: + tables: + - maz_maz_walk.csv # microMgraEquivMinutes.csv + +maz_to_tap: + walk: + table: maz_tap_walk.csv # microMgraTapEquivMinutes.csv + # if provided, this column will be used (together with tap_lines table) to trim the near tap set + # to only include the nearest tap to origin when more than one tap serves the same line + tap_line_distance_col: dist + +skim_time_periods: + time_window: 1440 + period_minutes: 30 + periods: [0, 11, 17, 30, 37, 48] # asim xborder + # periods: [0, 3, 9, 22, 29, 48] # ctramp xborder + labels: &skim_time_period_labels ['EA', 'AM', 'MD', 'PM', 'EV'] + +# transit virtual path builder settings +TVPB_SETTINGS: # find this in sandag bestPathTAP UEC + + tour_mode_choice: + units: utility + path_types: + WTW: + access: walk + egress: walk + max_paths_across_tap_sets: 4 + max_paths_per_tap_set: 1 + tap_tap_settings: + SPEC: tvpb_utility_tap_tap.csv + PREPROCESSOR: + SPEC: tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv + DF: df + # FIXME this has to be explicitly specified, since e.g. attribute columns are assigned in expression files + attribute_segments: + tod: *skim_time_period_labels + attributes_as_columns: + - tod + maz_tap_settings: + walk: + SPEC: tvpb_utility_walk_maz_tap.csv + CHOOSER_COLUMNS: + - walkTime + - dist + + CONSTANTS: + c_ivt: -0.028 + c_cost: -0.0011 + # transit "factors" from SANDAG + eb_fac: 1.0 + brt_fac: 0.9000 + lrt_fac: 0.6500 + cr_fac: 0.6500 + hyp_fac: 0.6500 + # coeffs used in maz_tap and tap_tap utility expressions + c_walk: 1.7 + c_fwt: 1.5 + c_waux: 2.5 + c_xwt: 2 + C_UNAVAILABLE: -9999 + # alternative-specific constants + exp_asc: 10 # express bus asc + brt_asc: -10 # BRT asc + lrt_asc: -20 # LRT alternative-specific constant + cr_asc: 0.00 # Commuter Rail alternative-specific constant + + trip_mode_choice: + units: utility + path_types: + WTW: + access: walk + egress: walk + max_paths_across_tap_sets: 4 + max_paths_per_tap_set: 1 + tap_tap_settings: + SPEC: tvpb_utility_tap_tap.csv + PREPROCESSOR: + SPEC: tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv + DF: df + # FIXME this has to be explicitly specified, since e.g. attribute columns are assigned in expression files + attribute_segments: + tod: *skim_time_period_labels + attributes_as_columns: + - tod + maz_tap_settings: + walk: + SPEC: tvpb_utility_walk_maz_tap.csv + CHOOSER_COLUMNS: + - walkTime + - dist + + CONSTANTS: + c_ivt: -0.03 + c_cost: -0.0003 + # transit "factors" from SANDAG + eb_fac: 1.0 + brt_fac: 0.9000 + lrt_fac: 0.6500 + cr_fac: 0.6500 + # coeffs used in maz_tap and tap_tap utility expressions + c_walk: 1.7 + c_fwt: 1.5 + c_waux: 2.5 + c_xwt: 2 + C_UNAVAILABLE: -9999 + # alternative-specific constants + exp_asc: 10 # express bus asc + brt_asc: -10 # BRT asc + lrt_asc: -20 # LRT alternative-specific constant + cr_asc: 0.00 # Commuter Rail alternative-specific constant + + diff --git a/activitysim/examples/example_sandag_xborder/configs/preprocessing.yaml b/activitysim/examples/prototype_sandag_xborder/configs/preprocessing.yaml similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/preprocessing.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/preprocessing.yaml diff --git a/activitysim/examples/example_sandag_xborder/configs/settings.yaml b/activitysim/examples/prototype_sandag_xborder/configs/settings.yaml similarity index 96% rename from activitysim/examples/example_sandag_xborder/configs/settings.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/settings.yaml index c8dd9d8abd..66ce520127 100755 --- a/activitysim/examples/example_sandag_xborder/configs/settings.yaml +++ b/activitysim/examples/prototype_sandag_xborder/configs/settings.yaml @@ -1,157 +1,157 @@ -inherit_settings: False - -# number of households to simulate - -households_sample_size: 0 -multiprocess: True -strict: False -mem_tick: 30 -num_processes: 30 -fail_fast: True - -############## -# -# chunking -# - -# chooser chunk size in gigabytes -# target top memory usage during activitysim run (including shared memory, loaded tables, and transient memory usage) -chunk_size: 400_000_000_000 -#chunk_size: 0 - -# minimum fraction of total chunk_size to reserve for adaptive chunking -min_available_chunk_ratio: 0.05 - -# initial number of chooser rows for first chunk in training mode -# when there is no pre-existing chunk_cache to set initial value -# ordinarily bigger is better as long as it is not so big it causes memory issues (e.g. accessibility with lots of zones) -default_initial_rows_per_chunk: 500 - -# method to calculate memory overhead when chunking is enabled -chunk_method: hybrid_uss - -# chunk training mode -# training to determine the chunking settings written to a cache file that is re-used for production runs -# training -# production -# disabled -chunk_training_mode: disabled - -# whether to preserve or delete subprocess chunk and mem logs when they are consolidated at end of multiprocess run -keep_chunk_logs: True -keep_mem_logs: True - -############## - -trace_hh_id: - -# input tables -input_table_list: - - tablename: households - filename: households_xborder.csv - index_col: household_id - - - tablename: persons - filename: persons_xborder.csv - index_col: person_id - - - tablename: land_use - filename: mazs_xborder.csv - index_col: zone_id - rename_columns: - MAZ: zone_id - - - tablename: tours - filename: tours_xborder.csv - index_col: tour_id - keep_columns: - - pass_type - - tour_type - - purpose_id - - tour_category - - number_of_participants - - tour_num - - tour_count - - household_id - - person_id - - -# set false to disable variability check in simple_simulate and interaction_simulate -check_for_variability: False - -# - shadow pricing global switches - -# turn shadow_pricing on and off for all models (e.g. school and work) -# shadow pricing is deprecated for less than full samples -# see shadow_pricing.yaml for additional settings -use_shadow_pricing: False - -# turn writing of sample_tables on and off for all models -# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) -want_dest_choice_sample_tables: False -want_dest_choice_presampling: True - -#resume_after: trip_scheduling - -models: - - initialize_landuse - - initialize_households - - initialize_tours - # --- STATIC cache prebuild steps - # single-process step to create attribute_combination list - - initialize_los - # multi-processable step to build STATIC cache - # (this step is a NOP if cache already exists and network_los.rebuild_tvpb_cache setting is False) - - initialize_tvpb - # # --- - - tour_scheduling_probabilistic - - tour_od_choice - - reassign_tour_purpose_by_poe - - tour_mode_choice_simulate - - stop_frequency - - trip_purpose - - trip_scheduling - - trip_destination - - trip_mode_choice - - write_trip_matrices - - write_tables - -multiprocess_steps: - - name: mp_initialize - begin: initialize_landuse - - name: mp_tvpb - begin: initialize_tvpb - num_processes: 5 - slice: - tables: - - attribute_combinations - - name: mp_households - begin: tour_scheduling_probabilistic - slice: - tables: - - households - - persons - - name: mp_summarize - begin: write_trip_matrices - -output_tables: - h5_store: False - action: include - prefix: final_ - # FIXME sort is an undocumented feature - sorts table by best index or ref_col according to traceable_table_indexes - sort: True - tables: - - checkpoints - - accessibility - - land_use - - households - - persons - - tours - - trips - - attribute_combinations - -output_summaries: - tours: - - tour_mode - - od_path_set - - do_path_set +inherit_settings: False + +# number of households to simulate + +households_sample_size: 0 +multiprocess: True +strict: False +mem_tick: 30 +num_processes: 30 +fail_fast: True + +############## +# +# chunking +# + +# chooser chunk size in gigabytes +# target top memory usage during activitysim run (including shared memory, loaded tables, and transient memory usage) +chunk_size: 400_000_000_000 +#chunk_size: 0 + +# minimum fraction of total chunk_size to reserve for adaptive chunking +min_available_chunk_ratio: 0.05 + +# initial number of chooser rows for first chunk in training mode +# when there is no pre-existing chunk_cache to set initial value +# ordinarily bigger is better as long as it is not so big it causes memory issues (e.g. accessibility with lots of zones) +default_initial_rows_per_chunk: 500 + +# method to calculate memory overhead when chunking is enabled +chunk_method: hybrid_uss + +# chunk training mode +# training to determine the chunking settings written to a cache file that is re-used for production runs +# training +# production +# disabled +chunk_training_mode: disabled + +# whether to preserve or delete subprocess chunk and mem logs when they are consolidated at end of multiprocess run +keep_chunk_logs: True +keep_mem_logs: True + +############## + +trace_hh_id: + +# input tables +input_table_list: + - tablename: households + filename: households_xborder.csv + index_col: household_id + + - tablename: persons + filename: persons_xborder.csv + index_col: person_id + + - tablename: land_use + filename: mazs_xborder.csv + index_col: zone_id + rename_columns: + MAZ: zone_id + + - tablename: tours + filename: tours_xborder.csv + index_col: tour_id + keep_columns: + - pass_type + - tour_type + - purpose_id + - tour_category + - number_of_participants + - tour_num + - tour_count + - household_id + - person_id + + +# set false to disable variability check in simple_simulate and interaction_simulate +check_for_variability: False + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False +want_dest_choice_presampling: True + +#resume_after: trip_scheduling + +models: + - initialize_landuse + - initialize_households + - initialize_tours + # --- STATIC cache prebuild steps + # single-process step to create attribute_combination list + - initialize_los + # multi-processable step to build STATIC cache + # (this step is a NOP if cache already exists and network_los.rebuild_tvpb_cache setting is False) + - initialize_tvpb + # # --- + - tour_scheduling_probabilistic + - tour_od_choice + - reassign_tour_purpose_by_poe + - tour_mode_choice_simulate + - stop_frequency + - trip_purpose + - trip_scheduling + - trip_destination + - trip_mode_choice + - write_trip_matrices + - write_tables + +multiprocess_steps: + - name: mp_initialize + begin: initialize_landuse + - name: mp_tvpb + begin: initialize_tvpb + num_processes: 5 + slice: + tables: + - attribute_combinations + - name: mp_households + begin: tour_scheduling_probabilistic + slice: + tables: + - households + - persons + - name: mp_summarize + begin: write_trip_matrices + +output_tables: + h5_store: False + action: include + prefix: final_ + # FIXME sort is an undocumented feature - sorts table by best index or ref_col according to traceable_table_indexes + sort: True + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + - attribute_combinations + +output_summaries: + tours: + - tour_mode + - od_path_set + - do_path_set diff --git a/activitysim/examples/example_sandag_xborder/configs/shadow_pricing.yaml b/activitysim/examples/prototype_sandag_xborder/configs/shadow_pricing.yaml similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/shadow_pricing.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/shadow_pricing.yaml diff --git a/activitysim/examples/example_sandag_xborder/configs/stop_frequency.yaml b/activitysim/examples/prototype_sandag_xborder/configs/stop_frequency.yaml similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/stop_frequency.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/stop_frequency.yaml diff --git a/activitysim/examples/example_sandag_xborder/configs/stop_frequency_alternatives.csv b/activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_alternatives.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/stop_frequency_alternatives.csv rename to activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_alternatives.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/stop_frequency_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_annotate_tours_preprocessor.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/stop_frequency_annotate_tours_preprocessor.csv rename to activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_annotate_tours_preprocessor.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/stop_frequency_coefficients_cargo.csv b/activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_coefficients_cargo.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/stop_frequency_coefficients_cargo.csv rename to activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_coefficients_cargo.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/stop_frequency_coefficients_other.csv b/activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_coefficients_other.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/stop_frequency_coefficients_other.csv rename to activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_coefficients_other.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/stop_frequency_coefficients_school.csv b/activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_coefficients_school.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/stop_frequency_coefficients_school.csv rename to activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_coefficients_school.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/stop_frequency_coefficients_shop.csv b/activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_coefficients_shop.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/stop_frequency_coefficients_shop.csv rename to activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_coefficients_shop.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/stop_frequency_coefficients_visit.csv b/activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_coefficients_visit.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/stop_frequency_coefficients_visit.csv rename to activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_coefficients_visit.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/stop_frequency_coefficients_work.csv b/activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_coefficients_work.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/stop_frequency_coefficients_work.csv rename to activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_coefficients_work.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/stop_frequency_other.csv b/activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_other.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/stop_frequency_other.csv rename to activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_other.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/stop_frequency_school.csv b/activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_school.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/stop_frequency_school.csv rename to activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_school.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/stop_frequency_shop.csv b/activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_shop.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/stop_frequency_shop.csv rename to activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_shop.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/stop_frequency_visit.csv b/activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_visit.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/stop_frequency_visit.csv rename to activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_visit.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/stop_frequency_work.csv b/activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_work.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/stop_frequency_work.csv rename to activitysim/examples/prototype_sandag_xborder/configs/stop_frequency_work.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/tour_departure_and_duration_alternatives.csv b/activitysim/examples/prototype_sandag_xborder/configs/tour_departure_and_duration_alternatives.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/tour_departure_and_duration_alternatives.csv rename to activitysim/examples/prototype_sandag_xborder/configs/tour_departure_and_duration_alternatives.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/tour_mode_choice.csv b/activitysim/examples/prototype_sandag_xborder/configs/tour_mode_choice.csv similarity index 98% rename from activitysim/examples/example_sandag_xborder/configs/tour_mode_choice.csv rename to activitysim/examples/prototype_sandag_xborder/configs/tour_mode_choice.csv index 89ab449abe..290b490e9e 100755 --- a/activitysim/examples/example_sandag_xborder/configs/tour_mode_choice.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/tour_mode_choice.csv @@ -1,34 +1,34 @@ -Label,Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK -util_auto_wait,Auto Wait Time,@(df.pass_type == 'no_pass') * df.std_wait,coef_wait,coef_wait,coef_wait, -util_auto_wait_sentri,Auto Wait Time - Sentri,@(df.pass_type == 'sentri') * df.sentri_wait,coef_wait,coef_wait,coef_wait, -util_auto_wait_ready,Auto Wait Time - Ready,@(df.pass_type == 'ready') * df.ready_wait,coef_wait,coef_wait,coef_wait, -util_ped_wait,Walk - Wait Time,@df.ped_wait,,,,coef_wait -util_trip_logsum_tour_da,Drive alone - Trip Logsum,@df.logsum_DRIVEALONE_outbound + df.logsum_DRIVEALONE_inbound,coef_trip_logsum,,, -util_trip_logsum_tour_s2,Shared Ride 2 - Trip Logsum,@df.logsum_SHARED2_outbound + df.logsum_SHARED2_inbound,,coef_trip_logsum,, -util_trip_logsum_tour_s3,Shared Ride 3 - Trip Logsum,@df.logsum_SHARED3_outbound + df.logsum_SHARED3_inbound,,,coef_trip_logsum, -util_trip_logsum_tour_walk,Walk - Trip Logsum,@df.logsum_WALK_outbound + df.logsum_WALK_inbound,,,,coef_trip_logsum -util_ASC_s2,Shared Ride 2 - ASC,1,,SHARED2_asc,, -util_ASC_s3,Shared Ride 3 - ASC,1,,,SHARED3_asc, -util_ASC_walk,Walk - ASC,1,,,,WALK_asc -util_sentri_s2,Crossing used Sentri - Shared Ride 2,@(df.pass_type == 'sentri'),,SHARED2_coef_sentri,, -util_sentri_s3,Crossing used Sentri - Shared Ride 3,@(df.pass_type == 'sentri'),,,SHARED3_coef_sentri, -util_ready_s2,Crossing used Sentri - Shared Ride 2,@(df.pass_type == 'ready'),,SHARED2_coef_ready,, -util_ready_s3,Crossing used Sentri - Shared Ride 3,@(df.pass_type == 'ready'),,,SHARED3_coef_ready, -util_visit_tour_s2,Visit Tour - Shared Ride 2,@df.tour_type == 'visit',,SHARED2_coef_visit,, -util_visit_tour_s3,Visit Tour - Shared Ride 3,@df.tour_type == 'visit',,,SHARED3_coef_visit, -util_visit_tour_walk,Visit Tour - Walk,@df.tour_type == 'visit',,,,WALK_coef_visit -util_other_tour_s2,Other Tour - Shared Ride 2,@df.tour_type == 'other',,SHARED2_coef_other,, -util_other_tour_s3,Other Tour - Shared Ride 3,@df.tour_type == 'other',,,SHARED3_coef_other, -util_other_tour_walk,Other Tour - Walk,@df.tour_type == 'other',,,,WALK_coef_other -util_calib_adj_s2,Calibration Adjustment - Shared Ride 2,1,,SHARED2_coef_calib_adj,, -util_calib_adj_s3,Calibration Adjustment - Shared Ride 3,1,,,SHARED3_coef_calib_adj, -util_calib_adj_walk,Calibration Adjustment - Walk,1,,,,WALK_coef_calib_adj -util_work_tour_s2,Work Tour - Shared Ride 2,@df.tour_type == 'work',,SHARED2_coef_work,, -util_work_tour_s3,Work Tour - Shared Ride 3,@df.tour_type == 'work',,,SHARED3_coef_work, -util_work_tour_walk,Work Tour - Walk,@df.tour_type == 'work',,,,WALK_coef_work -util_school_tour_s2,School Tour - Shared Ride 2,@df.tour_type == 'school',,SHARED2_coef_school,, -util_school_tour_s3,School Tour - Shared Ride 3,@df.tour_type == 'school',,,SHARED3_coef_school, -util_school_tour_walk,School Tour - Walk,@df.tour_type == 'school',,,,WALK_coef_school -util_shop_tour_s2,Shop Tour - Shared Ride 2,@df.tour_type == 'shop',,SHARED2_coef_shop,, -util_shop_tour_s3,Shop Tour - Shared Ride 3,@df.tour_type == 'shop',,,SHARED3_coef_shop, +Label,Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK +util_auto_wait,Auto Wait Time,@(df.pass_type == 'no_pass') * df.std_wait,coef_wait,coef_wait,coef_wait, +util_auto_wait_sentri,Auto Wait Time - Sentri,@(df.pass_type == 'sentri') * df.sentri_wait,coef_wait,coef_wait,coef_wait, +util_auto_wait_ready,Auto Wait Time - Ready,@(df.pass_type == 'ready') * df.ready_wait,coef_wait,coef_wait,coef_wait, +util_ped_wait,Walk - Wait Time,@df.ped_wait,,,,coef_wait +util_trip_logsum_tour_da,Drive alone - Trip Logsum,@df.logsum_DRIVEALONE_outbound + df.logsum_DRIVEALONE_inbound,coef_trip_logsum,,, +util_trip_logsum_tour_s2,Shared Ride 2 - Trip Logsum,@df.logsum_SHARED2_outbound + df.logsum_SHARED2_inbound,,coef_trip_logsum,, +util_trip_logsum_tour_s3,Shared Ride 3 - Trip Logsum,@df.logsum_SHARED3_outbound + df.logsum_SHARED3_inbound,,,coef_trip_logsum, +util_trip_logsum_tour_walk,Walk - Trip Logsum,@df.logsum_WALK_outbound + df.logsum_WALK_inbound,,,,coef_trip_logsum +util_ASC_s2,Shared Ride 2 - ASC,1,,SHARED2_asc,, +util_ASC_s3,Shared Ride 3 - ASC,1,,,SHARED3_asc, +util_ASC_walk,Walk - ASC,1,,,,WALK_asc +util_sentri_s2,Crossing used Sentri - Shared Ride 2,@(df.pass_type == 'sentri'),,SHARED2_coef_sentri,, +util_sentri_s3,Crossing used Sentri - Shared Ride 3,@(df.pass_type == 'sentri'),,,SHARED3_coef_sentri, +util_ready_s2,Crossing used Sentri - Shared Ride 2,@(df.pass_type == 'ready'),,SHARED2_coef_ready,, +util_ready_s3,Crossing used Sentri - Shared Ride 3,@(df.pass_type == 'ready'),,,SHARED3_coef_ready, +util_visit_tour_s2,Visit Tour - Shared Ride 2,@df.tour_type == 'visit',,SHARED2_coef_visit,, +util_visit_tour_s3,Visit Tour - Shared Ride 3,@df.tour_type == 'visit',,,SHARED3_coef_visit, +util_visit_tour_walk,Visit Tour - Walk,@df.tour_type == 'visit',,,,WALK_coef_visit +util_other_tour_s2,Other Tour - Shared Ride 2,@df.tour_type == 'other',,SHARED2_coef_other,, +util_other_tour_s3,Other Tour - Shared Ride 3,@df.tour_type == 'other',,,SHARED3_coef_other, +util_other_tour_walk,Other Tour - Walk,@df.tour_type == 'other',,,,WALK_coef_other +util_calib_adj_s2,Calibration Adjustment - Shared Ride 2,1,,SHARED2_coef_calib_adj,, +util_calib_adj_s3,Calibration Adjustment - Shared Ride 3,1,,,SHARED3_coef_calib_adj, +util_calib_adj_walk,Calibration Adjustment - Walk,1,,,,WALK_coef_calib_adj +util_work_tour_s2,Work Tour - Shared Ride 2,@df.tour_type == 'work',,SHARED2_coef_work,, +util_work_tour_s3,Work Tour - Shared Ride 3,@df.tour_type == 'work',,,SHARED3_coef_work, +util_work_tour_walk,Work Tour - Walk,@df.tour_type == 'work',,,,WALK_coef_work +util_school_tour_s2,School Tour - Shared Ride 2,@df.tour_type == 'school',,SHARED2_coef_school,, +util_school_tour_s3,School Tour - Shared Ride 3,@df.tour_type == 'school',,,SHARED3_coef_school, +util_school_tour_walk,School Tour - Walk,@df.tour_type == 'school',,,,WALK_coef_school +util_shop_tour_s2,Shop Tour - Shared Ride 2,@df.tour_type == 'shop',,SHARED2_coef_shop,, +util_shop_tour_s3,Shop Tour - Shared Ride 3,@df.tour_type == 'shop',,,SHARED3_coef_shop, util_shop_tour_walk,Shop Tour - Walk,@df.tour_type == 'shop',,,,WALK_coef_shop \ No newline at end of file diff --git a/activitysim/examples/example_sandag_xborder/configs/tour_mode_choice.yaml b/activitysim/examples/prototype_sandag_xborder/configs/tour_mode_choice.yaml similarity index 95% rename from activitysim/examples/example_sandag_xborder/configs/tour_mode_choice.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/tour_mode_choice.yaml index 8fcca34bf6..56bf36044b 100755 --- a/activitysim/examples/example_sandag_xborder/configs/tour_mode_choice.yaml +++ b/activitysim/examples/prototype_sandag_xborder/configs/tour_mode_choice.yaml @@ -1,69 +1,69 @@ -LOGIT_TYPE: NL - -tvpb_mode_path_types: - -NESTS: - name: root - coefficient: coef_nest_root - alternatives: - - name: AUTO - coefficient: coef_nest_AUTO - alternatives: - - DRIVEALONE - - SHARED2 - - SHARED3 - - name: NONMOTORIZED - coefficient: coef_nest_NONMOTORIZED - alternatives: - - WALK - -SPEC: tour_mode_choice.csv -COEFFICIENTS: tour_mode_choice_coefficients.csv -COEFFICIENT_TEMPLATE: tour_mode_choice_coefficients_template.csv - -CONSTANTS: - orig_col_name: origin - dest_col_name: destination - wait_time_col_regex: '_wait_[0-9]+$' - -use_TVPB_constants: False - -# so far, we can use the same spec as for non-joint tours -preprocessor: - SPEC: tour_mode_choice_annotate_choosers_preprocessor - DF: choosers - TABLES: - - land_use - - tours - -# to reduce memory needs filter chooser table to these fields -LOGSUM_CHOOSER_COLUMNS: - - tour_type - - hhsize - - density_index - - age - - age_16_p - - age_16_to_19 - - auto_ownership - - number_of_participants - - tour_category - - num_workers - - value_of_time - - free_parking_at_work - - income_segment - - demographic_segment - - c_ivt_for_segment - - c_cost_for_segment - - pass_type - - start - - end - - duration - - is_university - - primary_purpose - - person_id - - household_id - -CHOICE_COL_NAME: tour_mode -MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum -COMPUTE_TRIP_MODE_CHOICE_LOGSUMS: True - +LOGIT_TYPE: NL + +tvpb_mode_path_types: + +NESTS: + name: root + coefficient: coef_nest_root + alternatives: + - name: AUTO + coefficient: coef_nest_AUTO + alternatives: + - DRIVEALONE + - SHARED2 + - SHARED3 + - name: NONMOTORIZED + coefficient: coef_nest_NONMOTORIZED + alternatives: + - WALK + +SPEC: tour_mode_choice.csv +COEFFICIENTS: tour_mode_choice_coefficients.csv +COEFFICIENT_TEMPLATE: tour_mode_choice_coefficients_template.csv + +CONSTANTS: + orig_col_name: origin + dest_col_name: destination + wait_time_col_regex: '_wait_[0-9]+$' + +use_TVPB_constants: False + +# so far, we can use the same spec as for non-joint tours +preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + - tours + +# to reduce memory needs filter chooser table to these fields +LOGSUM_CHOOSER_COLUMNS: + - tour_type + - hhsize + - density_index + - age + - age_16_p + - age_16_to_19 + - auto_ownership + - number_of_participants + - tour_category + - num_workers + - value_of_time + - free_parking_at_work + - income_segment + - demographic_segment + - c_ivt_for_segment + - c_cost_for_segment + - pass_type + - start + - end + - duration + - is_university + - primary_purpose + - person_id + - household_id + +CHOICE_COL_NAME: tour_mode +MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum +COMPUTE_TRIP_MODE_CHOICE_LOGSUMS: True + diff --git a/activitysim/examples/example_sandag_xborder/configs/tour_mode_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/prototype_sandag_xborder/configs/tour_mode_choice_annotate_choosers_preprocessor.csv similarity index 99% rename from activitysim/examples/example_sandag_xborder/configs/tour_mode_choice_annotate_choosers_preprocessor.csv rename to activitysim/examples/prototype_sandag_xborder/configs/tour_mode_choice_annotate_choosers_preprocessor.csv index 83951672d3..559c0153f4 100755 --- a/activitysim/examples/example_sandag_xborder/configs/tour_mode_choice_annotate_choosers_preprocessor.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/tour_mode_choice_annotate_choosers_preprocessor.csv @@ -1,11 +1,11 @@ -Description,Target,Expression -list of POE wait time columns,_WAIT_PERIOD_COLS,land_use.filter(regex=wait_time_col_regex).columns.tolist() -pedestrian crossing wait time column name for each tour,_PED_WAIT_PERIOD_COLS,'ped_wait_' + df.start.astype(str) -standard crossing wait time column name for each tour,_STD_WAIT_PERIOD_COLS,'std_wait_' + df.start.astype(str) -ready crossing wait time column name for each tour,_READY_WAIT_PERIOD_COLS,'ready_wait_' + df.start.astype(str) -sentri crossing wait time column name for each tour,_SENTRI_WAIT_PERIOD_COLS,'sentri_wait_' + df.start.astype(str) -unpivot (a.k.a melt) the POE wait time data,_LONG_WAIT_PERIOD_TIMES,"land_use[land_use['poe_id'].notnull()].reset_index().melt(id_vars='zone_id', value_vars=_WAIT_PERIOD_COLS, var_name='wait_type_period', value_name='time').set_index(['zone_id','wait_type_period'])" -,std_wait,"_LONG_WAIT_PERIOD_TIMES.reindex([df[orig_col_name], _STD_WAIT_PERIOD_COLS])['time'].values" -,ready_wait,"_LONG_WAIT_PERIOD_TIMES.reindex([df[orig_col_name], _READY_WAIT_PERIOD_COLS])['time'].values" -,sentri_wait,"_LONG_WAIT_PERIOD_TIMES.reindex([df[orig_col_name], _SENTRI_WAIT_PERIOD_COLS])['time'].values" +Description,Target,Expression +list of POE wait time columns,_WAIT_PERIOD_COLS,land_use.filter(regex=wait_time_col_regex).columns.tolist() +pedestrian crossing wait time column name for each tour,_PED_WAIT_PERIOD_COLS,'ped_wait_' + df.start.astype(str) +standard crossing wait time column name for each tour,_STD_WAIT_PERIOD_COLS,'std_wait_' + df.start.astype(str) +ready crossing wait time column name for each tour,_READY_WAIT_PERIOD_COLS,'ready_wait_' + df.start.astype(str) +sentri crossing wait time column name for each tour,_SENTRI_WAIT_PERIOD_COLS,'sentri_wait_' + df.start.astype(str) +unpivot (a.k.a melt) the POE wait time data,_LONG_WAIT_PERIOD_TIMES,"land_use[land_use['poe_id'].notnull()].reset_index().melt(id_vars='zone_id', value_vars=_WAIT_PERIOD_COLS, var_name='wait_type_period', value_name='time').set_index(['zone_id','wait_type_period'])" +,std_wait,"_LONG_WAIT_PERIOD_TIMES.reindex([df[orig_col_name], _STD_WAIT_PERIOD_COLS])['time'].values" +,ready_wait,"_LONG_WAIT_PERIOD_TIMES.reindex([df[orig_col_name], _READY_WAIT_PERIOD_COLS])['time'].values" +,sentri_wait,"_LONG_WAIT_PERIOD_TIMES.reindex([df[orig_col_name], _SENTRI_WAIT_PERIOD_COLS])['time'].values" ,ped_wait,"_LONG_WAIT_PERIOD_TIMES.reindex([df[orig_col_name], _PED_WAIT_PERIOD_COLS])['time'].values" \ No newline at end of file diff --git a/activitysim/examples/example_sandag_xborder/configs/tour_mode_choice_coefficients.csv b/activitysim/examples/prototype_sandag_xborder/configs/tour_mode_choice_coefficients.csv similarity index 97% rename from activitysim/examples/example_sandag_xborder/configs/tour_mode_choice_coefficients.csv rename to activitysim/examples/prototype_sandag_xborder/configs/tour_mode_choice_coefficients.csv index 9dff5fe5db..50dafd5ab6 100755 --- a/activitysim/examples/example_sandag_xborder/configs/tour_mode_choice_coefficients.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/tour_mode_choice_coefficients.csv @@ -1,61 +1,61 @@ -coefficient_name,value,constrain -coef_one,1,T -coef_nest_root,1.00,T -coef_nest_AUTO_mandatory,.17,F -coef_nest_NONMOTORIZED_mandatory,0.17,F -coef_wait_mandatory,-0.0300,F -coef_trip_logsum_mandatory,0.200,F -SHARED2_asc_mandatory,-0.5460,F -SHARED3_asc_mandatory,-0.9820,F -WALK_asc_mandatory,-3.4000,F -SHARED2_coef_sentri_mandatory,0.1700,F -SHARED3_coef_sentri_mandatory,0.1230,F -SHARED2_coef_ready_mandatory,0.0,F -SHARED3_coef_ready_mandatory,0.0,F -SHARED2_coef_visit_mandatory,0.0,T -SHARED3_coef_visit_mandatory,0.00,T -WALK_coef_visit_mandatory,0.0,T -SHARED2_coef_other_mandatory,0.0,T -SHARED3_coef_other_mandatory,0.0,T -WALK_coef_other_mandatory,0.0,T -SHARED2_coef_calib_adj_mandatory,0.1608,F -SHARED3_coef_calib_adj_mandatory,0.2728,F -WALK_coef_calib_adj_mandatory,2.4348,F -SHARED2_coef_work_mandatory,0.0275,F -SHARED3_coef_work_mandatory,0.0935,F -WALK_coef_work_mandatory,-1.2362,F -SHARED2_coef_school_mandatory,-0.0868,F -SHARED3_coef_school_mandatory,0.0599,F -WALK_coef_school_mandatory,-0.9567,F -SHARED2_coef_shop_mandatory,0.0,T -SHARED3_coef_shop_mandatory,0.0,T -WALK_coef_shop_mandatory,0.0,T -coef_nest_AUTO_nonmandatory,.28,F -coef_nest_NONMOTORIZED_nonmandatory,.28,F -coef_wait_nonmandatory,-0.0120,F -coef_trip_logsum_nonmandatory,0.214,F -SHARED2_asc_nonmandatory,-1.0800,F -SHARED3_asc_nonmandatory,-1.3520,F -WALK_asc_nonmandatory,-1.9340,F -SHARED2_coef_sentri_nonmandatory,0.4210,F -SHARED3_coef_sentri_nonmandatory,-0.1550,F -SHARED2_coef_ready_nonmandatory,0.0,F -SHARED3_coef_ready_nonmandatory,0.0,F -SHARED2_coef_visit_nonmandatory,0.0754,F -SHARED3_coef_visit_nonmandatory,0.0684,F -WALK_coef_visit_nonmandatory,0.2257,F -SHARED2_coef_other_nonmandatory,0.0723,F -SHARED3_coef_other_nonmandatory,0.0925,F -WALK_coef_other_nonmandatory,0.4702,F -SHARED2_coef_shop_nonmandatory,-0.0608,F -SHARED3_coef_shop_nonmandatory,-0.0361,F -WALK_coef_shop_nonmandatory,0.6857,F -SHARED2_coef_calib_adj_nonmandatory,0.8914,F -SHARED3_coef_calib_adj_nonmandatory,0.9573,F -WALK_coef_calib_adj_nonmandatory,0.2989,F -SHARED2_coef_work_nonmandatory,0.0,T -SHARED3_coef_work_nonmandatory,0.0,T -WALK_coef_work_nonmandatory,0.0,T -SHARED2_coef_school_nonmandatory,0.0,T -SHARED3_coef_school_nonmandatory,0.0,T +coefficient_name,value,constrain +coef_one,1,T +coef_nest_root,1.00,T +coef_nest_AUTO_mandatory,.17,F +coef_nest_NONMOTORIZED_mandatory,0.17,F +coef_wait_mandatory,-0.0300,F +coef_trip_logsum_mandatory,0.200,F +SHARED2_asc_mandatory,-0.5460,F +SHARED3_asc_mandatory,-0.9820,F +WALK_asc_mandatory,-3.4000,F +SHARED2_coef_sentri_mandatory,0.1700,F +SHARED3_coef_sentri_mandatory,0.1230,F +SHARED2_coef_ready_mandatory,0.0,F +SHARED3_coef_ready_mandatory,0.0,F +SHARED2_coef_visit_mandatory,0.0,T +SHARED3_coef_visit_mandatory,0.00,T +WALK_coef_visit_mandatory,0.0,T +SHARED2_coef_other_mandatory,0.0,T +SHARED3_coef_other_mandatory,0.0,T +WALK_coef_other_mandatory,0.0,T +SHARED2_coef_calib_adj_mandatory,0.1608,F +SHARED3_coef_calib_adj_mandatory,0.2728,F +WALK_coef_calib_adj_mandatory,2.4348,F +SHARED2_coef_work_mandatory,0.0275,F +SHARED3_coef_work_mandatory,0.0935,F +WALK_coef_work_mandatory,-1.2362,F +SHARED2_coef_school_mandatory,-0.0868,F +SHARED3_coef_school_mandatory,0.0599,F +WALK_coef_school_mandatory,-0.9567,F +SHARED2_coef_shop_mandatory,0.0,T +SHARED3_coef_shop_mandatory,0.0,T +WALK_coef_shop_mandatory,0.0,T +coef_nest_AUTO_nonmandatory,.28,F +coef_nest_NONMOTORIZED_nonmandatory,.28,F +coef_wait_nonmandatory,-0.0120,F +coef_trip_logsum_nonmandatory,0.214,F +SHARED2_asc_nonmandatory,-1.0800,F +SHARED3_asc_nonmandatory,-1.3520,F +WALK_asc_nonmandatory,-1.9340,F +SHARED2_coef_sentri_nonmandatory,0.4210,F +SHARED3_coef_sentri_nonmandatory,-0.1550,F +SHARED2_coef_ready_nonmandatory,0.0,F +SHARED3_coef_ready_nonmandatory,0.0,F +SHARED2_coef_visit_nonmandatory,0.0754,F +SHARED3_coef_visit_nonmandatory,0.0684,F +WALK_coef_visit_nonmandatory,0.2257,F +SHARED2_coef_other_nonmandatory,0.0723,F +SHARED3_coef_other_nonmandatory,0.0925,F +WALK_coef_other_nonmandatory,0.4702,F +SHARED2_coef_shop_nonmandatory,-0.0608,F +SHARED3_coef_shop_nonmandatory,-0.0361,F +WALK_coef_shop_nonmandatory,0.6857,F +SHARED2_coef_calib_adj_nonmandatory,0.8914,F +SHARED3_coef_calib_adj_nonmandatory,0.9573,F +WALK_coef_calib_adj_nonmandatory,0.2989,F +SHARED2_coef_work_nonmandatory,0.0,T +SHARED3_coef_work_nonmandatory,0.0,T +WALK_coef_work_nonmandatory,0.0,T +SHARED2_coef_school_nonmandatory,0.0,T +SHARED3_coef_school_nonmandatory,0.0,T WALK_coef_school_nonmandatory,0.0,T \ No newline at end of file diff --git a/activitysim/examples/example_sandag_xborder/configs/tour_mode_choice_coefficients_template.csv b/activitysim/examples/prototype_sandag_xborder/configs/tour_mode_choice_coefficients_template.csv similarity index 99% rename from activitysim/examples/example_sandag_xborder/configs/tour_mode_choice_coefficients_template.csv rename to activitysim/examples/prototype_sandag_xborder/configs/tour_mode_choice_coefficients_template.csv index c247861116..920ec090d7 100755 --- a/activitysim/examples/example_sandag_xborder/configs/tour_mode_choice_coefficients_template.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/tour_mode_choice_coefficients_template.csv @@ -1,34 +1,34 @@ -coefficient_name,shop,work,other,visit,school -# same for all segments,,,,, -coef_one,,,,, -coef_nest_root,,,,, -#,,,,, -coef_nest_AUTO,coef_nest_AUTO_nonmandatory,coef_nest_AUTO_mandatory,coef_nest_AUTO_nonmandatory,coef_nest_AUTO_nonmandatory,coef_nest_AUTO_mandatory -coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED_nonmandatory,coef_nest_NONMOTORIZED_mandatory,coef_nest_NONMOTORIZED_nonmandatory,coef_nest_NONMOTORIZED_nonmandatory,coef_nest_NONMOTORIZED_mandatory -coef_wait,coef_wait_nonmandatory,coef_wait_mandatory,coef_wait_nonmandatory,coef_wait_nonmandatory,coef_wait_mandatory -coef_trip_logsum,coef_trip_logsum_nonmandatory,coef_trip_logsum_mandatory,coef_trip_logsum_nonmandatory,coef_trip_logsum_nonmandatory,coef_trip_logsum_mandatory -SHARED2_asc,SHARED2_asc_nonmandatory,SHARED2_asc_mandatory,SHARED2_asc_nonmandatory,SHARED2_asc_nonmandatory,SHARED2_asc_mandatory -SHARED3_asc,SHARED3_asc_nonmandatory,SHARED3_asc_mandatory,SHARED3_asc_nonmandatory,SHARED3_asc_nonmandatory,SHARED3_asc_mandatory -WALK_asc,WALK_asc_nonmandatory,WALK_asc_mandatory,WALK_asc_nonmandatory,WALK_asc_nonmandatory,WALK_asc_mandatory -SHARED2_coef_sentri,SHARED2_coef_sentri_nonmandatory,SHARED2_coef_sentri_mandatory,SHARED2_coef_sentri_nonmandatory,SHARED2_coef_sentri_nonmandatory,SHARED2_coef_sentri_mandatory -SHARED3_coef_sentri,SHARED3_coef_sentri_nonmandatory,SHARED3_coef_sentri_mandatory,SHARED3_coef_sentri_nonmandatory,SHARED3_coef_sentri_nonmandatory,SHARED3_coef_sentri_mandatory -SHARED2_coef_ready,SHARED2_coef_ready_nonmandatory,SHARED2_coef_ready_mandatory,SHARED2_coef_ready_nonmandatory,SHARED2_coef_ready_nonmandatory,SHARED2_coef_ready_mandatory -SHARED3_coef_ready,SHARED3_coef_ready_nonmandatory,SHARED3_coef_ready_mandatory,SHARED3_coef_ready_nonmandatory,SHARED3_coef_ready_nonmandatory,SHARED3_coef_ready_mandatory -SHARED2_coef_visit,SHARED2_coef_visit_nonmandatory,SHARED2_coef_visit_mandatory,SHARED2_coef_visit_nonmandatory,SHARED2_coef_visit_nonmandatory,SHARED2_coef_visit_mandatory -SHARED3_coef_visit,SHARED3_coef_visit_nonmandatory,SHARED3_coef_visit_mandatory,SHARED3_coef_visit_nonmandatory,SHARED3_coef_visit_nonmandatory,SHARED3_coef_visit_mandatory -WALK_coef_visit,WALK_coef_visit_nonmandatory,WALK_coef_visit_mandatory,WALK_coef_visit_nonmandatory,WALK_coef_visit_nonmandatory,WALK_coef_visit_mandatory -SHARED2_coef_other,SHARED2_coef_other_nonmandatory,SHARED2_coef_other_mandatory,SHARED2_coef_other_nonmandatory,SHARED2_coef_other_nonmandatory,SHARED2_coef_other_mandatory -SHARED3_coef_other,SHARED3_coef_other_nonmandatory,SHARED3_coef_other_mandatory,SHARED3_coef_other_nonmandatory,SHARED3_coef_other_nonmandatory,SHARED3_coef_other_mandatory -WALK_coef_other,WALK_coef_other_nonmandatory,WALK_coef_other_mandatory,WALK_coef_other_nonmandatory,WALK_coef_other_nonmandatory,WALK_coef_other_mandatory -SHARED2_coef_calib_adj,SHARED2_coef_calib_adj_nonmandatory,SHARED2_coef_calib_adj_mandatory,SHARED2_coef_calib_adj_nonmandatory,SHARED2_coef_calib_adj_nonmandatory,SHARED2_coef_calib_adj_mandatory -SHARED3_coef_calib_adj,SHARED3_coef_calib_adj_nonmandatory,SHARED3_coef_calib_adj_mandatory,SHARED3_coef_calib_adj_nonmandatory,SHARED3_coef_calib_adj_nonmandatory,SHARED3_coef_calib_adj_mandatory -WALK_coef_calib_adj,WALK_coef_calib_adj_nonmandatory,WALK_coef_calib_adj_mandatory,WALK_coef_calib_adj_nonmandatory,WALK_coef_calib_adj_nonmandatory,WALK_coef_calib_adj_mandatory -SHARED2_coef_work,SHARED2_coef_work_nonmandatory,SHARED2_coef_work_mandatory,SHARED2_coef_work_nonmandatory,SHARED2_coef_work_nonmandatory,SHARED2_coef_work_mandatory -SHARED3_coef_work,SHARED3_coef_work_nonmandatory,SHARED3_coef_work_mandatory,SHARED3_coef_work_nonmandatory,SHARED3_coef_work_nonmandatory,SHARED3_coef_work_mandatory -WALK_coef_work,WALK_coef_work_nonmandatory,WALK_coef_work_mandatory,WALK_coef_work_nonmandatory,WALK_coef_work_nonmandatory,WALK_coef_work_mandatory -SHARED2_coef_school,SHARED2_coef_school_nonmandatory,SHARED2_coef_school_mandatory,SHARED2_coef_school_nonmandatory,SHARED2_coef_school_nonmandatory,SHARED2_coef_school_mandatory -SHARED3_coef_school,SHARED3_coef_school_nonmandatory,SHARED3_coef_school_mandatory,SHARED3_coef_school_nonmandatory,SHARED3_coef_school_nonmandatory,SHARED3_coef_school_mandatory -WALK_coef_school,WALK_coef_school_nonmandatory,WALK_coef_school_mandatory,WALK_coef_school_nonmandatory,WALK_coef_school_nonmandatory,WALK_coef_school_mandatory -SHARED2_coef_shop,SHARED2_coef_shop_nonmandatory,SHARED2_coef_shop_mandatory,SHARED2_coef_shop_nonmandatory,SHARED2_coef_shop_nonmandatory,SHARED2_coef_shop_mandatory -SHARED3_coef_shop,SHARED3_coef_shop_nonmandatory,SHARED3_coef_shop_mandatory,SHARED3_coef_shop_nonmandatory,SHARED3_coef_shop_nonmandatory,SHARED3_coef_shop_mandatory +coefficient_name,shop,work,other,visit,school +# same for all segments,,,,, +coef_one,,,,, +coef_nest_root,,,,, +#,,,,, +coef_nest_AUTO,coef_nest_AUTO_nonmandatory,coef_nest_AUTO_mandatory,coef_nest_AUTO_nonmandatory,coef_nest_AUTO_nonmandatory,coef_nest_AUTO_mandatory +coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED_nonmandatory,coef_nest_NONMOTORIZED_mandatory,coef_nest_NONMOTORIZED_nonmandatory,coef_nest_NONMOTORIZED_nonmandatory,coef_nest_NONMOTORIZED_mandatory +coef_wait,coef_wait_nonmandatory,coef_wait_mandatory,coef_wait_nonmandatory,coef_wait_nonmandatory,coef_wait_mandatory +coef_trip_logsum,coef_trip_logsum_nonmandatory,coef_trip_logsum_mandatory,coef_trip_logsum_nonmandatory,coef_trip_logsum_nonmandatory,coef_trip_logsum_mandatory +SHARED2_asc,SHARED2_asc_nonmandatory,SHARED2_asc_mandatory,SHARED2_asc_nonmandatory,SHARED2_asc_nonmandatory,SHARED2_asc_mandatory +SHARED3_asc,SHARED3_asc_nonmandatory,SHARED3_asc_mandatory,SHARED3_asc_nonmandatory,SHARED3_asc_nonmandatory,SHARED3_asc_mandatory +WALK_asc,WALK_asc_nonmandatory,WALK_asc_mandatory,WALK_asc_nonmandatory,WALK_asc_nonmandatory,WALK_asc_mandatory +SHARED2_coef_sentri,SHARED2_coef_sentri_nonmandatory,SHARED2_coef_sentri_mandatory,SHARED2_coef_sentri_nonmandatory,SHARED2_coef_sentri_nonmandatory,SHARED2_coef_sentri_mandatory +SHARED3_coef_sentri,SHARED3_coef_sentri_nonmandatory,SHARED3_coef_sentri_mandatory,SHARED3_coef_sentri_nonmandatory,SHARED3_coef_sentri_nonmandatory,SHARED3_coef_sentri_mandatory +SHARED2_coef_ready,SHARED2_coef_ready_nonmandatory,SHARED2_coef_ready_mandatory,SHARED2_coef_ready_nonmandatory,SHARED2_coef_ready_nonmandatory,SHARED2_coef_ready_mandatory +SHARED3_coef_ready,SHARED3_coef_ready_nonmandatory,SHARED3_coef_ready_mandatory,SHARED3_coef_ready_nonmandatory,SHARED3_coef_ready_nonmandatory,SHARED3_coef_ready_mandatory +SHARED2_coef_visit,SHARED2_coef_visit_nonmandatory,SHARED2_coef_visit_mandatory,SHARED2_coef_visit_nonmandatory,SHARED2_coef_visit_nonmandatory,SHARED2_coef_visit_mandatory +SHARED3_coef_visit,SHARED3_coef_visit_nonmandatory,SHARED3_coef_visit_mandatory,SHARED3_coef_visit_nonmandatory,SHARED3_coef_visit_nonmandatory,SHARED3_coef_visit_mandatory +WALK_coef_visit,WALK_coef_visit_nonmandatory,WALK_coef_visit_mandatory,WALK_coef_visit_nonmandatory,WALK_coef_visit_nonmandatory,WALK_coef_visit_mandatory +SHARED2_coef_other,SHARED2_coef_other_nonmandatory,SHARED2_coef_other_mandatory,SHARED2_coef_other_nonmandatory,SHARED2_coef_other_nonmandatory,SHARED2_coef_other_mandatory +SHARED3_coef_other,SHARED3_coef_other_nonmandatory,SHARED3_coef_other_mandatory,SHARED3_coef_other_nonmandatory,SHARED3_coef_other_nonmandatory,SHARED3_coef_other_mandatory +WALK_coef_other,WALK_coef_other_nonmandatory,WALK_coef_other_mandatory,WALK_coef_other_nonmandatory,WALK_coef_other_nonmandatory,WALK_coef_other_mandatory +SHARED2_coef_calib_adj,SHARED2_coef_calib_adj_nonmandatory,SHARED2_coef_calib_adj_mandatory,SHARED2_coef_calib_adj_nonmandatory,SHARED2_coef_calib_adj_nonmandatory,SHARED2_coef_calib_adj_mandatory +SHARED3_coef_calib_adj,SHARED3_coef_calib_adj_nonmandatory,SHARED3_coef_calib_adj_mandatory,SHARED3_coef_calib_adj_nonmandatory,SHARED3_coef_calib_adj_nonmandatory,SHARED3_coef_calib_adj_mandatory +WALK_coef_calib_adj,WALK_coef_calib_adj_nonmandatory,WALK_coef_calib_adj_mandatory,WALK_coef_calib_adj_nonmandatory,WALK_coef_calib_adj_nonmandatory,WALK_coef_calib_adj_mandatory +SHARED2_coef_work,SHARED2_coef_work_nonmandatory,SHARED2_coef_work_mandatory,SHARED2_coef_work_nonmandatory,SHARED2_coef_work_nonmandatory,SHARED2_coef_work_mandatory +SHARED3_coef_work,SHARED3_coef_work_nonmandatory,SHARED3_coef_work_mandatory,SHARED3_coef_work_nonmandatory,SHARED3_coef_work_nonmandatory,SHARED3_coef_work_mandatory +WALK_coef_work,WALK_coef_work_nonmandatory,WALK_coef_work_mandatory,WALK_coef_work_nonmandatory,WALK_coef_work_nonmandatory,WALK_coef_work_mandatory +SHARED2_coef_school,SHARED2_coef_school_nonmandatory,SHARED2_coef_school_mandatory,SHARED2_coef_school_nonmandatory,SHARED2_coef_school_nonmandatory,SHARED2_coef_school_mandatory +SHARED3_coef_school,SHARED3_coef_school_nonmandatory,SHARED3_coef_school_mandatory,SHARED3_coef_school_nonmandatory,SHARED3_coef_school_nonmandatory,SHARED3_coef_school_mandatory +WALK_coef_school,WALK_coef_school_nonmandatory,WALK_coef_school_mandatory,WALK_coef_school_nonmandatory,WALK_coef_school_nonmandatory,WALK_coef_school_mandatory +SHARED2_coef_shop,SHARED2_coef_shop_nonmandatory,SHARED2_coef_shop_mandatory,SHARED2_coef_shop_nonmandatory,SHARED2_coef_shop_nonmandatory,SHARED2_coef_shop_mandatory +SHARED3_coef_shop,SHARED3_coef_shop_nonmandatory,SHARED3_coef_shop_mandatory,SHARED3_coef_shop_nonmandatory,SHARED3_coef_shop_nonmandatory,SHARED3_coef_shop_mandatory WALK_coef_shop,WALK_coef_shop_nonmandatory,WALK_coef_shop_mandatory,WALK_coef_shop_nonmandatory,WALK_coef_shop_nonmandatory,WALK_coef_shop_mandatory \ No newline at end of file diff --git a/activitysim/examples/example_sandag_xborder/configs/tour_od_choice.csv b/activitysim/examples/prototype_sandag_xborder/configs/tour_od_choice.csv similarity index 99% rename from activitysim/examples/example_sandag_xborder/configs/tour_od_choice.csv rename to activitysim/examples/prototype_sandag_xborder/configs/tour_od_choice.csv index 579f271e03..1c69e97883 100644 --- a/activitysim/examples/example_sandag_xborder/configs/tour_od_choice.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/tour_od_choice.csv @@ -1,23 +1,23 @@ -Label,Description,Expression,coefficient -util_size_variable,Size variable,@(df['size_term']).apply(np.log1p),1 -util_no_attractions,No attractions,@df['size_term']==0,-999 -local_dist,skim distance,"_DIST@skims[('SOV_NT_M_DIST','AM')]",1 -poe_accessibility,station logsum,@df['colonia_pop_accessibility'],1 -otay_mesa_mandatory,"POE is Otay Mesa, mandatory","@(df['poe_id']==1) & (df.is_mandatory)",coef_otay_mesa_mandatory -otay_mesa_non_mandatory,"POE is Otay Mesa, non-mandatory","@(df['poe_id']==1) & (1 - df.is_mandatory)",coef_otay_mesa_non_mandatory -tecate_mandatory,"POE is Tecate, mandatory","@(df['poe_id']==2) & (df.is_mandatory)",coef_tecate_mandatory -tecate_non_mandatory,"POE is Tecate, non-mandatory","@(df['poe_id']==2) & (1 - df.is_mandatory)",coef_tecate_non_mandatory -util_dist_0_2_mandatory,"Distance, piecewise linear from 0 to 2 miles, mandatory","@(_DIST > 0) * (np.minimum(_DIST, 2)) * (df.is_mandatory)",coef_dist_0_2_mandatory -util_dist_2_5_mandatory,"Distance, piecewise linear from 2 to 5 miles, mandatory","@(_DIST > 2) * (np.minimum(_DIST - 2, 3)) * (df.is_mandatory)",coef_dist_2_5_mandatory -util_dist_5_10_mandatory,"Distance, piecewise linear from 5 to 10 miles, mandatory","@(_DIST > 5) * (np.minimum(_DIST - 5, 5)) * (df.is_mandatory)",coef_dist_5_10_mandatory -util_dist_10_20_mandatory,"Distance, piecewise linear from 10 to 20 miles, mandatory","@(_DIST > 10) * (np.minimum(_DIST - 10, 10)) * (df.is_mandatory)",coef_dist_10_20_mandatory -util_dist_20_up_mandatory,"Distance, piecewise linear for 20+ miles, mandatory","@(_DIST > 20) * (_DIST - 20) * (df.is_mandatory)",coef_dist_20_up_mandatory -util_dist_0_2_non_mandatory,"Distance, piecewise linear from 0 to 2 miles, non_mandatory","@(_DIST > 0) * (np.minimum(_DIST, 2)) * (1 - df.is_mandatory)",coef_dist_0_2_non_mandatory -util_dist_2_5_non_mandatory,"Distance, piecewise linear from 2 to 5 miles, non_mandatory","@(_DIST > 2) * (np.minimum(_DIST - 2, 3)) * (1 - df.is_mandatory)",coef_dist_2_5_non_mandatory -util_dist_5_10_non_mandatory,"Distance, piecewise linear from 5 to 10 miles, non_mandatory","@(_DIST > 5) * (np.minimum(_DIST - 5, 5)) * (1 - df.is_mandatory)",coef_dist_5_10_non_mandatory -util_dist_10_20_non_mandatory,"Distance, piecewise linear from 10 to 20 miles, non_mandatory","@(_DIST > 10) * (np.minimum(_DIST - 10, 10)) * (1 - df.is_mandatory)",coef_dist_10_20_non_mandatory -util_dist_20_up_non_mandatory,"Distance, piecewise linear for 20+ miles, non_mandatory","@(_DIST > 20) * (_DIST - 20) * (1 - df.is_mandatory)",coef_dist_20_up_non_mandatory -util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count / df.prob), 60)",1 -otay_mesa_calibration_adjustment,"POE is Otay Mesa, calibration adjustment",@df['poe_id']==1,coef_otay_mesa_calibration_adj -tecate_calibration_adjustment,"POE is Tecate, calibration adjustment",@df['poe_id']==2,coef_tecate_calibration_adj +Label,Description,Expression,coefficient +util_size_variable,Size variable,@(df['size_term']).apply(np.log1p),1 +util_no_attractions,No attractions,@df['size_term']==0,-999 +local_dist,skim distance,"_DIST@skims[('SOV_NT_M_DIST','AM')]",1 +poe_accessibility,station logsum,@df['colonia_pop_accessibility'],1 +otay_mesa_mandatory,"POE is Otay Mesa, mandatory","@(df['poe_id']==1) & (df.is_mandatory)",coef_otay_mesa_mandatory +otay_mesa_non_mandatory,"POE is Otay Mesa, non-mandatory","@(df['poe_id']==1) & (1 - df.is_mandatory)",coef_otay_mesa_non_mandatory +tecate_mandatory,"POE is Tecate, mandatory","@(df['poe_id']==2) & (df.is_mandatory)",coef_tecate_mandatory +tecate_non_mandatory,"POE is Tecate, non-mandatory","@(df['poe_id']==2) & (1 - df.is_mandatory)",coef_tecate_non_mandatory +util_dist_0_2_mandatory,"Distance, piecewise linear from 0 to 2 miles, mandatory","@(_DIST > 0) * (np.minimum(_DIST, 2)) * (df.is_mandatory)",coef_dist_0_2_mandatory +util_dist_2_5_mandatory,"Distance, piecewise linear from 2 to 5 miles, mandatory","@(_DIST > 2) * (np.minimum(_DIST - 2, 3)) * (df.is_mandatory)",coef_dist_2_5_mandatory +util_dist_5_10_mandatory,"Distance, piecewise linear from 5 to 10 miles, mandatory","@(_DIST > 5) * (np.minimum(_DIST - 5, 5)) * (df.is_mandatory)",coef_dist_5_10_mandatory +util_dist_10_20_mandatory,"Distance, piecewise linear from 10 to 20 miles, mandatory","@(_DIST > 10) * (np.minimum(_DIST - 10, 10)) * (df.is_mandatory)",coef_dist_10_20_mandatory +util_dist_20_up_mandatory,"Distance, piecewise linear for 20+ miles, mandatory","@(_DIST > 20) * (_DIST - 20) * (df.is_mandatory)",coef_dist_20_up_mandatory +util_dist_0_2_non_mandatory,"Distance, piecewise linear from 0 to 2 miles, non_mandatory","@(_DIST > 0) * (np.minimum(_DIST, 2)) * (1 - df.is_mandatory)",coef_dist_0_2_non_mandatory +util_dist_2_5_non_mandatory,"Distance, piecewise linear from 2 to 5 miles, non_mandatory","@(_DIST > 2) * (np.minimum(_DIST - 2, 3)) * (1 - df.is_mandatory)",coef_dist_2_5_non_mandatory +util_dist_5_10_non_mandatory,"Distance, piecewise linear from 5 to 10 miles, non_mandatory","@(_DIST > 5) * (np.minimum(_DIST - 5, 5)) * (1 - df.is_mandatory)",coef_dist_5_10_non_mandatory +util_dist_10_20_non_mandatory,"Distance, piecewise linear from 10 to 20 miles, non_mandatory","@(_DIST > 10) * (np.minimum(_DIST - 10, 10)) * (1 - df.is_mandatory)",coef_dist_10_20_non_mandatory +util_dist_20_up_non_mandatory,"Distance, piecewise linear for 20+ miles, non_mandatory","@(_DIST > 20) * (_DIST - 20) * (1 - df.is_mandatory)",coef_dist_20_up_non_mandatory +util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count / df.prob), 60)",1 +otay_mesa_calibration_adjustment,"POE is Otay Mesa, calibration adjustment",@df['poe_id']==1,coef_otay_mesa_calibration_adj +tecate_calibration_adjustment,"POE is Tecate, calibration adjustment",@df['poe_id']==2,coef_tecate_calibration_adj util_mode_logsum,tour mode choice logsum,tour_mode_choice_logsum,1 \ No newline at end of file diff --git a/activitysim/examples/example_sandag_xborder/configs/tour_od_choice.yaml b/activitysim/examples/prototype_sandag_xborder/configs/tour_od_choice.yaml similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/tour_od_choice.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/tour_od_choice.yaml diff --git a/activitysim/examples/example_sandag_xborder/configs/tour_od_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/prototype_sandag_xborder/configs/tour_od_choice_annotate_choosers_preprocessor.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/tour_od_choice_annotate_choosers_preprocessor.csv rename to activitysim/examples/prototype_sandag_xborder/configs/tour_od_choice_annotate_choosers_preprocessor.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/tour_od_choice_coefficients.csv b/activitysim/examples/prototype_sandag_xborder/configs/tour_od_choice_coefficients.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/tour_od_choice_coefficients.csv rename to activitysim/examples/prototype_sandag_xborder/configs/tour_od_choice_coefficients.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/tour_od_choice_sample.csv b/activitysim/examples/prototype_sandag_xborder/configs/tour_od_choice_sample.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/tour_od_choice_sample.csv rename to activitysim/examples/prototype_sandag_xborder/configs/tour_od_choice_sample.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/tour_purpose_probs_by_poe.csv b/activitysim/examples/prototype_sandag_xborder/configs/tour_purpose_probs_by_poe.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/tour_purpose_probs_by_poe.csv rename to activitysim/examples/prototype_sandag_xborder/configs/tour_purpose_probs_by_poe.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/tour_scheduling_probabilistic.yaml b/activitysim/examples/prototype_sandag_xborder/configs/tour_scheduling_probabilistic.yaml similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/tour_scheduling_probabilistic.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/tour_scheduling_probabilistic.yaml diff --git a/activitysim/examples/example_sandag_xborder/configs/tour_scheduling_probs.csv b/activitysim/examples/prototype_sandag_xborder/configs/tour_scheduling_probs.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/tour_scheduling_probs.csv rename to activitysim/examples/prototype_sandag_xborder/configs/tour_scheduling_probs.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/trip_destination.csv b/activitysim/examples/prototype_sandag_xborder/configs/trip_destination.csv similarity index 99% rename from activitysim/examples/example_sandag_xborder/configs/trip_destination.csv rename to activitysim/examples/prototype_sandag_xborder/configs/trip_destination.csv index 725fdac6c2..12fe38ccd9 100644 --- a/activitysim/examples/example_sandag_xborder/configs/trip_destination.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/trip_destination.csv @@ -1,16 +1,16 @@ -Description,Expression,work,school,visit,shop,other -size term for purpose,"@np.log1p(size_terms.get(df.trip_dest, df.purpose))",1,1,1,1,1 -no attractions for purpose,"@size_terms.get(df.trip_dest, df.purpose) == 0",-999,-999,-999,-999,-999 -,"_od_DIST@odt_skims['SOV_NT_M_DIST']",1,1,1,1,1 -,"_dp_DIST@dpt_skims['SOV_NT_M_DIST']",1,1,1,1,1 -intermediate stops on half-tour,_stops_on_leg@df.trip_count - 1,1,1,1,1,1 -"Sample of alternatives correction factor","@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1 -mode choice logsum,od_logsum + dp_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum -,@(df.tour_duration <= 2) * (df.od_logsum + df.dp_logsum),coef_mode_choice_logsum_dur_leq2,coef_mode_choice_logsum_dur_leq2,coef_mode_choice_logsum_dur_leq2,coef_mode_choice_logsum_dur_leq2,coef_mode_choice_logsum_dur_leq2 -,@(df.tour_duration >= 8) * (df.od_logsum + df.dp_logsum),coef_mode_choice_logsum_dur_geq8,coef_mode_choice_logsum_dur_geq8,coef_mode_choice_logsum_dur_geq8,coef_mode_choice_logsum_dur_geq8,coef_mode_choice_logsum_dur_geq8 -first outbound trip,"@(df.trip_num == 1) & df.outbound & (_od_DIST > 1)",coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi -last outbound intermediate stop,"@(df.trip_num == _stops_on_leg) & df.outbound & (_dp_DIST > 1)",coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi -last outbound intermediate stop,"@(df.trip_num == _stops_on_leg) & df.outbound * np.clip(_dp_DIST - 1, 0, 3)",coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3 -first inbound trip,"@(df.trip_num == 1) & (1 - df.outbound) & (_od_DIST > 1)",coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi -last inbound intermediate stop,"@(df.trip_num == _stops_on_leg) & (1 - df.outbound) & (_dp_DIST > 1)",coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi +Description,Expression,work,school,visit,shop,other +size term for purpose,"@np.log1p(size_terms.get(df.trip_dest, df.purpose))",1,1,1,1,1 +no attractions for purpose,"@size_terms.get(df.trip_dest, df.purpose) == 0",-999,-999,-999,-999,-999 +,"_od_DIST@odt_skims['SOV_NT_M_DIST']",1,1,1,1,1 +,"_dp_DIST@dpt_skims['SOV_NT_M_DIST']",1,1,1,1,1 +intermediate stops on half-tour,_stops_on_leg@df.trip_count - 1,1,1,1,1,1 +"Sample of alternatives correction factor","@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1 +mode choice logsum,od_logsum + dp_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum,coef_mode_choice_logsum +,@(df.tour_duration <= 2) * (df.od_logsum + df.dp_logsum),coef_mode_choice_logsum_dur_leq2,coef_mode_choice_logsum_dur_leq2,coef_mode_choice_logsum_dur_leq2,coef_mode_choice_logsum_dur_leq2,coef_mode_choice_logsum_dur_leq2 +,@(df.tour_duration >= 8) * (df.od_logsum + df.dp_logsum),coef_mode_choice_logsum_dur_geq8,coef_mode_choice_logsum_dur_geq8,coef_mode_choice_logsum_dur_geq8,coef_mode_choice_logsum_dur_geq8,coef_mode_choice_logsum_dur_geq8 +first outbound trip,"@(df.trip_num == 1) & df.outbound & (_od_DIST > 1)",coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi +last outbound intermediate stop,"@(df.trip_num == _stops_on_leg) & df.outbound & (_dp_DIST > 1)",coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi +last outbound intermediate stop,"@(df.trip_num == _stops_on_leg) & df.outbound * np.clip(_dp_DIST - 1, 0, 3)",coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3 +first inbound trip,"@(df.trip_num == 1) & (1 - df.outbound) & (_od_DIST > 1)",coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi +last inbound intermediate stop,"@(df.trip_num == _stops_on_leg) & (1 - df.outbound) & (_dp_DIST > 1)",coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi last inbound intermediate stop,"@(df.trip_num == _stops_on_leg) & (1 - df.outbound) * np.clip(_dp_DIST - 1, 0, 3)",coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3 \ No newline at end of file diff --git a/activitysim/examples/example_sandag_xborder/configs/trip_destination.yaml b/activitysim/examples/prototype_sandag_xborder/configs/trip_destination.yaml similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/trip_destination.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/trip_destination.yaml diff --git a/activitysim/examples/example_sandag_xborder/configs/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_sandag_xborder/configs/trip_destination_annotate_trips_preprocessor.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/trip_destination_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_sandag_xborder/configs/trip_destination_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/trip_destination_coefficients.csv b/activitysim/examples/prototype_sandag_xborder/configs/trip_destination_coefficients.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/trip_destination_coefficients.csv rename to activitysim/examples/prototype_sandag_xborder/configs/trip_destination_coefficients.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/trip_destination_sample.csv b/activitysim/examples/prototype_sandag_xborder/configs/trip_destination_sample.csv similarity index 99% rename from activitysim/examples/example_sandag_xborder/configs/trip_destination_sample.csv rename to activitysim/examples/prototype_sandag_xborder/configs/trip_destination_sample.csv index 7fad8f0478..029c9f7ba4 100644 --- a/activitysim/examples/example_sandag_xborder/configs/trip_destination_sample.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/trip_destination_sample.csv @@ -1,12 +1,12 @@ -Description,Expression,work,school,visit,shop,other -Size variable,"@np.log1p(size_terms.get(df.trip_dest, df.purpose))",1,1,1,1,1 -No attractions,"@size_terms.get(df.trip_dest, df.purpose) == 0",-999,-999,-999,-999,-999 -,"_od_DIST@odt_skims['SOV_NT_M_DIST']",1,1,1,1,1 -,"_dp_DIST@dpt_skims['SOV_NT_M_DIST']",1,1,1,1,1 -intermediate stops on half-tour,_stops_on_leg@df.trip_count - 1,1,1,1,1,1 -first outbound trip,"@(df.trip_num == 1) & df.outbound & (_od_DIST > 1)",coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi -last outbound intermediate stop,"@(df.trip_num == _stops_on_leg) & df.outbound & (_dp_DIST > 1)",coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi -last outbound intermediate stop,"@(df.trip_num == _stops_on_leg) & df.outbound * np.clip(_dp_DIST - 1, 0, 3)",coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3 -first inbound trip,"@(df.trip_num == 1) & (1 - df.outbound) & (_od_DIST > 1)",coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi -last inbound intermediate stop,"@(df.trip_num == _stops_on_leg) & (1 - df.outbound) & (_dp_DIST > 1)",coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi -last inbound intermediate stop,"@(df.trip_num == _stops_on_leg) & (1 - df.outbound) * np.clip(_dp_DIST - 1, 0, 3)",coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3 +Description,Expression,work,school,visit,shop,other +Size variable,"@np.log1p(size_terms.get(df.trip_dest, df.purpose))",1,1,1,1,1 +No attractions,"@size_terms.get(df.trip_dest, df.purpose) == 0",-999,-999,-999,-999,-999 +,"_od_DIST@odt_skims['SOV_NT_M_DIST']",1,1,1,1,1 +,"_dp_DIST@dpt_skims['SOV_NT_M_DIST']",1,1,1,1,1 +intermediate stops on half-tour,_stops_on_leg@df.trip_count - 1,1,1,1,1,1 +first outbound trip,"@(df.trip_num == 1) & df.outbound & (_od_DIST > 1)",coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi,coef_first_outbound_dist_poe_gt_1mi +last outbound intermediate stop,"@(df.trip_num == _stops_on_leg) & df.outbound & (_dp_DIST > 1)",coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi,coef_last_outbound_dist_dest_gt_1mi +last outbound intermediate stop,"@(df.trip_num == _stops_on_leg) & df.outbound * np.clip(_dp_DIST - 1, 0, 3)",coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3,coef_last_outbound_dist_dest_gt1_max3 +first inbound trip,"@(df.trip_num == 1) & (1 - df.outbound) & (_od_DIST > 1)",coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi,coef_first_inbound_dist_poe_gt_1mi +last inbound intermediate stop,"@(df.trip_num == _stops_on_leg) & (1 - df.outbound) & (_dp_DIST > 1)",coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi,coef_last_inbound_dist_dest_gt_1mi +last inbound intermediate stop,"@(df.trip_num == _stops_on_leg) & (1 - df.outbound) * np.clip(_dp_DIST - 1, 0, 3)",coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3,coef_last_inbound_dist_dest_gt1_max3 diff --git a/activitysim/examples/example_sandag_xborder/configs/trip_mode_choice.csv b/activitysim/examples/prototype_sandag_xborder/configs/trip_mode_choice.csv similarity index 99% rename from activitysim/examples/example_sandag_xborder/configs/trip_mode_choice.csv rename to activitysim/examples/prototype_sandag_xborder/configs/trip_mode_choice.csv index 23ded3424a..d48d6de182 100644 --- a/activitysim/examples/example_sandag_xborder/configs/trip_mode_choice.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/trip_mode_choice.csv @@ -1,94 +1,94 @@ -Label,Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK,WALK_TRANSIT,TAXI,TNC_SINGLE,TNC_SHARED -util_DA_IVT,Drive alone - In-vehicle time,c_ivt * da_time_skims,1,,,,,,, -util_DA_op_cost,Drive alone - Operating cost,@c_cost * (df.cost_per_mile * df.da_dist_skims + df.da_cost_skims),1,,,,,,, -util_DA_park_cost,Drive alone - Parking cost,@c_cost * df.parking_cost,1,,,,,,, -util_S2_IVT,Shared ride 2 HOV - In -vehicle time,c_ivt * s2_time_skims,,1,,,,,, -util_S2_op_cost,Shared ride 2 HOV - Operating cost,@c_cost * (df.cost_per_mile * df.s2_dist_skims + df.s2_cost_skims),,1,,,,,, -util_S2_park_cost,Shared ride 2 HOV - Parking cost,@c_cost * df.parking_cost / cost_share_s2,,1,,,,,, -util_S3_IVT,Shared ride 3+ HOV - In-vehicle time,c_ivt * s3_time_skims,,,1,,,,, -util_S3_op_cost,Shared ride 3+ HOV - Operating cost,@c_cost * (df.cost_per_mile * df.s3_dist_skims + df.s3_cost_skims),,,1,,,,, -util_S3_park_cost,Shared ride 3+ HOV - Parking cost,@c_cost * df.parking_cost / cost_share_s3,,,1,,,,, -util_WALK_unavail,Walk - Mode unavailable,(1 - walk_available),,,,-999,,,, -util_WALK_time,Walk - Time,@df.walk_available * df.c_walk_time * df.walk_time,,,,1,,,, -util_WALK_TRANSIT_unavail,Walk-transit unavailable,walk_transit_logsum <= -500,,,,,-999,,, -util_WALK_TRANSIT,Walk-transit utility,(walk_transit_logsum > -500) * walk_transit_logsum,,,,,1,,, -util_TAXI_IVT,Taxi - In-vehicle time,c_ivt * s2_time_skims,,,,,,1,, -util_TAXI_wait,Taxi - Wait time,c_ivt * taxi_wait_time,,,,,,1,, -util_TAXI_cost,Taxi - Fare,@c_cost * ((taxi_base_fare + (df.s2_dist_skims * taxi_cost_per_mile) + (df.s2_time_skims * taxi_cost_per_minute)) * 100 + df.s2_cost_skims),,,,,,1,, -util_TNC_SINGLE_IVT,TNC Single - In-vehicle time,c_ivt * s2_time_skims,,,,,,,1, -util_TNC_SINGLE_wait,TNC Single - Wait time,c_ivt * 1.5 * tnc_single_wait_time,,,,,,,1, -util_TNC_SINGLE_cost,TNC Single - Cost,"@c_cost * ((tnc_single_base_fare + (df.s2_dist_skims * tnc_single_cost_per_mile) + (df.s2_time_skims * tnc_single_cost_per_minute).clip(lower=tnc_single_cost_minimum)) * 100 + df.s2_cost_skims)",,,,,,,1, -util_TNC_SHARED_IVT,TNC Shared - In-vehicle time,@df.c_ivt * df.s2_time_skims * tnc_shared_ivt_factor,,,,,,,,1 -util_TNC_SHARED_wait,TNC Shared - Wait time,c_ivt * 1.5 * tnc_shared_wait_time,,,,,,,,1 -util_TNC_SHARED_cost,TNC Shared - Cost,"@c_cost * ((tnc_shared_base_fare + (df.s2_dist_skims * tnc_shared_cost_per_mile) + (df.s2_time_skims * tnc_shared_cost_per_minute).clip(lower=tnc_shared_cost_minimum)) * 100 + df.s2_cost_skims)",,,,,,,,1 -util_S2_ASC_tour_DA,s2 ASC - Tour Mode DA,@df.tour_mode_is_da * s2_ASC_tour_da,,1,,,,,, -util_S3_ASC_tour_DA,s3 ASC - Tour Mode DA,@df.tour_mode_is_da * s3_ASC_tour_da,,,1,,,,, -util_WALK_ASC_tour_DA,walk ASC - Tour Mode DA,@df.tour_mode_is_da * walk_ASC_tour_da,,,,1,,,, -util_WALK_TRANSIT_ASC_tour_DA,walk transit ASC - Tour Mode DA,@df.tour_mode_is_da * walk_transit_ASC_tour_da,,,,,1,,, -util_TAXI_ASC_tour_DA,taxi ASC - Tour Mode DA,@df.tour_mode_is_da * taxi_ASC_tour_da,,,,,,1,, -util_TNC_SINGLE_ASC_tour_DA,tnc single ASC - Tour Mode DA,@df.tour_mode_is_da * tnc_single_ASC_tour_da,,,,,,,1, -util_TNC_SHARED_ASC_tour_DA,tnc shared ASC - Tour Mode DA,@df.tour_mode_is_da * tnc_shared_ASC_tour_da,,,,,,,,1 -util_S2_ASC_tour_S2,s2 ASC - Tour Mode S2,@df.tour_mode_is_s2 * s2_ASC_tour_s2,,1,,,,,, -util_S3_ASC_tour_S2,s3 ASC - Tour Mode S2,@df.tour_mode_is_s2 * s3_ASC_tour_s2,,,1,,,,, -util_WALK_ASC_tour_S2,walk ASC - Tour Mode S2,@df.tour_mode_is_s2 * walk_ASC_tour_s2,,,,1,,,, -util_WALK_TRANSIT_ASC_tour_S2,walk transit ASC - Tour Mode S2,@df.tour_mode_is_s2 * walk_transit_ASC_tour_s2,,,,,1,,, -util_TAXI_ASC_tour_S2,taxi ASC - Tour Mode S2,@df.tour_mode_is_s2 * taxi_ASC_tour_s2,,,,,,1,, -util_TNC_SINGLE_ASC_tour_S2,tnc single ASC - Tour Mode S2,@df.tour_mode_is_s2 * tnc_single_ASC_tour_s2,,,,,,,1, -util_TNC_SHARED_ASC_tour_S2,tnc shared ASC - Tour Mode S2,@df.tour_mode_is_s2 * tnc_shared_ASC_tour_s2,,,,,,,,1 -util_S2_ASC_tour_S3,s2 ASC - Tour Mode S3,@df.tour_mode_is_s3 * s2_ASC_tour_s3,,1,,,,,, -util_S3_ASC_tour_S3,s3 ASC - Tour Mode S3,@df.tour_mode_is_s3 * s3_ASC_tour_s3,,,1,,,,, -util_WALK_ASC_tour_S3,walk ASC - Tour Mode S3,@df.tour_mode_is_s3 * walk_ASC_tour_s3,,,,1,,,, -util_WALK_TRANSIT_ASC_tour_S3,walk transit ASC - Tour Mode S3,@df.tour_mode_is_s3 * walk_transit_ASC_tour_s3,,,,,1,,, -util_TAXI_ASC_tour_S3,taxi ASC - Tour Mode S3,@df.tour_mode_is_s3 * taxi_ASC_tour_s3,,,,,,1,, -util_TNC_SINGLE_ASC_tour_S3,tnc single ASC - Tour Mode S3,@df.tour_mode_is_s3 * tnc_single_ASC_tour_s3,,,,,,,1, -util_TNC_SHARED_ASC_tour_S3,tnc shared ASC - Tour Mode S3,@df.tour_mode_is_s3 * tnc_shared_ASC_tour_s3,,,,,,,,1 -util_S2_ASC_tour_WALK,s2 ASC - Tour Mode Walk,@df.tour_mode_is_walk * s2_ASC_tour_walk,,1,,,,,, -util_S3_ASC_tour_WALK,s3 ASC - Tour Mode Walk,@df.tour_mode_is_walk * s3_ASC_tour_walk,,,1,,,,, -util_WALK_ASC_tour_WALK,walk ASC - Tour Mode Walk,@df.tour_mode_is_walk * walk_ASC_tour_walk,,,,1,,,, -util_WALK_TRANSIT_ASC_tour_WALK,walk transit ASC - Tour Mode Walk,@df.tour_mode_is_walk * walk_transit_ASC_tour_walk,,,,,1,,, -util_TAXI_ASC_tour_WALK,taxi ASC - Tour Mode Walk,@df.tour_mode_is_walk * taxi_ASC_tour_walk,,,,,,1,, -util_TNC_SINGLE_ASC_tour_WALK,tnc single ASC - Tour Mode Walk,@df.tour_mode_is_walk * tnc_single_ASC_tour_walk,,,,,,,1, -util_TNC_SHARED_ASC_tour_WALK,tnc shared ASC - Tour Mode Walk,@df.tour_mode_is_walk * tnc_shared_ASC_tour_walk,,,,,,,,1 -util_sentri_S2,s2 Crossing used Sentri,@(df.pass_type == 'sentri') * s2_sentri_coef,,1,,,,,, -util_sentri_S3,s3 Crossing used Sentri,@(df.pass_type == 'sentri') * s3_sentri_coef,,,1,,,,, -util_sentri_WALK,walk Crossing used Sentri,@(df.pass_type == 'sentri') * walk_sentri_coef,,,,1,,,, -util_sentri_WALK_TRANSIT,walk transit Crossing used Sentri,@(df.pass_type == 'sentri') * walk_transit_sentri_coef,,,,,1,,, -util_ready_S2,s2 Crossing used Ready,@(df.pass_type == 'ready') * s2_ready_coef,,1,,,,,, -util_ready_S3,s3 Crossing used Ready,@(df.pass_type == 'ready') * s3_ready_coef,,,1,,,,, -util_ready_WALK,walk Crossing used Ready,@(df.pass_type == 'ready') * walk_ready_coef,,,,1,,,, -util_ready_WALK_TRANSIT,walk transit Crossing used Ready,@(df.pass_type == 'ready') * walk_transit_ready_coef,,,,,1,, -util_work_tour_S2,s2 Work Tour,@df.is_work_tour * s2_work_tour_coef,,1,,,,,, -util_work_tour_S3,s3 Work Tour,@df.is_work_tour * s3_work_tour_coef,,,1,,,,, -util_S2_calib_adj_tour_DA,s2 Calibration Adjustment - Tour Mode DA,@df.tour_mode_is_da * s2_calib_adj_tour_da,,1,,,,,, -util_S3_calib_adj_tour_DA,s3 Calibration Adjustment - Tour Mode DA,@df.tour_mode_is_da * s3_calib_adj_tour_da,,,1,,,,, -util_WALK_calib_adj_tour_DA,walk Calibration Adjustment - Tour Mode DA,@df.tour_mode_is_da * walk_calib_adj_tour_da,,,,1,,,, -util_WALK_TRANSIT_calib_adj_tour_DA,walk transit Calibration Adjustment - Tour Mode DA,@df.tour_mode_is_da * walk_transit_calib_adj_tour_da,,,,,1,,, -util_S2_calib_adj_tour_S2,s2 Calibration Adjustment - Tour Mode S2,@df.tour_mode_is_s2 * s2_calib_adj_tour_s2,,1,,,,,, -util_S3_calib_adj_tour_S2,s3 Calibration Adjustment - Tour Mode S2,@df.tour_mode_is_s2 * s3_calib_adj_tour_s2,,,1,,,,, -util_WALK_calib_adj_tour_S2,walk Calibration Adjustment - Tour Mode S2,@df.tour_mode_is_s2 * walk_calib_adj_tour_s2,,,,1,,,, -util_WALK_TRANSIT_calib_adj_tour_S2,walk transit Calibration Adjustment - Tour Mode S2,@df.tour_mode_is_s2 * walk_transit_calib_adj_tour_s2,,,,,1,,, -util_S2_calib_adj_tour_S3,s2 Calibration Adjustment - Tour Mode S3,@df.tour_mode_is_s3 * s2_calib_adj_tour_s3,,1,,,,,, -util_S3_calib_adj_tour_S3,s3 Calibration Adjustment - Tour Mode S3,@df.tour_mode_is_s3 * s3_calib_adj_tour_s3,,,1,,,,, -util_WALK_calib_adj_tour_S3,walk Calibration Adjustment - Tour Mode S3,@df.tour_mode_is_s3 * walk_calib_adj_tour_s3,,,,1,,,, -util_WALK_TRANSIT_calib_adj_tour_S3,walk transit Calibration Adjustment - Tour Mode S3,@df.tour_mode_is_s3 * walk_transit_calib_adj_tour_s3,,,,,1,,, -util_S2_calib_adj_tour_WALK,s2 Calibration Adjustment - Tour Mode Walk,@df.tour_mode_is_walk * s2_calib_adj_tour_walk,,1,,,,,, -util_S3_calib_adj_tour_WALK,s3 Calibration Adjustment - Tour Mode Walk,@df.tour_mode_is_walk * s3_calib_adj_tour_walk,,,1,,,,, -util_WALK_calib_adj_tour_WALK,walk Calibration Adjustment - Tour Mode Walk,@df.tour_mode_is_walk * walk_calib_adj_tour_walk,,,,1,,,, -util_WALK_TRANSIT_calib_adj_tour_WALK,walk transit Calibration Adjustment - Tour Mode Walk,@df.tour_mode_is_walk * walk_transit_calib_adj_tour_walk,,,,,1,,, -util_S2_ABM2_calib_adj_tour_DA,s2 ABM 2 Calibration Adjustment - Tour Mode DA,@df.tour_mode_is_da * s2_abm2_calib_adj_tour_da,,1,,,,,, -util_S3_ABM2_calib_adj_tour_DA,s3 ABM 2 Calibration Adjustment - Tour Mode DA,@df.tour_mode_is_da * s3_abm2_calib_adj_tour_da,,,1,,,,, -util_WALK_ABM2_calib_adj_tour_DA,walk ABM 2 Calibration Adjustment - Tour Mode DA,@df.tour_mode_is_da * walk_abm2_calib_adj_tour_da,,,,1,,,, -util_WALK_TRANSIT_ABM2_calib_adj_tour_DA,walk transit ABM 2 Calibration Adjustment - Tour Mode DA,@df.tour_mode_is_da * walk_transit_abm2_calib_adj_tour_da,,,,,1,,, -util_S2_ABM2_calib_adj_tour_S2,s2 ABM 2 Calibration Adjustment - Tour Mode S2,@df.tour_mode_is_s2 * s2_abm2_calib_adj_tour_s2,,1,,,,,, -util_S3_ABM2_calib_adj_tour_S2,s3 ABM 2 Calibration Adjustment - Tour Mode S2,@df.tour_mode_is_s2 * s3_abm2_calib_adj_tour_s2,,,1,,,,, -util_WALK_ABM2_calib_adj_tour_S2,walk ABM 2 Calibration Adjustment - Tour Mode S2,@df.tour_mode_is_s2 * walk_abm2_calib_adj_tour_s2,,,,1,,,, -util_WALK_TRANSIT_ABM2_calib_adj_tour_S2,walk transit ABM 2 Calibration Adjustment - Tour Mode S2,@df.tour_mode_is_s2 * walk_transit_abm2_calib_adj_tour_s2,,,,,1,,, -util_S2_ABM2_calib_adj_tour_S3,s2 ABM 2 Calibration Adjustment - Tour Mode S3,@df.tour_mode_is_s3 * s2_abm2_calib_adj_tour_s3,,1,,,,,, -util_S3_ABM2_calib_adj_tour_S3,s3 ABM 2 Calibration Adjustment - Tour Mode S3,@df.tour_mode_is_s3 * s3_abm2_calib_adj_tour_s3,,,1,,,,, -util_WALK_ABM2_calib_adj_tour_S3,walk ABM 2 Calibration Adjustment - Tour Mode S3,@df.tour_mode_is_s3 * walk_abm2_calib_adj_tour_s3,,,,1,,,, -util_WALK_TRANSIT_ABM2_calib_adj_tour_S3,walk transit ABM 2 Calibration Adjustment - Tour Mode S3,@df.tour_mode_is_s3 * walk_transit_abm2_calib_adj_tour_s3,,,,,1,,, -util_S2_ABM2_calib_adj_tour_WALK,s2 ABM 2 Calibration Adjustment - Tour Mode WALK,@df.tour_mode_is_walk * s2_abm2_calib_adj_tour_walk,,1,,,,,, -util_S3_ABM2_calib_adj_tour_WALK,s3 ABM 2 Calibration Adjustment - Tour Mode WALK,@df.tour_mode_is_walk * s3_abm2_calib_adj_tour_walk,,,1,,,,, -util_WALK_ABM2_calib_adj_tour_WALK,walk ABM 2 Calibration Adjustment - Tour Mode WALK,@df.tour_mode_is_walk * walk_abm2_calib_adj_tour_walk,,,,1,,,, -util_WALK_TRANSIT_ABM2_calib_adj_tour_WALK,walk transit ABM 2 Calibration Adjustment - Tour Mode WALK,@df.tour_mode_is_walk * walk_transit_abm2_calib_adj_tour_walk,,,,,1,,, +Label,Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK,WALK_TRANSIT,TAXI,TNC_SINGLE,TNC_SHARED +util_DA_IVT,Drive alone - In-vehicle time,c_ivt * da_time_skims,1,,,,,,, +util_DA_op_cost,Drive alone - Operating cost,@c_cost * (df.cost_per_mile * df.da_dist_skims + df.da_cost_skims),1,,,,,,, +util_DA_park_cost,Drive alone - Parking cost,@c_cost * df.parking_cost,1,,,,,,, +util_S2_IVT,Shared ride 2 HOV - In -vehicle time,c_ivt * s2_time_skims,,1,,,,,, +util_S2_op_cost,Shared ride 2 HOV - Operating cost,@c_cost * (df.cost_per_mile * df.s2_dist_skims + df.s2_cost_skims),,1,,,,,, +util_S2_park_cost,Shared ride 2 HOV - Parking cost,@c_cost * df.parking_cost / cost_share_s2,,1,,,,,, +util_S3_IVT,Shared ride 3+ HOV - In-vehicle time,c_ivt * s3_time_skims,,,1,,,,, +util_S3_op_cost,Shared ride 3+ HOV - Operating cost,@c_cost * (df.cost_per_mile * df.s3_dist_skims + df.s3_cost_skims),,,1,,,,, +util_S3_park_cost,Shared ride 3+ HOV - Parking cost,@c_cost * df.parking_cost / cost_share_s3,,,1,,,,, +util_WALK_unavail,Walk - Mode unavailable,(1 - walk_available),,,,-999,,,, +util_WALK_time,Walk - Time,@df.walk_available * df.c_walk_time * df.walk_time,,,,1,,,, +util_WALK_TRANSIT_unavail,Walk-transit unavailable,walk_transit_logsum <= -500,,,,,-999,,, +util_WALK_TRANSIT,Walk-transit utility,(walk_transit_logsum > -500) * walk_transit_logsum,,,,,1,,, +util_TAXI_IVT,Taxi - In-vehicle time,c_ivt * s2_time_skims,,,,,,1,, +util_TAXI_wait,Taxi - Wait time,c_ivt * taxi_wait_time,,,,,,1,, +util_TAXI_cost,Taxi - Fare,@c_cost * ((taxi_base_fare + (df.s2_dist_skims * taxi_cost_per_mile) + (df.s2_time_skims * taxi_cost_per_minute)) * 100 + df.s2_cost_skims),,,,,,1,, +util_TNC_SINGLE_IVT,TNC Single - In-vehicle time,c_ivt * s2_time_skims,,,,,,,1, +util_TNC_SINGLE_wait,TNC Single - Wait time,c_ivt * 1.5 * tnc_single_wait_time,,,,,,,1, +util_TNC_SINGLE_cost,TNC Single - Cost,"@c_cost * ((tnc_single_base_fare + (df.s2_dist_skims * tnc_single_cost_per_mile) + (df.s2_time_skims * tnc_single_cost_per_minute).clip(lower=tnc_single_cost_minimum)) * 100 + df.s2_cost_skims)",,,,,,,1, +util_TNC_SHARED_IVT,TNC Shared - In-vehicle time,@df.c_ivt * df.s2_time_skims * tnc_shared_ivt_factor,,,,,,,,1 +util_TNC_SHARED_wait,TNC Shared - Wait time,c_ivt * 1.5 * tnc_shared_wait_time,,,,,,,,1 +util_TNC_SHARED_cost,TNC Shared - Cost,"@c_cost * ((tnc_shared_base_fare + (df.s2_dist_skims * tnc_shared_cost_per_mile) + (df.s2_time_skims * tnc_shared_cost_per_minute).clip(lower=tnc_shared_cost_minimum)) * 100 + df.s2_cost_skims)",,,,,,,,1 +util_S2_ASC_tour_DA,s2 ASC - Tour Mode DA,@df.tour_mode_is_da * s2_ASC_tour_da,,1,,,,,, +util_S3_ASC_tour_DA,s3 ASC - Tour Mode DA,@df.tour_mode_is_da * s3_ASC_tour_da,,,1,,,,, +util_WALK_ASC_tour_DA,walk ASC - Tour Mode DA,@df.tour_mode_is_da * walk_ASC_tour_da,,,,1,,,, +util_WALK_TRANSIT_ASC_tour_DA,walk transit ASC - Tour Mode DA,@df.tour_mode_is_da * walk_transit_ASC_tour_da,,,,,1,,, +util_TAXI_ASC_tour_DA,taxi ASC - Tour Mode DA,@df.tour_mode_is_da * taxi_ASC_tour_da,,,,,,1,, +util_TNC_SINGLE_ASC_tour_DA,tnc single ASC - Tour Mode DA,@df.tour_mode_is_da * tnc_single_ASC_tour_da,,,,,,,1, +util_TNC_SHARED_ASC_tour_DA,tnc shared ASC - Tour Mode DA,@df.tour_mode_is_da * tnc_shared_ASC_tour_da,,,,,,,,1 +util_S2_ASC_tour_S2,s2 ASC - Tour Mode S2,@df.tour_mode_is_s2 * s2_ASC_tour_s2,,1,,,,,, +util_S3_ASC_tour_S2,s3 ASC - Tour Mode S2,@df.tour_mode_is_s2 * s3_ASC_tour_s2,,,1,,,,, +util_WALK_ASC_tour_S2,walk ASC - Tour Mode S2,@df.tour_mode_is_s2 * walk_ASC_tour_s2,,,,1,,,, +util_WALK_TRANSIT_ASC_tour_S2,walk transit ASC - Tour Mode S2,@df.tour_mode_is_s2 * walk_transit_ASC_tour_s2,,,,,1,,, +util_TAXI_ASC_tour_S2,taxi ASC - Tour Mode S2,@df.tour_mode_is_s2 * taxi_ASC_tour_s2,,,,,,1,, +util_TNC_SINGLE_ASC_tour_S2,tnc single ASC - Tour Mode S2,@df.tour_mode_is_s2 * tnc_single_ASC_tour_s2,,,,,,,1, +util_TNC_SHARED_ASC_tour_S2,tnc shared ASC - Tour Mode S2,@df.tour_mode_is_s2 * tnc_shared_ASC_tour_s2,,,,,,,,1 +util_S2_ASC_tour_S3,s2 ASC - Tour Mode S3,@df.tour_mode_is_s3 * s2_ASC_tour_s3,,1,,,,,, +util_S3_ASC_tour_S3,s3 ASC - Tour Mode S3,@df.tour_mode_is_s3 * s3_ASC_tour_s3,,,1,,,,, +util_WALK_ASC_tour_S3,walk ASC - Tour Mode S3,@df.tour_mode_is_s3 * walk_ASC_tour_s3,,,,1,,,, +util_WALK_TRANSIT_ASC_tour_S3,walk transit ASC - Tour Mode S3,@df.tour_mode_is_s3 * walk_transit_ASC_tour_s3,,,,,1,,, +util_TAXI_ASC_tour_S3,taxi ASC - Tour Mode S3,@df.tour_mode_is_s3 * taxi_ASC_tour_s3,,,,,,1,, +util_TNC_SINGLE_ASC_tour_S3,tnc single ASC - Tour Mode S3,@df.tour_mode_is_s3 * tnc_single_ASC_tour_s3,,,,,,,1, +util_TNC_SHARED_ASC_tour_S3,tnc shared ASC - Tour Mode S3,@df.tour_mode_is_s3 * tnc_shared_ASC_tour_s3,,,,,,,,1 +util_S2_ASC_tour_WALK,s2 ASC - Tour Mode Walk,@df.tour_mode_is_walk * s2_ASC_tour_walk,,1,,,,,, +util_S3_ASC_tour_WALK,s3 ASC - Tour Mode Walk,@df.tour_mode_is_walk * s3_ASC_tour_walk,,,1,,,,, +util_WALK_ASC_tour_WALK,walk ASC - Tour Mode Walk,@df.tour_mode_is_walk * walk_ASC_tour_walk,,,,1,,,, +util_WALK_TRANSIT_ASC_tour_WALK,walk transit ASC - Tour Mode Walk,@df.tour_mode_is_walk * walk_transit_ASC_tour_walk,,,,,1,,, +util_TAXI_ASC_tour_WALK,taxi ASC - Tour Mode Walk,@df.tour_mode_is_walk * taxi_ASC_tour_walk,,,,,,1,, +util_TNC_SINGLE_ASC_tour_WALK,tnc single ASC - Tour Mode Walk,@df.tour_mode_is_walk * tnc_single_ASC_tour_walk,,,,,,,1, +util_TNC_SHARED_ASC_tour_WALK,tnc shared ASC - Tour Mode Walk,@df.tour_mode_is_walk * tnc_shared_ASC_tour_walk,,,,,,,,1 +util_sentri_S2,s2 Crossing used Sentri,@(df.pass_type == 'sentri') * s2_sentri_coef,,1,,,,,, +util_sentri_S3,s3 Crossing used Sentri,@(df.pass_type == 'sentri') * s3_sentri_coef,,,1,,,,, +util_sentri_WALK,walk Crossing used Sentri,@(df.pass_type == 'sentri') * walk_sentri_coef,,,,1,,,, +util_sentri_WALK_TRANSIT,walk transit Crossing used Sentri,@(df.pass_type == 'sentri') * walk_transit_sentri_coef,,,,,1,,, +util_ready_S2,s2 Crossing used Ready,@(df.pass_type == 'ready') * s2_ready_coef,,1,,,,,, +util_ready_S3,s3 Crossing used Ready,@(df.pass_type == 'ready') * s3_ready_coef,,,1,,,,, +util_ready_WALK,walk Crossing used Ready,@(df.pass_type == 'ready') * walk_ready_coef,,,,1,,,, +util_ready_WALK_TRANSIT,walk transit Crossing used Ready,@(df.pass_type == 'ready') * walk_transit_ready_coef,,,,,1,, +util_work_tour_S2,s2 Work Tour,@df.is_work_tour * s2_work_tour_coef,,1,,,,,, +util_work_tour_S3,s3 Work Tour,@df.is_work_tour * s3_work_tour_coef,,,1,,,,, +util_S2_calib_adj_tour_DA,s2 Calibration Adjustment - Tour Mode DA,@df.tour_mode_is_da * s2_calib_adj_tour_da,,1,,,,,, +util_S3_calib_adj_tour_DA,s3 Calibration Adjustment - Tour Mode DA,@df.tour_mode_is_da * s3_calib_adj_tour_da,,,1,,,,, +util_WALK_calib_adj_tour_DA,walk Calibration Adjustment - Tour Mode DA,@df.tour_mode_is_da * walk_calib_adj_tour_da,,,,1,,,, +util_WALK_TRANSIT_calib_adj_tour_DA,walk transit Calibration Adjustment - Tour Mode DA,@df.tour_mode_is_da * walk_transit_calib_adj_tour_da,,,,,1,,, +util_S2_calib_adj_tour_S2,s2 Calibration Adjustment - Tour Mode S2,@df.tour_mode_is_s2 * s2_calib_adj_tour_s2,,1,,,,,, +util_S3_calib_adj_tour_S2,s3 Calibration Adjustment - Tour Mode S2,@df.tour_mode_is_s2 * s3_calib_adj_tour_s2,,,1,,,,, +util_WALK_calib_adj_tour_S2,walk Calibration Adjustment - Tour Mode S2,@df.tour_mode_is_s2 * walk_calib_adj_tour_s2,,,,1,,,, +util_WALK_TRANSIT_calib_adj_tour_S2,walk transit Calibration Adjustment - Tour Mode S2,@df.tour_mode_is_s2 * walk_transit_calib_adj_tour_s2,,,,,1,,, +util_S2_calib_adj_tour_S3,s2 Calibration Adjustment - Tour Mode S3,@df.tour_mode_is_s3 * s2_calib_adj_tour_s3,,1,,,,,, +util_S3_calib_adj_tour_S3,s3 Calibration Adjustment - Tour Mode S3,@df.tour_mode_is_s3 * s3_calib_adj_tour_s3,,,1,,,,, +util_WALK_calib_adj_tour_S3,walk Calibration Adjustment - Tour Mode S3,@df.tour_mode_is_s3 * walk_calib_adj_tour_s3,,,,1,,,, +util_WALK_TRANSIT_calib_adj_tour_S3,walk transit Calibration Adjustment - Tour Mode S3,@df.tour_mode_is_s3 * walk_transit_calib_adj_tour_s3,,,,,1,,, +util_S2_calib_adj_tour_WALK,s2 Calibration Adjustment - Tour Mode Walk,@df.tour_mode_is_walk * s2_calib_adj_tour_walk,,1,,,,,, +util_S3_calib_adj_tour_WALK,s3 Calibration Adjustment - Tour Mode Walk,@df.tour_mode_is_walk * s3_calib_adj_tour_walk,,,1,,,,, +util_WALK_calib_adj_tour_WALK,walk Calibration Adjustment - Tour Mode Walk,@df.tour_mode_is_walk * walk_calib_adj_tour_walk,,,,1,,,, +util_WALK_TRANSIT_calib_adj_tour_WALK,walk transit Calibration Adjustment - Tour Mode Walk,@df.tour_mode_is_walk * walk_transit_calib_adj_tour_walk,,,,,1,,, +util_S2_ABM2_calib_adj_tour_DA,s2 ABM 2 Calibration Adjustment - Tour Mode DA,@df.tour_mode_is_da * s2_abm2_calib_adj_tour_da,,1,,,,,, +util_S3_ABM2_calib_adj_tour_DA,s3 ABM 2 Calibration Adjustment - Tour Mode DA,@df.tour_mode_is_da * s3_abm2_calib_adj_tour_da,,,1,,,,, +util_WALK_ABM2_calib_adj_tour_DA,walk ABM 2 Calibration Adjustment - Tour Mode DA,@df.tour_mode_is_da * walk_abm2_calib_adj_tour_da,,,,1,,,, +util_WALK_TRANSIT_ABM2_calib_adj_tour_DA,walk transit ABM 2 Calibration Adjustment - Tour Mode DA,@df.tour_mode_is_da * walk_transit_abm2_calib_adj_tour_da,,,,,1,,, +util_S2_ABM2_calib_adj_tour_S2,s2 ABM 2 Calibration Adjustment - Tour Mode S2,@df.tour_mode_is_s2 * s2_abm2_calib_adj_tour_s2,,1,,,,,, +util_S3_ABM2_calib_adj_tour_S2,s3 ABM 2 Calibration Adjustment - Tour Mode S2,@df.tour_mode_is_s2 * s3_abm2_calib_adj_tour_s2,,,1,,,,, +util_WALK_ABM2_calib_adj_tour_S2,walk ABM 2 Calibration Adjustment - Tour Mode S2,@df.tour_mode_is_s2 * walk_abm2_calib_adj_tour_s2,,,,1,,,, +util_WALK_TRANSIT_ABM2_calib_adj_tour_S2,walk transit ABM 2 Calibration Adjustment - Tour Mode S2,@df.tour_mode_is_s2 * walk_transit_abm2_calib_adj_tour_s2,,,,,1,,, +util_S2_ABM2_calib_adj_tour_S3,s2 ABM 2 Calibration Adjustment - Tour Mode S3,@df.tour_mode_is_s3 * s2_abm2_calib_adj_tour_s3,,1,,,,,, +util_S3_ABM2_calib_adj_tour_S3,s3 ABM 2 Calibration Adjustment - Tour Mode S3,@df.tour_mode_is_s3 * s3_abm2_calib_adj_tour_s3,,,1,,,,, +util_WALK_ABM2_calib_adj_tour_S3,walk ABM 2 Calibration Adjustment - Tour Mode S3,@df.tour_mode_is_s3 * walk_abm2_calib_adj_tour_s3,,,,1,,,, +util_WALK_TRANSIT_ABM2_calib_adj_tour_S3,walk transit ABM 2 Calibration Adjustment - Tour Mode S3,@df.tour_mode_is_s3 * walk_transit_abm2_calib_adj_tour_s3,,,,,1,,, +util_S2_ABM2_calib_adj_tour_WALK,s2 ABM 2 Calibration Adjustment - Tour Mode WALK,@df.tour_mode_is_walk * s2_abm2_calib_adj_tour_walk,,1,,,,,, +util_S3_ABM2_calib_adj_tour_WALK,s3 ABM 2 Calibration Adjustment - Tour Mode WALK,@df.tour_mode_is_walk * s3_abm2_calib_adj_tour_walk,,,1,,,,, +util_WALK_ABM2_calib_adj_tour_WALK,walk ABM 2 Calibration Adjustment - Tour Mode WALK,@df.tour_mode_is_walk * walk_abm2_calib_adj_tour_walk,,,,1,,,, +util_WALK_TRANSIT_ABM2_calib_adj_tour_WALK,walk transit ABM 2 Calibration Adjustment - Tour Mode WALK,@df.tour_mode_is_walk * walk_transit_abm2_calib_adj_tour_walk,,,,,1,,, util_WALK_TRANSIT_ABM2_calib_adj_all_tour_modes,ABM 2 Calibration Adjustment - transit,@walk_transit_abm2_calib_adj_transit,,,,,1,,, \ No newline at end of file diff --git a/activitysim/examples/example_sandag_xborder/configs/trip_mode_choice.yaml b/activitysim/examples/prototype_sandag_xborder/configs/trip_mode_choice.yaml similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/trip_mode_choice.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/trip_mode_choice.yaml diff --git a/activitysim/examples/example_sandag_xborder/configs/trip_mode_choice_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_sandag_xborder/configs/trip_mode_choice_annotate_trips_preprocessor.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/trip_mode_choice_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_sandag_xborder/configs/trip_mode_choice_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/trip_mode_choice_coefficients.csv b/activitysim/examples/prototype_sandag_xborder/configs/trip_mode_choice_coefficients.csv similarity index 97% rename from activitysim/examples/example_sandag_xborder/configs/trip_mode_choice_coefficients.csv rename to activitysim/examples/prototype_sandag_xborder/configs/trip_mode_choice_coefficients.csv index a7ed2bebcb..2f13f09b6c 100644 --- a/activitysim/examples/example_sandag_xborder/configs/trip_mode_choice_coefficients.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/trip_mode_choice_coefficients.csv @@ -1,74 +1,74 @@ -coefficient_name,value,constrain -c_ivt_base,-0.021,F -c_cost,-0.0050,F -s2_ASC_tour_da,-0.8160,F -s2_ASC_tour_s2,1.5075,F -s2_ASC_tour_s3,1.4795,F -s2_ASC_tour_walk,0.7495,F -s3_ASC_tour_da,-1.2825,F -s3_ASC_tour_s2,1.3030,F -s3_ASC_tour_s3,2.7155,F -s3_ASC_tour_walk,0.9660,F -walk_ASC_tour_da,-0.6565,F -walk_ASC_tour_s2,0.1270,F -walk_ASC_tour_s3,1.0335,F -walk_ASC_tour_walk,3.0745,F -walk_transit_ASC_tour_da,-2.0185,F -walk_transit_ASC_tour_s2,-0.3370,F -walk_transit_ASC_tour_s3,1.1935,F -walk_transit_ASC_tour_walk,3.1525,F -taxi_ASC_tour_da,-5.0000,F -taxi_ASC_tour_s2,-5.0000,F -taxi_ASC_tour_s3,-5.0000,F -taxi_ASC_tour_walk,-5.0000,F -tnc_single_ASC_tour_da,-5.0000,F -tnc_single_ASC_tour_s2,-5.0000,F -tnc_single_ASC_tour_s3,-5.0000,F -tnc_single_ASC_tour_walk,-5.0000,F -tnc_shared_ASC_tour_da,-10.0000,F -tnc_shared_ASC_tour_s2,-10.0000,F -tnc_shared_ASC_tour_s3,-10.0000,F -tnc_shared_ASC_tour_walk,-10.0000,F -s2_sentri_coef,-0.5310,F -s2_ready_coef,0.0,F -s2_work_tour_coef,-0.7970,F -s2_calib_adj_tour_da,0.1280,F -s2_calib_adj_tour_s2,-0.5901,F -s2_calib_adj_tour_s3,-0.8562,F -s2_calib_adj_tour_walk,-0.6874,F -s2_abm2_calib_adj_tour_da,0.1177,F -s2_abm2_calib_adj_tour_s2,0.1003,F -s2_abm2_calib_adj_tour_s3,0.0927,F -s2_abm2_calib_adj_tour_walk,0.0615,F -s3_sentri_coef,-0.8825,F -s3_ready_coef,0.0,F -s3_work_tour_coef,-0.7525,F -s3_calib_adj_tour_da,0.3012,F -s3_calib_adj_tour_s2,-1.1094,F -s3_calib_adj_tour_s3,-1.3324,F -s3_calib_adj_tour_walk,-1.0476,F -s3_abm2_calib_adj_tour_da,0.1114,F -s3_abm2_calib_adj_tour_s2,0.1173,F -s3_abm2_calib_adj_tour_s3,0.1219,F -s3_abm2_calib_adj_tour_walk,-0.0099,F -walk_sentri_coef,-0.3875,F -walk_ready_coef,0.0,F -walk_calib_adj_tour_da,0.2003,F -walk_calib_adj_tour_s2,-0.0579,F -walk_calib_adj_tour_s3,-0.4514,F -walk_calib_adj_tour_walk,-0.0762,F -walk_abm2_calib_adj_tour_da,-0.8643,F -walk_abm2_calib_adj_tour_s2,-0.7036,F -walk_abm2_calib_adj_tour_s3,-0.6691,F -walk_abm2_calib_adj_tour_walk,-1.1779,F -walk_transit_sentri_coef,0.6285,F -walk_transit_ready_coef,0.0,F -walk_transit_calib_adj_tour_da,-0.7947,F -walk_transit_calib_adj_tour_s2,-2.0305,F -walk_transit_calib_adj_tour_s3,-2.4453,F -walk_transit_calib_adj_tour_walk,-2.6149,F -walk_transit_abm2_calib_adj_tour_da,-0.2530,F -walk_transit_abm2_calib_adj_tour_s2,-0.3951,F -walk_transit_abm2_calib_adj_tour_s3,-0.4158,F -walk_transit_abm2_calib_adj_tour_walk,-0.3565,F +coefficient_name,value,constrain +c_ivt_base,-0.021,F +c_cost,-0.0050,F +s2_ASC_tour_da,-0.8160,F +s2_ASC_tour_s2,1.5075,F +s2_ASC_tour_s3,1.4795,F +s2_ASC_tour_walk,0.7495,F +s3_ASC_tour_da,-1.2825,F +s3_ASC_tour_s2,1.3030,F +s3_ASC_tour_s3,2.7155,F +s3_ASC_tour_walk,0.9660,F +walk_ASC_tour_da,-0.6565,F +walk_ASC_tour_s2,0.1270,F +walk_ASC_tour_s3,1.0335,F +walk_ASC_tour_walk,3.0745,F +walk_transit_ASC_tour_da,-2.0185,F +walk_transit_ASC_tour_s2,-0.3370,F +walk_transit_ASC_tour_s3,1.1935,F +walk_transit_ASC_tour_walk,3.1525,F +taxi_ASC_tour_da,-5.0000,F +taxi_ASC_tour_s2,-5.0000,F +taxi_ASC_tour_s3,-5.0000,F +taxi_ASC_tour_walk,-5.0000,F +tnc_single_ASC_tour_da,-5.0000,F +tnc_single_ASC_tour_s2,-5.0000,F +tnc_single_ASC_tour_s3,-5.0000,F +tnc_single_ASC_tour_walk,-5.0000,F +tnc_shared_ASC_tour_da,-10.0000,F +tnc_shared_ASC_tour_s2,-10.0000,F +tnc_shared_ASC_tour_s3,-10.0000,F +tnc_shared_ASC_tour_walk,-10.0000,F +s2_sentri_coef,-0.5310,F +s2_ready_coef,0.0,F +s2_work_tour_coef,-0.7970,F +s2_calib_adj_tour_da,0.1280,F +s2_calib_adj_tour_s2,-0.5901,F +s2_calib_adj_tour_s3,-0.8562,F +s2_calib_adj_tour_walk,-0.6874,F +s2_abm2_calib_adj_tour_da,0.1177,F +s2_abm2_calib_adj_tour_s2,0.1003,F +s2_abm2_calib_adj_tour_s3,0.0927,F +s2_abm2_calib_adj_tour_walk,0.0615,F +s3_sentri_coef,-0.8825,F +s3_ready_coef,0.0,F +s3_work_tour_coef,-0.7525,F +s3_calib_adj_tour_da,0.3012,F +s3_calib_adj_tour_s2,-1.1094,F +s3_calib_adj_tour_s3,-1.3324,F +s3_calib_adj_tour_walk,-1.0476,F +s3_abm2_calib_adj_tour_da,0.1114,F +s3_abm2_calib_adj_tour_s2,0.1173,F +s3_abm2_calib_adj_tour_s3,0.1219,F +s3_abm2_calib_adj_tour_walk,-0.0099,F +walk_sentri_coef,-0.3875,F +walk_ready_coef,0.0,F +walk_calib_adj_tour_da,0.2003,F +walk_calib_adj_tour_s2,-0.0579,F +walk_calib_adj_tour_s3,-0.4514,F +walk_calib_adj_tour_walk,-0.0762,F +walk_abm2_calib_adj_tour_da,-0.8643,F +walk_abm2_calib_adj_tour_s2,-0.7036,F +walk_abm2_calib_adj_tour_s3,-0.6691,F +walk_abm2_calib_adj_tour_walk,-1.1779,F +walk_transit_sentri_coef,0.6285,F +walk_transit_ready_coef,0.0,F +walk_transit_calib_adj_tour_da,-0.7947,F +walk_transit_calib_adj_tour_s2,-2.0305,F +walk_transit_calib_adj_tour_s3,-2.4453,F +walk_transit_calib_adj_tour_walk,-2.6149,F +walk_transit_abm2_calib_adj_tour_da,-0.2530,F +walk_transit_abm2_calib_adj_tour_s2,-0.3951,F +walk_transit_abm2_calib_adj_tour_s3,-0.4158,F +walk_transit_abm2_calib_adj_tour_walk,-0.3565,F walk_transit_abm2_calib_adj_transit,-0.2000,F \ No newline at end of file diff --git a/activitysim/examples/example_sandag_xborder/configs/trip_mode_choice_coefficients_template.csv b/activitysim/examples/prototype_sandag_xborder/configs/trip_mode_choice_coefficients_template.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/trip_mode_choice_coefficients_template.csv rename to activitysim/examples/prototype_sandag_xborder/configs/trip_mode_choice_coefficients_template.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/trip_purpose.yaml b/activitysim/examples/prototype_sandag_xborder/configs/trip_purpose.yaml similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/trip_purpose.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/trip_purpose.yaml diff --git a/activitysim/examples/example_sandag_xborder/configs/trip_purpose_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_sandag_xborder/configs/trip_purpose_annotate_trips_preprocessor.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/trip_purpose_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_sandag_xborder/configs/trip_purpose_annotate_trips_preprocessor.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/trip_purpose_probs.csv b/activitysim/examples/prototype_sandag_xborder/configs/trip_purpose_probs.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/trip_purpose_probs.csv rename to activitysim/examples/prototype_sandag_xborder/configs/trip_purpose_probs.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/trip_scheduling.yaml b/activitysim/examples/prototype_sandag_xborder/configs/trip_scheduling.yaml similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/trip_scheduling.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/trip_scheduling.yaml diff --git a/activitysim/examples/example_sandag_xborder/configs/trip_scheduling_probs.csv b/activitysim/examples/prototype_sandag_xborder/configs/trip_scheduling_probs.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/configs/trip_scheduling_probs.csv rename to activitysim/examples/prototype_sandag_xborder/configs/trip_scheduling_probs.csv diff --git a/activitysim/examples/example_sandag_xborder/configs/tvpb_utility_drive_maz_tap.csv b/activitysim/examples/prototype_sandag_xborder/configs/tvpb_utility_drive_maz_tap.csv similarity index 99% rename from activitysim/examples/example_sandag_xborder/configs/tvpb_utility_drive_maz_tap.csv rename to activitysim/examples/prototype_sandag_xborder/configs/tvpb_utility_drive_maz_tap.csv index a5dadfaeb3..9cf267a4ba 100755 --- a/activitysim/examples/example_sandag_xborder/configs/tvpb_utility_drive_maz_tap.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/tvpb_utility_drive_maz_tap.csv @@ -1,3 +1,3 @@ -Label,Description,Expression,utility -util_drive_time,drive time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_drive * (df.DTIME + (df.WDIST / 5280 / walk_speed * 60))",1 -util_drive_cost,drive cost,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_cost_high_income, c_cost_low_income) * (df.DDIST + (df.WDIST / 5280)) * c_auto_operating_cost_per_mile",1 +Label,Description,Expression,utility +util_drive_time,drive time,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_ivt_high_income, c_ivt_low_income) * c_drive * (df.DTIME + (df.WDIST / 5280 / walk_speed * 60))",1 +util_drive_cost,drive cost,"@np.where(df.demographic_segment==C_HIGH_INCOME_SEGMENT_ID, c_cost_high_income, c_cost_low_income) * (df.DDIST + (df.WDIST / 5280)) * c_auto_operating_cost_per_mile",1 diff --git a/activitysim/examples/example_sandag_xborder/configs/tvpb_utility_tap_tap.csv b/activitysim/examples/prototype_sandag_xborder/configs/tvpb_utility_tap_tap.csv similarity index 99% rename from activitysim/examples/example_sandag_xborder/configs/tvpb_utility_tap_tap.csv rename to activitysim/examples/prototype_sandag_xborder/configs/tvpb_utility_tap_tap.csv index b27f1cdab0..9ceba88a15 100755 --- a/activitysim/examples/example_sandag_xborder/configs/tvpb_utility_tap_tap.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/tvpb_utility_tap_tap.csv @@ -1,33 +1,33 @@ -Label,Description,Expression,set1,set2,set3 -# Set 1,,,,, -set1_unavailable,Shut off set if unavailable,@(1 - df.set1_available),C_UNAVAILABLE,, -set1_total_ivt,total IVT,"@df.set1_available * c_ivt * (df.lb_ivt_set1 + eb_fac * (df.eb_ivt_set1 + df.ltd_ivt_set1) + brt_fac * (df.brt_red_ivt_set1 + df.brt_yel_ivt_set1) + lrt_fac * df.lrt_ivt_set1 + cr_fac * df.cr_ivt_set1)",1,, -set1_first_wait_time,First wait time,"@df.set1_available * c_fwt * c_ivt * df.first_wait_set1",1,, -set1_xfer_wait_time,set Transfer Wait Time,"@df.set1_available * c_xwt * c_ivt * df.xfer_wait_set1",1,, -set1_xfer_walk_time,set Walk transfer time,"@df.set1_available * c_waux * c_ivt * df.walk_time_set1",1,, -set1_fare,set Fare,"@df.set1_available * c_cost * df.fare_set1 * 100",1,, -set1_xfers,transfers penalty,@df.set1_available * (df.transfers_set1 > 0) * c_ivt * 1.5 * (-23 + 23 * np.exp(0.414 * df.transfers_set1)),1,, -set1_ASC,ASC,"@df.set1_available * df.asc_eb_set1 * np.where(df.asc_eb_set1 > 0, np.where(df.lb_ivt_set1 > 0, 0.5, 1), 1)",1,, -set1_cr_long_distance,CR long distance bonus,"@df.set1_available * (df.cr_ivt_set1 > 10) * c_ivt * -1.0 * (np.clip(df.cr_ivt_set1, a_min=None, a_max=60) + np.where(df.cr_ivt_set1 > 40, (df.cr_ivt_set1 - 40) * 0.6, 0))",1,, - -# Set 2,,,,, -set2_unavailable,Shut off set if unavailable,@(1 - df.set2_available),,C_UNAVAILABLE, -set2_total_ivt,total IVT,"@df.set2_available * c_ivt * (df.lb_ivt_set2 + eb_fac * (df.eb_ivt_set2 + df.ltd_ivt_set2) + brt_fac * (df.brt_red_ivt_set2 + df.brt_yel_ivt_set2) + lrt_fac * df.lrt_ivt_set2 + cr_fac * df.cr_ivt_set2)",,1, -set2_first_wait_time,First wait time,"@df.set2_available * c_fwt * c_ivt * df.first_wait_set2",,1, -set2_xfer_wait_time,set Transfer Wait Time,"@df.set2_available * c_xwt * c_ivt * df.xfer_wait_set2",,1, -set2_xfer_walk_time,set Walk transfer time,"@df.set2_available * c_waux * c_ivt * df.walk_time_set2",,1, -set2_fare,set Fare,"@df.set2_available * c_cost * df.fare_set2 * 100",,1, -set2_xfers,transfers penalty,@df.set2_available * (df.transfers_set2 > 0) * c_ivt * 1.5 * (-23 + 23 * np.exp(0.414 * df.transfers_set2)),,1, -set2_ASC,ASC,"@df.set2_available * df.asc_eb_set2 * np.where(df.asc_eb_set2 > 0, np.where(df.lb_ivt_set2 > 0, 0.5, 1), 1)",,1, -set2_cr_long_distance,CR long distance bonus,"@df.set2_available * (df.cr_ivt_set2 > 10) * c_ivt * -1.0 * (np.clip(df.cr_ivt_set2, a_min=None, a_max=60) + np.where(df.cr_ivt_set2 > 40, (df.cr_ivt_set2 - 40) * 0.6, 0))",,1, - -# Set 3,,,,, -set3_unavailable,Shut off set if unavailable,@(1 - df.set3_available),,,C_UNAVAILABLE -set3_total_ivt,total IVT,"@df.set3_available * c_ivt * (df.lb_ivt_set3 + eb_fac * (df.eb_ivt_set3 + df.ltd_ivt_set3) + brt_fac * (df.brt_red_ivt_set3 + df.brt_yel_ivt_set3) + lrt_fac * df.lrt_ivt_set3 + cr_fac * df.cr_ivt_set3)",,,1 -set3_first_wait_time,First wait time,"@df.set3_available * c_fwt * c_ivt * df.first_wait_set3",,,1 -set3_xfer_wait_time,set Transfer Wait Time,"@df.set3_available * c_xwt * c_ivt * df.xfer_wait_set3",,,1 -set3_xfer_walk_time,set Walk transfer time,"@df.set3_available * c_waux * c_ivt * df.walk_time_set3",,,1 -set3_fare,set Fare,"@df.set3_available * c_cost * df.fare_set3 * 100",,,1 -set3_xfers,transfers penalty,@df.set3_available * (df.transfers_set3 > 0) * c_ivt * 1.5 * (-23 + 23 * np.exp(0.414 * df.transfers_set3)),,,1 -set3_ASC,ASC,"@df.set3_available * df.asc_eb_set3 * np.where(df.asc_eb_set3 > 0, np.where(df.lb_ivt_set3 > 0, 0.5, 1), 1)",,,1 +Label,Description,Expression,set1,set2,set3 +# Set 1,,,,, +set1_unavailable,Shut off set if unavailable,@(1 - df.set1_available),C_UNAVAILABLE,, +set1_total_ivt,total IVT,"@df.set1_available * c_ivt * (df.lb_ivt_set1 + eb_fac * (df.eb_ivt_set1 + df.ltd_ivt_set1) + brt_fac * (df.brt_red_ivt_set1 + df.brt_yel_ivt_set1) + lrt_fac * df.lrt_ivt_set1 + cr_fac * df.cr_ivt_set1)",1,, +set1_first_wait_time,First wait time,"@df.set1_available * c_fwt * c_ivt * df.first_wait_set1",1,, +set1_xfer_wait_time,set Transfer Wait Time,"@df.set1_available * c_xwt * c_ivt * df.xfer_wait_set1",1,, +set1_xfer_walk_time,set Walk transfer time,"@df.set1_available * c_waux * c_ivt * df.walk_time_set1",1,, +set1_fare,set Fare,"@df.set1_available * c_cost * df.fare_set1 * 100",1,, +set1_xfers,transfers penalty,@df.set1_available * (df.transfers_set1 > 0) * c_ivt * 1.5 * (-23 + 23 * np.exp(0.414 * df.transfers_set1)),1,, +set1_ASC,ASC,"@df.set1_available * df.asc_eb_set1 * np.where(df.asc_eb_set1 > 0, np.where(df.lb_ivt_set1 > 0, 0.5, 1), 1)",1,, +set1_cr_long_distance,CR long distance bonus,"@df.set1_available * (df.cr_ivt_set1 > 10) * c_ivt * -1.0 * (np.clip(df.cr_ivt_set1, a_min=None, a_max=60) + np.where(df.cr_ivt_set1 > 40, (df.cr_ivt_set1 - 40) * 0.6, 0))",1,, + +# Set 2,,,,, +set2_unavailable,Shut off set if unavailable,@(1 - df.set2_available),,C_UNAVAILABLE, +set2_total_ivt,total IVT,"@df.set2_available * c_ivt * (df.lb_ivt_set2 + eb_fac * (df.eb_ivt_set2 + df.ltd_ivt_set2) + brt_fac * (df.brt_red_ivt_set2 + df.brt_yel_ivt_set2) + lrt_fac * df.lrt_ivt_set2 + cr_fac * df.cr_ivt_set2)",,1, +set2_first_wait_time,First wait time,"@df.set2_available * c_fwt * c_ivt * df.first_wait_set2",,1, +set2_xfer_wait_time,set Transfer Wait Time,"@df.set2_available * c_xwt * c_ivt * df.xfer_wait_set2",,1, +set2_xfer_walk_time,set Walk transfer time,"@df.set2_available * c_waux * c_ivt * df.walk_time_set2",,1, +set2_fare,set Fare,"@df.set2_available * c_cost * df.fare_set2 * 100",,1, +set2_xfers,transfers penalty,@df.set2_available * (df.transfers_set2 > 0) * c_ivt * 1.5 * (-23 + 23 * np.exp(0.414 * df.transfers_set2)),,1, +set2_ASC,ASC,"@df.set2_available * df.asc_eb_set2 * np.where(df.asc_eb_set2 > 0, np.where(df.lb_ivt_set2 > 0, 0.5, 1), 1)",,1, +set2_cr_long_distance,CR long distance bonus,"@df.set2_available * (df.cr_ivt_set2 > 10) * c_ivt * -1.0 * (np.clip(df.cr_ivt_set2, a_min=None, a_max=60) + np.where(df.cr_ivt_set2 > 40, (df.cr_ivt_set2 - 40) * 0.6, 0))",,1, + +# Set 3,,,,, +set3_unavailable,Shut off set if unavailable,@(1 - df.set3_available),,,C_UNAVAILABLE +set3_total_ivt,total IVT,"@df.set3_available * c_ivt * (df.lb_ivt_set3 + eb_fac * (df.eb_ivt_set3 + df.ltd_ivt_set3) + brt_fac * (df.brt_red_ivt_set3 + df.brt_yel_ivt_set3) + lrt_fac * df.lrt_ivt_set3 + cr_fac * df.cr_ivt_set3)",,,1 +set3_first_wait_time,First wait time,"@df.set3_available * c_fwt * c_ivt * df.first_wait_set3",,,1 +set3_xfer_wait_time,set Transfer Wait Time,"@df.set3_available * c_xwt * c_ivt * df.xfer_wait_set3",,,1 +set3_xfer_walk_time,set Walk transfer time,"@df.set3_available * c_waux * c_ivt * df.walk_time_set3",,,1 +set3_fare,set Fare,"@df.set3_available * c_cost * df.fare_set3 * 100",,,1 +set3_xfers,transfers penalty,@df.set3_available * (df.transfers_set3 > 0) * c_ivt * 1.5 * (-23 + 23 * np.exp(0.414 * df.transfers_set3)),,,1 +set3_ASC,ASC,"@df.set3_available * df.asc_eb_set3 * np.where(df.asc_eb_set3 > 0, np.where(df.lb_ivt_set3 > 0, 0.5, 1), 1)",,,1 set3_cr_long_distance,CR long distance bonus,"@df.set3_available * (df.cr_ivt_set3 > 10) * c_ivt * -1.0 * (np.clip(df.cr_ivt_set3, a_min=None, a_max=60) + np.where(df.cr_ivt_set3 > 40, (df.cr_ivt_set3 - 40) * 0.6, 0))",,,1 \ No newline at end of file diff --git a/activitysim/examples/example_sandag_xborder/configs/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv b/activitysim/examples/prototype_sandag_xborder/configs/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv similarity index 98% rename from activitysim/examples/example_sandag_xborder/configs/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv rename to activitysim/examples/prototype_sandag_xborder/configs/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv index 5e8f526ea6..99c0cf8f01 100755 --- a/activitysim/examples/example_sandag_xborder/configs/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/tvpb_utility_tap_tap_annotate_choosers_preprocessor.csv @@ -1,64 +1,64 @@ -Description,Target,Expression - -# set1,, -total IVT,total_ivt_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_TOTALIVTT')" -total number of transfers,raw_transfers_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_XFERS')" -commuter rail IVT,cr_ivt_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_CMRIVTT')" -light rail IVT,lrt_ivt_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_LRTIVTT')" -bus rapid transit red IVT,brt_red_ivt_set1,"los.get_tappairs3d(df.btap,df.atap,df.tod,'BUS_BRTREDIVTT')" -bus rapid transit yellow IVT,brt_yel_ivt_set1,"los.get_tappairs3d(df.btap,df.atap,df.tod,'BUS_BRTYELIVTT')" -express bus IVT,eb_ivt_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_EXPIVTT')" -limited bus IVT,ltd_ivt_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_LTDEXPIVTT')" -local bus IVT,lb_ivt_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_BUSIVTT')" -first wait,first_wait_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_FIRSTWAIT')" -transfer wait,xfer_wait_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_XFERWAIT')" -total walk time,walk_time_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_TOTALWALK')" -fare,fare_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_FARE')" -Transit available,set1_available,"(total_ivt_set1 > 0) * (total_ivt_set1 < 99999)" -Number transfers,transfers_set1,"np.clip(raw_transfers_set1, a_min=None, a_max=4)" -commuter rail ASC,asc_cr_set1,"set1_available * np.where(cr_ivt_set1 > 0, cr_asc * c_ivt, 0)" -light rail ASC,asc_lrt_set1,"set1_available * np.where(asc_cr_set1==0, np.where(lrt_ivt_set1 > 0, lrt_asc * c_ivt, 0), asc_cr_set1)" -bus rapid transit ASC,asc_brt_set1,"set1_available * np.where(asc_lrt_set1==0, np.where(brt_red_ivt_set1 + brt_yel_ivt_set1 > 0, brt_asc * c_ivt, 0), asc_lrt_set1)" -express bus ASC,asc_eb_set1,"set1_available * np.where(asc_brt_set1==0, np.where(eb_ivt_set1 + ltd_ivt_set1 > 0, exp_asc * c_ivt, 0), asc_brt_set1)" - -# set2,, -total IVT,total_ivt_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_TOTALIVTT')" -total number of transfers,raw_transfers_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_XFERS')" -commuter rail IVT,cr_ivt_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_CMRIVTT')" -light rail IVT,lrt_ivt_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_LRTIVTT')" -bus rapid transit red IVT,brt_red_ivt_set2,"los.get_tappairs3d(df.btap,df.atap,df.tod,'PREM_BRTREDIVTT')" -bus rapid transit yellow IVT,brt_yel_ivt_set2,"los.get_tappairs3d(df.btap,df.atap,df.tod,'PREM_BRTYELIVTT')" -express bus IVT,eb_ivt_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_EXPIVTT')" -limited bus IVT,ltd_ivt_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_LTDEXPIVTT')" -local bus IVT,lb_ivt_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_BUSIVTT')" -first wait,first_wait_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_FIRSTWAIT')" -transfer wait,xfer_wait_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_XFERWAIT')" -total walk time,walk_time_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_TOTALWALK')" -fare,fare_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_FARE')" -Transit available,set2_available,"(total_ivt_set2 > 0) * (total_ivt_set2 < 99999)" -Number Transfers,transfers_set2,"np.clip(raw_transfers_set2, a_min=None, a_max=4)" -commuter rail ASC,asc_cr_set2,"set2_available * np.where(cr_ivt_set2 > 0, cr_asc * c_ivt, 0)" -light rail ASC,asc_lrt_set2,"set2_available * np.where(asc_cr_set2==0, np.where(lrt_ivt_set2 > 0, lrt_asc * c_ivt, 0), asc_cr_set2)" -bus rapid transit ASC,asc_brt_set2,"set2_available * np.where(asc_lrt_set2==0, np.where(brt_red_ivt_set2 + brt_yel_ivt_set2 > 0, brt_asc * c_ivt, 0), asc_lrt_set2)" -express bus ASC,asc_eb_set2,"set2_available * np.where(asc_brt_set2==0, np.where(eb_ivt_set2 + ltd_ivt_set2 > 0, exp_asc * c_ivt, 0), asc_brt_set2)" - -# set3,, -total IVT,total_ivt_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_TOTALIVTT')" -total number of transfers,raw_transfers_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_XFERS')" -commuter rail IVT,cr_ivt_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_CMRIVTT')" -light rail IVT,lrt_ivt_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_LRTIVTT')" -bus rapid transit red IVT,brt_red_ivt_set3,"los.get_tappairs3d(df.btap,df.atap,df.tod,'ALLPEN_BRTREDIVTT')" -bus rapid transit yellow IVT,brt_yel_ivt_set3,"los.get_tappairs3d(df.btap,df.atap,df.tod,'ALLPEN_BRTYELIVTT')" -express bus IVT,eb_ivt_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_EXPIVTT')" -limited bus IVT,ltd_ivt_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_LTDEXPIVTT')" -local bus IVT,lb_ivt_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_BUSIVTT')" -first wait,first_wait_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_FIRSTWAIT')" -transfer wait,xfer_wait_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_XFERWAIT')" -total walk time,walk_time_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_TOTALWALK')" -fare,fare_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_FARE')" -Transit avaiable,set3_available,"(total_ivt_set3 > 0) * (total_ivt_set3 < 99999) * (raw_transfers_set3 != 0)" -Number Transfers,transfers_set3,"np.clip(raw_transfers_set3, a_min=None, a_max=4)" -commuter rail ASC,asc_cr_set3,"set3_available * np.where(cr_ivt_set3 > 0, cr_asc * c_ivt, 0)" -light rail ASC,asc_lrt_set3,"set3_available * np.where(asc_cr_set3==0, np.where(lrt_ivt_set3 > 0, lrt_asc * c_ivt, 0), asc_cr_set3)" -bus rapid transit ASC,asc_brt_set3,"set3_available * np.where(asc_lrt_set3==0, np.where(brt_red_ivt_set3 + brt_yel_ivt_set3 > 0, brt_asc * c_ivt, 0), asc_lrt_set3)" +Description,Target,Expression + +# set1,, +total IVT,total_ivt_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_TOTALIVTT')" +total number of transfers,raw_transfers_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_XFERS')" +commuter rail IVT,cr_ivt_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_CMRIVTT')" +light rail IVT,lrt_ivt_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_LRTIVTT')" +bus rapid transit red IVT,brt_red_ivt_set1,"los.get_tappairs3d(df.btap,df.atap,df.tod,'BUS_BRTREDIVTT')" +bus rapid transit yellow IVT,brt_yel_ivt_set1,"los.get_tappairs3d(df.btap,df.atap,df.tod,'BUS_BRTYELIVTT')" +express bus IVT,eb_ivt_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_EXPIVTT')" +limited bus IVT,ltd_ivt_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_LTDEXPIVTT')" +local bus IVT,lb_ivt_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_BUSIVTT')" +first wait,first_wait_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_FIRSTWAIT')" +transfer wait,xfer_wait_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_XFERWAIT')" +total walk time,walk_time_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_TOTALWALK')" +fare,fare_set1,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'BUS_FARE')" +Transit available,set1_available,"(total_ivt_set1 > 0) * (total_ivt_set1 < 99999)" +Number transfers,transfers_set1,"np.clip(raw_transfers_set1, a_min=None, a_max=4)" +commuter rail ASC,asc_cr_set1,"set1_available * np.where(cr_ivt_set1 > 0, cr_asc * c_ivt, 0)" +light rail ASC,asc_lrt_set1,"set1_available * np.where(asc_cr_set1==0, np.where(lrt_ivt_set1 > 0, lrt_asc * c_ivt, 0), asc_cr_set1)" +bus rapid transit ASC,asc_brt_set1,"set1_available * np.where(asc_lrt_set1==0, np.where(brt_red_ivt_set1 + brt_yel_ivt_set1 > 0, brt_asc * c_ivt, 0), asc_lrt_set1)" +express bus ASC,asc_eb_set1,"set1_available * np.where(asc_brt_set1==0, np.where(eb_ivt_set1 + ltd_ivt_set1 > 0, exp_asc * c_ivt, 0), asc_brt_set1)" + +# set2,, +total IVT,total_ivt_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_TOTALIVTT')" +total number of transfers,raw_transfers_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_XFERS')" +commuter rail IVT,cr_ivt_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_CMRIVTT')" +light rail IVT,lrt_ivt_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_LRTIVTT')" +bus rapid transit red IVT,brt_red_ivt_set2,"los.get_tappairs3d(df.btap,df.atap,df.tod,'PREM_BRTREDIVTT')" +bus rapid transit yellow IVT,brt_yel_ivt_set2,"los.get_tappairs3d(df.btap,df.atap,df.tod,'PREM_BRTYELIVTT')" +express bus IVT,eb_ivt_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_EXPIVTT')" +limited bus IVT,ltd_ivt_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_LTDEXPIVTT')" +local bus IVT,lb_ivt_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_BUSIVTT')" +first wait,first_wait_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_FIRSTWAIT')" +transfer wait,xfer_wait_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_XFERWAIT')" +total walk time,walk_time_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_TOTALWALK')" +fare,fare_set2,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'PREM_FARE')" +Transit available,set2_available,"(total_ivt_set2 > 0) * (total_ivt_set2 < 99999)" +Number Transfers,transfers_set2,"np.clip(raw_transfers_set2, a_min=None, a_max=4)" +commuter rail ASC,asc_cr_set2,"set2_available * np.where(cr_ivt_set2 > 0, cr_asc * c_ivt, 0)" +light rail ASC,asc_lrt_set2,"set2_available * np.where(asc_cr_set2==0, np.where(lrt_ivt_set2 > 0, lrt_asc * c_ivt, 0), asc_cr_set2)" +bus rapid transit ASC,asc_brt_set2,"set2_available * np.where(asc_lrt_set2==0, np.where(brt_red_ivt_set2 + brt_yel_ivt_set2 > 0, brt_asc * c_ivt, 0), asc_lrt_set2)" +express bus ASC,asc_eb_set2,"set2_available * np.where(asc_brt_set2==0, np.where(eb_ivt_set2 + ltd_ivt_set2 > 0, exp_asc * c_ivt, 0), asc_brt_set2)" + +# set3,, +total IVT,total_ivt_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_TOTALIVTT')" +total number of transfers,raw_transfers_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_XFERS')" +commuter rail IVT,cr_ivt_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_CMRIVTT')" +light rail IVT,lrt_ivt_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_LRTIVTT')" +bus rapid transit red IVT,brt_red_ivt_set3,"los.get_tappairs3d(df.btap,df.atap,df.tod,'ALLPEN_BRTREDIVTT')" +bus rapid transit yellow IVT,brt_yel_ivt_set3,"los.get_tappairs3d(df.btap,df.atap,df.tod,'ALLPEN_BRTYELIVTT')" +express bus IVT,eb_ivt_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_EXPIVTT')" +limited bus IVT,ltd_ivt_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_LTDEXPIVTT')" +local bus IVT,lb_ivt_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_BUSIVTT')" +first wait,first_wait_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_FIRSTWAIT')" +transfer wait,xfer_wait_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_XFERWAIT')" +total walk time,walk_time_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_TOTALWALK')" +fare,fare_set3,"los.get_tappairs3d(df.btap, df.atap, df.tod, 'ALLPEN_FARE')" +Transit avaiable,set3_available,"(total_ivt_set3 > 0) * (total_ivt_set3 < 99999) * (raw_transfers_set3 != 0)" +Number Transfers,transfers_set3,"np.clip(raw_transfers_set3, a_min=None, a_max=4)" +commuter rail ASC,asc_cr_set3,"set3_available * np.where(cr_ivt_set3 > 0, cr_asc * c_ivt, 0)" +light rail ASC,asc_lrt_set3,"set3_available * np.where(asc_cr_set3==0, np.where(lrt_ivt_set3 > 0, lrt_asc * c_ivt, 0), asc_cr_set3)" +bus rapid transit ASC,asc_brt_set3,"set3_available * np.where(asc_lrt_set3==0, np.where(brt_red_ivt_set3 + brt_yel_ivt_set3 > 0, brt_asc * c_ivt, 0), asc_lrt_set3)" express bus ASC,asc_eb_set3,"set3_available * np.where(asc_brt_set3==0, np.where(eb_ivt_set3 + ltd_ivt_set3 > 0, exp_asc * c_ivt, 0), asc_brt_set3)" \ No newline at end of file diff --git a/activitysim/examples/example_sandag_xborder/configs/tvpb_utility_walk_maz_tap.csv b/activitysim/examples/prototype_sandag_xborder/configs/tvpb_utility_walk_maz_tap.csv similarity index 98% rename from activitysim/examples/example_sandag_xborder/configs/tvpb_utility_walk_maz_tap.csv rename to activitysim/examples/prototype_sandag_xborder/configs/tvpb_utility_walk_maz_tap.csv index c7bb0e7d36..0a32ddbda7 100755 --- a/activitysim/examples/example_sandag_xborder/configs/tvpb_utility_walk_maz_tap.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/tvpb_utility_walk_maz_tap.csv @@ -1,4 +1,4 @@ -Label,Description,Expression,utility -#,,,FIXME column values shouldn't ever be na if different moides have different tables? -util_walk_available,walk available,@(df.walkTime==0) * C_UNAVAILABLE,1 -util_walk_time,walk time,"@c_ivt * c_walk * df.walkTime",1 +Label,Description,Expression,utility +#,,,FIXME column values shouldn't ever be na if different moides have different tables? +util_walk_available,walk available,@(df.walkTime==0) * C_UNAVAILABLE,1 +util_walk_time,walk time,"@c_ivt * c_walk * df.walkTime",1 diff --git a/activitysim/examples/example_sandag_xborder/configs/wait_time_mode.yaml b/activitysim/examples/prototype_sandag_xborder/configs/wait_time_mode.yaml similarity index 96% rename from activitysim/examples/example_sandag_xborder/configs/wait_time_mode.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/wait_time_mode.yaml index 13bbc27f61..b931be48b3 100755 --- a/activitysim/examples/example_sandag_xborder/configs/wait_time_mode.yaml +++ b/activitysim/examples/prototype_sandag_xborder/configs/wait_time_mode.yaml @@ -1,124 +1,124 @@ -inherit_settings: False - -# number of households to simulate -# households_sample_size: 100 -# chunk_size: 0 - -households_sample_size: 0 - -multiprocess: True -strict: False -mem_tick: 30 -num_processes: 30 -fail_fast: True - -############## -# -# chunking -# - -chunk_size: 50_000_000_000 - -# minimum fraction of total chunk_size to reserve for adaptive chunking -min_available_chunk_ratio: 0.05 - -# initial number of chooser rows for first chunk in training mode -# when there is no pre-existing chunk_cache to set initial value -# ordinarily bigger is better as long as it is not so big it causes memory issues (e.g. accessibility with lots of zones) -default_initial_rows_per_chunk: 500 - -# method to calculate memory overhead when chunking is enabled -chunk_method: hybrid_uss - -# chunk training mode -# training to determine the chunking settings written to a cache file that is re-used for production runs -# training -# production -# disabled -chunk_training_mode: disabled - -# whether to preserve or delete subprocess chunk and mem logs when they are consolidated at end of multiprocess run -keep_chunk_logs: True -keep_mem_logs: True - -############ - -trace_hh_id: - -# input tables -input_table_list: - - tablename: households - filename: households_xborder.csv - index_col: household_id - - - tablename: persons - filename: persons_xborder.csv - index_col: person_id - - - tablename: land_use - filename: mazs_xborder.csv - index_col: zone_id - rename_columns: - MAZ: zone_id - - - tablename: tours - filename: tours_xborder.csv - index_col: tour_id - - -# set false to disable variability check in simple_simulate and interaction_simulate -check_for_variability: False - -# - shadow pricing global switches - -# turn shadow_pricing on and off for all models (e.g. school and work) -# shadow pricing is deprecated for less than full samples -# see shadow_pricing.yaml for additional settings -use_shadow_pricing: False - -# turn writing of sample_tables on and off for all models -# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) -want_dest_choice_sample_tables: False -want_dest_choice_presampling: True - -#resume_after: _ - -models: - - initialize_landuse - - initialize_households - - initialize_tours - # --- STATIC cache prebuild steps - # single-process step to create attribute_combination list - - initialize_los - # multi-processable step to build STATIC cache - # (this step is a NOP if cache already exists and network_los.rebuild_tvpb_cache setting is False) - - initialize_tvpb - # # --- - - tour_scheduling_probabilistic - - tour_od_choice - - reassign_tour_purpose_by_poe - - tour_mode_choice_simulate - - write_tables - -multiprocess_steps: - - name: mp_initialize - begin: initialize_landuse - - name: mp_households - begin: tour_od_choice - slice: - tables: - - households - - persons - - name: mp_summarize - begin: write_tables - -output_tables: - h5_store: False - action: include - prefix: wait_time_ - sort: True - tables: - - tours - -output_summaries: - tours: +inherit_settings: False + +# number of households to simulate +# households_sample_size: 100 +# chunk_size: 0 + +households_sample_size: 0 + +multiprocess: True +strict: False +mem_tick: 30 +num_processes: 30 +fail_fast: True + +############## +# +# chunking +# + +chunk_size: 50_000_000_000 + +# minimum fraction of total chunk_size to reserve for adaptive chunking +min_available_chunk_ratio: 0.05 + +# initial number of chooser rows for first chunk in training mode +# when there is no pre-existing chunk_cache to set initial value +# ordinarily bigger is better as long as it is not so big it causes memory issues (e.g. accessibility with lots of zones) +default_initial_rows_per_chunk: 500 + +# method to calculate memory overhead when chunking is enabled +chunk_method: hybrid_uss + +# chunk training mode +# training to determine the chunking settings written to a cache file that is re-used for production runs +# training +# production +# disabled +chunk_training_mode: disabled + +# whether to preserve or delete subprocess chunk and mem logs when they are consolidated at end of multiprocess run +keep_chunk_logs: True +keep_mem_logs: True + +############ + +trace_hh_id: + +# input tables +input_table_list: + - tablename: households + filename: households_xborder.csv + index_col: household_id + + - tablename: persons + filename: persons_xborder.csv + index_col: person_id + + - tablename: land_use + filename: mazs_xborder.csv + index_col: zone_id + rename_columns: + MAZ: zone_id + + - tablename: tours + filename: tours_xborder.csv + index_col: tour_id + + +# set false to disable variability check in simple_simulate and interaction_simulate +check_for_variability: False + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + +# turn writing of sample_tables on and off for all models +# (if True, tables will be written if DEST_CHOICE_SAMPLE_TABLE_NAME is specified in individual model settings) +want_dest_choice_sample_tables: False +want_dest_choice_presampling: True + +#resume_after: _ + +models: + - initialize_landuse + - initialize_households + - initialize_tours + # --- STATIC cache prebuild steps + # single-process step to create attribute_combination list + - initialize_los + # multi-processable step to build STATIC cache + # (this step is a NOP if cache already exists and network_los.rebuild_tvpb_cache setting is False) + - initialize_tvpb + # # --- + - tour_scheduling_probabilistic + - tour_od_choice + - reassign_tour_purpose_by_poe + - tour_mode_choice_simulate + - write_tables + +multiprocess_steps: + - name: mp_initialize + begin: initialize_landuse + - name: mp_households + begin: tour_od_choice + slice: + tables: + - households + - persons + - name: mp_summarize + begin: write_tables + +output_tables: + h5_store: False + action: include + prefix: wait_time_ + sort: True + tables: + - tours + +output_summaries: + tours: diff --git a/activitysim/examples/example_sandag_xborder/configs/write_trip_matrices.yaml b/activitysim/examples/prototype_sandag_xborder/configs/write_trip_matrices.yaml similarity index 96% rename from activitysim/examples/example_sandag_xborder/configs/write_trip_matrices.yaml rename to activitysim/examples/prototype_sandag_xborder/configs/write_trip_matrices.yaml index fd3b79da32..37c6849ac3 100644 --- a/activitysim/examples/example_sandag_xborder/configs/write_trip_matrices.yaml +++ b/activitysim/examples/prototype_sandag_xborder/configs/write_trip_matrices.yaml @@ -1,108 +1,108 @@ -# read trips table post preprocessor and run expressions to code -# additional data fields, with one data fields for each matrix specified below - -preprocessor: - SPEC: write_trip_matrices_annotate_trips_preprocessor - DF: trips - TABLES: - - tours - -# divide trip counts by household expansion factor -HH_EXPANSION_WEIGHT_COL: sample_rate # added when households read in - -# save preprocessed trips table to pipeline if desired -SAVE_TRIPS_TABLE: False - -MATRICES: - - file_name: trips_ea.omx - tables: - - name: DRIVEALONE_EA - data_field: DRIVEALONE_EA - - name: SHARED2_EA - data_field: SHARED2_EA - - name: SHARED3_EA - data_field: SHARED3_EA - - name: WALK_EA - data_field: WALK_EA - - name: WALK_TRANSIT_WALK_EA - data_field: WALK_TRANSIT_WALK_EA - - name: TAXI_EA - data_field: TAXI_EA - - name: TNC_SINGLE_EA - data_field: TNC_SINGLE_EA - - name: TNC_SHARED_EA - data_field: TNC_SHARED_EA - - file_name: trips_am.omx - tables: - - name: DRIVEALONE_AM - data_field: DRIVEALONE_AM - - name: SHARED2_AM - data_field: SHARED2_AM - - name: SHARED3_AM - data_field: SHARED3_AM - - name: WALK_AM - data_field: WALK_AM - - name: WALK_TRANSIT_WALK_AM - data_field: WALK_TRANSIT_WALK_EA - - name: TAXI_AM - data_field: TAXI_AM - - name: TNC_SINGLE_AM - data_field: TNC_SINGLE_AM - - name: TNC_SHARED_AM - data_field: TNC_SHARED_AM - - file_name: trips_md.omx - tables: - - name: DRIVEALONE_MD - data_field: DRIVEALONE_MD - - name: SHARED2_MD - data_field: SHARED2_MD - - name: SHARED3_MD - data_field: SHARED3_MD - - name: WALK_MD - data_field: WALK_MD - - name: WALK_TRANSIT_WALK_MD - data_field: WALK_TRANSIT_WALK_EA - - name: TAXI_MD - data_field: TAXI_MD - - name: TNC_SINGLE_MD - data_field: TNC_SINGLE_MD - - name: TNC_SHARED_MD - data_field: TNC_SHARED_MD - - file_name: trips_pm.omx - tables: - - name: DRIVEALONE_PM - data_field: DRIVEALONE_PM - - name: SHARED2_PM - data_field: SHARED2_PM - - name: SHARED3_PM - data_field: SHARED3_PM - - name: WALK_PM - data_field: WALK_PM - - name: WALK_TRANSIT_WALK_PM - data_field: WALK_TRANSIT_WALK_EA - - name: TAXI_PM - data_field: TAXI_PM - - name: TNC_SINGLE_PM - data_field: TNC_SINGLE_PM - - name: TNC_SHARED_PM - data_field: TNC_SHARED_PM - - file_name: trips_ev.omx - tables: - - name: DRIVEALONE_EV - data_field: DRIVEALONE_EV - - name: SHARED2_EV - data_field: SHARED2_EV - - name: SHARED3_EV - data_field: SHARED3_EV - - name: WALK_EV - data_field: WALK_EV - - name: WALK_TRANSIT_WALK_EV - data_field: WALK_TRANSIT_WALK_EA - - name: TAXI_EV - data_field: TAXI_EV - - name: TNC_SINGLE_EV - data_field: TNC_SINGLE_EV - - name: TNC_SHARED_EV - data_field: TNC_SHARED_EV - -CONSTANTS: +# read trips table post preprocessor and run expressions to code +# additional data fields, with one data fields for each matrix specified below + +preprocessor: + SPEC: write_trip_matrices_annotate_trips_preprocessor + DF: trips + TABLES: + - tours + +# divide trip counts by household expansion factor +HH_EXPANSION_WEIGHT_COL: sample_rate # added when households read in + +# save preprocessed trips table to pipeline if desired +SAVE_TRIPS_TABLE: False + +MATRICES: + - file_name: trips_ea.omx + tables: + - name: DRIVEALONE_EA + data_field: DRIVEALONE_EA + - name: SHARED2_EA + data_field: SHARED2_EA + - name: SHARED3_EA + data_field: SHARED3_EA + - name: WALK_EA + data_field: WALK_EA + - name: WALK_TRANSIT_WALK_EA + data_field: WALK_TRANSIT_WALK_EA + - name: TAXI_EA + data_field: TAXI_EA + - name: TNC_SINGLE_EA + data_field: TNC_SINGLE_EA + - name: TNC_SHARED_EA + data_field: TNC_SHARED_EA + - file_name: trips_am.omx + tables: + - name: DRIVEALONE_AM + data_field: DRIVEALONE_AM + - name: SHARED2_AM + data_field: SHARED2_AM + - name: SHARED3_AM + data_field: SHARED3_AM + - name: WALK_AM + data_field: WALK_AM + - name: WALK_TRANSIT_WALK_AM + data_field: WALK_TRANSIT_WALK_EA + - name: TAXI_AM + data_field: TAXI_AM + - name: TNC_SINGLE_AM + data_field: TNC_SINGLE_AM + - name: TNC_SHARED_AM + data_field: TNC_SHARED_AM + - file_name: trips_md.omx + tables: + - name: DRIVEALONE_MD + data_field: DRIVEALONE_MD + - name: SHARED2_MD + data_field: SHARED2_MD + - name: SHARED3_MD + data_field: SHARED3_MD + - name: WALK_MD + data_field: WALK_MD + - name: WALK_TRANSIT_WALK_MD + data_field: WALK_TRANSIT_WALK_EA + - name: TAXI_MD + data_field: TAXI_MD + - name: TNC_SINGLE_MD + data_field: TNC_SINGLE_MD + - name: TNC_SHARED_MD + data_field: TNC_SHARED_MD + - file_name: trips_pm.omx + tables: + - name: DRIVEALONE_PM + data_field: DRIVEALONE_PM + - name: SHARED2_PM + data_field: SHARED2_PM + - name: SHARED3_PM + data_field: SHARED3_PM + - name: WALK_PM + data_field: WALK_PM + - name: WALK_TRANSIT_WALK_PM + data_field: WALK_TRANSIT_WALK_EA + - name: TAXI_PM + data_field: TAXI_PM + - name: TNC_SINGLE_PM + data_field: TNC_SINGLE_PM + - name: TNC_SHARED_PM + data_field: TNC_SHARED_PM + - file_name: trips_ev.omx + tables: + - name: DRIVEALONE_EV + data_field: DRIVEALONE_EV + - name: SHARED2_EV + data_field: SHARED2_EV + - name: SHARED3_EV + data_field: SHARED3_EV + - name: WALK_EV + data_field: WALK_EV + - name: WALK_TRANSIT_WALK_EV + data_field: WALK_TRANSIT_WALK_EA + - name: TAXI_EV + data_field: TAXI_EV + - name: TNC_SINGLE_EV + data_field: TNC_SINGLE_EV + - name: TNC_SHARED_EV + data_field: TNC_SHARED_EV + +CONSTANTS: diff --git a/activitysim/examples/example_sandag_xborder/configs/write_trip_matrices_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_sandag_xborder/configs/write_trip_matrices_annotate_trips_preprocessor.csv similarity index 98% rename from activitysim/examples/example_sandag_xborder/configs/write_trip_matrices_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_sandag_xborder/configs/write_trip_matrices_annotate_trips_preprocessor.csv index 09fd5f213b..2b50fd28ab 100644 --- a/activitysim/examples/example_sandag_xborder/configs/write_trip_matrices_annotate_trips_preprocessor.csv +++ b/activitysim/examples/prototype_sandag_xborder/configs/write_trip_matrices_annotate_trips_preprocessor.csv @@ -1,55 +1,55 @@ -Description,Target,Expression -# add additional fields,, -,tour_participants,trips.tour_id.map(tours.number_of_participants) -,distance,odt_skims['SOV_NT_M_DIST'] -# code time periods,, -,is_ea,trips.trip_period == 'EA' -,is_am,trips.trip_period == 'AM' -,is_md,trips.trip_period == 'MD' -,is_pm,trips.trip_period == 'PM' -,is_ev,trips.trip_period == 'EV' -# ea trips,, -,DRIVEALONE_EA,((trips.trip_mode == 'DRIVEALONE') & is_ea) * tour_participants -,SHARED2_EA,((trips.trip_mode == 'SHARED2') & is_ea) * tour_participants -,SHARED3_EA,((trips.trip_mode == 'SHARED3') & is_ea) * tour_participants -,WALK_EA,((trips.trip_mode == 'WALK') & is_ea) * tour_participants -,WALK_TRANSIT_WALK_EA,((trips.trip_mode == 'WALK_TRANSIT') & is_ea) * tour_participants -,TAXI_EA,((trips.trip_mode == 'TAXI') & is_ea) * tour_participants -,TNC_SINGLE_EA,((trips.trip_mode == 'TNC_SINGLE') & is_ea) * tour_participants -,TNC_SHARED_EA,((trips.trip_mode == 'TNC_SHARED') & is_ea) * tour_participants -# am trips,, -,DRIVEALONE_AM,((trips.trip_mode == 'DRIVEALONE') & is_am) * tour_participants -,SHARED2_AM,((trips.trip_mode == 'SHARED2') & is_am) * tour_participants -,SHARED3_AM,((trips.trip_mode == 'SHARED3') & is_am) * tour_participants -,WALK_AM,((trips.trip_mode == 'WALK') & is_am) * tour_participants -,WALK_TRANSIT_WALK_AM,((trips.trip_mode == 'WALK_TRANSIT') & is_am) * tour_participants -,TAXI_AM,((trips.trip_mode == 'TAXI') & is_am) * tour_participants -,TNC_SINGLE_AM,((trips.trip_mode == 'TNC_SINGLE') & is_am) * tour_participants -,TNC_SHARED_AM,((trips.trip_mode == 'TNC_SHARED') & is_am) * tour_participants -# md trips,, -,DRIVEALONE_MD,((trips.trip_mode == 'DRIVEALONE') & is_md) * tour_participants -,SHARED2_MD,((trips.trip_mode == 'SHARED2') & is_md) * tour_participants -,SHARED3_MD,((trips.trip_mode == 'SHARED3') & is_md) * tour_participants -,WALK_MD,((trips.trip_mode == 'WALK') & is_md) * tour_participants -,WALK_TRANSIT_WALK_MD,((trips.trip_mode == 'WALK_TRANSIT') & is_md) * tour_participants -,TAXI_MD,((trips.trip_mode == 'TAXI') & is_md) * tour_participants -,TNC_SINGLE_MD,((trips.trip_mode == 'TNC_SINGLE') & is_md) * tour_participants -,TNC_SHARED_MD,((trips.trip_mode == 'TNC_SHARED') & is_md) * tour_participants -# pm trips,, -,DRIVEALONE_PM,((trips.trip_mode == 'DRIVEALONE') & is_pm) * tour_participants -,SHARED2_PM,((trips.trip_mode == 'SHARED2') & is_pm) * tour_participants -,SHARED3_PM,((trips.trip_mode == 'SHARED3') & is_pm) * tour_participants -,WALK_PM,((trips.trip_mode == 'WALK') & is_pm) * tour_participants -,WALK_TRANSIT_WALK_PM,((trips.trip_mode == 'WALK_TRANSIT') & is_pm) * tour_participants -,TAXI_PM,((trips.trip_mode == 'TAXI') & is_pm) * tour_participants -,TNC_SINGLE_PM,((trips.trip_mode == 'TNC_SINGLE') & is_pm) * tour_participants -,TNC_SHARED_PM,((trips.trip_mode == 'TNC_SHARED') & is_pm) * tour_participants -# ev trips,, -,DRIVEALONE_EV,((trips.trip_mode == 'DRIVEALONE') & is_ev) * tour_participants -,SHARED2_EV,((trips.trip_mode == 'SHARED2') & is_ev) * tour_participants -,SHARED3_EV,((trips.trip_mode == 'SHARED3') & is_ev) * tour_participants -,WALK_EV,((trips.trip_mode == 'WALK') & is_ev) * tour_participants -,WALK_TRANSIT_WALK_EV,((trips.trip_mode == 'WALK_TRANSIT') & is_ev) * tour_participants -,TAXI_EV,((trips.trip_mode == 'TAXI') & is_ev) * tour_participants -,TNC_SINGLE_EV,((trips.trip_mode == 'TNC_SINGLE') & is_ev) * tour_participants -,TNC_SHARED_EV,((trips.trip_mode == 'TNC_SHARED') & is_ev) * tour_participants +Description,Target,Expression +# add additional fields,, +,tour_participants,trips.tour_id.map(tours.number_of_participants) +,distance,odt_skims['SOV_NT_M_DIST'] +# code time periods,, +,is_ea,trips.trip_period == 'EA' +,is_am,trips.trip_period == 'AM' +,is_md,trips.trip_period == 'MD' +,is_pm,trips.trip_period == 'PM' +,is_ev,trips.trip_period == 'EV' +# ea trips,, +,DRIVEALONE_EA,((trips.trip_mode == 'DRIVEALONE') & is_ea) * tour_participants +,SHARED2_EA,((trips.trip_mode == 'SHARED2') & is_ea) * tour_participants +,SHARED3_EA,((trips.trip_mode == 'SHARED3') & is_ea) * tour_participants +,WALK_EA,((trips.trip_mode == 'WALK') & is_ea) * tour_participants +,WALK_TRANSIT_WALK_EA,((trips.trip_mode == 'WALK_TRANSIT') & is_ea) * tour_participants +,TAXI_EA,((trips.trip_mode == 'TAXI') & is_ea) * tour_participants +,TNC_SINGLE_EA,((trips.trip_mode == 'TNC_SINGLE') & is_ea) * tour_participants +,TNC_SHARED_EA,((trips.trip_mode == 'TNC_SHARED') & is_ea) * tour_participants +# am trips,, +,DRIVEALONE_AM,((trips.trip_mode == 'DRIVEALONE') & is_am) * tour_participants +,SHARED2_AM,((trips.trip_mode == 'SHARED2') & is_am) * tour_participants +,SHARED3_AM,((trips.trip_mode == 'SHARED3') & is_am) * tour_participants +,WALK_AM,((trips.trip_mode == 'WALK') & is_am) * tour_participants +,WALK_TRANSIT_WALK_AM,((trips.trip_mode == 'WALK_TRANSIT') & is_am) * tour_participants +,TAXI_AM,((trips.trip_mode == 'TAXI') & is_am) * tour_participants +,TNC_SINGLE_AM,((trips.trip_mode == 'TNC_SINGLE') & is_am) * tour_participants +,TNC_SHARED_AM,((trips.trip_mode == 'TNC_SHARED') & is_am) * tour_participants +# md trips,, +,DRIVEALONE_MD,((trips.trip_mode == 'DRIVEALONE') & is_md) * tour_participants +,SHARED2_MD,((trips.trip_mode == 'SHARED2') & is_md) * tour_participants +,SHARED3_MD,((trips.trip_mode == 'SHARED3') & is_md) * tour_participants +,WALK_MD,((trips.trip_mode == 'WALK') & is_md) * tour_participants +,WALK_TRANSIT_WALK_MD,((trips.trip_mode == 'WALK_TRANSIT') & is_md) * tour_participants +,TAXI_MD,((trips.trip_mode == 'TAXI') & is_md) * tour_participants +,TNC_SINGLE_MD,((trips.trip_mode == 'TNC_SINGLE') & is_md) * tour_participants +,TNC_SHARED_MD,((trips.trip_mode == 'TNC_SHARED') & is_md) * tour_participants +# pm trips,, +,DRIVEALONE_PM,((trips.trip_mode == 'DRIVEALONE') & is_pm) * tour_participants +,SHARED2_PM,((trips.trip_mode == 'SHARED2') & is_pm) * tour_participants +,SHARED3_PM,((trips.trip_mode == 'SHARED3') & is_pm) * tour_participants +,WALK_PM,((trips.trip_mode == 'WALK') & is_pm) * tour_participants +,WALK_TRANSIT_WALK_PM,((trips.trip_mode == 'WALK_TRANSIT') & is_pm) * tour_participants +,TAXI_PM,((trips.trip_mode == 'TAXI') & is_pm) * tour_participants +,TNC_SINGLE_PM,((trips.trip_mode == 'TNC_SINGLE') & is_pm) * tour_participants +,TNC_SHARED_PM,((trips.trip_mode == 'TNC_SHARED') & is_pm) * tour_participants +# ev trips,, +,DRIVEALONE_EV,((trips.trip_mode == 'DRIVEALONE') & is_ev) * tour_participants +,SHARED2_EV,((trips.trip_mode == 'SHARED2') & is_ev) * tour_participants +,SHARED3_EV,((trips.trip_mode == 'SHARED3') & is_ev) * tour_participants +,WALK_EV,((trips.trip_mode == 'WALK') & is_ev) * tour_participants +,WALK_TRANSIT_WALK_EV,((trips.trip_mode == 'WALK_TRANSIT') & is_ev) * tour_participants +,TAXI_EV,((trips.trip_mode == 'TAXI') & is_ev) * tour_participants +,TNC_SINGLE_EV,((trips.trip_mode == 'TNC_SINGLE') & is_ev) * tour_participants +,TNC_SHARED_EV,((trips.trip_mode == 'TNC_SHARED') & is_ev) * tour_participants diff --git a/activitysim/examples/example_sandag_xborder/data/.gitignore b/activitysim/examples/prototype_sandag_xborder/data/.gitignore similarity index 100% rename from activitysim/examples/example_sandag_xborder/data/.gitignore rename to activitysim/examples/prototype_sandag_xborder/data/.gitignore diff --git a/activitysim/examples/example_sandag_xborder/data/households_xborder.csv b/activitysim/examples/prototype_sandag_xborder/data/households_xborder.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/data/households_xborder.csv rename to activitysim/examples/prototype_sandag_xborder/data/households_xborder.csv diff --git a/activitysim/examples/example_sandag_xborder/data/maz_maz_walk.csv b/activitysim/examples/prototype_sandag_xborder/data/maz_maz_walk.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/data/maz_maz_walk.csv rename to activitysim/examples/prototype_sandag_xborder/data/maz_maz_walk.csv diff --git a/activitysim/examples/example_sandag_xborder/data/maz_tap_walk.csv b/activitysim/examples/prototype_sandag_xborder/data/maz_tap_walk.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/data/maz_tap_walk.csv rename to activitysim/examples/prototype_sandag_xborder/data/maz_tap_walk.csv diff --git a/activitysim/examples/example_sandag_xborder/data/mazs_xborder.csv b/activitysim/examples/prototype_sandag_xborder/data/mazs_xborder.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/data/mazs_xborder.csv rename to activitysim/examples/prototype_sandag_xborder/data/mazs_xborder.csv diff --git a/activitysim/examples/example_sandag_xborder/data/persons_xborder.csv b/activitysim/examples/prototype_sandag_xborder/data/persons_xborder.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/data/persons_xborder.csv rename to activitysim/examples/prototype_sandag_xborder/data/persons_xborder.csv diff --git a/activitysim/examples/example_sandag_xborder/data/tap_lines.csv b/activitysim/examples/prototype_sandag_xborder/data/tap_lines.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/data/tap_lines.csv rename to activitysim/examples/prototype_sandag_xborder/data/tap_lines.csv diff --git a/activitysim/examples/example_sandag_xborder/data/taps.csv b/activitysim/examples/prototype_sandag_xborder/data/taps.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/data/taps.csv rename to activitysim/examples/prototype_sandag_xborder/data/taps.csv diff --git a/activitysim/examples/example_sandag_xborder/data/tours_xborder.csv b/activitysim/examples/prototype_sandag_xborder/data/tours_xborder.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/data/tours_xborder.csv rename to activitysim/examples/prototype_sandag_xborder/data/tours_xborder.csv diff --git a/activitysim/examples/example_sandag_xborder/data/traffic_skims_xborder_AM.omx b/activitysim/examples/prototype_sandag_xborder/data/traffic_skims_xborder_AM.omx similarity index 100% rename from activitysim/examples/example_sandag_xborder/data/traffic_skims_xborder_AM.omx rename to activitysim/examples/prototype_sandag_xborder/data/traffic_skims_xborder_AM.omx diff --git a/activitysim/examples/example_sandag_xborder/data/traffic_skims_xborder_EA.omx b/activitysim/examples/prototype_sandag_xborder/data/traffic_skims_xborder_EA.omx similarity index 100% rename from activitysim/examples/example_sandag_xborder/data/traffic_skims_xborder_EA.omx rename to activitysim/examples/prototype_sandag_xborder/data/traffic_skims_xborder_EA.omx diff --git a/activitysim/examples/example_sandag_xborder/data/traffic_skims_xborder_EV.omx b/activitysim/examples/prototype_sandag_xborder/data/traffic_skims_xborder_EV.omx similarity index 100% rename from activitysim/examples/example_sandag_xborder/data/traffic_skims_xborder_EV.omx rename to activitysim/examples/prototype_sandag_xborder/data/traffic_skims_xborder_EV.omx diff --git a/activitysim/examples/example_sandag_xborder/data/traffic_skims_xborder_MD.omx b/activitysim/examples/prototype_sandag_xborder/data/traffic_skims_xborder_MD.omx similarity index 100% rename from activitysim/examples/example_sandag_xborder/data/traffic_skims_xborder_MD.omx rename to activitysim/examples/prototype_sandag_xborder/data/traffic_skims_xborder_MD.omx diff --git a/activitysim/examples/example_sandag_xborder/data/traffic_skims_xborder_PM.omx b/activitysim/examples/prototype_sandag_xborder/data/traffic_skims_xborder_PM.omx similarity index 100% rename from activitysim/examples/example_sandag_xborder/data/traffic_skims_xborder_PM.omx rename to activitysim/examples/prototype_sandag_xborder/data/traffic_skims_xborder_PM.omx diff --git a/activitysim/examples/example_sandag_xborder/data/transit_skims_xborder.omx b/activitysim/examples/prototype_sandag_xborder/data/transit_skims_xborder.omx similarity index 100% rename from activitysim/examples/example_sandag_xborder/data/transit_skims_xborder.omx rename to activitysim/examples/prototype_sandag_xborder/data/transit_skims_xborder.omx diff --git a/activitysim/examples/example_sandag_xborder/extensions/.gitignore b/activitysim/examples/prototype_sandag_xborder/extensions/.gitignore similarity index 100% rename from activitysim/examples/example_sandag_xborder/extensions/.gitignore rename to activitysim/examples/prototype_sandag_xborder/extensions/.gitignore diff --git a/activitysim/examples/example_sandag_xborder/extensions/__init__.py b/activitysim/examples/prototype_sandag_xborder/extensions/__init__.py similarity index 100% rename from activitysim/examples/example_sandag_xborder/extensions/__init__.py rename to activitysim/examples/prototype_sandag_xborder/extensions/__init__.py diff --git a/activitysim/examples/example_sandag_xborder/extensions/reassign_tour_purpose.py b/activitysim/examples/prototype_sandag_xborder/extensions/reassign_tour_purpose.py similarity index 100% rename from activitysim/examples/example_sandag_xborder/extensions/reassign_tour_purpose.py rename to activitysim/examples/prototype_sandag_xborder/extensions/reassign_tour_purpose.py diff --git a/activitysim/examples/example_sandag_xborder/notebooks/cross_border_validation.ipynb b/activitysim/examples/prototype_sandag_xborder/notebooks/cross_border_validation.ipynb similarity index 100% rename from activitysim/examples/example_sandag_xborder/notebooks/cross_border_validation.ipynb rename to activitysim/examples/prototype_sandag_xborder/notebooks/cross_border_validation.ipynb diff --git a/activitysim/examples/example_sandag_xborder/output/.gitignore b/activitysim/examples/prototype_sandag_xborder/output/.gitignore similarity index 100% rename from activitysim/examples/example_sandag_xborder/output/.gitignore rename to activitysim/examples/prototype_sandag_xborder/output/.gitignore diff --git a/activitysim/examples/example_sandag_xborder/scripts/reduce_sandag_cb_skims_for_github.py b/activitysim/examples/prototype_sandag_xborder/scripts/reduce_sandag_cb_skims_for_github.py similarity index 100% rename from activitysim/examples/example_sandag_xborder/scripts/reduce_sandag_cb_skims_for_github.py rename to activitysim/examples/prototype_sandag_xborder/scripts/reduce_sandag_cb_skims_for_github.py diff --git a/activitysim/examples/example_sandag_xborder/scripts/sandag_crop_3_zone.py b/activitysim/examples/prototype_sandag_xborder/scripts/sandag_crop_3_zone.py similarity index 100% rename from activitysim/examples/example_sandag_xborder/scripts/sandag_crop_3_zone.py rename to activitysim/examples/prototype_sandag_xborder/scripts/sandag_crop_3_zone.py diff --git a/activitysim/examples/example_sandag_xborder/simulation.py b/activitysim/examples/prototype_sandag_xborder/simulation.py similarity index 100% rename from activitysim/examples/example_sandag_xborder/simulation.py rename to activitysim/examples/prototype_sandag_xborder/simulation.py diff --git a/activitysim/examples/example_sandag_xborder/test/configs/network_los.yaml b/activitysim/examples/prototype_sandag_xborder/test/configs/network_los.yaml similarity index 100% rename from activitysim/examples/example_sandag_xborder/test/configs/network_los.yaml rename to activitysim/examples/prototype_sandag_xborder/test/configs/network_los.yaml diff --git a/activitysim/examples/example_sandag_xborder/test/configs/settings.yaml b/activitysim/examples/prototype_sandag_xborder/test/configs/settings.yaml similarity index 100% rename from activitysim/examples/example_sandag_xborder/test/configs/settings.yaml rename to activitysim/examples/prototype_sandag_xborder/test/configs/settings.yaml diff --git a/activitysim/examples/example_sandag_xborder/test/output/.gitignore b/activitysim/examples/prototype_sandag_xborder/test/output/.gitignore similarity index 100% rename from activitysim/examples/example_sandag_xborder/test/output/.gitignore rename to activitysim/examples/prototype_sandag_xborder/test/output/.gitignore diff --git a/activitysim/examples/example_sandag_xborder/test/regress/final_trips.csv b/activitysim/examples/prototype_sandag_xborder/test/regress/final_trips.csv similarity index 100% rename from activitysim/examples/example_sandag_xborder/test/regress/final_trips.csv rename to activitysim/examples/prototype_sandag_xborder/test/regress/final_trips.csv diff --git a/activitysim/examples/example_sandag_xborder/test/test_sandag_xborder.py b/activitysim/examples/prototype_sandag_xborder/test/test_sandag_xborder.py similarity index 93% rename from activitysim/examples/example_sandag_xborder/test/test_sandag_xborder.py rename to activitysim/examples/prototype_sandag_xborder/test/test_sandag_xborder.py index 3d5b86fd3f..fa413cc99e 100644 --- a/activitysim/examples/example_sandag_xborder/test/test_sandag_xborder.py +++ b/activitysim/examples/prototype_sandag_xborder/test/test_sandag_xborder.py @@ -18,7 +18,7 @@ def teardown_function(func): def test_sandag_xborder(): def example_path(dirname): - resource = os.path.join('examples', 'example_sandag_xborder', dirname) + resource = os.path.join('examples', 'prototype_sandag_xborder', dirname) return pkg_resources.resource_filename('activitysim', resource) def test_path(dirname): diff --git a/activitysim/examples/example_semcog/.gitignore b/activitysim/examples/prototype_semcog/.gitignore similarity index 100% rename from activitysim/examples/example_semcog/.gitignore rename to activitysim/examples/prototype_semcog/.gitignore diff --git a/activitysim/examples/example_semcog/README.MD b/activitysim/examples/prototype_semcog/README.MD similarity index 100% rename from activitysim/examples/example_semcog/README.MD rename to activitysim/examples/prototype_semcog/README.MD diff --git a/activitysim/examples/example_semcog/change_log.txt b/activitysim/examples/prototype_semcog/change_log.txt similarity index 100% rename from activitysim/examples/example_semcog/change_log.txt rename to activitysim/examples/prototype_semcog/change_log.txt diff --git a/activitysim/examples/example_semcog/configs/_dummy_coefficients.csv b/activitysim/examples/prototype_semcog/configs/_dummy_coefficients.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/_dummy_coefficients.csv rename to activitysim/examples/prototype_semcog/configs/_dummy_coefficients.csv diff --git a/activitysim/examples/example_semcog/configs/accessibility.csv b/activitysim/examples/prototype_semcog/configs/accessibility.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/accessibility.csv rename to activitysim/examples/prototype_semcog/configs/accessibility.csv index 42128349f1..37558cf5db 100755 --- a/activitysim/examples/example_semcog/configs/accessibility.csv +++ b/activitysim/examples/prototype_semcog/configs/accessibility.csv @@ -1,59 +1,59 @@ -Description,Target,Expression -#,, -#,, auto peak -#,, -#,, assume peak occurs in AM for outbound and PM for inbound -peak round trip distance,_auPkTime,"skim_od[('SOV_TIME', 'AM')] + skim_do[('SOV_TIME', 'PM')]" -decay function,_decay, exp(_auPkTime * dispersion_parameter_automobile) -auto peak retail,auPkRetail,df.e05_retail * _decay -auto peak total,auPkTotal,df.tot_emp * _decay -#,, -#,, auto off-peak -#,, -#,, assume midday occurs entirely in the midday period -off-peak round trip distance,_auOpTime,"skim_od[('SOV_TIME', 'MD')] + skim_do[('SOV_TIME', 'MD')]" -decay function,_decay, exp(_auOpTime * dispersion_parameter_automobile) -auto off-peak retail,auOpRetail,df.e05_retail * _decay -auto off-peak total,auOpTotal,df.tot_emp * _decay -#,, -#,, transit peak -#,, -#,, assume peak outbound transit occurs in AM -o-d peak transit ivt,_inVehicleTime,"skim_od[('WLK_TRN_IVT', 'AM')]" -o-d peak transit ovt,_outOfVehicleTime,"skim_od[('WLK_TRN_IWAIT', 'AM')] + skim_od[('WLK_TRN_XWAIT', 'AM')] + skim_od[('WLK_TRN_WACC', 'AM')] + skim_od[('WLK_TRN_WAUX', 'AM')] + skim_od[('WLK_TRN_WEGR', 'AM')]" -o-d peak transit time,_trPkTime_od,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) -#,, assume peak inbound transit occurs in PM -d-o peak transit ivt,_inVehicleTime,"skim_do[('WLK_TRN_IVT', 'PM')]" -d-o peak transit ovt,_outOfVehicleTime,"skim_do[('WLK_TRN_IWAIT', 'PM')] + skim_do[('WLK_TRN_XWAIT', 'PM')] + skim_do[('WLK_TRN_WACC', 'PM')] + skim_do[('WLK_TRN_WAUX', 'PM')] + skim_do[('WLK_TRN_WEGR', 'PM')]" -d-o peak transit time,_trPkTime_do,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) -peak transit time,_trPkTime,_trPkTime_od + _trPkTime_do -round trip path is available,_rt_available,(_trPkTime_od > 0) & (_trPkTime_do > 0) -decay function,_decay,_rt_available * exp(_trPkTime * dispersion_parameter_transit) -transit peak retail,trPkRetail,df.e05_retail * _decay -transit peak total,trPkTotal,df.tot_emp * _decay -#,, -#,, transit off-peak -#,, -#,, assume off-peak outbound transit occurs in the MD time period -o-d off-peak transit ivt,_inVehicleTime,"skim_od[('WLK_TRN_IVT', 'MD')]" -o-d off-peak transit ovt,_outOfVehicleTime,"skim_od[('WLK_TRN_IWAIT', 'MD')] + skim_od[('WLK_TRN_XWAIT', 'MD')] + skim_od[('WLK_TRN_WACC', 'MD')] + skim_od[('WLK_TRN_WAUX', 'MD')] + skim_od[('WLK_TRN_WEGR', 'MD')]" -o-d off-peak transit time,_trOpTime_od,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) -#,, assume off-peak inbound transit occurs in the MD time period -d-o off-peak transit ivt,_inVehicleTime,"skim_do[('WLK_TRN_IVT', 'MD')]" -d-o off-peak transit ovt,_outOfVehicleTime,"skim_do[('WLK_TRN_IWAIT', 'MD')] + skim_do[('WLK_TRN_XWAIT', 'MD')] + skim_do[('WLK_TRN_WACC', 'MD')] + skim_do[('WLK_TRN_WAUX', 'MD')] + skim_do[('WLK_TRN_WEGR', 'MD')]" -d-o off-peak transit time,_trOpTime_do,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) -peak transit time,_trOpTime,_trOpTime_od + _trOpTime_do -#,,FIXME - _rt_available calculation appears to be wrong in mtctm1 accessibility.job -#round trip path is available,_rt_available,(_trOpTime > 0) -round trip path is available,_rt_available,(_trOpTime_od > 0) & (_trOpTime_do > 0) -decay function,_decay,_rt_available * exp(_trOpTime * dispersion_parameter_transit) -transit off-peak retail,trOpRetail,df.e05_retail * _decay -transit off-peak total,trOpTotal,df.tot_emp * _decay -#,, -#,, non motorized -#,, -non-motorized round trip distance,_nmDist,skim_od['DISTWALK'] + skim_do['DISTWALK'] -round trip path is available,_rt_available,_nmDist <= maximum_walk_distance -decay function,_decay,_rt_available * exp(_nmDist * dispersion_parameter_walk) -retail accessibility,nmRetail,df.e05_retail * _decay -total accessibility,nmTotal,df.tot_emp * _decay +Description,Target,Expression +#,, +#,, auto peak +#,, +#,, assume peak occurs in AM for outbound and PM for inbound +peak round trip distance,_auPkTime,"skim_od[('SOV_TIME', 'AM')] + skim_do[('SOV_TIME', 'PM')]" +decay function,_decay, exp(_auPkTime * dispersion_parameter_automobile) +auto peak retail,auPkRetail,df.e05_retail * _decay +auto peak total,auPkTotal,df.tot_emp * _decay +#,, +#,, auto off-peak +#,, +#,, assume midday occurs entirely in the midday period +off-peak round trip distance,_auOpTime,"skim_od[('SOV_TIME', 'MD')] + skim_do[('SOV_TIME', 'MD')]" +decay function,_decay, exp(_auOpTime * dispersion_parameter_automobile) +auto off-peak retail,auOpRetail,df.e05_retail * _decay +auto off-peak total,auOpTotal,df.tot_emp * _decay +#,, +#,, transit peak +#,, +#,, assume peak outbound transit occurs in AM +o-d peak transit ivt,_inVehicleTime,"skim_od[('WLK_TRN_IVT', 'AM')]" +o-d peak transit ovt,_outOfVehicleTime,"skim_od[('WLK_TRN_IWAIT', 'AM')] + skim_od[('WLK_TRN_XWAIT', 'AM')] + skim_od[('WLK_TRN_WACC', 'AM')] + skim_od[('WLK_TRN_WAUX', 'AM')] + skim_od[('WLK_TRN_WEGR', 'AM')]" +o-d peak transit time,_trPkTime_od,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) +#,, assume peak inbound transit occurs in PM +d-o peak transit ivt,_inVehicleTime,"skim_do[('WLK_TRN_IVT', 'PM')]" +d-o peak transit ovt,_outOfVehicleTime,"skim_do[('WLK_TRN_IWAIT', 'PM')] + skim_do[('WLK_TRN_XWAIT', 'PM')] + skim_do[('WLK_TRN_WACC', 'PM')] + skim_do[('WLK_TRN_WAUX', 'PM')] + skim_do[('WLK_TRN_WEGR', 'PM')]" +d-o peak transit time,_trPkTime_do,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) +peak transit time,_trPkTime,_trPkTime_od + _trPkTime_do +round trip path is available,_rt_available,(_trPkTime_od > 0) & (_trPkTime_do > 0) +decay function,_decay,_rt_available * exp(_trPkTime * dispersion_parameter_transit) +transit peak retail,trPkRetail,df.e05_retail * _decay +transit peak total,trPkTotal,df.tot_emp * _decay +#,, +#,, transit off-peak +#,, +#,, assume off-peak outbound transit occurs in the MD time period +o-d off-peak transit ivt,_inVehicleTime,"skim_od[('WLK_TRN_IVT', 'MD')]" +o-d off-peak transit ovt,_outOfVehicleTime,"skim_od[('WLK_TRN_IWAIT', 'MD')] + skim_od[('WLK_TRN_XWAIT', 'MD')] + skim_od[('WLK_TRN_WACC', 'MD')] + skim_od[('WLK_TRN_WAUX', 'MD')] + skim_od[('WLK_TRN_WEGR', 'MD')]" +o-d off-peak transit time,_trOpTime_od,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) +#,, assume off-peak inbound transit occurs in the MD time period +d-o off-peak transit ivt,_inVehicleTime,"skim_do[('WLK_TRN_IVT', 'MD')]" +d-o off-peak transit ovt,_outOfVehicleTime,"skim_do[('WLK_TRN_IWAIT', 'MD')] + skim_do[('WLK_TRN_XWAIT', 'MD')] + skim_do[('WLK_TRN_WACC', 'MD')] + skim_do[('WLK_TRN_WAUX', 'MD')] + skim_do[('WLK_TRN_WEGR', 'MD')]" +d-o off-peak transit time,_trOpTime_do,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) +peak transit time,_trOpTime,_trOpTime_od + _trOpTime_do +#,,FIXME - _rt_available calculation appears to be wrong in mtctm1 accessibility.job +#round trip path is available,_rt_available,(_trOpTime > 0) +round trip path is available,_rt_available,(_trOpTime_od > 0) & (_trOpTime_do > 0) +decay function,_decay,_rt_available * exp(_trOpTime * dispersion_parameter_transit) +transit off-peak retail,trOpRetail,df.e05_retail * _decay +transit off-peak total,trOpTotal,df.tot_emp * _decay +#,, +#,, non motorized +#,, +non-motorized round trip distance,_nmDist,skim_od['DISTWALK'] + skim_do['DISTWALK'] +round trip path is available,_rt_available,_nmDist <= maximum_walk_distance +decay function,_decay,_rt_available * exp(_nmDist * dispersion_parameter_walk) +retail accessibility,nmRetail,df.e05_retail * _decay +total accessibility,nmTotal,df.tot_emp * _decay diff --git a/activitysim/examples/example_semcog/configs/accessibility.yaml b/activitysim/examples/prototype_semcog/configs/accessibility.yaml similarity index 96% rename from activitysim/examples/example_semcog/configs/accessibility.yaml rename to activitysim/examples/prototype_semcog/configs/accessibility.yaml index 6bf19b6739..86ab866295 100755 --- a/activitysim/examples/example_semcog/configs/accessibility.yaml +++ b/activitysim/examples/prototype_semcog/configs/accessibility.yaml @@ -1,13 +1,13 @@ - -# columns from land_use table to add to df -land_use_columns: ['e05_retail', 'tot_emp'] - -CONSTANTS: - # dispersion parameters - dispersion_parameter_automobile: -0.05 - dispersion_parameter_transit: -0.05 - dispersion_parameter_walk: -1.00 - # maximum walk distance in miles - maximum_walk_distance: 3.0 - # perceived minute of in-vehicle time for every minute of out-of-vehicle time - out_of_vehicle_time_weight: 2.0 + +# columns from land_use table to add to df +land_use_columns: ['e05_retail', 'tot_emp'] + +CONSTANTS: + # dispersion parameters + dispersion_parameter_automobile: -0.05 + dispersion_parameter_transit: -0.05 + dispersion_parameter_walk: -1.00 + # maximum walk distance in miles + maximum_walk_distance: 3.0 + # perceived minute of in-vehicle time for every minute of out-of-vehicle time + out_of_vehicle_time_weight: 2.0 diff --git a/activitysim/examples/example_semcog/configs/annotate_households.csv b/activitysim/examples/prototype_semcog/configs/annotate_households.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/annotate_households.csv rename to activitysim/examples/prototype_semcog/configs/annotate_households.csv index 55a4210d5b..3ec08e3a7c 100755 --- a/activitysim/examples/example_semcog/configs/annotate_households.csv +++ b/activitysim/examples/prototype_semcog/configs/annotate_households.csv @@ -1,42 +1,42 @@ -Description,Target,Expression -#,, annotate households table after import -,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" -#,,FIXME households.income can be negative - so we clip? -income,income,households.HINCP.fillna(0) -income_in_thousands,income_in_thousands,(income / 1000).clip(lower=0) -income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[1, 2, 3, 4]).astype(int)" -#,, -,_MIN_VOT,setting('min_value_of_time') -,_MAX_VOT,setting('max_value_of_time') -,_MU,setting('distributed_vot_mu') -,_SIGMA,setting('distributed_vot_sigma') -median_value_of_time,median_value_of_time,"income_segment.map({k: v for k, v in setting('household_median_value_of_time').items()})" -hh_value_of_time,hh_value_of_time,"rng.lognormal_for_df(df, mu=np.log(median_value_of_time * _MU), sigma=_SIGMA).clip(_MIN_VOT, _MAX_VOT)" -#,, -#num_workers was renamed in import,, -,num_workers,"_PERSON_COUNT('(ESR==1)|(ESR==2)|(ESR==4)|(ESR==5)', persons, households)" -number of non_workers,num_non_workers,households.hhsize - num_workers -#,, -#,,we assume that everyone 16 and older is a potential driver -number of drivers,num_drivers,"_PERSON_COUNT('16 <= age', persons, households)" -num_adults,num_adults,"_PERSON_COUNT('18 <= age', persons, households)" -num_children,num_children,"_PERSON_COUNT('18 > age', persons, households)" -num_young_children,num_young_children,"_PERSON_COUNT('age <= 5', persons, households)" -num_children_5_to_15,num_children_5_to_15,"_PERSON_COUNT('5 <= age <= 15', persons, households)" -num_children_6_to_12,num_children_6_to_12,"_PERSON_COUNT('6 <= age <= 12', persons, households)" -num_children_16_to_17,num_children_16_to_17,"_PERSON_COUNT('16 <= age <= 17', persons, households)" -num_college_age,num_college_age,"_PERSON_COUNT('18 <= age <= 24', persons, households)" -num_young_adults,num_young_adults,"_PERSON_COUNT('25 <= age <= 34', persons, households)" -non_family,non_family,households.HHT.isin(HHT_NONFAMILY) -family,family,households.HHT.isin(HHT_FAMILY) -home_is_urban,home_is_urban,"reindex(land_use.AreaType, households.home_zone_id) < setting('urban_threshold')" -home_is_rural,home_is_rural,"reindex(land_use.AreaType, households.home_zone_id) > setting('rural_threshold')" -#,, default for work and school location logsums before auto_ownership model is run -,auto_ownership,households.auto_ownership -TAZ column to match settings file,TAZ,households.home_zone_id -number of pre-driving age children in the household,num_predrive_child,"_PERSON_COUNT('ptype == 7', persons, households)" -number of non-working adult in the household,num_nonworker_adults,"_PERSON_COUNT('ptype == 4', persons, households)" -number of full time workers,num_fullTime_workers,"_PERSON_COUNT('is_fulltime_worker', persons, households)" -number of part time workers,num_partTime_workers,"_PERSON_COUNT('is_parttime_worker', persons, households)" -number of retired adults in the household,_num_retired_adults,"_PERSON_COUNT('ptype == 5', persons, households)" -Retired Adults Only Households,retired_adults_only_hh,(households.hhsize > 0) & (households.hhsize == _num_retired_adults) +Description,Target,Expression +#,, annotate households table after import +,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)" +#,,FIXME households.income can be negative - so we clip? +income,income,households.HINCP.fillna(0) +income_in_thousands,income_in_thousands,(income / 1000).clip(lower=0) +income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[1, 2, 3, 4]).astype(int)" +#,, +,_MIN_VOT,setting('min_value_of_time') +,_MAX_VOT,setting('max_value_of_time') +,_MU,setting('distributed_vot_mu') +,_SIGMA,setting('distributed_vot_sigma') +median_value_of_time,median_value_of_time,"income_segment.map({k: v for k, v in setting('household_median_value_of_time').items()})" +hh_value_of_time,hh_value_of_time,"rng.lognormal_for_df(df, mu=np.log(median_value_of_time * _MU), sigma=_SIGMA).clip(_MIN_VOT, _MAX_VOT)" +#,, +#num_workers was renamed in import,, +,num_workers,"_PERSON_COUNT('(ESR==1)|(ESR==2)|(ESR==4)|(ESR==5)', persons, households)" +number of non_workers,num_non_workers,households.hhsize - num_workers +#,, +#,,we assume that everyone 16 and older is a potential driver +number of drivers,num_drivers,"_PERSON_COUNT('16 <= age', persons, households)" +num_adults,num_adults,"_PERSON_COUNT('18 <= age', persons, households)" +num_children,num_children,"_PERSON_COUNT('18 > age', persons, households)" +num_young_children,num_young_children,"_PERSON_COUNT('age <= 5', persons, households)" +num_children_5_to_15,num_children_5_to_15,"_PERSON_COUNT('5 <= age <= 15', persons, households)" +num_children_6_to_12,num_children_6_to_12,"_PERSON_COUNT('6 <= age <= 12', persons, households)" +num_children_16_to_17,num_children_16_to_17,"_PERSON_COUNT('16 <= age <= 17', persons, households)" +num_college_age,num_college_age,"_PERSON_COUNT('18 <= age <= 24', persons, households)" +num_young_adults,num_young_adults,"_PERSON_COUNT('25 <= age <= 34', persons, households)" +non_family,non_family,households.HHT.isin(HHT_NONFAMILY) +family,family,households.HHT.isin(HHT_FAMILY) +home_is_urban,home_is_urban,"reindex(land_use.AreaType, households.home_zone_id) < setting('urban_threshold')" +home_is_rural,home_is_rural,"reindex(land_use.AreaType, households.home_zone_id) > setting('rural_threshold')" +#,, default for work and school location logsums before auto_ownership model is run +,auto_ownership,households.auto_ownership +TAZ column to match settings file,TAZ,households.home_zone_id +number of pre-driving age children in the household,num_predrive_child,"_PERSON_COUNT('ptype == 7', persons, households)" +number of non-working adult in the household,num_nonworker_adults,"_PERSON_COUNT('ptype == 4', persons, households)" +number of full time workers,num_fullTime_workers,"_PERSON_COUNT('is_fulltime_worker', persons, households)" +number of part time workers,num_partTime_workers,"_PERSON_COUNT('is_parttime_worker', persons, households)" +number of retired adults in the household,_num_retired_adults,"_PERSON_COUNT('ptype == 5', persons, households)" +Retired Adults Only Households,retired_adults_only_hh,(households.hhsize > 0) & (households.hhsize == _num_retired_adults) diff --git a/activitysim/examples/example_psrc/configs/annotate_households_cdap.csv b/activitysim/examples/prototype_semcog/configs/annotate_households_cdap.csv similarity index 99% rename from activitysim/examples/example_psrc/configs/annotate_households_cdap.csv rename to activitysim/examples/prototype_semcog/configs/annotate_households_cdap.csv index a11620f76b..44b4fdcbfd 100755 --- a/activitysim/examples/example_psrc/configs/annotate_households_cdap.csv +++ b/activitysim/examples/prototype_semcog/configs/annotate_households_cdap.csv @@ -1,9 +1,9 @@ -Description,Target,Expression -#,, annotate households table after cdap model has run -num_under16_not_at_school,num_under16_not_at_school,persons.under16_not_at_school.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) -num_travel_active,num_travel_active,persons.travel_active.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) -num_travel_active_adults,num_travel_active_adults,(persons.adult & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) -num_travel_active_preschoolers,num_travel_active_preschoolers,((persons.ptype == PTYPE_PRESCHOOL) & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) -num_travel_active_children,num_travel_active_children,num_travel_active - num_travel_active_adults -num_travel_active_non_preschoolers,num_travel_active_non_preschoolers,num_travel_active - num_travel_active_preschoolers -participates_in_jtf_model,participates_in_jtf_model,(num_travel_active > 1) & (num_travel_active_non_preschoolers > 0) +Description,Target,Expression +#,, annotate households table after cdap model has run +num_under16_not_at_school,num_under16_not_at_school,persons.under16_not_at_school.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active,num_travel_active,persons.travel_active.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_adults,num_travel_active_adults,(persons.adult & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_preschoolers,num_travel_active_preschoolers,((persons.ptype == PTYPE_PRESCHOOL) & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8) +num_travel_active_children,num_travel_active_children,num_travel_active - num_travel_active_adults +num_travel_active_non_preschoolers,num_travel_active_non_preschoolers,num_travel_active - num_travel_active_preschoolers +participates_in_jtf_model,participates_in_jtf_model,(num_travel_active > 1) & (num_travel_active_non_preschoolers > 0) diff --git a/activitysim/examples/example_semcog/configs/annotate_households_workplace.csv b/activitysim/examples/prototype_semcog/configs/annotate_households_workplace.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/annotate_households_workplace.csv rename to activitysim/examples/prototype_semcog/configs/annotate_households_workplace.csv index b835abf499..1b53e91daf 100755 --- a/activitysim/examples/example_semcog/configs/annotate_households_workplace.csv +++ b/activitysim/examples/prototype_semcog/configs/annotate_households_workplace.csv @@ -1,5 +1,5 @@ -Description,Target,Expression -#,, annotate households table after workplace_location model has run -#,, hh_work_auto_savings_ratio is sum of persons work_auto_savings_ratio -,hh_work_auto_savings_ratio,persons.work_auto_savings_ratio.groupby(persons.household_id).sum().reindex(households.index).fillna(0.0) -#,,handle persons with no locatcion +Description,Target,Expression +#,, annotate households table after workplace_location model has run +#,, hh_work_auto_savings_ratio is sum of persons work_auto_savings_ratio +,hh_work_auto_savings_ratio,persons.work_auto_savings_ratio.groupby(persons.household_id).sum().reindex(households.index).fillna(0.0) +#,,handle persons with no locatcion diff --git a/activitysim/examples/example_semcog/configs/annotate_landuse.csv b/activitysim/examples/prototype_semcog/configs/annotate_landuse.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/annotate_landuse.csv rename to activitysim/examples/prototype_semcog/configs/annotate_landuse.csv index 251ab46564..4ef3e4310b 100755 --- a/activitysim/examples/example_semcog/configs/annotate_landuse.csv +++ b/activitysim/examples/prototype_semcog/configs/annotate_landuse.csv @@ -1,5 +1,5 @@ -Description,Target,Expression -#,, annotate landuse table after import -household_density,household_density,land_use.tot_hhs / (land_use.tot_acres) -employment_density,employment_density,land_use.tot_emp / (land_use.tot_acres) -density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1) +Description,Target,Expression +#,, annotate landuse table after import +household_density,household_density,land_use.tot_hhs / (land_use.tot_acres) +employment_density,employment_density,land_use.tot_emp / (land_use.tot_acres) +density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1) diff --git a/activitysim/examples/example_semcog/configs/annotate_persons.csv b/activitysim/examples/prototype_semcog/configs/annotate_persons.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/annotate_persons.csv rename to activitysim/examples/prototype_semcog/configs/annotate_persons.csv index 52b2d756f2..b89e292f96 100755 --- a/activitysim/examples/example_semcog/configs/annotate_persons.csv +++ b/activitysim/examples/prototype_semcog/configs/annotate_persons.csv @@ -1,67 +1,67 @@ -Description,Target,Expression -#,, annotate persons table after import -age_0_to_5,age_0_to_5,"persons.age.between(0,5)" -age_6_to_12,age_6_to_12,"persons.age.between(6,12)" -age_16_to_19,age_16_to_19,"persons.age.between(16, 19)" -age_16_p,age_16_p,persons.age >= 16 -adult,adult,persons.age >= 18 -male,male,persons.sex == 1 -female,female,persons.sex == 2 -,esr,persons.ESR.fillna(0) -,wkhp,persons.WKHP.fillna(0) -,wkw,persons.WKW.fillna(0) -,schg,persons.SCHG.fillna(0) -,mil,persons.MIL.fillna(0) -employment status type,pemploy,np.zeros(len(persons)) -,pemploy,"np.where(persons.age < 16, PEMPLOY_CHILD, PEMPLOY_PART)" -,pemploy,"np.where((persons['age'] >= 16) & ((esr == 3) | (esr == 6)), PEMPLOY_NOT, pemploy)" -,pemploy,"np.where((persons.age>=16)&((esr != 3)&(esr != 6))&(wkhp >= 35) & (wkw >= 1) & (wkw <= 4), PEMPLOY_FULL, pemploy)" -student category,pstudent,np.zeros(len(persons)) -,pstudent,"np.where((pemploy == 1) & (persons.age >= 16), PSTUDENT_NOT, pstudent)" -,pstudent,"np.where((pemploy == 1) & (persons.age < 16), PSTUDENT_GRADE_OR_HIGH, pstudent)" -,pstudent,"np.where((schg < 1) & (persons.age >= 16), PSTUDENT_NOT, pstudent)" -,pstudent,"np.where((schg < 1) & (persons.age < 16), PSTUDENT_GRADE_OR_HIGH, pstudent)" -,pstudent,"np.where((schg >= 15) & (persons.age >= 16) & (pemploy != 1), PSTUDENT_UNIVERSITY, pstudent)" -,pstudent,"np.where((schg >= 15) & (persons.age < 16) & (pemploy != 1), PSTUDENT_GRADE_OR_HIGH, pstudent)" -,pstudent,"np.where((persons.age <= 19) & (pemploy != 1) & (schg >=1) & (schg<=14), PSTUDENT_GRADE_OR_HIGH, pstudent)" -,pstudent,"np.where((persons.age > 19) & (pemploy != 1) & (schg >=1) & (schg<=14), PSTUDENT_UNIVERSITY, pstudent)" -,pstudent,"np.where(pstudent == 0, 3, pstudent)" -person type,ptype,np.zeros(len(persons)) -,ptype,"np.where((pemploy == 1), PTYPE_FULL, PTYPE_NONWORK)" -,ptype,"np.where((pstudent == 3) & (pemploy == 2), PTYPE_PART, ptype)" -,ptype,"np.where((pstudent == 3) & (persons['age'] >= 65) & ((pemploy == 3) | (pemploy == 4)), PTYPE_RETIRED, ptype)" -,ptype,"np.where((pstudent == 3) & (persons['age'] < 6) & ((pemploy == 3) | (pemploy == 4)), PTYPE_PRESCHOOL, ptype)" -,ptype,"np.where((pstudent == 3) & (persons['age'] >= 6) & (persons['age'] <= 64) & ((pemploy == 3) | (pemploy == 4)), PTYPE_NONWORK, ptype)" -,ptype,"np.where((pstudent == 2) & ((pemploy == 2) | (pemploy == 3) | (pemploy == 4)), PTYPE_UNIVERSITY, ptype)" -,ptype,"np.where((pstudent == 1) & (persons['age'] < 6) & ((pemploy == 2) | (pemploy == 3) | (pemploy == 4)), PTYPE_PRESCHOOL, ptype)" -,ptype,"np.where((pstudent == 1) & (persons['age'] >= 16) & ((pemploy == 2) | (pemploy == 3) | (pemploy == 4)), PTYPE_DRIVING, ptype)" -,ptype,"np.where((pstudent == 1) & (persons['age'] >= 6) & (persons['age'] < 16) & ((pemploy == 2) | (pemploy == 3) | (pemploy == 4)), PTYPE_SCHOOL, ptype)" -presence of non_worker other than self in household,has_non_worker,"other_than(persons.household_id, ptype == PTYPE_NONWORK)" -presence of retiree other than self in household,has_retiree,"other_than(persons.household_id, ptype == PTYPE_RETIRED)" -presence of preschooler other than self in household,has_preschool_kid,"other_than(persons.household_id, ptype == PTYPE_PRESCHOOL)" -presence of driving_kid other than self in household,has_driving_kid,"other_than(persons.household_id, ptype == PTYPE_DRIVING)" -presence of school_kid other than self in household,has_school_kid,"other_than(persons.household_id, ptype == PTYPE_SCHOOL)" -presence of full_time worker other than self in household (independent of person type),has_full_time,"other_than(persons.household_id, pemploy==PEMPLOY_FULL)" -presence of part_time worker other than self in household (independent of person type),has_part_time,"other_than(persons.household_id, pemploy==PEMPLOY_PART)" -presence of university student other than self in household,has_university,"other_than(persons.household_id, ptype == PTYPE_UNIVERSITY)" -student_is_employed,student_is_employed,"np.where(((ptype == PTYPE_UNIVERSITY) | (ptype == PTYPE_DRIVING)) & ((pemploy == PEMPLOY_FULL) | (pemploy == PEMPLOY_PART)), True, False)" -nonstudent_to_school,nonstudent_to_school,"np.where(((ptype == PTYPE_FULL) | (ptype == PTYPE_PART) | (ptype == PTYPE_NONWORK) | (ptype == PTYPE_RETIRED)) & ((pstudent == PSTUDENT_GRADE_OR_HIGH) | (pstudent == PSTUDENT_UNIVERSITY)), True, False)" -is_student,is_student,"np.where((pstudent == PSTUDENT_GRADE_OR_HIGH) | (pstudent == PSTUDENT_UNIVERSITY), True, False)" -preschool age can go to preschool,is_student,"np.where((pstudent == PSTUDENT_GRADE_OR_HIGH) | (pstudent == PSTUDENT_UNIVERSITY) & (persons.age > GRADE_SCHOOL_MIN_AGE), True, is_student)" -is_gradeschool,is_gradeschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MAX_AGE) -is_highschool,is_highschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age > GRADE_SCHOOL_MAX_AGE) -is_university,is_university,pstudent == PSTUDENT_UNIVERSITY -school_segment gradeschool,school_segment,"np.where(is_gradeschool, SCHOOL_SEGMENT_GRADE, SCHOOL_SEGMENT_NONE)" -school_segment highschool,school_segment,"np.where(is_highschool, SCHOOL_SEGMENT_HIGH, school_segment)" -school_segment university,school_segment,"np.where(is_university, SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)" -#,, -is_worker,is_worker,"np.where((pemploy == PEMPLOY_FULL) |( pemploy == PEMPLOY_PART), True, False)" -#,, -home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)" -hh_child,hh_child,"reindex(households.children, persons.household_id)" -person number,PNUM,persons.member_id -income,income,"reindex(households.HINCP, persons.household_id)" -income_in_thousands,income_in_thousands,(income / 1000).clip(lower=0) -income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[1, 2, 3, 4]).astype(int)" -is_fulltime_worker,is_fulltime_worker,"((age_16_p) & (wkhp >=35) & (wkw>=1) & (wkw<=4) & (~esr.isin([3,6])))" -is_parttime_worker,is_parttime_worker,"((age_16_p) & (~esr.isin([3,6])) & (is_fulltime_worker == False))" +Description,Target,Expression +#,, annotate persons table after import +age_0_to_5,age_0_to_5,"persons.age.between(0,5)" +age_6_to_12,age_6_to_12,"persons.age.between(6,12)" +age_16_to_19,age_16_to_19,"persons.age.between(16, 19)" +age_16_p,age_16_p,persons.age >= 16 +adult,adult,persons.age >= 18 +male,male,persons.sex == 1 +female,female,persons.sex == 2 +,esr,persons.ESR.fillna(0) +,wkhp,persons.WKHP.fillna(0) +,wkw,persons.WKW.fillna(0) +,schg,persons.SCHG.fillna(0) +,mil,persons.MIL.fillna(0) +employment status type,pemploy,np.zeros(len(persons)) +,pemploy,"np.where(persons.age < 16, PEMPLOY_CHILD, PEMPLOY_PART)" +,pemploy,"np.where((persons['age'] >= 16) & ((esr == 3) | (esr == 6)), PEMPLOY_NOT, pemploy)" +,pemploy,"np.where((persons.age>=16)&((esr != 3)&(esr != 6))&(wkhp >= 35) & (wkw >= 1) & (wkw <= 4), PEMPLOY_FULL, pemploy)" +student category,pstudent,np.zeros(len(persons)) +,pstudent,"np.where((pemploy == 1) & (persons.age >= 16), PSTUDENT_NOT, pstudent)" +,pstudent,"np.where((pemploy == 1) & (persons.age < 16), PSTUDENT_GRADE_OR_HIGH, pstudent)" +,pstudent,"np.where((schg < 1) & (persons.age >= 16), PSTUDENT_NOT, pstudent)" +,pstudent,"np.where((schg < 1) & (persons.age < 16), PSTUDENT_GRADE_OR_HIGH, pstudent)" +,pstudent,"np.where((schg >= 15) & (persons.age >= 16) & (pemploy != 1), PSTUDENT_UNIVERSITY, pstudent)" +,pstudent,"np.where((schg >= 15) & (persons.age < 16) & (pemploy != 1), PSTUDENT_GRADE_OR_HIGH, pstudent)" +,pstudent,"np.where((persons.age <= 19) & (pemploy != 1) & (schg >=1) & (schg<=14), PSTUDENT_GRADE_OR_HIGH, pstudent)" +,pstudent,"np.where((persons.age > 19) & (pemploy != 1) & (schg >=1) & (schg<=14), PSTUDENT_UNIVERSITY, pstudent)" +,pstudent,"np.where(pstudent == 0, 3, pstudent)" +person type,ptype,np.zeros(len(persons)) +,ptype,"np.where((pemploy == 1), PTYPE_FULL, PTYPE_NONWORK)" +,ptype,"np.where((pstudent == 3) & (pemploy == 2), PTYPE_PART, ptype)" +,ptype,"np.where((pstudent == 3) & (persons['age'] >= 65) & ((pemploy == 3) | (pemploy == 4)), PTYPE_RETIRED, ptype)" +,ptype,"np.where((pstudent == 3) & (persons['age'] < 6) & ((pemploy == 3) | (pemploy == 4)), PTYPE_PRESCHOOL, ptype)" +,ptype,"np.where((pstudent == 3) & (persons['age'] >= 6) & (persons['age'] <= 64) & ((pemploy == 3) | (pemploy == 4)), PTYPE_NONWORK, ptype)" +,ptype,"np.where((pstudent == 2) & ((pemploy == 2) | (pemploy == 3) | (pemploy == 4)), PTYPE_UNIVERSITY, ptype)" +,ptype,"np.where((pstudent == 1) & (persons['age'] < 6) & ((pemploy == 2) | (pemploy == 3) | (pemploy == 4)), PTYPE_PRESCHOOL, ptype)" +,ptype,"np.where((pstudent == 1) & (persons['age'] >= 16) & ((pemploy == 2) | (pemploy == 3) | (pemploy == 4)), PTYPE_DRIVING, ptype)" +,ptype,"np.where((pstudent == 1) & (persons['age'] >= 6) & (persons['age'] < 16) & ((pemploy == 2) | (pemploy == 3) | (pemploy == 4)), PTYPE_SCHOOL, ptype)" +presence of non_worker other than self in household,has_non_worker,"other_than(persons.household_id, ptype == PTYPE_NONWORK)" +presence of retiree other than self in household,has_retiree,"other_than(persons.household_id, ptype == PTYPE_RETIRED)" +presence of preschooler other than self in household,has_preschool_kid,"other_than(persons.household_id, ptype == PTYPE_PRESCHOOL)" +presence of driving_kid other than self in household,has_driving_kid,"other_than(persons.household_id, ptype == PTYPE_DRIVING)" +presence of school_kid other than self in household,has_school_kid,"other_than(persons.household_id, ptype == PTYPE_SCHOOL)" +presence of full_time worker other than self in household (independent of person type),has_full_time,"other_than(persons.household_id, pemploy==PEMPLOY_FULL)" +presence of part_time worker other than self in household (independent of person type),has_part_time,"other_than(persons.household_id, pemploy==PEMPLOY_PART)" +presence of university student other than self in household,has_university,"other_than(persons.household_id, ptype == PTYPE_UNIVERSITY)" +student_is_employed,student_is_employed,"np.where(((ptype == PTYPE_UNIVERSITY) | (ptype == PTYPE_DRIVING)) & ((pemploy == PEMPLOY_FULL) | (pemploy == PEMPLOY_PART)), True, False)" +nonstudent_to_school,nonstudent_to_school,"np.where(((ptype == PTYPE_FULL) | (ptype == PTYPE_PART) | (ptype == PTYPE_NONWORK) | (ptype == PTYPE_RETIRED)) & ((pstudent == PSTUDENT_GRADE_OR_HIGH) | (pstudent == PSTUDENT_UNIVERSITY)), True, False)" +is_student,is_student,"np.where((pstudent == PSTUDENT_GRADE_OR_HIGH) | (pstudent == PSTUDENT_UNIVERSITY), True, False)" +preschool age can go to preschool,is_student,"np.where((pstudent == PSTUDENT_GRADE_OR_HIGH) | (pstudent == PSTUDENT_UNIVERSITY) & (persons.age > GRADE_SCHOOL_MIN_AGE), True, is_student)" +is_gradeschool,is_gradeschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MAX_AGE) +is_highschool,is_highschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age > GRADE_SCHOOL_MAX_AGE) +is_university,is_university,pstudent == PSTUDENT_UNIVERSITY +school_segment gradeschool,school_segment,"np.where(is_gradeschool, SCHOOL_SEGMENT_GRADE, SCHOOL_SEGMENT_NONE)" +school_segment highschool,school_segment,"np.where(is_highschool, SCHOOL_SEGMENT_HIGH, school_segment)" +school_segment university,school_segment,"np.where(is_university, SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)" +#,, +is_worker,is_worker,"np.where((pemploy == PEMPLOY_FULL) |( pemploy == PEMPLOY_PART), True, False)" +#,, +home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)" +hh_child,hh_child,"reindex(households.children, persons.household_id)" +person number,PNUM,persons.member_id +income,income,"reindex(households.HINCP, persons.household_id)" +income_in_thousands,income_in_thousands,(income / 1000).clip(lower=0) +income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[1, 2, 3, 4]).astype(int)" +is_fulltime_worker,is_fulltime_worker,"((age_16_p) & (wkhp >=35) & (wkw>=1) & (wkw<=4) & (~esr.isin([3,6])))" +is_parttime_worker,is_parttime_worker,"((age_16_p) & (~esr.isin([3,6])) & (is_fulltime_worker == False))" diff --git a/activitysim/examples/example_semcog/configs/annotate_persons_after_hh.csv b/activitysim/examples/prototype_semcog/configs/annotate_persons_after_hh.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/annotate_persons_after_hh.csv rename to activitysim/examples/prototype_semcog/configs/annotate_persons_after_hh.csv index ebb8b06a58..7e5743c8ec 100755 --- a/activitysim/examples/example_semcog/configs/annotate_persons_after_hh.csv +++ b/activitysim/examples/prototype_semcog/configs/annotate_persons_after_hh.csv @@ -1,22 +1,22 @@ -Description,Target,Expression -#,, annotate persons table after annotate_households -#,, adults get full hh_value_of_time and children get 60% -,_hh_vot,"reindex(households.hh_value_of_time, persons.household_id)" -,value_of_time,"_hh_vot.where(persons.age>=18, _hh_vot * 0.667)" -,_hh_income,"reindex(households.HINCP, persons.household_id)" -,_num_adults,"reindex(households.num_adults, persons.household_id)" -,_num_predrive_child,"reindex(households.num_predrive_child, persons.household_id)" -,_num_nonworker_adults,"reindex(households.num_nonworker_adults, persons.household_id)" -,_num_full_time_workers,"reindex(households.num_fullTime_workers, persons.household_id)" -Income less than 25K,is_income_less25K,(_hh_income)<25000 -Income 25K to 60K,is_income_25K_to_60K,((_hh_income)>=25000) & ((_hh_income)<60000) -Income 60K to 120K,is_income_60K_to_120K, ((_hh_income)>=60000) & ((_hh_income)<120000) -Income greater than 60K,is_income_greater60K,((_hh_income)>=60000) -Income greater than 120K,is_income_greater120K,((_hh_income)>=120000) -Presence of nonworker in HHs,is_non_worker_in_HH,_num_nonworker_adults>0 -all the adults in the HH are full time workers,is_all_adults_full_time_workers,(_num_adults) == (_num_full_time_workers) -Presence of predrive child in HHs,is_pre_drive_child_in_HH,_num_predrive_child>0 -,_has_young_children,"reindex(households.num_young_children, persons.household_id)" -,_has_children_6_to_12,"reindex(households.num_children_6_to_12, persons.household_id)" -has_young_children,has_young_children,_has_young_children>0 -has_children_6_to_12,has_children_6_to_12,_has_children_6_to_12>0 +Description,Target,Expression +#,, annotate persons table after annotate_households +#,, adults get full hh_value_of_time and children get 60% +,_hh_vot,"reindex(households.hh_value_of_time, persons.household_id)" +,value_of_time,"_hh_vot.where(persons.age>=18, _hh_vot * 0.667)" +,_hh_income,"reindex(households.HINCP, persons.household_id)" +,_num_adults,"reindex(households.num_adults, persons.household_id)" +,_num_predrive_child,"reindex(households.num_predrive_child, persons.household_id)" +,_num_nonworker_adults,"reindex(households.num_nonworker_adults, persons.household_id)" +,_num_full_time_workers,"reindex(households.num_fullTime_workers, persons.household_id)" +Income less than 25K,is_income_less25K,(_hh_income)<25000 +Income 25K to 60K,is_income_25K_to_60K,((_hh_income)>=25000) & ((_hh_income)<60000) +Income 60K to 120K,is_income_60K_to_120K, ((_hh_income)>=60000) & ((_hh_income)<120000) +Income greater than 60K,is_income_greater60K,((_hh_income)>=60000) +Income greater than 120K,is_income_greater120K,((_hh_income)>=120000) +Presence of nonworker in HHs,is_non_worker_in_HH,_num_nonworker_adults>0 +all the adults in the HH are full time workers,is_all_adults_full_time_workers,(_num_adults) == (_num_full_time_workers) +Presence of predrive child in HHs,is_pre_drive_child_in_HH,_num_predrive_child>0 +,_has_young_children,"reindex(households.num_young_children, persons.household_id)" +,_has_children_6_to_12,"reindex(households.num_children_6_to_12, persons.household_id)" +has_young_children,has_young_children,_has_young_children>0 +has_children_6_to_12,has_children_6_to_12,_has_children_6_to_12>0 diff --git a/activitysim/examples/example_semcog/configs/annotate_persons_cdap.csv b/activitysim/examples/prototype_semcog/configs/annotate_persons_cdap.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/annotate_persons_cdap.csv rename to activitysim/examples/prototype_semcog/configs/annotate_persons_cdap.csv index d1344ea4a2..6e426628ca 100755 --- a/activitysim/examples/example_semcog/configs/annotate_persons_cdap.csv +++ b/activitysim/examples/prototype_semcog/configs/annotate_persons_cdap.csv @@ -1,7 +1,7 @@ -Description,Target,Expression -#,, annotate persons table after cdap model has run -travel_active,travel_active,persons.cdap_activity != CDAP_ACTIVITY_HOME -under16_not_at_school,under16_not_at_school,"persons.ptype.isin([PTYPE_SCHOOL, PTYPE_PRESCHOOL]) & persons.cdap_activity.isin(['N', 'H'])" -has_preschool_kid_at_home,has_preschool_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_PRESCHOOL) & (persons.cdap_activity == 'H'))" -has_school_kid_at_home,has_school_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_SCHOOL) & (persons.cdap_activity == 'H'))" -,cdap_activity,"np.where((persons.work_from_home) & (persons.cdap_activity=='M'), np.random.choice(['N','H']), persons.cdap_activity)" +Description,Target,Expression +#,, annotate persons table after cdap model has run +travel_active,travel_active,persons.cdap_activity != CDAP_ACTIVITY_HOME +under16_not_at_school,under16_not_at_school,"persons.ptype.isin([PTYPE_SCHOOL, PTYPE_PRESCHOOL]) & persons.cdap_activity.isin(['N', 'H'])" +has_preschool_kid_at_home,has_preschool_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_PRESCHOOL) & (persons.cdap_activity == 'H'))" +has_school_kid_at_home,has_school_kid_at_home,"other_than(persons.household_id, (persons.ptype == PTYPE_SCHOOL) & (persons.cdap_activity == 'H'))" +,cdap_activity,"np.where((persons.work_from_home) & (persons.cdap_activity=='M'), np.random.choice(['N','H']), persons.cdap_activity)" diff --git a/activitysim/examples/example_psrc/configs/annotate_persons_jtp.csv b/activitysim/examples/prototype_semcog/configs/annotate_persons_jtp.csv similarity index 98% rename from activitysim/examples/example_psrc/configs/annotate_persons_jtp.csv rename to activitysim/examples/prototype_semcog/configs/annotate_persons_jtp.csv index 3d5e08db0c..a72c866057 100755 --- a/activitysim/examples/example_psrc/configs/annotate_persons_jtp.csv +++ b/activitysim/examples/prototype_semcog/configs/annotate_persons_jtp.csv @@ -1,3 +1,3 @@ -Description,Target,Expression -#,, annotate persons table after joint_tour_participation model has run -num_joint_tours,num_joint_tours,"joint_tour_participants.groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" +Description,Target,Expression +#,, annotate persons table after joint_tour_participation model has run +num_joint_tours,num_joint_tours,"joint_tour_participants.groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" diff --git a/activitysim/examples/example_semcog/configs/annotate_persons_mtf.csv b/activitysim/examples/prototype_semcog/configs/annotate_persons_mtf.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/annotate_persons_mtf.csv rename to activitysim/examples/prototype_semcog/configs/annotate_persons_mtf.csv index b235b8ed69..4f8f5a8b09 100755 --- a/activitysim/examples/example_semcog/configs/annotate_persons_mtf.csv +++ b/activitysim/examples/prototype_semcog/configs/annotate_persons_mtf.csv @@ -1,10 +1,10 @@ -Description,Target,Expression -#,, annotate persons table after mandatory_tour_frequency model has run -,_PERSON_TOUR_COUNT,"lambda exp, persons, tours: tours.query(exp).groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" -,_Q,"lambda s: ""'{}'"".format(s)" -work_and_school_and_worker,work_and_school_and_worker,(persons.mandatory_tour_frequency == 'work_and_school') & persons.is_worker -work_and_school_and_student,work_and_school_and_student,(persons.mandatory_tour_frequency == 'work_and_school') & persons.is_student -number of mandatory tours for each person,num_mand,"_PERSON_TOUR_COUNT('tour_category==%s' % _Q('mandatory'), persons, tours).fillna(0)" -number of work tours for each person,num_work_tours,"_PERSON_TOUR_COUNT('tour_type==%s' % _Q('work'), persons, tours).fillna(0)" -presence of pre school kid with mandatory tours,has_pre_school_child_with_mandatory,"other_than(persons.household_id, (persons.ptype == 8) & (num_mand > 0))" -presense of driving age school children with mandatory tours,has_driving_age_child_with_mandatory,"other_than(persons.household_id, (persons.ptype == 6) & (num_mand > 0))" +Description,Target,Expression +#,, annotate persons table after mandatory_tour_frequency model has run +,_PERSON_TOUR_COUNT,"lambda exp, persons, tours: tours.query(exp).groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8)" +,_Q,"lambda s: ""'{}'"".format(s)" +work_and_school_and_worker,work_and_school_and_worker,(persons.mandatory_tour_frequency == 'work_and_school') & persons.is_worker +work_and_school_and_student,work_and_school_and_student,(persons.mandatory_tour_frequency == 'work_and_school') & persons.is_student +number of mandatory tours for each person,num_mand,"_PERSON_TOUR_COUNT('tour_category==%s' % _Q('mandatory'), persons, tours).fillna(0)" +number of work tours for each person,num_work_tours,"_PERSON_TOUR_COUNT('tour_type==%s' % _Q('work'), persons, tours).fillna(0)" +presence of pre school kid with mandatory tours,has_pre_school_child_with_mandatory,"other_than(persons.household_id, (persons.ptype == 8) & (num_mand > 0))" +presense of driving age school children with mandatory tours,has_driving_age_child_with_mandatory,"other_than(persons.household_id, (persons.ptype == 6) & (num_mand > 0))" diff --git a/activitysim/examples/example_semcog/configs/annotate_persons_nmtf.csv b/activitysim/examples/prototype_semcog/configs/annotate_persons_nmtf.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/annotate_persons_nmtf.csv rename to activitysim/examples/prototype_semcog/configs/annotate_persons_nmtf.csv index 17a1a001fb..11f8b111e4 100755 --- a/activitysim/examples/example_semcog/configs/annotate_persons_nmtf.csv +++ b/activitysim/examples/prototype_semcog/configs/annotate_persons_nmtf.csv @@ -1,15 +1,15 @@ -Description,Target,Expression -#,, annotate persons table after non_mandatory_tour_frequency model has run -num_non_mand,num_non_mand,tours[tours.tour_category=='non_mandatory'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) -num_escort_tours,num_escort_tours,tours[tours.tour_type == 'escort'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) -num_eatout_tours,num_eatout_tours,tours[tours.tour_type == 'eatout'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) -num_shop_tours,num_shop_tours,tours[tours.tour_type == 'shopping'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) -num_maint_tours,num_maint_tours,tours[tours.tour_type == 'othmaint'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) -num_discr_tours,num_discr_tours,tours[tours.tour_type == 'othdiscr'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) -num_social_tours,num_social_tours,tours[tours.tour_type == 'social'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) -num_non_escort_tours,num_non_escort_tours,num_non_mand-num_escort_tours -total shopping and maintenance tours,num_shop_maint_tours,num_shop_tours + num_maint_tours -"total shopping, maintenance and escort tours",num_shop_maint_escort_tours,num_shop_tours + num_maint_tours + num_escort_tours -number of additional shopping and maintenance tours,num_add_shop_maint_tours,"np.where (num_shop_maint_tours>0, 1, 0) * (num_shop_maint_tours - 1)" -total social and discretionary tours,num_soc_discr_tours,num_social_tours + num_discr_tours -number of additional social and discretionary,num_add_soc_discr_tours,"np.where (num_soc_discr_tours>0, 1, 0) * (num_soc_discr_tours - 1)" +Description,Target,Expression +#,, annotate persons table after non_mandatory_tour_frequency model has run +num_non_mand,num_non_mand,tours[tours.tour_category=='non_mandatory'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_escort_tours,num_escort_tours,tours[tours.tour_type == 'escort'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_eatout_tours,num_eatout_tours,tours[tours.tour_type == 'eatout'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_shop_tours,num_shop_tours,tours[tours.tour_type == 'shopping'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_maint_tours,num_maint_tours,tours[tours.tour_type == 'othmaint'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_discr_tours,num_discr_tours,tours[tours.tour_type == 'othdiscr'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_social_tours,num_social_tours,tours[tours.tour_type == 'social'].groupby('person_id').size().reindex(persons.index).fillna(0).astype(np.int8) +num_non_escort_tours,num_non_escort_tours,num_non_mand-num_escort_tours +total shopping and maintenance tours,num_shop_maint_tours,num_shop_tours + num_maint_tours +"total shopping, maintenance and escort tours",num_shop_maint_escort_tours,num_shop_tours + num_maint_tours + num_escort_tours +number of additional shopping and maintenance tours,num_add_shop_maint_tours,"np.where (num_shop_maint_tours>0, 1, 0) * (num_shop_maint_tours - 1)" +total social and discretionary tours,num_soc_discr_tours,num_social_tours + num_discr_tours +number of additional social and discretionary,num_add_soc_discr_tours,"np.where (num_soc_discr_tours>0, 1, 0) * (num_soc_discr_tours - 1)" diff --git a/activitysim/examples/example_semcog/configs/annotate_persons_school.csv b/activitysim/examples/prototype_semcog/configs/annotate_persons_school.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/annotate_persons_school.csv rename to activitysim/examples/prototype_semcog/configs/annotate_persons_school.csv index 43625623f8..553b124c3b 100755 --- a/activitysim/examples/example_semcog/configs/annotate_persons_school.csv +++ b/activitysim/examples/prototype_semcog/configs/annotate_persons_school.csv @@ -1,7 +1,7 @@ -Description,Target,Expression -#,, annotate persons table after school_location model has run -,distance_to_school,"np.where(persons.school_zone_id>=0,skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, 'DIST'),np.nan)" -#,, this uses the free flow travel time in both directions. MTC TM1 was MD and MD -temp auto_time_to_school,_auto_time_to_school,"skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, ('SOV_TIME', 'MD'))" -temp auto_time_return,_auto_time_return,"skim_dict.lookup(persons.school_zone_id, persons.home_zone_id, ('SOV_TIME', 'MD'))" -free flow roundtrip_auto_time_to_school,roundtrip_auto_time_to_school,"np.where(persons.school_zone_id>=0,_auto_time_to_school + _auto_time_return,0)" +Description,Target,Expression +#,, annotate persons table after school_location model has run +,distance_to_school,"np.where(persons.school_zone_id>=0,skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, 'DIST'),np.nan)" +#,, this uses the free flow travel time in both directions. MTC TM1 was MD and MD +temp auto_time_to_school,_auto_time_to_school,"skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, ('SOV_TIME', 'MD'))" +temp auto_time_return,_auto_time_return,"skim_dict.lookup(persons.school_zone_id, persons.home_zone_id, ('SOV_TIME', 'MD'))" +free flow roundtrip_auto_time_to_school,roundtrip_auto_time_to_school,"np.where(persons.school_zone_id>=0,_auto_time_to_school + _auto_time_return,0)" diff --git a/activitysim/examples/example_semcog/configs/annotate_persons_workplace.csv b/activitysim/examples/prototype_semcog/configs/annotate_persons_workplace.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/annotate_persons_workplace.csv rename to activitysim/examples/prototype_semcog/configs/annotate_persons_workplace.csv index 04d0c37b00..36d6a01f8f 100755 --- a/activitysim/examples/example_semcog/configs/annotate_persons_workplace.csv +++ b/activitysim/examples/prototype_semcog/configs/annotate_persons_workplace.csv @@ -1,32 +1,32 @@ -Description,Target,Expression -#,, annotate persons table after workplace_location model has run -,distance_to_work,"np.where(persons.workplace_zone_id>=0,skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, 'DIST'),np.nan)" -workplace_in_cbd,workplace_in_cbd,"reindex(land_use.AreaType, persons.workplace_zone_id) < setting('cbd_threshold')" -work_zone_area_type,work_zone_area_type,"reindex(land_use.AreaType, persons.workplace_zone_id)" -#,, auto time to work - free flow travel time in both directions. MTC TM1 was MD and MD -#,,roundtrip_auto_time_to_work -,_auto_time_home_to_work,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('SOV_TIME', 'MD'))" -,_auto_time_work_to_home,"skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('SOV_TIME', 'MD'))" -,roundtrip_auto_time_to_work,"np.where(persons.workplace_zone_id>=0,_auto_time_home_to_work + _auto_time_work_to_home,0)" -#,,_roundtrip_walk_time_to_work -,_MAX_TIME_TO_WORK,999 -,_WALK_SPEED_MPH,3 -,_walk_time_home_to_work,"60 * skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, 'DISTWALK')/_WALK_SPEED_MPH" -,_walk_time_work_to_home,"60 * skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, 'DISTWALK')/_WALK_SPEED_MPH" -,_work_walk_available,(_walk_time_home_to_work > 0) & (_walk_time_work_to_home > 0) -,_roundtrip_walk_time_to_work,"np.where(_work_walk_available, _walk_time_home_to_work + _walk_time_work_to_home, _MAX_TIME_TO_WORK)" -#,,_roundtrip_transit_time_to_work -,_transit_ivt_home_to_work,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_IVT', 'MD'))" -,_transit_ivt_work_to_home,"skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_IVT', 'MD'))" -,_work_transit_available,(_transit_ivt_home_to_work > 0) & (_transit_ivt_work_to_home > 0) -,_transit_iwait,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_IWAIT', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_IWAIT', 'MD'))" -,_transit_xwait,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_XWAIT', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_XWAIT', 'MD'))" -,_transit_waux,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WAUX', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WAUX', 'MD'))" -,_transit_wacc,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WACC', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WACC', 'MD'))" -,_transit_wegr,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WEGR', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WEGR', 'MD'))" -,_roundtrip_transit_time_to_work,_transit_ivt_home_to_work + _transit_ivt_work_to_home + _transit_iwait + _transit_xwait + _transit_waux + _transit_wacc + _transit_wegr -#,,work_auto_savings_ratio -,_min_work_walk_transit,"np.where(_work_transit_available, np.minimum(_roundtrip_transit_time_to_work, _roundtrip_walk_time_to_work), _roundtrip_walk_time_to_work)" -,work_auto_savings,"np.where(persons.is_worker, _min_work_walk_transit - roundtrip_auto_time_to_work, 0)" -#,,auto savings over walk or transit capped at 120 and normalized to unity -,work_auto_savings_ratio,"(work_auto_savings / 120.0).clip(-1.0, 1.0)" +Description,Target,Expression +#,, annotate persons table after workplace_location model has run +,distance_to_work,"np.where(persons.workplace_zone_id>=0,skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, 'DIST'),np.nan)" +workplace_in_cbd,workplace_in_cbd,"reindex(land_use.AreaType, persons.workplace_zone_id) < setting('cbd_threshold')" +work_zone_area_type,work_zone_area_type,"reindex(land_use.AreaType, persons.workplace_zone_id)" +#,, auto time to work - free flow travel time in both directions. MTC TM1 was MD and MD +#,,roundtrip_auto_time_to_work +,_auto_time_home_to_work,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('SOV_TIME', 'MD'))" +,_auto_time_work_to_home,"skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('SOV_TIME', 'MD'))" +,roundtrip_auto_time_to_work,"np.where(persons.workplace_zone_id>=0,_auto_time_home_to_work + _auto_time_work_to_home,0)" +#,,_roundtrip_walk_time_to_work +,_MAX_TIME_TO_WORK,999 +,_WALK_SPEED_MPH,3 +,_walk_time_home_to_work,"60 * skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, 'DISTWALK')/_WALK_SPEED_MPH" +,_walk_time_work_to_home,"60 * skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, 'DISTWALK')/_WALK_SPEED_MPH" +,_work_walk_available,(_walk_time_home_to_work > 0) & (_walk_time_work_to_home > 0) +,_roundtrip_walk_time_to_work,"np.where(_work_walk_available, _walk_time_home_to_work + _walk_time_work_to_home, _MAX_TIME_TO_WORK)" +#,,_roundtrip_transit_time_to_work +,_transit_ivt_home_to_work,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_IVT', 'MD'))" +,_transit_ivt_work_to_home,"skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_IVT', 'MD'))" +,_work_transit_available,(_transit_ivt_home_to_work > 0) & (_transit_ivt_work_to_home > 0) +,_transit_iwait,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_IWAIT', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_IWAIT', 'MD'))" +,_transit_xwait,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_XWAIT', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_XWAIT', 'MD'))" +,_transit_waux,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WAUX', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WAUX', 'MD'))" +,_transit_wacc,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WACC', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WACC', 'MD'))" +,_transit_wegr,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WLK_TRN_WEGR', 'MD')) + skim_dict.lookup(persons.workplace_zone_id, persons.home_zone_id, ('WLK_TRN_WEGR', 'MD'))" +,_roundtrip_transit_time_to_work,_transit_ivt_home_to_work + _transit_ivt_work_to_home + _transit_iwait + _transit_xwait + _transit_waux + _transit_wacc + _transit_wegr +#,,work_auto_savings_ratio +,_min_work_walk_transit,"np.where(_work_transit_available, np.minimum(_roundtrip_transit_time_to_work, _roundtrip_walk_time_to_work), _roundtrip_walk_time_to_work)" +,work_auto_savings,"np.where(persons.is_worker, _min_work_walk_transit - roundtrip_auto_time_to_work, 0)" +#,,auto savings over walk or transit capped at 120 and normalized to unity +,work_auto_savings_ratio,"(work_auto_savings / 120.0).clip(-1.0, 1.0)" diff --git a/activitysim/examples/example_semcog/configs/atwork_subtour_destination.csv b/activitysim/examples/prototype_semcog/configs/atwork_subtour_destination.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/atwork_subtour_destination.csv rename to activitysim/examples/prototype_semcog/configs/atwork_subtour_destination.csv index 7f97ca84c7..c0e44b6796 100755 --- a/activitysim/examples/example_semcog/configs/atwork_subtour_destination.csv +++ b/activitysim/examples/prototype_semcog/configs/atwork_subtour_destination.csv @@ -1,12 +1,12 @@ -Label,Description,Expression,atwork -local_dist,local_dist,_DIST@skims['DIST'],1 -util_dist,util_dist,@_DIST,coef_dist_atwork -util_dist_squared,util_dist_squared,"@(_DIST).clip(0,20)**2",coef_dist_squared_atwork -util_dist_cubed,util_dist_cubed,"@(_DIST).clip(0,20)**3",coef_dist_cubed_atwork -util_dist_logged,util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_atwork -# util_size_variable_atwork,Size variable atwork,@df['atwork'].apply(np.log1p),coef_size_variable_atwork -# util_no_attractions_atwork_size_variable_is_0,"No attractions, atwork size variable is 0",atwork==0,coef_no_attractions_atwork_size_variable_is_0 -util_size_variable_atwork,Size variable atwork,@df['size_term'].apply(np.log1p),coef_size_variable_atwork -util_no_attractions_atwork_size_variable_is_0,"No attractions, atwork size variable is 0",size_term==0,coef_no_attractions_atwork_size_variable_is_0 -util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum_atwork -util_sample_of_alternatives_correction_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",coef_sample_of_alternatives_correction_factor +Label,Description,Expression,atwork +local_dist,local_dist,_DIST@skims['DIST'],1 +util_dist,util_dist,@_DIST,coef_dist_atwork +util_dist_squared,util_dist_squared,"@(_DIST).clip(0,20)**2",coef_dist_squared_atwork +util_dist_cubed,util_dist_cubed,"@(_DIST).clip(0,20)**3",coef_dist_cubed_atwork +util_dist_logged,util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_atwork +# util_size_variable_atwork,Size variable atwork,@df['atwork'].apply(np.log1p),coef_size_variable_atwork +# util_no_attractions_atwork_size_variable_is_0,"No attractions, atwork size variable is 0",atwork==0,coef_no_attractions_atwork_size_variable_is_0 +util_size_variable_atwork,Size variable atwork,@df['size_term'].apply(np.log1p),coef_size_variable_atwork +util_no_attractions_atwork_size_variable_is_0,"No attractions, atwork size variable is 0",size_term==0,coef_no_attractions_atwork_size_variable_is_0 +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_choice_logsum_atwork +util_sample_of_alternatives_correction_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",coef_sample_of_alternatives_correction_factor diff --git a/activitysim/examples/example_semcog/configs/atwork_subtour_destination.yaml b/activitysim/examples/prototype_semcog/configs/atwork_subtour_destination.yaml similarity index 96% rename from activitysim/examples/example_semcog/configs/atwork_subtour_destination.yaml rename to activitysim/examples/prototype_semcog/configs/atwork_subtour_destination.yaml index 4697a3a075..ea7a48e378 100755 --- a/activitysim/examples/example_semcog/configs/atwork_subtour_destination.yaml +++ b/activitysim/examples/prototype_semcog/configs/atwork_subtour_destination.yaml @@ -1,26 +1,26 @@ - -SPEC: atwork_subtour_destination.csv -SAMPLE_SPEC: atwork_subtour_destination_sample.csv -COEFFICIENTS: atwork_subtour_destination_coeffs.csv - - -SAMPLE_SIZE: 30 - -SIMULATE_CHOOSER_COLUMNS: - - person_id - - income_segment - - workplace_zone_id - -LOGSUM_SETTINGS: tour_mode_choice.yaml - -# model-specific logsum-related settings -CHOOSER_ORIG_COL_NAME: workplace_zone_id -ALT_DEST_COL_NAME: alt_dest -IN_PERIOD: 14 -OUT_PERIOD: 14 - -# optional (comment out if not desired) -DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum - -# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table -DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample + +SPEC: atwork_subtour_destination.csv +SAMPLE_SPEC: atwork_subtour_destination_sample.csv +COEFFICIENTS: atwork_subtour_destination_coeffs.csv + + +SAMPLE_SIZE: 30 + +SIMULATE_CHOOSER_COLUMNS: + - person_id + - income_segment + - workplace_zone_id + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: workplace_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 14 + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample diff --git a/activitysim/examples/example_semcog/configs/atwork_subtour_destination_coeffs.csv b/activitysim/examples/prototype_semcog/configs/atwork_subtour_destination_coeffs.csv similarity index 97% rename from activitysim/examples/example_semcog/configs/atwork_subtour_destination_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/atwork_subtour_destination_coeffs.csv index 0a37e30c44..158789c9da 100755 --- a/activitysim/examples/example_semcog/configs/atwork_subtour_destination_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/atwork_subtour_destination_coeffs.csv @@ -1,9 +1,9 @@ -coefficient_name,value,constrain -coef_dist_atwork,-0.021259921,F -coef_dist_squared_atwork,-0.017765289,F -coef_dist_cubed_atwork,0.000703328,F -coef_dist_logged_atwork,-0.844157837,F -coef_size_variable_atwork,1,T -coef_no_attractions_atwork_size_variable_is_0,-999,T -coef_mode_choice_logsum_atwork,0.79,F -coef_sample_of_alternatives_correction_factor,1,T +coefficient_name,value,constrain +coef_dist_atwork,-0.021259921,F +coef_dist_squared_atwork,-0.017765289,F +coef_dist_cubed_atwork,0.000703328,F +coef_dist_logged_atwork,-0.844157837,F +coef_size_variable_atwork,1,T +coef_no_attractions_atwork_size_variable_is_0,-999,T +coef_mode_choice_logsum_atwork,0.79,F +coef_sample_of_alternatives_correction_factor,1,T diff --git a/activitysim/examples/example_semcog/configs/atwork_subtour_destination_sample.csv b/activitysim/examples/prototype_semcog/configs/atwork_subtour_destination_sample.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/atwork_subtour_destination_sample.csv rename to activitysim/examples/prototype_semcog/configs/atwork_subtour_destination_sample.csv index 944956e60c..bacf3e5c27 100755 --- a/activitysim/examples/example_semcog/configs/atwork_subtour_destination_sample.csv +++ b/activitysim/examples/prototype_semcog/configs/atwork_subtour_destination_sample.csv @@ -1,10 +1,10 @@ -Label,Description,Expression,atwork -local_dist,local_dist,_DIST@skims['DIST'],1 -util_dist,util_dist,@_DIST,coef_dist_atwork -util_dist_squared,util_dist_squared,"@(_DIST).clip(0,20)**2",coef_dist_squared_atwork -util_dist_cubed,util_dist_cubed,"@(_DIST).clip(0,20)**3",coef_dist_cubed_atwork -util_dist_logged,util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_atwork -# util_size_variable_atwork,Size variable atwork,@df['atwork'].apply(np.log1p),coef_size_variable_atwork -# util_no_attractions_atwork_size_variable_is_0,"No attractions, atwork size variable is 0",atwork==0,coef_no_attractions_atwork_size_variable_is_0 -util_size_variable_atwork,Size variable atwork,@df['size_term'].apply(np.log1p),coef_size_variable_atwork -util_no_attractions_atwork_size_variable_is_0,"No attractions, atwork size variable is 0",size_term==0,coef_no_attractions_atwork_size_variable_is_0 +Label,Description,Expression,atwork +local_dist,local_dist,_DIST@skims['DIST'],1 +util_dist,util_dist,@_DIST,coef_dist_atwork +util_dist_squared,util_dist_squared,"@(_DIST).clip(0,20)**2",coef_dist_squared_atwork +util_dist_cubed,util_dist_cubed,"@(_DIST).clip(0,20)**3",coef_dist_cubed_atwork +util_dist_logged,util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_atwork +# util_size_variable_atwork,Size variable atwork,@df['atwork'].apply(np.log1p),coef_size_variable_atwork +# util_no_attractions_atwork_size_variable_is_0,"No attractions, atwork size variable is 0",atwork==0,coef_no_attractions_atwork_size_variable_is_0 +util_size_variable_atwork,Size variable atwork,@df['size_term'].apply(np.log1p),coef_size_variable_atwork +util_no_attractions_atwork_size_variable_is_0,"No attractions, atwork size variable is 0",size_term==0,coef_no_attractions_atwork_size_variable_is_0 diff --git a/activitysim/examples/example_semcog/configs/atwork_subtour_frequency.csv b/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/atwork_subtour_frequency.csv rename to activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency.csv index 154c1d8972..a4eec1a888 100755 --- a/activitysim/examples/example_semcog/configs/atwork_subtour_frequency.csv +++ b/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency.csv @@ -1,23 +1,23 @@ -Label,Expression,no_subtours,eat,business1,maint,business2,eat_business -util_dummy_for_full_time_worker,pemploy==1,coefficient_dummy_for_full_time_worker_no_subtours,coefficient_dummy_for_full_time_worker_eat,coefficient_dummy_for_full_time_worker_business1,coefficient_dummy_for_full_time_worker_maint,coefficient_dummy_for_full_time_worker_business2,coefficient_dummy_for_full_time_worker_eat_business -util_dummy_for_non_full_time_worker,pemploy!=1,coefficient_dummy_for_non_full_time_worker_no_subtours,coefficient_dummy_for_non_full_time_worker_eat,coefficient_dummy_for_non_full_time_worker_business1,coefficient_dummy_for_non_full_time_worker_maint,coefficient_dummy_for_non_full_time_worker_business2,coefficient_dummy_for_non_full_time_worker_eat_business -util_dummy_for_non_workers,"ptype in [4, 5]",coefficient_dummy_for_non_workers_no_subtours,coefficient_dummy_for_non_workers_eat,coefficient_dummy_for_non_workers_business1,coefficient_dummy_for_non_workers_maint,coefficient_dummy_for_non_workers_business2,coefficient_dummy_for_non_workers_eat_business -util_medium_hh_income_dummy,income_segment == 2,coefficient_medium_hh_income_dummy_no_subtours,coefficient_medium_hh_income_dummy_eat,coefficient_medium_hh_income_dummy_business1,coefficient_medium_hh_income_dummy_maint,coefficient_medium_hh_income_dummy_business2,coefficient_medium_hh_income_dummy_eat_business -util_high_hh_income_dummy,(income_segment > 2) & (income_segment < 5),coefficient_high_hh_income_dummy_no_subtours,coefficient_high_hh_income_dummy_eat,coefficient_high_hh_income_dummy_business1,coefficient_high_hh_income_dummy_maint,coefficient_high_hh_income_dummy_business2,coefficient_high_hh_income_dummy_eat_business -util_zero_cars_owned_by_hh_dummy, auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business -util_individual_discretionary_tours_made_by_full_time_worker,@(df.pemploy==1)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business -util_individual_discretionary_tours_made_by_part_time_worker,@(df.pemploy==2)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business -util_individual_eating_out_tours_made_by_person,num_eatout_tours,coefficient_individual_eating_out_tours_made_by_person_no_subtours,coefficient_individual_eating_out_tours_made_by_person_eat,coefficient_individual_eating_out_tours_made_by_person_business1,coefficient_individual_eating_out_tours_made_by_person_maint,coefficient_individual_eating_out_tours_made_by_person_business2,coefficient_individual_eating_out_tours_made_by_person_eat_business -util_main_shop_escort_tours_allocated_to_full_time_worker,@(df.pemploy==1)*df.num_maint_shop_escort,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business -util_main_shop_escort_tours_allocated_to_part_time_worker,@(df.pemploy==2)*df.num_maint_shop_escort,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business -util_participation_in_joint_shop_main_eat_tours,num_joint_maint_shop_eat,coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,coefficient_participation_in_joint_shop_main_eat_tours_eat,coefficient_participation_in_joint_shop_main_eat_tours_business1,coefficient_participation_in_joint_shop_main_eat_tours_maint,coefficient_participation_in_joint_shop_main_eat_tours_business2,coefficient_participation_in_joint_shop_main_eat_tours_eat_business -util_participation_in_joint_discretionary_tours,num_joint_discr,coefficient_participation_in_joint_discretionary_tours_no_subtours,coefficient_participation_in_joint_discretionary_tours_eat,coefficient_participation_in_joint_discretionary_tours_business1,coefficient_participation_in_joint_discretionary_tours_maint,coefficient_participation_in_joint_discretionary_tours_business2,coefficient_participation_in_joint_discretionary_tours_eat_business -util_log_of_the_work_tour_duration,@np.log((df.duration/2.25)+0.5),coefficient_log_of_the_work_tour_duration_no_subtours,coefficient_log_of_the_work_tour_duration_eat,coefficient_log_of_the_work_tour_duration_business1,coefficient_log_of_the_work_tour_duration_maint,coefficient_log_of_the_work_tour_duration_business2,coefficient_log_of_the_work_tour_duration_eat_business -util_dummy_for_drive_alone_mode_for_work_tour,work_tour_is_SOV,coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business -util_two_work_tours_by_person,num_work_tours==2,coefficient_two_work_tours_by_person_no_subtours,coefficient_two_work_tours_by_person_eat,coefficient_two_work_tours_by_person_business1,coefficient_two_work_tours_by_person_maint,coefficient_two_work_tours_by_person_business2,coefficient_two_work_tours_by_person_eat_business -util_workplace_urban_area_dummy,work_zone_area_type<4,coefficient_workplace_urban_area_dummy_no_subtours,coefficient_workplace_urban_area_dummy_eat,coefficient_workplace_urban_area_dummy_business1,coefficient_workplace_urban_area_dummy_maint,coefficient_workplace_urban_area_dummy_business2,coefficient_workplace_urban_area_dummy_eat_business -util_workplace_suburban_area_dummy,(work_zone_area_type>3) & (work_zone_area_type<6),coefficient_workplace_suburban_area_dummy_no_subtours,coefficient_workplace_suburban_area_dummy_eat,coefficient_workplace_suburban_area_dummy_business1,coefficient_workplace_suburban_area_dummy_maint,coefficient_workplace_suburban_area_dummy_business2,coefficient_workplace_suburban_area_dummy_eat_business -util_auto_accessibility_to_retail_for_work_taz,auOpRetail,coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,coefficient_auto_accessibility_to_retail_for_work_taz_eat,coefficient_auto_accessibility_to_retail_for_work_taz_business1,coefficient_auto_accessibility_to_retail_for_work_taz_maint,coefficient_auto_accessibility_to_retail_for_work_taz_business2,coefficient_auto_accessibility_to_retail_for_work_taz_eat_business -util_walk_accessibility_to_retail_for_work_taz,nmRetail,coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,coefficient_walk_accessibility_to_retail_for_work_taz_eat,coefficient_walk_accessibility_to_retail_for_work_taz_business1,coefficient_walk_accessibility_to_retail_for_work_taz_maint,coefficient_walk_accessibility_to_retail_for_work_taz_business2,coefficient_walk_accessibility_to_retail_for_work_taz_eat_business -util_dummy_for_worker_or_student_with_non_mandatory_tour,(is_worker | is_student) * num_non_mand,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business -util_at_work_sub_tour_alternative_specific_constant,1,coefficient_at_work_sub_tour_alternative_specific_constant_no_subtours,coefficient_at_work_sub_tour_alternative_specific_constant_eat,coefficient_at_work_sub_tour_alternative_specific_constant_business1,coefficient_at_work_sub_tour_alternative_specific_constant_maint,coefficient_at_work_sub_tour_alternative_specific_constant_business2,coefficient_at_work_sub_tour_alternative_specific_constant_eat_business +Label,Expression,no_subtours,eat,business1,maint,business2,eat_business +util_dummy_for_full_time_worker,pemploy==1,coefficient_dummy_for_full_time_worker_no_subtours,coefficient_dummy_for_full_time_worker_eat,coefficient_dummy_for_full_time_worker_business1,coefficient_dummy_for_full_time_worker_maint,coefficient_dummy_for_full_time_worker_business2,coefficient_dummy_for_full_time_worker_eat_business +util_dummy_for_non_full_time_worker,pemploy!=1,coefficient_dummy_for_non_full_time_worker_no_subtours,coefficient_dummy_for_non_full_time_worker_eat,coefficient_dummy_for_non_full_time_worker_business1,coefficient_dummy_for_non_full_time_worker_maint,coefficient_dummy_for_non_full_time_worker_business2,coefficient_dummy_for_non_full_time_worker_eat_business +util_dummy_for_non_workers,"ptype in [4, 5]",coefficient_dummy_for_non_workers_no_subtours,coefficient_dummy_for_non_workers_eat,coefficient_dummy_for_non_workers_business1,coefficient_dummy_for_non_workers_maint,coefficient_dummy_for_non_workers_business2,coefficient_dummy_for_non_workers_eat_business +util_medium_hh_income_dummy,income_segment == 2,coefficient_medium_hh_income_dummy_no_subtours,coefficient_medium_hh_income_dummy_eat,coefficient_medium_hh_income_dummy_business1,coefficient_medium_hh_income_dummy_maint,coefficient_medium_hh_income_dummy_business2,coefficient_medium_hh_income_dummy_eat_business +util_high_hh_income_dummy,(income_segment > 2) & (income_segment < 5),coefficient_high_hh_income_dummy_no_subtours,coefficient_high_hh_income_dummy_eat,coefficient_high_hh_income_dummy_business1,coefficient_high_hh_income_dummy_maint,coefficient_high_hh_income_dummy_business2,coefficient_high_hh_income_dummy_eat_business +util_zero_cars_owned_by_hh_dummy, auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business +util_individual_discretionary_tours_made_by_full_time_worker,@(df.pemploy==1)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business +util_individual_discretionary_tours_made_by_part_time_worker,@(df.pemploy==2)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business +util_individual_eating_out_tours_made_by_person,num_eatout_tours,coefficient_individual_eating_out_tours_made_by_person_no_subtours,coefficient_individual_eating_out_tours_made_by_person_eat,coefficient_individual_eating_out_tours_made_by_person_business1,coefficient_individual_eating_out_tours_made_by_person_maint,coefficient_individual_eating_out_tours_made_by_person_business2,coefficient_individual_eating_out_tours_made_by_person_eat_business +util_main_shop_escort_tours_allocated_to_full_time_worker,@(df.pemploy==1)*df.num_maint_shop_escort,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business +util_main_shop_escort_tours_allocated_to_part_time_worker,@(df.pemploy==2)*df.num_maint_shop_escort,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business +util_participation_in_joint_shop_main_eat_tours,num_joint_maint_shop_eat,coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,coefficient_participation_in_joint_shop_main_eat_tours_eat,coefficient_participation_in_joint_shop_main_eat_tours_business1,coefficient_participation_in_joint_shop_main_eat_tours_maint,coefficient_participation_in_joint_shop_main_eat_tours_business2,coefficient_participation_in_joint_shop_main_eat_tours_eat_business +util_participation_in_joint_discretionary_tours,num_joint_discr,coefficient_participation_in_joint_discretionary_tours_no_subtours,coefficient_participation_in_joint_discretionary_tours_eat,coefficient_participation_in_joint_discretionary_tours_business1,coefficient_participation_in_joint_discretionary_tours_maint,coefficient_participation_in_joint_discretionary_tours_business2,coefficient_participation_in_joint_discretionary_tours_eat_business +util_log_of_the_work_tour_duration,@np.log((df.duration/2.25)+0.5),coefficient_log_of_the_work_tour_duration_no_subtours,coefficient_log_of_the_work_tour_duration_eat,coefficient_log_of_the_work_tour_duration_business1,coefficient_log_of_the_work_tour_duration_maint,coefficient_log_of_the_work_tour_duration_business2,coefficient_log_of_the_work_tour_duration_eat_business +util_dummy_for_drive_alone_mode_for_work_tour,work_tour_is_SOV,coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business +util_two_work_tours_by_person,num_work_tours==2,coefficient_two_work_tours_by_person_no_subtours,coefficient_two_work_tours_by_person_eat,coefficient_two_work_tours_by_person_business1,coefficient_two_work_tours_by_person_maint,coefficient_two_work_tours_by_person_business2,coefficient_two_work_tours_by_person_eat_business +util_workplace_urban_area_dummy,work_zone_area_type<4,coefficient_workplace_urban_area_dummy_no_subtours,coefficient_workplace_urban_area_dummy_eat,coefficient_workplace_urban_area_dummy_business1,coefficient_workplace_urban_area_dummy_maint,coefficient_workplace_urban_area_dummy_business2,coefficient_workplace_urban_area_dummy_eat_business +util_workplace_suburban_area_dummy,(work_zone_area_type>3) & (work_zone_area_type<6),coefficient_workplace_suburban_area_dummy_no_subtours,coefficient_workplace_suburban_area_dummy_eat,coefficient_workplace_suburban_area_dummy_business1,coefficient_workplace_suburban_area_dummy_maint,coefficient_workplace_suburban_area_dummy_business2,coefficient_workplace_suburban_area_dummy_eat_business +util_auto_accessibility_to_retail_for_work_taz,auOpRetail,coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,coefficient_auto_accessibility_to_retail_for_work_taz_eat,coefficient_auto_accessibility_to_retail_for_work_taz_business1,coefficient_auto_accessibility_to_retail_for_work_taz_maint,coefficient_auto_accessibility_to_retail_for_work_taz_business2,coefficient_auto_accessibility_to_retail_for_work_taz_eat_business +util_walk_accessibility_to_retail_for_work_taz,nmRetail,coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,coefficient_walk_accessibility_to_retail_for_work_taz_eat,coefficient_walk_accessibility_to_retail_for_work_taz_business1,coefficient_walk_accessibility_to_retail_for_work_taz_maint,coefficient_walk_accessibility_to_retail_for_work_taz_business2,coefficient_walk_accessibility_to_retail_for_work_taz_eat_business +util_dummy_for_worker_or_student_with_non_mandatory_tour,(is_worker | is_student) * num_non_mand,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business +util_at_work_sub_tour_alternative_specific_constant,1,coefficient_at_work_sub_tour_alternative_specific_constant_no_subtours,coefficient_at_work_sub_tour_alternative_specific_constant_eat,coefficient_at_work_sub_tour_alternative_specific_constant_business1,coefficient_at_work_sub_tour_alternative_specific_constant_maint,coefficient_at_work_sub_tour_alternative_specific_constant_business2,coefficient_at_work_sub_tour_alternative_specific_constant_eat_business diff --git a/activitysim/examples/example_psrc/configs/atwork_subtour_frequency.yaml b/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency.yaml similarity index 95% rename from activitysim/examples/example_psrc/configs/atwork_subtour_frequency.yaml rename to activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency.yaml index 125bf2c1fd..1b5d27101d 100755 --- a/activitysim/examples/example_psrc/configs/atwork_subtour_frequency.yaml +++ b/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency.yaml @@ -1,11 +1,11 @@ - -SPEC: atwork_subtour_frequency.csv -COEFFICIENTS: atwork_subtour_frequency_coeffs.csv - -preprocessor: - SPEC: atwork_subtour_frequency_annotate_tours_preprocessor - DF: df - TABLES: - - land_use - - tours - - joint_tour_participants + +SPEC: atwork_subtour_frequency.csv +COEFFICIENTS: atwork_subtour_frequency_coeffs.csv + +preprocessor: + SPEC: atwork_subtour_frequency_annotate_tours_preprocessor + DF: df + TABLES: + - land_use + - tours + - joint_tour_participants diff --git a/activitysim/examples/example_semcog/configs/atwork_subtour_frequency_alternatives.csv b/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency_alternatives.csv similarity index 96% rename from activitysim/examples/example_semcog/configs/atwork_subtour_frequency_alternatives.csv rename to activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency_alternatives.csv index ed7c13c58d..ba9941919d 100755 --- a/activitysim/examples/example_semcog/configs/atwork_subtour_frequency_alternatives.csv +++ b/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency_alternatives.csv @@ -1,8 +1,8 @@ -#,,,alt file for building tours even though simulation is simple_simulate not interaction_simulate -alt,eat,business,maint -no_subtours,0,0,0 -eat,1,0,0 -business1,0,1,0 -maint,0,0,1 -business2,0,2,0 -eat_business,1,1,0 +#,,,alt file for building tours even though simulation is simple_simulate not interaction_simulate +alt,eat,business,maint +no_subtours,0,0,0 +eat,1,0,0 +business1,0,1,0 +maint,0,0,1 +business2,0,2,0 +eat_business,1,1,0 diff --git a/activitysim/examples/example_semcog/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv index 34728e6a7d..ec500df508 100755 --- a/activitysim/examples/example_semcog/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency_annotate_tours_preprocessor.csv @@ -1,9 +1,9 @@ -Description,Target,Expression -,num_maint_shop_escort,df.num_maint_tours+df.num_shop_tours+df.num_escort_tours -#,num_joint_maint_shop_eat,"reindex_i(tours[(tours.tour_category=='joint') & tours.tour_type.isin(['othmaint', 'shopping', 'eatout'])].groupby('person_id').size(), df.person_id)" -#,num_eatout_tours,"reindex_i(tours[~tours.is_joint & (tours.tour_type==EATOUT_TOUR)].groupby('person_id').size(), df.person_id)" -joint tour participants annotated with tour type,_PARTICIPANTS,"pd.merge(joint_tour_participants[['tour_id', 'person_id']], tours[['tour_type']], left_on='tour_id', right_index=True, how='left')" -,num_joint_discr,"reindex_i(_PARTICIPANTS[_PARTICIPANTS.tour_type=='othdiscr'].groupby('person_id').size(), df.person_id)" -,num_joint_maint_shop_eat,"reindex_i(_PARTICIPANTS[_PARTICIPANTS.tour_type.isin(['othmaint', 'shopping', 'eatout'])].groupby('person_id').size(), df.person_id)" -#,, -,work_tour_is_SOV,df.tour_mode.isin(['DRIVEALONE']) +Description,Target,Expression +,num_maint_shop_escort,df.num_maint_tours+df.num_shop_tours+df.num_escort_tours +#,num_joint_maint_shop_eat,"reindex_i(tours[(tours.tour_category=='joint') & tours.tour_type.isin(['othmaint', 'shopping', 'eatout'])].groupby('person_id').size(), df.person_id)" +#,num_eatout_tours,"reindex_i(tours[~tours.is_joint & (tours.tour_type==EATOUT_TOUR)].groupby('person_id').size(), df.person_id)" +joint tour participants annotated with tour type,_PARTICIPANTS,"pd.merge(joint_tour_participants[['tour_id', 'person_id']], tours[['tour_type']], left_on='tour_id', right_index=True, how='left')" +,num_joint_discr,"reindex_i(_PARTICIPANTS[_PARTICIPANTS.tour_type=='othdiscr'].groupby('person_id').size(), df.person_id)" +,num_joint_maint_shop_eat,"reindex_i(_PARTICIPANTS[_PARTICIPANTS.tour_type.isin(['othmaint', 'shopping', 'eatout'])].groupby('person_id').size(), df.person_id)" +#,, +,work_tour_is_SOV,df.tour_mode.isin(['DRIVEALONE']) diff --git a/activitysim/examples/example_semcog/configs/atwork_subtour_frequency_coeffs.csv b/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency_coeffs.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/atwork_subtour_frequency_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency_coeffs.csv index 79640c1d29..c59a4e2ee3 100755 --- a/activitysim/examples/example_semcog/configs/atwork_subtour_frequency_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/atwork_subtour_frequency_coeffs.csv @@ -1,133 +1,133 @@ -coefficient_name,value,constrain -coefficient_dummy_for_full_time_worker_business1,-7.375,F -coefficient_dummy_for_full_time_worker_business2,-14.28,F -coefficient_dummy_for_full_time_worker_eat,-7.28,F -coefficient_dummy_for_full_time_worker_eat_business,-14.79,F -coefficient_dummy_for_full_time_worker_maint,-8.093,F -coefficient_dummy_for_full_time_worker_no_subtours,-0.6,F -coefficient_dummy_for_non_full_time_worker_business1,-8.319,F -coefficient_dummy_for_non_full_time_worker_business2,-14.28,F -coefficient_dummy_for_non_full_time_worker_eat,-8.604,F -coefficient_dummy_for_non_full_time_worker_eat_business,-14.79,F -coefficient_dummy_for_non_full_time_worker_maint,-8.214,F -coefficient_dummy_for_non_full_time_worker_no_subtours,-0.6,F -coefficient_dummy_for_non_workers_business1,-5,T -coefficient_dummy_for_non_workers_business2,-5,T -coefficient_dummy_for_non_workers_eat,0,T -coefficient_dummy_for_non_workers_eat_business,-5,T -coefficient_dummy_for_non_workers_maint,-5,T -coefficient_dummy_for_non_workers_no_subtours,0,T -coefficient_medium_hh_income_dummy_business1,0.5555,F -coefficient_medium_hh_income_dummy_business2,1.111,F -coefficient_medium_hh_income_dummy_eat,0.61,F -coefficient_medium_hh_income_dummy_eat_business,1.1655,F -coefficient_medium_hh_income_dummy_maint,0.1527,F -coefficient_medium_hh_income_dummy_no_subtours,0,T -coefficient_high_hh_income_dummy_business1,1.066,F -coefficient_high_hh_income_dummy_business2,2.132,F -coefficient_high_hh_income_dummy_eat,0.8693,F -coefficient_high_hh_income_dummy_eat_business,1.9353,F -coefficient_high_hh_income_dummy_maint,0.1651,F -coefficient_high_hh_income_dummy_no_subtours,0,T -coefficient_zero_cars_owned_by_hh_dummy_business1,-0.3391,F -coefficient_zero_cars_owned_by_hh_dummy_business2,0,T -coefficient_zero_cars_owned_by_hh_dummy_eat,0,T -coefficient_zero_cars_owned_by_hh_dummy_eat_business,-0.3391,F -coefficient_zero_cars_owned_by_hh_dummy_maint,0.1762,F -coefficient_zero_cars_owned_by_hh_dummy_no_subtours,0,T -coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,0.7045,F -coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,1.409,F -coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,0.2334,F -coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business,0.9379,F -coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,0.5061,F -coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,0,T -coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,0.7045,F -coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,1.409,F -coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,0.6776,F -coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business,1.3821,F -coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,0.5061,F -coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,0,T -coefficient_individual_eating_out_tours_made_by_person_business1,0.5434,F -coefficient_individual_eating_out_tours_made_by_person_business2,1.0868,F -coefficient_individual_eating_out_tours_made_by_person_eat,0.5491,F -coefficient_individual_eating_out_tours_made_by_person_eat_business,1.0925,F -coefficient_individual_eating_out_tours_made_by_person_maint,0.9166,F -coefficient_individual_eating_out_tours_made_by_person_no_subtours,0,T -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,-0.1903,F -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,-0.3806,F -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,0.052,F -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business,-0.2423,F -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,0.1446,F -coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,0,T -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,-0.1903,F -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,-0.3806,F -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,-0.3099,F -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business,-0.5002,F -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,-0.2723,F -coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,0,T -coefficient_participation_in_joint_shop_main_eat_tours_business1,0.083,F -coefficient_participation_in_joint_shop_main_eat_tours_business2,0.166,F -coefficient_participation_in_joint_shop_main_eat_tours_eat,0.2458,F -coefficient_participation_in_joint_shop_main_eat_tours_eat_business,0.3288,F -coefficient_participation_in_joint_shop_main_eat_tours_maint,0.0803,F -coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,0,T -coefficient_participation_in_joint_discretionary_tours_business1,-0.2637,F -coefficient_participation_in_joint_discretionary_tours_business2,-0.5274,F -coefficient_participation_in_joint_discretionary_tours_eat,0.3588,F -coefficient_participation_in_joint_discretionary_tours_eat_business,0.0951,F -coefficient_participation_in_joint_discretionary_tours_maint,0.5822,F -coefficient_participation_in_joint_discretionary_tours_no_subtours,0,T -coefficient_log_of_the_work_tour_duration_business1,1.142,F -coefficient_log_of_the_work_tour_duration_business2,2.284,F -coefficient_log_of_the_work_tour_duration_eat,1.55,F -coefficient_log_of_the_work_tour_duration_eat_business,2.692,F -coefficient_log_of_the_work_tour_duration_maint,1.659,F -coefficient_log_of_the_work_tour_duration_no_subtours,0,T -coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,0.9901,F -coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,1.9802,F -coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,0.4804,F -coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business,1.4705,F -coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,1.153,F -coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,0,T -coefficient_two_work_tours_by_person_business1,0.3753,F -coefficient_two_work_tours_by_person_business2,0.7506,F -coefficient_two_work_tours_by_person_eat,-0.9862,F -coefficient_two_work_tours_by_person_eat_business,-0.6109,F -coefficient_two_work_tours_by_person_maint,-0.2312,F -coefficient_two_work_tours_by_person_no_subtours,0,T -coefficient_workplace_urban_area_dummy_business1,-0.2235,F -coefficient_workplace_urban_area_dummy_business2,-0.447,F -coefficient_workplace_urban_area_dummy_eat,-0.4182,F -coefficient_workplace_urban_area_dummy_eat_business,-0.6417,F -coefficient_workplace_urban_area_dummy_maint,-0.1479,F -coefficient_workplace_urban_area_dummy_no_subtours,0,T -coefficient_workplace_suburban_area_dummy_business1,-0.1102,F -coefficient_workplace_suburban_area_dummy_business2,-0.2204,F -coefficient_workplace_suburban_area_dummy_eat,-0.2916,F -coefficient_workplace_suburban_area_dummy_eat_business,-0.4018,F -coefficient_workplace_suburban_area_dummy_maint,0,T -coefficient_workplace_suburban_area_dummy_no_subtours,0,T -coefficient_auto_accessibility_to_retail_for_work_taz_business1,0.0534,F -coefficient_auto_accessibility_to_retail_for_work_taz_business2,0.1067,F -coefficient_auto_accessibility_to_retail_for_work_taz_eat,0.015,F -coefficient_auto_accessibility_to_retail_for_work_taz_eat_business,0.0683,F -coefficient_auto_accessibility_to_retail_for_work_taz_maint,0.0265,F -coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,0,T -coefficient_walk_accessibility_to_retail_for_work_taz_business1,0,T -coefficient_walk_accessibility_to_retail_for_work_taz_business2,0,T -coefficient_walk_accessibility_to_retail_for_work_taz_eat,0.06,F -coefficient_walk_accessibility_to_retail_for_work_taz_eat_business,0.06,F -coefficient_walk_accessibility_to_retail_for_work_taz_maint,0.04,F -coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,0,T -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,0,T -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,0,T -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,0,T -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business,0,T -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,-0.3573,F -coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,0,T -coefficient_at_work_sub_tour_alternative_specific_constant_business1,-0.5372,F -coefficient_at_work_sub_tour_alternative_specific_constant_business2,-2.1337,F -coefficient_at_work_sub_tour_alternative_specific_constant_eat,0.8576,F -coefficient_at_work_sub_tour_alternative_specific_constant_eat_business,-0.9721,F -coefficient_at_work_sub_tour_alternative_specific_constant_maint,-0.6198,F +coefficient_name,value,constrain +coefficient_dummy_for_full_time_worker_business1,-7.375,F +coefficient_dummy_for_full_time_worker_business2,-14.28,F +coefficient_dummy_for_full_time_worker_eat,-7.28,F +coefficient_dummy_for_full_time_worker_eat_business,-14.79,F +coefficient_dummy_for_full_time_worker_maint,-8.093,F +coefficient_dummy_for_full_time_worker_no_subtours,-0.6,F +coefficient_dummy_for_non_full_time_worker_business1,-8.319,F +coefficient_dummy_for_non_full_time_worker_business2,-14.28,F +coefficient_dummy_for_non_full_time_worker_eat,-8.604,F +coefficient_dummy_for_non_full_time_worker_eat_business,-14.79,F +coefficient_dummy_for_non_full_time_worker_maint,-8.214,F +coefficient_dummy_for_non_full_time_worker_no_subtours,-0.6,F +coefficient_dummy_for_non_workers_business1,-5,T +coefficient_dummy_for_non_workers_business2,-5,T +coefficient_dummy_for_non_workers_eat,0,T +coefficient_dummy_for_non_workers_eat_business,-5,T +coefficient_dummy_for_non_workers_maint,-5,T +coefficient_dummy_for_non_workers_no_subtours,0,T +coefficient_medium_hh_income_dummy_business1,0.5555,F +coefficient_medium_hh_income_dummy_business2,1.111,F +coefficient_medium_hh_income_dummy_eat,0.61,F +coefficient_medium_hh_income_dummy_eat_business,1.1655,F +coefficient_medium_hh_income_dummy_maint,0.1527,F +coefficient_medium_hh_income_dummy_no_subtours,0,T +coefficient_high_hh_income_dummy_business1,1.066,F +coefficient_high_hh_income_dummy_business2,2.132,F +coefficient_high_hh_income_dummy_eat,0.8693,F +coefficient_high_hh_income_dummy_eat_business,1.9353,F +coefficient_high_hh_income_dummy_maint,0.1651,F +coefficient_high_hh_income_dummy_no_subtours,0,T +coefficient_zero_cars_owned_by_hh_dummy_business1,-0.3391,F +coefficient_zero_cars_owned_by_hh_dummy_business2,0,T +coefficient_zero_cars_owned_by_hh_dummy_eat,0,T +coefficient_zero_cars_owned_by_hh_dummy_eat_business,-0.3391,F +coefficient_zero_cars_owned_by_hh_dummy_maint,0.1762,F +coefficient_zero_cars_owned_by_hh_dummy_no_subtours,0,T +coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,0.7045,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,1.409,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,0.2334,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business,0.9379,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,0.5061,F +coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,0,T +coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,0.7045,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,1.409,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,0.6776,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business,1.3821,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,0.5061,F +coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,0,T +coefficient_individual_eating_out_tours_made_by_person_business1,0.5434,F +coefficient_individual_eating_out_tours_made_by_person_business2,1.0868,F +coefficient_individual_eating_out_tours_made_by_person_eat,0.5491,F +coefficient_individual_eating_out_tours_made_by_person_eat_business,1.0925,F +coefficient_individual_eating_out_tours_made_by_person_maint,0.9166,F +coefficient_individual_eating_out_tours_made_by_person_no_subtours,0,T +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business1,-0.1903,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_business2,-0.3806,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat,0.052,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_eat_business,-0.2423,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_maint,0.1446,F +coefficient_main_shop_escort_tours_allocated_to_full_time_worker_no_subtours,0,T +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business1,-0.1903,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_business2,-0.3806,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat,-0.3099,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_eat_business,-0.5002,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_maint,-0.2723,F +coefficient_main_shop_escort_tours_allocated_to_part_time_worker_no_subtours,0,T +coefficient_participation_in_joint_shop_main_eat_tours_business1,0.083,F +coefficient_participation_in_joint_shop_main_eat_tours_business2,0.166,F +coefficient_participation_in_joint_shop_main_eat_tours_eat,0.2458,F +coefficient_participation_in_joint_shop_main_eat_tours_eat_business,0.3288,F +coefficient_participation_in_joint_shop_main_eat_tours_maint,0.0803,F +coefficient_participation_in_joint_shop_main_eat_tours_no_subtours,0,T +coefficient_participation_in_joint_discretionary_tours_business1,-0.2637,F +coefficient_participation_in_joint_discretionary_tours_business2,-0.5274,F +coefficient_participation_in_joint_discretionary_tours_eat,0.3588,F +coefficient_participation_in_joint_discretionary_tours_eat_business,0.0951,F +coefficient_participation_in_joint_discretionary_tours_maint,0.5822,F +coefficient_participation_in_joint_discretionary_tours_no_subtours,0,T +coefficient_log_of_the_work_tour_duration_business1,1.142,F +coefficient_log_of_the_work_tour_duration_business2,2.284,F +coefficient_log_of_the_work_tour_duration_eat,1.55,F +coefficient_log_of_the_work_tour_duration_eat_business,2.692,F +coefficient_log_of_the_work_tour_duration_maint,1.659,F +coefficient_log_of_the_work_tour_duration_no_subtours,0,T +coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,0.9901,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,1.9802,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,0.4804,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business,1.4705,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,1.153,F +coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,0,T +coefficient_two_work_tours_by_person_business1,0.3753,F +coefficient_two_work_tours_by_person_business2,0.7506,F +coefficient_two_work_tours_by_person_eat,-0.9862,F +coefficient_two_work_tours_by_person_eat_business,-0.6109,F +coefficient_two_work_tours_by_person_maint,-0.2312,F +coefficient_two_work_tours_by_person_no_subtours,0,T +coefficient_workplace_urban_area_dummy_business1,-0.2235,F +coefficient_workplace_urban_area_dummy_business2,-0.447,F +coefficient_workplace_urban_area_dummy_eat,-0.4182,F +coefficient_workplace_urban_area_dummy_eat_business,-0.6417,F +coefficient_workplace_urban_area_dummy_maint,-0.1479,F +coefficient_workplace_urban_area_dummy_no_subtours,0,T +coefficient_workplace_suburban_area_dummy_business1,-0.1102,F +coefficient_workplace_suburban_area_dummy_business2,-0.2204,F +coefficient_workplace_suburban_area_dummy_eat,-0.2916,F +coefficient_workplace_suburban_area_dummy_eat_business,-0.4018,F +coefficient_workplace_suburban_area_dummy_maint,0,T +coefficient_workplace_suburban_area_dummy_no_subtours,0,T +coefficient_auto_accessibility_to_retail_for_work_taz_business1,0.0534,F +coefficient_auto_accessibility_to_retail_for_work_taz_business2,0.1067,F +coefficient_auto_accessibility_to_retail_for_work_taz_eat,0.015,F +coefficient_auto_accessibility_to_retail_for_work_taz_eat_business,0.0683,F +coefficient_auto_accessibility_to_retail_for_work_taz_maint,0.0265,F +coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_business1,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_business2,0,T +coefficient_walk_accessibility_to_retail_for_work_taz_eat,0.06,F +coefficient_walk_accessibility_to_retail_for_work_taz_eat_business,0.06,F +coefficient_walk_accessibility_to_retail_for_work_taz_maint,0.04,F +coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business,0,T +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,-0.3573,F +coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,0,T +coefficient_at_work_sub_tour_alternative_specific_constant_business1,-0.5372,F +coefficient_at_work_sub_tour_alternative_specific_constant_business2,-2.1337,F +coefficient_at_work_sub_tour_alternative_specific_constant_eat,0.8576,F +coefficient_at_work_sub_tour_alternative_specific_constant_eat_business,-0.9721,F +coefficient_at_work_sub_tour_alternative_specific_constant_maint,-0.6198,F coefficient_at_work_sub_tour_alternative_specific_constant_no_subtours,0,T \ No newline at end of file diff --git a/activitysim/examples/example_semcog/configs/auto_ownership.csv b/activitysim/examples/prototype_semcog/configs/auto_ownership.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/auto_ownership.csv rename to activitysim/examples/prototype_semcog/configs/auto_ownership.csv index 66bde074b9..e9ad2c58e2 100755 --- a/activitysim/examples/example_semcog/configs/auto_ownership.csv +++ b/activitysim/examples/prototype_semcog/configs/auto_ownership.csv @@ -1,25 +1,25 @@ -Label,Description,Expression,cars0,cars1,cars2,cars3,cars4 -util_drivers_2,2 Adults (age 16+),num_drivers==2,,coef_cars1_drivers_2,coef_cars2_drivers_2,coef_cars3_drivers_2,coef_cars4_drivers_2 -util_drivers_3,3 Adults (age 16+),num_drivers==3,,coef_cars1_drivers_3,coef_cars2_drivers_3,coef_cars3_drivers_3,coef_cars4_drivers_3 -util_drivers_4_up,4+ Adults (age 16+),num_drivers>3,,coef_cars1_drivers_4_up,coef_cars2_drivers_4_up,coef_cars3_drivers_4_up,coef_cars4_drivers_4_up -util_persons_16_17,Persons age 16-17,num_children_16_to_17,,coef_cars1_persons_16_17,coef_cars2_persons_16_17,coef_cars34_persons_16_17,coef_cars34_persons_16_17 -util_persons_18_24,Persons age 18-24,num_college_age,,coef_cars1_persons_18_24,coef_cars2_persons_18_24,coef_cars34_persons_18_24,coef_cars34_persons_18_24 -util_persons_25_34,Persons age 35-34,num_young_adults,,coef_cars1_persons_25_34,coef_cars2_persons_25_34,coef_cars34_persons_25_34,coef_cars34_persons_25_34 -util_presence_children_0_4,Presence of children age 0-4,num_young_children>0,,coef_cars1_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4 -util_presence_children_5_17,Presence of children age 5-17,(num_children_5_to_15+num_children_16_to_17)>0,,coef_cars1_presence_children_5_17,coef_cars2_presence_children_5_17,coef_cars34_presence_children_5_17,coef_cars34_presence_children_5_17 -util_num_workers_clip_3,"Number of workers, capped at 3",@df.num_workers.clip(upper=3),,coef_cars1_num_workers_clip_3,coef_cars2_num_workers_clip_3,coef_cars3_num_workers_clip_3,coef_cars4_num_workers_clip_3 -util_hh_income_0_30k,"Piecewise Linear household income, $0-30k","@df.income_in_thousands.clip(0, 30)",,coef_cars1_hh_income_0_30k,coef_cars2_hh_income_0_30k,coef_cars3_hh_income_0_30k,coef_cars4_hh_income_0_30k -util_hh_income_30_75k,"Piecewise Linear household income, $30-75k","@(df.income_in_thousands-30).clip(0, 45)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up -util_hh_income_75k_up,"Piecewise Linear household income, $75k+, capped at $125k","@(df.income_in_thousands-75).clip(0, 50)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up -util_density_0_10_no_workers,"Density index up to 10, if 0 workers","@(df.num_workers==0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers -util_density_10_up_no_workers,"Density index in excess of 10, if 0 workers",@(df.num_workers==0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_no_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers -util_density_0_10_workers,"Density index up to 10, if 1+ workers","@(df.num_workers>0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers -util_density_10_up_workers,"Density index in excess of 10, if 1+ workers",@(df.num_workers>0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers -util_asc,Constants,1,,coef_cars1_asc,coef_cars2_asc,coef_cars3_asc,coef_cars4_asc -util_retail_auto_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 0 workers",(num_workers==0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers -util_retail_auto_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 1+ workers",(num_workers>0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers -util_retail_transit_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 0 workers",(num_workers==0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers -util_retail_transit_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 1+ workers",(num_workers>0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers -util_retail_non_motor_no_workers,"Retail accessibility by non-motorized, if 0 workers",(num_workers==0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor -util_retail_non_motor_workers,"Retail accessibility by non-motorized, if 1+ workers",(num_workers>0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor -util_auto_time_saving_per_worker,Auto time savings per worker to work,"@np.where(df.num_workers > 0, df.hh_work_auto_savings_ratio / df.num_workers, 0)",,coef_cars1_auto_time_saving_per_worker,coef_cars2_auto_time_saving_per_worker,coef_cars3_auto_time_saving_per_worker,coef_cars4_auto_time_saving_per_worker +Label,Description,Expression,cars0,cars1,cars2,cars3,cars4 +util_drivers_2,2 Adults (age 16+),num_drivers==2,,coef_cars1_drivers_2,coef_cars2_drivers_2,coef_cars3_drivers_2,coef_cars4_drivers_2 +util_drivers_3,3 Adults (age 16+),num_drivers==3,,coef_cars1_drivers_3,coef_cars2_drivers_3,coef_cars3_drivers_3,coef_cars4_drivers_3 +util_drivers_4_up,4+ Adults (age 16+),num_drivers>3,,coef_cars1_drivers_4_up,coef_cars2_drivers_4_up,coef_cars3_drivers_4_up,coef_cars4_drivers_4_up +util_persons_16_17,Persons age 16-17,num_children_16_to_17,,coef_cars1_persons_16_17,coef_cars2_persons_16_17,coef_cars34_persons_16_17,coef_cars34_persons_16_17 +util_persons_18_24,Persons age 18-24,num_college_age,,coef_cars1_persons_18_24,coef_cars2_persons_18_24,coef_cars34_persons_18_24,coef_cars34_persons_18_24 +util_persons_25_34,Persons age 35-34,num_young_adults,,coef_cars1_persons_25_34,coef_cars2_persons_25_34,coef_cars34_persons_25_34,coef_cars34_persons_25_34 +util_presence_children_0_4,Presence of children age 0-4,num_young_children>0,,coef_cars1_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4,coef_cars234_presence_children_0_4 +util_presence_children_5_17,Presence of children age 5-17,(num_children_5_to_15+num_children_16_to_17)>0,,coef_cars1_presence_children_5_17,coef_cars2_presence_children_5_17,coef_cars34_presence_children_5_17,coef_cars34_presence_children_5_17 +util_num_workers_clip_3,"Number of workers, capped at 3",@df.num_workers.clip(upper=3),,coef_cars1_num_workers_clip_3,coef_cars2_num_workers_clip_3,coef_cars3_num_workers_clip_3,coef_cars4_num_workers_clip_3 +util_hh_income_0_30k,"Piecewise Linear household income, $0-30k","@df.income_in_thousands.clip(0, 30)",,coef_cars1_hh_income_0_30k,coef_cars2_hh_income_0_30k,coef_cars3_hh_income_0_30k,coef_cars4_hh_income_0_30k +util_hh_income_30_75k,"Piecewise Linear household income, $30-75k","@(df.income_in_thousands-30).clip(0, 45)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up +util_hh_income_75k_up,"Piecewise Linear household income, $75k+, capped at $125k","@(df.income_in_thousands-75).clip(0, 50)",,coef_cars1_hh_income_30_up,coef_cars2_hh_income_30_up,coef_cars3_hh_income_30_up,coef_cars4_hh_income_30_up +util_density_0_10_no_workers,"Density index up to 10, if 0 workers","@(df.num_workers==0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers +util_density_10_up_no_workers,"Density index in excess of 10, if 0 workers",@(df.num_workers==0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_no_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers +util_density_0_10_workers,"Density index up to 10, if 1+ workers","@(df.num_workers>0)*df.density_index.clip(0, 10)",,coef_cars1_density_0_10_no_workers,coef_cars2_density_0_10_no_workers,coef_cars34_density_0_10_no_workers,coef_cars34_density_0_10_no_workers +util_density_10_up_workers,"Density index in excess of 10, if 1+ workers",@(df.num_workers>0)*(df.density_index-10).clip(0),,coef_cars1_density_10_up_workers,coef_cars2_density_10_up_no_workers,coef_cars34_density_10_up_no_workers,coef_cars34_density_10_up_no_workers +util_asc,Constants,1,,coef_cars1_asc,coef_cars2_asc,coef_cars3_asc,coef_cars4_asc +util_retail_auto_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 0 workers",(num_workers==0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers,coef_retail_auto_no_workers +util_retail_auto_workers,"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 1+ workers",(num_workers>0)*(0.66*auPkRetail+0.34*auOpRetail),,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers,coef_retail_auto_workers +util_retail_transit_no_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 0 workers",(num_workers==0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers,coef_retail_transit_no_workers +util_retail_transit_workers,"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 1+ workers",(num_workers>0)*(0.66*trPkRetail+0.34*trOpRetail),,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers,coef_retail_transit_workers +util_retail_non_motor_no_workers,"Retail accessibility by non-motorized, if 0 workers",(num_workers==0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor +util_retail_non_motor_workers,"Retail accessibility by non-motorized, if 1+ workers",(num_workers>0)*nmRetail,,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor,coef_retail_non_motor +util_auto_time_saving_per_worker,Auto time savings per worker to work,"@np.where(df.num_workers > 0, df.hh_work_auto_savings_ratio / df.num_workers, 0)",,coef_cars1_auto_time_saving_per_worker,coef_cars2_auto_time_saving_per_worker,coef_cars3_auto_time_saving_per_worker,coef_cars4_auto_time_saving_per_worker diff --git a/activitysim/examples/example_semcog/configs/auto_ownership.yaml b/activitysim/examples/prototype_semcog/configs/auto_ownership.yaml similarity index 94% rename from activitysim/examples/example_semcog/configs/auto_ownership.yaml rename to activitysim/examples/prototype_semcog/configs/auto_ownership.yaml index 55943670da..25f3249e61 100755 --- a/activitysim/examples/example_semcog/configs/auto_ownership.yaml +++ b/activitysim/examples/prototype_semcog/configs/auto_ownership.yaml @@ -1,6 +1,6 @@ - -SPEC: auto_ownership.csv -COEFFICIENTS: auto_ownership_coeffs.csv - -#LOGIT_TYPE: NL -LOGIT_TYPE: MNL + +SPEC: auto_ownership.csv +COEFFICIENTS: auto_ownership_coeffs.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL diff --git a/activitysim/examples/example_semcog/configs/auto_ownership_coeffs.csv b/activitysim/examples/prototype_semcog/configs/auto_ownership_coeffs.csv similarity index 97% rename from activitysim/examples/example_semcog/configs/auto_ownership_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/auto_ownership_coeffs.csv index 945d916745..adad6e9911 100755 --- a/activitysim/examples/example_semcog/configs/auto_ownership_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/auto_ownership_coeffs.csv @@ -1,68 +1,68 @@ -coefficient_name,value,constrain -coef_cars1_drivers_2,0.0,T -coef_cars1_drivers_3,0.0,T -coef_cars1_persons_16_17,0.0,T -coef_cars234_asc_marin,0.0,T -coef_cars1_persons_25_34,0.0,T -coef_cars1_num_workers_clip_3,0.0,T -coef_cars1_hh_income_30_up,0.0,T -coef_cars1_density_0_10_no_workers,0.0,T -coef_cars1_density_10_up_workers,-0.0006916064567687393,F -coef_retail_non_motor,-0.03,T -coef_cars4_asc,-2.1157137267630084,F -coef_cars3_asc,-0.5389664867851066,F -coef_cars34_persons_16_17,-1.0131713092471806,F -coef_cars2_asc,0.5089927738334394,F -coef_cars34_persons_18_24,-0.22633082627703238,F -coef_cars2_persons_18_24,-0.2779863655452459,F -coef_cars2_persons_16_17,-0.8499905464429038,F -coef_cars34_persons_25_34,-0.5072697829616668,F -coef_cars1_asc_county,-0.5660000000000001,F -coef_retail_transit_workers,-0.26599406864996145,F -coef_cars2_persons_25_34,-0.2845857507016052,F -coef_cars2_asc_county,-0.4429,F -coef_cars1_persons_18_24,0.3122834693531618,F -coef_cars34_density_0_10_no_workers,-1.0611910844303392,F -coef_retail_transit_no_workers,-0.4610891860644177,F -coef_cars1_asc_marin,-0.2434,F -coef_cars34_asc_county,-0.2372,F -coef_cars2_density_0_10_no_workers,-0.4334566014350984,F -coef_cars34_density_10_up_no_workers,-0.1766,T -coef_cars2_density_10_up_no_workers,0.22349472733850723,F -coef_cars2_density_10_up_workers,-0.1106,F -coef_cars1_density_10_up_no_workers,-0.6278796181279449,F -coef_cars2_hh_income_30_up,0.013802348149352129,F -coef_cars3_hh_income_30_up,0.01756826620528943,F -coef_cars4_hh_income_30_up,0.020241067403575532,F -coef_cars1_presence_children_5_17,0.2201929251562138,F -coef_cars1_hh_income_0_30k,0.08754817729196587,F -coef_cars2_hh_income_0_30k,0.11363199169081308,F -coef_cars3_hh_income_0_30k,0.10919644885632351,F -coef_cars4_hh_income_0_30k,0.12371561063030768,F -coef_retail_auto_no_workers,-0.27201647971031934,F -coef_cars34_asc_san_francisco,0.1458,F -coef_retail_auto_workers,-0.3440504516404184,F -coef_cars2_presence_children_5_17,0.3267549927594411,F -coef_cars2_num_workers_clip_3,0.7131266645999695,F -coef_cars1_presence_children_0_4,-0.13410330297845457,F -coef_cars1_asc_san_francisco,0.4259,F -coef_cars2_asc_san_francisco,0.4683,F -coef_cars1_auto_time_saving_per_worker,1.3138947141281918,F -coef_cars34_presence_children_5_17,0.09288972465342095,F -coef_cars3_auto_time_saving_per_worker,0.9830482363478317,F -coef_cars2_auto_time_saving_per_worker,1.0902251470013522,F -coef_cars3_num_workers_clip_3,1.000861534518726,F -coef_cars234_presence_children_0_4,0.049938987712858804,F -coef_cars4_auto_time_saving_per_worker,0.8990148505984551,F -coef_cars4_num_workers_clip_3,1.079450657634896,F -coef_cars1_asc,3.1493581264401556,F -coef_cars1_drivers_4_up,-1.4515192922101592,F -coef_cars4_drivers_2,2.4056683705077937,F -coef_cars2_drivers_2,2.817372734348654,F -coef_cars3_drivers_2,2.6801310124351496,F -coef_cars2_drivers_3,2.5819723625758484,F -coef_cars4_drivers_3,3.964857311371601,F -coef_cars3_drivers_3,4.277120564378806,F -coef_cars2_drivers_4_up,1.5182056527267993,F -coef_cars3_drivers_4_up,3.4916291197074343,F -coef_cars4_drivers_4_up,4.509213244436791,F +coefficient_name,value,constrain +coef_cars1_drivers_2,0.0,T +coef_cars1_drivers_3,0.0,T +coef_cars1_persons_16_17,0.0,T +coef_cars234_asc_marin,0.0,T +coef_cars1_persons_25_34,0.0,T +coef_cars1_num_workers_clip_3,0.0,T +coef_cars1_hh_income_30_up,0.0,T +coef_cars1_density_0_10_no_workers,0.0,T +coef_cars1_density_10_up_workers,-0.0006916064567687393,F +coef_retail_non_motor,-0.03,T +coef_cars4_asc,-2.1157137267630084,F +coef_cars3_asc,-0.5389664867851066,F +coef_cars34_persons_16_17,-1.0131713092471806,F +coef_cars2_asc,0.5089927738334394,F +coef_cars34_persons_18_24,-0.22633082627703238,F +coef_cars2_persons_18_24,-0.2779863655452459,F +coef_cars2_persons_16_17,-0.8499905464429038,F +coef_cars34_persons_25_34,-0.5072697829616668,F +coef_cars1_asc_county,-0.5660000000000001,F +coef_retail_transit_workers,-0.26599406864996145,F +coef_cars2_persons_25_34,-0.2845857507016052,F +coef_cars2_asc_county,-0.4429,F +coef_cars1_persons_18_24,0.3122834693531618,F +coef_cars34_density_0_10_no_workers,-1.0611910844303392,F +coef_retail_transit_no_workers,-0.4610891860644177,F +coef_cars1_asc_marin,-0.2434,F +coef_cars34_asc_county,-0.2372,F +coef_cars2_density_0_10_no_workers,-0.4334566014350984,F +coef_cars34_density_10_up_no_workers,-0.1766,T +coef_cars2_density_10_up_no_workers,0.22349472733850723,F +coef_cars2_density_10_up_workers,-0.1106,F +coef_cars1_density_10_up_no_workers,-0.6278796181279449,F +coef_cars2_hh_income_30_up,0.013802348149352129,F +coef_cars3_hh_income_30_up,0.01756826620528943,F +coef_cars4_hh_income_30_up,0.020241067403575532,F +coef_cars1_presence_children_5_17,0.2201929251562138,F +coef_cars1_hh_income_0_30k,0.08754817729196587,F +coef_cars2_hh_income_0_30k,0.11363199169081308,F +coef_cars3_hh_income_0_30k,0.10919644885632351,F +coef_cars4_hh_income_0_30k,0.12371561063030768,F +coef_retail_auto_no_workers,-0.27201647971031934,F +coef_cars34_asc_san_francisco,0.1458,F +coef_retail_auto_workers,-0.3440504516404184,F +coef_cars2_presence_children_5_17,0.3267549927594411,F +coef_cars2_num_workers_clip_3,0.7131266645999695,F +coef_cars1_presence_children_0_4,-0.13410330297845457,F +coef_cars1_asc_san_francisco,0.4259,F +coef_cars2_asc_san_francisco,0.4683,F +coef_cars1_auto_time_saving_per_worker,1.3138947141281918,F +coef_cars34_presence_children_5_17,0.09288972465342095,F +coef_cars3_auto_time_saving_per_worker,0.9830482363478317,F +coef_cars2_auto_time_saving_per_worker,1.0902251470013522,F +coef_cars3_num_workers_clip_3,1.000861534518726,F +coef_cars234_presence_children_0_4,0.049938987712858804,F +coef_cars4_auto_time_saving_per_worker,0.8990148505984551,F +coef_cars4_num_workers_clip_3,1.079450657634896,F +coef_cars1_asc,3.1493581264401556,F +coef_cars1_drivers_4_up,-1.4515192922101592,F +coef_cars4_drivers_2,2.4056683705077937,F +coef_cars2_drivers_2,2.817372734348654,F +coef_cars3_drivers_2,2.6801310124351496,F +coef_cars2_drivers_3,2.5819723625758484,F +coef_cars4_drivers_3,3.964857311371601,F +coef_cars3_drivers_3,4.277120564378806,F +coef_cars2_drivers_4_up,1.5182056527267993,F +coef_cars3_drivers_4_up,3.4916291197074343,F +coef_cars4_drivers_4_up,4.509213244436791,F diff --git a/activitysim/examples/example_semcog/configs/cdap.yaml b/activitysim/examples/prototype_semcog/configs/cdap.yaml similarity index 94% rename from activitysim/examples/example_semcog/configs/cdap.yaml rename to activitysim/examples/prototype_semcog/configs/cdap.yaml index 3c4cc5719a..546db913a7 100755 --- a/activitysim/examples/example_semcog/configs/cdap.yaml +++ b/activitysim/examples/prototype_semcog/configs/cdap.yaml @@ -1,36 +1,36 @@ -COEFFICIENTS: _dummy_coefficients.csv -INTERACTION_COEFFICIENTS: cdap_interaction_coefficients.csv -INDIV_AND_HHSIZE1_SPEC: cdap_indiv_and_hhsize1.csv - -FIXED_RELATIVE_PROPORTIONS_SPEC: cdap_fixed_relative_proportions.csv - - -CONSTANTS: - FULL: 1 - PART: 2 - UNIVERSITY: 3 - NONWORK: 4 - RETIRED: 5 - DRIVING: 6 - SCHOOL: 7 - PRESCHOOL: 8 - -PERSON_TYPE_MAP: - WORKER: - - 1 - - 2 - CHILD: - - 6 - - 7 - - 8 - -annotate_persons: - SPEC: annotate_persons_cdap - DF: persons - - -annotate_households: - SPEC: annotate_households_cdap - DF: households - TABLES: - - persons +COEFFICIENTS: _dummy_coefficients.csv +INTERACTION_COEFFICIENTS: cdap_interaction_coefficients.csv +INDIV_AND_HHSIZE1_SPEC: cdap_indiv_and_hhsize1.csv + +FIXED_RELATIVE_PROPORTIONS_SPEC: cdap_fixed_relative_proportions.csv + + +CONSTANTS: + FULL: 1 + PART: 2 + UNIVERSITY: 3 + NONWORK: 4 + RETIRED: 5 + DRIVING: 6 + SCHOOL: 7 + PRESCHOOL: 8 + +PERSON_TYPE_MAP: + WORKER: + - 1 + - 2 + CHILD: + - 6 + - 7 + - 8 + +annotate_persons: + SPEC: annotate_persons_cdap + DF: persons + + +annotate_households: + SPEC: annotate_households_cdap + DF: households + TABLES: + - persons diff --git a/activitysim/examples/example_semcog/configs/cdap_fixed_relative_proportions.csv b/activitysim/examples/prototype_semcog/configs/cdap_fixed_relative_proportions.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/cdap_fixed_relative_proportions.csv rename to activitysim/examples/prototype_semcog/configs/cdap_fixed_relative_proportions.csv index 6e9454da4c..c7c97fcaff 100755 --- a/activitysim/examples/example_semcog/configs/cdap_fixed_relative_proportions.csv +++ b/activitysim/examples/prototype_semcog/configs/cdap_fixed_relative_proportions.csv @@ -1,11 +1,11 @@ -Description,Expression,M,N,H -Full-time worker,ptype == 1 & not work_from_home,0.79647,0.09368,0.10985 -Full-time worker,ptype == 1 & work_from_home,0,0.460276126,0.539723874 -Part-time worker,ptype == 2 & not work_from_home,0.61678,0.25757,0.12565 -Part-time worker,ptype == 2 & work_from_home,0,0.672120453,0.327879547 -University student,ptype == 3,0.69229,0.15641,0.1513 -Non-working adult,ptype == 4,0,0.67169,0.32831 -Retired,ptype == 5,0,0.54295,0.45705 -Driving-age child who is in school,ptype == 6,0.77609,0.06004,0.16387 -Pre-driving-age child who is in school,ptype == 7,0.68514,0.09144,0.22342 -Child who is too young for school,ptype == 8,0.14056,0.06512,0.79432 +Description,Expression,M,N,H +Full-time worker,ptype == 1 & not work_from_home,0.79647,0.09368,0.10985 +Full-time worker,ptype == 1 & work_from_home,0,0.460276126,0.539723874 +Part-time worker,ptype == 2 & not work_from_home,0.61678,0.25757,0.12565 +Part-time worker,ptype == 2 & work_from_home,0,0.672120453,0.327879547 +University student,ptype == 3,0.69229,0.15641,0.1513 +Non-working adult,ptype == 4,0,0.67169,0.32831 +Retired,ptype == 5,0,0.54295,0.45705 +Driving-age child who is in school,ptype == 6,0.77609,0.06004,0.16387 +Pre-driving-age child who is in school,ptype == 7,0.68514,0.09144,0.22342 +Child who is too young for school,ptype == 8,0.14056,0.06512,0.79432 diff --git a/activitysim/examples/example_semcog/configs/cdap_indiv_and_hhsize1.csv b/activitysim/examples/prototype_semcog/configs/cdap_indiv_and_hhsize1.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/cdap_indiv_and_hhsize1.csv rename to activitysim/examples/prototype_semcog/configs/cdap_indiv_and_hhsize1.csv index 8768a9e428..a3ce4a01c4 100755 --- a/activitysim/examples/example_semcog/configs/cdap_indiv_and_hhsize1.csv +++ b/activitysim/examples/prototype_semcog/configs/cdap_indiv_and_hhsize1.csv @@ -1,66 +1,66 @@ -Description,Expression,M,N,H -Full-time worker alternative-specific constants,ptype == 1,1.378734579,0.622662391, -Part-time worker alternative-specific constants,ptype == 2,-0.718823738,0.636032467, -University student alternative-specific constants,ptype == 3,2.353595176,0.609709846, -Non-working adult alternative-specific constants,ptype == 4,-999,0.594645386, -Retired alternative-specific constants,ptype == 5,-999,0.408202071, -Driving-age child who is in school alternative-specific constants,ptype == 6,2.330918685,-0.599119112, -Pre-driving-age child who is in school alternative-specific constants,ptype == 7,3.295863529,0.57142434, -Pre-driving-age child who is in school interaction with age 6 to 9,(ptype == 7) & (age >= 6) & (age <= 9),-0.2943,, -Pre-driving-age child who is in school interaction with age 13 to 15,(ptype == 7) & (age >= 13) & (age <= 15),-0.7141,-0.672, -Pre-driving-age child who is too young for school alternative-specific constants,ptype == 8,1.052531189,-0.837567776, -# corrected tm1 age bug,,,, -Pre-driving-age child who is too young for school interaction with age 0 to 1,(ptype == 8) & (age >= 0) & (age <= 1),-0.4515,, -Pre-driving-age child who is too young for school interaction with age 4 to 5,(ptype == 8) & (age >= 4) & (age <= 5),0.6107,, -#,,,, -Full-time worker interaction with age less than 40,(ptype == 1) & (age < 40),0.2091,, -Retired interaction with age more than 80,(ptype == 5) & (age > 80),,,0.7666 -Full-time worker interaction with female gender,(ptype == 1) & (sex == 2),-0.1259,, -Non-working adult interaction with female gender,(ptype == 4) & (sex == 2),-0.743,, -Retired interaction with female,(ptype == 5) & (sex == 2),0.4769,, -Non-working adult interaction with more cars than workers,(ptype == 4) & (auto_ownership > num_workers),0.6515,0.8168, -Retired interaction with more cars than workers,(ptype == 5) & (auto_ownership > num_workers),2.992,1.056, -Pre-driving-age child who is too young for school interaction with more cars than workers,(ptype == 8) & (auto_ownership > num_workers),,0.2991, -Full-time worker interaction with fewer cars than workers,(ptype == 1) & (auto_ownership < num_workers),,,0.5039 -Non-working adult interaction with fewer cars than workers,(ptype == 4) & (auto_ownership < num_workers),,,0.8965 -Retired interaction with fewer cars than workers,(ptype == 5) & (auto_ownership < num_workers),,,0.5496 -Driving-age child who is in school interaction with fewer cars than workers,(ptype == 6) & (auto_ownership < num_workers),,,0.6475 -Pre-driving-age child who is in school interaction with fewer cars than workers,(ptype == 7) & (auto_ownership < num_workers),,,0.5862 -Pre-driving-age child who is too young for school interaction with fewer cars than workers,(ptype == 8) & (auto_ownership < num_workers),,,0.5061 -Full-time worker interaction with income less than $20k,(ptype == 1) & (income_in_thousands < 20),,,0.5313 -Retired interaction with income less than $20k,(ptype == 5) & (income_in_thousands < 20),,,0.533 -Part-time worker interaction with income less than $20k,(ptype == 2) & (income_in_thousands < 20),,,0.3232 -Part-time worker interaction with income between $50k and $100k,(ptype == 2) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,-0.4032 -Part-time worker interaction with income more than $100k,(ptype == 2) & (income_in_thousands >= 100),,0.4207,-0.3534 -Non-working adult interaction with income between $50k and $100k,(ptype == 4) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,-0.5602 -Non-working adult interaction with income more than $100k,(ptype == 4) & (income_in_thousands >= 100),,,-0.7188 -Driving-age child who is in school interaction with less than $20k,(ptype == 6) & (income_in_thousands < 20),,,1.307 -Driving-age child who is in school interaction income between $50k and $100k,(ptype == 6) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,-0.5031 -Driving-age child who is in school interaction with income more than $100k,(ptype == 6) & (income_in_thousands >= 100),,,-2.046 -Pre-driving-age child who is too young for school interaction with income between $50k and $100k,(ptype == 8) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,-0.5708 -Pre-driving-age child who is too young for school interaction with income more than $100k,(ptype == 8) & (income_in_thousands >= 100),,,-0.6186 -Full-time worker intraction with peak accessibility to all employment,(ptype == 1) * auPkTotal,0.1212,, -Part-time worker interaction with peak accessibility to all employment,(ptype == 2) * auPkTotal,0.2004,, -Non-working adult interaction with peak accessibility to all employment,(ptype == 4) * auPkTotal,0.2314,, -Retired interaction with peak accessibility to all employment,(ptype == 5) * auPkTotal,0.2792,, -Non-working adult interaction with off-peak accessibility to retail,(ptype == 4) * auOpRetail,,0.07207, -Retired interaction with off-peak accessibility to retail,(ptype == 5) * auOpRetail,,0.07207, -University student interaction with off-peak accessibility to retail,(ptype == 3) * auOpRetail,,0.07207, -Driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 6) * auOpRetail,,0.08233, -Pre-driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 7) * auOpRetail,,0.08233, -Pre-driving-age child who is too young for school interaction with off-peak accessibility to retail,(ptype == 8) * auOpRetail,,0.08233, -# commented out because not used in mtctm1,,,, -# Full-time worker interaction with usual work location is home,(ptype == 1) * usualWorkLocationIsHome,-1.758,,0.1813 -# Part-time worker interaction with usual work location is home,(ptype == 2) * usualWorkLocationIsHome,-1.758,,0.1813 -# Full-time worker interaction with no usual work location,(ptype == 1) * noUsualWorkLocation,-0.5935,, -# Part-time worker interaction with no usual work location,(ptype == 2) * noUsualWorkLocation,-0.5935,, -# Driving-age child who is in school interaction with no usual school location,(ptype == 6) * noUsualWorkLocation,-0.866,, -# Pre-driving age child who is in school interaction with no usual school location,(ptype == 7) * noUsualWorkLocation,-0.866,, -#tm1 scenario test,,,, -#Simulate telecommuting by reducing mandatory patterns,(ptype == 1) * (income_in_thousands >= 50),-0.142930042,, -Telecommutes 1 day per week,telecommute_frequency=='1_day_week',,0.526,0.496 -Telecommutes 2-3 days per week,telecommute_frequency=='2_3_days_week',,1.387,1.584 -Telecommutes 4 days per week,telecommute_frequency=='4_days_week',,1.848,1.711 -#Turning Mandatory pattern off for FT and PT who work from home (sandag),,,, -"Full time worker, works from home",(ptype == 1) & (work_from_home),-999,, -"Part time worker, works from home",(ptype == 2) & (work_from_home),-999,, +Description,Expression,M,N,H +Full-time worker alternative-specific constants,ptype == 1,1.378734579,0.622662391, +Part-time worker alternative-specific constants,ptype == 2,-0.718823738,0.636032467, +University student alternative-specific constants,ptype == 3,2.353595176,0.609709846, +Non-working adult alternative-specific constants,ptype == 4,-999,0.594645386, +Retired alternative-specific constants,ptype == 5,-999,0.408202071, +Driving-age child who is in school alternative-specific constants,ptype == 6,2.330918685,-0.599119112, +Pre-driving-age child who is in school alternative-specific constants,ptype == 7,3.295863529,0.57142434, +Pre-driving-age child who is in school interaction with age 6 to 9,(ptype == 7) & (age >= 6) & (age <= 9),-0.2943,, +Pre-driving-age child who is in school interaction with age 13 to 15,(ptype == 7) & (age >= 13) & (age <= 15),-0.7141,-0.672, +Pre-driving-age child who is too young for school alternative-specific constants,ptype == 8,1.052531189,-0.837567776, +# corrected tm1 age bug,,,, +Pre-driving-age child who is too young for school interaction with age 0 to 1,(ptype == 8) & (age >= 0) & (age <= 1),-0.4515,, +Pre-driving-age child who is too young for school interaction with age 4 to 5,(ptype == 8) & (age >= 4) & (age <= 5),0.6107,, +#,,,, +Full-time worker interaction with age less than 40,(ptype == 1) & (age < 40),0.2091,, +Retired interaction with age more than 80,(ptype == 5) & (age > 80),,,0.7666 +Full-time worker interaction with female gender,(ptype == 1) & (sex == 2),-0.1259,, +Non-working adult interaction with female gender,(ptype == 4) & (sex == 2),-0.743,, +Retired interaction with female,(ptype == 5) & (sex == 2),0.4769,, +Non-working adult interaction with more cars than workers,(ptype == 4) & (auto_ownership > num_workers),0.6515,0.8168, +Retired interaction with more cars than workers,(ptype == 5) & (auto_ownership > num_workers),2.992,1.056, +Pre-driving-age child who is too young for school interaction with more cars than workers,(ptype == 8) & (auto_ownership > num_workers),,0.2991, +Full-time worker interaction with fewer cars than workers,(ptype == 1) & (auto_ownership < num_workers),,,0.5039 +Non-working adult interaction with fewer cars than workers,(ptype == 4) & (auto_ownership < num_workers),,,0.8965 +Retired interaction with fewer cars than workers,(ptype == 5) & (auto_ownership < num_workers),,,0.5496 +Driving-age child who is in school interaction with fewer cars than workers,(ptype == 6) & (auto_ownership < num_workers),,,0.6475 +Pre-driving-age child who is in school interaction with fewer cars than workers,(ptype == 7) & (auto_ownership < num_workers),,,0.5862 +Pre-driving-age child who is too young for school interaction with fewer cars than workers,(ptype == 8) & (auto_ownership < num_workers),,,0.5061 +Full-time worker interaction with income less than $20k,(ptype == 1) & (income_in_thousands < 20),,,0.5313 +Retired interaction with income less than $20k,(ptype == 5) & (income_in_thousands < 20),,,0.533 +Part-time worker interaction with income less than $20k,(ptype == 2) & (income_in_thousands < 20),,,0.3232 +Part-time worker interaction with income between $50k and $100k,(ptype == 2) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,-0.4032 +Part-time worker interaction with income more than $100k,(ptype == 2) & (income_in_thousands >= 100),,0.4207,-0.3534 +Non-working adult interaction with income between $50k and $100k,(ptype == 4) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,-0.5602 +Non-working adult interaction with income more than $100k,(ptype == 4) & (income_in_thousands >= 100),,,-0.7188 +Driving-age child who is in school interaction with less than $20k,(ptype == 6) & (income_in_thousands < 20),,,1.307 +Driving-age child who is in school interaction income between $50k and $100k,(ptype == 6) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,-0.5031 +Driving-age child who is in school interaction with income more than $100k,(ptype == 6) & (income_in_thousands >= 100),,,-2.046 +Pre-driving-age child who is too young for school interaction with income between $50k and $100k,(ptype == 8) & (income_in_thousands >= 50) & (income_in_thousands <= 100),,,-0.5708 +Pre-driving-age child who is too young for school interaction with income more than $100k,(ptype == 8) & (income_in_thousands >= 100),,,-0.6186 +Full-time worker intraction with peak accessibility to all employment,(ptype == 1) * auPkTotal,0.1212,, +Part-time worker interaction with peak accessibility to all employment,(ptype == 2) * auPkTotal,0.2004,, +Non-working adult interaction with peak accessibility to all employment,(ptype == 4) * auPkTotal,0.2314,, +Retired interaction with peak accessibility to all employment,(ptype == 5) * auPkTotal,0.2792,, +Non-working adult interaction with off-peak accessibility to retail,(ptype == 4) * auOpRetail,,0.07207, +Retired interaction with off-peak accessibility to retail,(ptype == 5) * auOpRetail,,0.07207, +University student interaction with off-peak accessibility to retail,(ptype == 3) * auOpRetail,,0.07207, +Driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 6) * auOpRetail,,0.08233, +Pre-driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 7) * auOpRetail,,0.08233, +Pre-driving-age child who is too young for school interaction with off-peak accessibility to retail,(ptype == 8) * auOpRetail,,0.08233, +# commented out because not used in mtctm1,,,, +# Full-time worker interaction with usual work location is home,(ptype == 1) * usualWorkLocationIsHome,-1.758,,0.1813 +# Part-time worker interaction with usual work location is home,(ptype == 2) * usualWorkLocationIsHome,-1.758,,0.1813 +# Full-time worker interaction with no usual work location,(ptype == 1) * noUsualWorkLocation,-0.5935,, +# Part-time worker interaction with no usual work location,(ptype == 2) * noUsualWorkLocation,-0.5935,, +# Driving-age child who is in school interaction with no usual school location,(ptype == 6) * noUsualWorkLocation,-0.866,, +# Pre-driving age child who is in school interaction with no usual school location,(ptype == 7) * noUsualWorkLocation,-0.866,, +#tm1 scenario test,,,, +#Simulate telecommuting by reducing mandatory patterns,(ptype == 1) * (income_in_thousands >= 50),-0.142930042,, +Telecommutes 1 day per week,telecommute_frequency=='1_day_week',,0.526,0.496 +Telecommutes 2-3 days per week,telecommute_frequency=='2_3_days_week',,1.387,1.584 +Telecommutes 4 days per week,telecommute_frequency=='4_days_week',,1.848,1.711 +#Turning Mandatory pattern off for FT and PT who work from home (sandag),,,, +"Full time worker, works from home",(ptype == 1) & (work_from_home),-999,, +"Part time worker, works from home",(ptype == 2) & (work_from_home),-999,, diff --git a/activitysim/examples/example_semcog/configs/cdap_interaction_coefficients.csv b/activitysim/examples/prototype_semcog/configs/cdap_interaction_coefficients.csv similarity index 92% rename from activitysim/examples/example_semcog/configs/cdap_interaction_coefficients.csv rename to activitysim/examples/prototype_semcog/configs/cdap_interaction_coefficients.csv index 43a4ec3c73..f2d3b025c5 100755 --- a/activitysim/examples/example_semcog/configs/cdap_interaction_coefficients.csv +++ b/activitysim/examples/prototype_semcog/configs/cdap_interaction_coefficients.csv @@ -1,140 +1,140 @@ -activity,interaction_ptypes,coefficient -# 2-way interactions,, -H,11,1.626 -H,12,0.7407 -H,13,1.183 -H,14,0.9436 -H,15,1.298 -H,16,2.064 -H,17,1.501 -H,18,0.9912 -H,22,0.8911 -H,23,1.642 -H,24,0.7057 -H,25,0.463 -H,26,3.057 -H,27,0.7685 -H,28,1.07 -H,33,1.018 -H,34,1.781 -H,35,0.4835 -H,36,1.546 -H,37,1.552 -H,38,1.34 -H,44,1.352 -H,45,1.209 -H,46,0.5243 -H,47,0.8112 -H,48,1.167 -H,55,1.407 -H,56,0.8632 -H,57,0.8632 -H,58,0.8632 -H,66,2.198 -H,67,0.977 -H,68,1.467 -H,77,2.8 -H,78,1.434 -H,88,1.378 -M,11,0.141 -M,12,0.08845 -M,13,0.4273 -M,16,0.3842 -M,17,0.2623 -M,18,0.5118 -M,22,1.135 -M,23,0.173 -M,26,1.103 -M,27,0.3079 -M,28,0.5074 -M,33,0.8726 -M,36,-0.0021 -M,37,0.2975 -M,38,0.2254 -M,66,0.4794 -M,67,0.5151 -M,68,0.5516 -M,77,0.9731 -M,78,0.5961 -M,88,1.651 -N,11,1.123 -N,12,0.4947 -N,13,0.5523 -N,14,0.02186 -N,15,0.3115 -N,16,0.4095 -N,17,0.6008 -N,18,0.751 -N,22,1.032 -N,23,0.3355 -N,24,0.7477 -N,25,0.09831 -N,26,0.495 -N,27,0.8984 -N,28,1.452 -N,33,1.054 -N,34,0.193 -N,35,0.4065 -N,36,1.62 -N,37,0.5165 -N,38,0.8973 -N,44,0.6984 -N,45,0.1864 -N,46,0.6801 -N,47,0.5646 -N,48,1.164 -N,55,0.7291 -N,56,0.2919 -N,57,0.2919 -N,58,0.2919 -N,66,1.512 -N,67,1.422 -N,68,1.273 -N,77,1.553 -N,78,0.6184 -N,88,0.8771 -# 3-way interactions,, -H,124,0.9573 -H,122,0.9573 -H,144,0.9573 -H,126,0.2939 -H,146,0.2939 -H,222,0.9881 -H,224,0.9881 -H,244,0.9881 -H,226,0.4374 -H,246,0.4374 -H,446,0.4374 -H,266,0.4747 -H,466,0.4747 -M,111,0.3133 -M,112,0.3495 -M,114,0.3495 -M,666,-0.3906 -N,112,0.4637 -N,114,0.4637 -N,124,0.3491 -N,122,0.3491 -N,144,0.3491 -N,166,0.3553 -N,222,-1.386 -N,224,-1.386 -N,444,-1.386 -N,246,-0.8571 -N,226,-0.8571 -N,446,-0.8571 -# cdap_final_rules,, -M,5,-999 -M,4,-999 -# cdap_all_people,, -M,***,-0.0671 -N,***,-0.3653 -H,***,-1.1810 -M,****,-0.6104 -N,****,-1.3460 -H,****,-3.7330 -M,*****,-1.5280 -N,*****,-3.4530 -H,*****,-8.6210 - - +activity,interaction_ptypes,coefficient +# 2-way interactions,, +H,11,1.626 +H,12,0.7407 +H,13,1.183 +H,14,0.9436 +H,15,1.298 +H,16,2.064 +H,17,1.501 +H,18,0.9912 +H,22,0.8911 +H,23,1.642 +H,24,0.7057 +H,25,0.463 +H,26,3.057 +H,27,0.7685 +H,28,1.07 +H,33,1.018 +H,34,1.781 +H,35,0.4835 +H,36,1.546 +H,37,1.552 +H,38,1.34 +H,44,1.352 +H,45,1.209 +H,46,0.5243 +H,47,0.8112 +H,48,1.167 +H,55,1.407 +H,56,0.8632 +H,57,0.8632 +H,58,0.8632 +H,66,2.198 +H,67,0.977 +H,68,1.467 +H,77,2.8 +H,78,1.434 +H,88,1.378 +M,11,0.141 +M,12,0.08845 +M,13,0.4273 +M,16,0.3842 +M,17,0.2623 +M,18,0.5118 +M,22,1.135 +M,23,0.173 +M,26,1.103 +M,27,0.3079 +M,28,0.5074 +M,33,0.8726 +M,36,-0.0021 +M,37,0.2975 +M,38,0.2254 +M,66,0.4794 +M,67,0.5151 +M,68,0.5516 +M,77,0.9731 +M,78,0.5961 +M,88,1.651 +N,11,1.123 +N,12,0.4947 +N,13,0.5523 +N,14,0.02186 +N,15,0.3115 +N,16,0.4095 +N,17,0.6008 +N,18,0.751 +N,22,1.032 +N,23,0.3355 +N,24,0.7477 +N,25,0.09831 +N,26,0.495 +N,27,0.8984 +N,28,1.452 +N,33,1.054 +N,34,0.193 +N,35,0.4065 +N,36,1.62 +N,37,0.5165 +N,38,0.8973 +N,44,0.6984 +N,45,0.1864 +N,46,0.6801 +N,47,0.5646 +N,48,1.164 +N,55,0.7291 +N,56,0.2919 +N,57,0.2919 +N,58,0.2919 +N,66,1.512 +N,67,1.422 +N,68,1.273 +N,77,1.553 +N,78,0.6184 +N,88,0.8771 +# 3-way interactions,, +H,124,0.9573 +H,122,0.9573 +H,144,0.9573 +H,126,0.2939 +H,146,0.2939 +H,222,0.9881 +H,224,0.9881 +H,244,0.9881 +H,226,0.4374 +H,246,0.4374 +H,446,0.4374 +H,266,0.4747 +H,466,0.4747 +M,111,0.3133 +M,112,0.3495 +M,114,0.3495 +M,666,-0.3906 +N,112,0.4637 +N,114,0.4637 +N,124,0.3491 +N,122,0.3491 +N,144,0.3491 +N,166,0.3553 +N,222,-1.386 +N,224,-1.386 +N,444,-1.386 +N,246,-0.8571 +N,226,-0.8571 +N,446,-0.8571 +# cdap_final_rules,, +M,5,-999 +M,4,-999 +# cdap_all_people,, +M,***,-0.0671 +N,***,-0.3653 +H,***,-1.1810 +M,****,-0.6104 +N,****,-1.3460 +H,****,-3.7330 +M,*****,-1.5280 +N,*****,-3.4530 +H,*****,-8.6210 + + diff --git a/activitysim/examples/example_semcog/configs/constants.yaml b/activitysim/examples/prototype_semcog/configs/constants.yaml similarity index 95% rename from activitysim/examples/example_semcog/configs/constants.yaml rename to activitysim/examples/prototype_semcog/configs/constants.yaml index 68313e53d1..7d3864fd9d 100755 --- a/activitysim/examples/example_semcog/configs/constants.yaml +++ b/activitysim/examples/prototype_semcog/configs/constants.yaml @@ -1,63 +1,63 @@ -## ActivitySim -## See full license in LICENSE.txt. - - -#HHT_NONE: 0 -#HHT_FAMILY_MARRIED: 1 -#HHT_FAMILY_MALE: 2 -#HHT_FAMILY_FEMALE: 3 -#HHT_NONFAMILY_MALE_ALONE: 4 -#HHT_NONFAMILY_MALE_NOTALONE: 5 -#HHT_NONFAMILY_FEMALE_ALONE: 6 -#HHT_NONFAMILY_FEMALE_NOTALONE: 7 - -# convenience for expression files -HHT_NONFAMILY: [4, 5, 6, 7] -HHT_FAMILY: [1, 2, 3] - -PSTUDENT_GRADE_OR_HIGH: 1 -PSTUDENT_UNIVERSITY: 2 -PSTUDENT_NOT: 3 - -GRADE_SCHOOL_MAX_AGE: 14 -GRADE_SCHOOL_MIN_AGE: 5 - -SCHOOL_SEGMENT_NONE: 0 -SCHOOL_SEGMENT_GRADE: 1 -SCHOOL_SEGMENT_HIGH: 2 -SCHOOL_SEGMENT_UNIV: 3 - -INCOME_SEGMENT_LOW: 1 -INCOME_SEGMENT_MED: 2 -INCOME_SEGMENT_HIGH: 3 -INCOME_SEGMENT_VERYHIGH: 4 - -PEMPLOY_FULL: 1 -PEMPLOY_PART: 2 -PEMPLOY_NOT: 3 -PEMPLOY_CHILD: 4 - -PTYPE_FULL: &ptype_full 1 -PTYPE_PART: &ptype_part 2 -PTYPE_UNIVERSITY: &ptype_university 3 -PTYPE_NONWORK: &ptype_nonwork 4 -PTYPE_RETIRED: &ptype_retired 5 -PTYPE_DRIVING: &ptype_driving 6 -PTYPE_SCHOOL: &ptype_school 7 -PTYPE_PRESCHOOL: &ptype_preschool 8 - -# these appear as column headers in non_mandatory_tour_frequency.csv -PTYPE_NAME: - *ptype_full: PTYPE_FULL - *ptype_part: PTYPE_PART - *ptype_university: PTYPE_UNIVERSITY - *ptype_nonwork: PTYPE_NONWORK - *ptype_retired: PTYPE_RETIRED - *ptype_driving: PTYPE_DRIVING - *ptype_school: PTYPE_SCHOOL - *ptype_preschool: PTYPE_PRESCHOOL - - -CDAP_ACTIVITY_MANDATORY: M -CDAP_ACTIVITY_NONMANDATORY: N -CDAP_ACTIVITY_HOME: H +## ActivitySim +## See full license in LICENSE.txt. + + +#HHT_NONE: 0 +#HHT_FAMILY_MARRIED: 1 +#HHT_FAMILY_MALE: 2 +#HHT_FAMILY_FEMALE: 3 +#HHT_NONFAMILY_MALE_ALONE: 4 +#HHT_NONFAMILY_MALE_NOTALONE: 5 +#HHT_NONFAMILY_FEMALE_ALONE: 6 +#HHT_NONFAMILY_FEMALE_NOTALONE: 7 + +# convenience for expression files +HHT_NONFAMILY: [4, 5, 6, 7] +HHT_FAMILY: [1, 2, 3] + +PSTUDENT_GRADE_OR_HIGH: 1 +PSTUDENT_UNIVERSITY: 2 +PSTUDENT_NOT: 3 + +GRADE_SCHOOL_MAX_AGE: 14 +GRADE_SCHOOL_MIN_AGE: 5 + +SCHOOL_SEGMENT_NONE: 0 +SCHOOL_SEGMENT_GRADE: 1 +SCHOOL_SEGMENT_HIGH: 2 +SCHOOL_SEGMENT_UNIV: 3 + +INCOME_SEGMENT_LOW: 1 +INCOME_SEGMENT_MED: 2 +INCOME_SEGMENT_HIGH: 3 +INCOME_SEGMENT_VERYHIGH: 4 + +PEMPLOY_FULL: 1 +PEMPLOY_PART: 2 +PEMPLOY_NOT: 3 +PEMPLOY_CHILD: 4 + +PTYPE_FULL: &ptype_full 1 +PTYPE_PART: &ptype_part 2 +PTYPE_UNIVERSITY: &ptype_university 3 +PTYPE_NONWORK: &ptype_nonwork 4 +PTYPE_RETIRED: &ptype_retired 5 +PTYPE_DRIVING: &ptype_driving 6 +PTYPE_SCHOOL: &ptype_school 7 +PTYPE_PRESCHOOL: &ptype_preschool 8 + +# these appear as column headers in non_mandatory_tour_frequency.csv +PTYPE_NAME: + *ptype_full: PTYPE_FULL + *ptype_part: PTYPE_PART + *ptype_university: PTYPE_UNIVERSITY + *ptype_nonwork: PTYPE_NONWORK + *ptype_retired: PTYPE_RETIRED + *ptype_driving: PTYPE_DRIVING + *ptype_school: PTYPE_SCHOOL + *ptype_preschool: PTYPE_PRESCHOOL + + +CDAP_ACTIVITY_MANDATORY: M +CDAP_ACTIVITY_NONMANDATORY: N +CDAP_ACTIVITY_HOME: H diff --git a/activitysim/examples/example_semcog/configs/destination_choice_size_terms.csv b/activitysim/examples/prototype_semcog/configs/destination_choice_size_terms.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/destination_choice_size_terms.csv rename to activitysim/examples/prototype_semcog/configs/destination_choice_size_terms.csv index ceb2a35918..ef359c9048 100755 --- a/activitysim/examples/example_semcog/configs/destination_choice_size_terms.csv +++ b/activitysim/examples/prototype_semcog/configs/destination_choice_size_terms.csv @@ -1,26 +1,26 @@ -model_selector,segment,tot_hhs,e01_nrm,e02_constr,e03_manuf,e04_whole,e05_retail,e06_trans,e07_utility,e08_infor,e09_finan,e10_pstsvc,e11_compmgt,e12_admsvc,e13_edusvc,e14_medfac,e15_hospit,e16_leisure,e17_othsvc,e18_pubadm,K_8,G9_12,Univ_Enrollment -workplace,work_low,0,0,0.31,0.504,0.086,0.905,0.215,0.008,0.059,0.238,0.162,0,0.512,0.457,0.984,0.163,1,0.429,0.07,0,0,0 -workplace,work_med,0,0.001,0.359,0.944,0.153,0.883,0.254,0.035,0.095,0.29,0.389,0.004,0.345,0.511,1,0.186,0.661,0.348,0.205,0,0,0 -workplace,work_high,0,0.005,0.297,1,0.148,0.593,0.178,0.05,0.11,0.355,0.35,0.007,0.228,0.587,0.843,0.123,0.33,0.228,0.213,0,0,0 -workplace,work_veryhigh,0,0.006,0.162,1,0.133,0.363,0.101,0.055,0.104,0.34,0.475,0.005,0.164,0.498,0.699,0.094,0.214,0.14,0.143,0,0,0 -school,university,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 -school,gradeschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 -school,highschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0 -non_mandatory,escort,0.192,0,0,0,0,0.188,0,0,0,0,0,0,0,0,0.164,0.078,0.791,0,0,1,0.768,0 -non_mandatory,shopping,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.005,0.003,0,0,0,0 -non_mandatory,eatout,0.004,0,0,0,0,0.283,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 -non_mandatory,othmaint,0.029,0,0,0,0.026,0.587,0,0,0,0.268,0,0,0.445,0.161,1,0.556,0.336,0.501,0.478,0,0,0 -non_mandatory,social,0.487,0,0,0,0,0,0,0,0,0,0,0,0,0.244,0,0.177,1,0,0,0,0,0 -non_mandatory,othdiscr,0.184,0,0,0,0,0.185,0,0,0,0,0,0,0,0.899,0.511,0.004,1.081,1,0,0,0,0 -atwork,atwork,0.03,0,0,0,0.049,1,0,0,0,0.141,0,0,0.482,0.048,0.092,0.013,0,0,0.053,0,0,0 -trip,work,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0 -trip,escort,0.001,0,0,0,0,0.225,0,0,0,0,0,0,0,0.144,0.144,0.144,0.144,0,0,0.465,0.166,0 -trip,shopping,0.001,0,0,0,0,0.999,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -trip,eatout,0,0,0,0,0,0.742,0,0,0,0,0,0,0,0.258,0.258,0.258,0.258,0,0,0,0,0 -trip,othmaint,0.001,0,0,0,0,0.481,0,0,0,0,0,0,0,0.518,0.518,0.518,0.518,0,0,0,0,0 -trip,social,0.001,0,0,0,0,0.521,0,0,0,0,0,0,0,0.478,0.478,0.478,0.478,0,0,0,0,0 -trip,othdiscr,0.252,0,0,0,0,0.212,0,0,0,0,0,0,0,0.273,0.273,0.273,0.273,0.165,0.165,0,0.098,0 -trip,univ,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 -# not needed as school is not chosen as an intermediate trip destination,,,,,,,,,,,,,,,,,,,,,,,0 -#trip,gradeschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 -#trip,highschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0 +model_selector,segment,tot_hhs,e01_nrm,e02_constr,e03_manuf,e04_whole,e05_retail,e06_trans,e07_utility,e08_infor,e09_finan,e10_pstsvc,e11_compmgt,e12_admsvc,e13_edusvc,e14_medfac,e15_hospit,e16_leisure,e17_othsvc,e18_pubadm,K_8,G9_12,Univ_Enrollment +workplace,work_low,0,0,0.31,0.504,0.086,0.905,0.215,0.008,0.059,0.238,0.162,0,0.512,0.457,0.984,0.163,1,0.429,0.07,0,0,0 +workplace,work_med,0,0.001,0.359,0.944,0.153,0.883,0.254,0.035,0.095,0.29,0.389,0.004,0.345,0.511,1,0.186,0.661,0.348,0.205,0,0,0 +workplace,work_high,0,0.005,0.297,1,0.148,0.593,0.178,0.05,0.11,0.355,0.35,0.007,0.228,0.587,0.843,0.123,0.33,0.228,0.213,0,0,0 +workplace,work_veryhigh,0,0.006,0.162,1,0.133,0.363,0.101,0.055,0.104,0.34,0.475,0.005,0.164,0.498,0.699,0.094,0.214,0.14,0.143,0,0,0 +school,university,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +school,gradeschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0 +school,highschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0 +non_mandatory,escort,0.192,0,0,0,0,0.188,0,0,0,0,0,0,0,0,0.164,0.078,0.791,0,0,1,0.768,0 +non_mandatory,shopping,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.005,0.003,0,0,0,0 +non_mandatory,eatout,0.004,0,0,0,0,0.283,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0 +non_mandatory,othmaint,0.029,0,0,0,0.026,0.587,0,0,0,0.268,0,0,0.445,0.161,1,0.556,0.336,0.501,0.478,0,0,0 +non_mandatory,social,0.487,0,0,0,0,0,0,0,0,0,0,0,0,0.244,0,0.177,1,0,0,0,0,0 +non_mandatory,othdiscr,0.184,0,0,0,0,0.185,0,0,0,0,0,0,0,0.899,0.511,0.004,1.081,1,0,0,0,0 +atwork,atwork,0.03,0,0,0,0.049,1,0,0,0,0.141,0,0,0.482,0.048,0.092,0.013,0,0,0.053,0,0,0 +trip,work,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0 +trip,escort,0.001,0,0,0,0,0.225,0,0,0,0,0,0,0,0.144,0.144,0.144,0.144,0,0,0.465,0.166,0 +trip,shopping,0.001,0,0,0,0,0.999,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +trip,eatout,0,0,0,0,0,0.742,0,0,0,0,0,0,0,0.258,0.258,0.258,0.258,0,0,0,0,0 +trip,othmaint,0.001,0,0,0,0,0.481,0,0,0,0,0,0,0,0.518,0.518,0.518,0.518,0,0,0,0,0 +trip,social,0.001,0,0,0,0,0.521,0,0,0,0,0,0,0,0.478,0.478,0.478,0.478,0,0,0,0,0 +trip,othdiscr,0.252,0,0,0,0,0.212,0,0,0,0,0,0,0,0.273,0.273,0.273,0.273,0.165,0.165,0,0.098,0 +trip,univ,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 +# not needed as school is not chosen as an intermediate trip destination,,,,,,,,,,,,,,,,,,,,,,,0 +#trip,gradeschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 +#trip,highschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0 diff --git a/activitysim/examples/example_semcog/configs/free_parking.csv b/activitysim/examples/prototype_semcog/configs/free_parking.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/free_parking.csv rename to activitysim/examples/prototype_semcog/configs/free_parking.csv index 6bda3519a0..2505a9bf70 100755 --- a/activitysim/examples/example_semcog/configs/free_parking.csv +++ b/activitysim/examples/prototype_semcog/configs/free_parking.csv @@ -1,6 +1,6 @@ -Label,Description,Expression,free,pay -util_income_very_high,Very high income household dummy,@df.income>=100000,coef_income_very_high,0 -util_income_high,High income housheold dummy,@(df.income>=60000) & (df.income<100000),coef_income_high,0 -util_hh_size_4_up,Household size is greater than 3 dummy,@df.hhsize>3,coef_hh_size_4_up,0 -util_more_autos_than_workers,More automobiles than workers dummy,@df.auto_ownership>df.num_workers,coef_more_autos_than_workers,0 -util_fewer_autos_than_workers,Fewer automobiles than workers dummy,@df.auto_ownership=100000,coef_income_very_high,0 +util_income_high,High income housheold dummy,@(df.income>=60000) & (df.income<100000),coef_income_high,0 +util_hh_size_4_up,Household size is greater than 3 dummy,@df.hhsize>3,coef_hh_size_4_up,0 +util_more_autos_than_workers,More automobiles than workers dummy,@df.auto_ownership>df.num_workers,coef_more_autos_than_workers,0 +util_fewer_autos_than_workers,Fewer automobiles than workers dummy,@df.auto_ownership=30) & (income_in_thousands<60),coef_medium_income_households,, -util_household_has_more_cars_than_workers,Household has more cars than workers (dummy),more_cars_than_workers,coef_household_has_more_cars_than_workers_adults,,coef_household_has_more_cars_than_workers_mixed -util_household_in_urban_area,Household is located in an urban area type (dummy),home_is_urban,coef_household_in_urban_area,, -util_household_in_suburban_area,Household is located in a suburban area type (dummy),~(home_is_urban | home_is_rural),coef_household_in_suburban_area_adults,,coef_household_in_suburban_area_mixed -util_log_max_overlap_of_adults_time_windows,Log of max pair-wise overlap of household adults time windows,log_time_window_overlap_adult,coef_log_max_overlap_of_adults_time_windows,, -util_log_max_overlap_of_childrens_time_windows,Log of max pair-wise overlap of household childrens time windows,log_time_window_overlap_child,,coef_log_max_overlap_of_childrens_time_windows, -util_log_max_overlap_of_time_windows,Log of max pair-wise overlap of household adults and childrens time windows,log_time_window_overlap_adult_child,,,coef_log_max_overlap_of_time_windows -util_two_acive_adults,Two adults must have Mand or Non Mand activity patterns to have adult-only joint travel,num_travel_active_adults<2,coef_unavailable,, -util_two_active_children,Two children must have Mand or Non Mand activity patterns to have children-only joint travel,num_travel_active_children<2,,coef_unavailable, +Label,Description,Expression,adults,children,mixed +util_asc,Alternative-specific constant,1,,coef_asc_children,coef_asc_mixed +util_tour_purpose_is_eating_out,Joint tour purpose is eating out (dummy),tour_type=='eat',,coef_tour_purpose_is_eating_out_children,coef_tour_purpose_is_eating_out_mixed +util_tour_purpose_is_discretionary,Joint tour purpose is discretionary (dummy),tour_type=='disc',coef_tour_purpose_is_discretionary_adults,coef_tour_purpose_is_discretionary_children, +util_number_of_full_time_workers,Number of Full-Time Workers in the household,num_full_max3,coef_number_of_full_time_workers_adults,,coef_number_of_full_time_workers_mixed +util_number_of_part_time_workers,Number of Part-Time Workers in the household,num_part_max3,coef_number_of_part_time_workers_adults,,coef_number_of_part_time_workers_mixed +util_number_of_university_students,Number of University students in the household,num_univ_max3,coef_number_of_university_students,, +util_number_of_non_workers,Number of Non-Workers in the household,num_nonwork_max3,coef_number_of_non_workers_adults,,coef_number_of_non_workers_mixed +util_number_of_children_too_young_for_school,Number of Children too Young for School in the household,num_preschool_max3,,coef_number_of_children_too_young_for_school_children,coef_number_of_children_too_young_for_school_mixed +util_number_of_pre_driving_age_children,Number of Pre-driving Age Children in the household,num_school_max3,,coef_number_of_pre_driving_age_children_children,coef_number_of_pre_driving_age_children_mixed +util_number_of_driving_age_children,Number of Driving-age Children in the household,num_driving_max3,,coef_number_of_driving_age_children_children,coef_number_of_driving_age_children_mixed +util_low_income_households,Low income households (dummy),income_in_thousands<30,coef_low_income_households_adults,,coef_low_income_households_mixed +util_medium_income_households,Medium income households (dummy),(income_in_thousands>=30) & (income_in_thousands<60),coef_medium_income_households,, +util_household_has_more_cars_than_workers,Household has more cars than workers (dummy),more_cars_than_workers,coef_household_has_more_cars_than_workers_adults,,coef_household_has_more_cars_than_workers_mixed +util_household_in_urban_area,Household is located in an urban area type (dummy),home_is_urban,coef_household_in_urban_area,, +util_household_in_suburban_area,Household is located in a suburban area type (dummy),~(home_is_urban | home_is_rural),coef_household_in_suburban_area_adults,,coef_household_in_suburban_area_mixed +util_log_max_overlap_of_adults_time_windows,Log of max pair-wise overlap of household adults time windows,log_time_window_overlap_adult,coef_log_max_overlap_of_adults_time_windows,, +util_log_max_overlap_of_childrens_time_windows,Log of max pair-wise overlap of household childrens time windows,log_time_window_overlap_child,,coef_log_max_overlap_of_childrens_time_windows, +util_log_max_overlap_of_time_windows,Log of max pair-wise overlap of household adults and childrens time windows,log_time_window_overlap_adult_child,,,coef_log_max_overlap_of_time_windows +util_two_acive_adults,Two adults must have Mand or Non Mand activity patterns to have adult-only joint travel,num_travel_active_adults<2,coef_unavailable,, +util_two_active_children,Two children must have Mand or Non Mand activity patterns to have children-only joint travel,num_travel_active_children<2,,coef_unavailable, util_travel_active_adult,At least one adult and at least one child must have Mand or Non Mand activity patterns to have adult/child joint travel,(num_travel_active_adults == 0) | (num_travel_active_children == 0),,,coef_unavailable \ No newline at end of file diff --git a/activitysim/examples/example_psrc/configs/joint_tour_composition.yaml b/activitysim/examples/prototype_semcog/configs/joint_tour_composition.yaml similarity index 95% rename from activitysim/examples/example_psrc/configs/joint_tour_composition.yaml rename to activitysim/examples/prototype_semcog/configs/joint_tour_composition.yaml index 55ee2015ec..a699bb337d 100755 --- a/activitysim/examples/example_psrc/configs/joint_tour_composition.yaml +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_composition.yaml @@ -1,11 +1,11 @@ -LOGIT_TYPE: MNL - -SPEC: joint_tour_composition.csv -COEFFICIENTS: joint_tour_composition_coeffs.csv - -preprocessor: - SPEC: joint_tour_composition_annotate_households_preprocessor - DF: households -# TABLES: -# - persons -# - accessibility +LOGIT_TYPE: MNL + +SPEC: joint_tour_composition.csv +COEFFICIENTS: joint_tour_composition_coeffs.csv + +preprocessor: + SPEC: joint_tour_composition_annotate_households_preprocessor + DF: households +# TABLES: +# - persons +# - accessibility diff --git a/activitysim/examples/example_semcog/configs/joint_tour_composition_annotate_households_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/joint_tour_composition_annotate_households_preprocessor.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/joint_tour_composition_annotate_households_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/joint_tour_composition_annotate_households_preprocessor.csv index c53d990944..3267e37939 100755 --- a/activitysim/examples/example_semcog/configs/joint_tour_composition_annotate_households_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_composition_annotate_households_preprocessor.csv @@ -1,22 +1,22 @@ -Description,Target,Expression -#,, -,_HH_OVERLAPS,"hh_time_window_overlap(households, persons)" -,time_window_overlap_adult,_HH_OVERLAPS['aa']/2.25 -,time_window_overlap_child,_HH_OVERLAPS['cc']/2.25 -,time_window_overlap_adult_child,_HH_OVERLAPS['ac']/2.25 -logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) -logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) -logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) -#,, -,_HH_PERSON_COUNT,"lambda exp, households, persons: persons.query(exp).groupby('household_id').size().reindex(households.index).fillna(0)" -,_num_full,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_FULL, households, persons)" -,_num_part,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_PART, households, persons)" -,num_full_max3,"_num_full.clip(0,3)" -,num_part_max3,"_num_part.clip(0,3)" -,num_univ_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_UNIVERSITY, households, persons).clip(0,3)" -,num_nonwork_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_NONWORK, households, persons).clip(0,3)" -,num_preschool_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_PRESCHOOL, households, persons).clip(0,3)" -,num_school_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_SCHOOL, households, persons).clip(0,3)" -,num_driving_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_DRIVING, households, persons).clip(0,3)" -#,, -,more_cars_than_workers,households.auto_ownership > (_num_full + _num_part) +Description,Target,Expression +#,, +,_HH_OVERLAPS,"hh_time_window_overlap(households, persons)" +,time_window_overlap_adult,_HH_OVERLAPS['aa']/2.25 +,time_window_overlap_child,_HH_OVERLAPS['cc']/2.25 +,time_window_overlap_adult_child,_HH_OVERLAPS['ac']/2.25 +logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) +logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) +logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) +#,, +,_HH_PERSON_COUNT,"lambda exp, households, persons: persons.query(exp).groupby('household_id').size().reindex(households.index).fillna(0)" +,_num_full,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_FULL, households, persons)" +,_num_part,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_PART, households, persons)" +,num_full_max3,"_num_full.clip(0,3)" +,num_part_max3,"_num_part.clip(0,3)" +,num_univ_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_UNIVERSITY, households, persons).clip(0,3)" +,num_nonwork_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_NONWORK, households, persons).clip(0,3)" +,num_preschool_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_PRESCHOOL, households, persons).clip(0,3)" +,num_school_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_SCHOOL, households, persons).clip(0,3)" +,num_driving_max3,"_HH_PERSON_COUNT('ptype == %s' % PTYPE_DRIVING, households, persons).clip(0,3)" +#,, +,more_cars_than_workers,households.auto_ownership > (_num_full + _num_part) diff --git a/activitysim/examples/example_semcog/configs/joint_tour_composition_coeffs.csv b/activitysim/examples/prototype_semcog/configs/joint_tour_composition_coeffs.csv similarity index 97% rename from activitysim/examples/example_semcog/configs/joint_tour_composition_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/joint_tour_composition_coeffs.csv index ad082c2d1e..4d929c2b5f 100755 --- a/activitysim/examples/example_semcog/configs/joint_tour_composition_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_composition_coeffs.csv @@ -1,32 +1,32 @@ -coefficient_name,value,constrain -coef_unavailable,-999,T -coef_asc_children,5.3517,F -coef_asc_mixed,5.6290,fF -coef_tour_purpose_is_eating_out_children,-0.9678,F -coef_tour_purpose_is_eating_out_mixed,-0.8027,F -coef_tour_purpose_is_discretionary_adults,0.7648,F -coef_tour_purpose_is_discretionary_children,0.5101,F -coef_number_of_full_time_workers_adults,1.024,F -coef_number_of_full_time_workers_mixed,0.3624,F -coef_number_of_part_time_workers_adults,0.5412,F -coef_number_of_part_time_workers_mixed,0.3164,F -coef_number_of_university_students,0.8245,F -coef_number_of_non_workers_adults,0.6263,F -coef_number_of_non_workers_mixed,-0.3724,F -coef_number_of_children_too_young_for_school_children,0.7306,F -coef_number_of_children_too_young_for_school_mixed,0.7906,F -coef_number_of_pre_driving_age_children_children,0.7306,F -coef_number_of_pre_driving_age_children_mixed,0.3532,F -coef_number_of_driving_age_children_children,-0.2667,F -coef_number_of_driving_age_children_mixed,-0.9399,F -coef_low_income_households_adults,1.248,F -coef_low_income_households_mixed,0.5755,F -coef_medium_income_households,0.8369,F -coef_household_has_more_cars_than_workers_adults,1.386,F -coef_household_has_more_cars_than_workers_mixed,0.751,F -coef_household_in_urban_area,0.5741,F -coef_household_in_suburban_area_adults,0.5105,F -coef_household_in_suburban_area_mixed,0.1283,F -coef_log_max_overlap_of_adults_time_windows,1.192,F -coef_log_max_overlap_of_childrens_time_windows,1.841,F -coef_log_max_overlap_of_time_windows,1.958,F +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_asc_children,5.3517,F +coef_asc_mixed,5.6290,fF +coef_tour_purpose_is_eating_out_children,-0.9678,F +coef_tour_purpose_is_eating_out_mixed,-0.8027,F +coef_tour_purpose_is_discretionary_adults,0.7648,F +coef_tour_purpose_is_discretionary_children,0.5101,F +coef_number_of_full_time_workers_adults,1.024,F +coef_number_of_full_time_workers_mixed,0.3624,F +coef_number_of_part_time_workers_adults,0.5412,F +coef_number_of_part_time_workers_mixed,0.3164,F +coef_number_of_university_students,0.8245,F +coef_number_of_non_workers_adults,0.6263,F +coef_number_of_non_workers_mixed,-0.3724,F +coef_number_of_children_too_young_for_school_children,0.7306,F +coef_number_of_children_too_young_for_school_mixed,0.7906,F +coef_number_of_pre_driving_age_children_children,0.7306,F +coef_number_of_pre_driving_age_children_mixed,0.3532,F +coef_number_of_driving_age_children_children,-0.2667,F +coef_number_of_driving_age_children_mixed,-0.9399,F +coef_low_income_households_adults,1.248,F +coef_low_income_households_mixed,0.5755,F +coef_medium_income_households,0.8369,F +coef_household_has_more_cars_than_workers_adults,1.386,F +coef_household_has_more_cars_than_workers_mixed,0.751,F +coef_household_in_urban_area,0.5741,F +coef_household_in_suburban_area_adults,0.5105,F +coef_household_in_suburban_area_mixed,0.1283,F +coef_log_max_overlap_of_adults_time_windows,1.192,F +coef_log_max_overlap_of_childrens_time_windows,1.841,F +coef_log_max_overlap_of_time_windows,1.958,F diff --git a/activitysim/examples/example_semcog/configs/joint_tour_destination.csv b/activitysim/examples/prototype_semcog/configs/joint_tour_destination.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/joint_tour_destination.csv rename to activitysim/examples/prototype_semcog/configs/joint_tour_destination.csv index be4e18bf64..9d8571d401 100755 --- a/activitysim/examples/example_semcog/configs/joint_tour_destination.csv +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_destination.csv @@ -1,15 +1,15 @@ -Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr -local_dist,_DIST@skims['DIST'],1,1,1,1,1,1 -util_dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_othmaint,coef_dist_social,coef_dist_othdiscr -util_dist_squared,"@(_DIST).clip(0,20)**2",coef_dist_squared_escort,coef_dist_squared_shopping,coef_dist_squared_eatout,coef_dist_squared_othmaint,,coef_dist_squared_othdiscr -util_dist_cubed,"@(_DIST).clip(0,20)**3",coef_dist_cubed_escort,coef_dist_cubed_shopping,coef_dist_cubed_eatout,coef_dist_cubed_othmaint,,coef_dist_cubed_othdiscr -util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_escort,coef_dist_logged_shopping,coef_dist_logged_eatout,coef_dist_logged_othmaint,coef_dist_logged_social,coef_dist_logged_othdiscr -util_dist_low,@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,0,0,0,0,0,0 -util_dist_med,@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,0,0,0,coef_dist_med_inc_othmaint,0,coef_dist_med_inc_othdiscr -util_dist_high,@(df['income_segment']==WORK_HIGH_SEGMENT_ID) * _DIST,0,0,0,coef_dist_high_inc_othmaint,0,0 -util_dist_hh_child,@(df['num_children']>0) * _DIST,coef_dist_hh_child_escort,0,coef_dist_hh_child_eatout,0,coef_dist_hh_child_social,coef_dist_hh_child_othdiscr -util_dist_joint_tour,@(df['tour_type']=='joint') * _DIST,0,coef_dist_joint_tour_shopping,0,0,coef_dist_joint_tour_social,coef_dist_joint_tour_othdiscr -Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1 -No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999 -Mode choice logsum,mode_choice_logsum,coef_mode_logsum_escort,coef_mode_logsum_shopping,coef_mode_logsum_eatout,coef_mode_logsum_othmaint,coef_mode_logsum_social,coef_mode_logsum_othdiscr -Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1 +Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr +local_dist,_DIST@skims['DIST'],1,1,1,1,1,1 +util_dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_othmaint,coef_dist_social,coef_dist_othdiscr +util_dist_squared,"@(_DIST).clip(0,20)**2",coef_dist_squared_escort,coef_dist_squared_shopping,coef_dist_squared_eatout,coef_dist_squared_othmaint,,coef_dist_squared_othdiscr +util_dist_cubed,"@(_DIST).clip(0,20)**3",coef_dist_cubed_escort,coef_dist_cubed_shopping,coef_dist_cubed_eatout,coef_dist_cubed_othmaint,,coef_dist_cubed_othdiscr +util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_escort,coef_dist_logged_shopping,coef_dist_logged_eatout,coef_dist_logged_othmaint,coef_dist_logged_social,coef_dist_logged_othdiscr +util_dist_low,@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,0,0,0,0,0,0 +util_dist_med,@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,0,0,0,coef_dist_med_inc_othmaint,0,coef_dist_med_inc_othdiscr +util_dist_high,@(df['income_segment']==WORK_HIGH_SEGMENT_ID) * _DIST,0,0,0,coef_dist_high_inc_othmaint,0,0 +util_dist_hh_child,@(df['num_children']>0) * _DIST,coef_dist_hh_child_escort,0,coef_dist_hh_child_eatout,0,coef_dist_hh_child_social,coef_dist_hh_child_othdiscr +util_dist_joint_tour,@(df['tour_type']=='joint') * _DIST,0,coef_dist_joint_tour_shopping,0,0,coef_dist_joint_tour_social,coef_dist_joint_tour_othdiscr +Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1 +No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999 +Mode choice logsum,mode_choice_logsum,coef_mode_logsum_escort,coef_mode_logsum_shopping,coef_mode_logsum_eatout,coef_mode_logsum_othmaint,coef_mode_logsum_social,coef_mode_logsum_othdiscr +Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1 diff --git a/activitysim/examples/example_semcog/configs/joint_tour_destination.yaml b/activitysim/examples/prototype_semcog/configs/joint_tour_destination.yaml similarity index 95% rename from activitysim/examples/example_semcog/configs/joint_tour_destination.yaml rename to activitysim/examples/prototype_semcog/configs/joint_tour_destination.yaml index 57ae660b69..bac02c2d72 100755 --- a/activitysim/examples/example_semcog/configs/joint_tour_destination.yaml +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_destination.yaml @@ -1,52 +1,52 @@ - -SAMPLE_SPEC: joint_tour_destination_sample.csv -SPEC: joint_tour_destination.csv -COEFFICIENTS: joint_tour_destination_coeffs.csv - -SEGMENTS: - - shopping - - othmaint - - othdiscr - - eatout - - social - - -SAMPLE_SIZE: 30 - -# we can't use use household income_segment as this will also be set for non-workers -CHOOSER_SEGMENT_COLUMN_NAME: tour_type - -SIMULATE_CHOOSER_COLUMNS: - - tour_type - - TAZ - - person_id - - income_segment - - num_children - -LOGSUM_SETTINGS: tour_mode_choice.yaml - -# model-specific logsum-related settings -CHOOSER_ORIG_COL_NAME: TAZ -ALT_DEST_COL_NAME: alt_dest -IN_PERIOD: 14 -OUT_PERIOD: 14 - -SIZE_TERM_SELECTOR: non_mandatory - -# optional (comment out if not desired) -DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum - -# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table -DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample - -SEGMENT_IDS: - work_low: 1 - work_med: 2 - work_high: 3 - work_veryhigh: 4 - -CONSTANTS: - WORK_LOW_SEGMENT_ID: 1 - WORK_MED_SEGMENT_ID: 2 - WORK_HIGH_SEGMENT_ID: 3 - WORK_VERYHIGH_SEGMENT_ID: 4 + +SAMPLE_SPEC: joint_tour_destination_sample.csv +SPEC: joint_tour_destination.csv +COEFFICIENTS: joint_tour_destination_coeffs.csv + +SEGMENTS: + - shopping + - othmaint + - othdiscr + - eatout + - social + + +SAMPLE_SIZE: 30 + +# we can't use use household income_segment as this will also be set for non-workers +CHOOSER_SEGMENT_COLUMN_NAME: tour_type + +SIMULATE_CHOOSER_COLUMNS: + - tour_type + - TAZ + - person_id + - income_segment + - num_children + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: TAZ +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 14 + +SIZE_TERM_SELECTOR: non_mandatory + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample + +SEGMENT_IDS: + work_low: 1 + work_med: 2 + work_high: 3 + work_veryhigh: 4 + +CONSTANTS: + WORK_LOW_SEGMENT_ID: 1 + WORK_MED_SEGMENT_ID: 2 + WORK_HIGH_SEGMENT_ID: 3 + WORK_VERYHIGH_SEGMENT_ID: 4 diff --git a/activitysim/examples/example_semcog/configs/joint_tour_destination_coeffs.csv b/activitysim/examples/prototype_semcog/configs/joint_tour_destination_coeffs.csv similarity index 97% rename from activitysim/examples/example_semcog/configs/joint_tour_destination_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/joint_tour_destination_coeffs.csv index db6fa3941e..b9b82be0cd 100755 --- a/activitysim/examples/example_semcog/configs/joint_tour_destination_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_destination_coeffs.csv @@ -1,39 +1,39 @@ -coefficient_name,value,constrain -coef_mode_logsum_escort,0.43,F -coef_dist_escort,-0.024726043,F -coef_dist_squared_escort,-0.00174728,F -coef_dist_cubed_escort,3.68E-06,F -coef_dist_logged_escort,-1.236681595,F -coef_dist_hh_child_escort,-0.115868258,F -coef_mode_logsum_shopping,0.65,F -coef_dist_shopping,-0.098281692,F -coef_dist_squared_shopping,-0.020314759,F -coef_dist_cubed_shopping,0.000801126,F -coef_dist_logged_shopping,-0.792030818,F -coef_dist_joint_tour_shopping,0.035663397,F -coef_mode_logsum_eatout,0.56,F -coef_dist_eatout,-0.039022477,F -coef_dist_squared_eatout,-0.02117814,F -coef_dist_cubed_eatout,0.000793071,F -coef_dist_logged_eatout,-0.680658809,F -coef_dist_hh_child_eatout,-0.059764452,F -coef_mode_logsum_othmaint,0.38,F -coef_dist_othmaint,-0.058808778,F -coef_dist_squared_othmaint,-0.009297153,F -coef_dist_cubed_othmaint,0.000314133,F -coef_dist_logged_othmaint,-0.759800765,F -coef_dist_med_inc_othmaint,-0.007251455,F -coef_dist_high_inc_othmaint,-0.01144311,F -coef_mode_logsum_social,0.68,F -coef_dist_social,-0.070898603,F -coef_dist_logged_social,-0.67038649,F -coef_dist_hh_child_social,-0.054753072,F -coef_dist_joint_tour_social,0.030177219,F -coef_mode_logsum_othdiscr,0.75,F -coef_dist_othdiscr,-0.03374735,F -coef_dist_squared_othdiscr,-0.012479607,F -coef_dist_cubed_othdiscr,0.000434109,F -coef_dist_logged_othdiscr,-0.680070996,F -coef_dist_med_inc_othdiscr,0.036605959,F -coef_dist_hh_child_othdiscr,-0.021556002,F -coef_dist_joint_tour_othdiscr,0.011538605,F +coefficient_name,value,constrain +coef_mode_logsum_escort,0.43,F +coef_dist_escort,-0.024726043,F +coef_dist_squared_escort,-0.00174728,F +coef_dist_cubed_escort,3.68E-06,F +coef_dist_logged_escort,-1.236681595,F +coef_dist_hh_child_escort,-0.115868258,F +coef_mode_logsum_shopping,0.65,F +coef_dist_shopping,-0.098281692,F +coef_dist_squared_shopping,-0.020314759,F +coef_dist_cubed_shopping,0.000801126,F +coef_dist_logged_shopping,-0.792030818,F +coef_dist_joint_tour_shopping,0.035663397,F +coef_mode_logsum_eatout,0.56,F +coef_dist_eatout,-0.039022477,F +coef_dist_squared_eatout,-0.02117814,F +coef_dist_cubed_eatout,0.000793071,F +coef_dist_logged_eatout,-0.680658809,F +coef_dist_hh_child_eatout,-0.059764452,F +coef_mode_logsum_othmaint,0.38,F +coef_dist_othmaint,-0.058808778,F +coef_dist_squared_othmaint,-0.009297153,F +coef_dist_cubed_othmaint,0.000314133,F +coef_dist_logged_othmaint,-0.759800765,F +coef_dist_med_inc_othmaint,-0.007251455,F +coef_dist_high_inc_othmaint,-0.01144311,F +coef_mode_logsum_social,0.68,F +coef_dist_social,-0.070898603,F +coef_dist_logged_social,-0.67038649,F +coef_dist_hh_child_social,-0.054753072,F +coef_dist_joint_tour_social,0.030177219,F +coef_mode_logsum_othdiscr,0.75,F +coef_dist_othdiscr,-0.03374735,F +coef_dist_squared_othdiscr,-0.012479607,F +coef_dist_cubed_othdiscr,0.000434109,F +coef_dist_logged_othdiscr,-0.680070996,F +coef_dist_med_inc_othdiscr,0.036605959,F +coef_dist_hh_child_othdiscr,-0.021556002,F +coef_dist_joint_tour_othdiscr,0.011538605,F diff --git a/activitysim/examples/example_semcog/configs/joint_tour_destination_sample.csv b/activitysim/examples/prototype_semcog/configs/joint_tour_destination_sample.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/joint_tour_destination_sample.csv rename to activitysim/examples/prototype_semcog/configs/joint_tour_destination_sample.csv index 2f0500d883..5c4bf8a90f 100755 --- a/activitysim/examples/example_semcog/configs/joint_tour_destination_sample.csv +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_destination_sample.csv @@ -1,13 +1,13 @@ -Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr -local_dist,_DIST@skims['DIST'],1,1,1,1,1,1 -util_dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_othmaint,coef_dist_social,coef_dist_othdiscr -util_dist_squared,"@(_DIST).clip(0,20)**2",coef_dist_squared_escort,coef_dist_squared_shopping,coef_dist_squared_eatout,coef_dist_squared_othmaint,,coef_dist_squared_othdiscr -util_dist_cubed,"@(_DIST).clip(0,20)**3",coef_dist_cubed_escort,coef_dist_cubed_shopping,coef_dist_cubed_eatout,coef_dist_cubed_othmaint,,coef_dist_cubed_othdiscr -util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_escort,coef_dist_logged_shopping,coef_dist_logged_eatout,coef_dist_logged_othmaint,coef_dist_logged_social,coef_dist_logged_othdiscr -util_dist_low,@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,0,0,0,0,0,0 -util_dist_med,@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,0,0,0,coef_dist_med_inc_othmaint,0,coef_dist_med_inc_othdiscr -util_dist_high,@(df['income_segment']==WORK_HIGH_SEGMENT_ID) * _DIST,0,0,0,coef_dist_high_inc_othmaint,0,0 -util_dist_hh_child,@(df['num_children']>0) * _DIST,coef_dist_hh_child_escort,0,coef_dist_hh_child_eatout,0,coef_dist_hh_child_social,coef_dist_hh_child_othdiscr -util_dist_joint_tour,@(df['tour_type']=='joint') * _DIST,0,coef_dist_joint_tour_shopping,0,0,coef_dist_joint_tour_social,coef_dist_joint_tour_othdiscr -Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1 -No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999 +Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr +local_dist,_DIST@skims['DIST'],1,1,1,1,1,1 +util_dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_othmaint,coef_dist_social,coef_dist_othdiscr +util_dist_squared,"@(_DIST).clip(0,20)**2",coef_dist_squared_escort,coef_dist_squared_shopping,coef_dist_squared_eatout,coef_dist_squared_othmaint,,coef_dist_squared_othdiscr +util_dist_cubed,"@(_DIST).clip(0,20)**3",coef_dist_cubed_escort,coef_dist_cubed_shopping,coef_dist_cubed_eatout,coef_dist_cubed_othmaint,,coef_dist_cubed_othdiscr +util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_escort,coef_dist_logged_shopping,coef_dist_logged_eatout,coef_dist_logged_othmaint,coef_dist_logged_social,coef_dist_logged_othdiscr +util_dist_low,@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,0,0,0,0,0,0 +util_dist_med,@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,0,0,0,coef_dist_med_inc_othmaint,0,coef_dist_med_inc_othdiscr +util_dist_high,@(df['income_segment']==WORK_HIGH_SEGMENT_ID) * _DIST,0,0,0,coef_dist_high_inc_othmaint,0,0 +util_dist_hh_child,@(df['num_children']>0) * _DIST,coef_dist_hh_child_escort,0,coef_dist_hh_child_eatout,0,coef_dist_hh_child_social,coef_dist_hh_child_othdiscr +util_dist_joint_tour,@(df['tour_type']=='joint') * _DIST,0,coef_dist_joint_tour_shopping,0,0,coef_dist_joint_tour_social,coef_dist_joint_tour_othdiscr +Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1 +No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999 diff --git a/activitysim/examples/example_semcog/configs/joint_tour_frequency.csv b/activitysim/examples/prototype_semcog/configs/joint_tour_frequency.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/joint_tour_frequency.csv rename to activitysim/examples/prototype_semcog/configs/joint_tour_frequency.csv index d89911f1fb..48d46a8fbf 100755 --- a/activitysim/examples/example_semcog/configs/joint_tour_frequency.csv +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_frequency.csv @@ -1,78 +1,78 @@ -Label,Description,Expression,0_tours,1_Shop,1_Main,1_Eat,1_Visit,1_Disc,2_SS,2_SM,2_SE,2_SV,2_SD,2_MM,2_ME,2_MV,2_MD,2_EE,2_EV,2_ED,2_VV,2_VD,2_DD -util_alternative_specific_constants,alternative_specific_constants,1,coef_asc_0_tours,coef_asc_1_Shop,coef_asc_1_Main,coef_asc_1_Eat,coef_asc_1_Visit,coef_asc_1_Disc,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours -util_alternative_specific_constants_calibration,alternative_specific_constants_calibration,1,asc_calib_0_tours,asc_calib_1_Shop,asc_calib_1_Main,asc_calib_1_Eat,asc_calib_1_Visit,asc_calib_1_Disc,asc_calib_2_SS,asc_calib_2_SM,asc_calib_2_SE,asc_calib_2_SV,asc_calib_2_SD,asc_calib_2_MM,asc_calib_2_ME,asc_calib_2_MV,asc_calib_2_MD,asc_calib_2_EE,asc_calib_2_EV,asc_calib_2_ED,asc_calib_2_VV,asc_calib_2_VD,asc_calib_2_DD -#_zero_tours,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeHomeMaxThree_zero_tours,fullTimeHomeMaxThree_zero_tours,cdap_home_full_max3,coef_fullTimeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, -util_partTimeHomeMaxThree_zero_tours,partTimeHomeMaxThree_zero_tours,cdap_home_part_max3,coef_partTimeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, -util_nonWorkerHomeMaxThree_zero_tours,nonWorkerHomeMaxThree_zero_tours,cdap_home_nonwork_max3,coef_nonWorkerHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, -util_retireeHomeMaxThree_zero_tours,retireeHomeMaxThree_zero_tours,cdap_home_retired_max3,coef_retireeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, -util_universityHomeMaxThree_univ_and_driving_zero_tours,universityHomeMaxThree_univ_and_driving_zero_tours,cdap_home_univ_driving_max3,coef_universityHomeMaxThree_univ_and_driving_zero_tours,,,,,,,,,,,,,,,,,,,, -util_preDrivingHomeMaxThree_preschool_and_school_zero_tours,preDrivingHomeMaxThree_preschool_and_school_zero_tours,cdap_home_nondriving_child_max3,coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours,,,,,,,,,,,,,,,,,,,, -#_shopping,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeNonMandMaxThree_shopping,fullTimeNonMandMaxThree_shopping,cdap_nonmand_full_max3,,coef_fullTimeNonMandMaxThree_shopping,,,,,2 * coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,,,,,,,,,, -util_partTimeNonMandMaxThree_shopping,partTimeNonMandMaxThree_shopping,cdap_nonmand_part_max3,,coef_partTimeNonMandMaxThree_shopping,,,,,2 * coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,,,,,,,,,, -util_nonWorkerNonMandMaxThree_shopping,nonWorkerNonMandMaxThree_shopping,cdap_nonmand_nonwork_max3,,coef_nonWorkerNonMandMaxThree_shopping,,,,,2 * coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,,,,,,,,,, -util_retireeNonMandMaxThree_shopping,retireeNonMandMaxThree_shopping,cdap_nonmand_retired_max3,,coef_retireeNonMandMaxThree_shopping,,,,,2 * coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,,,,,,,,,, -util_universityNonMandMaxThree_shopping,universityNonMandMaxThree_shopping,cdap_nonmand_univ_driving_max3,,coef_universityNonMandMaxThree_shopping,,,,,2 * coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,,,,,,,,,, -util_preDrivingNonMandMaxThree_shopping,preDrivingNonMandMaxThree_shopping,cdap_nonmand_nondriving_child_max3,,coef_preDrivingNonMandMaxThree_shopping,,,,,2 * coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,,,,,,,,,, -util_fullTimeMandMaxThree_shopping,fullTimeMandMaxThree_shopping,cdap_mand_full_max3,,coef_fullTimeMandMaxThree_shopping,,,,,2 * coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,,,,,,,,,, -util_logTimeWindowOverlapAdult_shopping,logTimeWindowOverlapAdult_shopping,log_time_window_overlap_adult,,coef_logTimeWindowOverlapAdult_shopping,,,,,2 * coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,,,,,,,,,, -util_logTimeWindowOverlapChild_shopping,logTimeWindowOverlapChild_shopping,log_time_window_overlap_child,,coef_logTimeWindowOverlapChild_shopping,,,,,2 * coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,,,,,,,,,, -util_logTimeWindowOverlapAdultChild_shopping,logTimeWindowOverlapAdultChild_shopping,log_time_window_overlap_adult_child,,coef_logTimeWindowOverlapAdultChild_shopping,,,,,2 * coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,,,,,,,,,, -util_fewerCarsThanDrivers_shopping,fewerCarsThanDrivers_shopping,(auto_ownership > 0) & (auto_ownership < num_drivers),,coef_fewerCarsThanDrivers_shopping,,,,,2 * coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,,,,,,,,,, -util_moreCarsThanWorkers_shopping,moreCarsThanWorkers_shopping,auto_ownership > num_workers,,coef_moreCarsThanWorkers_shopping,,,,,2 * coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,,,,,,,,,, -#_Maintenance,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeNonMandMaxThree_Maintenance,fullTimeNonMandMaxThree_Maintenance,cdap_nonmand_full_max3,,,coef_fullTimeNonMandMaxThree_maint,,,,,coef_fullTimeNonMandMaxThree_maint,,,,2 * coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,,,,,, -util_partTimeNonMandMaxThree_Maintenance,partTimeNonMandMaxThree_Maintenance,cdap_nonmand_part_max3,,,coef_partTimeNonMandMaxThree_maint,,,,,coef_partTimeNonMandMaxThree_maint,,,,2 * coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,,,,,, -util_nonWorkerNonMandMaxThree_Maintenance,nonWorkerNonMandMaxThree_Maintenance,cdap_nonmand_nonwork_max3,,,coef_nonWorkerNonMandMaxThree_maint,,,,,coef_nonWorkerNonMandMaxThree_maint,,,,2 * coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,,,,,, -util_retireeNonMandMaxThree_Maintenance,retireeNonMandMaxThree_Maintenance,cdap_nonmand_retired_max3,,,coef_retireeNonMandMaxThree_maint,,,,,coef_retireeNonMandMaxThree_maint,,,,2 * coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,,,,,, -util_universityNonMandMaxThree_Maintenance,universityNonMandMaxThree_Maintenance,cdap_nonmand_univ_driving_max3,,,coef_universityNonMandMaxThree_maint,,,,,coef_universityNonMandMaxThree_maint,,,,2 * coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,,,,,, -util_preDrivingNonMandMaxThree_Maintenance,preDrivingNonMandMaxThree_Maintenance,cdap_nonmand_nondriving_child_max3,,,coef_preDrivingNonMandMaxThree_maint,,,,,coef_preDrivingNonMandMaxThree_maint,,,,2 * coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,,,,,, -util_fullTimeMandMaxThree_Maintenance,fullTimeMandMaxThree_Maintenance,cdap_mand_full_max3,,,coef_fullTimeMandMaxThree_maint,,,,,coef_fullTimeMandMaxThree_maint,,,,2 * coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,,,,,, -util_drivingAgeStuMandMaxThree_Maintenance,drivingAgeStuMandMaxThree_Maintenance,cdap_mand_univ_driving_max3,,,coef_drivingAgeStuMandMaxThree_maint,,,,,coef_drivingAgeStuMandMaxThree_maint,,,,2 * coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,,,,,, -util_preDrivingAgeMandMaxThree_Maintenance,preDrivingAgeMandMaxThree_Maintenance,cdap_mand_nondriving_child_max3,,,coef_preDrivingAgeMandMaxThree_maint,,,,,coef_preDrivingAgeMandMaxThree_maint,,,,2 * coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,,,,,, -util_logTimeWindowOverlapAdult_Maintenance,logTimeWindowOverlapAdult_Maintenance,log_time_window_overlap_adult,,,coef_logTimeWindowOverlapAdult_maint,,,,,coef_logTimeWindowOverlapAdult_maint,,,,2 * coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,,,,,, -util_logTimeWindowOverlapChild_Maintenance,logTimeWindowOverlapChild_Maintenance,log_time_window_overlap_child,,,coef_logTimeWindowOverlapChild_maint,,,,,coef_logTimeWindowOverlapChild_maint,,,,2 * coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,,,,,, -util_logTimeWindowOverlapAdultChild_Maintenance,logTimeWindowOverlapAdultChild_Maintenance,log_time_window_overlap_adult_child,,,coef_logTimeWindowOverlapAdultChild_maint,,,,,coef_logTimeWindowOverlapAdultChild_maint,,,,2 * coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,,,,,, -util_fewerCarsThanDrivers_Maintenance,fewerCarsThanDrivers_Maintenance,(auto_ownership > 0) & (auto_ownership < num_drivers),,,coef_fewerCarsThanDrivers_maint,,,,,coef_fewerCarsThanDrivers_maint,,,,2 * coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,,,,,, -#_eatout,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeNonMandMaxThree_eatout,fullTimeNonMandMaxThree_eatout,cdap_nonmand_full_max3,,,,coef_fullTimeNonMandMaxThree_eatout,,,,,coef_fullTimeNonMandMaxThree_eatout,,,,coef_fullTimeNonMandMaxThree_eatout,,,2 * coef_fullTimeNonMandMaxThree_eatout,coef_fullTimeNonMandMaxThree_eatout,coef_fullTimeNonMandMaxThree_eatout,,, -util_partTimeNonMandMaxThree_eatout,partTimeNonMandMaxThree_eatout,cdap_nonmand_part_max3,,,,coef_partTimeNonMandMaxThree_eatout,,,,,coef_partTimeNonMandMaxThree_eatout,,,,coef_partTimeNonMandMaxThree_eatout,,,2 * coef_partTimeNonMandMaxThree_eatout,coef_partTimeNonMandMaxThree_eatout,coef_partTimeNonMandMaxThree_eatout,,, -util_nonWorkerNonMandMaxThree_eatout,nonWorkerNonMandMaxThree_eatout,cdap_nonmand_nonwork_max3,,,,coef_nonWorkerNonMandMaxThree_eatout,,,,,coef_nonWorkerNonMandMaxThree_eatout,,,,coef_nonWorkerNonMandMaxThree_eatout,,,2 * coef_nonWorkerNonMandMaxThree_eatout,coef_nonWorkerNonMandMaxThree_eatout,coef_nonWorkerNonMandMaxThree_eatout,,, -util_retireeNonMandMaxThree_eatout,retireeNonMandMaxThree_eatout,cdap_nonmand_retired_max3,,,,coef_retireeNonMandMaxThree_eatout,,,,,coef_retireeNonMandMaxThree_eatout,,,,coef_retireeNonMandMaxThree_eatout,,,2 * coef_retireeNonMandMaxThree_eatout,coef_retireeNonMandMaxThree_eatout,coef_retireeNonMandMaxThree_eatout,,, -util_universityNonMandMaxThree_eatout,universityNonMandMaxThree_eatout,cdap_nonmand_univ_driving_max3,,,,coef_universityNonMandMaxThree_eatout,,,,,coef_universityNonMandMaxThree_eatout,,,,coef_universityNonMandMaxThree_eatout,,,2 * coef_universityNonMandMaxThree_eatout,coef_universityNonMandMaxThree_eatout,coef_universityNonMandMaxThree_eatout,,, -util_preDrivingNonMandMaxThree_eatout,preDrivingNonMandMaxThree_eatout,cdap_nonmand_nondriving_child_max3,,,,coef_preDrivingNonMandMaxThree_eatout,,,,,coef_preDrivingNonMandMaxThree_eatout,,,,coef_preDrivingNonMandMaxThree_eatout,,,2 * coef_preDrivingNonMandMaxThree_eatout,coef_preDrivingNonMandMaxThree_eatout,coef_preDrivingNonMandMaxThree_eatout,,, -util_logTimeWindowOverlapAdult_eatout,logTimeWindowOverlapAdult_eatout,log_time_window_overlap_adult,,,,coef_logTimeWindowOverlapAdult_eatout,,,,,coef_logTimeWindowOverlapAdult_eatout,,,,coef_logTimeWindowOverlapAdult_eatout,,,2 * coef_logTimeWindowOverlapAdult_eatout,coef_logTimeWindowOverlapAdult_eatout,coef_logTimeWindowOverlapAdult_eatout,,, -util_logTimeWindowOverlapAdultChild_eatout,logTimeWindowOverlapAdultChild_eatout,log_time_window_overlap_adult_child,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,2 * coef_logTimeWindowOverlapAdultChild_eatout,coef_logTimeWindowOverlapAdultChild_eatout,coef_logTimeWindowOverlapAdultChild_eatout,,, -util_incomeBetween50And100_eatout,incomeBetween50And100_eatout,income_between_50_and_100,,,,coef_incomeBetween50And100_eatout,,,,,coef_incomeBetween50And100_eatout,,,,coef_incomeBetween50And100_eatout,,,2 * coef_incomeBetween50And100_eatout,coef_incomeBetween50And100_eatout,coef_incomeBetween50And100_eatout,,, -util_incomeGreaterThan100_eatout,incomeGreaterThan100_eatout,income_greater_than_100,,,,coef_incomeGreaterThan100_eatout,,,,,coef_incomeGreaterThan100_eatout,,,,coef_incomeGreaterThan100_eatout,,,2 * coef_incomeGreaterThan100_eatout,coef_incomeGreaterThan100_eatout,coef_incomeGreaterThan100_eatout,,, -util_incomeMissing_dummy_always_zero_eatout,incomeMissing_dummy_always_zero_eatout,income_missing,,,,coef_incomeMissing_dummy_always_zero_eatout,,,,,coef_incomeMissing_dummy_always_zero_eatout,,,,coef_incomeMissing_dummy_always_zero_eatout,,,2 * coef_incomeMissing_dummy_always_zero_eatout,coef_incomeMissing_dummy_always_zero_eatout,coef_incomeMissing_dummy_always_zero_eatout,,, -util_moreCarsThanWorkers_eatout,moreCarsThanWorkers_eatout,auto_ownership > num_workers,,,,coef_moreCarsThanWorkers_eatout,,,,,coef_moreCarsThanWorkers_eatout,,,,coef_moreCarsThanWorkers_eatout,,,2 * coef_moreCarsThanWorkers_eatout,coef_moreCarsThanWorkers_eatout,coef_moreCarsThanWorkers_eatout,,, -util_walkRetailAccessibility_eatout,walkRetailAccessibility_eatout,non_motorized_retail_accessibility,,,,coef_walkRetailAccessibility_eatout,,,,,coef_walkRetailAccessibility_eatout,,,,coef_walkRetailAccessibility_eatout,,,2 * coef_walkRetailAccessibility_eatout,coef_walkRetailAccessibility_eatout,coef_walkRetailAccessibility_eatout,,, -#_visiting,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeNonMandMaxThree_visiting,fullTimeNonMandMaxThree_visiting,cdap_nonmand_full_max3,,,,,coef_fullTimeNonMandMaxThree_visiting,,,,,coef_fullTimeNonMandMaxThree_visiting,,,,coef_fullTimeNonMandMaxThree_visiting,,,coef_fullTimeNonMandMaxThree_visiting,,2 * coef_fullTimeNonMandMaxThree_visiting,coef_fullTimeNonMandMaxThree_visiting, -util_partTimeNonMandMaxThree_visiting,partTimeNonMandMaxThree_visiting,cdap_nonmand_part_max3,,,,,coef_partTimeNonMandMaxThree_visiting,,,,,coef_partTimeNonMandMaxThree_visiting,,,,coef_partTimeNonMandMaxThree_visiting,,,coef_partTimeNonMandMaxThree_visiting,,2 * coef_partTimeNonMandMaxThree_visiting,coef_partTimeNonMandMaxThree_visiting, -util_nonWorkerNonMandMaxThree_visiting,nonWorkerNonMandMaxThree_visiting,cdap_nonmand_nonwork_max3,,,,,coef_nonWorkerNonMandMaxThree_visiting,,,,,coef_nonWorkerNonMandMaxThree_visiting,,,,coef_nonWorkerNonMandMaxThree_visiting,,,coef_nonWorkerNonMandMaxThree_visiting,,2 * coef_nonWorkerNonMandMaxThree_visiting,coef_nonWorkerNonMandMaxThree_visiting, -util_retireeNonMandMaxThree_visiting,retireeNonMandMaxThree_visiting,cdap_nonmand_retired_max3,,,,,coef_retireeNonMandMaxThree_visiting,,,,,coef_retireeNonMandMaxThree_visiting,,,,coef_retireeNonMandMaxThree_visiting,,,coef_retireeNonMandMaxThree_visiting,,2 * coef_retireeNonMandMaxThree_visiting,coef_retireeNonMandMaxThree_visiting, -util_universityNonMandMaxThree_visiting,universityNonMandMaxThree_visiting,cdap_nonmand_univ_driving_max3,,,,,coef_universityNonMandMaxThree_visiting,,,,,coef_universityNonMandMaxThree_visiting,,,,coef_universityNonMandMaxThree_visiting,,,coef_universityNonMandMaxThree_visiting,,2 * coef_universityNonMandMaxThree_visiting,coef_universityNonMandMaxThree_visiting, -util_preDrivingNonMandMaxThree_visiting,preDrivingNonMandMaxThree_visiting,cdap_nonmand_nondriving_child_max3,,,,,coef_preDrivingNonMandMaxThree_visiting,,,,,coef_preDrivingNonMandMaxThree_visiting,,,,coef_preDrivingNonMandMaxThree_visiting,,,coef_preDrivingNonMandMaxThree_visiting,,2 * coef_preDrivingNonMandMaxThree_visiting,coef_preDrivingNonMandMaxThree_visiting, -util_timeWindowOverlapAdult_visiting,timeWindowOverlapAdult_visiting,time_window_overlap_adult,,,,,coef_timeWindowOverlapAdult_visiting,,,,,coef_timeWindowOverlapAdult_visiting,,,,coef_timeWindowOverlapAdult_visiting,,,coef_timeWindowOverlapAdult_visiting,,2 * coef_timeWindowOverlapAdult_visiting,coef_timeWindowOverlapAdult_visiting, -util_timeWindowOverlapChild_visiting,timeWindowOverlapChild_visiting,time_window_overlap_child,,,,,coef_timeWindowOverlapChild_visiting,,,,,coef_timeWindowOverlapChild_visiting,,,,coef_timeWindowOverlapChild_visiting,,,coef_timeWindowOverlapChild_visiting,,2 * coef_timeWindowOverlapChild_visiting,coef_timeWindowOverlapChild_visiting, -util_timeWindowOverlapAdultChild_visiting,timeWindowOverlapAdultChild_visiting,time_window_overlap_adult_child,,,,,coef_timeWindowOverlapAdultChild_visiting,,,,,coef_timeWindowOverlapAdultChild_visiting,,,,coef_timeWindowOverlapAdultChild_visiting,,,coef_timeWindowOverlapAdultChild_visiting,,2 * coef_timeWindowOverlapAdultChild_visiting,coef_timeWindowOverlapAdultChild_visiting, -util_zeroAutomobiles_visiting,zeroAutomobiles_visiting,auto_ownership == 0,,,,,coef_zeroAutomobiles_visiting,,,,,coef_zeroAutomobiles_visiting,,,,coef_zeroAutomobiles_visiting,,,coef_zeroAutomobiles_visiting,,2 * coef_zeroAutomobiles_visiting,coef_zeroAutomobiles_visiting, -#_discretionary,,,,,,,,,,,,,,,,,,,,,,, -util_fullTimeNonMandMaxThree_disc,fullTimeNonMandMaxThree_disc,cdap_nonmand_full_max3,,,,,,coef_fullTimeNonMandMaxThree_disc,,,,,coef_fullTimeNonMandMaxThree_disc,,,,coef_fullTimeNonMandMaxThree_disc,,,coef_fullTimeNonMandMaxThree_disc,,coef_fullTimeNonMandMaxThree_disc,2 * coef_fullTimeNonMandMaxThree_disc -util_partTimeNonMandMaxThree_disc,partTimeNonMandMaxThree_disc,cdap_nonmand_part_max3,,,,,,coef_partTimeNonMandMaxThree_disc,,,,,coef_partTimeNonMandMaxThree_disc,,,,coef_partTimeNonMandMaxThree_disc,,,coef_partTimeNonMandMaxThree_disc,,coef_partTimeNonMandMaxThree_disc,2 * coef_partTimeNonMandMaxThree_disc -util_nonWorkerNonMandMaxThree_disc,nonWorkerNonMandMaxThree_disc,cdap_nonmand_nonwork_max3,,,,,,coef_nonWorkerNonMandMaxThree_disc,,,,,coef_nonWorkerNonMandMaxThree_disc,,,,coef_nonWorkerNonMandMaxThree_disc,,,coef_nonWorkerNonMandMaxThree_disc,,coef_nonWorkerNonMandMaxThree_disc,2 * coef_nonWorkerNonMandMaxThree_disc -util_retireeNonMandMaxThree_disc,retireeNonMandMaxThree_disc,cdap_nonmand_retired_max3,,,,,,coef_retireeNonMandMaxThree_disc,,,,,coef_retireeNonMandMaxThree_disc,,,,coef_retireeNonMandMaxThree_disc,,,coef_retireeNonMandMaxThree_disc,,coef_retireeNonMandMaxThree_disc,2 * coef_retireeNonMandMaxThree_disc -util_universityNonMandMaxThree_disc,universityNonMandMaxThree_disc,cdap_nonmand_univ_driving_max3,,,,,,coef_universityNonMandMaxThree_disc,,,,,coef_universityNonMandMaxThree_disc,,,,coef_universityNonMandMaxThree_disc,,,coef_universityNonMandMaxThree_disc,,coef_universityNonMandMaxThree_disc,2 * coef_universityNonMandMaxThree_disc -util_preDrivingNonMandMaxThree_disc,preDrivingNonMandMaxThree_disc,cdap_nonmand_nondriving_child_max3,,,,,,coef_preDrivingNonMandMaxThree_disc,,,,,coef_preDrivingNonMandMaxThree_disc,,,,coef_preDrivingNonMandMaxThree_disc,,,coef_preDrivingNonMandMaxThree_disc,,coef_preDrivingNonMandMaxThree_disc,2 * coef_preDrivingNonMandMaxThree_disc -util_drivingAgeStuMandMaxThree_disc,drivingAgeStuMandMaxThree_disc,cdap_mand_univ_driving_max3,,,,,,coef_drivingAgeStuMandMaxThree_disc,,,,,coef_drivingAgeStuMandMaxThree_disc,,,,coef_drivingAgeStuMandMaxThree_disc,,,coef_drivingAgeStuMandMaxThree_disc,,coef_drivingAgeStuMandMaxThree_disc,2 * coef_drivingAgeStuMandMaxThree_disc -util_preDrivingAgeMandMaxThree_disc,preDrivingAgeMandMaxThree_disc,cdap_mand_nondriving_child_max3,,,,,,coef_preDrivingAgeMandMaxThree_disc,,,,,coef_preDrivingAgeMandMaxThree_disc,,,,coef_preDrivingAgeMandMaxThree_disc,,,coef_preDrivingAgeMandMaxThree_disc,,coef_preDrivingAgeMandMaxThree_disc,2 * coef_preDrivingAgeMandMaxThree_disc -util_logTimeWindowOverlapAdult_disc,logTimeWindowOverlapAdult_disc,log_time_window_overlap_adult,,,,,,coef_logTimeWindowOverlapAdult_disc,,,,,coef_logTimeWindowOverlapAdult_disc,,,,coef_logTimeWindowOverlapAdult_disc,,,coef_logTimeWindowOverlapAdult_disc,,coef_logTimeWindowOverlapAdult_disc,2 * coef_logTimeWindowOverlapAdult_disc -util_logTimeWindowOverlapChild_disc,logTimeWindowOverlapChild_disc,log_time_window_overlap_child,,,,,,coef_logTimeWindowOverlapChild_disc,,,,,coef_logTimeWindowOverlapChild_disc,,,,coef_logTimeWindowOverlapChild_disc,,,coef_logTimeWindowOverlapChild_disc,,coef_logTimeWindowOverlapChild_disc,2 * coef_logTimeWindowOverlapChild_disc -util_logTimeWindowOverlapAdultChild_disc,logTimeWindowOverlapAdultChild_disc,log_time_window_overlap_adult_child,,,,,,coef_logTimeWindowOverlapAdultChild_disc,,,,,coef_logTimeWindowOverlapAdultChild_disc,,,,coef_logTimeWindowOverlapAdultChild_disc,,,coef_logTimeWindowOverlapAdultChild_disc,,coef_logTimeWindowOverlapAdultChild_disc,2 * coef_logTimeWindowOverlapAdultChild_disc -util_incomeBetween50And100_disc,incomeBetween50And100_disc,income_between_50_and_100,,,,,,coef_incomeBetween50And100_disc,,,,,coef_incomeBetween50And100_disc,,,,coef_incomeBetween50And100_disc,,,coef_incomeBetween50And100_disc,,coef_incomeBetween50And100_disc,2 * coef_incomeBetween50And100_disc -util_incomeGreaterThan100_disc,incomeGreaterThan100_disc,income_greater_than_100,,,,,,coef_incomeGreaterThan100_disc,,,,,coef_incomeGreaterThan100_disc,,,,coef_incomeGreaterThan100_disc,,,coef_incomeGreaterThan100_disc,,coef_incomeGreaterThan100_disc,2 * coef_incomeGreaterThan100_disc -util_incomeMissing_dummy_always_zero_disc,incomeMissing_dummy_always_zero_disc,income_missing,,,,,,coef_incomeMissing_dummy_always_zero_disc,,,,,coef_incomeMissing_dummy_always_zero_disc,,,,coef_incomeMissing_dummy_always_zero_disc,,,coef_incomeMissing_dummy_always_zero_disc,,coef_incomeMissing_dummy_always_zero_disc,2 * coef_incomeMissing_dummy_always_zero_disc -util_zeroAutomobiles_dis,zeroAutomobiles_disc,auto_ownership == 0,,,,,,coef_zeroAutomobiles_disc,,,,,coef_zeroAutomobiles_disc,,,,coef_zeroAutomobiles_disc,,,coef_zeroAutomobiles_disc,,coef_zeroAutomobiles_disc,2 * coef_zeroAutomobiles_disc +Label,Description,Expression,0_tours,1_Shop,1_Main,1_Eat,1_Visit,1_Disc,2_SS,2_SM,2_SE,2_SV,2_SD,2_MM,2_ME,2_MV,2_MD,2_EE,2_EV,2_ED,2_VV,2_VD,2_DD +util_alternative_specific_constants,alternative_specific_constants,1,coef_asc_0_tours,coef_asc_1_Shop,coef_asc_1_Main,coef_asc_1_Eat,coef_asc_1_Visit,coef_asc_1_Disc,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours,coef_asc_2_tours +util_alternative_specific_constants_calibration,alternative_specific_constants_calibration,1,asc_calib_0_tours,asc_calib_1_Shop,asc_calib_1_Main,asc_calib_1_Eat,asc_calib_1_Visit,asc_calib_1_Disc,asc_calib_2_SS,asc_calib_2_SM,asc_calib_2_SE,asc_calib_2_SV,asc_calib_2_SD,asc_calib_2_MM,asc_calib_2_ME,asc_calib_2_MV,asc_calib_2_MD,asc_calib_2_EE,asc_calib_2_EV,asc_calib_2_ED,asc_calib_2_VV,asc_calib_2_VD,asc_calib_2_DD +#_zero_tours,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeHomeMaxThree_zero_tours,fullTimeHomeMaxThree_zero_tours,cdap_home_full_max3,coef_fullTimeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, +util_partTimeHomeMaxThree_zero_tours,partTimeHomeMaxThree_zero_tours,cdap_home_part_max3,coef_partTimeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, +util_nonWorkerHomeMaxThree_zero_tours,nonWorkerHomeMaxThree_zero_tours,cdap_home_nonwork_max3,coef_nonWorkerHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, +util_retireeHomeMaxThree_zero_tours,retireeHomeMaxThree_zero_tours,cdap_home_retired_max3,coef_retireeHomeMaxThree_zero_tours,,,,,,,,,,,,,,,,,,,, +util_universityHomeMaxThree_univ_and_driving_zero_tours,universityHomeMaxThree_univ_and_driving_zero_tours,cdap_home_univ_driving_max3,coef_universityHomeMaxThree_univ_and_driving_zero_tours,,,,,,,,,,,,,,,,,,,, +util_preDrivingHomeMaxThree_preschool_and_school_zero_tours,preDrivingHomeMaxThree_preschool_and_school_zero_tours,cdap_home_nondriving_child_max3,coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours,,,,,,,,,,,,,,,,,,,, +#_shopping,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeNonMandMaxThree_shopping,fullTimeNonMandMaxThree_shopping,cdap_nonmand_full_max3,,coef_fullTimeNonMandMaxThree_shopping,,,,,2 * coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,coef_fullTimeNonMandMaxThree_shopping,,,,,,,,,, +util_partTimeNonMandMaxThree_shopping,partTimeNonMandMaxThree_shopping,cdap_nonmand_part_max3,,coef_partTimeNonMandMaxThree_shopping,,,,,2 * coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,coef_partTimeNonMandMaxThree_shopping,,,,,,,,,, +util_nonWorkerNonMandMaxThree_shopping,nonWorkerNonMandMaxThree_shopping,cdap_nonmand_nonwork_max3,,coef_nonWorkerNonMandMaxThree_shopping,,,,,2 * coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,coef_nonWorkerNonMandMaxThree_shopping,,,,,,,,,, +util_retireeNonMandMaxThree_shopping,retireeNonMandMaxThree_shopping,cdap_nonmand_retired_max3,,coef_retireeNonMandMaxThree_shopping,,,,,2 * coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,coef_retireeNonMandMaxThree_shopping,,,,,,,,,, +util_universityNonMandMaxThree_shopping,universityNonMandMaxThree_shopping,cdap_nonmand_univ_driving_max3,,coef_universityNonMandMaxThree_shopping,,,,,2 * coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,coef_universityNonMandMaxThree_shopping,,,,,,,,,, +util_preDrivingNonMandMaxThree_shopping,preDrivingNonMandMaxThree_shopping,cdap_nonmand_nondriving_child_max3,,coef_preDrivingNonMandMaxThree_shopping,,,,,2 * coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,coef_preDrivingNonMandMaxThree_shopping,,,,,,,,,, +util_fullTimeMandMaxThree_shopping,fullTimeMandMaxThree_shopping,cdap_mand_full_max3,,coef_fullTimeMandMaxThree_shopping,,,,,2 * coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,coef_fullTimeMandMaxThree_shopping,,,,,,,,,, +util_logTimeWindowOverlapAdult_shopping,logTimeWindowOverlapAdult_shopping,log_time_window_overlap_adult,,coef_logTimeWindowOverlapAdult_shopping,,,,,2 * coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,coef_logTimeWindowOverlapAdult_shopping,,,,,,,,,, +util_logTimeWindowOverlapChild_shopping,logTimeWindowOverlapChild_shopping,log_time_window_overlap_child,,coef_logTimeWindowOverlapChild_shopping,,,,,2 * coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,coef_logTimeWindowOverlapChild_shopping,,,,,,,,,, +util_logTimeWindowOverlapAdultChild_shopping,logTimeWindowOverlapAdultChild_shopping,log_time_window_overlap_adult_child,,coef_logTimeWindowOverlapAdultChild_shopping,,,,,2 * coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,coef_logTimeWindowOverlapAdultChild_shopping,,,,,,,,,, +util_fewerCarsThanDrivers_shopping,fewerCarsThanDrivers_shopping,(auto_ownership > 0) & (auto_ownership < num_drivers),,coef_fewerCarsThanDrivers_shopping,,,,,2 * coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,coef_fewerCarsThanDrivers_shopping,,,,,,,,,, +util_moreCarsThanWorkers_shopping,moreCarsThanWorkers_shopping,auto_ownership > num_workers,,coef_moreCarsThanWorkers_shopping,,,,,2 * coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,coef_moreCarsThanWorkers_shopping,,,,,,,,,, +#_Maintenance,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeNonMandMaxThree_Maintenance,fullTimeNonMandMaxThree_Maintenance,cdap_nonmand_full_max3,,,coef_fullTimeNonMandMaxThree_maint,,,,,coef_fullTimeNonMandMaxThree_maint,,,,2 * coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,coef_fullTimeNonMandMaxThree_maint,,,,,, +util_partTimeNonMandMaxThree_Maintenance,partTimeNonMandMaxThree_Maintenance,cdap_nonmand_part_max3,,,coef_partTimeNonMandMaxThree_maint,,,,,coef_partTimeNonMandMaxThree_maint,,,,2 * coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,coef_partTimeNonMandMaxThree_maint,,,,,, +util_nonWorkerNonMandMaxThree_Maintenance,nonWorkerNonMandMaxThree_Maintenance,cdap_nonmand_nonwork_max3,,,coef_nonWorkerNonMandMaxThree_maint,,,,,coef_nonWorkerNonMandMaxThree_maint,,,,2 * coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,coef_nonWorkerNonMandMaxThree_maint,,,,,, +util_retireeNonMandMaxThree_Maintenance,retireeNonMandMaxThree_Maintenance,cdap_nonmand_retired_max3,,,coef_retireeNonMandMaxThree_maint,,,,,coef_retireeNonMandMaxThree_maint,,,,2 * coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,coef_retireeNonMandMaxThree_maint,,,,,, +util_universityNonMandMaxThree_Maintenance,universityNonMandMaxThree_Maintenance,cdap_nonmand_univ_driving_max3,,,coef_universityNonMandMaxThree_maint,,,,,coef_universityNonMandMaxThree_maint,,,,2 * coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,coef_universityNonMandMaxThree_maint,,,,,, +util_preDrivingNonMandMaxThree_Maintenance,preDrivingNonMandMaxThree_Maintenance,cdap_nonmand_nondriving_child_max3,,,coef_preDrivingNonMandMaxThree_maint,,,,,coef_preDrivingNonMandMaxThree_maint,,,,2 * coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,coef_preDrivingNonMandMaxThree_maint,,,,,, +util_fullTimeMandMaxThree_Maintenance,fullTimeMandMaxThree_Maintenance,cdap_mand_full_max3,,,coef_fullTimeMandMaxThree_maint,,,,,coef_fullTimeMandMaxThree_maint,,,,2 * coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,coef_fullTimeMandMaxThree_maint,,,,,, +util_drivingAgeStuMandMaxThree_Maintenance,drivingAgeStuMandMaxThree_Maintenance,cdap_mand_univ_driving_max3,,,coef_drivingAgeStuMandMaxThree_maint,,,,,coef_drivingAgeStuMandMaxThree_maint,,,,2 * coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,coef_drivingAgeStuMandMaxThree_maint,,,,,, +util_preDrivingAgeMandMaxThree_Maintenance,preDrivingAgeMandMaxThree_Maintenance,cdap_mand_nondriving_child_max3,,,coef_preDrivingAgeMandMaxThree_maint,,,,,coef_preDrivingAgeMandMaxThree_maint,,,,2 * coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,coef_preDrivingAgeMandMaxThree_maint,,,,,, +util_logTimeWindowOverlapAdult_Maintenance,logTimeWindowOverlapAdult_Maintenance,log_time_window_overlap_adult,,,coef_logTimeWindowOverlapAdult_maint,,,,,coef_logTimeWindowOverlapAdult_maint,,,,2 * coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,coef_logTimeWindowOverlapAdult_maint,,,,,, +util_logTimeWindowOverlapChild_Maintenance,logTimeWindowOverlapChild_Maintenance,log_time_window_overlap_child,,,coef_logTimeWindowOverlapChild_maint,,,,,coef_logTimeWindowOverlapChild_maint,,,,2 * coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,coef_logTimeWindowOverlapChild_maint,,,,,, +util_logTimeWindowOverlapAdultChild_Maintenance,logTimeWindowOverlapAdultChild_Maintenance,log_time_window_overlap_adult_child,,,coef_logTimeWindowOverlapAdultChild_maint,,,,,coef_logTimeWindowOverlapAdultChild_maint,,,,2 * coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,coef_logTimeWindowOverlapAdultChild_maint,,,,,, +util_fewerCarsThanDrivers_Maintenance,fewerCarsThanDrivers_Maintenance,(auto_ownership > 0) & (auto_ownership < num_drivers),,,coef_fewerCarsThanDrivers_maint,,,,,coef_fewerCarsThanDrivers_maint,,,,2 * coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,coef_fewerCarsThanDrivers_maint,,,,,, +#_eatout,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeNonMandMaxThree_eatout,fullTimeNonMandMaxThree_eatout,cdap_nonmand_full_max3,,,,coef_fullTimeNonMandMaxThree_eatout,,,,,coef_fullTimeNonMandMaxThree_eatout,,,,coef_fullTimeNonMandMaxThree_eatout,,,2 * coef_fullTimeNonMandMaxThree_eatout,coef_fullTimeNonMandMaxThree_eatout,coef_fullTimeNonMandMaxThree_eatout,,, +util_partTimeNonMandMaxThree_eatout,partTimeNonMandMaxThree_eatout,cdap_nonmand_part_max3,,,,coef_partTimeNonMandMaxThree_eatout,,,,,coef_partTimeNonMandMaxThree_eatout,,,,coef_partTimeNonMandMaxThree_eatout,,,2 * coef_partTimeNonMandMaxThree_eatout,coef_partTimeNonMandMaxThree_eatout,coef_partTimeNonMandMaxThree_eatout,,, +util_nonWorkerNonMandMaxThree_eatout,nonWorkerNonMandMaxThree_eatout,cdap_nonmand_nonwork_max3,,,,coef_nonWorkerNonMandMaxThree_eatout,,,,,coef_nonWorkerNonMandMaxThree_eatout,,,,coef_nonWorkerNonMandMaxThree_eatout,,,2 * coef_nonWorkerNonMandMaxThree_eatout,coef_nonWorkerNonMandMaxThree_eatout,coef_nonWorkerNonMandMaxThree_eatout,,, +util_retireeNonMandMaxThree_eatout,retireeNonMandMaxThree_eatout,cdap_nonmand_retired_max3,,,,coef_retireeNonMandMaxThree_eatout,,,,,coef_retireeNonMandMaxThree_eatout,,,,coef_retireeNonMandMaxThree_eatout,,,2 * coef_retireeNonMandMaxThree_eatout,coef_retireeNonMandMaxThree_eatout,coef_retireeNonMandMaxThree_eatout,,, +util_universityNonMandMaxThree_eatout,universityNonMandMaxThree_eatout,cdap_nonmand_univ_driving_max3,,,,coef_universityNonMandMaxThree_eatout,,,,,coef_universityNonMandMaxThree_eatout,,,,coef_universityNonMandMaxThree_eatout,,,2 * coef_universityNonMandMaxThree_eatout,coef_universityNonMandMaxThree_eatout,coef_universityNonMandMaxThree_eatout,,, +util_preDrivingNonMandMaxThree_eatout,preDrivingNonMandMaxThree_eatout,cdap_nonmand_nondriving_child_max3,,,,coef_preDrivingNonMandMaxThree_eatout,,,,,coef_preDrivingNonMandMaxThree_eatout,,,,coef_preDrivingNonMandMaxThree_eatout,,,2 * coef_preDrivingNonMandMaxThree_eatout,coef_preDrivingNonMandMaxThree_eatout,coef_preDrivingNonMandMaxThree_eatout,,, +util_logTimeWindowOverlapAdult_eatout,logTimeWindowOverlapAdult_eatout,log_time_window_overlap_adult,,,,coef_logTimeWindowOverlapAdult_eatout,,,,,coef_logTimeWindowOverlapAdult_eatout,,,,coef_logTimeWindowOverlapAdult_eatout,,,2 * coef_logTimeWindowOverlapAdult_eatout,coef_logTimeWindowOverlapAdult_eatout,coef_logTimeWindowOverlapAdult_eatout,,, +util_logTimeWindowOverlapAdultChild_eatout,logTimeWindowOverlapAdultChild_eatout,log_time_window_overlap_adult_child,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,,coef_logTimeWindowOverlapAdultChild_eatout,,,2 * coef_logTimeWindowOverlapAdultChild_eatout,coef_logTimeWindowOverlapAdultChild_eatout,coef_logTimeWindowOverlapAdultChild_eatout,,, +util_incomeBetween50And100_eatout,incomeBetween50And100_eatout,income_between_50_and_100,,,,coef_incomeBetween50And100_eatout,,,,,coef_incomeBetween50And100_eatout,,,,coef_incomeBetween50And100_eatout,,,2 * coef_incomeBetween50And100_eatout,coef_incomeBetween50And100_eatout,coef_incomeBetween50And100_eatout,,, +util_incomeGreaterThan100_eatout,incomeGreaterThan100_eatout,income_greater_than_100,,,,coef_incomeGreaterThan100_eatout,,,,,coef_incomeGreaterThan100_eatout,,,,coef_incomeGreaterThan100_eatout,,,2 * coef_incomeGreaterThan100_eatout,coef_incomeGreaterThan100_eatout,coef_incomeGreaterThan100_eatout,,, +util_incomeMissing_dummy_always_zero_eatout,incomeMissing_dummy_always_zero_eatout,income_missing,,,,coef_incomeMissing_dummy_always_zero_eatout,,,,,coef_incomeMissing_dummy_always_zero_eatout,,,,coef_incomeMissing_dummy_always_zero_eatout,,,2 * coef_incomeMissing_dummy_always_zero_eatout,coef_incomeMissing_dummy_always_zero_eatout,coef_incomeMissing_dummy_always_zero_eatout,,, +util_moreCarsThanWorkers_eatout,moreCarsThanWorkers_eatout,auto_ownership > num_workers,,,,coef_moreCarsThanWorkers_eatout,,,,,coef_moreCarsThanWorkers_eatout,,,,coef_moreCarsThanWorkers_eatout,,,2 * coef_moreCarsThanWorkers_eatout,coef_moreCarsThanWorkers_eatout,coef_moreCarsThanWorkers_eatout,,, +util_walkRetailAccessibility_eatout,walkRetailAccessibility_eatout,non_motorized_retail_accessibility,,,,coef_walkRetailAccessibility_eatout,,,,,coef_walkRetailAccessibility_eatout,,,,coef_walkRetailAccessibility_eatout,,,2 * coef_walkRetailAccessibility_eatout,coef_walkRetailAccessibility_eatout,coef_walkRetailAccessibility_eatout,,, +#_visiting,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeNonMandMaxThree_visiting,fullTimeNonMandMaxThree_visiting,cdap_nonmand_full_max3,,,,,coef_fullTimeNonMandMaxThree_visiting,,,,,coef_fullTimeNonMandMaxThree_visiting,,,,coef_fullTimeNonMandMaxThree_visiting,,,coef_fullTimeNonMandMaxThree_visiting,,2 * coef_fullTimeNonMandMaxThree_visiting,coef_fullTimeNonMandMaxThree_visiting, +util_partTimeNonMandMaxThree_visiting,partTimeNonMandMaxThree_visiting,cdap_nonmand_part_max3,,,,,coef_partTimeNonMandMaxThree_visiting,,,,,coef_partTimeNonMandMaxThree_visiting,,,,coef_partTimeNonMandMaxThree_visiting,,,coef_partTimeNonMandMaxThree_visiting,,2 * coef_partTimeNonMandMaxThree_visiting,coef_partTimeNonMandMaxThree_visiting, +util_nonWorkerNonMandMaxThree_visiting,nonWorkerNonMandMaxThree_visiting,cdap_nonmand_nonwork_max3,,,,,coef_nonWorkerNonMandMaxThree_visiting,,,,,coef_nonWorkerNonMandMaxThree_visiting,,,,coef_nonWorkerNonMandMaxThree_visiting,,,coef_nonWorkerNonMandMaxThree_visiting,,2 * coef_nonWorkerNonMandMaxThree_visiting,coef_nonWorkerNonMandMaxThree_visiting, +util_retireeNonMandMaxThree_visiting,retireeNonMandMaxThree_visiting,cdap_nonmand_retired_max3,,,,,coef_retireeNonMandMaxThree_visiting,,,,,coef_retireeNonMandMaxThree_visiting,,,,coef_retireeNonMandMaxThree_visiting,,,coef_retireeNonMandMaxThree_visiting,,2 * coef_retireeNonMandMaxThree_visiting,coef_retireeNonMandMaxThree_visiting, +util_universityNonMandMaxThree_visiting,universityNonMandMaxThree_visiting,cdap_nonmand_univ_driving_max3,,,,,coef_universityNonMandMaxThree_visiting,,,,,coef_universityNonMandMaxThree_visiting,,,,coef_universityNonMandMaxThree_visiting,,,coef_universityNonMandMaxThree_visiting,,2 * coef_universityNonMandMaxThree_visiting,coef_universityNonMandMaxThree_visiting, +util_preDrivingNonMandMaxThree_visiting,preDrivingNonMandMaxThree_visiting,cdap_nonmand_nondriving_child_max3,,,,,coef_preDrivingNonMandMaxThree_visiting,,,,,coef_preDrivingNonMandMaxThree_visiting,,,,coef_preDrivingNonMandMaxThree_visiting,,,coef_preDrivingNonMandMaxThree_visiting,,2 * coef_preDrivingNonMandMaxThree_visiting,coef_preDrivingNonMandMaxThree_visiting, +util_timeWindowOverlapAdult_visiting,timeWindowOverlapAdult_visiting,time_window_overlap_adult,,,,,coef_timeWindowOverlapAdult_visiting,,,,,coef_timeWindowOverlapAdult_visiting,,,,coef_timeWindowOverlapAdult_visiting,,,coef_timeWindowOverlapAdult_visiting,,2 * coef_timeWindowOverlapAdult_visiting,coef_timeWindowOverlapAdult_visiting, +util_timeWindowOverlapChild_visiting,timeWindowOverlapChild_visiting,time_window_overlap_child,,,,,coef_timeWindowOverlapChild_visiting,,,,,coef_timeWindowOverlapChild_visiting,,,,coef_timeWindowOverlapChild_visiting,,,coef_timeWindowOverlapChild_visiting,,2 * coef_timeWindowOverlapChild_visiting,coef_timeWindowOverlapChild_visiting, +util_timeWindowOverlapAdultChild_visiting,timeWindowOverlapAdultChild_visiting,time_window_overlap_adult_child,,,,,coef_timeWindowOverlapAdultChild_visiting,,,,,coef_timeWindowOverlapAdultChild_visiting,,,,coef_timeWindowOverlapAdultChild_visiting,,,coef_timeWindowOverlapAdultChild_visiting,,2 * coef_timeWindowOverlapAdultChild_visiting,coef_timeWindowOverlapAdultChild_visiting, +util_zeroAutomobiles_visiting,zeroAutomobiles_visiting,auto_ownership == 0,,,,,coef_zeroAutomobiles_visiting,,,,,coef_zeroAutomobiles_visiting,,,,coef_zeroAutomobiles_visiting,,,coef_zeroAutomobiles_visiting,,2 * coef_zeroAutomobiles_visiting,coef_zeroAutomobiles_visiting, +#_discretionary,,,,,,,,,,,,,,,,,,,,,,, +util_fullTimeNonMandMaxThree_disc,fullTimeNonMandMaxThree_disc,cdap_nonmand_full_max3,,,,,,coef_fullTimeNonMandMaxThree_disc,,,,,coef_fullTimeNonMandMaxThree_disc,,,,coef_fullTimeNonMandMaxThree_disc,,,coef_fullTimeNonMandMaxThree_disc,,coef_fullTimeNonMandMaxThree_disc,2 * coef_fullTimeNonMandMaxThree_disc +util_partTimeNonMandMaxThree_disc,partTimeNonMandMaxThree_disc,cdap_nonmand_part_max3,,,,,,coef_partTimeNonMandMaxThree_disc,,,,,coef_partTimeNonMandMaxThree_disc,,,,coef_partTimeNonMandMaxThree_disc,,,coef_partTimeNonMandMaxThree_disc,,coef_partTimeNonMandMaxThree_disc,2 * coef_partTimeNonMandMaxThree_disc +util_nonWorkerNonMandMaxThree_disc,nonWorkerNonMandMaxThree_disc,cdap_nonmand_nonwork_max3,,,,,,coef_nonWorkerNonMandMaxThree_disc,,,,,coef_nonWorkerNonMandMaxThree_disc,,,,coef_nonWorkerNonMandMaxThree_disc,,,coef_nonWorkerNonMandMaxThree_disc,,coef_nonWorkerNonMandMaxThree_disc,2 * coef_nonWorkerNonMandMaxThree_disc +util_retireeNonMandMaxThree_disc,retireeNonMandMaxThree_disc,cdap_nonmand_retired_max3,,,,,,coef_retireeNonMandMaxThree_disc,,,,,coef_retireeNonMandMaxThree_disc,,,,coef_retireeNonMandMaxThree_disc,,,coef_retireeNonMandMaxThree_disc,,coef_retireeNonMandMaxThree_disc,2 * coef_retireeNonMandMaxThree_disc +util_universityNonMandMaxThree_disc,universityNonMandMaxThree_disc,cdap_nonmand_univ_driving_max3,,,,,,coef_universityNonMandMaxThree_disc,,,,,coef_universityNonMandMaxThree_disc,,,,coef_universityNonMandMaxThree_disc,,,coef_universityNonMandMaxThree_disc,,coef_universityNonMandMaxThree_disc,2 * coef_universityNonMandMaxThree_disc +util_preDrivingNonMandMaxThree_disc,preDrivingNonMandMaxThree_disc,cdap_nonmand_nondriving_child_max3,,,,,,coef_preDrivingNonMandMaxThree_disc,,,,,coef_preDrivingNonMandMaxThree_disc,,,,coef_preDrivingNonMandMaxThree_disc,,,coef_preDrivingNonMandMaxThree_disc,,coef_preDrivingNonMandMaxThree_disc,2 * coef_preDrivingNonMandMaxThree_disc +util_drivingAgeStuMandMaxThree_disc,drivingAgeStuMandMaxThree_disc,cdap_mand_univ_driving_max3,,,,,,coef_drivingAgeStuMandMaxThree_disc,,,,,coef_drivingAgeStuMandMaxThree_disc,,,,coef_drivingAgeStuMandMaxThree_disc,,,coef_drivingAgeStuMandMaxThree_disc,,coef_drivingAgeStuMandMaxThree_disc,2 * coef_drivingAgeStuMandMaxThree_disc +util_preDrivingAgeMandMaxThree_disc,preDrivingAgeMandMaxThree_disc,cdap_mand_nondriving_child_max3,,,,,,coef_preDrivingAgeMandMaxThree_disc,,,,,coef_preDrivingAgeMandMaxThree_disc,,,,coef_preDrivingAgeMandMaxThree_disc,,,coef_preDrivingAgeMandMaxThree_disc,,coef_preDrivingAgeMandMaxThree_disc,2 * coef_preDrivingAgeMandMaxThree_disc +util_logTimeWindowOverlapAdult_disc,logTimeWindowOverlapAdult_disc,log_time_window_overlap_adult,,,,,,coef_logTimeWindowOverlapAdult_disc,,,,,coef_logTimeWindowOverlapAdult_disc,,,,coef_logTimeWindowOverlapAdult_disc,,,coef_logTimeWindowOverlapAdult_disc,,coef_logTimeWindowOverlapAdult_disc,2 * coef_logTimeWindowOverlapAdult_disc +util_logTimeWindowOverlapChild_disc,logTimeWindowOverlapChild_disc,log_time_window_overlap_child,,,,,,coef_logTimeWindowOverlapChild_disc,,,,,coef_logTimeWindowOverlapChild_disc,,,,coef_logTimeWindowOverlapChild_disc,,,coef_logTimeWindowOverlapChild_disc,,coef_logTimeWindowOverlapChild_disc,2 * coef_logTimeWindowOverlapChild_disc +util_logTimeWindowOverlapAdultChild_disc,logTimeWindowOverlapAdultChild_disc,log_time_window_overlap_adult_child,,,,,,coef_logTimeWindowOverlapAdultChild_disc,,,,,coef_logTimeWindowOverlapAdultChild_disc,,,,coef_logTimeWindowOverlapAdultChild_disc,,,coef_logTimeWindowOverlapAdultChild_disc,,coef_logTimeWindowOverlapAdultChild_disc,2 * coef_logTimeWindowOverlapAdultChild_disc +util_incomeBetween50And100_disc,incomeBetween50And100_disc,income_between_50_and_100,,,,,,coef_incomeBetween50And100_disc,,,,,coef_incomeBetween50And100_disc,,,,coef_incomeBetween50And100_disc,,,coef_incomeBetween50And100_disc,,coef_incomeBetween50And100_disc,2 * coef_incomeBetween50And100_disc +util_incomeGreaterThan100_disc,incomeGreaterThan100_disc,income_greater_than_100,,,,,,coef_incomeGreaterThan100_disc,,,,,coef_incomeGreaterThan100_disc,,,,coef_incomeGreaterThan100_disc,,,coef_incomeGreaterThan100_disc,,coef_incomeGreaterThan100_disc,2 * coef_incomeGreaterThan100_disc +util_incomeMissing_dummy_always_zero_disc,incomeMissing_dummy_always_zero_disc,income_missing,,,,,,coef_incomeMissing_dummy_always_zero_disc,,,,,coef_incomeMissing_dummy_always_zero_disc,,,,coef_incomeMissing_dummy_always_zero_disc,,,coef_incomeMissing_dummy_always_zero_disc,,coef_incomeMissing_dummy_always_zero_disc,2 * coef_incomeMissing_dummy_always_zero_disc +util_zeroAutomobiles_dis,zeroAutomobiles_disc,auto_ownership == 0,,,,,,coef_zeroAutomobiles_disc,,,,,coef_zeroAutomobiles_disc,,,,coef_zeroAutomobiles_disc,,,coef_zeroAutomobiles_disc,,coef_zeroAutomobiles_disc,2 * coef_zeroAutomobiles_disc diff --git a/activitysim/examples/example_psrc/configs/joint_tour_frequency.yaml b/activitysim/examples/prototype_semcog/configs/joint_tour_frequency.yaml similarity index 95% rename from activitysim/examples/example_psrc/configs/joint_tour_frequency.yaml rename to activitysim/examples/prototype_semcog/configs/joint_tour_frequency.yaml index d5a70b1dfc..61e1f1bdcf 100755 --- a/activitysim/examples/example_psrc/configs/joint_tour_frequency.yaml +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_frequency.yaml @@ -1,11 +1,11 @@ -LOGIT_TYPE: MNL - -SPEC: joint_tour_frequency.csv -COEFFICIENTS: joint_tour_frequency_coeffs.csv - -preprocessor: - SPEC: joint_tour_frequency_annotate_households_preprocessor - DF: households - TABLES: - #- persons - - accessibility +LOGIT_TYPE: MNL + +SPEC: joint_tour_frequency.csv +COEFFICIENTS: joint_tour_frequency_coeffs.csv + +preprocessor: + SPEC: joint_tour_frequency_annotate_households_preprocessor + DF: households + TABLES: + #- persons + - accessibility diff --git a/activitysim/examples/example_semcog/configs/joint_tour_frequency_alternatives.csv b/activitysim/examples/prototype_semcog/configs/joint_tour_frequency_alternatives.csv similarity index 94% rename from activitysim/examples/example_semcog/configs/joint_tour_frequency_alternatives.csv rename to activitysim/examples/prototype_semcog/configs/joint_tour_frequency_alternatives.csv index fefa93432c..7bf93731f9 100755 --- a/activitysim/examples/example_semcog/configs/joint_tour_frequency_alternatives.csv +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_frequency_alternatives.csv @@ -1,23 +1,23 @@ -#,,,,,alt file for building joint tours -alt,shopping,othmaint,eatout,social,othdiscr -0_tours,0,0,0,0,0 -1_Shop,1,0,0,0,0 -1_Main,0,1,0,0,0 -1_Eat,0,0,1,0,0 -1_Visit,0,0,0,1,0 -1_Disc,0,0,0,0,1 -2_SS,2,0,0,0,0 -2_SM,1,1,0,0,0 -2_SE,1,0,1,0,0 -2_SV,1,0,0,1,0 -2_SD,1,0,0,0,1 -2_MM,0,2,0,0,0 -2_ME,0,1,1,0,0 -2_MV,0,1,0,1,0 -2_MD,0,1,0,0,1 -2_EE,0,0,2,0,0 -2_EV,0,0,1,1,0 -2_ED,0,0,1,0,1 -2_VV,0,0,0,2,0 -2_VD,0,0,0,1,1 -2_DD,0,0,0,0,2 +#,,,,,alt file for building joint tours +alt,shopping,othmaint,eatout,social,othdiscr +0_tours,0,0,0,0,0 +1_Shop,1,0,0,0,0 +1_Main,0,1,0,0,0 +1_Eat,0,0,1,0,0 +1_Visit,0,0,0,1,0 +1_Disc,0,0,0,0,1 +2_SS,2,0,0,0,0 +2_SM,1,1,0,0,0 +2_SE,1,0,1,0,0 +2_SV,1,0,0,1,0 +2_SD,1,0,0,0,1 +2_MM,0,2,0,0,0 +2_ME,0,1,1,0,0 +2_MV,0,1,0,1,0 +2_MD,0,1,0,0,1 +2_EE,0,0,2,0,0 +2_EV,0,0,1,1,0 +2_ED,0,0,1,0,1 +2_VV,0,0,0,2,0 +2_VD,0,0,0,1,1 +2_DD,0,0,0,0,2 diff --git a/activitysim/examples/example_semcog/configs/joint_tour_frequency_annotate_households_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/joint_tour_frequency_annotate_households_preprocessor.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/joint_tour_frequency_annotate_households_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/joint_tour_frequency_annotate_households_preprocessor.csv index b6dd9b930e..c5580ed715 100755 --- a/activitysim/examples/example_semcog/configs/joint_tour_frequency_annotate_households_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_frequency_annotate_households_preprocessor.csv @@ -1,32 +1,32 @@ -Description,Target,Expression -,_PTYPE_CDAP_PATTERN_COUNT,"lambda ptype, activity, households, persons: persons.query('ptype == %s and cdap_activity==\'%s\'' % (ptype, activity)).groupby('household_id').size().reindex(households.index).fillna(0)" -,_PEMPLOY_CDAP_PATTERN_COUNT,"lambda pemploy, activity, households, persons: persons.query('pemploy == %s and cdap_activity==\'%s\'' % (pemploy, activity)).groupby('household_id').size().reindex(households.index).fillna(0)" -,_2_PTYPE_CDAP_PATTERN_COUNT,"lambda ptype1, ptype2, activity, households, persons: persons.query('(ptype == %s or ptype == %s) and cdap_activity==\'%s\'' % (ptype1, ptype2, activity)).groupby('household_id').size().reindex(households.index).fillna(0)" -#,, -,_HH_OVERLAPS,"hh_time_window_overlap(households, persons)" -,time_window_overlap_adult,_HH_OVERLAPS['aa']/2.25 -,time_window_overlap_child,_HH_OVERLAPS['cc']/2.25 -,time_window_overlap_adult_child,_HH_OVERLAPS['ac']/2.25 -#,, -,cdap_home_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'H', households, persons).clip(0,3)" -,cdap_home_part_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_PART, 'H', households, persons).clip(0,3)" -,cdap_home_nonwork_max3,"_PTYPE_CDAP_PATTERN_COUNT(4, 'H', households, persons).clip(0,3)" -,cdap_home_retired_max3,"_PTYPE_CDAP_PATTERN_COUNT(5, 'H', households, persons).clip(0,3)" -,cdap_home_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'H', households, persons).clip(0,3)" -,cdap_home_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'H', households, persons).clip(0,3)" -,cdap_nonmand_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'N', households, persons).clip(0,3)" -,cdap_nonmand_part_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_PART, 'N', households, persons).clip(0,3)" -,cdap_nonmand_nonwork_max3,"_PTYPE_CDAP_PATTERN_COUNT(4, 'N', households, persons).clip(0,3)" -,cdap_nonmand_retired_max3,"_PTYPE_CDAP_PATTERN_COUNT(5, 'N', households, persons).clip(0,3)" -,cdap_nonmand_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'N', households, persons).clip(0,3)" -,cdap_nonmand_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'N', households, persons).clip(0,3)" -,cdap_mand_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'M', households, persons).clip(0,3)" -,cdap_mand_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'M', households, persons).clip(0,3)" -,cdap_mand_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'M', households, persons).clip(0,3)" -,income_between_50_and_100,(households.income > 50000) & (households.income <= 100000) -,income_greater_than_100,households.income > 100000 -,income_missing,0 -logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) -logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) -logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) -nmRetail,non_motorized_retail_accessibility,"reindex(accessibility.nmRetail, households.TAZ)" +Description,Target,Expression +,_PTYPE_CDAP_PATTERN_COUNT,"lambda ptype, activity, households, persons: persons.query('ptype == %s and cdap_activity==\'%s\'' % (ptype, activity)).groupby('household_id').size().reindex(households.index).fillna(0)" +,_PEMPLOY_CDAP_PATTERN_COUNT,"lambda pemploy, activity, households, persons: persons.query('pemploy == %s and cdap_activity==\'%s\'' % (pemploy, activity)).groupby('household_id').size().reindex(households.index).fillna(0)" +,_2_PTYPE_CDAP_PATTERN_COUNT,"lambda ptype1, ptype2, activity, households, persons: persons.query('(ptype == %s or ptype == %s) and cdap_activity==\'%s\'' % (ptype1, ptype2, activity)).groupby('household_id').size().reindex(households.index).fillna(0)" +#,, +,_HH_OVERLAPS,"hh_time_window_overlap(households, persons)" +,time_window_overlap_adult,_HH_OVERLAPS['aa']/2.25 +,time_window_overlap_child,_HH_OVERLAPS['cc']/2.25 +,time_window_overlap_adult_child,_HH_OVERLAPS['ac']/2.25 +#,, +,cdap_home_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'H', households, persons).clip(0,3)" +,cdap_home_part_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_PART, 'H', households, persons).clip(0,3)" +,cdap_home_nonwork_max3,"_PTYPE_CDAP_PATTERN_COUNT(4, 'H', households, persons).clip(0,3)" +,cdap_home_retired_max3,"_PTYPE_CDAP_PATTERN_COUNT(5, 'H', households, persons).clip(0,3)" +,cdap_home_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'H', households, persons).clip(0,3)" +,cdap_home_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'H', households, persons).clip(0,3)" +,cdap_nonmand_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'N', households, persons).clip(0,3)" +,cdap_nonmand_part_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_PART, 'N', households, persons).clip(0,3)" +,cdap_nonmand_nonwork_max3,"_PTYPE_CDAP_PATTERN_COUNT(4, 'N', households, persons).clip(0,3)" +,cdap_nonmand_retired_max3,"_PTYPE_CDAP_PATTERN_COUNT(5, 'N', households, persons).clip(0,3)" +,cdap_nonmand_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'N', households, persons).clip(0,3)" +,cdap_nonmand_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'N', households, persons).clip(0,3)" +,cdap_mand_full_max3,"_PEMPLOY_CDAP_PATTERN_COUNT(PEMPLOY_FULL, 'M', households, persons).clip(0,3)" +,cdap_mand_univ_driving_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(3, 6, 'M', households, persons).clip(0,3)" +,cdap_mand_nondriving_child_max3,"_2_PTYPE_CDAP_PATTERN_COUNT(7, 8, 'M', households, persons).clip(0,3)" +,income_between_50_and_100,(households.income > 50000) & (households.income <= 100000) +,income_greater_than_100,households.income > 100000 +,income_missing,0 +logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) +logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) +logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) +nmRetail,non_motorized_retail_accessibility,"reindex(accessibility.nmRetail, households.TAZ)" diff --git a/activitysim/examples/example_semcog/configs/joint_tour_frequency_coeffs.csv b/activitysim/examples/prototype_semcog/configs/joint_tour_frequency_coeffs.csv similarity index 97% rename from activitysim/examples/example_semcog/configs/joint_tour_frequency_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/joint_tour_frequency_coeffs.csv index cc65958632..2f7f803653 100755 --- a/activitysim/examples/example_semcog/configs/joint_tour_frequency_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_frequency_coeffs.csv @@ -1,106 +1,106 @@ -coefficient_name,value,constrain -# asc,, -coef_asc_0_tours,0,T -coef_asc_1_Shop,-6.0149,F -coef_asc_1_Main,-5.7389,F -coef_asc_1_Eat,-6.3757,F -coef_asc_1_Visit,-5.8818,F -coef_asc_1_Disc,-5.4806,F -coef_asc_2_tours,-14.4576,F -# zero_tours,, -coef_fullTimeHomeMaxThree_zero_tours,1.175,F -coef_partTimeHomeMaxThree_zero_tours,1.447,F -coef_nonWorkerHomeMaxThree_zero_tours,1.514,F -coef_retireeHomeMaxThree_zero_tours,0.6053,F -coef_universityHomeMaxThree_univ_and_driving_zero_tours,0.5685,F -coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours,0.53,F -# shopping,, -coef_fullTimeNonMandMaxThree_shopping,0.2052,F -coef_partTimeNonMandMaxThree_shopping,0.1866,F -coef_nonWorkerNonMandMaxThree_shopping,0.7078,F -coef_retireeNonMandMaxThree_shopping,0.941,F -coef_universityNonMandMaxThree_shopping,0.7648,F -coef_preDrivingNonMandMaxThree_shopping,0.5474,F -coef_fullTimeMandMaxThree_shopping,-0.2424,F -coef_logTimeWindowOverlapAdult_shopping,0.5945,F -coef_logTimeWindowOverlapChild_shopping,0.1416,F -coef_logTimeWindowOverlapAdultChild_shopping,0.1086,F -coef_fewerCarsThanDrivers_shopping,0.2523,F -coef_moreCarsThanWorkers_shopping,-0.3027,F -# maintenance,, -coef_fullTimeNonMandMaxThree_maint,0.3173,F -coef_partTimeNonMandMaxThree_maint,0.2452,F -coef_nonWorkerNonMandMaxThree_maint,0.4643,F -coef_retireeNonMandMaxThree_maint,0.905,F -coef_universityNonMandMaxThree_maint,0.2643,F -coef_preDrivingNonMandMaxThree_maint,0.6482,F -coef_fullTimeMandMaxThree_maint,-0.3009,F -coef_drivingAgeStuMandMaxThree_maint,-0.3237,F -coef_preDrivingAgeMandMaxThree_maint,0.2299,F -coef_logTimeWindowOverlapAdult_maint,0.3714,F -coef_logTimeWindowOverlapChild_maint,0.176,F -coef_logTimeWindowOverlapAdultChild_maint,0.2443,F -coef_fewerCarsThanDrivers_maint,0.461,F -# eatout,, -coef_fullTimeNonMandMaxThree_eatout,0.2275,F -coef_partTimeNonMandMaxThree_eatout,0.3765,F -coef_nonWorkerNonMandMaxThree_eatout,0.182,F -coef_retireeNonMandMaxThree_eatout,0.4264,F -coef_universityNonMandMaxThree_eatout,0.4097,F -coef_preDrivingNonMandMaxThree_eatout,0.3851,F -coef_logTimeWindowOverlapAdult_eatout,0.4856,F -coef_logTimeWindowOverlapAdultChild_eatout,0.0921,F -coef_incomeBetween50And100_eatout,0.2977,F -coef_incomeGreaterThan100_eatout,0.4492,F -coef_incomeMissing_dummy_always_zero_eatout,0.278,F -coef_moreCarsThanWorkers_eatout,0.3825,F -coef_walkRetailAccessibility_eatout,0.062,F -# visiting,, -coef_fullTimeNonMandMaxThree_visiting,0.6445,F -coef_partTimeNonMandMaxThree_visiting,0.1332,F -coef_nonWorkerNonMandMaxThree_visiting,0.5475,F -coef_retireeNonMandMaxThree_visiting,0.5579,F -coef_universityNonMandMaxThree_visiting,0.2809,F -coef_preDrivingNonMandMaxThree_visiting,0.6008,F -coef_timeWindowOverlapAdult_visiting,0.0596,F -coef_timeWindowOverlapChild_visiting,0.0092,F -coef_timeWindowOverlapAdultChild_visiting,0.0256,F -coef_zeroAutomobiles_visiting,-0.98,F -# discretionary,, -coef_fullTimeNonMandMaxThree_disc,0.1275,F -coef_partTimeNonMandMaxThree_disc,0.4979,F -coef_nonWorkerNonMandMaxThree_disc,0.2871,F -coef_retireeNonMandMaxThree_disc,0.6136,F -coef_universityNonMandMaxThree_disc,0.7546,F -coef_preDrivingNonMandMaxThree_disc,0.5331,F -coef_drivingAgeStuMandMaxThree_disc,0.1932,F -coef_preDrivingAgeMandMaxThree_disc,0.3862,F -coef_logTimeWindowOverlapAdult_disc,0.3428,F -coef_logTimeWindowOverlapChild_disc,0.1162,F -coef_logTimeWindowOverlapAdultChild_disc,0.2212,F -coef_incomeBetween50And100_disc,0.3167,F -coef_incomeGreaterThan100_disc,0.486,F -coef_incomeMissing_dummy_always_zero_disc,0.3723,F -coef_zeroAutomobiles_disc,-0.909,F -#Phase1_Calibration,, -asc_calib_0_tours,0,T -asc_calib_1_Shop,1.053698044,F -asc_calib_1_Main,0.732431708,F -asc_calib_1_Eat,0.877259241,F -asc_calib_1_Visit,0.436534239,F -asc_calib_1_Disc,0.619623918,F -asc_calib_2_SS,2.404591356,F -asc_calib_2_SM,2.998240224,F -asc_calib_2_SE,2.042554293,F -asc_calib_2_SV,0.054676458,F -asc_calib_2_SD,2.467068755,F -asc_calib_2_MM,3.614067275,F -asc_calib_2_ME,4.359003674,F -asc_calib_2_MV,5.998710535,F -asc_calib_2_MD,5.114616381,F -asc_calib_2_EE,3.295790528,F -asc_calib_2_EV,5.235744732,F -asc_calib_2_ED,2.359825593,F -asc_calib_2_VV,1.285350946,F -asc_calib_2_VD,4.250918308,F -asc_calib_2_DD,2.958796827,F +coefficient_name,value,constrain +# asc,, +coef_asc_0_tours,0,T +coef_asc_1_Shop,-6.0149,F +coef_asc_1_Main,-5.7389,F +coef_asc_1_Eat,-6.3757,F +coef_asc_1_Visit,-5.8818,F +coef_asc_1_Disc,-5.4806,F +coef_asc_2_tours,-14.4576,F +# zero_tours,, +coef_fullTimeHomeMaxThree_zero_tours,1.175,F +coef_partTimeHomeMaxThree_zero_tours,1.447,F +coef_nonWorkerHomeMaxThree_zero_tours,1.514,F +coef_retireeHomeMaxThree_zero_tours,0.6053,F +coef_universityHomeMaxThree_univ_and_driving_zero_tours,0.5685,F +coef_preDrivingHomeMaxThree_preschool_and_school_zero_tours,0.53,F +# shopping,, +coef_fullTimeNonMandMaxThree_shopping,0.2052,F +coef_partTimeNonMandMaxThree_shopping,0.1866,F +coef_nonWorkerNonMandMaxThree_shopping,0.7078,F +coef_retireeNonMandMaxThree_shopping,0.941,F +coef_universityNonMandMaxThree_shopping,0.7648,F +coef_preDrivingNonMandMaxThree_shopping,0.5474,F +coef_fullTimeMandMaxThree_shopping,-0.2424,F +coef_logTimeWindowOverlapAdult_shopping,0.5945,F +coef_logTimeWindowOverlapChild_shopping,0.1416,F +coef_logTimeWindowOverlapAdultChild_shopping,0.1086,F +coef_fewerCarsThanDrivers_shopping,0.2523,F +coef_moreCarsThanWorkers_shopping,-0.3027,F +# maintenance,, +coef_fullTimeNonMandMaxThree_maint,0.3173,F +coef_partTimeNonMandMaxThree_maint,0.2452,F +coef_nonWorkerNonMandMaxThree_maint,0.4643,F +coef_retireeNonMandMaxThree_maint,0.905,F +coef_universityNonMandMaxThree_maint,0.2643,F +coef_preDrivingNonMandMaxThree_maint,0.6482,F +coef_fullTimeMandMaxThree_maint,-0.3009,F +coef_drivingAgeStuMandMaxThree_maint,-0.3237,F +coef_preDrivingAgeMandMaxThree_maint,0.2299,F +coef_logTimeWindowOverlapAdult_maint,0.3714,F +coef_logTimeWindowOverlapChild_maint,0.176,F +coef_logTimeWindowOverlapAdultChild_maint,0.2443,F +coef_fewerCarsThanDrivers_maint,0.461,F +# eatout,, +coef_fullTimeNonMandMaxThree_eatout,0.2275,F +coef_partTimeNonMandMaxThree_eatout,0.3765,F +coef_nonWorkerNonMandMaxThree_eatout,0.182,F +coef_retireeNonMandMaxThree_eatout,0.4264,F +coef_universityNonMandMaxThree_eatout,0.4097,F +coef_preDrivingNonMandMaxThree_eatout,0.3851,F +coef_logTimeWindowOverlapAdult_eatout,0.4856,F +coef_logTimeWindowOverlapAdultChild_eatout,0.0921,F +coef_incomeBetween50And100_eatout,0.2977,F +coef_incomeGreaterThan100_eatout,0.4492,F +coef_incomeMissing_dummy_always_zero_eatout,0.278,F +coef_moreCarsThanWorkers_eatout,0.3825,F +coef_walkRetailAccessibility_eatout,0.062,F +# visiting,, +coef_fullTimeNonMandMaxThree_visiting,0.6445,F +coef_partTimeNonMandMaxThree_visiting,0.1332,F +coef_nonWorkerNonMandMaxThree_visiting,0.5475,F +coef_retireeNonMandMaxThree_visiting,0.5579,F +coef_universityNonMandMaxThree_visiting,0.2809,F +coef_preDrivingNonMandMaxThree_visiting,0.6008,F +coef_timeWindowOverlapAdult_visiting,0.0596,F +coef_timeWindowOverlapChild_visiting,0.0092,F +coef_timeWindowOverlapAdultChild_visiting,0.0256,F +coef_zeroAutomobiles_visiting,-0.98,F +# discretionary,, +coef_fullTimeNonMandMaxThree_disc,0.1275,F +coef_partTimeNonMandMaxThree_disc,0.4979,F +coef_nonWorkerNonMandMaxThree_disc,0.2871,F +coef_retireeNonMandMaxThree_disc,0.6136,F +coef_universityNonMandMaxThree_disc,0.7546,F +coef_preDrivingNonMandMaxThree_disc,0.5331,F +coef_drivingAgeStuMandMaxThree_disc,0.1932,F +coef_preDrivingAgeMandMaxThree_disc,0.3862,F +coef_logTimeWindowOverlapAdult_disc,0.3428,F +coef_logTimeWindowOverlapChild_disc,0.1162,F +coef_logTimeWindowOverlapAdultChild_disc,0.2212,F +coef_incomeBetween50And100_disc,0.3167,F +coef_incomeGreaterThan100_disc,0.486,F +coef_incomeMissing_dummy_always_zero_disc,0.3723,F +coef_zeroAutomobiles_disc,-0.909,F +#Phase1_Calibration,, +asc_calib_0_tours,0,T +asc_calib_1_Shop,1.053698044,F +asc_calib_1_Main,0.732431708,F +asc_calib_1_Eat,0.877259241,F +asc_calib_1_Visit,0.436534239,F +asc_calib_1_Disc,0.619623918,F +asc_calib_2_SS,2.404591356,F +asc_calib_2_SM,2.998240224,F +asc_calib_2_SE,2.042554293,F +asc_calib_2_SV,0.054676458,F +asc_calib_2_SD,2.467068755,F +asc_calib_2_MM,3.614067275,F +asc_calib_2_ME,4.359003674,F +asc_calib_2_MV,5.998710535,F +asc_calib_2_MD,5.114616381,F +asc_calib_2_EE,3.295790528,F +asc_calib_2_EV,5.235744732,F +asc_calib_2_ED,2.359825593,F +asc_calib_2_VV,1.285350946,F +asc_calib_2_VD,4.250918308,F +asc_calib_2_DD,2.958796827,F diff --git a/activitysim/examples/example_mtc/configs/joint_tour_participation.csv b/activitysim/examples/prototype_semcog/configs/joint_tour_participation.csv old mode 100644 new mode 100755 similarity index 99% rename from activitysim/examples/example_mtc/configs/joint_tour_participation.csv rename to activitysim/examples/prototype_semcog/configs/joint_tour_participation.csv index da2e65d1ea..cd692d8d27 --- a/activitysim/examples/example_mtc/configs/joint_tour_participation.csv +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_participation.csv @@ -1,67 +1,67 @@ -Label,Description,Expression,participate,not_participate -util_full_time_worker_mixed_party,"Full-Time Worker, mixed party",person_is_full & tour_composition_is_mixed,coef_full_time_worker_mixed_party,coef_full_time_worker_mixed_party_not -util_part_time_worker_adults_only_party,"Part-Time Worker, adults-only party",person_is_part & tour_composition_is_adults,coef_part_time_worker_adults_only_party,coef_part_time_worker_adults_only_party_not -util_part_time_worker_mixed_party,"Part-Time Worker, mixed party",person_is_part & tour_composition_is_mixed,coef_part_time_worker_mixed_party, -util_university_student_mixed_party,"University Student, mixed party",person_is_univ & tour_composition_is_mixed,coef_university_student_mixed_party, -util_non_worker_adults_only_party,"Non-Worker, adults-only party",person_is_nonwork & tour_composition_is_adults,coef_non_worker_adults_only_party, -util_non_worker_mixed_party,"Non-Worker, mixed party",person_is_nonwork & tour_composition_is_mixed,coef_non_worker_mixed_party, -util_child_too_young_for_school_children_only_party,"Child too Young for School, children-only party",person_is_preschool & tour_composition_is_children,coef_child_too_young_for_school_children_only_party, -util_child_too_young_for_school_mixed_party,"Child too Young for School, mixed party",person_is_preschool & tour_composition_is_mixed,coef_child_too_young_for_school_mixed_party, -util_pre_driving_age_student_children_only_party,"Pre-driving age Student, children-only party",person_is_school & tour_composition_is_children,coef_pre_driving_age_student_children_only_party, -util_pre_driving_age_student_mixed_party,"Pre-driving age Student, mixed party",person_is_school & tour_composition_is_mixed,coef_pre_driving_age_student_mixed_party, -util_driving_age_student_children_only_party,"Driving-age Student, children-only party",person_is_driving & tour_composition_is_children,coef_driving_age_student_children_only_party, -util_driving_age_student_mixed_party,"Driving-age Student, mixed party",person_is_driving & tour_composition_is_mixed,coef_driving_age_student_mixed_party, -#,,,, -util_full_time_worker_specific_to_eating_out_joint_tours,"Full-Time Worker, specific to eating out joint tours",person_is_full & tour_type_is_eat,coef_full_time_worker_specific_to_eating_out_joint_tours,coef_full_time_worker_specific_to_eating_out_joint_tours_not -util_full_time_worker_specific_to_discretionary_joint_tours,"Full-Time Worker, specific to discretionary joint tours",person_is_full & tour_type_is_disc,coef_full_time_worker_specific_to_discretionary_joint_tours,coef_full_time_worker_specific_to_discretionary_joint_tours_not -util_part_time_worker_specific_to_eating_out_joint_tours,"Part-Time Worker, specific to eating out joint tours",person_is_part & tour_type_is_eat,coef_part_time_worker_specific_to_eating_out_joint_tours, -util_part_time_worker_specific_to_discretionary_joint_tours,"Part-Time Worker, specific to discretionary joint tours",person_is_part & tour_type_is_disc,coef_part_time_worker_specific_to_discretionary_joint_tours, -util_university_student_specific_to_eating_out_joint_tours,"University Student, specific to eating out joint tours",person_is_univ & tour_type_is_eat,coef_university_student_specific_to_eating_out_joint_tours, -util_university_student_specific_to_discretionary_joint_tours,"University Student, specific to discretionary joint tours",person_is_univ & tour_type_is_disc,coef_university_student_specific_to_discretionary_joint_tours, -util_non_worker_specific_to_eating_out_joint_tours,"Non-worker, specific to eating out joint tours",person_is_nonwork & tour_type_is_eat,coef_non_worker_specific_to_eating_out_joint_tours, -util_non_worker_specific_to_discretionary_joint_tours,"Non-worker, specific to discretionary joint tours",person_is_nonwork & tour_type_is_disc,coef_non_worker_specific_to_discretionary_joint_tours, -util_child_too_young_for_school_specific_to_eating_out_joint_tours,"Child too Young for School, specific to eating out joint tours",person_is_preschool & tour_type_is_eat,coef_child_too_young_for_school_specific_to_eating_out_joint_tours, -util_child_too_young_for_school_specific_to_discretionary_joint_tours,"Child too Young for School, specific to discretionary joint tours",person_is_preschool & tour_type_is_disc,coef_child_too_young_for_school_specific_to_discretionary_joint_tours, -util_pre_driving_age_student_specific_to_eating_out_joint_tours,"Pre-driving Age Student, specific to eating out joint tours",person_is_school & tour_type_is_eat,coef_pre_driving_age_student_specific_to_eating_out_joint_tours, -util_pre_driving_age_student_specific_to_discretionary_joint_tours,"Pre-driving age Student, specific to discretionary joint tours",person_is_school & tour_type_is_disc,coef_pre_driving_age_student_specific_to_discretionary_joint_tours, -util_driving_age_student_specific_to_eating_out_joint_tours,"Driving-age Student, specific to eating out joint tours",person_is_driving & tour_type_is_eat,coef_driving_age_student_specific_to_eating_out_joint_tours, -util_driving_age_student_specific_to_discretionary_joint_tours,"Driving-age Student, specific to discretionary joint tours",person_is_driving & tour_type_is_disc,coef_driving_age_student_specific_to_discretionary_joint_tours, -#,,,, -util_household_in_urban_area_adult_adult_only_party,"Household in urban area, adult, adult-only party",home_is_urban & adult & tour_composition_is_adults,coef_household_in_urban_area_adult_adult_only_party, -util_household_in_urban_area_adult_mixed_party,"Household in urban area, adult, mixed party",home_is_urban & adult & tour_composition_is_mixed,coef_household_in_urban_area_adult_mixed_party, -util_household_in_urban_area_child_child_only_party,"Household in urban area, child, child-only party",home_is_urban & ~adult & tour_composition_is_children,coef_household_in_urban_area_child_child_only_party, -util_household_in_urban_area_child_mixed_party,"Household in urban area, child, mixed party",home_is_urban & ~adult & tour_composition_is_mixed,coef_household_in_urban_area_child_mixed_party, -util_household_in_suburban_area_adult_adult_only_party,"Household in suburban area, adult, adult-only party",home_is_suburban & adult & tour_composition_is_adults,coef_household_in_suburban_area_adult_adult_only_party, -util_household_in_suburban_area_adult_mixed_party,"Household in suburban area, adult, mixed party",home_is_suburban & adult & tour_composition_is_mixed,coef_household_in_suburban_area_adult_mixed_party, -util_household_in_suburban_area_child_child_only_party,"Household in suburban area, child, child-only party",home_is_suburban & ~adult & tour_composition_is_children,coef_household_in_suburban_area_child_child_only_party, -util_household_in_suburban_area_child_mixed_party,"Household in suburban area, child, mixed party",home_is_suburban & ~adult & tour_composition_is_mixed,coef_household_in_suburban_area_child_mixed_party, -util_adult_more_automobiles_than_workers_adult_only_party,"Adult, more automobiles than workers, adult-only party",adult & more_cars_than_workers & tour_composition_is_adults,coef_adult_more_automobiles_than_workers_adult_only_party, -util_adult_more_automobiles_than_workers_mixed_party,"Adult, more automobiles than workers, mixed party",adult & more_cars_than_workers & tour_composition_is_mixed,coef_adult_more_automobiles_than_workers_mixed_party, -util_child_more_automobiles_than_workers_child_only_party,"Child, more automobiles than workers, child-only party",adult & more_cars_than_workers & tour_composition_is_children,coef_child_more_automobiles_than_workers_child_only_party, -util_child_more_automobiles_than_workers_mixed_party,"Child, more automobiles than workers, mixed party",adult & more_cars_than_workers & tour_composition_is_mixed,coef_child_more_automobiles_than_workers_mixed_party, -#,,,, -util_dummy_for_high_income_for_adult_in_adult_party,Dummy for high income for adult in adult party,high_income & tour_composition_is_adults,coef_dummy_for_high_income_for_adult_in_adult_party, -util_dummy_for_high_income_for_adult_in_mixed_party,Dummy for high income for adult in mixed party,high_income & tour_composition_is_mixed,coef_dummy_for_high_income_for_adult_in_mixed_party, -util_dummy_for_high_income_for_child_in_children_party,Dummy for high income for child in children party,high_income & tour_composition_is_children,coef_dummy_for_high_income_for_child_in_children_party, -util_dummy_for_high_income_for_child_in_mixed_party,Dummy for high income for child in mixed party,high_income & tour_composition_is_mixed,coef_dummy_for_high_income_for_child_in_mixed_party, -util_adult_number_of_joint_tours_adult_only,"Adult, number of joint tours, adult-only",(adult & tour_composition_is_adults) * num_hh_joint_tours,coef_adult_number_of_joint_tours_adult_only, -util_adult_number_of_joint_tours_mixed,"Adult, number of joint tours, mixed",(adult & tour_composition_is_mixed) * num_hh_joint_tours,coef_adult_number_of_joint_tours_mixed, -util_child_number_of_joint_tours_child_only,"Child, number of joint tours, child only",(~adult & tour_composition_is_children) * num_hh_joint_tours,coef_child_number_of_joint_tours_child_only, -util_child_number_of_joint_tours_mixed,"Child, number of joint tours, mixed",(~adult & tour_composition_is_mixed) * num_hh_joint_tours,coef_child_number_of_joint_tours_mixed, -util_adult_number_of_other_adults_in_the_household_adults_only_party,"Adult, number of other adults in the household, adults-only party",(adult & tour_composition_is_adults) * (num_adults - 1),coef_adult_number_of_other_adults_in_the_household_adults_only_party, -util_adult_number_of_other_adults_in_the_household_mixed_party,"Adult, number of other adults in the household, mixed party",(adult & tour_composition_is_mixed) * (num_adults - 1),coef_adult_number_of_other_adults_in_the_household_mixed_party, -util_child_number_of_other_children_in_the_household_child_only_party,"Child, number of other children in the household, child-only party",(~adult & tour_composition_is_children) * (num_children - 1),coef_child_number_of_other_children_in_the_household_child_only_party, -util_child_number_of_other_children_in_the_household_mixed,"Child, number of other children in the household, mixed",(~adult & tour_composition_is_mixed) * (num_children - 1),coef_child_number_of_other_children_in_the_household_mixed, -#,,,, -util_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,"Adult, log of max window overlap with an adult, adult-only party",(adult & tour_composition_is_adults) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party, -util_adult_log_of_max_window_overlap_with_a_child_mixed,"Adult, log of max window overlap with a child, mixed",(adult & tour_composition_is_mixed) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_a_child_mixed, -util_child_log_of_max_window_overlap_with_an_adult_mixed,"Child, log of max window overlap with an adult, mixed",(~adult & tour_composition_is_mixed) * log_time_window_overlap_adult,coef_child_log_of_max_window_overlap_with_an_adult_mixed, -util_child_log_of_max_window_overlap_with_a_child_child,"Child, log of max window overlap with a child, child",(~adult & tour_composition_is_children) * log_time_window_overlap_adult,coef_child_log_of_max_window_overlap_with_a_child_child, -#,,,, -util_adults_are_prohibited_in_participating_in_child_only_tours,Adults are prohibited in participating in child-only tours,adult & tour_composition_is_children,coef_unavailable, -util_children_are_prohibited_in_participating_in_adult_only_tours,Children are prohibited in participating in adult-only tours,~adult & tour_composition_is_adults,coef_unavailable, -util_persons_with_home_activity_patterns_are_prohibilted_from_participating,Persons with Home activity patterns are prohibilted from participating,~travel_active,coef_unavailable, -util_if_only_two_available_adults_both_must_participate_in_adult_only_tour,"If only two available adults, both must participate in adult-only tour",adult & travel_active & tour_composition_is_adults & (num_travel_active_adults<3),,coef_unavailable -util_if_only_one_available_adult_traveler_must_participate_in_mixed_tour,"If only one available adult, traveler must participate in mixed tour",adult & travel_active & tour_composition_is_mixed & (num_travel_active_adults<2),,coef_unavailable -util_if_only_two_available_children_both_must_participate_in_child_only_tour,"If only two available children, both must participate in child-only tour",~adult & travel_active & tour_composition_is_children & (num_travel_active_children<3),,coef_unavailable -util_if_only_one_available_child_traveler_must_participate_in_mixed_tour,"If only one available child, traveler must participate in mixed tour",~adult & travel_active & tour_composition_is_mixed & (num_travel_active_children<2),,coef_unavailable +Label,Description,Expression,participate,not_participate +util_full_time_worker_mixed_party,"Full-Time Worker, mixed party",person_is_full & tour_composition_is_mixed,coef_full_time_worker_mixed_party,coef_full_time_worker_mixed_party_not +util_part_time_worker_adults_only_party,"Part-Time Worker, adults-only party",person_is_part & tour_composition_is_adults,coef_part_time_worker_adults_only_party,coef_part_time_worker_adults_only_party_not +util_part_time_worker_mixed_party,"Part-Time Worker, mixed party",person_is_part & tour_composition_is_mixed,coef_part_time_worker_mixed_party, +util_university_student_mixed_party,"University Student, mixed party",person_is_univ & tour_composition_is_mixed,coef_university_student_mixed_party, +util_non_worker_adults_only_party,"Non-Worker, adults-only party",person_is_nonwork & tour_composition_is_adults,coef_non_worker_adults_only_party, +util_non_worker_mixed_party,"Non-Worker, mixed party",person_is_nonwork & tour_composition_is_mixed,coef_non_worker_mixed_party, +util_child_too_young_for_school_children_only_party,"Child too Young for School, children-only party",person_is_preschool & tour_composition_is_children,coef_child_too_young_for_school_children_only_party, +util_child_too_young_for_school_mixed_party,"Child too Young for School, mixed party",person_is_preschool & tour_composition_is_mixed,coef_child_too_young_for_school_mixed_party, +util_pre_driving_age_student_children_only_party,"Pre-driving age Student, children-only party",person_is_school & tour_composition_is_children,coef_pre_driving_age_student_children_only_party, +util_pre_driving_age_student_mixed_party,"Pre-driving age Student, mixed party",person_is_school & tour_composition_is_mixed,coef_pre_driving_age_student_mixed_party, +util_driving_age_student_children_only_party,"Driving-age Student, children-only party",person_is_driving & tour_composition_is_children,coef_driving_age_student_children_only_party, +util_driving_age_student_mixed_party,"Driving-age Student, mixed party",person_is_driving & tour_composition_is_mixed,coef_driving_age_student_mixed_party, +#,,,, +util_full_time_worker_specific_to_eating_out_joint_tours,"Full-Time Worker, specific to eating out joint tours",person_is_full & tour_type_is_eat,coef_full_time_worker_specific_to_eating_out_joint_tours,coef_full_time_worker_specific_to_eating_out_joint_tours_not +util_full_time_worker_specific_to_discretionary_joint_tours,"Full-Time Worker, specific to discretionary joint tours",person_is_full & tour_type_is_disc,coef_full_time_worker_specific_to_discretionary_joint_tours,coef_full_time_worker_specific_to_discretionary_joint_tours_not +util_part_time_worker_specific_to_eating_out_joint_tours,"Part-Time Worker, specific to eating out joint tours",person_is_part & tour_type_is_eat,coef_part_time_worker_specific_to_eating_out_joint_tours, +util_part_time_worker_specific_to_discretionary_joint_tours,"Part-Time Worker, specific to discretionary joint tours",person_is_part & tour_type_is_disc,coef_part_time_worker_specific_to_discretionary_joint_tours, +util_university_student_specific_to_eating_out_joint_tours,"University Student, specific to eating out joint tours",person_is_univ & tour_type_is_eat,coef_university_student_specific_to_eating_out_joint_tours, +util_university_student_specific_to_discretionary_joint_tours,"University Student, specific to discretionary joint tours",person_is_univ & tour_type_is_disc,coef_university_student_specific_to_discretionary_joint_tours, +util_non_worker_specific_to_eating_out_joint_tours,"Non-worker, specific to eating out joint tours",person_is_nonwork & tour_type_is_eat,coef_non_worker_specific_to_eating_out_joint_tours, +util_non_worker_specific_to_discretionary_joint_tours,"Non-worker, specific to discretionary joint tours",person_is_nonwork & tour_type_is_disc,coef_non_worker_specific_to_discretionary_joint_tours, +util_child_too_young_for_school_specific_to_eating_out_joint_tours,"Child too Young for School, specific to eating out joint tours",person_is_preschool & tour_type_is_eat,coef_child_too_young_for_school_specific_to_eating_out_joint_tours, +util_child_too_young_for_school_specific_to_discretionary_joint_tours,"Child too Young for School, specific to discretionary joint tours",person_is_preschool & tour_type_is_disc,coef_child_too_young_for_school_specific_to_discretionary_joint_tours, +util_pre_driving_age_student_specific_to_eating_out_joint_tours,"Pre-driving Age Student, specific to eating out joint tours",person_is_school & tour_type_is_eat,coef_pre_driving_age_student_specific_to_eating_out_joint_tours, +util_pre_driving_age_student_specific_to_discretionary_joint_tours,"Pre-driving age Student, specific to discretionary joint tours",person_is_school & tour_type_is_disc,coef_pre_driving_age_student_specific_to_discretionary_joint_tours, +util_driving_age_student_specific_to_eating_out_joint_tours,"Driving-age Student, specific to eating out joint tours",person_is_driving & tour_type_is_eat,coef_driving_age_student_specific_to_eating_out_joint_tours, +util_driving_age_student_specific_to_discretionary_joint_tours,"Driving-age Student, specific to discretionary joint tours",person_is_driving & tour_type_is_disc,coef_driving_age_student_specific_to_discretionary_joint_tours, +#,,,, +util_household_in_urban_area_adult_adult_only_party,"Household in urban area, adult, adult-only party",home_is_urban & adult & tour_composition_is_adults,coef_household_in_urban_area_adult_adult_only_party, +util_household_in_urban_area_adult_mixed_party,"Household in urban area, adult, mixed party",home_is_urban & adult & tour_composition_is_mixed,coef_household_in_urban_area_adult_mixed_party, +util_household_in_urban_area_child_child_only_party,"Household in urban area, child, child-only party",home_is_urban & ~adult & tour_composition_is_children,coef_household_in_urban_area_child_child_only_party, +util_household_in_urban_area_child_mixed_party,"Household in urban area, child, mixed party",home_is_urban & ~adult & tour_composition_is_mixed,coef_household_in_urban_area_child_mixed_party, +util_household_in_suburban_area_adult_adult_only_party,"Household in suburban area, adult, adult-only party",home_is_suburban & adult & tour_composition_is_adults,coef_household_in_suburban_area_adult_adult_only_party, +util_household_in_suburban_area_adult_mixed_party,"Household in suburban area, adult, mixed party",home_is_suburban & adult & tour_composition_is_mixed,coef_household_in_suburban_area_adult_mixed_party, +util_household_in_suburban_area_child_child_only_party,"Household in suburban area, child, child-only party",home_is_suburban & ~adult & tour_composition_is_children,coef_household_in_suburban_area_child_child_only_party, +util_household_in_suburban_area_child_mixed_party,"Household in suburban area, child, mixed party",home_is_suburban & ~adult & tour_composition_is_mixed,coef_household_in_suburban_area_child_mixed_party, +util_adult_more_automobiles_than_workers_adult_only_party,"Adult, more automobiles than workers, adult-only party",adult & more_cars_than_workers & tour_composition_is_adults,coef_adult_more_automobiles_than_workers_adult_only_party, +util_adult_more_automobiles_than_workers_mixed_party,"Adult, more automobiles than workers, mixed party",adult & more_cars_than_workers & tour_composition_is_mixed,coef_adult_more_automobiles_than_workers_mixed_party, +util_child_more_automobiles_than_workers_child_only_party,"Child, more automobiles than workers, child-only party",adult & more_cars_than_workers & tour_composition_is_children,coef_child_more_automobiles_than_workers_child_only_party, +util_child_more_automobiles_than_workers_mixed_party,"Child, more automobiles than workers, mixed party",adult & more_cars_than_workers & tour_composition_is_mixed,coef_child_more_automobiles_than_workers_mixed_party, +#,,,, +util_dummy_for_high_income_for_adult_in_adult_party,Dummy for high income for adult in adult party,high_income & tour_composition_is_adults,coef_dummy_for_high_income_for_adult_in_adult_party, +util_dummy_for_high_income_for_adult_in_mixed_party,Dummy for high income for adult in mixed party,high_income & tour_composition_is_mixed,coef_dummy_for_high_income_for_adult_in_mixed_party, +util_dummy_for_high_income_for_child_in_children_party,Dummy for high income for child in children party,high_income & tour_composition_is_children,coef_dummy_for_high_income_for_child_in_children_party, +util_dummy_for_high_income_for_child_in_mixed_party,Dummy for high income for child in mixed party,high_income & tour_composition_is_mixed,coef_dummy_for_high_income_for_child_in_mixed_party, +util_adult_number_of_joint_tours_adult_only,"Adult, number of joint tours, adult-only",(adult & tour_composition_is_adults) * num_hh_joint_tours,coef_adult_number_of_joint_tours_adult_only, +util_adult_number_of_joint_tours_mixed,"Adult, number of joint tours, mixed",(adult & tour_composition_is_mixed) * num_hh_joint_tours,coef_adult_number_of_joint_tours_mixed, +util_child_number_of_joint_tours_child_only,"Child, number of joint tours, child only",(~adult & tour_composition_is_children) * num_hh_joint_tours,coef_child_number_of_joint_tours_child_only, +util_child_number_of_joint_tours_mixed,"Child, number of joint tours, mixed",(~adult & tour_composition_is_mixed) * num_hh_joint_tours,coef_child_number_of_joint_tours_mixed, +util_adult_number_of_other_adults_in_the_household_adults_only_party,"Adult, number of other adults in the household, adults-only party",(adult & tour_composition_is_adults) * (num_adults - 1),coef_adult_number_of_other_adults_in_the_household_adults_only_party, +util_adult_number_of_other_adults_in_the_household_mixed_party,"Adult, number of other adults in the household, mixed party",(adult & tour_composition_is_mixed) * (num_adults - 1),coef_adult_number_of_other_adults_in_the_household_mixed_party, +util_child_number_of_other_children_in_the_household_child_only_party,"Child, number of other children in the household, child-only party",(~adult & tour_composition_is_children) * (num_children - 1),coef_child_number_of_other_children_in_the_household_child_only_party, +util_child_number_of_other_children_in_the_household_mixed,"Child, number of other children in the household, mixed",(~adult & tour_composition_is_mixed) * (num_children - 1),coef_child_number_of_other_children_in_the_household_mixed, +#,,,, +util_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,"Adult, log of max window overlap with an adult, adult-only party",(adult & tour_composition_is_adults) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party, +util_adult_log_of_max_window_overlap_with_a_child_mixed,"Adult, log of max window overlap with a child, mixed",(adult & tour_composition_is_mixed) * log_time_window_overlap_adult,coef_adult_log_of_max_window_overlap_with_a_child_mixed, +util_child_log_of_max_window_overlap_with_an_adult_mixed,"Child, log of max window overlap with an adult, mixed",(~adult & tour_composition_is_mixed) * log_time_window_overlap_adult,coef_child_log_of_max_window_overlap_with_an_adult_mixed, +util_child_log_of_max_window_overlap_with_a_child_child,"Child, log of max window overlap with a child, child",(~adult & tour_composition_is_children) * log_time_window_overlap_adult,coef_child_log_of_max_window_overlap_with_a_child_child, +#,,,, +util_adults_are_prohibited_in_participating_in_child_only_tours,Adults are prohibited in participating in child-only tours,adult & tour_composition_is_children,coef_unavailable, +util_children_are_prohibited_in_participating_in_adult_only_tours,Children are prohibited in participating in adult-only tours,~adult & tour_composition_is_adults,coef_unavailable, +util_persons_with_home_activity_patterns_are_prohibilted_from_participating,Persons with Home activity patterns are prohibilted from participating,~travel_active,coef_unavailable, +util_if_only_two_available_adults_both_must_participate_in_adult_only_tour,"If only two available adults, both must participate in adult-only tour",adult & travel_active & tour_composition_is_adults & (num_travel_active_adults<3),,coef_unavailable +util_if_only_one_available_adult_traveler_must_participate_in_mixed_tour,"If only one available adult, traveler must participate in mixed tour",adult & travel_active & tour_composition_is_mixed & (num_travel_active_adults<2),,coef_unavailable +util_if_only_two_available_children_both_must_participate_in_child_only_tour,"If only two available children, both must participate in child-only tour",~adult & travel_active & tour_composition_is_children & (num_travel_active_children<3),,coef_unavailable +util_if_only_one_available_child_traveler_must_participate_in_mixed_tour,"If only one available child, traveler must participate in mixed tour",~adult & travel_active & tour_composition_is_mixed & (num_travel_active_children<2),,coef_unavailable diff --git a/activitysim/examples/example_psrc/configs/joint_tour_participation.yaml b/activitysim/examples/prototype_semcog/configs/joint_tour_participation.yaml similarity index 95% rename from activitysim/examples/example_psrc/configs/joint_tour_participation.yaml rename to activitysim/examples/prototype_semcog/configs/joint_tour_participation.yaml index aee45349f9..59941e8324 100755 --- a/activitysim/examples/example_psrc/configs/joint_tour_participation.yaml +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_participation.yaml @@ -1,20 +1,20 @@ - -SPEC: joint_tour_participation.csv -COEFFICIENTS: joint_tour_participation_coeffs.csv - -LOGIT_TYPE: MNL - -#max_participation_choice_iterations: 5000 - -preprocessor: - SPEC: joint_tour_participation_annotate_participants_preprocessor - DF: participants -# TABLES: -# - persons -# - accessibility - -annotate_persons: - SPEC: annotate_persons_jtp - DF: persons - TABLES: - - joint_tour_participants + +SPEC: joint_tour_participation.csv +COEFFICIENTS: joint_tour_participation_coeffs.csv + +LOGIT_TYPE: MNL + +#max_participation_choice_iterations: 5000 + +preprocessor: + SPEC: joint_tour_participation_annotate_participants_preprocessor + DF: participants +# TABLES: +# - persons +# - accessibility + +annotate_persons: + SPEC: annotate_persons_jtp + DF: persons + TABLES: + - joint_tour_participants diff --git a/activitysim/examples/example_semcog/configs/joint_tour_participation_annotate_participants_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/joint_tour_participation_annotate_participants_preprocessor.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/joint_tour_participation_annotate_participants_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/joint_tour_participation_annotate_participants_preprocessor.csv index f17023dc7a..cb78bff306 100755 --- a/activitysim/examples/example_semcog/configs/joint_tour_participation_annotate_participants_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_participation_annotate_participants_preprocessor.csv @@ -1,24 +1,24 @@ -Description,Target,Expression -,_P_OVERLAPS,person_time_window_overlap(persons) -,time_window_overlap_adult,"reindex(_P_OVERLAPS.aa, participants.person_id)/2.25" -,time_window_overlap_child,"reindex(_P_OVERLAPS.cc, participants.person_id)/2.25" -,time_window_overlap_adult_child,"reindex(_P_OVERLAPS.ac, participants.person_id)/2.25" -logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) -logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) -logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) -#,, -,person_is_full,participants.ptype == PTYPE_FULL -,person_is_part,participants.ptype == PTYPE_PART -,person_is_univ,participants.ptype == PTYPE_UNIVERSITY -,person_is_nonwork,participants.ptype == PTYPE_NONWORK -,person_is_driving,participants.ptype == PTYPE_DRIVING -,person_is_school,participants.ptype == PTYPE_SCHOOL -,person_is_preschool,participants.ptype == PTYPE_PRESCHOOL -,tour_type_is_eat,participants.tour_type=='eat' -,tour_type_is_disc,participants.tour_type=='disc' -,tour_composition_is_adults,participants.composition=='adults' -,tour_composition_is_children,participants.composition=='children' -,tour_composition_is_mixed,participants.composition=='mixed' -,home_is_suburban,~(participants.home_is_urban | participants.home_is_rural) -,high_income,participants.income_in_thousands > 60 -,more_cars_than_workers,participants.auto_ownership > participants.num_workers +Description,Target,Expression +,_P_OVERLAPS,person_time_window_overlap(persons) +,time_window_overlap_adult,"reindex(_P_OVERLAPS.aa, participants.person_id)/2.25" +,time_window_overlap_child,"reindex(_P_OVERLAPS.cc, participants.person_id)/2.25" +,time_window_overlap_adult_child,"reindex(_P_OVERLAPS.ac, participants.person_id)/2.25" +logTimeWindowOverlapAdult,log_time_window_overlap_adult,np.log1p(time_window_overlap_adult) +logTimeWindowOverlapChild,log_time_window_overlap_child,np.log1p(time_window_overlap_child) +logTimeWindowOverlapAdultChild,log_time_window_overlap_adult_child,np.log1p(time_window_overlap_adult_child) +#,, +,person_is_full,participants.ptype == PTYPE_FULL +,person_is_part,participants.ptype == PTYPE_PART +,person_is_univ,participants.ptype == PTYPE_UNIVERSITY +,person_is_nonwork,participants.ptype == PTYPE_NONWORK +,person_is_driving,participants.ptype == PTYPE_DRIVING +,person_is_school,participants.ptype == PTYPE_SCHOOL +,person_is_preschool,participants.ptype == PTYPE_PRESCHOOL +,tour_type_is_eat,participants.tour_type=='eat' +,tour_type_is_disc,participants.tour_type=='disc' +,tour_composition_is_adults,participants.composition=='adults' +,tour_composition_is_children,participants.composition=='children' +,tour_composition_is_mixed,participants.composition=='mixed' +,home_is_suburban,~(participants.home_is_urban | participants.home_is_rural) +,high_income,participants.income_in_thousands > 60 +,more_cars_than_workers,participants.auto_ownership > participants.num_workers diff --git a/activitysim/examples/example_psrc/configs/joint_tour_participation_coeffs.csv b/activitysim/examples/prototype_semcog/configs/joint_tour_participation_coeffs.csv similarity index 98% rename from activitysim/examples/example_psrc/configs/joint_tour_participation_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/joint_tour_participation_coeffs.csv index 604c0acb69..455f08be9e 100755 --- a/activitysim/examples/example_psrc/configs/joint_tour_participation_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_participation_coeffs.csv @@ -1,68 +1,68 @@ -coefficient_name,value,constrain -coef_unavailable,-999,T -coef_full_time_worker_mixed_party,-3.566,F -coef_full_time_worker_mixed_party_not,0.5,T -coef_part_time_worker_adults_only_party,-3.566,F -coef_part_time_worker_adults_only_party_not,0.5,T -coef_part_time_worker_mixed_party,-0.3655,F -coef_university_student_mixed_party,-3.041,F -coef_non_worker_adults_only_party,-3.164,F -coef_non_worker_mixed_party,0.7152,F -coef_child_too_young_for_school_children_only_party,-2.786,F -coef_child_too_young_for_school_mixed_party,-1.893,F -coef_pre_driving_age_student_children_only_party,-0.7217,F -coef_pre_driving_age_student_mixed_party,-1.752,F -coef_driving_age_student_children_only_party,-1.822,F -coef_driving_age_student_mixed_party,-1.353,F -#,, -coef_full_time_worker_specific_to_eating_out_joint_tours,0.7157,F -coef_full_time_worker_specific_to_eating_out_joint_tours_not,0.5,T -coef_full_time_worker_specific_to_discretionary_joint_tours,0.4392,F -coef_full_time_worker_specific_to_discretionary_joint_tours_not,0.5,T -coef_part_time_worker_specific_to_eating_out_joint_tours,2.188,F -coef_part_time_worker_specific_to_discretionary_joint_tours,0.285,F -coef_university_student_specific_to_eating_out_joint_tours,-0.82,F -coef_university_student_specific_to_discretionary_joint_tours,0,T -coef_non_worker_specific_to_eating_out_joint_tours,0.1617,F -coef_non_worker_specific_to_discretionary_joint_tours,-0.1835,F -coef_child_too_young_for_school_specific_to_eating_out_joint_tours,0.6589,F -coef_child_too_young_for_school_specific_to_discretionary_joint_tours,0.1284,F -coef_pre_driving_age_student_specific_to_eating_out_joint_tours,1.391,F -coef_pre_driving_age_student_specific_to_discretionary_joint_tours,0.6626,F -coef_driving_age_student_specific_to_eating_out_joint_tours,2.344,F -coef_driving_age_student_specific_to_discretionary_joint_tours,-0.6675,F -#,, -coef_household_in_urban_area_adult_adult_only_party,0,T -coef_household_in_urban_area_adult_mixed_party,-0.137,F -coef_household_in_urban_area_child_child_only_party,1.21,F -coef_household_in_urban_area_child_mixed_party,0.6265,F -coef_household_in_suburban_area_adult_adult_only_party,0,T -coef_household_in_suburban_area_adult_mixed_party,-0.06007,F -coef_household_in_suburban_area_child_child_only_party,0,T -coef_household_in_suburban_area_child_mixed_party,0,T -coef_adult_more_automobiles_than_workers_adult_only_party,-0.2133,F -coef_adult_more_automobiles_than_workers_mixed_party,-0.6031,F -coef_child_more_automobiles_than_workers_child_only_party,-0.4214,F -coef_child_more_automobiles_than_workers_mixed_party,-0.3776,F -#,, -coef_dummy_for_high_income_for_adult_in_adult_party,-0.1682,F -coef_dummy_for_high_income_for_adult_in_mixed_party,-0.02613,F -coef_dummy_for_high_income_for_child_in_children_party,-0.5619,F -coef_dummy_for_high_income_for_child_in_mixed_party,-0.1528,F -coef_adult_number_of_joint_tours_adult_only,-0.3242,F -coef_adult_number_of_joint_tours_mixed,-0.3584,F -coef_child_number_of_joint_tours_child_only,0.1047,F -coef_child_number_of_joint_tours_mixed,-0.5089,F -coef_adult_number_of_other_adults_in_the_household_adults_only_party,0,T -coef_adult_number_of_other_adults_in_the_household_mixed_party,0,T -coef_child_number_of_other_children_in_the_household_child_only_party,0,T -coef_child_number_of_other_children_in_the_household_mixed,0,T -#,, -coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,0.8436,F -coef_adult_log_of_max_window_overlap_with_a_child_mixed,2.189,F -coef_child_log_of_max_window_overlap_with_an_adult_mixed,1.538,F -coef_child_log_of_max_window_overlap_with_a_child_child,1.296,F - - - - +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_full_time_worker_mixed_party,-3.566,F +coef_full_time_worker_mixed_party_not,0.5,T +coef_part_time_worker_adults_only_party,-3.566,F +coef_part_time_worker_adults_only_party_not,0.5,T +coef_part_time_worker_mixed_party,-0.3655,F +coef_university_student_mixed_party,-3.041,F +coef_non_worker_adults_only_party,-3.164,F +coef_non_worker_mixed_party,0.7152,F +coef_child_too_young_for_school_children_only_party,-2.786,F +coef_child_too_young_for_school_mixed_party,-1.893,F +coef_pre_driving_age_student_children_only_party,-0.7217,F +coef_pre_driving_age_student_mixed_party,-1.752,F +coef_driving_age_student_children_only_party,-1.822,F +coef_driving_age_student_mixed_party,-1.353,F +#,, +coef_full_time_worker_specific_to_eating_out_joint_tours,0.7157,F +coef_full_time_worker_specific_to_eating_out_joint_tours_not,0.5,T +coef_full_time_worker_specific_to_discretionary_joint_tours,0.4392,F +coef_full_time_worker_specific_to_discretionary_joint_tours_not,0.5,T +coef_part_time_worker_specific_to_eating_out_joint_tours,2.188,F +coef_part_time_worker_specific_to_discretionary_joint_tours,0.285,F +coef_university_student_specific_to_eating_out_joint_tours,-0.82,F +coef_university_student_specific_to_discretionary_joint_tours,0,T +coef_non_worker_specific_to_eating_out_joint_tours,0.1617,F +coef_non_worker_specific_to_discretionary_joint_tours,-0.1835,F +coef_child_too_young_for_school_specific_to_eating_out_joint_tours,0.6589,F +coef_child_too_young_for_school_specific_to_discretionary_joint_tours,0.1284,F +coef_pre_driving_age_student_specific_to_eating_out_joint_tours,1.391,F +coef_pre_driving_age_student_specific_to_discretionary_joint_tours,0.6626,F +coef_driving_age_student_specific_to_eating_out_joint_tours,2.344,F +coef_driving_age_student_specific_to_discretionary_joint_tours,-0.6675,F +#,, +coef_household_in_urban_area_adult_adult_only_party,0,T +coef_household_in_urban_area_adult_mixed_party,-0.137,F +coef_household_in_urban_area_child_child_only_party,1.21,F +coef_household_in_urban_area_child_mixed_party,0.6265,F +coef_household_in_suburban_area_adult_adult_only_party,0,T +coef_household_in_suburban_area_adult_mixed_party,-0.06007,F +coef_household_in_suburban_area_child_child_only_party,0,T +coef_household_in_suburban_area_child_mixed_party,0,T +coef_adult_more_automobiles_than_workers_adult_only_party,-0.2133,F +coef_adult_more_automobiles_than_workers_mixed_party,-0.6031,F +coef_child_more_automobiles_than_workers_child_only_party,-0.4214,F +coef_child_more_automobiles_than_workers_mixed_party,-0.3776,F +#,, +coef_dummy_for_high_income_for_adult_in_adult_party,-0.1682,F +coef_dummy_for_high_income_for_adult_in_mixed_party,-0.02613,F +coef_dummy_for_high_income_for_child_in_children_party,-0.5619,F +coef_dummy_for_high_income_for_child_in_mixed_party,-0.1528,F +coef_adult_number_of_joint_tours_adult_only,-0.3242,F +coef_adult_number_of_joint_tours_mixed,-0.3584,F +coef_child_number_of_joint_tours_child_only,0.1047,F +coef_child_number_of_joint_tours_mixed,-0.5089,F +coef_adult_number_of_other_adults_in_the_household_adults_only_party,0,T +coef_adult_number_of_other_adults_in_the_household_mixed_party,0,T +coef_child_number_of_other_children_in_the_household_child_only_party,0,T +coef_child_number_of_other_children_in_the_household_mixed,0,T +#,, +coef_adult_log_of_max_window_overlap_with_an_adult_adult_only_party,0.8436,F +coef_adult_log_of_max_window_overlap_with_a_child_mixed,2.189,F +coef_child_log_of_max_window_overlap_with_an_adult_mixed,1.538,F +coef_child_log_of_max_window_overlap_with_a_child_child,1.296,F + + + + diff --git a/activitysim/examples/example_psrc/configs/joint_tour_scheduling.yaml b/activitysim/examples/prototype_semcog/configs/joint_tour_scheduling.yaml similarity index 94% rename from activitysim/examples/example_psrc/configs/joint_tour_scheduling.yaml rename to activitysim/examples/prototype_semcog/configs/joint_tour_scheduling.yaml index f57d09340f..e81afe238e 100755 --- a/activitysim/examples/example_psrc/configs/joint_tour_scheduling.yaml +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_scheduling.yaml @@ -1,13 +1,12 @@ - -SPEC: tour_scheduling_joint.csv -COEFFICIENTS: tour_scheduling_joint_coeffs.csv - -LOGIT_TYPE: MNL - -preprocessor: - SPEC: joint_tour_scheduling_annotate_tours_preprocessor - DF: joint_tours - TABLES: - - land_use - - households - - joint_tour_participants +LOGIT_TYPE: MNL + +SPEC: tour_scheduling_joint.csv +COEFFICIENTS: tour_scheduling_joint_coeffs.csv + +preprocessor: + SPEC: joint_tour_scheduling_annotate_tours_preprocessor + DF: joint_tours + TABLES: + - land_use + - households + - joint_tour_participants diff --git a/activitysim/examples/example_semcog/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv index 7a1e4e485d..594b79624a 100755 --- a/activitysim/examples/example_semcog/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/joint_tour_scheduling_annotate_tours_preprocessor.csv @@ -1,2 +1,2 @@ -Description,Target,Expression -,origin_to_destination_distance,"skim_dict.lookup(joint_tours.origin, joint_tours.destination, ('SOV_DIST', 'MD'))" +Description,Target,Expression +,origin_to_destination_distance,"skim_dict.lookup(joint_tours.origin, joint_tours.destination, ('SOV_DIST', 'MD'))" diff --git a/activitysim/examples/example_semcog/configs/logging.yaml b/activitysim/examples/prototype_semcog/configs/logging.yaml similarity index 95% rename from activitysim/examples/example_semcog/configs/logging.yaml rename to activitysim/examples/prototype_semcog/configs/logging.yaml index efc1b2d01b..6dfca578a4 100755 --- a/activitysim/examples/example_semcog/configs/logging.yaml +++ b/activitysim/examples/prototype_semcog/configs/logging.yaml @@ -1,53 +1,53 @@ -# Config for logging -# ------------------ -# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema - -logging: - version: 1 - disable_existing_loggers: true - - - # Configuring the default (root) logger is highly recommended - root: - level: NOTSET - handlers: [console] - - loggers: - - activitysim: - level: DEBUG - handlers: [console, logfile] - propagate: false - - orca: - level: WARN - handlers: [console, logfile] - propagate: false - - handlers: - - logfile: - class: logging.FileHandler - filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] - mode: w - formatter: fileFormatter - level: NOTSET - - console: - class: logging.StreamHandler - stream: ext://sys.stdout - formatter: simpleFormatter - level: NOTSET - - formatters: - - simpleFormatter: - class: logging.Formatter - # format: '%(levelname)s - %(name)s - %(message)s' - format: '%(levelname)s - %(message)s' - datefmt: '%d/%m/%Y %H:%M:%S' - - fileFormatter: - class: logging.Formatter - format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' - datefmt: '%d/%m/%Y %H:%M:%S' +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: NOTSET + handlers: [console] + + loggers: + + activitysim: + level: DEBUG + handlers: [console, logfile] + propagate: false + + orca: + level: WARN + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + level: NOTSET + + formatters: + + simpleFormatter: + class: logging.Formatter + # format: '%(levelname)s - %(name)s - %(message)s' + format: '%(levelname)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' diff --git a/activitysim/examples/example_psrc/configs/mandatory_tour_frequency.csv b/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency.csv similarity index 99% rename from activitysim/examples/example_psrc/configs/mandatory_tour_frequency.csv rename to activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency.csv index 17f68125ab..848bbf77aa 100755 --- a/activitysim/examples/example_psrc/configs/mandatory_tour_frequency.csv +++ b/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency.csv @@ -1,101 +1,101 @@ -Label,Description,Expression,work1,work2,school1,school2,work_and_school -util_ft_worker,Full-time worker alternative-specific constants,ptype == 1,0,coef_ft_worker_work2_asc,,, -util_pt_worker,Part-time worker alternative-specific constants,ptype == 2,0,coef_pt_worker_work2_asc,,, -util_univ,University student alternative-specific constants,ptype == 3,coef_univ_work1_asc,coef_univ_work2_asc,0,coef_univ_school2_asc,coef_univ_work_and_school_asc -util_non_working_adult,Non-working adult alternative-specific constants,ptype == 4,,,,, -util_retired,Retired alternative-specific constants,ptype == 5,,,,, -util_driving_age_child,Driving-age child alternative-specific constants,ptype == 6,,,0,coef_driving_age_child_school2_asc,coef_driving_age_child_work_and_school_asc -util_pre_driving_age_child,Pre-driving age child who is in school alternative-specific constants,ptype == 7,,,0,coef_pre_driving_age_child_school2_asc, -util_female_ft_worker,Female - Full-time worker interaction,(ptype == 1) & female,0,coef_female_work2,coef_female_school1,,coef_female_work_and_school -util_female_pt_worker,Female - Part-time worker interaction,(ptype == 2) & female,0,coef_female_work2,coef_female_school1,,coef_female_work_and_school -util_female_univ,Female - University student interaction,(ptype == 3) & female,coef_female_work1,coef_female_work2,coef_female_school1,coef_female_school2,coef_female_work_and_school -util_female_non_working_adult,Female - Non-working adult interaction,(ptype == 4) & female,0,coef_female_work2,coef_female_school1,, -util_female_retired,Female - Retired interaction,(ptype == 5) & female,0,coef_female_work2,coef_female_school1,, -util_female_driving_age_child,Female - Driving-age child interaction,(ptype == 6) & female,coef_female_work1,,0,coef_female_school2,coef_female_work_and_school -util_female_pre_driving,Female - Pre-driving age child who is in school interaction,(ptype == 7) & female,coef_female_work1,,0,coef_female_school2, -util_under_35_ft,Under 35 - Full-time worker interaction,(ptype == 1) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,,coef_under_35_work_and_school -util_under_35_pt,Under 35 - Part-time worker interaction,(ptype == 2) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,,coef_under_35_work_and_school -util_under_35_univ,Under 35 - University student interaction,(ptype == 3) & (age <= 35),coef_under_35_work1,coef_under_35_work2,0,coef_under_35_school2,coef_under_35_work_and_school -util_under_35_non_working,Under 35 - Non-working adult interaction,(ptype == 4) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,, -util_can_walk_to_work_ft,Can walk to work - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, -util_can_walk_to_work_pt,Can walk to work - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, -util_can_walk_to_work_univ,Can walk to work - University student interaction,(ptype == 3) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, -util_can_walk_to_work_non_working_adult,Can walk to work - Non-working adult interaction,(ptype == 4) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, -util_can_walk_to_work_retired,Can walk to work - Retired interaction,(ptype == 5) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, -util_can_walk_to_school_univ,Can walk to school - University student interaction,(ptype == 3) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, -util_can_walk_to_school_driving_age_child,Can walk to school - Driving-age child interaction,(ptype == 6) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, -util_can_walk_to_school_pre_driving_age_child,Can walk to school - Pre-driving age child who is in school interaction,(ptype == 7) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, -util_can_walk_to_work_or_school_ft,Can walk to work or school - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_pt,Can walk to work or school - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_univ,Can walk to work or school - University student interaction,(ptype == 3) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_can_walk_to_work_or_school_driving_age_child,Can walk to work or school - Driving-age child interaction,(ptype == 6) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school -util_round_trip_auto_time_to_work_ft,Round trip auto time to work - Full-time worker interaction,(ptype == 1) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 -util_round_trip_auto_time_to_work_pt,Round trip auto time to work - Part-time worker interaction,(ptype == 2) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 -util_round_trip_auto_time_to_work_univ,Round trip auto time to work - University student interaction,(ptype == 3) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 -util_round_trip_auto_time_to_work_non_working_adult,Round trip auto time to work - Non-working adult interaction,(ptype == 4) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,, -util_round_trip_auto_time_to_work_retired,Round trip auto time to work - Retired,(ptype == 5) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,, -util_round_trip_auto_time_to_school_univ,Round trip auto time to school - University student interaction,(ptype == 3) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2,coef_round_trip_auto_time_to_work_work_and_school -util_round_trip_auto_time_to_school_driving_age_child,Round trip auto time to school - Driving-age child interaction,(ptype == 6) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2,coef_round_trip_auto_time_to_work_work_and_school -util_round_trip_auto_time_to_school_pre_driving_age_child,Round trip auto time to school - Pre-driving age child who is in school interaction,(ptype == 7) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2, -util_student_employted_univ,Student is employed - University student interaction,(ptype == 3) & student_is_employed,coef_student_employed,coef_student_employed,,,coef_student_employed -util_student_employted_driving_age_child,Student is employed - Driving-age child interaction,(ptype == 6) & student_is_employed,coef_student_employed,coef_student_employed,,,coef_student_employed -util_non_student_goes_to_school_ft,Non-student goes to school - Full-time worker interaction,(ptype == 1) & nonstudent_to_school,,,coef_non_student_goes_to_school,,coef_non_student_goes_to_school -util_non_student_goes_to_school_pt,Non-student goes to school - Part-time worker interaction,(ptype == 2) & nonstudent_to_school,,,coef_non_student_goes_to_school,,coef_non_student_goes_to_school -util_non_student_goes_to_school_non_working_adult,Non-student goes to school - Non-working adult interaction,(ptype == 4) & nonstudent_to_school,,,coef_non_student_goes_to_school,, -util_non_student_goes_to_school_retired,Non-student goes to school - Retired interaction,(ptype == 5) & nonstudent_to_school,,,coef_non_student_goes_to_school,, -util_no_cars_in_hh_ft,No cars in household - Full-time worker interaction,(ptype == 1) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,,coef_no_cars_in_hh_work_and_school -util_no_cars_in_hh_pt,No cars in household - Part-time worker interaction,(ptype == 2) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,,coef_no_cars_in_hh_work_and_school -util_no_cars_in_hh_unif,No cars in household - University student interaction,(ptype == 3) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,coef_no_cars_in_hh_school2,coef_no_cars_in_hh_work_and_school -util_no_cars_in_hh_non_working_adult,No cars in household - Non-working adult interaction,(ptype == 4) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,, -util_no_cars_in_hh_retired,No cars in household - Retired interaction,(ptype == 5) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,, -util_no_cars_in_hh_driving_age_student,No cars in household - Driving-age student interaction,(ptype == 6) & (auto_ownership == 0),,,,coef_no_cars_in_hh_school2,coef_no_cars_in_hh_work_and_school -util_no_cars_in_hh_pre_driving_age,No cars in household - Pre-driving age child who is in school interaction,(ptype == 7) & (auto_ownership == 0),,,,coef_no_cars_in_hh_school2, -util_fewer_cars_than_drivers_univ,Fewer cars than drivers in household - University student interaction,(ptype == 3) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2, -util_fewer_cars_than_drivers_driving_age_student,Fewer cars than drivers in household - Driving-age student interaction,(ptype == 6) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2, -util_fewer_cars_than_drivers_pre_driving_age,Fewer cars than drivers in household - Pre-driving age child who is in school interaction,(ptype == 7) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2, -util_num_preschool_in_hh_ft,Number of preschool children in household - Full-time worker interaction,(ptype == 1) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,,coef_num_preschool_in_hh_work_and_school -util_num_preschool_in_hh_pt,Number of preschool children in household - Part-time worker interaction,(ptype == 2) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,,coef_num_preschool_in_hh_work_and_school -util_num_preschool_in_hh_univ,Number of preschool children in household - University student interaction,(ptype == 3) * (num_young_children),coef_num_preschool_in_hh_work1,coef_num_preschool_in_hh_work2,0,coef_num_preschool_in_hh_school2,coef_num_preschool_in_hh_work_and_school -util_num_preschool_in_hh_non_working_adult,Number of preschool children in household - Non-working adult interaction,(ptype == 4) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,, -util_num_preschool_in_hh_retired,Number of preschool children in household - Retired interaction,(ptype == 5) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,, -util_num_preschool_in_hh_driving_age_student,Number of preschool children in household - Driving-age student interaction,(ptype == 6) * (num_young_children),coef_num_preschool_in_hh_work1,,0,coef_num_preschool_in_hh_school2,coef_num_preschool_in_hh_work_and_school -util_num_preschool_in_hh_pre_driving_age_in_school,Number of preschool children in household - Pre-driving age child who is in school interaction,(ptype == 7) * (num_young_children),coef_num_preschool_in_hh_work1,,0,coef_num_preschool_in_hh_school2, -util_num_nonworkers_in_hh_ft,Number of non-workers in the household - Full-time worker interaction,(ptype == 1) * num_non_workers,,,coef_num_non_workers_in_hh_school1,, -util_num_nonworkers_in_hh_pt,Number of non-workers in the household - Part-time worker interaction,(ptype == 2) * num_non_workers,,,coef_num_non_workers_in_hh_school1,, -util_hh_income_gt_50k_ft,Household income higher than $50k - Full-time worker interaction,(ptype == 1) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,,coef_hh_income_gt_50k_worker_work_and_school -util_hh_income_gt_50k_pt,Household income higher than $50k - Part-time worker interaction,(ptype == 2) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,,coef_hh_income_gt_50k_worker_work_and_school -util_hh_income_gt_50k_univ,Household income higher than $50k - University student interaction,(ptype == 3) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,coef_hh_income_gt_50k_work,0,,coef_hh_income_gt_50k_student_work_and_school -util_hh_income_gt_50k_non_working_adult,Household income higher than $50k - Non-working adult interaction,(ptype == 4) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,, -util_hh_income_gt_50k_retired,Household income higher than $50k - Retired interaction,(ptype == 5) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,, -util_hh_income_gt_50k_driving_age_student,Household income higher than $50k - Driving-age student interaction,(ptype == 6) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,,0,,coef_hh_income_gt_50k_student_work_and_school -util_hh_income_gt_50k_pre_driving_age_student,Household income higher than $50k - Pre-driving age child who is in school interaction,(ptype == 7) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,,0,, -util_non_family_hh_ft,Non-family household - Full-time worker interaction,(ptype == 1) & non_family,0,,coef_non_family_hh_category1,,coef_non_family_hh_category1 -util_non_family_hh_pt,Non-family household - Part-time worker interaction,(ptype == 2) & non_family,0,,coef_non_family_hh_category1,,coef_non_family_hh_category1 -util_non_family_hh_univ,Non-family household - University student interaction,(ptype == 3) & non_family,coef_non_family_hh_category2,coef_non_family_hh_category2,0,,coef_non_family_hh_category2 -util_non_family_hh_non_working_adult,Non-family household - Non-working adult interaction,(ptype == 4) & non_family,0,,coef_non_family_hh_category1,, -util_non_family_hh_retired,Non-family household - Retired interaction,(ptype == 5) & non_family,0,,coef_non_family_hh_category1,, -util_non_family_hh_driving_age_student,Non-family household - Driving-age student interaction,(ptype == 6) & non_family,coef_non_family_hh_category2,,0,,coef_non_family_hh_category2 -util_non_family_hh_pre_driving_age_student,Non-family household - Pre-driving age child who is in school interaction,(ptype == 7) & non_family,coef_non_family_hh_category2,,0,, -util_num_under_16_not_at_school_ft,Number of children under 16 not at school - Full-time worker interaction,(ptype == 1) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,,coef_num_under_16_not_at_school_work_and_school -util_num_under_16_not_at_school_pt,Number of children under 16 not at school - Part-time worker interaction,(ptype == 2) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,,coef_num_under_16_not_at_school_work_and_school -util_num_under_16_not_at_school_univ,Number of children under 16 not at school - University student interaction,(ptype == 3) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,coef_num_under_16_not_at_school_school2,coef_num_under_16_not_at_school_work_and_school -util_num_under_16_not_at_school_non_working_adult,Number of children under 16 not at school - Non-working adult interaction,(ptype == 4) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,, -util_num_under_16_not_at_school_retired,Number of children under 16 not at school - Retired,(ptype == 5) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,, -util_num_under_16_not_at_school_driving_age_student,Number of children under 16 not at school - Driving-age student interaction,(ptype == 6) * num_under16_not_at_school,,,,coef_num_under_16_not_at_school_school2,coef_num_under_16_not_at_school_work_and_school -util_num_under_16_not_at_school_pre_driving_age,Number of children under 16 not at school - Pre-driving age child who is in school interaction,(ptype == 7) * num_under16_not_at_school,,,,coef_num_under_16_not_at_school_school2, -util_nome_urban_ft,Home is in urban area - Full-time worker interaction,(ptype == 1) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,,coef_home_urban_work_and_school -util_nome_urban_pt,Home is in urban area - Part-time worker interaction,(ptype == 2) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,,coef_home_urban_work_and_school -util_nome_urban_univ,Home is in urban area - University student interaction,(ptype == 3) & home_is_urban,coef_home_urban_work1,coef_home_urban_work2,0,coef_home_urban_school2,coef_home_urban_work_and_school -util_nome_urban_non_working_adult,Home is in urban area - Non-working adult interaction,(ptype == 4) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,, -util_nome_urban_retired,Home is in urban area - Retired interaction,(ptype == 5) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,, -util_nome_urban_driving_age_student,Home is in urban area - Driving-age student interaction,(ptype == 6) & home_is_urban,coef_home_urban_work1,,0,coef_home_urban_school2,coef_home_urban_work_and_school -util_nome_urban_pre_driving_age_student,Home is in urban area - Pre-driving age child who is in school interaction,(ptype == 7) & home_is_urban,coef_home_urban_work1,,0,coef_home_urban_school2, -util_availability_ft,Unavailable: Full-time worker,ptype == 1,,,,coef_unavailable, -util_availability_pt,Unavailable: Part-time worker,ptype == 2,,,,coef_unavailable, -util_availability_non_working_adult,Unavailable: Non-working adult,ptype == 4,,,,coef_unavailable,coef_unavailable -util_availability_retired,Unavailable: Retired,ptype == 5,,,,coef_unavailable,coef_unavailable -util_availability_driving_age_child,Unavailable: Driving-age child,ptype == 6,coef_unavailable,coef_unavailable,,, -util_availability_pre_driving_age_student,Unavailable: Pre-driving age child who is in school,ptype == 7,,coef_unavailable,,,coef_unavailable -util_availability_pre_driving_age_not_in_school,Unavailable: Pre-driving age child who is not in school,ptype == 8,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable -util_availability_work_tours_no_usual_work_location,Unavailable: Work tours for those with no usual work location,~(workplace_zone_id > -1),coef_unavailable,coef_unavailable,,,coef_unavailable -util_availability_school_tours_no_usual_school_location,Unavailable: School tours for those with no usual school location,~(school_zone_id > -1),,,coef_unavailable,coef_unavailable,coef_unavailable +Label,Description,Expression,work1,work2,school1,school2,work_and_school +util_ft_worker,Full-time worker alternative-specific constants,ptype == 1,0,coef_ft_worker_work2_asc,,, +util_pt_worker,Part-time worker alternative-specific constants,ptype == 2,0,coef_pt_worker_work2_asc,,, +util_univ,University student alternative-specific constants,ptype == 3,coef_univ_work1_asc,coef_univ_work2_asc,0,coef_univ_school2_asc,coef_univ_work_and_school_asc +util_non_working_adult,Non-working adult alternative-specific constants,ptype == 4,,,,, +util_retired,Retired alternative-specific constants,ptype == 5,,,,, +util_driving_age_child,Driving-age child alternative-specific constants,ptype == 6,,,0,coef_driving_age_child_school2_asc,coef_driving_age_child_work_and_school_asc +util_pre_driving_age_child,Pre-driving age child who is in school alternative-specific constants,ptype == 7,,,0,coef_pre_driving_age_child_school2_asc, +util_female_ft_worker,Female - Full-time worker interaction,(ptype == 1) & female,0,coef_female_work2,coef_female_school1,,coef_female_work_and_school +util_female_pt_worker,Female - Part-time worker interaction,(ptype == 2) & female,0,coef_female_work2,coef_female_school1,,coef_female_work_and_school +util_female_univ,Female - University student interaction,(ptype == 3) & female,coef_female_work1,coef_female_work2,coef_female_school1,coef_female_school2,coef_female_work_and_school +util_female_non_working_adult,Female - Non-working adult interaction,(ptype == 4) & female,0,coef_female_work2,coef_female_school1,, +util_female_retired,Female - Retired interaction,(ptype == 5) & female,0,coef_female_work2,coef_female_school1,, +util_female_driving_age_child,Female - Driving-age child interaction,(ptype == 6) & female,coef_female_work1,,0,coef_female_school2,coef_female_work_and_school +util_female_pre_driving,Female - Pre-driving age child who is in school interaction,(ptype == 7) & female,coef_female_work1,,0,coef_female_school2, +util_under_35_ft,Under 35 - Full-time worker interaction,(ptype == 1) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,,coef_under_35_work_and_school +util_under_35_pt,Under 35 - Part-time worker interaction,(ptype == 2) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,,coef_under_35_work_and_school +util_under_35_univ,Under 35 - University student interaction,(ptype == 3) & (age <= 35),coef_under_35_work1,coef_under_35_work2,0,coef_under_35_school2,coef_under_35_work_and_school +util_under_35_non_working,Under 35 - Non-working adult interaction,(ptype == 4) & (age <= 35),0,coef_under_35_work2,coef_under_35_school1,, +util_can_walk_to_work_ft,Can walk to work - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, +util_can_walk_to_work_pt,Can walk to work - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, +util_can_walk_to_work_univ,Can walk to work - University student interaction,(ptype == 3) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, +util_can_walk_to_work_non_working_adult,Can walk to work - Non-working adult interaction,(ptype == 4) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, +util_can_walk_to_work_retired,Can walk to work - Retired interaction,(ptype == 5) & (distance_to_work < 3),,coef_can_walk_to_work_work2,,, +util_can_walk_to_school_univ,Can walk to school - University student interaction,(ptype == 3) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, +util_can_walk_to_school_driving_age_child,Can walk to school - Driving-age child interaction,(ptype == 6) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, +util_can_walk_to_school_pre_driving_age_child,Can walk to school - Pre-driving age child who is in school interaction,(ptype == 7) & (distance_to_school < 3),,,,coef_can_walk_to_work_school2, +util_can_walk_to_work_or_school_ft,Can walk to work or school - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_pt,Can walk to work or school - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_univ,Can walk to work or school - University student interaction,(ptype == 3) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school +util_can_walk_to_work_or_school_driving_age_child,Can walk to work or school - Driving-age child interaction,(ptype == 6) & (distance_to_work < 3 | distance_to_school < 3),,,,,coef_can_walk_to_work_and_school +util_round_trip_auto_time_to_work_ft,Round trip auto time to work - Full-time worker interaction,(ptype == 1) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 +util_round_trip_auto_time_to_work_pt,Round trip auto time to work - Part-time worker interaction,(ptype == 2) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 +util_round_trip_auto_time_to_work_univ,Round trip auto time to work - University student interaction,(ptype == 3) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,,coef_round_trip_auto_time_to_work_school2 +util_round_trip_auto_time_to_work_non_working_adult,Round trip auto time to work - Non-working adult interaction,(ptype == 4) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,, +util_round_trip_auto_time_to_work_retired,Round trip auto time to work - Retired,(ptype == 5) * roundtrip_auto_time_to_work,,coef_round_trip_auto_time_to_work_work2,,, +util_round_trip_auto_time_to_school_univ,Round trip auto time to school - University student interaction,(ptype == 3) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2,coef_round_trip_auto_time_to_work_work_and_school +util_round_trip_auto_time_to_school_driving_age_child,Round trip auto time to school - Driving-age child interaction,(ptype == 6) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2,coef_round_trip_auto_time_to_work_work_and_school +util_round_trip_auto_time_to_school_pre_driving_age_child,Round trip auto time to school - Pre-driving age child who is in school interaction,(ptype == 7) * roundtrip_auto_time_to_school,,,,coef_round_trip_auto_time_to_work_school2, +util_student_employted_univ,Student is employed - University student interaction,(ptype == 3) & student_is_employed,coef_student_employed,coef_student_employed,,,coef_student_employed +util_student_employted_driving_age_child,Student is employed - Driving-age child interaction,(ptype == 6) & student_is_employed,coef_student_employed,coef_student_employed,,,coef_student_employed +util_non_student_goes_to_school_ft,Non-student goes to school - Full-time worker interaction,(ptype == 1) & nonstudent_to_school,,,coef_non_student_goes_to_school,,coef_non_student_goes_to_school +util_non_student_goes_to_school_pt,Non-student goes to school - Part-time worker interaction,(ptype == 2) & nonstudent_to_school,,,coef_non_student_goes_to_school,,coef_non_student_goes_to_school +util_non_student_goes_to_school_non_working_adult,Non-student goes to school - Non-working adult interaction,(ptype == 4) & nonstudent_to_school,,,coef_non_student_goes_to_school,, +util_non_student_goes_to_school_retired,Non-student goes to school - Retired interaction,(ptype == 5) & nonstudent_to_school,,,coef_non_student_goes_to_school,, +util_no_cars_in_hh_ft,No cars in household - Full-time worker interaction,(ptype == 1) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,,coef_no_cars_in_hh_work_and_school +util_no_cars_in_hh_pt,No cars in household - Part-time worker interaction,(ptype == 2) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,,coef_no_cars_in_hh_work_and_school +util_no_cars_in_hh_unif,No cars in household - University student interaction,(ptype == 3) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,coef_no_cars_in_hh_school2,coef_no_cars_in_hh_work_and_school +util_no_cars_in_hh_non_working_adult,No cars in household - Non-working adult interaction,(ptype == 4) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,, +util_no_cars_in_hh_retired,No cars in household - Retired interaction,(ptype == 5) & (auto_ownership == 0),,coef_no_cars_in_hh_work2,,, +util_no_cars_in_hh_driving_age_student,No cars in household - Driving-age student interaction,(ptype == 6) & (auto_ownership == 0),,,,coef_no_cars_in_hh_school2,coef_no_cars_in_hh_work_and_school +util_no_cars_in_hh_pre_driving_age,No cars in household - Pre-driving age child who is in school interaction,(ptype == 7) & (auto_ownership == 0),,,,coef_no_cars_in_hh_school2, +util_fewer_cars_than_drivers_univ,Fewer cars than drivers in household - University student interaction,(ptype == 3) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2, +util_fewer_cars_than_drivers_driving_age_student,Fewer cars than drivers in household - Driving-age student interaction,(ptype == 6) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2, +util_fewer_cars_than_drivers_pre_driving_age,Fewer cars than drivers in household - Pre-driving age child who is in school interaction,(ptype == 7) & (auto_ownership < num_drivers),,,,coef_few_cars_than_drivers_school2, +util_num_preschool_in_hh_ft,Number of preschool children in household - Full-time worker interaction,(ptype == 1) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,,coef_num_preschool_in_hh_work_and_school +util_num_preschool_in_hh_pt,Number of preschool children in household - Part-time worker interaction,(ptype == 2) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,,coef_num_preschool_in_hh_work_and_school +util_num_preschool_in_hh_univ,Number of preschool children in household - University student interaction,(ptype == 3) * (num_young_children),coef_num_preschool_in_hh_work1,coef_num_preschool_in_hh_work2,0,coef_num_preschool_in_hh_school2,coef_num_preschool_in_hh_work_and_school +util_num_preschool_in_hh_non_working_adult,Number of preschool children in household - Non-working adult interaction,(ptype == 4) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,, +util_num_preschool_in_hh_retired,Number of preschool children in household - Retired interaction,(ptype == 5) * (num_young_children),0,coef_num_preschool_in_hh_work2,coef_num_preschool_in_hh_school1,, +util_num_preschool_in_hh_driving_age_student,Number of preschool children in household - Driving-age student interaction,(ptype == 6) * (num_young_children),coef_num_preschool_in_hh_work1,,0,coef_num_preschool_in_hh_school2,coef_num_preschool_in_hh_work_and_school +util_num_preschool_in_hh_pre_driving_age_in_school,Number of preschool children in household - Pre-driving age child who is in school interaction,(ptype == 7) * (num_young_children),coef_num_preschool_in_hh_work1,,0,coef_num_preschool_in_hh_school2, +util_num_nonworkers_in_hh_ft,Number of non-workers in the household - Full-time worker interaction,(ptype == 1) * num_non_workers,,,coef_num_non_workers_in_hh_school1,, +util_num_nonworkers_in_hh_pt,Number of non-workers in the household - Part-time worker interaction,(ptype == 2) * num_non_workers,,,coef_num_non_workers_in_hh_school1,, +util_hh_income_gt_50k_ft,Household income higher than $50k - Full-time worker interaction,(ptype == 1) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,,coef_hh_income_gt_50k_worker_work_and_school +util_hh_income_gt_50k_pt,Household income higher than $50k - Part-time worker interaction,(ptype == 2) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,,coef_hh_income_gt_50k_worker_work_and_school +util_hh_income_gt_50k_univ,Household income higher than $50k - University student interaction,(ptype == 3) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,coef_hh_income_gt_50k_work,0,,coef_hh_income_gt_50k_student_work_and_school +util_hh_income_gt_50k_non_working_adult,Household income higher than $50k - Non-working adult interaction,(ptype == 4) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,, +util_hh_income_gt_50k_retired,Household income higher than $50k - Retired interaction,(ptype == 5) & (income_in_thousands > 50),0,,coef_hh_income_gt_50k_school1,, +util_hh_income_gt_50k_driving_age_student,Household income higher than $50k - Driving-age student interaction,(ptype == 6) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,,0,,coef_hh_income_gt_50k_student_work_and_school +util_hh_income_gt_50k_pre_driving_age_student,Household income higher than $50k - Pre-driving age child who is in school interaction,(ptype == 7) & (income_in_thousands > 50),coef_hh_income_gt_50k_work,,0,, +util_non_family_hh_ft,Non-family household - Full-time worker interaction,(ptype == 1) & non_family,0,,coef_non_family_hh_category1,,coef_non_family_hh_category1 +util_non_family_hh_pt,Non-family household - Part-time worker interaction,(ptype == 2) & non_family,0,,coef_non_family_hh_category1,,coef_non_family_hh_category1 +util_non_family_hh_univ,Non-family household - University student interaction,(ptype == 3) & non_family,coef_non_family_hh_category2,coef_non_family_hh_category2,0,,coef_non_family_hh_category2 +util_non_family_hh_non_working_adult,Non-family household - Non-working adult interaction,(ptype == 4) & non_family,0,,coef_non_family_hh_category1,, +util_non_family_hh_retired,Non-family household - Retired interaction,(ptype == 5) & non_family,0,,coef_non_family_hh_category1,, +util_non_family_hh_driving_age_student,Non-family household - Driving-age student interaction,(ptype == 6) & non_family,coef_non_family_hh_category2,,0,,coef_non_family_hh_category2 +util_non_family_hh_pre_driving_age_student,Non-family household - Pre-driving age child who is in school interaction,(ptype == 7) & non_family,coef_non_family_hh_category2,,0,, +util_num_under_16_not_at_school_ft,Number of children under 16 not at school - Full-time worker interaction,(ptype == 1) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,,coef_num_under_16_not_at_school_work_and_school +util_num_under_16_not_at_school_pt,Number of children under 16 not at school - Part-time worker interaction,(ptype == 2) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,,coef_num_under_16_not_at_school_work_and_school +util_num_under_16_not_at_school_univ,Number of children under 16 not at school - University student interaction,(ptype == 3) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,coef_num_under_16_not_at_school_school2,coef_num_under_16_not_at_school_work_and_school +util_num_under_16_not_at_school_non_working_adult,Number of children under 16 not at school - Non-working adult interaction,(ptype == 4) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,, +util_num_under_16_not_at_school_retired,Number of children under 16 not at school - Retired,(ptype == 5) * num_under16_not_at_school,,coef_num_under_16_not_at_school_work2,,, +util_num_under_16_not_at_school_driving_age_student,Number of children under 16 not at school - Driving-age student interaction,(ptype == 6) * num_under16_not_at_school,,,,coef_num_under_16_not_at_school_school2,coef_num_under_16_not_at_school_work_and_school +util_num_under_16_not_at_school_pre_driving_age,Number of children under 16 not at school - Pre-driving age child who is in school interaction,(ptype == 7) * num_under16_not_at_school,,,,coef_num_under_16_not_at_school_school2, +util_nome_urban_ft,Home is in urban area - Full-time worker interaction,(ptype == 1) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,,coef_home_urban_work_and_school +util_nome_urban_pt,Home is in urban area - Part-time worker interaction,(ptype == 2) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,,coef_home_urban_work_and_school +util_nome_urban_univ,Home is in urban area - University student interaction,(ptype == 3) & home_is_urban,coef_home_urban_work1,coef_home_urban_work2,0,coef_home_urban_school2,coef_home_urban_work_and_school +util_nome_urban_non_working_adult,Home is in urban area - Non-working adult interaction,(ptype == 4) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,, +util_nome_urban_retired,Home is in urban area - Retired interaction,(ptype == 5) & home_is_urban,0,coef_home_urban_work2,coef_home_urban_school1,, +util_nome_urban_driving_age_student,Home is in urban area - Driving-age student interaction,(ptype == 6) & home_is_urban,coef_home_urban_work1,,0,coef_home_urban_school2,coef_home_urban_work_and_school +util_nome_urban_pre_driving_age_student,Home is in urban area - Pre-driving age child who is in school interaction,(ptype == 7) & home_is_urban,coef_home_urban_work1,,0,coef_home_urban_school2, +util_availability_ft,Unavailable: Full-time worker,ptype == 1,,,,coef_unavailable, +util_availability_pt,Unavailable: Part-time worker,ptype == 2,,,,coef_unavailable, +util_availability_non_working_adult,Unavailable: Non-working adult,ptype == 4,,,,coef_unavailable,coef_unavailable +util_availability_retired,Unavailable: Retired,ptype == 5,,,,coef_unavailable,coef_unavailable +util_availability_driving_age_child,Unavailable: Driving-age child,ptype == 6,coef_unavailable,coef_unavailable,,, +util_availability_pre_driving_age_student,Unavailable: Pre-driving age child who is in school,ptype == 7,,coef_unavailable,,,coef_unavailable +util_availability_pre_driving_age_not_in_school,Unavailable: Pre-driving age child who is not in school,ptype == 8,coef_unavailable,coef_unavailable,,coef_unavailable,coef_unavailable +util_availability_work_tours_no_usual_work_location,Unavailable: Work tours for those with no usual work location,~(workplace_zone_id > -1),coef_unavailable,coef_unavailable,,,coef_unavailable +util_availability_school_tours_no_usual_school_location,Unavailable: School tours for those with no usual school location,~(school_zone_id > -1),,,coef_unavailable,coef_unavailable,coef_unavailable diff --git a/activitysim/examples/example_psrc/configs/mandatory_tour_frequency.yaml b/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency.yaml similarity index 94% rename from activitysim/examples/example_psrc/configs/mandatory_tour_frequency.yaml rename to activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency.yaml index ce768bbaa7..de8e115fd5 100755 --- a/activitysim/examples/example_psrc/configs/mandatory_tour_frequency.yaml +++ b/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency.yaml @@ -1,10 +1,10 @@ - -SPEC: mandatory_tour_frequency.csv -COEFFICIENTS: mandatory_tour_frequency_coeffs.csv - -annotate_persons: - SPEC: annotate_persons_mtf - DF: persons - TABLES: - - tours - + +SPEC: mandatory_tour_frequency.csv +COEFFICIENTS: mandatory_tour_frequency_coeffs.csv + +annotate_persons: + SPEC: annotate_persons_mtf + DF: persons + TABLES: + - tours + diff --git a/activitysim/examples/example_semcog/configs/mandatory_tour_frequency_alternatives.csv b/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency_alternatives.csv similarity index 96% rename from activitysim/examples/example_semcog/configs/mandatory_tour_frequency_alternatives.csv rename to activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency_alternatives.csv index e4e04d48ef..025decbb1c 100755 --- a/activitysim/examples/example_semcog/configs/mandatory_tour_frequency_alternatives.csv +++ b/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency_alternatives.csv @@ -1,7 +1,7 @@ -#,,alt file for building tours even though simulation is simple_simulate not interaction_simulate -alt,work,school -work1,1,0 -work2,2,0 -school1,0,1 -school2,0,2 -work_and_school,1,1 +#,,alt file for building tours even though simulation is simple_simulate not interaction_simulate +alt,work,school +work1,1,0 +work2,2,0 +school1,0,1 +school2,0,2 +work_and_school,1,1 diff --git a/activitysim/examples/example_semcog/configs/mandatory_tour_frequency_coeffs.csv b/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency_coeffs.csv similarity index 97% rename from activitysim/examples/example_semcog/configs/mandatory_tour_frequency_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency_coeffs.csv index d712395c00..c0909e681f 100755 --- a/activitysim/examples/example_semcog/configs/mandatory_tour_frequency_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/mandatory_tour_frequency_coeffs.csv @@ -1,54 +1,54 @@ -coefficient_name,value,constrain -coef_unavailable,-999,T -coef_ft_worker_work2_asc,-3.3781,F -coef_pt_worker_work2_asc,-3.0476,F -coef_univ_work1_asc,-2.630262534,F -coef_univ_work2_asc,-4.5,F -coef_univ_school2_asc,-3.841285259,F -coef_univ_work_and_school_asc,-4.529863127,F -coef_driving_age_child_school2_asc,-3.136,F -coef_driving_age_child_work_and_school_asc,-4.4362,F -coef_pre_driving_age_child_school2_asc,-3.9703,F -coef_female_work1,0.1737,F -coef_female_work2,-0.2255,F -coef_female_school1,0.1592,F -coef_female_school2,0.114,F -coef_female_work_and_school,-0.3442,F -coef_female_univ_work1,0.1737,F -coef_under_35_work1,-0.4629,F -coef_under_35_work2,-0.1375,F -coef_under_35_school1,0.7218,F -coef_under_35_school2,1.275,F -coef_under_35_work_and_school,0.9761,F -coef_can_walk_to_work_work2,0.5268,F -coef_can_walk_to_work_school2,0.7114,F -coef_can_walk_to_work_and_school,0.1391,F -coef_round_trip_auto_time_to_work_work2,-0.0035,F -coef_round_trip_auto_time_to_work_school2,-0.0034,F -coef_round_trip_auto_time_to_work_work_and_school,-0.0031,F -coef_student_employed,3.014,F -coef_non_student_goes_to_school,3.883,F -coef_no_cars_in_hh_work2,-1.306,F -coef_no_cars_in_hh_school2,-1.413,F -coef_no_cars_in_hh_work_and_school,-1.302,F -coef_few_cars_than_drivers_school2,-0.5759,F -coef_num_preschool_in_hh_work1,0.2191,F -coef_num_preschool_in_hh_work2,-0.1478,F -coef_num_preschool_in_hh_school1,-0.1335,F -coef_num_preschool_in_hh_school2,-0.5577,F -coef_num_preschool_in_hh_work_and_school,-0.1251,F -coef_num_non_workers_in_hh_school1,0.2574,F -coef_hh_income_gt_50k_work,-0.0528,F -coef_hh_income_gt_50k_school1,0.0347,F -coef_hh_income_gt_50k_worker_work_and_school,0.0347,F -coef_hh_income_gt_50k_student_work_and_school,-0.0528,F -coef_non_family_hh_category1,-0.25,F -coef_non_family_hh_category2,-0.1792,F -coef_num_under_16_not_at_school_work2,0.1804, -coef_num_under_16_not_at_school_school2,0.0866, -coef_num_under_16_not_at_school_work_and_school,-0.1955, -coef_home_urban_work1,-0.2831, -coef_home_urban_work2,0.2308, -coef_home_urban_school1,-0.1361, -coef_home_urban_school2,0.317, -coef_home_urban_work_and_school,-0.3509, +coefficient_name,value,constrain +coef_unavailable,-999,T +coef_ft_worker_work2_asc,-3.3781,F +coef_pt_worker_work2_asc,-3.0476,F +coef_univ_work1_asc,-2.630262534,F +coef_univ_work2_asc,-4.5,F +coef_univ_school2_asc,-3.841285259,F +coef_univ_work_and_school_asc,-4.529863127,F +coef_driving_age_child_school2_asc,-3.136,F +coef_driving_age_child_work_and_school_asc,-4.4362,F +coef_pre_driving_age_child_school2_asc,-3.9703,F +coef_female_work1,0.1737,F +coef_female_work2,-0.2255,F +coef_female_school1,0.1592,F +coef_female_school2,0.114,F +coef_female_work_and_school,-0.3442,F +coef_female_univ_work1,0.1737,F +coef_under_35_work1,-0.4629,F +coef_under_35_work2,-0.1375,F +coef_under_35_school1,0.7218,F +coef_under_35_school2,1.275,F +coef_under_35_work_and_school,0.9761,F +coef_can_walk_to_work_work2,0.5268,F +coef_can_walk_to_work_school2,0.7114,F +coef_can_walk_to_work_and_school,0.1391,F +coef_round_trip_auto_time_to_work_work2,-0.0035,F +coef_round_trip_auto_time_to_work_school2,-0.0034,F +coef_round_trip_auto_time_to_work_work_and_school,-0.0031,F +coef_student_employed,3.014,F +coef_non_student_goes_to_school,3.883,F +coef_no_cars_in_hh_work2,-1.306,F +coef_no_cars_in_hh_school2,-1.413,F +coef_no_cars_in_hh_work_and_school,-1.302,F +coef_few_cars_than_drivers_school2,-0.5759,F +coef_num_preschool_in_hh_work1,0.2191,F +coef_num_preschool_in_hh_work2,-0.1478,F +coef_num_preschool_in_hh_school1,-0.1335,F +coef_num_preschool_in_hh_school2,-0.5577,F +coef_num_preschool_in_hh_work_and_school,-0.1251,F +coef_num_non_workers_in_hh_school1,0.2574,F +coef_hh_income_gt_50k_work,-0.0528,F +coef_hh_income_gt_50k_school1,0.0347,F +coef_hh_income_gt_50k_worker_work_and_school,0.0347,F +coef_hh_income_gt_50k_student_work_and_school,-0.0528,F +coef_non_family_hh_category1,-0.25,F +coef_non_family_hh_category2,-0.1792,F +coef_num_under_16_not_at_school_work2,0.1804, +coef_num_under_16_not_at_school_school2,0.0866, +coef_num_under_16_not_at_school_work_and_school,-0.1955, +coef_home_urban_work1,-0.2831, +coef_home_urban_work2,0.2308, +coef_home_urban_school1,-0.1361, +coef_home_urban_school2,0.317, +coef_home_urban_work_and_school,-0.3509, diff --git a/activitysim/examples/example_semcog/configs/mandatory_tour_scheduling.yaml b/activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling.yaml similarity index 95% rename from activitysim/examples/example_semcog/configs/mandatory_tour_scheduling.yaml rename to activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling.yaml index 7e74373cb3..ea9ca0a2db 100755 --- a/activitysim/examples/example_semcog/configs/mandatory_tour_scheduling.yaml +++ b/activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling.yaml @@ -1,52 +1,52 @@ - -SIMULATE_CHOOSER_COLUMNS: - - age - - female - - ptype - - is_university - - is_income_less25K - - is_income_25K_to_60K - - is_income_60K_to_120K - - is_income_greater120K - - is_pre_drive_child_in_HH - - is_non_worker_in_HH - - auto_ownership - - is_all_adults_full_time_workers - - distance_to_school - - roundtrip_auto_time_to_work - - roundtrip_auto_time_to_school - - free_parking_at_work - - workplace_zone_id - - school_zone_id - - home_zone_id - - TAZ - - transit_pass_ownership - - transit_pass_subsidy - -LOGSUM_SETTINGS: tour_mode_choice.yaml - -TOUR_SPEC_SEGMENTS: - work: work - school: school - univ: univ - -ALTS_PREPROCESSOR: - work: - SPEC: mandatory_tour_scheduling_annotate_alts_preprocessor.csv - DF: alt_tdd - -SPEC_SEGMENTS: - work: - 'SPEC': tour_scheduling_work.csv - 'COEFFICIENTS': tour_scheduling_work_coeffs.csv - school: - 'SPEC': tour_scheduling_school.csv - 'COEFFICIENTS': tour_scheduling_school_coeffs.csv - univ: - 'SPEC': tour_scheduling_university.csv - 'COEFFICIENTS': tour_scheduling_university_coeffs.csv - -DESTINATION_FOR_TOUR_PURPOSE: - work: workplace_zone_id - school: school_zone_id - univ: school_zone_id + +SIMULATE_CHOOSER_COLUMNS: + - age + - female + - ptype + - is_university + - is_income_less25K + - is_income_25K_to_60K + - is_income_60K_to_120K + - is_income_greater120K + - is_pre_drive_child_in_HH + - is_non_worker_in_HH + - auto_ownership + - is_all_adults_full_time_workers + - distance_to_school + - roundtrip_auto_time_to_work + - roundtrip_auto_time_to_school + - free_parking_at_work + - workplace_zone_id + - school_zone_id + - home_zone_id + - TAZ + - transit_pass_ownership + - transit_pass_subsidy + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +TOUR_SPEC_SEGMENTS: + work: work + school: school + univ: univ + +ALTS_PREPROCESSOR: + work: + SPEC: mandatory_tour_scheduling_annotate_alts_preprocessor.csv + DF: alt_tdd + +SPEC_SEGMENTS: + work: + 'SPEC': tour_scheduling_work.csv + 'COEFFICIENTS': tour_scheduling_work_coeffs.csv + school: + 'SPEC': tour_scheduling_school.csv + 'COEFFICIENTS': tour_scheduling_school_coeffs.csv + univ: + 'SPEC': tour_scheduling_university.csv + 'COEFFICIENTS': tour_scheduling_university_coeffs.csv + +DESTINATION_FOR_TOUR_PURPOSE: + work: workplace_zone_id + school: school_zone_id + univ: school_zone_id diff --git a/activitysim/examples/example_semcog/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv index 68e51af194..6c2089653a 100755 --- a/activitysim/examples/example_semcog/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling_annotate_alts_preprocessor.csv @@ -1,4 +1,4 @@ -Description,Target,Expression -departure_shift,departureLinearShift1,"np.minimum(9-df.start,48)*(df.start<=9) + np.minimum(df.start-9,21)*(df.start>9)" -arrival_shift,arrivalLinearShift1,"np.minimum(30-df.end,48)*(df.end<=30) + np.minimum(df.end-30,21)*(df.end>30)" -duration_shift,durationShift,"(np.minimum(21-df.duration,47)*(df.duration<=20)) + (np.minimum(df.duration-21,20)*(df.duration>21))" +Description,Target,Expression +departure_shift,departureLinearShift1,"np.minimum(9-df.start,48)*(df.start<=9) + np.minimum(df.start-9,21)*(df.start>9)" +arrival_shift,arrivalLinearShift1,"np.minimum(30-df.end,48)*(df.end<=30) + np.minimum(df.end-30,21)*(df.end>30)" +duration_shift,durationShift,"(np.minimum(21-df.duration,47)*(df.duration<=20)) + (np.minimum(df.duration-21,20)*(df.duration>21))" diff --git a/activitysim/examples/example_semcog/configs/mandatory_tour_scheduling_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling_annotate_tours_preprocessor.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/mandatory_tour_scheduling_annotate_tours_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling_annotate_tours_preprocessor.csv index 68e51af194..6c2089653a 100755 --- a/activitysim/examples/example_semcog/configs/mandatory_tour_scheduling_annotate_tours_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/mandatory_tour_scheduling_annotate_tours_preprocessor.csv @@ -1,4 +1,4 @@ -Description,Target,Expression -departure_shift,departureLinearShift1,"np.minimum(9-df.start,48)*(df.start<=9) + np.minimum(df.start-9,21)*(df.start>9)" -arrival_shift,arrivalLinearShift1,"np.minimum(30-df.end,48)*(df.end<=30) + np.minimum(df.end-30,21)*(df.end>30)" -duration_shift,durationShift,"(np.minimum(21-df.duration,47)*(df.duration<=20)) + (np.minimum(df.duration-21,20)*(df.duration>21))" +Description,Target,Expression +departure_shift,departureLinearShift1,"np.minimum(9-df.start,48)*(df.start<=9) + np.minimum(df.start-9,21)*(df.start>9)" +arrival_shift,arrivalLinearShift1,"np.minimum(30-df.end,48)*(df.end<=30) + np.minimum(df.end-30,21)*(df.end>30)" +duration_shift,durationShift,"(np.minimum(21-df.duration,47)*(df.duration<=20)) + (np.minimum(df.duration-21,20)*(df.duration>21))" diff --git a/activitysim/examples/example_semcog/configs/network_los.yaml b/activitysim/examples/prototype_semcog/configs/network_los.yaml similarity index 96% rename from activitysim/examples/example_semcog/configs/network_los.yaml rename to activitysim/examples/prototype_semcog/configs/network_los.yaml index 769bffb58c..43a28e5337 100755 --- a/activitysim/examples/example_semcog/configs/network_los.yaml +++ b/activitysim/examples/prototype_semcog/configs/network_los.yaml @@ -1,14 +1,14 @@ -# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) -read_skim_cache: False -# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs -write_skim_cache: True - -zone_system: 1 - -taz_skims: skims*.omx - -skim_time_periods: - time_window: 1440 - period_minutes: 30 - periods: [0, 6, 12, 24, 32, 48] - labels: ['EA', 'AM', 'MD', 'PM', 'EV'] +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +read_skim_cache: False +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +write_skim_cache: True + +zone_system: 1 + +taz_skims: skims*.omx + +skim_time_periods: + time_window: 1440 + period_minutes: 30 + periods: [0, 6, 12, 24, 32, 48] + labels: ['EA', 'AM', 'MD', 'PM', 'EV'] diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_destination.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_destination.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination.csv index 8d5449c3e9..baf6f3b531 100755 --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_destination.csv +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination.csv @@ -1,19 +1,19 @@ -Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr -local_dist,_DIST@skims['DIST'],1,1,1,1,1,1 -util_dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_othmaint,coef_dist_social,coef_dist_othdiscr -util_dist_squared,"@(_DIST).clip(0,20)**2",coef_dist_squared_escort,coef_dist_squared_shopping,coef_dist_squared_eatout,coef_dist_squared_othmaint,,coef_dist_squared_othdiscr -util_dist_cubed,"@(_DIST).clip(0,20)**3",coef_dist_cubed_escort,coef_dist_cubed_shopping,coef_dist_cubed_eatout,coef_dist_cubed_othmaint,,coef_dist_cubed_othdiscr -util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_escort,coef_dist_logged_shopping,coef_dist_logged_eatout,coef_dist_logged_othmaint,coef_dist_logged_social,coef_dist_logged_othdiscr -util_dist_low,@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,0,0,0,0,0,0 -util_dist_med,@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,0,0,0,coef_dist_med_inc_othmaint,0,coef_dist_med_inc_othdiscr -util_dist_high,@(df['income_segment']==WORK_HIGH_SEGMENT_ID) * _DIST,0,0,0,coef_dist_high_inc_othmaint,0,0 -util_dist_part_time,@(df['pemploy']==2) * _DIST,0,coef_dist_part_time_shopping,0,coef_dist_part_time_othmaint,0,0 -util_dist_full_time,@(df['pemploy']==1) * _DIST,0,0,0,coef_dist_full_time_othmaint,coef_dist_full_time_social,0 -util_dist_student,@(df['is_student']==True) * _DIST,0,0,0,coef_dist_student_othmaint,0,0 -util_dist_hh_child,@(df['hh_child']>0) * _DIST,coef_dist_hh_child_escort,0,coef_dist_hh_child_eatout,0,coef_dist_hh_child_social,coef_dist_hh_child_othdiscr -util_dist_child_0_5,@(df['age_0_to_5']==True) * _DIST,0,0,0,0,0,0 -util_dist_child_6_12,@(df['age_6_to_12']==True) * _DIST,0,0,0,0,0,0 -Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1 -No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999 -Mode choice logsum,mode_choice_logsum,coef_mode_logsum_escort,coef_mode_logsum_shopping,coef_mode_logsum_eatout,coef_mode_logsum_othmaint,coef_mode_logsum_social,coef_mode_logsum_othdiscr -Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1 +Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr +local_dist,_DIST@skims['DIST'],1,1,1,1,1,1 +util_dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_othmaint,coef_dist_social,coef_dist_othdiscr +util_dist_squared,"@(_DIST).clip(0,20)**2",coef_dist_squared_escort,coef_dist_squared_shopping,coef_dist_squared_eatout,coef_dist_squared_othmaint,,coef_dist_squared_othdiscr +util_dist_cubed,"@(_DIST).clip(0,20)**3",coef_dist_cubed_escort,coef_dist_cubed_shopping,coef_dist_cubed_eatout,coef_dist_cubed_othmaint,,coef_dist_cubed_othdiscr +util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_escort,coef_dist_logged_shopping,coef_dist_logged_eatout,coef_dist_logged_othmaint,coef_dist_logged_social,coef_dist_logged_othdiscr +util_dist_low,@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,0,0,0,0,0,0 +util_dist_med,@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,0,0,0,coef_dist_med_inc_othmaint,0,coef_dist_med_inc_othdiscr +util_dist_high,@(df['income_segment']==WORK_HIGH_SEGMENT_ID) * _DIST,0,0,0,coef_dist_high_inc_othmaint,0,0 +util_dist_part_time,@(df['pemploy']==2) * _DIST,0,coef_dist_part_time_shopping,0,coef_dist_part_time_othmaint,0,0 +util_dist_full_time,@(df['pemploy']==1) * _DIST,0,0,0,coef_dist_full_time_othmaint,coef_dist_full_time_social,0 +util_dist_student,@(df['is_student']==True) * _DIST,0,0,0,coef_dist_student_othmaint,0,0 +util_dist_hh_child,@(df['hh_child']>0) * _DIST,coef_dist_hh_child_escort,0,coef_dist_hh_child_eatout,0,coef_dist_hh_child_social,coef_dist_hh_child_othdiscr +util_dist_child_0_5,@(df['age_0_to_5']==True) * _DIST,0,0,0,0,0,0 +util_dist_child_6_12,@(df['age_6_to_12']==True) * _DIST,0,0,0,0,0,0 +Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1 +No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999 +Mode choice logsum,mode_choice_logsum,coef_mode_logsum_escort,coef_mode_logsum_shopping,coef_mode_logsum_eatout,coef_mode_logsum_othmaint,coef_mode_logsum_social,coef_mode_logsum_othdiscr +Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1 diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_destination.yaml b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination.yaml similarity index 95% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_destination.yaml rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination.yaml index 4a976c0a3e..61ade06f15 100755 --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_destination.yaml +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination.yaml @@ -1,63 +1,63 @@ -SAMPLE_SPEC: non_mandatory_tour_destination_sample.csv -SPEC: non_mandatory_tour_destination.csv -COEFFICIENTS: non_mandatory_tour_destination_coeffs.csv - -SAMPLE_SIZE: 30 - -SIZE_TERM_SELECTOR: non_mandatory - -# we can't use use household income_segment as this will also be set for non-workers -CHOOSER_SEGMENT_COLUMN_NAME: tour_type - -# optional (comment out if not desired) -DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum - -# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table -DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample - - -SEGMENTS: - - shopping - - othmaint - - othdiscr - - eatout - - social - - escort - -SIMULATE_CHOOSER_COLUMNS: - - tour_type - - TAZ - - person_id - - income_segment - - pemploy - - is_student - - age_0_to_5 - - age_6_to_12 - - hh_child - -LOGSUM_SETTINGS: tour_mode_choice.yaml - -# model-specific logsum-related settings -CHOOSER_ORIG_COL_NAME: TAZ -ALT_DEST_COL_NAME: alt_dest -IN_PERIOD: 14 -OUT_PERIOD: 14 - -SEGMENT_IDS: - work_low: 1 - work_med: 2 - work_high: 3 - work_veryhigh: 4 - -CONSTANTS: - WORK_LOW_SEGMENT_ID: 1 - WORK_MED_SEGMENT_ID: 2 - WORK_HIGH_SEGMENT_ID: 3 - WORK_VERYHIGH_SEGMENT_ID: 4 - - -preprocessor: - SPEC: non_mandatory_tour_destination_annotate_tours_preprocessor - DF: tours - TABLES: +SAMPLE_SPEC: non_mandatory_tour_destination_sample.csv +SPEC: non_mandatory_tour_destination.csv +COEFFICIENTS: non_mandatory_tour_destination_coeffs.csv + +SAMPLE_SIZE: 30 + +SIZE_TERM_SELECTOR: non_mandatory + +# we can't use use household income_segment as this will also be set for non-workers +CHOOSER_SEGMENT_COLUMN_NAME: tour_type + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: tour_destination_sample + + +SEGMENTS: + - shopping + - othmaint + - othdiscr + - eatout + - social + - escort + +SIMULATE_CHOOSER_COLUMNS: + - tour_type + - TAZ + - person_id + - income_segment + - pemploy + - is_student + - age_0_to_5 + - age_6_to_12 + - hh_child + +LOGSUM_SETTINGS: tour_mode_choice.yaml + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: TAZ +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 14 + +SEGMENT_IDS: + work_low: 1 + work_med: 2 + work_high: 3 + work_veryhigh: 4 + +CONSTANTS: + WORK_LOW_SEGMENT_ID: 1 + WORK_MED_SEGMENT_ID: 2 + WORK_HIGH_SEGMENT_ID: 3 + WORK_VERYHIGH_SEGMENT_ID: 4 + + +preprocessor: + SPEC: non_mandatory_tour_destination_annotate_tours_preprocessor + DF: tours + TABLES: - persons \ No newline at end of file diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_destination_coeffs.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination_coeffs.csv similarity index 97% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_destination_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination_coeffs.csv index 5df12a98e1..d7c0ae3b82 100755 --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_destination_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination_coeffs.csv @@ -1,44 +1,44 @@ -coefficient_name,value,constrain -coef_mode_logsum_escort,0.43,F -coef_dist_escort,-0.024726043,F -coef_dist_squared_escort,-0.00174728,F -coef_dist_cubed_escort,3.68E-06,F -coef_dist_logged_escort,-1.236681595,F -coef_dist_hh_child_escort,-0.115868258,F -coef_mode_logsum_shopping,0.65,F -coef_dist_shopping,-0.098281692,F -coef_dist_squared_shopping,-0.020314759,F -coef_dist_cubed_shopping,0.000801126,F -coef_dist_logged_shopping,-0.792030818,F -coef_dist_part_time_shopping,0.027590804,F -coef_dist_joint_tour_shopping,0.035663397,F -coef_mode_logsum_eatout,0.56,F -coef_dist_eatout,-0.039022477,F -coef_dist_squared_eatout,-0.02117814,F -coef_dist_cubed_eatout,0.000793071,F -coef_dist_logged_eatout,-0.680658809,F -coef_dist_hh_child_eatout,-0.059764452,F -coef_mode_logsum_othmaint,0.38,F -coef_dist_othmaint,-0.058808778,F -coef_dist_squared_othmaint,-0.009297153,F -coef_dist_cubed_othmaint,0.000314133,F -coef_dist_logged_othmaint,-0.759800765,F -coef_dist_med_inc_othmaint,-0.007251455,F -coef_dist_high_inc_othmaint,-0.01144311,F -coef_dist_part_time_othmaint,0.020816995,F -coef_dist_full_time_othmaint,0.044631414,F -coef_dist_student_othmaint,-0.038852608,F -coef_mode_logsum_social,0.68,F -coef_dist_social,-0.070898603,F -coef_dist_logged_social,-0.67038649,F -coef_dist_full_time_social,-0.020050832,F -coef_dist_hh_child_social,-0.054753072,F -coef_dist_joint_tour_social,0.030177219,F -coef_mode_logsum_othdiscr,0.75,F -coef_dist_othdiscr,-0.03374735,F -coef_dist_squared_othdiscr,-0.012479607,F -coef_dist_cubed_othdiscr,0.000434109,F -coef_dist_logged_othdiscr,-0.680070996,F -coef_dist_med_inc_othdiscr,0.036605959,F -coef_dist_hh_child_othdiscr,-0.021556002,F -coef_dist_joint_tour_othdiscr,0.011538605,F +coefficient_name,value,constrain +coef_mode_logsum_escort,0.43,F +coef_dist_escort,-0.024726043,F +coef_dist_squared_escort,-0.00174728,F +coef_dist_cubed_escort,3.68E-06,F +coef_dist_logged_escort,-1.236681595,F +coef_dist_hh_child_escort,-0.115868258,F +coef_mode_logsum_shopping,0.65,F +coef_dist_shopping,-0.098281692,F +coef_dist_squared_shopping,-0.020314759,F +coef_dist_cubed_shopping,0.000801126,F +coef_dist_logged_shopping,-0.792030818,F +coef_dist_part_time_shopping,0.027590804,F +coef_dist_joint_tour_shopping,0.035663397,F +coef_mode_logsum_eatout,0.56,F +coef_dist_eatout,-0.039022477,F +coef_dist_squared_eatout,-0.02117814,F +coef_dist_cubed_eatout,0.000793071,F +coef_dist_logged_eatout,-0.680658809,F +coef_dist_hh_child_eatout,-0.059764452,F +coef_mode_logsum_othmaint,0.38,F +coef_dist_othmaint,-0.058808778,F +coef_dist_squared_othmaint,-0.009297153,F +coef_dist_cubed_othmaint,0.000314133,F +coef_dist_logged_othmaint,-0.759800765,F +coef_dist_med_inc_othmaint,-0.007251455,F +coef_dist_high_inc_othmaint,-0.01144311,F +coef_dist_part_time_othmaint,0.020816995,F +coef_dist_full_time_othmaint,0.044631414,F +coef_dist_student_othmaint,-0.038852608,F +coef_mode_logsum_social,0.68,F +coef_dist_social,-0.070898603,F +coef_dist_logged_social,-0.67038649,F +coef_dist_full_time_social,-0.020050832,F +coef_dist_hh_child_social,-0.054753072,F +coef_dist_joint_tour_social,0.030177219,F +coef_mode_logsum_othdiscr,0.75,F +coef_dist_othdiscr,-0.03374735,F +coef_dist_squared_othdiscr,-0.012479607,F +coef_dist_cubed_othdiscr,0.000434109,F +coef_dist_logged_othdiscr,-0.680070996,F +coef_dist_med_inc_othdiscr,0.036605959,F +coef_dist_hh_child_othdiscr,-0.021556002,F +coef_dist_joint_tour_othdiscr,0.011538605,F diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_destination_sample.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination_sample.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_destination_sample.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination_sample.csv index f6cc670d14..1ab7b426d8 100755 --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_destination_sample.csv +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_destination_sample.csv @@ -1,17 +1,17 @@ -Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr -local_dist,_DIST@skims['DIST'],1,1,1,1,1,1 -util_dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_othmaint,coef_dist_social,coef_dist_othdiscr -util_dist_squared,"@(_DIST).clip(0,20)**2",coef_dist_squared_escort,coef_dist_squared_shopping,coef_dist_squared_eatout,coef_dist_squared_othmaint,,coef_dist_squared_othdiscr -util_dist_cubed,"@(_DIST).clip(0,20)**3",coef_dist_cubed_escort,coef_dist_cubed_shopping,coef_dist_cubed_eatout,coef_dist_cubed_othmaint,,coef_dist_cubed_othdiscr -util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_escort,coef_dist_logged_shopping,coef_dist_logged_eatout,coef_dist_logged_othmaint,coef_dist_logged_social,coef_dist_logged_othdiscr -util_dist_low,@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,0,0,0,0,0,0 -util_dist_med,@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,0,0,0,coef_dist_med_inc_othmaint,0,coef_dist_med_inc_othdiscr -util_dist_high,@(df['income_segment']==WORK_HIGH_SEGMENT_ID) * _DIST,0,0,0,coef_dist_high_inc_othmaint,0,0 -util_dist_part_time,@(df['pemploy']==2) * _DIST,0,coef_dist_part_time_shopping,0,coef_dist_part_time_othmaint,0,0 -util_dist_full_time,@(df['pemploy']==1) * _DIST,0,0,0,coef_dist_full_time_othmaint,coef_dist_full_time_social,0 -util_dist_student,@(df['is_student']==True) * _DIST,0,0,0,coef_dist_student_othmaint,0,0 -util_dist_hh_child,@(df['hh_child']>0) * _DIST,coef_dist_hh_child_escort,0,coef_dist_hh_child_eatout,0,coef_dist_hh_child_social,coef_dist_hh_child_othdiscr -util_dist_child_0_5,@(df['age_0_to_5']==True) * _DIST,0,0,0,0,0,0 -util_dist_child_6_12,@(df['age_6_to_12']==True) * _DIST,0,0,0,0,0,0 -Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1 -No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999 +Description,Expression,escort,shopping,eatout,othmaint,social,othdiscr +local_dist,_DIST@skims['DIST'],1,1,1,1,1,1 +util_dist,@_DIST,coef_dist_escort,coef_dist_shopping,coef_dist_eatout,coef_dist_othmaint,coef_dist_social,coef_dist_othdiscr +util_dist_squared,"@(_DIST).clip(0,20)**2",coef_dist_squared_escort,coef_dist_squared_shopping,coef_dist_squared_eatout,coef_dist_squared_othmaint,,coef_dist_squared_othdiscr +util_dist_cubed,"@(_DIST).clip(0,20)**3",coef_dist_cubed_escort,coef_dist_cubed_shopping,coef_dist_cubed_eatout,coef_dist_cubed_othmaint,,coef_dist_cubed_othdiscr +util_dist_logged,@(_DIST).apply(np.log1p),coef_dist_logged_escort,coef_dist_logged_shopping,coef_dist_logged_eatout,coef_dist_logged_othmaint,coef_dist_logged_social,coef_dist_logged_othdiscr +util_dist_low,@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,0,0,0,0,0,0 +util_dist_med,@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,0,0,0,coef_dist_med_inc_othmaint,0,coef_dist_med_inc_othdiscr +util_dist_high,@(df['income_segment']==WORK_HIGH_SEGMENT_ID) * _DIST,0,0,0,coef_dist_high_inc_othmaint,0,0 +util_dist_part_time,@(df['pemploy']==2) * _DIST,0,coef_dist_part_time_shopping,0,coef_dist_part_time_othmaint,0,0 +util_dist_full_time,@(df['pemploy']==1) * _DIST,0,0,0,coef_dist_full_time_othmaint,coef_dist_full_time_social,0 +util_dist_student,@(df['is_student']==True) * _DIST,0,0,0,coef_dist_student_othmaint,0,0 +util_dist_hh_child,@(df['hh_child']>0) * _DIST,coef_dist_hh_child_escort,0,coef_dist_hh_child_eatout,0,coef_dist_hh_child_social,coef_dist_hh_child_othdiscr +util_dist_child_0_5,@(df['age_0_to_5']==True) * _DIST,0,0,0,0,0,0 +util_dist_child_6_12,@(df['age_6_to_12']==True) * _DIST,0,0,0,0,0,0 +Size variable,@df['size_term'].apply(np.log1p),1,1,1,1,1,1 +No attractions,@df['size_term']==0,-999,-999,-999,-999,-999,-999 diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency.csv index c295ea5b07..76c60822e2 100755 --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency.csv +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency.csv @@ -1,211 +1,211 @@ -Label,Description,Expression,PTYPE_FULL,PTYPE_PART,PTYPE_UNIVERSITY,PTYPE_NONWORK,PTYPE_RETIRED,PTYPE_DRIVING,PTYPE_SCHOOL,PTYPE_PRESCHOOL -util_escorting_tour,Escorting Tour,escort,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour -util_discretionary_tour,Discretionary Tour,othdiscr,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour -util_shopping_tour,Shopping Tour,shopping,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour -util_maintenance_tour,Maintenance Tour,othmaint,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour -util_visiting_or_social_tour,Visiting/Social Tour,social,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour -util_eating_out_tour,Eating Out Tour,eatout,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour -util_total_number_of_tours_is_0_no_prior_tours,Total Number of Tours = 0 (No Prior Tours),(tot_tours == 0) & (num_mand == 0) & (num_hh_joint_tours == 0),coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours -util_total_number_of_tours_is_0_prior_tours,Total Number of Tours = 0 (1 or more Prior Tours),(tot_tours == 0) & ((num_mand > 0) | (num_hh_joint_tours > 0)),coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours -util_total_number_of_tours_is_1,Total Number of Tours = 1,tot_tours == 1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1 -util_total_number_of_tours_is_2,Total Number of Tours = 2,tot_tours == 2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2 -util_total_number_of_tours_is_3,Total Number of Tours = 3,tot_tours == 3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3 -util_total_number_of_tours_is_4,Total Number of Tours = 4,tot_tours == 4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4 -util_total_number_of_tours_is_5,Total Number of Tours = 5,tot_tours == 5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5 -util_total_number_of_tours_is_6_plus,Total Number of Tours = 6+,tot_tours > 5,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus -util_number_of_mandatory_tours_and_tour_frequency_is_0,Number of Mandatory tours & tour frequency =0,num_mand*(tot_tours == 0),coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0 -util_number_of_mandatory_tours_and_tour_frequency_is_1,Number of Mandatory tours & tour frequency =1,num_mand*(tot_tours == 1),coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1 -util_number_of_mandatory_tours_and_tour_frequency_is_2,Number of Mandatory tours & tour frequency =2,num_mand*(tot_tours == 2),coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2 -util_number_of_mandatory_tours_and_tour_frequency_is_3,Number of Mandatory tours & tour frequency =3,num_mand*(tot_tours == 3),coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3 -util_number_of_mandatory_tours_and_tour_frequency_is_4,Number of Mandatory tours & tour frequency =4,num_mand*(tot_tours == 4),coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4 -util_number_of_mandatory_tours_and_tour_frequency_is_5_plus,Number of Mandatory tours & tour frequency = 5+,num_mand*(tot_tours > 4),coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus -util_number_of_joint_tours_and_tour_frequency_is_0,Number of Joint tours & tour frequency =0,num_hh_joint_tours*(tot_tours == 0),coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0 -util_number_of_joint_tours_and_tour_frequency_is_1,Number of Joint tours & tour frequency =1,num_hh_joint_tours*(tot_tours == 1),coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1 -util_number_of_joint_tours_and_tour_frequency_is_2,Number of Joint tours & tour frequency =2,num_hh_joint_tours*(tot_tours == 2),coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2 -util_number_of_joint_tours_and_tour_frequency_is_3,Number of Joint tours & tour frequency =3,num_hh_joint_tours*(tot_tours == 3),coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3 -util_number_of_joint_tours_and_tour_frequency_is_4,Number of Joint tours & tour frequency =4,num_hh_joint_tours*(tot_tours == 4),coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4 -util_number_of_joint_tours_and_tour_frequency_is_5_plus,Number of Joint tours & tour frequency = 5+,num_hh_joint_tours*(tot_tours > 4),coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus -util_number_of_joint_shopping_tours,Number of Joint Shopping tours,shopping * num_hh_joint_shop_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours -util_number_of_joint_maintenance_tours,Number of Joint Maintenance tours,othmaint * num_hh_joint_maint_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours -util_number_of_joint_eating_out_tours,Number of Joint Eating Out tours,eatout * num_hh_joint_eatout_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours -util_number_of_joint_visit_tours,Number of Joint Visit tours,social * num_hh_joint_social_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours -util_number_of_joint_discretionary_tours,Number of Joint Discretionary tours,othdiscr * num_hh_joint_othdiscr_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours -util_logged_maximum_residual_window_tour_frequency_is_0,"Logged Maximum Residual Window, tour frequency =0",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 0),coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0 -util_logged_maximum_residual_window_tour_frequency_is_1,"Logged Maximum Residual Window, tour frequency =1",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 1),coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1 -util_logged_maximum_residual_window_tour_frequency_is_2,"Logged Maximum Residual Window, tour frequency =2",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 2),coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2 -util_logged_maximum_residual_window_tour_frequency_is_3,"Logged Maximum Residual Window, tour frequency =3",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 3),coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3 -util_logged_maximum_residual_window_tour_frequency_is_4,"Logged Maximum Residual Window, tour frequency =4",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 4),coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4 -util_logged_maximum_residual_window_tour_frequency_is_5_plus,"Logged Maximum Residual Window, tour frequency =5+",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours > 4),coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus -util_mediumlow_income_group_and_tour_frequency_is_1,Dummy for Mediumlow Income group (20K-50K) & tour frequency=1,medium_low_income & (tot_tours == 1),coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1 -util_mediumlow_income_group_and_tour_frequency_is_2,Dummy for Mediumlow Income group (20K-50K) & tour frequency=2,medium_low_income & (tot_tours == 2),coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2 -util_mediumlow_income_group_and_tour_frequency_is_3,Dummy for Mediumlow Income group (20K-50K) & tour frequency=3,medium_low_income & (tot_tours == 3),coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3 -util_mediumlow_income_group_and_tour_frequency_is_4,Dummy for Mediumlow Income group (20K-50K) & tour frequency=4,medium_low_income & (tot_tours == 4),coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4 -util_mediumlow_income_group_and_tour_frequency_is_5_plus,Dummy for Mediumlow Income group (20K-50K) & tour frequency=5+,medium_low_income & (tot_tours > 4),coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus -util_mediumhigh_income_group_and_tour_frequency_is_1,Dummy for MediumHigh Income group (50K-100K) & tour frequency=1,medium_high_income & (tot_tours == 1),coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1 -util_mediumhigh_income_group_and_tour_frequency_is_2,Dummy for MediumHigh Income group (50K-100K) & tour frequency=2,medium_high_income & (tot_tours == 2),coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2 -util_mediumhigh_income_group_and_tour_frequency_is_3,Dummy for MediumHigh Income group (50K-100K) & tour frequency=3,medium_high_income & (tot_tours == 3),coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3 -util_mediumhigh_income_group_and_tour_frequency_is_4,Dummy for MediumHigh Income group (50K-100K) & tour frequency=4,medium_high_income & (tot_tours == 4),coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4 -util_mediumhigh_income_group_and_tour_frequency_is_5_plus,Dummy for MediumHigh Income group (50K-100K) & tour frequency=5+,medium_high_income & (tot_tours > 4),coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus -util_high_income_group_and_tour_frequency_is_1,Dummy for High Income group (>100K) & tour frequency=1,high_income & (tot_tours == 1),coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1 -util_high_income_group_and_tour_frequency_is_2,Dummy for High Income group (>100K) & tour frequency=2,high_income & (tot_tours == 2),coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2 -util_high_income_group_and_tour_frequency_is_3,Dummy for High Income group (>100K) & tour frequency=3,high_income & (tot_tours == 3),coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3 -util_high_income_group_and_tour_frequency_is_4,Dummy for High Income group (>100K) & tour frequency=4,high_income & (tot_tours == 4),coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4 -util_high_income_group_and_tour_frequency_is_5_plus,Dummy for High Income group (>100K) & tour frequency=5+,high_income & (tot_tours > 4),coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus -util_mediumlow_income_group_and_shopping_tour,Dummy for Mediumlow Income group (20K-50K) & shopping tour,medium_low_income * shopping,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour -util_mediumhigh_income_group_and_shopping_tour,Dummy for Mediumhigh Income group (50K-100K) & shopping tour,medium_high_income * shopping,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour -util_high_income_group_and_shopping_tour,Dummy for High Income group (>100K) & shopping tour,high_income * shopping,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour -util_mediumlow_income_group_and_maintenance_tour,Dummy for Mediumlow Income group (20K-50K) & maintenance tour,medium_low_income * othmaint,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour -util_mediumhigh_income_group_and_maintenance_tour,Dummy for Mediumhigh Income group (50K-100K) & maintenance tour,medium_high_income * othmaint,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour -util_high_income_group_and_maintenance_tour,Dummy for High Income group (>100K) & maintenance tour,high_income * othmaint,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour -util_mediumlow_income_group_and_eating_out_tour,Dummy for Mediumlow Income group (20K-50K) & Eating out tour,medium_low_income * eatout,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour -util_mediumhigh_income_group_and_eating_out_tour,Dummy for Mediumhigh Income group (50K-100K) & Eating out tour,medium_high_income * eatout,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour -util_high_income_group_and_eating_out_tour,Dummy for High Income group (>100K) & Eating out tour,high_income * eatout,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour -util_mediumlow_income_group_and_discretionary_tour,Dummy for Mediumlow Income group (20K-50K) & Discretionary tour,medium_low_income * othdiscr,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour -util_mediumhigh_income_group_and_discretionary_tour,Dummy for Mediumhigh Income group (50K-100K) & Discretionary tour,medium_high_income * othdiscr,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour -util_high_income_group_and_discretionary_tour,Dummy for High Income group (>100K) & Discretionary tour,high_income * othdiscr,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour -util_mediumlow_income_group_and_visiting_tour,Dummy for Mediumlow Income group (20K-50K) & Visiting tour,medium_low_income * social,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour -util_mediumhigh_income_group_and_visiting_tour,Dummy for Mediumhigh Income group (50K-100K) & Visiting tour,medium_high_income * social,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour -util_high_income_group_and_visiting_tour,Dummy for High Income group (>100K) & Visiting tour,high_income * social,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour -util_female_and_tour_frequency_is_1,Dummy for Female & tour frequency =1,female & (tot_tours == 1),coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1 -util_female_and_tour_frequency_is_2,Dummy for Female & tour frequency =2,female & (tot_tours == 2),coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2 -util_female_and_tour_frequency_is_3,Dummy for Female & tour frequency =3,female & (tot_tours == 3),coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3 -util_female_and_tour_frequency_is_4,Dummy for Female & tour frequency =4,female & (tot_tours == 4),coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4 -util_female_and_tour_frequency_is_5,Dummy for Female & tour frequency =5,female & (tot_tours == 5),coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5 -util_female_and_escorting_tour,Dummy for Female & Escorting Tour,female * escort,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour -util_female_and_shopping_tour,Dummy for Female & Shopping Tour,female * shopping,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour -util_female_and_maintenance_tour,Dummy for Female & Maintenance Tour,female * othmaint,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour -util_female_and_eatingout_tour,Dummy for Female & EatingOut Tour,female * eatout,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour -util_female_and_discretionary_tour,Dummy for Female & Discretionary Tour,female * othdiscr,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour -util_zero_car_ownership_and_tour_frequency_is_1,Dummy for zero car ownership & tour frequency =1,no_cars & (tot_tours == 1),coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1 -util_zero_car_ownership_and_tour_frequency_is_2,Dummy for zero car ownership & tour frequency =2,no_cars & (tot_tours == 2),coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2 -util_zero_car_ownership_and_tour_frequency_is_3,Dummy for zero car ownership & tour frequency =3,no_cars & (tot_tours == 3),coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3 -util_zero_car_ownership_and_tour_frequency_is_4,Dummy for zero car ownership & tour frequency =4,no_cars & (tot_tours == 4),coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4 -util_zero_car_ownership_and_tour_frequency_is_5_plus,Dummy for zero car ownership & tour frequency =5+,no_cars & (tot_tours > 4),coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus -util_car_shortage_vs_workers_and_tour_frequency_is_1,Dummy for Car Shortage vs Workers & tour frequency =1,~no_cars & (car_sufficiency < 0) & (tot_tours == 1),coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1 -util_car_shortage_vs_workers_and_tour_frequency_is_2,Dummy for Car Shortage vs Workers & tour frequency =2,~no_cars & (car_sufficiency < 0) & (tot_tours == 2),coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2 -util_car_shortage_vs_workers_and_tour_frequency_is_3,Dummy for Car Shortage vs Workers & tour frequency =3,~no_cars & (car_sufficiency < 0) & (tot_tours == 3),coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3 -util_car_shortage_vs_workers_and_tour_frequency_is_4,Dummy for Car Shortage vs Workers & tour frequency =4,~no_cars & (car_sufficiency < 0) & (tot_tours == 4),coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4 -util_car_shortage_vs_workers_and_tour_frequency_is_5_plus,Dummy for Car Shortage vs Workers & tour frequency =5+,~no_cars & (car_sufficiency < 0) & (tot_tours > 4),coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus -util_car_surplus_vs_workers_and_tour_frequency_is_1,Dummy for Car Surplus vs Workers & tour frequency =1,~no_cars & (car_sufficiency > 0) & (tot_tours == 1),coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1 -util_car_surplus_vs_workers_and_tour_frequency_is_2,Dummy for Car Surplus vs Workers & tour frequency =2,~no_cars & (car_sufficiency > 0) & (tot_tours == 2),coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2 -util_car_surplus_vs_workers_and_tour_frequency_is_3,Dummy for Car Surplus vs Workers & tour frequency =3,~no_cars & (car_sufficiency > 0) & (tot_tours == 3),coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3 -util_car_surplus_vs_workers_and_tour_frequency_is_4,Dummy for Car Surplus vs Workers & tour frequency =4,~no_cars & (car_sufficiency > 0) & (tot_tours == 4),coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4 -util_car_surplus_vs_workers_and_tour_frequency_is_5_plus,Dummy for Car Surplus vs Workers & tour frequency =5+,~no_cars & (car_sufficiency > 0) & (tot_tours > 4),coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus -util_presence_of_non_worker_and_tour_frequency_is_1,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =1,has_non_worker & (tot_tours == 1),coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1 -util_presence_of_non_worker_and_tour_frequency_is_2,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =2,has_non_worker & (tot_tours == 2),coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2 -util_presence_of_non_worker_and_tour_frequency_is_3,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =3,has_non_worker & (tot_tours == 3),coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3 -util_presence_of_non_worker_and_tour_frequency_is_4,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =4,has_non_worker & (tot_tours == 4),coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4 -util_presence_of_non_worker_and_tour_frequency_is_5,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =5,has_non_worker & (tot_tours == 5),coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5 -util_presence_of_retiree_and_tour_frequency_is_1,Dummy for Presence of Retiree(other than modeled person) & tour frequency =1,has_retiree & (tot_tours == 1),coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1 -util_presence_of_retiree_and_tour_frequency_is_2,Dummy for Presence of Retiree(other than modeled person) & tour frequency =2,has_retiree & (tot_tours == 2),coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2 -util_presence_of_retiree_and_tour_frequency_is_3,Dummy for Presence of Retiree(other than modeled person) & tour frequency =3,has_retiree & (tot_tours == 3),coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3 -util_presence_of_retiree_and_tour_frequency_is_4,Dummy for Presence of Retiree(other than modeled person) & tour frequency =4,has_retiree & (tot_tours == 4),coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4 -util_presence_of_retiree_and_tour_frequency_is_5,Dummy for Presence of Retiree(other than modeled person) & tour frequency =5,has_retiree & (tot_tours == 5),coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5 -util_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =1,has_preschool_kid & (tot_tours == 1),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1 -util_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =2,has_preschool_kid & (tot_tours == 2),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2 -util_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =3,has_preschool_kid & (tot_tours == 3),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3 -util_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =4,has_preschool_kid & (tot_tours == 4),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4 -util_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =5,has_preschool_kid & (tot_tours == 5),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5 -util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =1,has_school_kid & (tot_tours == 1),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1 -util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =2,has_school_kid & (tot_tours == 2),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2 -util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =3,has_school_kid & (tot_tours == 3),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3 -util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =4,has_school_kid & (tot_tours == 4),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4 -util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =5,has_school_kid & (tot_tours == 5),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5 -util_presence_of_full_time_worker_and_escorting_tour,Dummy for Presence of Full time Worker (other than modeled person) & Escorting tour ,has_full_time * escort,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour -util_presence_of_part_time_worker_and_escorting_tour,Dummy for Presence of Part time Worker (other than modeled person) & Escorting tour ,has_part_time * escort,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour -util_presence_of_non_worker_and_escorting_tour,Dummy for Presence of Non-Worker (other than modeled person) & Escorting tour ,has_non_worker * escort,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour -util_presence_of_retiree_and_escorting_tour,Dummy for Presence of Retiree (other than modeled person) & Escorting tour ,has_retiree * escort,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour -util_presence_of_university_student_and_escorting_tour,Dummy for Presence of University Student (other than modeled person) & Escorting tour ,has_university * escort,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour -util_presence_of_driving_school_kid_and_escorting_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Escorting tour ,has_driving_kid * escort,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour -util_presence_of_pre_driving_school_kid_and_escorting_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Escorting tour ,has_school_kid * escort,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour -util_presence_of_pre_school_kid_and_escorting_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Escorting tour ,has_preschool_kid * escort,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour -util_at_home_pre_driving_school_kid_and_escorting_tour,Dummy for At home Pre-Driving School Kid & Escorting tour ,has_school_kid_at_home * escort,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour -util_at_home_pre_school_kid_and_escorting_tour,Dummy for At homef Pre-School Kid & Escorting tour ,has_preschool_kid_at_home * escort,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour -util_presence_of_full_time_worker_and_shopping_tour,Dummy for Presence of Full time Worker (other than modeled person) & Shopping tour ,has_full_time * shopping,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour -util_presence_of_part_time_worker_and_shopping_tour,Dummy for Presence of Part time Worker (other than modeled person) & Shopping tour ,has_part_time * shopping,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour -util_presence_of_non_worker_and_shopping_tour,Dummy for Presence of Non-Worker (other than modeled person) & Shopping tour ,has_non_worker * shopping,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour -util_presence_of_retiree_and_shopping_tour,Dummy for Presence of Retiree (other than modeled person) & Shopping tour ,has_retiree * shopping,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour -util_presence_of_university_student_and_shopping_tour,Dummy for Presence of University Student (other than modeled person) & Shopping tour ,has_university * shopping,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour -util_presence_of_driving_school_kid_and_shopping_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Shopping tour ,has_driving_kid * shopping,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour -util_presence_of_pre_driving_school_kid_and_shopping_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Shopping tour ,has_school_kid * shopping,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour -util_presence_of_pre_school_kid_and_shopping_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Shopping tour ,has_preschool_kid * shopping,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour -util_at_home_pre_driving_school_kid_and_shopping_tour,Dummy for At home Pre-Driving School Kid & Shopping tour ,has_school_kid_at_home * shopping,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour -util_at_home_pre_school_kid_and_shopping_tour,Dummy for At homef Pre-School Kid & Shopping tour ,has_preschool_kid_at_home * shopping,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour -util_presence_of_full_time_worker_and_maintenance_tour,Dummy for Presence of Full time Worker (other than modeled person) & Maintenance tour ,has_full_time * othmaint,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour -util_presence_of_part_time_worker_and_maintenance_tour,Dummy for Presence of Part time Worker (other than modeled person) & Maintenance tour ,has_part_time * othmaint,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour -util_presence_of_non_worker_and_maintenance_tour,Dummy for Presence of Non-Worker(other than modeled person) & Maintenance tour ,has_non_worker * othmaint,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour -util_presence_of_retiree_and_maintenance_tour,Dummy for Presence of Retiree (other than modeled person) & Maintenance tour ,has_retiree * othmaint,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour -util_presence_of_university_student_and_maintenance_tour,Dummy for Presence of University Student (other than modeled person) & Maintenance tour ,has_university * othmaint,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour -util_presence_of_driving_school_kid_and_maintenance_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Maintenance tour ,has_driving_kid * othmaint,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour -util_presence_of_pre_driving_school_kid_and_maintenance_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Maintenance tour ,has_school_kid * othmaint,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour -util_presence_of_pre_school_kid_and_maintenance_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Maintenance tour ,has_preschool_kid * othmaint,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour -util_at_home_pre_driving_school_kid_and_maintenance_tour,Dummy for At home Pre-Driving School Kid & Maintenance tour ,has_school_kid_at_home * othmaint,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour -util_at_home_pre_school_kid_and_maintenance_tour,Dummy for At homef Pre-School Kid & Maintenance tour ,has_preschool_kid_at_home * othmaint,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour -util_presence_of_full_time_worker_and_eating_out_tour,Dummy for Presence of Full time Worker (other than modeled person) & Eating Out tour ,has_full_time * eatout,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour -util_presence_of_part_time_worker_and_eating_out_tour,Dummy for Presence of Part time Worker (other than modeled person) & Eating Out tour ,has_part_time * eatout,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour -util_presence_of_non_worker_and_eating_out_tour,Dummy for Presence of Non-Worker (other than modeled person) & Eating Out tour ,has_non_worker * eatout,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour -util_presence_of_retiree_and_eating_out_tour,Dummy for Presence of Retiree (other than modeled person) & Eating Out tour ,has_retiree * eatout,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour -util_presence_of_university_student_and_eating_out_tour,Dummy for Presence of University Student (other than modeled person) & Eating Out tour ,has_university * eatout,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour -util_presence_of_driving_school_kid_and_eating_out_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Eating Out tour ,has_driving_kid * eatout,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour -util_presence_of_pre_driving_school_kid_and_eating_out_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Eating Out tour ,has_school_kid * eatout,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour -util_presence_of_pre_school_kid_and_eating_out_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Eating Out tour ,has_preschool_kid * eatout,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour -util_at_home_pre_driving_school_kid_and_eating_out_tour,Dummy for At home Pre-Driving School Kid & Eating Out tour ,has_school_kid_at_home * eatout,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour -util_at_home_pre_school_kid_and_eating_out_tour,Dummy for At homef Pre-School Kid & Eating Out tour ,has_preschool_kid_at_home * eatout,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour -util_presence_of_full_time_worker_and_discretionary_tour,Dummy for Presence of Full time Worker (other than modeled person) & Discretionary tour ,has_full_time * othdiscr,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour -util_presence_of_part_time_worker_and_discretionary_tour,Dummy for Presence of Part time Worker (other than modeled person) & Discretionary tour ,has_part_time * othdiscr,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour -util_presence_of_non_worker_and_discretionary_tour,Dummy for Presence of Non-Worker (other than modeled person) & Discretionary tour ,has_non_worker * othdiscr,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour -util_presence_of_retiree_and_discretionary_tour,Dummy for Presence of Retiree (other than modeled person) & Discretionary tour ,has_retiree * othdiscr,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour -util_presence_of_university_student_and_discretionary_tour,Dummy for Presence of University Student (other than modeled person) & Discretionary tour ,has_university * othdiscr,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour -util_presence_of_driving_school_kid_and_discretionary_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Discretionary tour ,has_driving_kid * othdiscr,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour -util_presence_of_pre_driving_school_kid_and_discretionary_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Discretionary tour ,has_school_kid * othdiscr,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour -util_presence_of_pre_school_kid_and_discretionary_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Discretionary tour ,has_preschool_kid * othdiscr,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour -util_at_home_pre_driving_school_kid_and_discretionary_tour,Dummy for At home Pre-Driving School Kid & Discretionary tour ,has_school_kid_at_home * othdiscr,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour -util_at_home_pre_school_kid_and_discretionary_tour,Dummy for At homef Pre-School Kid & Discretionary tour ,has_preschool_kid_at_home * othdiscr,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour -util_walk_access_to_retail_and_tour_frequency_is_1,Walk Access to Retail & Tour Frequency =1,nmRetail * (tot_tours == 1),coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1 -util_walk_access_to_retail_and_tour_frequency_is_2,Walk Access to Retail & Tour Frequency =2,nmRetail * (tot_tours == 2),coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2 -util_walk_access_to_retail_and_tour_frequency_is_3,Walk Access to Retail & Tour Frequency =3,nmRetail * (tot_tours == 3),coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3 -util_walk_access_to_retail_and_tour_frequency_is_4,Walk Access to Retail & Tour Frequency =4,nmRetail * (tot_tours == 4),coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4 -util_walk_access_to_retail_and_tour_frequency_is_5_plus,Walk Access to Retail & Tour Frequency =5+,nmRetail * (tot_tours > 4),coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus -util_transit_access_to_retail_and_tour_frequency_is_1,Transit Access to Retail & Tour Frequency =1,trOpRetail * (tot_tours == 1),coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1 -util_transit_access_to_retail_and_tour_frequency_is_2,Transit Access to Retail & Tour Frequency =2,trOpRetail * (tot_tours == 2),coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2 -util_transit_access_to_retail_and_tour_frequency_is_3,Transit Access to Retail & Tour Frequency =3,trOpRetail * (tot_tours == 3),coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3 -util_transit_access_to_retail_and_tour_frequency_is_4,Transit Access to Retail & Tour Frequency =4,trOpRetail * (tot_tours == 4),coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4 -util_transit_access_to_retail_and_tour_frequency_is_5_plus,Transit Access to Retail & Tour Frequency =5+,trOpRetail * (tot_tours > 4),coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus -util_auto_access_to_retail_and_tour_frequency_is_1,Auto Access to Retail & Tour Frequency =1,auOpRetail * (tot_tours == 1),coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1 -util_auto_access_to_retail_and_tour_frequency_is_2,Auto Access to Retail & Tour Frequency =2,auOpRetail * (tot_tours == 2),coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2 -util_auto_access_to_retail_and_tour_frequency_is_3,Auto Access to Retail & Tour Frequency =3,auOpRetail * (tot_tours == 3),coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3 -util_auto_access_to_retail_and_tour_frequency_is_4,Auto Access to Retail & Tour Frequency =4,auOpRetail * (tot_tours == 4),coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4 -util_auto_access_to_retail_and_tour_frequency_is_5_plus,Auto Access to Retail & Tour Frequency =5+,auOpRetail * (tot_tours > 4),coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus -util_walk_access_to_retail_and_escorting,Walk Access to Retail & Escorting ,nmRetail * escort,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting -util_transit_access_to_retail_and_escorting,Transit Access to Retail & Escorting ,trOpRetail * escort,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting -util_auto_access_to_retail_and_escorting,Auto Access to Retail & Escorting ,auOpRetail * escort,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting -util_walk_access_to_retail_and_shopping,Walk Access to Retail & Shopping ,nmRetail * shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping -util_transit_access_to_retail_and_shopping,Transit Access to Retail & Shopping ,trOpRetail * shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping -util_auto_access_to_retail_and_shopping,Auto Access to Retail & Shopping ,auOpRetail * shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping -util_walk_access_to_retail_and_maintenance,Walk Access to Retail & Maintenance ,nmRetail * othmaint,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance -util_transit_access_to_retail_and_maintenance,Transit Access to Retail & Maintenance ,trOpRetail * othmaint,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance -util_auto_access_to_retail_and_maintenance,Auto Access to Retail & Maintenance ,auOpRetail * othmaint,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance -util_walk_access_to_retail_and_eating_out,Walk Access to Retail & Eating Out ,nmRetail * eatout,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out -util_transit_access_to_retail_and_eating_out,Transit Access to Retail & Eating Out ,trOpRetail * eatout,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out -util_auto_access_to_retail_and_eating_out,Auto Access to Retail & Eating Out ,auOpRetail * eatout,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out -util_walk_access_to_retail_and_discretionary,Walk Access to Retail & Discretionary ,nmRetail * othdiscr,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary -util_transit_access_to_retail_and_discretionary,Transit Access to Retail & Discretionary ,trOpRetail * othdiscr,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary -util_auto_access_to_retail_and_discretionary,Auto Access to Retail & Discretionary ,auOpRetail * othdiscr,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary -util_urban_and_tour_frequency_is_1,Urban Areatype & Tour Frequency =1,home_is_urban & (tot_tours == 1),coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1 -util_urban_and_tour_frequency_is_2,Urban Areatype & Tour Frequency =2,home_is_urban & (tot_tours == 2),coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2 -util_urban_and_tour_frequency_is_3,Urban Areatype & Tour Frequency =3,home_is_urban & (tot_tours == 3),coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3 -util_urban_and_tour_frequency_is_4,Urban Areatype & Tour Frequency =4,home_is_urban & (tot_tours == 4),coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4 -util_urban_and_tour_frequency_is_5_plus,Urban Areatype & Tour Frequency =5+,home_is_urban & (tot_tours > 4),coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus -util_urban_and_escorting_tour,Urban Areatype & Escorting tour,home_is_urban * escort,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour -util_urban_and_shopping_tour,Urban Areatype &Shopping tour,home_is_urban * shopping,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour -util_urban_and_maintenance_tour,Urban Areatype & Maintenance tour,home_is_urban * othmaint,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour -util_urban_and_eatingout_tour,Urban Areatype & EatingOut tour,home_is_urban * eatout,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour -util_urban_and_discretionary_tour,Urban Areatype & Discretionary tour,home_is_urban * othdiscr,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour -util_1_escort_tour_constant,1 Escort Tour Constant,escort == 1,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant -util_2_plus_escort_tours_constant,2+ Escort Tours Constant,escort >= 2,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant -util_1_plus_shopping_tours_constant,1+ Shopping Tours Constant,shopping >= 1,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant -util_1_plus_maintenance_tours_constant,1+ Maintenance Tours Constant,othmaint >= 1,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant -util_1_plus_eating_out_tours_constant,1+ Eating Out Tours Constant,eatout >= 1,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant -util_1_plus_visting_tours_constant,1+ Visting Tours Constant,social >= 1,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant -util_1_plus_other_discretionary_tours_constant,1+ Other Discretionary Tours Constant,othdiscr >= 1,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant +Label,Description,Expression,PTYPE_FULL,PTYPE_PART,PTYPE_UNIVERSITY,PTYPE_NONWORK,PTYPE_RETIRED,PTYPE_DRIVING,PTYPE_SCHOOL,PTYPE_PRESCHOOL +util_escorting_tour,Escorting Tour,escort,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour,coef_escorting_tour +util_discretionary_tour,Discretionary Tour,othdiscr,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour,coef_discretionary_tour +util_shopping_tour,Shopping Tour,shopping,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour,coef_shopping_tour +util_maintenance_tour,Maintenance Tour,othmaint,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour,coef_maintenance_tour +util_visiting_or_social_tour,Visiting/Social Tour,social,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour,coef_visiting_or_social_tour +util_eating_out_tour,Eating Out Tour,eatout,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour,coef_eating_out_tour +util_total_number_of_tours_is_0_no_prior_tours,Total Number of Tours = 0 (No Prior Tours),(tot_tours == 0) & (num_mand == 0) & (num_hh_joint_tours == 0),coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours,coef_total_number_of_tours_is_0_no_prior_tours +util_total_number_of_tours_is_0_prior_tours,Total Number of Tours = 0 (1 or more Prior Tours),(tot_tours == 0) & ((num_mand > 0) | (num_hh_joint_tours > 0)),coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours,coef_total_number_of_tours_is_0_prior_tours +util_total_number_of_tours_is_1,Total Number of Tours = 1,tot_tours == 1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1,coef_total_number_of_tours_is_1 +util_total_number_of_tours_is_2,Total Number of Tours = 2,tot_tours == 2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2,coef_total_number_of_tours_is_2 +util_total_number_of_tours_is_3,Total Number of Tours = 3,tot_tours == 3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3,coef_total_number_of_tours_is_3 +util_total_number_of_tours_is_4,Total Number of Tours = 4,tot_tours == 4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4,coef_total_number_of_tours_is_4 +util_total_number_of_tours_is_5,Total Number of Tours = 5,tot_tours == 5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5,coef_total_number_of_tours_is_5 +util_total_number_of_tours_is_6_plus,Total Number of Tours = 6+,tot_tours > 5,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus,coef_total_number_of_tours_is_6_plus +util_number_of_mandatory_tours_and_tour_frequency_is_0,Number of Mandatory tours & tour frequency =0,num_mand*(tot_tours == 0),coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0,coef_number_of_mandatory_tours_and_tour_frequency_is_0 +util_number_of_mandatory_tours_and_tour_frequency_is_1,Number of Mandatory tours & tour frequency =1,num_mand*(tot_tours == 1),coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1,coef_number_of_mandatory_tours_and_tour_frequency_is_1 +util_number_of_mandatory_tours_and_tour_frequency_is_2,Number of Mandatory tours & tour frequency =2,num_mand*(tot_tours == 2),coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2,coef_number_of_mandatory_tours_and_tour_frequency_is_2 +util_number_of_mandatory_tours_and_tour_frequency_is_3,Number of Mandatory tours & tour frequency =3,num_mand*(tot_tours == 3),coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3,coef_number_of_mandatory_tours_and_tour_frequency_is_3 +util_number_of_mandatory_tours_and_tour_frequency_is_4,Number of Mandatory tours & tour frequency =4,num_mand*(tot_tours == 4),coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4,coef_number_of_mandatory_tours_and_tour_frequency_is_4 +util_number_of_mandatory_tours_and_tour_frequency_is_5_plus,Number of Mandatory tours & tour frequency = 5+,num_mand*(tot_tours > 4),coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus +util_number_of_joint_tours_and_tour_frequency_is_0,Number of Joint tours & tour frequency =0,num_hh_joint_tours*(tot_tours == 0),coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0,coef_number_of_joint_tours_and_tour_frequency_is_0 +util_number_of_joint_tours_and_tour_frequency_is_1,Number of Joint tours & tour frequency =1,num_hh_joint_tours*(tot_tours == 1),coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1,coef_number_of_joint_tours_and_tour_frequency_is_1 +util_number_of_joint_tours_and_tour_frequency_is_2,Number of Joint tours & tour frequency =2,num_hh_joint_tours*(tot_tours == 2),coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2,coef_number_of_joint_tours_and_tour_frequency_is_2 +util_number_of_joint_tours_and_tour_frequency_is_3,Number of Joint tours & tour frequency =3,num_hh_joint_tours*(tot_tours == 3),coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3,coef_number_of_joint_tours_and_tour_frequency_is_3 +util_number_of_joint_tours_and_tour_frequency_is_4,Number of Joint tours & tour frequency =4,num_hh_joint_tours*(tot_tours == 4),coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4,coef_number_of_joint_tours_and_tour_frequency_is_4 +util_number_of_joint_tours_and_tour_frequency_is_5_plus,Number of Joint tours & tour frequency = 5+,num_hh_joint_tours*(tot_tours > 4),coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus,coef_number_of_joint_tours_and_tour_frequency_is_5_plus +util_number_of_joint_shopping_tours,Number of Joint Shopping tours,shopping * num_hh_joint_shop_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours,coef_number_of_joint_shopping_tours +util_number_of_joint_maintenance_tours,Number of Joint Maintenance tours,othmaint * num_hh_joint_maint_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours,coef_number_of_joint_maintenance_tours +util_number_of_joint_eating_out_tours,Number of Joint Eating Out tours,eatout * num_hh_joint_eatout_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours,coef_number_of_joint_eating_out_tours +util_number_of_joint_visit_tours,Number of Joint Visit tours,social * num_hh_joint_social_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours,coef_number_of_joint_visit_tours +util_number_of_joint_discretionary_tours,Number of Joint Discretionary tours,othdiscr * num_hh_joint_othdiscr_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours,coef_number_of_joint_discretionary_tours +util_logged_maximum_residual_window_tour_frequency_is_0,"Logged Maximum Residual Window, tour frequency =0",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 0),coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0,coef_logged_maximum_residual_window_tour_frequency_is_0 +util_logged_maximum_residual_window_tour_frequency_is_1,"Logged Maximum Residual Window, tour frequency =1",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 1),coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1,coef_logged_maximum_residual_window_tour_frequency_is_1 +util_logged_maximum_residual_window_tour_frequency_is_2,"Logged Maximum Residual Window, tour frequency =2",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 2),coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2,coef_logged_maximum_residual_window_tour_frequency_is_2 +util_logged_maximum_residual_window_tour_frequency_is_3,"Logged Maximum Residual Window, tour frequency =3",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 3),coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3,coef_logged_maximum_residual_window_tour_frequency_is_3 +util_logged_maximum_residual_window_tour_frequency_is_4,"Logged Maximum Residual Window, tour frequency =4",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours == 4),coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4,coef_logged_maximum_residual_window_tour_frequency_is_4 +util_logged_maximum_residual_window_tour_frequency_is_5_plus,"Logged Maximum Residual Window, tour frequency =5+",((num_mand > 0) | (num_hh_joint_tours > 0)) * log_max_window*(tot_tours > 4),coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus,coef_logged_maximum_residual_window_tour_frequency_is_5_plus +util_mediumlow_income_group_and_tour_frequency_is_1,Dummy for Mediumlow Income group (20K-50K) & tour frequency=1,medium_low_income & (tot_tours == 1),coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1,coef_mediumlow_income_group_and_tour_frequency_is_1 +util_mediumlow_income_group_and_tour_frequency_is_2,Dummy for Mediumlow Income group (20K-50K) & tour frequency=2,medium_low_income & (tot_tours == 2),coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2,coef_mediumlow_income_group_and_tour_frequency_is_2 +util_mediumlow_income_group_and_tour_frequency_is_3,Dummy for Mediumlow Income group (20K-50K) & tour frequency=3,medium_low_income & (tot_tours == 3),coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3,coef_mediumlow_income_group_and_tour_frequency_is_3 +util_mediumlow_income_group_and_tour_frequency_is_4,Dummy for Mediumlow Income group (20K-50K) & tour frequency=4,medium_low_income & (tot_tours == 4),coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4,coef_mediumlow_income_group_and_tour_frequency_is_4 +util_mediumlow_income_group_and_tour_frequency_is_5_plus,Dummy for Mediumlow Income group (20K-50K) & tour frequency=5+,medium_low_income & (tot_tours > 4),coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus,coef_mediumlow_income_group_and_tour_frequency_is_5_plus +util_mediumhigh_income_group_and_tour_frequency_is_1,Dummy for MediumHigh Income group (50K-100K) & tour frequency=1,medium_high_income & (tot_tours == 1),coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1,coef_mediumhigh_income_group_and_tour_frequency_is_1 +util_mediumhigh_income_group_and_tour_frequency_is_2,Dummy for MediumHigh Income group (50K-100K) & tour frequency=2,medium_high_income & (tot_tours == 2),coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2,coef_mediumhigh_income_group_and_tour_frequency_is_2 +util_mediumhigh_income_group_and_tour_frequency_is_3,Dummy for MediumHigh Income group (50K-100K) & tour frequency=3,medium_high_income & (tot_tours == 3),coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3,coef_mediumhigh_income_group_and_tour_frequency_is_3 +util_mediumhigh_income_group_and_tour_frequency_is_4,Dummy for MediumHigh Income group (50K-100K) & tour frequency=4,medium_high_income & (tot_tours == 4),coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4,coef_mediumhigh_income_group_and_tour_frequency_is_4 +util_mediumhigh_income_group_and_tour_frequency_is_5_plus,Dummy for MediumHigh Income group (50K-100K) & tour frequency=5+,medium_high_income & (tot_tours > 4),coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,coef_mediumhigh_income_group_and_tour_frequency_is_5_plus +util_high_income_group_and_tour_frequency_is_1,Dummy for High Income group (>100K) & tour frequency=1,high_income & (tot_tours == 1),coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1,coef_high_income_group_and_tour_frequency_is_1 +util_high_income_group_and_tour_frequency_is_2,Dummy for High Income group (>100K) & tour frequency=2,high_income & (tot_tours == 2),coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2,coef_high_income_group_and_tour_frequency_is_2 +util_high_income_group_and_tour_frequency_is_3,Dummy for High Income group (>100K) & tour frequency=3,high_income & (tot_tours == 3),coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3,coef_high_income_group_and_tour_frequency_is_3 +util_high_income_group_and_tour_frequency_is_4,Dummy for High Income group (>100K) & tour frequency=4,high_income & (tot_tours == 4),coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4,coef_high_income_group_and_tour_frequency_is_4 +util_high_income_group_and_tour_frequency_is_5_plus,Dummy for High Income group (>100K) & tour frequency=5+,high_income & (tot_tours > 4),coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus,coef_high_income_group_and_tour_frequency_is_5_plus +util_mediumlow_income_group_and_shopping_tour,Dummy for Mediumlow Income group (20K-50K) & shopping tour,medium_low_income * shopping,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour,coef_mediumlow_income_group_and_shopping_tour +util_mediumhigh_income_group_and_shopping_tour,Dummy for Mediumhigh Income group (50K-100K) & shopping tour,medium_high_income * shopping,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour,coef_mediumhigh_income_group_and_shopping_tour +util_high_income_group_and_shopping_tour,Dummy for High Income group (>100K) & shopping tour,high_income * shopping,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour,coef_high_income_group_and_shopping_tour +util_mediumlow_income_group_and_maintenance_tour,Dummy for Mediumlow Income group (20K-50K) & maintenance tour,medium_low_income * othmaint,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour,coef_mediumlow_income_group_and_maintenance_tour +util_mediumhigh_income_group_and_maintenance_tour,Dummy for Mediumhigh Income group (50K-100K) & maintenance tour,medium_high_income * othmaint,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour,coef_mediumhigh_income_group_and_maintenance_tour +util_high_income_group_and_maintenance_tour,Dummy for High Income group (>100K) & maintenance tour,high_income * othmaint,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour,coef_high_income_group_and_maintenance_tour +util_mediumlow_income_group_and_eating_out_tour,Dummy for Mediumlow Income group (20K-50K) & Eating out tour,medium_low_income * eatout,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour,coef_mediumlow_income_group_and_eating_out_tour +util_mediumhigh_income_group_and_eating_out_tour,Dummy for Mediumhigh Income group (50K-100K) & Eating out tour,medium_high_income * eatout,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour,coef_mediumhigh_income_group_and_eating_out_tour +util_high_income_group_and_eating_out_tour,Dummy for High Income group (>100K) & Eating out tour,high_income * eatout,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour,coef_high_income_group_and_eating_out_tour +util_mediumlow_income_group_and_discretionary_tour,Dummy for Mediumlow Income group (20K-50K) & Discretionary tour,medium_low_income * othdiscr,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour,coef_mediumlow_income_group_and_discretionary_tour +util_mediumhigh_income_group_and_discretionary_tour,Dummy for Mediumhigh Income group (50K-100K) & Discretionary tour,medium_high_income * othdiscr,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour,coef_mediumhigh_income_group_and_discretionary_tour +util_high_income_group_and_discretionary_tour,Dummy for High Income group (>100K) & Discretionary tour,high_income * othdiscr,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour,coef_high_income_group_and_discretionary_tour +util_mediumlow_income_group_and_visiting_tour,Dummy for Mediumlow Income group (20K-50K) & Visiting tour,medium_low_income * social,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour,coef_mediumlow_income_group_and_visiting_tour +util_mediumhigh_income_group_and_visiting_tour,Dummy for Mediumhigh Income group (50K-100K) & Visiting tour,medium_high_income * social,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour,coef_mediumhigh_income_group_and_visiting_tour +util_high_income_group_and_visiting_tour,Dummy for High Income group (>100K) & Visiting tour,high_income * social,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour,coef_high_income_group_and_visiting_tour +util_female_and_tour_frequency_is_1,Dummy for Female & tour frequency =1,female & (tot_tours == 1),coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1,coef_female_and_tour_frequency_is_1 +util_female_and_tour_frequency_is_2,Dummy for Female & tour frequency =2,female & (tot_tours == 2),coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2,coef_female_and_tour_frequency_is_2 +util_female_and_tour_frequency_is_3,Dummy for Female & tour frequency =3,female & (tot_tours == 3),coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3,coef_female_and_tour_frequency_is_3 +util_female_and_tour_frequency_is_4,Dummy for Female & tour frequency =4,female & (tot_tours == 4),coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4,coef_female_and_tour_frequency_is_4 +util_female_and_tour_frequency_is_5,Dummy for Female & tour frequency =5,female & (tot_tours == 5),coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5,coef_female_and_tour_frequency_is_5 +util_female_and_escorting_tour,Dummy for Female & Escorting Tour,female * escort,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour,coef_female_and_escorting_tour +util_female_and_shopping_tour,Dummy for Female & Shopping Tour,female * shopping,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour,coef_female_and_shopping_tour +util_female_and_maintenance_tour,Dummy for Female & Maintenance Tour,female * othmaint,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour,coef_female_and_maintenance_tour +util_female_and_eatingout_tour,Dummy for Female & EatingOut Tour,female * eatout,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour,coef_female_and_eatingout_tour +util_female_and_discretionary_tour,Dummy for Female & Discretionary Tour,female * othdiscr,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour,coef_female_and_discretionary_tour +util_zero_car_ownership_and_tour_frequency_is_1,Dummy for zero car ownership & tour frequency =1,no_cars & (tot_tours == 1),coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1,coef_zero_car_ownership_and_tour_frequency_is_1 +util_zero_car_ownership_and_tour_frequency_is_2,Dummy for zero car ownership & tour frequency =2,no_cars & (tot_tours == 2),coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2,coef_zero_car_ownership_and_tour_frequency_is_2 +util_zero_car_ownership_and_tour_frequency_is_3,Dummy for zero car ownership & tour frequency =3,no_cars & (tot_tours == 3),coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3,coef_zero_car_ownership_and_tour_frequency_is_3 +util_zero_car_ownership_and_tour_frequency_is_4,Dummy for zero car ownership & tour frequency =4,no_cars & (tot_tours == 4),coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4,coef_zero_car_ownership_and_tour_frequency_is_4 +util_zero_car_ownership_and_tour_frequency_is_5_plus,Dummy for zero car ownership & tour frequency =5+,no_cars & (tot_tours > 4),coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus,coef_zero_car_ownership_and_tour_frequency_is_5_plus +util_car_shortage_vs_workers_and_tour_frequency_is_1,Dummy for Car Shortage vs Workers & tour frequency =1,~no_cars & (car_sufficiency < 0) & (tot_tours == 1),coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1,coef_car_shortage_vs_workers_and_tour_frequency_is_1 +util_car_shortage_vs_workers_and_tour_frequency_is_2,Dummy for Car Shortage vs Workers & tour frequency =2,~no_cars & (car_sufficiency < 0) & (tot_tours == 2),coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2,coef_car_shortage_vs_workers_and_tour_frequency_is_2 +util_car_shortage_vs_workers_and_tour_frequency_is_3,Dummy for Car Shortage vs Workers & tour frequency =3,~no_cars & (car_sufficiency < 0) & (tot_tours == 3),coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3,coef_car_shortage_vs_workers_and_tour_frequency_is_3 +util_car_shortage_vs_workers_and_tour_frequency_is_4,Dummy for Car Shortage vs Workers & tour frequency =4,~no_cars & (car_sufficiency < 0) & (tot_tours == 4),coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4,coef_car_shortage_vs_workers_and_tour_frequency_is_4 +util_car_shortage_vs_workers_and_tour_frequency_is_5_plus,Dummy for Car Shortage vs Workers & tour frequency =5+,~no_cars & (car_sufficiency < 0) & (tot_tours > 4),coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus +util_car_surplus_vs_workers_and_tour_frequency_is_1,Dummy for Car Surplus vs Workers & tour frequency =1,~no_cars & (car_sufficiency > 0) & (tot_tours == 1),coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1,coef_car_surplus_vs_workers_and_tour_frequency_is_1 +util_car_surplus_vs_workers_and_tour_frequency_is_2,Dummy for Car Surplus vs Workers & tour frequency =2,~no_cars & (car_sufficiency > 0) & (tot_tours == 2),coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2,coef_car_surplus_vs_workers_and_tour_frequency_is_2 +util_car_surplus_vs_workers_and_tour_frequency_is_3,Dummy for Car Surplus vs Workers & tour frequency =3,~no_cars & (car_sufficiency > 0) & (tot_tours == 3),coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3,coef_car_surplus_vs_workers_and_tour_frequency_is_3 +util_car_surplus_vs_workers_and_tour_frequency_is_4,Dummy for Car Surplus vs Workers & tour frequency =4,~no_cars & (car_sufficiency > 0) & (tot_tours == 4),coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4,coef_car_surplus_vs_workers_and_tour_frequency_is_4 +util_car_surplus_vs_workers_and_tour_frequency_is_5_plus,Dummy for Car Surplus vs Workers & tour frequency =5+,~no_cars & (car_sufficiency > 0) & (tot_tours > 4),coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus +util_presence_of_non_worker_and_tour_frequency_is_1,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =1,has_non_worker & (tot_tours == 1),coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1,coef_presence_of_non_worker_and_tour_frequency_is_1 +util_presence_of_non_worker_and_tour_frequency_is_2,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =2,has_non_worker & (tot_tours == 2),coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2,coef_presence_of_non_worker_and_tour_frequency_is_2 +util_presence_of_non_worker_and_tour_frequency_is_3,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =3,has_non_worker & (tot_tours == 3),coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3,coef_presence_of_non_worker_and_tour_frequency_is_3 +util_presence_of_non_worker_and_tour_frequency_is_4,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =4,has_non_worker & (tot_tours == 4),coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4,coef_presence_of_non_worker_and_tour_frequency_is_4 +util_presence_of_non_worker_and_tour_frequency_is_5,Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =5,has_non_worker & (tot_tours == 5),coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5,coef_presence_of_non_worker_and_tour_frequency_is_5 +util_presence_of_retiree_and_tour_frequency_is_1,Dummy for Presence of Retiree(other than modeled person) & tour frequency =1,has_retiree & (tot_tours == 1),coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1,coef_presence_of_retiree_and_tour_frequency_is_1 +util_presence_of_retiree_and_tour_frequency_is_2,Dummy for Presence of Retiree(other than modeled person) & tour frequency =2,has_retiree & (tot_tours == 2),coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2,coef_presence_of_retiree_and_tour_frequency_is_2 +util_presence_of_retiree_and_tour_frequency_is_3,Dummy for Presence of Retiree(other than modeled person) & tour frequency =3,has_retiree & (tot_tours == 3),coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3,coef_presence_of_retiree_and_tour_frequency_is_3 +util_presence_of_retiree_and_tour_frequency_is_4,Dummy for Presence of Retiree(other than modeled person) & tour frequency =4,has_retiree & (tot_tours == 4),coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4,coef_presence_of_retiree_and_tour_frequency_is_4 +util_presence_of_retiree_and_tour_frequency_is_5,Dummy for Presence of Retiree(other than modeled person) & tour frequency =5,has_retiree & (tot_tours == 5),coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5,coef_presence_of_retiree_and_tour_frequency_is_5 +util_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =1,has_preschool_kid & (tot_tours == 1),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1 +util_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =2,has_preschool_kid & (tot_tours == 2),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2 +util_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =3,has_preschool_kid & (tot_tours == 3),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3 +util_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =4,has_preschool_kid & (tot_tours == 4),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4 +util_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =5,has_preschool_kid & (tot_tours == 5),coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5 +util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =1,has_school_kid & (tot_tours == 1),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1 +util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =2,has_school_kid & (tot_tours == 2),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2 +util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =3,has_school_kid & (tot_tours == 3),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3 +util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =4,has_school_kid & (tot_tours == 4),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4 +util_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =5,has_school_kid & (tot_tours == 5),coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5 +util_presence_of_full_time_worker_and_escorting_tour,Dummy for Presence of Full time Worker (other than modeled person) & Escorting tour ,has_full_time * escort,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour,coef_presence_of_full_time_worker_and_escorting_tour +util_presence_of_part_time_worker_and_escorting_tour,Dummy for Presence of Part time Worker (other than modeled person) & Escorting tour ,has_part_time * escort,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour,coef_presence_of_part_time_worker_and_escorting_tour +util_presence_of_non_worker_and_escorting_tour,Dummy for Presence of Non-Worker (other than modeled person) & Escorting tour ,has_non_worker * escort,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour,coef_presence_of_non_worker_and_escorting_tour +util_presence_of_retiree_and_escorting_tour,Dummy for Presence of Retiree (other than modeled person) & Escorting tour ,has_retiree * escort,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour,coef_presence_of_retiree_and_escorting_tour +util_presence_of_university_student_and_escorting_tour,Dummy for Presence of University Student (other than modeled person) & Escorting tour ,has_university * escort,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour,coef_presence_of_university_student_and_escorting_tour +util_presence_of_driving_school_kid_and_escorting_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Escorting tour ,has_driving_kid * escort,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour,coef_presence_of_driving_school_kid_and_escorting_tour +util_presence_of_pre_driving_school_kid_and_escorting_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Escorting tour ,has_school_kid * escort,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour,coef_presence_of_pre_driving_school_kid_and_escorting_tour +util_presence_of_pre_school_kid_and_escorting_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Escorting tour ,has_preschool_kid * escort,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour,coef_presence_of_pre_school_kid_and_escorting_tour +util_at_home_pre_driving_school_kid_and_escorting_tour,Dummy for At home Pre-Driving School Kid & Escorting tour ,has_school_kid_at_home * escort,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour,coef_at_home_pre_driving_school_kid_and_escorting_tour +util_at_home_pre_school_kid_and_escorting_tour,Dummy for At homef Pre-School Kid & Escorting tour ,has_preschool_kid_at_home * escort,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour,coef_at_home_pre_school_kid_and_escorting_tour +util_presence_of_full_time_worker_and_shopping_tour,Dummy for Presence of Full time Worker (other than modeled person) & Shopping tour ,has_full_time * shopping,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour,coef_presence_of_full_time_worker_and_shopping_tour +util_presence_of_part_time_worker_and_shopping_tour,Dummy for Presence of Part time Worker (other than modeled person) & Shopping tour ,has_part_time * shopping,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour,coef_presence_of_part_time_worker_and_shopping_tour +util_presence_of_non_worker_and_shopping_tour,Dummy for Presence of Non-Worker (other than modeled person) & Shopping tour ,has_non_worker * shopping,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour,coef_presence_of_non_worker_and_shopping_tour +util_presence_of_retiree_and_shopping_tour,Dummy for Presence of Retiree (other than modeled person) & Shopping tour ,has_retiree * shopping,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour,coef_presence_of_retiree_and_shopping_tour +util_presence_of_university_student_and_shopping_tour,Dummy for Presence of University Student (other than modeled person) & Shopping tour ,has_university * shopping,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour,coef_presence_of_university_student_and_shopping_tour +util_presence_of_driving_school_kid_and_shopping_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Shopping tour ,has_driving_kid * shopping,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour,coef_presence_of_driving_school_kid_and_shopping_tour +util_presence_of_pre_driving_school_kid_and_shopping_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Shopping tour ,has_school_kid * shopping,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour,coef_presence_of_pre_driving_school_kid_and_shopping_tour +util_presence_of_pre_school_kid_and_shopping_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Shopping tour ,has_preschool_kid * shopping,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour,coef_presence_of_pre_school_kid_and_shopping_tour +util_at_home_pre_driving_school_kid_and_shopping_tour,Dummy for At home Pre-Driving School Kid & Shopping tour ,has_school_kid_at_home * shopping,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour,coef_at_home_pre_driving_school_kid_and_shopping_tour +util_at_home_pre_school_kid_and_shopping_tour,Dummy for At homef Pre-School Kid & Shopping tour ,has_preschool_kid_at_home * shopping,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour,coef_at_home_pre_school_kid_and_shopping_tour +util_presence_of_full_time_worker_and_maintenance_tour,Dummy for Presence of Full time Worker (other than modeled person) & Maintenance tour ,has_full_time * othmaint,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour,coef_presence_of_full_time_worker_and_maintenance_tour +util_presence_of_part_time_worker_and_maintenance_tour,Dummy for Presence of Part time Worker (other than modeled person) & Maintenance tour ,has_part_time * othmaint,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour,coef_presence_of_part_time_worker_and_maintenance_tour +util_presence_of_non_worker_and_maintenance_tour,Dummy for Presence of Non-Worker(other than modeled person) & Maintenance tour ,has_non_worker * othmaint,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour,coef_presence_of_non_worker_and_maintenance_tour +util_presence_of_retiree_and_maintenance_tour,Dummy for Presence of Retiree (other than modeled person) & Maintenance tour ,has_retiree * othmaint,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour,coef_presence_of_retiree_and_maintenance_tour +util_presence_of_university_student_and_maintenance_tour,Dummy for Presence of University Student (other than modeled person) & Maintenance tour ,has_university * othmaint,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour,coef_presence_of_university_student_and_maintenance_tour +util_presence_of_driving_school_kid_and_maintenance_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Maintenance tour ,has_driving_kid * othmaint,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour,coef_presence_of_driving_school_kid_and_maintenance_tour +util_presence_of_pre_driving_school_kid_and_maintenance_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Maintenance tour ,has_school_kid * othmaint,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour,coef_presence_of_pre_driving_school_kid_and_maintenance_tour +util_presence_of_pre_school_kid_and_maintenance_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Maintenance tour ,has_preschool_kid * othmaint,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour,coef_presence_of_pre_school_kid_and_maintenance_tour +util_at_home_pre_driving_school_kid_and_maintenance_tour,Dummy for At home Pre-Driving School Kid & Maintenance tour ,has_school_kid_at_home * othmaint,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour,coef_at_home_pre_driving_school_kid_and_maintenance_tour +util_at_home_pre_school_kid_and_maintenance_tour,Dummy for At homef Pre-School Kid & Maintenance tour ,has_preschool_kid_at_home * othmaint,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour,coef_at_home_pre_school_kid_and_maintenance_tour +util_presence_of_full_time_worker_and_eating_out_tour,Dummy for Presence of Full time Worker (other than modeled person) & Eating Out tour ,has_full_time * eatout,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour,coef_presence_of_full_time_worker_and_eating_out_tour +util_presence_of_part_time_worker_and_eating_out_tour,Dummy for Presence of Part time Worker (other than modeled person) & Eating Out tour ,has_part_time * eatout,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour,coef_presence_of_part_time_worker_and_eating_out_tour +util_presence_of_non_worker_and_eating_out_tour,Dummy for Presence of Non-Worker (other than modeled person) & Eating Out tour ,has_non_worker * eatout,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour,coef_presence_of_non_worker_and_eating_out_tour +util_presence_of_retiree_and_eating_out_tour,Dummy for Presence of Retiree (other than modeled person) & Eating Out tour ,has_retiree * eatout,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour,coef_presence_of_retiree_and_eating_out_tour +util_presence_of_university_student_and_eating_out_tour,Dummy for Presence of University Student (other than modeled person) & Eating Out tour ,has_university * eatout,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour,coef_presence_of_university_student_and_eating_out_tour +util_presence_of_driving_school_kid_and_eating_out_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Eating Out tour ,has_driving_kid * eatout,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour,coef_presence_of_driving_school_kid_and_eating_out_tour +util_presence_of_pre_driving_school_kid_and_eating_out_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Eating Out tour ,has_school_kid * eatout,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour,coef_presence_of_pre_driving_school_kid_and_eating_out_tour +util_presence_of_pre_school_kid_and_eating_out_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Eating Out tour ,has_preschool_kid * eatout,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour,coef_presence_of_pre_school_kid_and_eating_out_tour +util_at_home_pre_driving_school_kid_and_eating_out_tour,Dummy for At home Pre-Driving School Kid & Eating Out tour ,has_school_kid_at_home * eatout,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour,coef_at_home_pre_driving_school_kid_and_eating_out_tour +util_at_home_pre_school_kid_and_eating_out_tour,Dummy for At homef Pre-School Kid & Eating Out tour ,has_preschool_kid_at_home * eatout,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour,coef_at_home_pre_school_kid_and_eating_out_tour +util_presence_of_full_time_worker_and_discretionary_tour,Dummy for Presence of Full time Worker (other than modeled person) & Discretionary tour ,has_full_time * othdiscr,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour,coef_presence_of_full_time_worker_and_discretionary_tour +util_presence_of_part_time_worker_and_discretionary_tour,Dummy for Presence of Part time Worker (other than modeled person) & Discretionary tour ,has_part_time * othdiscr,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour,coef_presence_of_part_time_worker_and_discretionary_tour +util_presence_of_non_worker_and_discretionary_tour,Dummy for Presence of Non-Worker (other than modeled person) & Discretionary tour ,has_non_worker * othdiscr,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour,coef_presence_of_non_worker_and_discretionary_tour +util_presence_of_retiree_and_discretionary_tour,Dummy for Presence of Retiree (other than modeled person) & Discretionary tour ,has_retiree * othdiscr,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour,coef_presence_of_retiree_and_discretionary_tour +util_presence_of_university_student_and_discretionary_tour,Dummy for Presence of University Student (other than modeled person) & Discretionary tour ,has_university * othdiscr,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour,coef_presence_of_university_student_and_discretionary_tour +util_presence_of_driving_school_kid_and_discretionary_tour,Dummy for Presence of Driving School Kid (other than modeled person) & Discretionary tour ,has_driving_kid * othdiscr,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour,coef_presence_of_driving_school_kid_and_discretionary_tour +util_presence_of_pre_driving_school_kid_and_discretionary_tour,Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Discretionary tour ,has_school_kid * othdiscr,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour,coef_presence_of_pre_driving_school_kid_and_discretionary_tour +util_presence_of_pre_school_kid_and_discretionary_tour,Dummy for Presence of Pre-School Kid (other than modeled person) & Discretionary tour ,has_preschool_kid * othdiscr,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour,coef_presence_of_pre_school_kid_and_discretionary_tour +util_at_home_pre_driving_school_kid_and_discretionary_tour,Dummy for At home Pre-Driving School Kid & Discretionary tour ,has_school_kid_at_home * othdiscr,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour,coef_at_home_pre_driving_school_kid_and_discretionary_tour +util_at_home_pre_school_kid_and_discretionary_tour,Dummy for At homef Pre-School Kid & Discretionary tour ,has_preschool_kid_at_home * othdiscr,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour,coef_at_home_pre_school_kid_and_discretionary_tour +util_walk_access_to_retail_and_tour_frequency_is_1,Walk Access to Retail & Tour Frequency =1,nmRetail * (tot_tours == 1),coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1,coef_walk_access_to_retail_and_tour_frequency_is_1 +util_walk_access_to_retail_and_tour_frequency_is_2,Walk Access to Retail & Tour Frequency =2,nmRetail * (tot_tours == 2),coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2,coef_walk_access_to_retail_and_tour_frequency_is_2 +util_walk_access_to_retail_and_tour_frequency_is_3,Walk Access to Retail & Tour Frequency =3,nmRetail * (tot_tours == 3),coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3,coef_walk_access_to_retail_and_tour_frequency_is_3 +util_walk_access_to_retail_and_tour_frequency_is_4,Walk Access to Retail & Tour Frequency =4,nmRetail * (tot_tours == 4),coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4,coef_walk_access_to_retail_and_tour_frequency_is_4 +util_walk_access_to_retail_and_tour_frequency_is_5_plus,Walk Access to Retail & Tour Frequency =5+,nmRetail * (tot_tours > 4),coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus,coef_walk_access_to_retail_and_tour_frequency_is_5_plus +util_transit_access_to_retail_and_tour_frequency_is_1,Transit Access to Retail & Tour Frequency =1,trOpRetail * (tot_tours == 1),coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1,coef_transit_access_to_retail_and_tour_frequency_is_1 +util_transit_access_to_retail_and_tour_frequency_is_2,Transit Access to Retail & Tour Frequency =2,trOpRetail * (tot_tours == 2),coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2,coef_transit_access_to_retail_and_tour_frequency_is_2 +util_transit_access_to_retail_and_tour_frequency_is_3,Transit Access to Retail & Tour Frequency =3,trOpRetail * (tot_tours == 3),coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3,coef_transit_access_to_retail_and_tour_frequency_is_3 +util_transit_access_to_retail_and_tour_frequency_is_4,Transit Access to Retail & Tour Frequency =4,trOpRetail * (tot_tours == 4),coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4,coef_transit_access_to_retail_and_tour_frequency_is_4 +util_transit_access_to_retail_and_tour_frequency_is_5_plus,Transit Access to Retail & Tour Frequency =5+,trOpRetail * (tot_tours > 4),coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus,coef_transit_access_to_retail_and_tour_frequency_is_5_plus +util_auto_access_to_retail_and_tour_frequency_is_1,Auto Access to Retail & Tour Frequency =1,auOpRetail * (tot_tours == 1),coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1,coef_auto_access_to_retail_and_tour_frequency_is_1 +util_auto_access_to_retail_and_tour_frequency_is_2,Auto Access to Retail & Tour Frequency =2,auOpRetail * (tot_tours == 2),coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2,coef_auto_access_to_retail_and_tour_frequency_is_2 +util_auto_access_to_retail_and_tour_frequency_is_3,Auto Access to Retail & Tour Frequency =3,auOpRetail * (tot_tours == 3),coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3,coef_auto_access_to_retail_and_tour_frequency_is_3 +util_auto_access_to_retail_and_tour_frequency_is_4,Auto Access to Retail & Tour Frequency =4,auOpRetail * (tot_tours == 4),coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4,coef_auto_access_to_retail_and_tour_frequency_is_4 +util_auto_access_to_retail_and_tour_frequency_is_5_plus,Auto Access to Retail & Tour Frequency =5+,auOpRetail * (tot_tours > 4),coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus,coef_auto_access_to_retail_and_tour_frequency_is_5_plus +util_walk_access_to_retail_and_escorting,Walk Access to Retail & Escorting ,nmRetail * escort,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting,coef_walk_access_to_retail_and_escorting +util_transit_access_to_retail_and_escorting,Transit Access to Retail & Escorting ,trOpRetail * escort,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting,coef_transit_access_to_retail_and_escorting +util_auto_access_to_retail_and_escorting,Auto Access to Retail & Escorting ,auOpRetail * escort,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting,coef_auto_access_to_retail_and_escorting +util_walk_access_to_retail_and_shopping,Walk Access to Retail & Shopping ,nmRetail * shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping,coef_walk_access_to_retail_and_shopping +util_transit_access_to_retail_and_shopping,Transit Access to Retail & Shopping ,trOpRetail * shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping,coef_transit_access_to_retail_and_shopping +util_auto_access_to_retail_and_shopping,Auto Access to Retail & Shopping ,auOpRetail * shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping,coef_auto_access_to_retail_and_shopping +util_walk_access_to_retail_and_maintenance,Walk Access to Retail & Maintenance ,nmRetail * othmaint,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance,coef_walk_access_to_retail_and_maintenance +util_transit_access_to_retail_and_maintenance,Transit Access to Retail & Maintenance ,trOpRetail * othmaint,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance,coef_transit_access_to_retail_and_maintenance +util_auto_access_to_retail_and_maintenance,Auto Access to Retail & Maintenance ,auOpRetail * othmaint,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance,coef_auto_access_to_retail_and_maintenance +util_walk_access_to_retail_and_eating_out,Walk Access to Retail & Eating Out ,nmRetail * eatout,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out,coef_walk_access_to_retail_and_eating_out +util_transit_access_to_retail_and_eating_out,Transit Access to Retail & Eating Out ,trOpRetail * eatout,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out,coef_transit_access_to_retail_and_eating_out +util_auto_access_to_retail_and_eating_out,Auto Access to Retail & Eating Out ,auOpRetail * eatout,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out,coef_auto_access_to_retail_and_eating_out +util_walk_access_to_retail_and_discretionary,Walk Access to Retail & Discretionary ,nmRetail * othdiscr,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary,coef_walk_access_to_retail_and_discretionary +util_transit_access_to_retail_and_discretionary,Transit Access to Retail & Discretionary ,trOpRetail * othdiscr,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary,coef_transit_access_to_retail_and_discretionary +util_auto_access_to_retail_and_discretionary,Auto Access to Retail & Discretionary ,auOpRetail * othdiscr,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary,coef_auto_access_to_retail_and_discretionary +util_urban_and_tour_frequency_is_1,Urban Areatype & Tour Frequency =1,home_is_urban & (tot_tours == 1),coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1,coef_urban_and_tour_frequency_is_1 +util_urban_and_tour_frequency_is_2,Urban Areatype & Tour Frequency =2,home_is_urban & (tot_tours == 2),coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2,coef_urban_and_tour_frequency_is_2 +util_urban_and_tour_frequency_is_3,Urban Areatype & Tour Frequency =3,home_is_urban & (tot_tours == 3),coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3,coef_urban_and_tour_frequency_is_3 +util_urban_and_tour_frequency_is_4,Urban Areatype & Tour Frequency =4,home_is_urban & (tot_tours == 4),coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4,coef_urban_and_tour_frequency_is_4 +util_urban_and_tour_frequency_is_5_plus,Urban Areatype & Tour Frequency =5+,home_is_urban & (tot_tours > 4),coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus,coef_urban_and_tour_frequency_is_5_plus +util_urban_and_escorting_tour,Urban Areatype & Escorting tour,home_is_urban * escort,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour,coef_urban_and_escorting_tour +util_urban_and_shopping_tour,Urban Areatype &Shopping tour,home_is_urban * shopping,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour,coef_urban_and_shopping_tour +util_urban_and_maintenance_tour,Urban Areatype & Maintenance tour,home_is_urban * othmaint,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour,coef_urban_and_maintenance_tour +util_urban_and_eatingout_tour,Urban Areatype & EatingOut tour,home_is_urban * eatout,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour,coef_urban_and_eatingout_tour +util_urban_and_discretionary_tour,Urban Areatype & Discretionary tour,home_is_urban * othdiscr,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour,coef_urban_and_discretionary_tour +util_1_escort_tour_constant,1 Escort Tour Constant,escort == 1,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant,coef_1_escort_tour_constant +util_2_plus_escort_tours_constant,2+ Escort Tours Constant,escort >= 2,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant,coef_2_plus_escort_tours_constant +util_1_plus_shopping_tours_constant,1+ Shopping Tours Constant,shopping >= 1,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant,coef_1_plus_shopping_tours_constant +util_1_plus_maintenance_tours_constant,1+ Maintenance Tours Constant,othmaint >= 1,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant,coef_1_plus_maintenance_tours_constant +util_1_plus_eating_out_tours_constant,1+ Eating Out Tours Constant,eatout >= 1,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant,coef_1_plus_eating_out_tours_constant +util_1_plus_visting_tours_constant,1+ Visting Tours Constant,social >= 1,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant,coef_1_plus_visting_tours_constant +util_1_plus_other_discretionary_tours_constant,1+ Other Discretionary Tours Constant,othdiscr >= 1,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant,coef_1_plus_other_discretionary_tours_constant util_0_auto_household_and_escorting_tour,Dummy for 0-auto household & Escorting Tour,escort * no_cars,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour,coef_0_auto_household_and_escorting_tour \ No newline at end of file diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency.yaml b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency.yaml similarity index 96% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency.yaml rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency.yaml index bd1c6f12d3..239b5a38a6 100755 --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency.yaml +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency.yaml @@ -1,42 +1,42 @@ - -SEGMENT_COL: ptype -SPEC: non_mandatory_tour_frequency.csv - -SPEC_SEGMENTS: - - NAME: PTYPE_FULL - PTYPE: 1 - COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv - - NAME: PTYPE_PART - PTYPE: 2 - COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv - - NAME: PTYPE_UNIVERSITY - PTYPE: 3 - COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv - - NAME: PTYPE_NONWORK - PTYPE: 4 - COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv - - NAME: PTYPE_RETIRED - PTYPE: 5 - COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv - - NAME: PTYPE_DRIVING - PTYPE: 6 - COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv - - NAME: PTYPE_SCHOOL - PTYPE: 7 - COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv - - NAME: PTYPE_PRESCHOOL - PTYPE: 8 - COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv - -annotate_persons: - SPEC: annotate_persons_nmtf - DF: persons - TABLES: - - tours - -preprocessor: - SPEC: non_mandatory_tour_frequency_annotate_persons_preprocessor - DF: persons - TABLES: - - tours -# - accessibility + +SEGMENT_COL: ptype +SPEC: non_mandatory_tour_frequency.csv + +SPEC_SEGMENTS: + - NAME: PTYPE_FULL + PTYPE: 1 + COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv + - NAME: PTYPE_PART + PTYPE: 2 + COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv + - NAME: PTYPE_UNIVERSITY + PTYPE: 3 + COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv + - NAME: PTYPE_NONWORK + PTYPE: 4 + COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv + - NAME: PTYPE_RETIRED + PTYPE: 5 + COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv + - NAME: PTYPE_DRIVING + PTYPE: 6 + COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv + - NAME: PTYPE_SCHOOL + PTYPE: 7 + COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv + - NAME: PTYPE_PRESCHOOL + PTYPE: 8 + COEFFICIENTS: non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv + +annotate_persons: + SPEC: annotate_persons_nmtf + DF: persons + TABLES: + - tours + +preprocessor: + SPEC: non_mandatory_tour_frequency_annotate_persons_preprocessor + DF: persons + TABLES: + - tours +# - accessibility diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_alternatives.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_alternatives.csv similarity index 92% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_alternatives.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_alternatives.csv index 1c0052f963..b9765aa75a 100755 --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_alternatives.csv +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_alternatives.csv @@ -1,97 +1,97 @@ -escort,shopping,othmaint,othdiscr,eatout,social -0,0,0,0,0,0 -0,0,0,1,0,0 -0,0,0,0,0,1 -0,0,0,1,0,1 -0,0,0,0,1,0 -0,0,0,1,1,0 -0,0,0,0,1,1 -0,0,0,1,1,1 -0,0,1,0,0,0 -0,0,1,1,0,0 -0,0,1,0,0,1 -0,0,1,1,0,1 -0,0,1,0,1,0 -0,0,1,1,1,0 -0,0,1,0,1,1 -0,0,1,1,1,1 -0,1,0,0,0,0 -0,1,0,1,0,0 -0,1,0,0,0,1 -0,1,0,1,0,1 -0,1,0,0,1,0 -0,1,0,1,1,0 -0,1,0,0,1,1 -0,1,0,1,1,1 -0,1,1,0,0,0 -0,1,1,1,0,0 -0,1,1,0,0,1 -0,1,1,1,0,1 -0,1,1,0,1,0 -0,1,1,1,1,0 -0,1,1,0,1,1 -0,1,1,1,1,1 -1,0,0,0,0,0 -1,0,0,1,0,0 -1,0,0,0,0,1 -1,0,0,1,0,1 -1,0,0,0,1,0 -1,0,0,1,1,0 -1,0,0,0,1,1 -1,0,0,1,1,1 -1,0,1,0,0,0 -1,0,1,1,0,0 -1,0,1,0,0,1 -1,0,1,1,0,1 -1,0,1,0,1,0 -1,0,1,1,1,0 -1,0,1,0,1,1 -1,0,1,1,1,1 -1,1,0,0,0,0 -1,1,0,1,0,0 -1,1,0,0,0,1 -1,1,0,1,0,1 -1,1,0,0,1,0 -1,1,0,1,1,0 -1,1,0,0,1,1 -1,1,0,1,1,1 -1,1,1,0,0,0 -1,1,1,1,0,0 -1,1,1,0,0,1 -1,1,1,1,0,1 -1,1,1,0,1,0 -1,1,1,1,1,0 -1,1,1,0,1,1 -1,1,1,1,1,1 -2,0,0,0,0,0 -2,0,0,1,0,0 -2,0,0,0,0,1 -2,0,0,1,0,1 -2,0,0,0,1,0 -2,0,0,1,1,0 -2,0,0,0,1,1 -2,0,0,1,1,1 -2,0,1,0,0,0 -2,0,1,1,0,0 -2,0,1,0,0,1 -2,0,1,1,0,1 -2,0,1,0,1,0 -2,0,1,1,1,0 -2,0,1,0,1,1 -2,0,1,1,1,1 -2,1,0,0,0,0 -2,1,0,1,0,0 -2,1,0,0,0,1 -2,1,0,1,0,1 -2,1,0,0,1,0 -2,1,0,1,1,0 -2,1,0,0,1,1 -2,1,0,1,1,1 -2,1,1,0,0,0 -2,1,1,1,0,0 -2,1,1,0,0,1 -2,1,1,1,0,1 -2,1,1,0,1,0 -2,1,1,1,1,0 -2,1,1,0,1,1 -2,1,1,1,1,1 +escort,shopping,othmaint,othdiscr,eatout,social +0,0,0,0,0,0 +0,0,0,1,0,0 +0,0,0,0,0,1 +0,0,0,1,0,1 +0,0,0,0,1,0 +0,0,0,1,1,0 +0,0,0,0,1,1 +0,0,0,1,1,1 +0,0,1,0,0,0 +0,0,1,1,0,0 +0,0,1,0,0,1 +0,0,1,1,0,1 +0,0,1,0,1,0 +0,0,1,1,1,0 +0,0,1,0,1,1 +0,0,1,1,1,1 +0,1,0,0,0,0 +0,1,0,1,0,0 +0,1,0,0,0,1 +0,1,0,1,0,1 +0,1,0,0,1,0 +0,1,0,1,1,0 +0,1,0,0,1,1 +0,1,0,1,1,1 +0,1,1,0,0,0 +0,1,1,1,0,0 +0,1,1,0,0,1 +0,1,1,1,0,1 +0,1,1,0,1,0 +0,1,1,1,1,0 +0,1,1,0,1,1 +0,1,1,1,1,1 +1,0,0,0,0,0 +1,0,0,1,0,0 +1,0,0,0,0,1 +1,0,0,1,0,1 +1,0,0,0,1,0 +1,0,0,1,1,0 +1,0,0,0,1,1 +1,0,0,1,1,1 +1,0,1,0,0,0 +1,0,1,1,0,0 +1,0,1,0,0,1 +1,0,1,1,0,1 +1,0,1,0,1,0 +1,0,1,1,1,0 +1,0,1,0,1,1 +1,0,1,1,1,1 +1,1,0,0,0,0 +1,1,0,1,0,0 +1,1,0,0,0,1 +1,1,0,1,0,1 +1,1,0,0,1,0 +1,1,0,1,1,0 +1,1,0,0,1,1 +1,1,0,1,1,1 +1,1,1,0,0,0 +1,1,1,1,0,0 +1,1,1,0,0,1 +1,1,1,1,0,1 +1,1,1,0,1,0 +1,1,1,1,1,0 +1,1,1,0,1,1 +1,1,1,1,1,1 +2,0,0,0,0,0 +2,0,0,1,0,0 +2,0,0,0,0,1 +2,0,0,1,0,1 +2,0,0,0,1,0 +2,0,0,1,1,0 +2,0,0,0,1,1 +2,0,0,1,1,1 +2,0,1,0,0,0 +2,0,1,1,0,0 +2,0,1,0,0,1 +2,0,1,1,0,1 +2,0,1,0,1,0 +2,0,1,1,1,0 +2,0,1,0,1,1 +2,0,1,1,1,1 +2,1,0,0,0,0 +2,1,0,1,0,0 +2,1,0,0,0,1 +2,1,0,1,0,1 +2,1,0,0,1,0 +2,1,0,1,1,0 +2,1,0,0,1,1 +2,1,0,1,1,1 +2,1,1,0,0,0 +2,1,1,1,0,0 +2,1,1,0,0,1 +2,1,1,1,0,1 +2,1,1,0,1,0 +2,1,1,1,1,0 +2,1,1,0,1,1 +2,1,1,1,1,1 diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv index c764831615..fc6616aba2 100755 --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_annotate_persons_preprocessor.csv @@ -1,21 +1,21 @@ -Description,Target,Expression -#,, -,max_window,person_max_window(persons)/2.25 -,log_max_window,np.log1p(max_window) -,medium_low_income,(persons.income_in_thousands > 20) & (persons.income_in_thousands <= 50) -,medium_high_income,(persons.income_in_thousands > 50) & (persons.income_in_thousands <= 100) -,high_income,(persons.income_in_thousands > 100) -,no_cars,(persons.auto_ownership == 0) -,car_sufficiency,persons.auto_ownership-persons.num_workers -#,, -# UEC file comments says these are joint tour counts per persons but code is for household counts,, -,_JOINT_TOURS,tours[tours.tour_category=='joint'] -,num_hh_joint_tours,"reindex_i(_JOINT_TOURS.groupby('household_id').size(), persons.household_id)" -,num_hh_joint_shop_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='shopping'].groupby('household_id').size(), persons.household_id)" -,num_hh_joint_eatout_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='eatout'].groupby('household_id').size(), persons.household_id)" -,num_hh_joint_maint_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='maint'].groupby('household_id').size(), persons.household_id)" -,num_hh_joint_social_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='social'].groupby('household_id').size(), persons.household_id)" -,num_hh_joint_othdiscr_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='othdiscr'].groupby('household_id').size(), persons.household_id)" -# non_mandatory tour frequency extension,, -,has_mandatory_tour,(persons.num_mand > 0) * 1 -,has_joint_tour,(persons.num_joint_tours > 0) * 1 +Description,Target,Expression +#,, +,max_window,person_max_window(persons)/2.25 +,log_max_window,np.log1p(max_window) +,medium_low_income,(persons.income_in_thousands > 20) & (persons.income_in_thousands <= 50) +,medium_high_income,(persons.income_in_thousands > 50) & (persons.income_in_thousands <= 100) +,high_income,(persons.income_in_thousands > 100) +,no_cars,(persons.auto_ownership == 0) +,car_sufficiency,persons.auto_ownership-persons.num_workers +#,, +# UEC file comments says these are joint tour counts per persons but code is for household counts,, +,_JOINT_TOURS,tours[tours.tour_category=='joint'] +,num_hh_joint_tours,"reindex_i(_JOINT_TOURS.groupby('household_id').size(), persons.household_id)" +,num_hh_joint_shop_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='shopping'].groupby('household_id').size(), persons.household_id)" +,num_hh_joint_eatout_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='eatout'].groupby('household_id').size(), persons.household_id)" +,num_hh_joint_maint_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='maint'].groupby('household_id').size(), persons.household_id)" +,num_hh_joint_social_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='social'].groupby('household_id').size(), persons.household_id)" +,num_hh_joint_othdiscr_tours,"reindex_i(_JOINT_TOURS[_JOINT_TOURS.tour_type=='othdiscr'].groupby('household_id').size(), persons.household_id)" +# non_mandatory tour frequency extension,, +,has_mandatory_tour,(persons.num_mand > 0) * 1 +,has_joint_tour,(persons.num_joint_tours > 0) * 1 diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv index 68b0882011..d24ffa777e 100755 --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_DRIVING.csv @@ -1,211 +1,211 @@ -coefficient_name,value,constrain -coef_escorting_tour,0,T -coef_discretionary_tour,0,T -coef_shopping_tour,0,T -coef_maintenance_tour,0,T -coef_visiting_or_social_tour,0,T -coef_eating_out_tour,0,T -coef_total_number_of_tours_is_0_no_prior_tours,-999,T -coef_total_number_of_tours_is_0_prior_tours,0,T -coef_total_number_of_tours_is_1,-7.1506,F -coef_total_number_of_tours_is_2,-11.1214,F -coef_total_number_of_tours_is_3,-13.175,F -coef_total_number_of_tours_is_4,-999,T -coef_total_number_of_tours_is_5,-999,T -coef_total_number_of_tours_is_6_plus,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.234,F -coef_number_of_mandatory_tours_and_tour_frequency_is_2,-0.9231,F -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-6.5835,F -coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_0,0,T -coef_number_of_joint_tours_and_tour_frequency_is_1,-0.2162,F -coef_number_of_joint_tours_and_tour_frequency_is_2,-0.3587,F -coef_number_of_joint_tours_and_tour_frequency_is_3,-4.2701,F -coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_shopping_tours,0,T -coef_number_of_joint_maintenance_tours,0,T -coef_number_of_joint_eating_out_tours,0,T -coef_number_of_joint_visit_tours,0,T -coef_number_of_joint_discretionary_tours,0,T -coef_logged_maximum_residual_window_tour_frequency_is_0,0,T -coef_logged_maximum_residual_window_tour_frequency_is_1,1.3298,F -coef_logged_maximum_residual_window_tour_frequency_is_2,1.3759,F -coef_logged_maximum_residual_window_tour_frequency_is_3,3.2808,F -coef_logged_maximum_residual_window_tour_frequency_is_4,3.2808,F -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,3.2808,F -coef_mediumlow_income_group_and_tour_frequency_is_1,0,T -coef_mediumlow_income_group_and_tour_frequency_is_2,0,T -coef_mediumlow_income_group_and_tour_frequency_is_3,0,T -coef_mediumlow_income_group_and_tour_frequency_is_4,0,T -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_1,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_2,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_3,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_4,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0,T -coef_high_income_group_and_tour_frequency_is_1,0,T -coef_high_income_group_and_tour_frequency_is_2,0,T -coef_high_income_group_and_tour_frequency_is_3,0,T -coef_high_income_group_and_tour_frequency_is_4,0,T -coef_high_income_group_and_tour_frequency_is_5_plus,0,T -coef_mediumlow_income_group_and_shopping_tour,0,T -coef_mediumhigh_income_group_and_shopping_tour,0.2443,F -coef_high_income_group_and_shopping_tour,0.2443,F -coef_mediumlow_income_group_and_maintenance_tour,0,T -coef_mediumhigh_income_group_and_maintenance_tour,0.3982,F -coef_high_income_group_and_maintenance_tour,0.3982,F -coef_mediumlow_income_group_and_eating_out_tour,0,T -coef_mediumhigh_income_group_and_eating_out_tour,0.4916,F -coef_high_income_group_and_eating_out_tour,0.4916,F -coef_mediumlow_income_group_and_discretionary_tour,0.9169,F -coef_mediumhigh_income_group_and_discretionary_tour,1.405,F -coef_high_income_group_and_discretionary_tour,2.327,F -coef_mediumlow_income_group_and_visiting_tour,0,T -coef_mediumhigh_income_group_and_visiting_tour,0.2858,F -coef_high_income_group_and_visiting_tour,0.2858,F -coef_female_and_tour_frequency_is_1,0,T -coef_female_and_tour_frequency_is_2,0,T -coef_female_and_tour_frequency_is_3,0,T -coef_female_and_tour_frequency_is_4,0,T -coef_female_and_tour_frequency_is_5,0,T -coef_female_and_escorting_tour,0,T -coef_female_and_shopping_tour,0,T -coef_female_and_maintenance_tour,0,T -coef_female_and_eatingout_tour,0,T -coef_female_and_discretionary_tour,0,T -coef_zero_car_ownership_and_tour_frequency_is_1,-0.6369,F -coef_zero_car_ownership_and_tour_frequency_is_2,-0.6369,F -coef_zero_car_ownership_and_tour_frequency_is_3,-0.6369,F -coef_zero_car_ownership_and_tour_frequency_is_4,-0.6369,F -coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.6369,F -coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.6369,F -coef_car_shortage_vs_workers_and_tour_frequency_is_2,-0.6369,F -coef_car_shortage_vs_workers_and_tour_frequency_is_3,-0.6369,F -coef_car_shortage_vs_workers_and_tour_frequency_is_4,-0.6369,F -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-0.6369,F -coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.2902,F -coef_car_surplus_vs_workers_and_tour_frequency_is_2,2.0352,F -coef_car_surplus_vs_workers_and_tour_frequency_is_3,2.0352,F -coef_car_surplus_vs_workers_and_tour_frequency_is_4,2.0352,F -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,2.0352,F -coef_presence_of_non_worker_and_tour_frequency_is_1,0,T -coef_presence_of_non_worker_and_tour_frequency_is_2,-0.6571,F -coef_presence_of_non_worker_and_tour_frequency_is_3,-1.4044,F -coef_presence_of_non_worker_and_tour_frequency_is_4,-1.4044,F -coef_presence_of_non_worker_and_tour_frequency_is_5,-1.4044,F -coef_presence_of_retiree_and_tour_frequency_is_1,0,T -coef_presence_of_retiree_and_tour_frequency_is_2,0,T -coef_presence_of_retiree_and_tour_frequency_is_3,0,T -coef_presence_of_retiree_and_tour_frequency_is_4,0,T -coef_presence_of_retiree_and_tour_frequency_is_5,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,-0.3219,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,-1.0874,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,-1.0874,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,-1.0874,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,-1.0874,F -coef_presence_of_full_time_worker_and_escorting_tour,0,T -coef_presence_of_part_time_worker_and_escorting_tour,0,T -coef_presence_of_non_worker_and_escorting_tour,0,T -coef_presence_of_retiree_and_escorting_tour,0,T -coef_presence_of_university_student_and_escorting_tour,0,T -coef_presence_of_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_school_kid_and_escorting_tour,0,T -coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T -coef_at_home_pre_school_kid_and_escorting_tour,0,T -coef_presence_of_full_time_worker_and_shopping_tour,0,T -coef_presence_of_part_time_worker_and_shopping_tour,0,T -coef_presence_of_non_worker_and_shopping_tour,0,T -coef_presence_of_retiree_and_shopping_tour,0,T -coef_presence_of_university_student_and_shopping_tour,0,T -coef_presence_of_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_school_kid_and_shopping_tour,0,T -coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T -coef_at_home_pre_school_kid_and_shopping_tour,0,T -coef_presence_of_full_time_worker_and_maintenance_tour,0,T -coef_presence_of_part_time_worker_and_maintenance_tour,0,T -coef_presence_of_non_worker_and_maintenance_tour,0,T -coef_presence_of_retiree_and_maintenance_tour,0,T -coef_presence_of_university_student_and_maintenance_tour,0,T -coef_presence_of_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_school_kid_and_maintenance_tour,0,T -coef_presence_of_full_time_worker_and_eating_out_tour,0,T -coef_presence_of_part_time_worker_and_eating_out_tour,0,T -coef_presence_of_non_worker_and_eating_out_tour,0,T -coef_presence_of_retiree_and_eating_out_tour,0,T -coef_presence_of_university_student_and_eating_out_tour,0,T -coef_presence_of_driving_school_kid_and_eating_out_tour,-0.6377,F -coef_presence_of_pre_driving_school_kid_and_eating_out_tour,-1.5698,F -coef_presence_of_pre_school_kid_and_eating_out_tour,-0.2987,F -coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_school_kid_and_eating_out_tour,0,T -coef_presence_of_full_time_worker_and_discretionary_tour,0,T -coef_presence_of_part_time_worker_and_discretionary_tour,0,T -coef_presence_of_non_worker_and_discretionary_tour,0,T -coef_presence_of_retiree_and_discretionary_tour,0,T -coef_presence_of_university_student_and_discretionary_tour,-1.2834,F -coef_presence_of_driving_school_kid_and_discretionary_tour,-0.9202,F -coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_school_kid_and_discretionary_tour,0,T -coef_walk_access_to_retail_and_tour_frequency_is_1,0,T -coef_walk_access_to_retail_and_tour_frequency_is_2,0,T -coef_walk_access_to_retail_and_tour_frequency_is_3,0,T -coef_walk_access_to_retail_and_tour_frequency_is_4,0,T -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_transit_access_to_retail_and_tour_frequency_is_1,0,T -coef_transit_access_to_retail_and_tour_frequency_is_2,0,T -coef_transit_access_to_retail_and_tour_frequency_is_3,0,T -coef_transit_access_to_retail_and_tour_frequency_is_4,0,T -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_auto_access_to_retail_and_tour_frequency_is_1,0.1004,F -coef_auto_access_to_retail_and_tour_frequency_is_2,0.1004,F -coef_auto_access_to_retail_and_tour_frequency_is_3,0.1004,F -coef_auto_access_to_retail_and_tour_frequency_is_4,0.1004,F -coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0.1004,F -coef_walk_access_to_retail_and_escorting,0,T -coef_transit_access_to_retail_and_escorting,0,T -coef_auto_access_to_retail_and_escorting,0,T -coef_walk_access_to_retail_and_shopping,0,T -coef_transit_access_to_retail_and_shopping,0,T -coef_auto_access_to_retail_and_shopping,0,T -coef_walk_access_to_retail_and_maintenance,0,T -coef_transit_access_to_retail_and_maintenance,0,T -coef_auto_access_to_retail_and_maintenance,0,T -coef_walk_access_to_retail_and_eating_out,0,T -coef_transit_access_to_retail_and_eating_out,0,T -coef_auto_access_to_retail_and_eating_out,0,T -coef_walk_access_to_retail_and_discretionary,0,T -coef_transit_access_to_retail_and_discretionary,0,T -coef_auto_access_to_retail_and_discretionary,0,T -coef_urban_and_tour_frequency_is_1,0,T -coef_urban_and_tour_frequency_is_2,0,T -coef_urban_and_tour_frequency_is_3,0,T -coef_urban_and_tour_frequency_is_4,0,T -coef_urban_and_tour_frequency_is_5_plus,0,T -coef_urban_and_escorting_tour,0,T -coef_urban_and_shopping_tour,0,T -coef_urban_and_maintenance_tour,1.0394,F -coef_urban_and_eatingout_tour,0,T -coef_urban_and_discretionary_tour,0,T -coef_1_escort_tour_constant,-0.4934,F -coef_2_plus_escort_tours_constant,1.4155,F -coef_1_plus_shopping_tours_constant,0.532,F -coef_1_plus_maintenance_tours_constant,-0.4344,F -coef_1_plus_eating_out_tours_constant,-0.0242,F -coef_1_plus_visting_tours_constant,0.2367,F -coef_1_plus_other_discretionary_tours_constant,-0.2602,F +coefficient_name,value,constrain +coef_escorting_tour,0,T +coef_discretionary_tour,0,T +coef_shopping_tour,0,T +coef_maintenance_tour,0,T +coef_visiting_or_social_tour,0,T +coef_eating_out_tour,0,T +coef_total_number_of_tours_is_0_no_prior_tours,-999,T +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,-7.1506,F +coef_total_number_of_tours_is_2,-11.1214,F +coef_total_number_of_tours_is_3,-13.175,F +coef_total_number_of_tours_is_4,-999,T +coef_total_number_of_tours_is_5,-999,T +coef_total_number_of_tours_is_6_plus,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.234,F +coef_number_of_mandatory_tours_and_tour_frequency_is_2,-0.9231,F +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-6.5835,F +coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_0,0,T +coef_number_of_joint_tours_and_tour_frequency_is_1,-0.2162,F +coef_number_of_joint_tours_and_tour_frequency_is_2,-0.3587,F +coef_number_of_joint_tours_and_tour_frequency_is_3,-4.2701,F +coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_shopping_tours,0,T +coef_number_of_joint_maintenance_tours,0,T +coef_number_of_joint_eating_out_tours,0,T +coef_number_of_joint_visit_tours,0,T +coef_number_of_joint_discretionary_tours,0,T +coef_logged_maximum_residual_window_tour_frequency_is_0,0,T +coef_logged_maximum_residual_window_tour_frequency_is_1,1.3298,F +coef_logged_maximum_residual_window_tour_frequency_is_2,1.3759,F +coef_logged_maximum_residual_window_tour_frequency_is_3,3.2808,F +coef_logged_maximum_residual_window_tour_frequency_is_4,3.2808,F +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,3.2808,F +coef_mediumlow_income_group_and_tour_frequency_is_1,0,T +coef_mediumlow_income_group_and_tour_frequency_is_2,0,T +coef_mediumlow_income_group_and_tour_frequency_is_3,0,T +coef_mediumlow_income_group_and_tour_frequency_is_4,0,T +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_1,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_2,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_3,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_4,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0,T +coef_high_income_group_and_tour_frequency_is_1,0,T +coef_high_income_group_and_tour_frequency_is_2,0,T +coef_high_income_group_and_tour_frequency_is_3,0,T +coef_high_income_group_and_tour_frequency_is_4,0,T +coef_high_income_group_and_tour_frequency_is_5_plus,0,T +coef_mediumlow_income_group_and_shopping_tour,0,T +coef_mediumhigh_income_group_and_shopping_tour,0.2443,F +coef_high_income_group_and_shopping_tour,0.2443,F +coef_mediumlow_income_group_and_maintenance_tour,0,T +coef_mediumhigh_income_group_and_maintenance_tour,0.3982,F +coef_high_income_group_and_maintenance_tour,0.3982,F +coef_mediumlow_income_group_and_eating_out_tour,0,T +coef_mediumhigh_income_group_and_eating_out_tour,0.4916,F +coef_high_income_group_and_eating_out_tour,0.4916,F +coef_mediumlow_income_group_and_discretionary_tour,0.9169,F +coef_mediumhigh_income_group_and_discretionary_tour,1.405,F +coef_high_income_group_and_discretionary_tour,2.327,F +coef_mediumlow_income_group_and_visiting_tour,0,T +coef_mediumhigh_income_group_and_visiting_tour,0.2858,F +coef_high_income_group_and_visiting_tour,0.2858,F +coef_female_and_tour_frequency_is_1,0,T +coef_female_and_tour_frequency_is_2,0,T +coef_female_and_tour_frequency_is_3,0,T +coef_female_and_tour_frequency_is_4,0,T +coef_female_and_tour_frequency_is_5,0,T +coef_female_and_escorting_tour,0,T +coef_female_and_shopping_tour,0,T +coef_female_and_maintenance_tour,0,T +coef_female_and_eatingout_tour,0,T +coef_female_and_discretionary_tour,0,T +coef_zero_car_ownership_and_tour_frequency_is_1,-0.6369,F +coef_zero_car_ownership_and_tour_frequency_is_2,-0.6369,F +coef_zero_car_ownership_and_tour_frequency_is_3,-0.6369,F +coef_zero_car_ownership_and_tour_frequency_is_4,-0.6369,F +coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.6369,F +coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.6369,F +coef_car_shortage_vs_workers_and_tour_frequency_is_2,-0.6369,F +coef_car_shortage_vs_workers_and_tour_frequency_is_3,-0.6369,F +coef_car_shortage_vs_workers_and_tour_frequency_is_4,-0.6369,F +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-0.6369,F +coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.2902,F +coef_car_surplus_vs_workers_and_tour_frequency_is_2,2.0352,F +coef_car_surplus_vs_workers_and_tour_frequency_is_3,2.0352,F +coef_car_surplus_vs_workers_and_tour_frequency_is_4,2.0352,F +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,2.0352,F +coef_presence_of_non_worker_and_tour_frequency_is_1,0,T +coef_presence_of_non_worker_and_tour_frequency_is_2,-0.6571,F +coef_presence_of_non_worker_and_tour_frequency_is_3,-1.4044,F +coef_presence_of_non_worker_and_tour_frequency_is_4,-1.4044,F +coef_presence_of_non_worker_and_tour_frequency_is_5,-1.4044,F +coef_presence_of_retiree_and_tour_frequency_is_1,0,T +coef_presence_of_retiree_and_tour_frequency_is_2,0,T +coef_presence_of_retiree_and_tour_frequency_is_3,0,T +coef_presence_of_retiree_and_tour_frequency_is_4,0,T +coef_presence_of_retiree_and_tour_frequency_is_5,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,-0.3219,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,-1.0874,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,-1.0874,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,-1.0874,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,-1.0874,F +coef_presence_of_full_time_worker_and_escorting_tour,0,T +coef_presence_of_part_time_worker_and_escorting_tour,0,T +coef_presence_of_non_worker_and_escorting_tour,0,T +coef_presence_of_retiree_and_escorting_tour,0,T +coef_presence_of_university_student_and_escorting_tour,0,T +coef_presence_of_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_school_kid_and_escorting_tour,0,T +coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T +coef_at_home_pre_school_kid_and_escorting_tour,0,T +coef_presence_of_full_time_worker_and_shopping_tour,0,T +coef_presence_of_part_time_worker_and_shopping_tour,0,T +coef_presence_of_non_worker_and_shopping_tour,0,T +coef_presence_of_retiree_and_shopping_tour,0,T +coef_presence_of_university_student_and_shopping_tour,0,T +coef_presence_of_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_school_kid_and_shopping_tour,0,T +coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T +coef_at_home_pre_school_kid_and_shopping_tour,0,T +coef_presence_of_full_time_worker_and_maintenance_tour,0,T +coef_presence_of_part_time_worker_and_maintenance_tour,0,T +coef_presence_of_non_worker_and_maintenance_tour,0,T +coef_presence_of_retiree_and_maintenance_tour,0,T +coef_presence_of_university_student_and_maintenance_tour,0,T +coef_presence_of_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_school_kid_and_maintenance_tour,0,T +coef_presence_of_full_time_worker_and_eating_out_tour,0,T +coef_presence_of_part_time_worker_and_eating_out_tour,0,T +coef_presence_of_non_worker_and_eating_out_tour,0,T +coef_presence_of_retiree_and_eating_out_tour,0,T +coef_presence_of_university_student_and_eating_out_tour,0,T +coef_presence_of_driving_school_kid_and_eating_out_tour,-0.6377,F +coef_presence_of_pre_driving_school_kid_and_eating_out_tour,-1.5698,F +coef_presence_of_pre_school_kid_and_eating_out_tour,-0.2987,F +coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_school_kid_and_eating_out_tour,0,T +coef_presence_of_full_time_worker_and_discretionary_tour,0,T +coef_presence_of_part_time_worker_and_discretionary_tour,0,T +coef_presence_of_non_worker_and_discretionary_tour,0,T +coef_presence_of_retiree_and_discretionary_tour,0,T +coef_presence_of_university_student_and_discretionary_tour,-1.2834,F +coef_presence_of_driving_school_kid_and_discretionary_tour,-0.9202,F +coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_school_kid_and_discretionary_tour,0,T +coef_walk_access_to_retail_and_tour_frequency_is_1,0,T +coef_walk_access_to_retail_and_tour_frequency_is_2,0,T +coef_walk_access_to_retail_and_tour_frequency_is_3,0,T +coef_walk_access_to_retail_and_tour_frequency_is_4,0,T +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_transit_access_to_retail_and_tour_frequency_is_1,0,T +coef_transit_access_to_retail_and_tour_frequency_is_2,0,T +coef_transit_access_to_retail_and_tour_frequency_is_3,0,T +coef_transit_access_to_retail_and_tour_frequency_is_4,0,T +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_auto_access_to_retail_and_tour_frequency_is_1,0.1004,F +coef_auto_access_to_retail_and_tour_frequency_is_2,0.1004,F +coef_auto_access_to_retail_and_tour_frequency_is_3,0.1004,F +coef_auto_access_to_retail_and_tour_frequency_is_4,0.1004,F +coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0.1004,F +coef_walk_access_to_retail_and_escorting,0,T +coef_transit_access_to_retail_and_escorting,0,T +coef_auto_access_to_retail_and_escorting,0,T +coef_walk_access_to_retail_and_shopping,0,T +coef_transit_access_to_retail_and_shopping,0,T +coef_auto_access_to_retail_and_shopping,0,T +coef_walk_access_to_retail_and_maintenance,0,T +coef_transit_access_to_retail_and_maintenance,0,T +coef_auto_access_to_retail_and_maintenance,0,T +coef_walk_access_to_retail_and_eating_out,0,T +coef_transit_access_to_retail_and_eating_out,0,T +coef_auto_access_to_retail_and_eating_out,0,T +coef_walk_access_to_retail_and_discretionary,0,T +coef_transit_access_to_retail_and_discretionary,0,T +coef_auto_access_to_retail_and_discretionary,0,T +coef_urban_and_tour_frequency_is_1,0,T +coef_urban_and_tour_frequency_is_2,0,T +coef_urban_and_tour_frequency_is_3,0,T +coef_urban_and_tour_frequency_is_4,0,T +coef_urban_and_tour_frequency_is_5_plus,0,T +coef_urban_and_escorting_tour,0,T +coef_urban_and_shopping_tour,0,T +coef_urban_and_maintenance_tour,1.0394,F +coef_urban_and_eatingout_tour,0,T +coef_urban_and_discretionary_tour,0,T +coef_1_escort_tour_constant,-0.4934,F +coef_2_plus_escort_tours_constant,1.4155,F +coef_1_plus_shopping_tours_constant,0.532,F +coef_1_plus_maintenance_tours_constant,-0.4344,F +coef_1_plus_eating_out_tours_constant,-0.0242,F +coef_1_plus_visting_tours_constant,0.2367,F +coef_1_plus_other_discretionary_tours_constant,-0.2602,F coef_0_auto_household_and_escorting_tour,-2, \ No newline at end of file diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv similarity index 98% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv index 6b9d8d7622..1c1d5221bd 100755 --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_FULL.csv @@ -1,211 +1,211 @@ -coefficient_name,value,constrain -coef_escorting_tour,0,T -coef_discretionary_tour,0,T -coef_shopping_tour,0,T -coef_maintenance_tour,0,T -coef_visiting_or_social_tour,0,T -coef_eating_out_tour,0,T -coef_total_number_of_tours_is_0_no_prior_tours,-999,T -coef_total_number_of_tours_is_0_prior_tours,0,T -coef_total_number_of_tours_is_1,-7.3572,F -coef_total_number_of_tours_is_2,-10.647,F -coef_total_number_of_tours_is_3,-13.5005,F -coef_total_number_of_tours_is_4,-16.3965,F -coef_total_number_of_tours_is_5,-19.6843,F -coef_total_number_of_tours_is_6_plus,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_1,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_2,-0.8887,F -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-2.3343,F -coef_number_of_mandatory_tours_and_tour_frequency_is_4,-2.3343,F -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-2.3343,F -coef_number_of_joint_tours_and_tour_frequency_is_0,0,T -coef_number_of_joint_tours_and_tour_frequency_is_1,0,T -coef_number_of_joint_tours_and_tour_frequency_is_2,0,T -coef_number_of_joint_tours_and_tour_frequency_is_3,0,T -coef_number_of_joint_tours_and_tour_frequency_is_4,0,T -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,0,T -coef_number_of_joint_shopping_tours,0,T -coef_number_of_joint_maintenance_tours,0,T -coef_number_of_joint_eating_out_tours,-0.5866,F -coef_number_of_joint_visit_tours,0,T -coef_number_of_joint_discretionary_tours,0,T -coef_logged_maximum_residual_window_tour_frequency_is_0,0,T -coef_logged_maximum_residual_window_tour_frequency_is_1,1.2562,F -coef_logged_maximum_residual_window_tour_frequency_is_2,1.2868,F -coef_logged_maximum_residual_window_tour_frequency_is_3,1.3993,F -coef_logged_maximum_residual_window_tour_frequency_is_4,1.3993,F -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.3993,F -coef_mediumlow_income_group_and_tour_frequency_is_1,0.4981,F -coef_mediumlow_income_group_and_tour_frequency_is_2,0.8345,F -coef_mediumlow_income_group_and_tour_frequency_is_3,1.0213,F -coef_mediumlow_income_group_and_tour_frequency_is_4,1.0213,F -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,1.0213,F -coef_mediumhigh_income_group_and_tour_frequency_is_1,0.4981,F -coef_mediumhigh_income_group_and_tour_frequency_is_2,0.8345,F -coef_mediumhigh_income_group_and_tour_frequency_is_3,1.0213,F -coef_mediumhigh_income_group_and_tour_frequency_is_4,1.0213,F -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.0213,F -coef_high_income_group_and_tour_frequency_is_1,0.5189,F -coef_high_income_group_and_tour_frequency_is_2,1.1336,F -coef_high_income_group_and_tour_frequency_is_3,1.3899,F -coef_high_income_group_and_tour_frequency_is_4,1.3899,F -coef_high_income_group_and_tour_frequency_is_5_plus,1.3899,F -coef_mediumlow_income_group_and_shopping_tour,0,T -coef_mediumhigh_income_group_and_shopping_tour,0,T -coef_high_income_group_and_shopping_tour,0,T -coef_mediumlow_income_group_and_maintenance_tour,0,T -coef_mediumhigh_income_group_and_maintenance_tour,0,T -coef_high_income_group_and_maintenance_tour,0,T -coef_mediumlow_income_group_and_eating_out_tour,0,T -coef_mediumhigh_income_group_and_eating_out_tour,0.5581,F -coef_high_income_group_and_eating_out_tour,0.5581,F -coef_mediumlow_income_group_and_discretionary_tour,0,T -coef_mediumhigh_income_group_and_discretionary_tour,0.2565,F -coef_high_income_group_and_discretionary_tour,0.2565,F -coef_mediumlow_income_group_and_visiting_tour,0,T -coef_mediumhigh_income_group_and_visiting_tour,-0.2423,F -coef_high_income_group_and_visiting_tour,-0.2423,F -coef_female_and_tour_frequency_is_1,-0.0766,F -coef_female_and_tour_frequency_is_2,-0.1062,F -coef_female_and_tour_frequency_is_3,-0.3274,F -coef_female_and_tour_frequency_is_4,-0.3274,F -coef_female_and_tour_frequency_is_5,-0.3274,F -coef_female_and_escorting_tour,0.1824,F -coef_female_and_shopping_tour,0,T -coef_female_and_maintenance_tour,0,T -coef_female_and_eatingout_tour,0,T -coef_female_and_discretionary_tour,0,T -coef_zero_car_ownership_and_tour_frequency_is_1,-0.3486,F -coef_zero_car_ownership_and_tour_frequency_is_2,-0.3486,F -coef_zero_car_ownership_and_tour_frequency_is_3,-0.3486,F -coef_zero_car_ownership_and_tour_frequency_is_4,-0.3486,F -coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.3486,F -coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.1304,F -coef_car_surplus_vs_workers_and_tour_frequency_is_2,0.1304,F -coef_car_surplus_vs_workers_and_tour_frequency_is_3,0.1304,F -coef_car_surplus_vs_workers_and_tour_frequency_is_4,0.1304,F -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0.1304,F -coef_presence_of_non_worker_and_tour_frequency_is_1,0,T -coef_presence_of_non_worker_and_tour_frequency_is_2,0,T -coef_presence_of_non_worker_and_tour_frequency_is_3,0,T -coef_presence_of_non_worker_and_tour_frequency_is_4,0,T -coef_presence_of_non_worker_and_tour_frequency_is_5,0,T -coef_presence_of_retiree_and_tour_frequency_is_1,0,T -coef_presence_of_retiree_and_tour_frequency_is_2,0,T -coef_presence_of_retiree_and_tour_frequency_is_3,0,T -coef_presence_of_retiree_and_tour_frequency_is_4,0,T -coef_presence_of_retiree_and_tour_frequency_is_5,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_full_time_worker_and_escorting_tour,0,T -coef_presence_of_part_time_worker_and_escorting_tour,0,T -coef_presence_of_non_worker_and_escorting_tour,-0.4815,F -coef_presence_of_retiree_and_escorting_tour,-0.808,F -coef_presence_of_university_student_and_escorting_tour,0,T -coef_presence_of_driving_school_kid_and_escorting_tour,0.3601,F -coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.3974,F -coef_presence_of_pre_school_kid_and_escorting_tour,0.6842,F -coef_at_home_pre_driving_school_kid_and_escorting_tour,-0.2746,F -coef_at_home_pre_school_kid_and_escorting_tour,-1.5675,F -coef_presence_of_full_time_worker_and_shopping_tour,-0.3059,F -coef_presence_of_part_time_worker_and_shopping_tour,-0.1541,F -coef_presence_of_non_worker_and_shopping_tour,-0.416,F -coef_presence_of_retiree_and_shopping_tour,0,T -coef_presence_of_university_student_and_shopping_tour,0,T -coef_presence_of_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_school_kid_and_shopping_tour,-0.208,F -coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T -coef_at_home_pre_school_kid_and_shopping_tour,0,T -coef_presence_of_full_time_worker_and_maintenance_tour,-0.1685,F -coef_presence_of_part_time_worker_and_maintenance_tour,-0.1584,F -coef_presence_of_non_worker_and_maintenance_tour,-0.3237,F -coef_presence_of_retiree_and_maintenance_tour,0,T -coef_presence_of_university_student_and_maintenance_tour,0,T -coef_presence_of_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_school_kid_and_maintenance_tour,0,T -coef_presence_of_full_time_worker_and_eating_out_tour,-0.3571,F -coef_presence_of_part_time_worker_and_eating_out_tour,0,T -coef_presence_of_non_worker_and_eating_out_tour,-0.2014,F -coef_presence_of_retiree_and_eating_out_tour,-0.5708,F -coef_presence_of_university_student_and_eating_out_tour,0,T -coef_presence_of_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_school_kid_and_eating_out_tour,-0.4225,F -coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_school_kid_and_eating_out_tour,0,T -coef_presence_of_full_time_worker_and_discretionary_tour,-0.667,F -coef_presence_of_part_time_worker_and_discretionary_tour,-0.2102,F -coef_presence_of_non_worker_and_discretionary_tour,-0.4281,F -coef_presence_of_retiree_and_discretionary_tour,-0.9104,F -coef_presence_of_university_student_and_discretionary_tour,-0.8551,F -coef_presence_of_driving_school_kid_and_discretionary_tour,-0.3963,F -coef_presence_of_pre_driving_school_kid_and_discretionary_tour,-0.3959,F -coef_presence_of_pre_school_kid_and_discretionary_tour,-0.5081,F -coef_at_home_pre_driving_school_kid_and_discretionary_tour,-0.4703,F -coef_at_home_pre_school_kid_and_discretionary_tour,-0.4703,F -coef_walk_access_to_retail_and_tour_frequency_is_1,0,T -coef_walk_access_to_retail_and_tour_frequency_is_2,0,T -coef_walk_access_to_retail_and_tour_frequency_is_3,0,T -coef_walk_access_to_retail_and_tour_frequency_is_4,0,T -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_transit_access_to_retail_and_tour_frequency_is_1,0.0226,F -coef_transit_access_to_retail_and_tour_frequency_is_2,0.0226,F -coef_transit_access_to_retail_and_tour_frequency_is_3,0.0226,F -coef_transit_access_to_retail_and_tour_frequency_is_4,0.0226,F -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0.0226,F -coef_auto_access_to_retail_and_tour_frequency_is_1,0,T -coef_auto_access_to_retail_and_tour_frequency_is_2,0,T -coef_auto_access_to_retail_and_tour_frequency_is_3,0,T -coef_auto_access_to_retail_and_tour_frequency_is_4,0,T -coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_walk_access_to_retail_and_escorting,0.0451,F -coef_transit_access_to_retail_and_escorting,0,T -coef_auto_access_to_retail_and_escorting,0,T -coef_walk_access_to_retail_and_shopping,0.033,F -coef_transit_access_to_retail_and_shopping,0,T -coef_auto_access_to_retail_and_shopping,0.1067,F -coef_walk_access_to_retail_and_maintenance,0,T -coef_transit_access_to_retail_and_maintenance,0,T -coef_auto_access_to_retail_and_maintenance,0.0749,F -coef_walk_access_to_retail_and_eating_out,0.145,F -coef_transit_access_to_retail_and_eating_out,0,T -coef_auto_access_to_retail_and_eating_out,0,T -coef_walk_access_to_retail_and_discretionary,0.0567,F -coef_transit_access_to_retail_and_discretionary,0,T -coef_auto_access_to_retail_and_discretionary,0.0844,F -coef_urban_and_tour_frequency_is_1,0,T -coef_urban_and_tour_frequency_is_2,0,T -coef_urban_and_tour_frequency_is_3,0,T -coef_urban_and_tour_frequency_is_4,0,T -coef_urban_and_tour_frequency_is_5_plus,0,T -coef_urban_and_escorting_tour,-0.4316,F -coef_urban_and_shopping_tour,0,T -coef_urban_and_maintenance_tour,0,T -coef_urban_and_eatingout_tour,0,T -coef_urban_and_discretionary_tour,0,T -coef_1_escort_tour_constant,0.0298,F -coef_2_plus_escort_tours_constant,0.7402,F -coef_1_plus_shopping_tours_constant,0.4774,F -coef_1_plus_maintenance_tours_constant,0.1202,F -coef_1_plus_eating_out_tours_constant,0.0097,F -coef_1_plus_visting_tours_constant,0.0522,F -coef_1_plus_other_discretionary_tours_constant,0.7412,F +coefficient_name,value,constrain +coef_escorting_tour,0,T +coef_discretionary_tour,0,T +coef_shopping_tour,0,T +coef_maintenance_tour,0,T +coef_visiting_or_social_tour,0,T +coef_eating_out_tour,0,T +coef_total_number_of_tours_is_0_no_prior_tours,-999,T +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,-7.3572,F +coef_total_number_of_tours_is_2,-10.647,F +coef_total_number_of_tours_is_3,-13.5005,F +coef_total_number_of_tours_is_4,-16.3965,F +coef_total_number_of_tours_is_5,-19.6843,F +coef_total_number_of_tours_is_6_plus,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_1,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_2,-0.8887,F +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-2.3343,F +coef_number_of_mandatory_tours_and_tour_frequency_is_4,-2.3343,F +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-2.3343,F +coef_number_of_joint_tours_and_tour_frequency_is_0,0,T +coef_number_of_joint_tours_and_tour_frequency_is_1,0,T +coef_number_of_joint_tours_and_tour_frequency_is_2,0,T +coef_number_of_joint_tours_and_tour_frequency_is_3,0,T +coef_number_of_joint_tours_and_tour_frequency_is_4,0,T +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,0,T +coef_number_of_joint_shopping_tours,0,T +coef_number_of_joint_maintenance_tours,0,T +coef_number_of_joint_eating_out_tours,-0.5866,F +coef_number_of_joint_visit_tours,0,T +coef_number_of_joint_discretionary_tours,0,T +coef_logged_maximum_residual_window_tour_frequency_is_0,0,T +coef_logged_maximum_residual_window_tour_frequency_is_1,1.2562,F +coef_logged_maximum_residual_window_tour_frequency_is_2,1.2868,F +coef_logged_maximum_residual_window_tour_frequency_is_3,1.3993,F +coef_logged_maximum_residual_window_tour_frequency_is_4,1.3993,F +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.3993,F +coef_mediumlow_income_group_and_tour_frequency_is_1,0.4981,F +coef_mediumlow_income_group_and_tour_frequency_is_2,0.8345,F +coef_mediumlow_income_group_and_tour_frequency_is_3,1.0213,F +coef_mediumlow_income_group_and_tour_frequency_is_4,1.0213,F +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,1.0213,F +coef_mediumhigh_income_group_and_tour_frequency_is_1,0.4981,F +coef_mediumhigh_income_group_and_tour_frequency_is_2,0.8345,F +coef_mediumhigh_income_group_and_tour_frequency_is_3,1.0213,F +coef_mediumhigh_income_group_and_tour_frequency_is_4,1.0213,F +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.0213,F +coef_high_income_group_and_tour_frequency_is_1,0.5189,F +coef_high_income_group_and_tour_frequency_is_2,1.1336,F +coef_high_income_group_and_tour_frequency_is_3,1.3899,F +coef_high_income_group_and_tour_frequency_is_4,1.3899,F +coef_high_income_group_and_tour_frequency_is_5_plus,1.3899,F +coef_mediumlow_income_group_and_shopping_tour,0,T +coef_mediumhigh_income_group_and_shopping_tour,0,T +coef_high_income_group_and_shopping_tour,0,T +coef_mediumlow_income_group_and_maintenance_tour,0,T +coef_mediumhigh_income_group_and_maintenance_tour,0,T +coef_high_income_group_and_maintenance_tour,0,T +coef_mediumlow_income_group_and_eating_out_tour,0,T +coef_mediumhigh_income_group_and_eating_out_tour,0.5581,F +coef_high_income_group_and_eating_out_tour,0.5581,F +coef_mediumlow_income_group_and_discretionary_tour,0,T +coef_mediumhigh_income_group_and_discretionary_tour,0.2565,F +coef_high_income_group_and_discretionary_tour,0.2565,F +coef_mediumlow_income_group_and_visiting_tour,0,T +coef_mediumhigh_income_group_and_visiting_tour,-0.2423,F +coef_high_income_group_and_visiting_tour,-0.2423,F +coef_female_and_tour_frequency_is_1,-0.0766,F +coef_female_and_tour_frequency_is_2,-0.1062,F +coef_female_and_tour_frequency_is_3,-0.3274,F +coef_female_and_tour_frequency_is_4,-0.3274,F +coef_female_and_tour_frequency_is_5,-0.3274,F +coef_female_and_escorting_tour,0.1824,F +coef_female_and_shopping_tour,0,T +coef_female_and_maintenance_tour,0,T +coef_female_and_eatingout_tour,0,T +coef_female_and_discretionary_tour,0,T +coef_zero_car_ownership_and_tour_frequency_is_1,-0.3486,F +coef_zero_car_ownership_and_tour_frequency_is_2,-0.3486,F +coef_zero_car_ownership_and_tour_frequency_is_3,-0.3486,F +coef_zero_car_ownership_and_tour_frequency_is_4,-0.3486,F +coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.3486,F +coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.1304,F +coef_car_surplus_vs_workers_and_tour_frequency_is_2,0.1304,F +coef_car_surplus_vs_workers_and_tour_frequency_is_3,0.1304,F +coef_car_surplus_vs_workers_and_tour_frequency_is_4,0.1304,F +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0.1304,F +coef_presence_of_non_worker_and_tour_frequency_is_1,0,T +coef_presence_of_non_worker_and_tour_frequency_is_2,0,T +coef_presence_of_non_worker_and_tour_frequency_is_3,0,T +coef_presence_of_non_worker_and_tour_frequency_is_4,0,T +coef_presence_of_non_worker_and_tour_frequency_is_5,0,T +coef_presence_of_retiree_and_tour_frequency_is_1,0,T +coef_presence_of_retiree_and_tour_frequency_is_2,0,T +coef_presence_of_retiree_and_tour_frequency_is_3,0,T +coef_presence_of_retiree_and_tour_frequency_is_4,0,T +coef_presence_of_retiree_and_tour_frequency_is_5,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_full_time_worker_and_escorting_tour,0,T +coef_presence_of_part_time_worker_and_escorting_tour,0,T +coef_presence_of_non_worker_and_escorting_tour,-0.4815,F +coef_presence_of_retiree_and_escorting_tour,-0.808,F +coef_presence_of_university_student_and_escorting_tour,0,T +coef_presence_of_driving_school_kid_and_escorting_tour,0.3601,F +coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.3974,F +coef_presence_of_pre_school_kid_and_escorting_tour,0.6842,F +coef_at_home_pre_driving_school_kid_and_escorting_tour,-0.2746,F +coef_at_home_pre_school_kid_and_escorting_tour,-1.5675,F +coef_presence_of_full_time_worker_and_shopping_tour,-0.3059,F +coef_presence_of_part_time_worker_and_shopping_tour,-0.1541,F +coef_presence_of_non_worker_and_shopping_tour,-0.416,F +coef_presence_of_retiree_and_shopping_tour,0,T +coef_presence_of_university_student_and_shopping_tour,0,T +coef_presence_of_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_school_kid_and_shopping_tour,-0.208,F +coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T +coef_at_home_pre_school_kid_and_shopping_tour,0,T +coef_presence_of_full_time_worker_and_maintenance_tour,-0.1685,F +coef_presence_of_part_time_worker_and_maintenance_tour,-0.1584,F +coef_presence_of_non_worker_and_maintenance_tour,-0.3237,F +coef_presence_of_retiree_and_maintenance_tour,0,T +coef_presence_of_university_student_and_maintenance_tour,0,T +coef_presence_of_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_school_kid_and_maintenance_tour,0,T +coef_presence_of_full_time_worker_and_eating_out_tour,-0.3571,F +coef_presence_of_part_time_worker_and_eating_out_tour,0,T +coef_presence_of_non_worker_and_eating_out_tour,-0.2014,F +coef_presence_of_retiree_and_eating_out_tour,-0.5708,F +coef_presence_of_university_student_and_eating_out_tour,0,T +coef_presence_of_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_school_kid_and_eating_out_tour,-0.4225,F +coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_school_kid_and_eating_out_tour,0,T +coef_presence_of_full_time_worker_and_discretionary_tour,-0.667,F +coef_presence_of_part_time_worker_and_discretionary_tour,-0.2102,F +coef_presence_of_non_worker_and_discretionary_tour,-0.4281,F +coef_presence_of_retiree_and_discretionary_tour,-0.9104,F +coef_presence_of_university_student_and_discretionary_tour,-0.8551,F +coef_presence_of_driving_school_kid_and_discretionary_tour,-0.3963,F +coef_presence_of_pre_driving_school_kid_and_discretionary_tour,-0.3959,F +coef_presence_of_pre_school_kid_and_discretionary_tour,-0.5081,F +coef_at_home_pre_driving_school_kid_and_discretionary_tour,-0.4703,F +coef_at_home_pre_school_kid_and_discretionary_tour,-0.4703,F +coef_walk_access_to_retail_and_tour_frequency_is_1,0,T +coef_walk_access_to_retail_and_tour_frequency_is_2,0,T +coef_walk_access_to_retail_and_tour_frequency_is_3,0,T +coef_walk_access_to_retail_and_tour_frequency_is_4,0,T +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_transit_access_to_retail_and_tour_frequency_is_1,0.0226,F +coef_transit_access_to_retail_and_tour_frequency_is_2,0.0226,F +coef_transit_access_to_retail_and_tour_frequency_is_3,0.0226,F +coef_transit_access_to_retail_and_tour_frequency_is_4,0.0226,F +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0.0226,F +coef_auto_access_to_retail_and_tour_frequency_is_1,0,T +coef_auto_access_to_retail_and_tour_frequency_is_2,0,T +coef_auto_access_to_retail_and_tour_frequency_is_3,0,T +coef_auto_access_to_retail_and_tour_frequency_is_4,0,T +coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_walk_access_to_retail_and_escorting,0.0451,F +coef_transit_access_to_retail_and_escorting,0,T +coef_auto_access_to_retail_and_escorting,0,T +coef_walk_access_to_retail_and_shopping,0.033,F +coef_transit_access_to_retail_and_shopping,0,T +coef_auto_access_to_retail_and_shopping,0.1067,F +coef_walk_access_to_retail_and_maintenance,0,T +coef_transit_access_to_retail_and_maintenance,0,T +coef_auto_access_to_retail_and_maintenance,0.0749,F +coef_walk_access_to_retail_and_eating_out,0.145,F +coef_transit_access_to_retail_and_eating_out,0,T +coef_auto_access_to_retail_and_eating_out,0,T +coef_walk_access_to_retail_and_discretionary,0.0567,F +coef_transit_access_to_retail_and_discretionary,0,T +coef_auto_access_to_retail_and_discretionary,0.0844,F +coef_urban_and_tour_frequency_is_1,0,T +coef_urban_and_tour_frequency_is_2,0,T +coef_urban_and_tour_frequency_is_3,0,T +coef_urban_and_tour_frequency_is_4,0,T +coef_urban_and_tour_frequency_is_5_plus,0,T +coef_urban_and_escorting_tour,-0.4316,F +coef_urban_and_shopping_tour,0,T +coef_urban_and_maintenance_tour,0,T +coef_urban_and_eatingout_tour,0,T +coef_urban_and_discretionary_tour,0,T +coef_1_escort_tour_constant,0.0298,F +coef_2_plus_escort_tours_constant,0.7402,F +coef_1_plus_shopping_tours_constant,0.4774,F +coef_1_plus_maintenance_tours_constant,0.1202,F +coef_1_plus_eating_out_tours_constant,0.0097,F +coef_1_plus_visting_tours_constant,0.0522,F +coef_1_plus_other_discretionary_tours_constant,0.7412,F coef_0_auto_household_and_escorting_tour,-2,T \ No newline at end of file diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv similarity index 98% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv index 86b81420d1..3fe9f45ae5 100755 --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_NONWORK.csv @@ -1,211 +1,211 @@ -coefficient_name,value,constrain -coef_escorting_tour,0,T -coef_discretionary_tour,0,T -coef_shopping_tour,0,T -coef_maintenance_tour,0,T -coef_visiting_or_social_tour,0,T -coef_eating_out_tour,0,T -coef_total_number_of_tours_is_0_no_prior_tours,-999,T -coef_total_number_of_tours_is_0_prior_tours,0,T -coef_total_number_of_tours_is_1,-8.9791,F -coef_total_number_of_tours_is_2,-12.0248,F -coef_total_number_of_tours_is_3,-14.8516,F -coef_total_number_of_tours_is_4,-17.7037,F -coef_total_number_of_tours_is_5,-999,T -coef_total_number_of_tours_is_6_plus,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.6766,F -coef_number_of_mandatory_tours_and_tour_frequency_is_2,-1.0518,F -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-1.0518,F -coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_0,0,T -coef_number_of_joint_tours_and_tour_frequency_is_1,-0.1699,F -coef_number_of_joint_tours_and_tour_frequency_is_2,-0.4285,F -coef_number_of_joint_tours_and_tour_frequency_is_3,-0.6551,F -coef_number_of_joint_tours_and_tour_frequency_is_4,-1.0411,F -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-1.0411,F -coef_number_of_joint_shopping_tours,-0.2391,F -coef_number_of_joint_maintenance_tours,0,T -coef_number_of_joint_eating_out_tours,-0.7727,F -coef_number_of_joint_visit_tours,0,T -coef_number_of_joint_discretionary_tours,0,T -coef_logged_maximum_residual_window_tour_frequency_is_0,0,T -coef_logged_maximum_residual_window_tour_frequency_is_1,1.7637,F -coef_logged_maximum_residual_window_tour_frequency_is_2,1.7928,F -coef_logged_maximum_residual_window_tour_frequency_is_3,1.7928,F -coef_logged_maximum_residual_window_tour_frequency_is_4,1.7928,F -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.7928,F -coef_mediumlow_income_group_and_tour_frequency_is_1,0.5709,F -coef_mediumlow_income_group_and_tour_frequency_is_2,0.8315,F -coef_mediumlow_income_group_and_tour_frequency_is_3,0.8315,F -coef_mediumlow_income_group_and_tour_frequency_is_4,0.8315,F -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0.8315,F -coef_mediumhigh_income_group_and_tour_frequency_is_1,0.7426,F -coef_mediumhigh_income_group_and_tour_frequency_is_2,0.8546,F -coef_mediumhigh_income_group_and_tour_frequency_is_3,1.0792,F -coef_mediumhigh_income_group_and_tour_frequency_is_4,1.0792,F -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.0792,F -coef_high_income_group_and_tour_frequency_is_1,1.0633,F -coef_high_income_group_and_tour_frequency_is_2,1.0633,F -coef_high_income_group_and_tour_frequency_is_3,1.7742,F -coef_high_income_group_and_tour_frequency_is_4,2.3941,F -coef_high_income_group_and_tour_frequency_is_5_plus,2.3941,F -coef_mediumlow_income_group_and_shopping_tour,0.7734,F -coef_mediumhigh_income_group_and_shopping_tour,0.8906,F -coef_high_income_group_and_shopping_tour,0.9776,F -coef_mediumlow_income_group_and_maintenance_tour,0,T -coef_mediumhigh_income_group_and_maintenance_tour,0,T -coef_high_income_group_and_maintenance_tour,0,T -coef_mediumlow_income_group_and_eating_out_tour,0.2766,F -coef_mediumhigh_income_group_and_eating_out_tour,0.4631,F -coef_high_income_group_and_eating_out_tour,0.7086,F -coef_mediumlow_income_group_and_discretionary_tour,0.1707,F -coef_mediumhigh_income_group_and_discretionary_tour,0.5009,F -coef_high_income_group_and_discretionary_tour,0.8846,F -coef_mediumlow_income_group_and_visiting_tour,-0.267,F -coef_mediumhigh_income_group_and_visiting_tour,-0.267,F -coef_high_income_group_and_visiting_tour,-0.9449,F -coef_female_and_tour_frequency_is_1,0.3902,F -coef_female_and_tour_frequency_is_2,0.5323,F -coef_female_and_tour_frequency_is_3,0.7452,F -coef_female_and_tour_frequency_is_4,1.1294,F -coef_female_and_tour_frequency_is_5,1.1294,F -coef_female_and_escorting_tour,0,T -coef_female_and_shopping_tour,0,T -coef_female_and_maintenance_tour,-0.2464,F -coef_female_and_eatingout_tour,0,T -coef_female_and_discretionary_tour,0,T -coef_zero_car_ownership_and_tour_frequency_is_1,-0.3623,F -coef_zero_car_ownership_and_tour_frequency_is_2,-1.272,F -coef_zero_car_ownership_and_tour_frequency_is_3,-1.9307,F -coef_zero_car_ownership_and_tour_frequency_is_4,-1.9307,F -coef_zero_car_ownership_and_tour_frequency_is_5_plus,-1.9307,F -coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.3623,F -coef_car_shortage_vs_workers_and_tour_frequency_is_2,-1.272,F -coef_car_shortage_vs_workers_and_tour_frequency_is_3,-1.9307,F -coef_car_shortage_vs_workers_and_tour_frequency_is_4,-1.9307,F -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-1.9307,F -coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.7738,F -coef_car_surplus_vs_workers_and_tour_frequency_is_2,0.7738,F -coef_car_surplus_vs_workers_and_tour_frequency_is_3,0.7738,F -coef_car_surplus_vs_workers_and_tour_frequency_is_4,0.7738,F -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0.7738,F -coef_presence_of_non_worker_and_tour_frequency_is_1,-0.3763,F -coef_presence_of_non_worker_and_tour_frequency_is_2,-0.719,F -coef_presence_of_non_worker_and_tour_frequency_is_3,-1.0229,F -coef_presence_of_non_worker_and_tour_frequency_is_4,-1.0229,F -coef_presence_of_non_worker_and_tour_frequency_is_5,-1.0229,F -coef_presence_of_retiree_and_tour_frequency_is_1,-0.464,F -coef_presence_of_retiree_and_tour_frequency_is_2,-0.4795,F -coef_presence_of_retiree_and_tour_frequency_is_3,-0.4795,F -coef_presence_of_retiree_and_tour_frequency_is_4,-0.4795,F -coef_presence_of_retiree_and_tour_frequency_is_5,-0.4795,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.7161,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-0.7161,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-0.7161,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-0.7161,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.7161,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0.1486,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0.484,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0.484,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0.484,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0.484,F -coef_presence_of_full_time_worker_and_escorting_tour,0.3947,F -coef_presence_of_part_time_worker_and_escorting_tour,-0.5861,F -coef_presence_of_non_worker_and_escorting_tour,0,T -coef_presence_of_retiree_and_escorting_tour,0,T -coef_presence_of_university_student_and_escorting_tour,0,T -coef_presence_of_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.3773,F -coef_presence_of_pre_school_kid_and_escorting_tour,0.7194,F -coef_at_home_pre_driving_school_kid_and_escorting_tour,-1.148,F -coef_at_home_pre_school_kid_and_escorting_tour,-0.1373,F -coef_presence_of_full_time_worker_and_shopping_tour,0,T -coef_presence_of_part_time_worker_and_shopping_tour,0,T -coef_presence_of_non_worker_and_shopping_tour,0,T -coef_presence_of_retiree_and_shopping_tour,0,T -coef_presence_of_university_student_and_shopping_tour,0,T -coef_presence_of_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_school_kid_and_shopping_tour,0,T -coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T -coef_at_home_pre_school_kid_and_shopping_tour,0,T -coef_presence_of_full_time_worker_and_maintenance_tour,0,T -coef_presence_of_part_time_worker_and_maintenance_tour,0,T -coef_presence_of_non_worker_and_maintenance_tour,0,T -coef_presence_of_retiree_and_maintenance_tour,0,T -coef_presence_of_university_student_and_maintenance_tour,0,T -coef_presence_of_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_school_kid_and_maintenance_tour,0,T -coef_presence_of_full_time_worker_and_eating_out_tour,-0.4667,F -coef_presence_of_part_time_worker_and_eating_out_tour,0,T -coef_presence_of_non_worker_and_eating_out_tour,-0.4976,F -coef_presence_of_retiree_and_eating_out_tour,-0.6911,F -coef_presence_of_university_student_and_eating_out_tour,0,T -coef_presence_of_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_driving_school_kid_and_eating_out_tour,-0.3926,F -coef_at_home_pre_school_kid_and_eating_out_tour,-0.3926,F -coef_presence_of_full_time_worker_and_discretionary_tour,-0.3545,F -coef_presence_of_part_time_worker_and_discretionary_tour,-0.3545,F -coef_presence_of_non_worker_and_discretionary_tour,0,T -coef_presence_of_retiree_and_discretionary_tour,0,T -coef_presence_of_university_student_and_discretionary_tour,0,T -coef_presence_of_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_school_kid_and_discretionary_tour,0,T -coef_walk_access_to_retail_and_tour_frequency_is_1,0.0713,F -coef_walk_access_to_retail_and_tour_frequency_is_2,0.1256,F -coef_walk_access_to_retail_and_tour_frequency_is_3,0.1508,F -coef_walk_access_to_retail_and_tour_frequency_is_4,0.1508,F -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.1508,F -coef_transit_access_to_retail_and_tour_frequency_is_1,0,T -coef_transit_access_to_retail_and_tour_frequency_is_2,0,T -coef_transit_access_to_retail_and_tour_frequency_is_3,0,T -coef_transit_access_to_retail_and_tour_frequency_is_4,0,T -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_auto_access_to_retail_and_tour_frequency_is_1,0,T -coef_auto_access_to_retail_and_tour_frequency_is_2,0,T -coef_auto_access_to_retail_and_tour_frequency_is_3,0,T -coef_auto_access_to_retail_and_tour_frequency_is_4,0,T -coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_walk_access_to_retail_and_escorting,0,T -coef_transit_access_to_retail_and_escorting,0,T -coef_auto_access_to_retail_and_escorting,0,T -coef_walk_access_to_retail_and_shopping,0.0598,F -coef_transit_access_to_retail_and_shopping,0,T -coef_auto_access_to_retail_and_shopping,0,T -coef_walk_access_to_retail_and_maintenance,0,T -coef_transit_access_to_retail_and_maintenance,0,T -coef_auto_access_to_retail_and_maintenance,0.0956,F -coef_walk_access_to_retail_and_eating_out,0,T -coef_transit_access_to_retail_and_eating_out,0,T -coef_auto_access_to_retail_and_eating_out,0,T -coef_walk_access_to_retail_and_discretionary,0.0772,F -coef_transit_access_to_retail_and_discretionary,0,T -coef_auto_access_to_retail_and_discretionary,0,T -coef_urban_and_tour_frequency_is_1,0,T -coef_urban_and_tour_frequency_is_2,0,T -coef_urban_and_tour_frequency_is_3,0,T -coef_urban_and_tour_frequency_is_4,0,T -coef_urban_and_tour_frequency_is_5_plus,0,T -coef_urban_and_escorting_tour,0,T -coef_urban_and_shopping_tour,0,T -coef_urban_and_maintenance_tour,0,T -coef_urban_and_eatingout_tour,0,T -coef_urban_and_discretionary_tour,0,T -coef_1_escort_tour_constant,-0.0629,F -coef_2_plus_escort_tours_constant,0.9273,F -coef_1_plus_shopping_tours_constant,0.4683,F -coef_1_plus_maintenance_tours_constant,-0.0653,F -coef_1_plus_eating_out_tours_constant,-0.1429,F -coef_1_plus_visting_tours_constant,-0.1272,F -coef_1_plus_other_discretionary_tours_constant,0.3334,F +coefficient_name,value,constrain +coef_escorting_tour,0,T +coef_discretionary_tour,0,T +coef_shopping_tour,0,T +coef_maintenance_tour,0,T +coef_visiting_or_social_tour,0,T +coef_eating_out_tour,0,T +coef_total_number_of_tours_is_0_no_prior_tours,-999,T +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,-8.9791,F +coef_total_number_of_tours_is_2,-12.0248,F +coef_total_number_of_tours_is_3,-14.8516,F +coef_total_number_of_tours_is_4,-17.7037,F +coef_total_number_of_tours_is_5,-999,T +coef_total_number_of_tours_is_6_plus,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.6766,F +coef_number_of_mandatory_tours_and_tour_frequency_is_2,-1.0518,F +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-1.0518,F +coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_0,0,T +coef_number_of_joint_tours_and_tour_frequency_is_1,-0.1699,F +coef_number_of_joint_tours_and_tour_frequency_is_2,-0.4285,F +coef_number_of_joint_tours_and_tour_frequency_is_3,-0.6551,F +coef_number_of_joint_tours_and_tour_frequency_is_4,-1.0411,F +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-1.0411,F +coef_number_of_joint_shopping_tours,-0.2391,F +coef_number_of_joint_maintenance_tours,0,T +coef_number_of_joint_eating_out_tours,-0.7727,F +coef_number_of_joint_visit_tours,0,T +coef_number_of_joint_discretionary_tours,0,T +coef_logged_maximum_residual_window_tour_frequency_is_0,0,T +coef_logged_maximum_residual_window_tour_frequency_is_1,1.7637,F +coef_logged_maximum_residual_window_tour_frequency_is_2,1.7928,F +coef_logged_maximum_residual_window_tour_frequency_is_3,1.7928,F +coef_logged_maximum_residual_window_tour_frequency_is_4,1.7928,F +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.7928,F +coef_mediumlow_income_group_and_tour_frequency_is_1,0.5709,F +coef_mediumlow_income_group_and_tour_frequency_is_2,0.8315,F +coef_mediumlow_income_group_and_tour_frequency_is_3,0.8315,F +coef_mediumlow_income_group_and_tour_frequency_is_4,0.8315,F +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0.8315,F +coef_mediumhigh_income_group_and_tour_frequency_is_1,0.7426,F +coef_mediumhigh_income_group_and_tour_frequency_is_2,0.8546,F +coef_mediumhigh_income_group_and_tour_frequency_is_3,1.0792,F +coef_mediumhigh_income_group_and_tour_frequency_is_4,1.0792,F +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.0792,F +coef_high_income_group_and_tour_frequency_is_1,1.0633,F +coef_high_income_group_and_tour_frequency_is_2,1.0633,F +coef_high_income_group_and_tour_frequency_is_3,1.7742,F +coef_high_income_group_and_tour_frequency_is_4,2.3941,F +coef_high_income_group_and_tour_frequency_is_5_plus,2.3941,F +coef_mediumlow_income_group_and_shopping_tour,0.7734,F +coef_mediumhigh_income_group_and_shopping_tour,0.8906,F +coef_high_income_group_and_shopping_tour,0.9776,F +coef_mediumlow_income_group_and_maintenance_tour,0,T +coef_mediumhigh_income_group_and_maintenance_tour,0,T +coef_high_income_group_and_maintenance_tour,0,T +coef_mediumlow_income_group_and_eating_out_tour,0.2766,F +coef_mediumhigh_income_group_and_eating_out_tour,0.4631,F +coef_high_income_group_and_eating_out_tour,0.7086,F +coef_mediumlow_income_group_and_discretionary_tour,0.1707,F +coef_mediumhigh_income_group_and_discretionary_tour,0.5009,F +coef_high_income_group_and_discretionary_tour,0.8846,F +coef_mediumlow_income_group_and_visiting_tour,-0.267,F +coef_mediumhigh_income_group_and_visiting_tour,-0.267,F +coef_high_income_group_and_visiting_tour,-0.9449,F +coef_female_and_tour_frequency_is_1,0.3902,F +coef_female_and_tour_frequency_is_2,0.5323,F +coef_female_and_tour_frequency_is_3,0.7452,F +coef_female_and_tour_frequency_is_4,1.1294,F +coef_female_and_tour_frequency_is_5,1.1294,F +coef_female_and_escorting_tour,0,T +coef_female_and_shopping_tour,0,T +coef_female_and_maintenance_tour,-0.2464,F +coef_female_and_eatingout_tour,0,T +coef_female_and_discretionary_tour,0,T +coef_zero_car_ownership_and_tour_frequency_is_1,-0.3623,F +coef_zero_car_ownership_and_tour_frequency_is_2,-1.272,F +coef_zero_car_ownership_and_tour_frequency_is_3,-1.9307,F +coef_zero_car_ownership_and_tour_frequency_is_4,-1.9307,F +coef_zero_car_ownership_and_tour_frequency_is_5_plus,-1.9307,F +coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.3623,F +coef_car_shortage_vs_workers_and_tour_frequency_is_2,-1.272,F +coef_car_shortage_vs_workers_and_tour_frequency_is_3,-1.9307,F +coef_car_shortage_vs_workers_and_tour_frequency_is_4,-1.9307,F +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-1.9307,F +coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.7738,F +coef_car_surplus_vs_workers_and_tour_frequency_is_2,0.7738,F +coef_car_surplus_vs_workers_and_tour_frequency_is_3,0.7738,F +coef_car_surplus_vs_workers_and_tour_frequency_is_4,0.7738,F +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0.7738,F +coef_presence_of_non_worker_and_tour_frequency_is_1,-0.3763,F +coef_presence_of_non_worker_and_tour_frequency_is_2,-0.719,F +coef_presence_of_non_worker_and_tour_frequency_is_3,-1.0229,F +coef_presence_of_non_worker_and_tour_frequency_is_4,-1.0229,F +coef_presence_of_non_worker_and_tour_frequency_is_5,-1.0229,F +coef_presence_of_retiree_and_tour_frequency_is_1,-0.464,F +coef_presence_of_retiree_and_tour_frequency_is_2,-0.4795,F +coef_presence_of_retiree_and_tour_frequency_is_3,-0.4795,F +coef_presence_of_retiree_and_tour_frequency_is_4,-0.4795,F +coef_presence_of_retiree_and_tour_frequency_is_5,-0.4795,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.7161,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-0.7161,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-0.7161,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-0.7161,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.7161,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0.1486,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0.484,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0.484,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0.484,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0.484,F +coef_presence_of_full_time_worker_and_escorting_tour,0.3947,F +coef_presence_of_part_time_worker_and_escorting_tour,-0.5861,F +coef_presence_of_non_worker_and_escorting_tour,0,T +coef_presence_of_retiree_and_escorting_tour,0,T +coef_presence_of_university_student_and_escorting_tour,0,T +coef_presence_of_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.3773,F +coef_presence_of_pre_school_kid_and_escorting_tour,0.7194,F +coef_at_home_pre_driving_school_kid_and_escorting_tour,-1.148,F +coef_at_home_pre_school_kid_and_escorting_tour,-0.1373,F +coef_presence_of_full_time_worker_and_shopping_tour,0,T +coef_presence_of_part_time_worker_and_shopping_tour,0,T +coef_presence_of_non_worker_and_shopping_tour,0,T +coef_presence_of_retiree_and_shopping_tour,0,T +coef_presence_of_university_student_and_shopping_tour,0,T +coef_presence_of_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_school_kid_and_shopping_tour,0,T +coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T +coef_at_home_pre_school_kid_and_shopping_tour,0,T +coef_presence_of_full_time_worker_and_maintenance_tour,0,T +coef_presence_of_part_time_worker_and_maintenance_tour,0,T +coef_presence_of_non_worker_and_maintenance_tour,0,T +coef_presence_of_retiree_and_maintenance_tour,0,T +coef_presence_of_university_student_and_maintenance_tour,0,T +coef_presence_of_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_school_kid_and_maintenance_tour,0,T +coef_presence_of_full_time_worker_and_eating_out_tour,-0.4667,F +coef_presence_of_part_time_worker_and_eating_out_tour,0,T +coef_presence_of_non_worker_and_eating_out_tour,-0.4976,F +coef_presence_of_retiree_and_eating_out_tour,-0.6911,F +coef_presence_of_university_student_and_eating_out_tour,0,T +coef_presence_of_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_driving_school_kid_and_eating_out_tour,-0.3926,F +coef_at_home_pre_school_kid_and_eating_out_tour,-0.3926,F +coef_presence_of_full_time_worker_and_discretionary_tour,-0.3545,F +coef_presence_of_part_time_worker_and_discretionary_tour,-0.3545,F +coef_presence_of_non_worker_and_discretionary_tour,0,T +coef_presence_of_retiree_and_discretionary_tour,0,T +coef_presence_of_university_student_and_discretionary_tour,0,T +coef_presence_of_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_school_kid_and_discretionary_tour,0,T +coef_walk_access_to_retail_and_tour_frequency_is_1,0.0713,F +coef_walk_access_to_retail_and_tour_frequency_is_2,0.1256,F +coef_walk_access_to_retail_and_tour_frequency_is_3,0.1508,F +coef_walk_access_to_retail_and_tour_frequency_is_4,0.1508,F +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.1508,F +coef_transit_access_to_retail_and_tour_frequency_is_1,0,T +coef_transit_access_to_retail_and_tour_frequency_is_2,0,T +coef_transit_access_to_retail_and_tour_frequency_is_3,0,T +coef_transit_access_to_retail_and_tour_frequency_is_4,0,T +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_auto_access_to_retail_and_tour_frequency_is_1,0,T +coef_auto_access_to_retail_and_tour_frequency_is_2,0,T +coef_auto_access_to_retail_and_tour_frequency_is_3,0,T +coef_auto_access_to_retail_and_tour_frequency_is_4,0,T +coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_walk_access_to_retail_and_escorting,0,T +coef_transit_access_to_retail_and_escorting,0,T +coef_auto_access_to_retail_and_escorting,0,T +coef_walk_access_to_retail_and_shopping,0.0598,F +coef_transit_access_to_retail_and_shopping,0,T +coef_auto_access_to_retail_and_shopping,0,T +coef_walk_access_to_retail_and_maintenance,0,T +coef_transit_access_to_retail_and_maintenance,0,T +coef_auto_access_to_retail_and_maintenance,0.0956,F +coef_walk_access_to_retail_and_eating_out,0,T +coef_transit_access_to_retail_and_eating_out,0,T +coef_auto_access_to_retail_and_eating_out,0,T +coef_walk_access_to_retail_and_discretionary,0.0772,F +coef_transit_access_to_retail_and_discretionary,0,T +coef_auto_access_to_retail_and_discretionary,0,T +coef_urban_and_tour_frequency_is_1,0,T +coef_urban_and_tour_frequency_is_2,0,T +coef_urban_and_tour_frequency_is_3,0,T +coef_urban_and_tour_frequency_is_4,0,T +coef_urban_and_tour_frequency_is_5_plus,0,T +coef_urban_and_escorting_tour,0,T +coef_urban_and_shopping_tour,0,T +coef_urban_and_maintenance_tour,0,T +coef_urban_and_eatingout_tour,0,T +coef_urban_and_discretionary_tour,0,T +coef_1_escort_tour_constant,-0.0629,F +coef_2_plus_escort_tours_constant,0.9273,F +coef_1_plus_shopping_tours_constant,0.4683,F +coef_1_plus_maintenance_tours_constant,-0.0653,F +coef_1_plus_eating_out_tours_constant,-0.1429,F +coef_1_plus_visting_tours_constant,-0.1272,F +coef_1_plus_other_discretionary_tours_constant,0.3334,F coef_0_auto_household_and_escorting_tour,-2,T \ No newline at end of file diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv index c015b2108f..18f2e3b888 100755 --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PART.csv @@ -1,211 +1,211 @@ -coefficient_name,value,constrain -coef_escorting_tour,0,T -coef_discretionary_tour,0,T -coef_shopping_tour,0,T -coef_maintenance_tour,0,T -coef_visiting_or_social_tour,0,T -coef_eating_out_tour,0,T -coef_total_number_of_tours_is_0_no_prior_tours,-999,T -coef_total_number_of_tours_is_0_prior_tours,0,T -coef_total_number_of_tours_is_1,-7.6391,F -coef_total_number_of_tours_is_2,-10.4557,F -coef_total_number_of_tours_is_3,-14.0176,F -coef_total_number_of_tours_is_4,-16.9717,F -coef_total_number_of_tours_is_5,-999,T -coef_total_number_of_tours_is_6_plus,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.239,F -coef_number_of_mandatory_tours_and_tour_frequency_is_2,-1.8208,F -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-2.5923,F -coef_number_of_mandatory_tours_and_tour_frequency_is_4,-2.5923,F -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-2.5923,F -coef_number_of_joint_tours_and_tour_frequency_is_0,0,T -coef_number_of_joint_tours_and_tour_frequency_is_1,0,T -coef_number_of_joint_tours_and_tour_frequency_is_2,-1.1986,F -coef_number_of_joint_tours_and_tour_frequency_is_3,-1.1986,F -coef_number_of_joint_tours_and_tour_frequency_is_4,-1.1986,F -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_shopping_tours,0,T -coef_number_of_joint_maintenance_tours,0,T -coef_number_of_joint_eating_out_tours,0,T -coef_number_of_joint_visit_tours,0,T -coef_number_of_joint_discretionary_tours,0,T -coef_logged_maximum_residual_window_tour_frequency_is_0,0,T -coef_logged_maximum_residual_window_tour_frequency_is_1,1.5748,F -coef_logged_maximum_residual_window_tour_frequency_is_2,2.0026,F -coef_logged_maximum_residual_window_tour_frequency_is_3,2.0026,F -coef_logged_maximum_residual_window_tour_frequency_is_4,2.0026,F -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,2.0026,F -coef_mediumlow_income_group_and_tour_frequency_is_1,0.5981,F -coef_mediumlow_income_group_and_tour_frequency_is_2,0.9178,F -coef_mediumlow_income_group_and_tour_frequency_is_3,1.7539,F -coef_mediumlow_income_group_and_tour_frequency_is_4,1.7539,F -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,1.7539,F -coef_mediumhigh_income_group_and_tour_frequency_is_1,0.8682,F -coef_mediumhigh_income_group_and_tour_frequency_is_2,1.5362,F -coef_mediumhigh_income_group_and_tour_frequency_is_3,1.9331,F -coef_mediumhigh_income_group_and_tour_frequency_is_4,1.9331,F -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.9331,F -coef_high_income_group_and_tour_frequency_is_1,0.8682,F -coef_high_income_group_and_tour_frequency_is_2,1.5362,F -coef_high_income_group_and_tour_frequency_is_3,1.9331,F -coef_high_income_group_and_tour_frequency_is_4,1.9331,F -coef_high_income_group_and_tour_frequency_is_5_plus,1.9331,F -coef_mediumlow_income_group_and_shopping_tour,0.4421,F -coef_mediumhigh_income_group_and_shopping_tour,0.4421,F -coef_high_income_group_and_shopping_tour,0.7066,F -coef_mediumlow_income_group_and_maintenance_tour,0.6763,F -coef_mediumhigh_income_group_and_maintenance_tour,0.6763,F -coef_high_income_group_and_maintenance_tour,0.6763,F -coef_mediumlow_income_group_and_eating_out_tour,0,T -coef_mediumhigh_income_group_and_eating_out_tour,0,T -coef_high_income_group_and_eating_out_tour,0,T -coef_mediumlow_income_group_and_discretionary_tour,0.296,F -coef_mediumhigh_income_group_and_discretionary_tour,0.296,F -coef_high_income_group_and_discretionary_tour,0.296,F -coef_mediumlow_income_group_and_visiting_tour,-0.6868,F -coef_mediumhigh_income_group_and_visiting_tour,-0.6868,F -coef_high_income_group_and_visiting_tour,-0.6868,F -coef_female_and_tour_frequency_is_1,0,T -coef_female_and_tour_frequency_is_2,0,T -coef_female_and_tour_frequency_is_3,0,T -coef_female_and_tour_frequency_is_4,0,T -coef_female_and_tour_frequency_is_5,0,T -coef_female_and_escorting_tour,0,T -coef_female_and_shopping_tour,0.4524,F -coef_female_and_maintenance_tour,0,T -coef_female_and_eatingout_tour,0,T -coef_female_and_discretionary_tour,0.3072,F -coef_zero_car_ownership_and_tour_frequency_is_1,-0.5498,F -coef_zero_car_ownership_and_tour_frequency_is_2,-0.5498,F -coef_zero_car_ownership_and_tour_frequency_is_3,-0.5498,F -coef_zero_car_ownership_and_tour_frequency_is_4,-0.5498,F -coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.5498,F -coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.5498,F -coef_car_shortage_vs_workers_and_tour_frequency_is_2,-0.5498,F -coef_car_shortage_vs_workers_and_tour_frequency_is_3,-0.5498,F -coef_car_shortage_vs_workers_and_tour_frequency_is_4,-0.5498,F -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-0.5498,F -coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T -coef_presence_of_non_worker_and_tour_frequency_is_1,0,T -coef_presence_of_non_worker_and_tour_frequency_is_2,0,T -coef_presence_of_non_worker_and_tour_frequency_is_3,0,T -coef_presence_of_non_worker_and_tour_frequency_is_4,0,T -coef_presence_of_non_worker_and_tour_frequency_is_5,0,T -coef_presence_of_retiree_and_tour_frequency_is_1,0,T -coef_presence_of_retiree_and_tour_frequency_is_2,0,T -coef_presence_of_retiree_and_tour_frequency_is_3,0,T -coef_presence_of_retiree_and_tour_frequency_is_4,0,T -coef_presence_of_retiree_and_tour_frequency_is_5,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.1559,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-0.5681,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-0.5681,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-0.5681,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.5681,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_full_time_worker_and_escorting_tour,0,T -coef_presence_of_part_time_worker_and_escorting_tour,0,T -coef_presence_of_non_worker_and_escorting_tour,-0.5263,F -coef_presence_of_retiree_and_escorting_tour,-0.7516,F -coef_presence_of_university_student_and_escorting_tour,0,T -coef_presence_of_driving_school_kid_and_escorting_tour,0.4164,F -coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.5795,F -coef_presence_of_pre_school_kid_and_escorting_tour,0.5414,F -coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T -coef_at_home_pre_school_kid_and_escorting_tour,0,T -coef_presence_of_full_time_worker_and_shopping_tour,0,T -coef_presence_of_part_time_worker_and_shopping_tour,0,T -coef_presence_of_non_worker_and_shopping_tour,0,T -coef_presence_of_retiree_and_shopping_tour,0,T -coef_presence_of_university_student_and_shopping_tour,0,T -coef_presence_of_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_school_kid_and_shopping_tour,0,T -coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T -coef_at_home_pre_school_kid_and_shopping_tour,0,T -coef_presence_of_full_time_worker_and_maintenance_tour,-0.3131,F -coef_presence_of_part_time_worker_and_maintenance_tour,-0.5621,F -coef_presence_of_non_worker_and_maintenance_tour,0,T -coef_presence_of_retiree_and_maintenance_tour,0,T -coef_presence_of_university_student_and_maintenance_tour,0,T -coef_presence_of_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_school_kid_and_maintenance_tour,0,T -coef_presence_of_full_time_worker_and_eating_out_tour,0,T -coef_presence_of_part_time_worker_and_eating_out_tour,0,T -coef_presence_of_non_worker_and_eating_out_tour,-0.6545,F -coef_presence_of_retiree_and_eating_out_tour,-1.389,F -coef_presence_of_university_student_and_eating_out_tour,-1.4318,F -coef_presence_of_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_school_kid_and_eating_out_tour,0,T -coef_presence_of_full_time_worker_and_discretionary_tour,0,T -coef_presence_of_part_time_worker_and_discretionary_tour,0,T -coef_presence_of_non_worker_and_discretionary_tour,-1.0371,F -coef_presence_of_retiree_and_discretionary_tour,0,T -coef_presence_of_university_student_and_discretionary_tour,0,T -coef_presence_of_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_school_kid_and_discretionary_tour,0,T -coef_walk_access_to_retail_and_tour_frequency_is_1,0.0899,F -coef_walk_access_to_retail_and_tour_frequency_is_2,0.1447,F -coef_walk_access_to_retail_and_tour_frequency_is_3,0.3479,F -coef_walk_access_to_retail_and_tour_frequency_is_4,0.3479,F -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.3479,F -coef_transit_access_to_retail_and_tour_frequency_is_1,0,T -coef_transit_access_to_retail_and_tour_frequency_is_2,0,T -coef_transit_access_to_retail_and_tour_frequency_is_3,0,T -coef_transit_access_to_retail_and_tour_frequency_is_4,0,T -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_auto_access_to_retail_and_tour_frequency_is_1,0,T -coef_auto_access_to_retail_and_tour_frequency_is_2,0,T -coef_auto_access_to_retail_and_tour_frequency_is_3,0,T -coef_auto_access_to_retail_and_tour_frequency_is_4,0,T -coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_walk_access_to_retail_and_escorting,0,T -coef_transit_access_to_retail_and_escorting,0,T -coef_auto_access_to_retail_and_escorting,0,T -coef_walk_access_to_retail_and_shopping,0,T -coef_transit_access_to_retail_and_shopping,0,T -coef_auto_access_to_retail_and_shopping,0,T -coef_walk_access_to_retail_and_maintenance,0,T -coef_transit_access_to_retail_and_maintenance,0,T -coef_auto_access_to_retail_and_maintenance,0,T -coef_walk_access_to_retail_and_eating_out,0,T -coef_transit_access_to_retail_and_eating_out,0,T -coef_auto_access_to_retail_and_eating_out,0,T -coef_walk_access_to_retail_and_discretionary,0,T -coef_transit_access_to_retail_and_discretionary,0,T -coef_auto_access_to_retail_and_discretionary,0,T -coef_urban_and_tour_frequency_is_1,0,T -coef_urban_and_tour_frequency_is_2,0,T -coef_urban_and_tour_frequency_is_3,0,T -coef_urban_and_tour_frequency_is_4,0,T -coef_urban_and_tour_frequency_is_5_plus,0,T -coef_urban_and_escorting_tour,-0.3929,F -coef_urban_and_shopping_tour,0,T -coef_urban_and_maintenance_tour,0,T -coef_urban_and_eatingout_tour,0,T -coef_urban_and_discretionary_tour,0,T -coef_1_escort_tour_constant,0.5272,F -coef_2_plus_escort_tours_constant,1.5987,F -coef_1_plus_shopping_tours_constant,0.7569,F -coef_1_plus_maintenance_tours_constant,0.5533,F -coef_1_plus_eating_out_tours_constant,0.6914,F -coef_1_plus_visting_tours_constant,0.1405,F -coef_1_plus_other_discretionary_tours_constant,0.7989,F +coefficient_name,value,constrain +coef_escorting_tour,0,T +coef_discretionary_tour,0,T +coef_shopping_tour,0,T +coef_maintenance_tour,0,T +coef_visiting_or_social_tour,0,T +coef_eating_out_tour,0,T +coef_total_number_of_tours_is_0_no_prior_tours,-999,T +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,-7.6391,F +coef_total_number_of_tours_is_2,-10.4557,F +coef_total_number_of_tours_is_3,-14.0176,F +coef_total_number_of_tours_is_4,-16.9717,F +coef_total_number_of_tours_is_5,-999,T +coef_total_number_of_tours_is_6_plus,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.239,F +coef_number_of_mandatory_tours_and_tour_frequency_is_2,-1.8208,F +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-2.5923,F +coef_number_of_mandatory_tours_and_tour_frequency_is_4,-2.5923,F +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-2.5923,F +coef_number_of_joint_tours_and_tour_frequency_is_0,0,T +coef_number_of_joint_tours_and_tour_frequency_is_1,0,T +coef_number_of_joint_tours_and_tour_frequency_is_2,-1.1986,F +coef_number_of_joint_tours_and_tour_frequency_is_3,-1.1986,F +coef_number_of_joint_tours_and_tour_frequency_is_4,-1.1986,F +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_shopping_tours,0,T +coef_number_of_joint_maintenance_tours,0,T +coef_number_of_joint_eating_out_tours,0,T +coef_number_of_joint_visit_tours,0,T +coef_number_of_joint_discretionary_tours,0,T +coef_logged_maximum_residual_window_tour_frequency_is_0,0,T +coef_logged_maximum_residual_window_tour_frequency_is_1,1.5748,F +coef_logged_maximum_residual_window_tour_frequency_is_2,2.0026,F +coef_logged_maximum_residual_window_tour_frequency_is_3,2.0026,F +coef_logged_maximum_residual_window_tour_frequency_is_4,2.0026,F +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,2.0026,F +coef_mediumlow_income_group_and_tour_frequency_is_1,0.5981,F +coef_mediumlow_income_group_and_tour_frequency_is_2,0.9178,F +coef_mediumlow_income_group_and_tour_frequency_is_3,1.7539,F +coef_mediumlow_income_group_and_tour_frequency_is_4,1.7539,F +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,1.7539,F +coef_mediumhigh_income_group_and_tour_frequency_is_1,0.8682,F +coef_mediumhigh_income_group_and_tour_frequency_is_2,1.5362,F +coef_mediumhigh_income_group_and_tour_frequency_is_3,1.9331,F +coef_mediumhigh_income_group_and_tour_frequency_is_4,1.9331,F +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.9331,F +coef_high_income_group_and_tour_frequency_is_1,0.8682,F +coef_high_income_group_and_tour_frequency_is_2,1.5362,F +coef_high_income_group_and_tour_frequency_is_3,1.9331,F +coef_high_income_group_and_tour_frequency_is_4,1.9331,F +coef_high_income_group_and_tour_frequency_is_5_plus,1.9331,F +coef_mediumlow_income_group_and_shopping_tour,0.4421,F +coef_mediumhigh_income_group_and_shopping_tour,0.4421,F +coef_high_income_group_and_shopping_tour,0.7066,F +coef_mediumlow_income_group_and_maintenance_tour,0.6763,F +coef_mediumhigh_income_group_and_maintenance_tour,0.6763,F +coef_high_income_group_and_maintenance_tour,0.6763,F +coef_mediumlow_income_group_and_eating_out_tour,0,T +coef_mediumhigh_income_group_and_eating_out_tour,0,T +coef_high_income_group_and_eating_out_tour,0,T +coef_mediumlow_income_group_and_discretionary_tour,0.296,F +coef_mediumhigh_income_group_and_discretionary_tour,0.296,F +coef_high_income_group_and_discretionary_tour,0.296,F +coef_mediumlow_income_group_and_visiting_tour,-0.6868,F +coef_mediumhigh_income_group_and_visiting_tour,-0.6868,F +coef_high_income_group_and_visiting_tour,-0.6868,F +coef_female_and_tour_frequency_is_1,0,T +coef_female_and_tour_frequency_is_2,0,T +coef_female_and_tour_frequency_is_3,0,T +coef_female_and_tour_frequency_is_4,0,T +coef_female_and_tour_frequency_is_5,0,T +coef_female_and_escorting_tour,0,T +coef_female_and_shopping_tour,0.4524,F +coef_female_and_maintenance_tour,0,T +coef_female_and_eatingout_tour,0,T +coef_female_and_discretionary_tour,0.3072,F +coef_zero_car_ownership_and_tour_frequency_is_1,-0.5498,F +coef_zero_car_ownership_and_tour_frequency_is_2,-0.5498,F +coef_zero_car_ownership_and_tour_frequency_is_3,-0.5498,F +coef_zero_car_ownership_and_tour_frequency_is_4,-0.5498,F +coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.5498,F +coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.5498,F +coef_car_shortage_vs_workers_and_tour_frequency_is_2,-0.5498,F +coef_car_shortage_vs_workers_and_tour_frequency_is_3,-0.5498,F +coef_car_shortage_vs_workers_and_tour_frequency_is_4,-0.5498,F +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-0.5498,F +coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T +coef_presence_of_non_worker_and_tour_frequency_is_1,0,T +coef_presence_of_non_worker_and_tour_frequency_is_2,0,T +coef_presence_of_non_worker_and_tour_frequency_is_3,0,T +coef_presence_of_non_worker_and_tour_frequency_is_4,0,T +coef_presence_of_non_worker_and_tour_frequency_is_5,0,T +coef_presence_of_retiree_and_tour_frequency_is_1,0,T +coef_presence_of_retiree_and_tour_frequency_is_2,0,T +coef_presence_of_retiree_and_tour_frequency_is_3,0,T +coef_presence_of_retiree_and_tour_frequency_is_4,0,T +coef_presence_of_retiree_and_tour_frequency_is_5,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.1559,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-0.5681,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-0.5681,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-0.5681,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.5681,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_full_time_worker_and_escorting_tour,0,T +coef_presence_of_part_time_worker_and_escorting_tour,0,T +coef_presence_of_non_worker_and_escorting_tour,-0.5263,F +coef_presence_of_retiree_and_escorting_tour,-0.7516,F +coef_presence_of_university_student_and_escorting_tour,0,T +coef_presence_of_driving_school_kid_and_escorting_tour,0.4164,F +coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.5795,F +coef_presence_of_pre_school_kid_and_escorting_tour,0.5414,F +coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T +coef_at_home_pre_school_kid_and_escorting_tour,0,T +coef_presence_of_full_time_worker_and_shopping_tour,0,T +coef_presence_of_part_time_worker_and_shopping_tour,0,T +coef_presence_of_non_worker_and_shopping_tour,0,T +coef_presence_of_retiree_and_shopping_tour,0,T +coef_presence_of_university_student_and_shopping_tour,0,T +coef_presence_of_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_school_kid_and_shopping_tour,0,T +coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T +coef_at_home_pre_school_kid_and_shopping_tour,0,T +coef_presence_of_full_time_worker_and_maintenance_tour,-0.3131,F +coef_presence_of_part_time_worker_and_maintenance_tour,-0.5621,F +coef_presence_of_non_worker_and_maintenance_tour,0,T +coef_presence_of_retiree_and_maintenance_tour,0,T +coef_presence_of_university_student_and_maintenance_tour,0,T +coef_presence_of_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_school_kid_and_maintenance_tour,0,T +coef_presence_of_full_time_worker_and_eating_out_tour,0,T +coef_presence_of_part_time_worker_and_eating_out_tour,0,T +coef_presence_of_non_worker_and_eating_out_tour,-0.6545,F +coef_presence_of_retiree_and_eating_out_tour,-1.389,F +coef_presence_of_university_student_and_eating_out_tour,-1.4318,F +coef_presence_of_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_school_kid_and_eating_out_tour,0,T +coef_presence_of_full_time_worker_and_discretionary_tour,0,T +coef_presence_of_part_time_worker_and_discretionary_tour,0,T +coef_presence_of_non_worker_and_discretionary_tour,-1.0371,F +coef_presence_of_retiree_and_discretionary_tour,0,T +coef_presence_of_university_student_and_discretionary_tour,0,T +coef_presence_of_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_school_kid_and_discretionary_tour,0,T +coef_walk_access_to_retail_and_tour_frequency_is_1,0.0899,F +coef_walk_access_to_retail_and_tour_frequency_is_2,0.1447,F +coef_walk_access_to_retail_and_tour_frequency_is_3,0.3479,F +coef_walk_access_to_retail_and_tour_frequency_is_4,0.3479,F +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.3479,F +coef_transit_access_to_retail_and_tour_frequency_is_1,0,T +coef_transit_access_to_retail_and_tour_frequency_is_2,0,T +coef_transit_access_to_retail_and_tour_frequency_is_3,0,T +coef_transit_access_to_retail_and_tour_frequency_is_4,0,T +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_auto_access_to_retail_and_tour_frequency_is_1,0,T +coef_auto_access_to_retail_and_tour_frequency_is_2,0,T +coef_auto_access_to_retail_and_tour_frequency_is_3,0,T +coef_auto_access_to_retail_and_tour_frequency_is_4,0,T +coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_walk_access_to_retail_and_escorting,0,T +coef_transit_access_to_retail_and_escorting,0,T +coef_auto_access_to_retail_and_escorting,0,T +coef_walk_access_to_retail_and_shopping,0,T +coef_transit_access_to_retail_and_shopping,0,T +coef_auto_access_to_retail_and_shopping,0,T +coef_walk_access_to_retail_and_maintenance,0,T +coef_transit_access_to_retail_and_maintenance,0,T +coef_auto_access_to_retail_and_maintenance,0,T +coef_walk_access_to_retail_and_eating_out,0,T +coef_transit_access_to_retail_and_eating_out,0,T +coef_auto_access_to_retail_and_eating_out,0,T +coef_walk_access_to_retail_and_discretionary,0,T +coef_transit_access_to_retail_and_discretionary,0,T +coef_auto_access_to_retail_and_discretionary,0,T +coef_urban_and_tour_frequency_is_1,0,T +coef_urban_and_tour_frequency_is_2,0,T +coef_urban_and_tour_frequency_is_3,0,T +coef_urban_and_tour_frequency_is_4,0,T +coef_urban_and_tour_frequency_is_5_plus,0,T +coef_urban_and_escorting_tour,-0.3929,F +coef_urban_and_shopping_tour,0,T +coef_urban_and_maintenance_tour,0,T +coef_urban_and_eatingout_tour,0,T +coef_urban_and_discretionary_tour,0,T +coef_1_escort_tour_constant,0.5272,F +coef_2_plus_escort_tours_constant,1.5987,F +coef_1_plus_shopping_tours_constant,0.7569,F +coef_1_plus_maintenance_tours_constant,0.5533,F +coef_1_plus_eating_out_tours_constant,0.6914,F +coef_1_plus_visting_tours_constant,0.1405,F +coef_1_plus_other_discretionary_tours_constant,0.7989,F coef_0_auto_household_and_escorting_tour,-2,T \ No newline at end of file diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv index 3085ba2cb0..7514ed50a1 100755 --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_PRESCHOOL.csv @@ -1,211 +1,211 @@ -coefficient_name,value,constrain -coef_escorting_tour,2.491,F -coef_discretionary_tour,0.903,F -coef_shopping_tour,0,T -coef_maintenance_tour,1.022,F -coef_visiting_or_social_tour,0.769,F -coef_eating_out_tour,0,T -coef_total_number_of_tours_is_0_no_prior_tours,-999,T -coef_total_number_of_tours_is_0_prior_tours,0,T -coef_total_number_of_tours_is_1,-5.759,F -coef_total_number_of_tours_is_2,-11.517,F -coef_total_number_of_tours_is_3,-17.276,F -coef_total_number_of_tours_is_4,-23.035,F -coef_total_number_of_tours_is_5,-999,T -coef_total_number_of_tours_is_6_plus,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_1,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_2,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_3,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_4,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,0,T -coef_number_of_joint_tours_and_tour_frequency_is_0,0,T -coef_number_of_joint_tours_and_tour_frequency_is_1,0,T -coef_number_of_joint_tours_and_tour_frequency_is_2,0,T -coef_number_of_joint_tours_and_tour_frequency_is_3,0,T -coef_number_of_joint_tours_and_tour_frequency_is_4,0,T -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,0,T -coef_number_of_joint_shopping_tours,0,T -coef_number_of_joint_maintenance_tours,0,T -coef_number_of_joint_eating_out_tours,0,T -coef_number_of_joint_visit_tours,0,T -coef_number_of_joint_discretionary_tours,0,T -coef_logged_maximum_residual_window_tour_frequency_is_0,0,T -coef_logged_maximum_residual_window_tour_frequency_is_1,0,T -coef_logged_maximum_residual_window_tour_frequency_is_2,0,T -coef_logged_maximum_residual_window_tour_frequency_is_3,0,T -coef_logged_maximum_residual_window_tour_frequency_is_4,0,T -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,0,T -coef_mediumlow_income_group_and_tour_frequency_is_1,0,T -coef_mediumlow_income_group_and_tour_frequency_is_2,0,T -coef_mediumlow_income_group_and_tour_frequency_is_3,0,T -coef_mediumlow_income_group_and_tour_frequency_is_4,0,T -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_1,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_2,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_3,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_4,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0,T -coef_high_income_group_and_tour_frequency_is_1,0,T -coef_high_income_group_and_tour_frequency_is_2,0,T -coef_high_income_group_and_tour_frequency_is_3,0,T -coef_high_income_group_and_tour_frequency_is_4,0,T -coef_high_income_group_and_tour_frequency_is_5_plus,0,T -coef_mediumlow_income_group_and_shopping_tour,0,T -coef_mediumhigh_income_group_and_shopping_tour,0,T -coef_high_income_group_and_shopping_tour,0,T -coef_mediumlow_income_group_and_maintenance_tour,0,T -coef_mediumhigh_income_group_and_maintenance_tour,0,T -coef_high_income_group_and_maintenance_tour,0,T -coef_mediumlow_income_group_and_eating_out_tour,0,T -coef_mediumhigh_income_group_and_eating_out_tour,0,T -coef_high_income_group_and_eating_out_tour,0,T -coef_mediumlow_income_group_and_discretionary_tour,0,T -coef_mediumhigh_income_group_and_discretionary_tour,0,T -coef_high_income_group_and_discretionary_tour,0,T -coef_mediumlow_income_group_and_visiting_tour,0,T -coef_mediumhigh_income_group_and_visiting_tour,0,T -coef_high_income_group_and_visiting_tour,0,T -coef_female_and_tour_frequency_is_1,0,T -coef_female_and_tour_frequency_is_2,0,T -coef_female_and_tour_frequency_is_3,0,T -coef_female_and_tour_frequency_is_4,0,T -coef_female_and_tour_frequency_is_5,0,T -coef_female_and_escorting_tour,0,T -coef_female_and_shopping_tour,0,T -coef_female_and_maintenance_tour,0,T -coef_female_and_eatingout_tour,0,T -coef_female_and_discretionary_tour,0,T -coef_zero_car_ownership_and_tour_frequency_is_1,0,T -coef_zero_car_ownership_and_tour_frequency_is_2,0,T -coef_zero_car_ownership_and_tour_frequency_is_3,0,T -coef_zero_car_ownership_and_tour_frequency_is_4,0,T -coef_zero_car_ownership_and_tour_frequency_is_5_plus,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T -coef_presence_of_non_worker_and_tour_frequency_is_1,0,T -coef_presence_of_non_worker_and_tour_frequency_is_2,0,T -coef_presence_of_non_worker_and_tour_frequency_is_3,0,T -coef_presence_of_non_worker_and_tour_frequency_is_4,0,T -coef_presence_of_non_worker_and_tour_frequency_is_5,0,T -coef_presence_of_retiree_and_tour_frequency_is_1,0,T -coef_presence_of_retiree_and_tour_frequency_is_2,0,T -coef_presence_of_retiree_and_tour_frequency_is_3,0,T -coef_presence_of_retiree_and_tour_frequency_is_4,0,T -coef_presence_of_retiree_and_tour_frequency_is_5,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_full_time_worker_and_escorting_tour,-0.893,F -coef_presence_of_part_time_worker_and_escorting_tour,0,T -coef_presence_of_non_worker_and_escorting_tour,0.89,F -coef_presence_of_retiree_and_escorting_tour,0,T -coef_presence_of_university_student_and_escorting_tour,0,T -coef_presence_of_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_school_kid_and_escorting_tour,0,T -coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T -coef_at_home_pre_school_kid_and_escorting_tour,0,T -coef_presence_of_full_time_worker_and_shopping_tour,0,T -coef_presence_of_part_time_worker_and_shopping_tour,1.155,F -coef_presence_of_non_worker_and_shopping_tour,0.808,F -coef_presence_of_retiree_and_shopping_tour,0,T -coef_presence_of_university_student_and_shopping_tour,0,T -coef_presence_of_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_school_kid_and_shopping_tour,0,T -coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T -coef_at_home_pre_school_kid_and_shopping_tour,0,T -coef_presence_of_full_time_worker_and_maintenance_tour,0,T -coef_presence_of_part_time_worker_and_maintenance_tour,0,T -coef_presence_of_non_worker_and_maintenance_tour,0,T -coef_presence_of_retiree_and_maintenance_tour,0,T -coef_presence_of_university_student_and_maintenance_tour,0,T -coef_presence_of_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_school_kid_and_maintenance_tour,0,T -coef_presence_of_full_time_worker_and_eating_out_tour,0,T -coef_presence_of_part_time_worker_and_eating_out_tour,1.037,F -coef_presence_of_non_worker_and_eating_out_tour,1.157,F -coef_presence_of_retiree_and_eating_out_tour,0,T -coef_presence_of_university_student_and_eating_out_tour,0,T -coef_presence_of_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_school_kid_and_eating_out_tour,0,T -coef_presence_of_full_time_worker_and_discretionary_tour,0,T -coef_presence_of_part_time_worker_and_discretionary_tour,0,T -coef_presence_of_non_worker_and_discretionary_tour,0.791,F -coef_presence_of_retiree_and_discretionary_tour,0,T -coef_presence_of_university_student_and_discretionary_tour,0,T -coef_presence_of_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_school_kid_and_discretionary_tour,0,T -coef_walk_access_to_retail_and_tour_frequency_is_1,0,T -coef_walk_access_to_retail_and_tour_frequency_is_2,0,T -coef_walk_access_to_retail_and_tour_frequency_is_3,0,T -coef_walk_access_to_retail_and_tour_frequency_is_4,0,T -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_transit_access_to_retail_and_tour_frequency_is_1,0,T -coef_transit_access_to_retail_and_tour_frequency_is_2,0,T -coef_transit_access_to_retail_and_tour_frequency_is_3,0,T -coef_transit_access_to_retail_and_tour_frequency_is_4,0,T -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_auto_access_to_retail_and_tour_frequency_is_1,0,T -coef_auto_access_to_retail_and_tour_frequency_is_2,0,T -coef_auto_access_to_retail_and_tour_frequency_is_3,0,T -coef_auto_access_to_retail_and_tour_frequency_is_4,0,T -coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_walk_access_to_retail_and_escorting,0,T -coef_transit_access_to_retail_and_escorting,0,T -coef_auto_access_to_retail_and_escorting,0,T -coef_walk_access_to_retail_and_shopping,0,T -coef_transit_access_to_retail_and_shopping,0,T -coef_auto_access_to_retail_and_shopping,0,T -coef_walk_access_to_retail_and_maintenance,0,T -coef_transit_access_to_retail_and_maintenance,0,T -coef_auto_access_to_retail_and_maintenance,0,T -coef_walk_access_to_retail_and_eating_out,0,T -coef_transit_access_to_retail_and_eating_out,0,T -coef_auto_access_to_retail_and_eating_out,0,T -coef_walk_access_to_retail_and_discretionary,0,T -coef_transit_access_to_retail_and_discretionary,0,T -coef_auto_access_to_retail_and_discretionary,0,T -coef_urban_and_tour_frequency_is_1,0,T -coef_urban_and_tour_frequency_is_2,0,T -coef_urban_and_tour_frequency_is_3,0,T -coef_urban_and_tour_frequency_is_4,0,T -coef_urban_and_tour_frequency_is_5_plus,0,T -coef_urban_and_escorting_tour,0,T -coef_urban_and_shopping_tour,0,T -coef_urban_and_maintenance_tour,0,T -coef_urban_and_eatingout_tour,0,T -coef_urban_and_discretionary_tour,0,T -coef_1_escort_tour_constant,0.3622,F -coef_2_plus_escort_tours_constant,2.2219,F -coef_1_plus_shopping_tours_constant,1.6919,F -coef_1_plus_maintenance_tours_constant,0.6788,F -coef_1_plus_eating_out_tours_constant,0.9612,F -coef_1_plus_visting_tours_constant,0.4424,F -coef_1_plus_other_discretionary_tours_constant,1.4935,F +coefficient_name,value,constrain +coef_escorting_tour,2.491,F +coef_discretionary_tour,0.903,F +coef_shopping_tour,0,T +coef_maintenance_tour,1.022,F +coef_visiting_or_social_tour,0.769,F +coef_eating_out_tour,0,T +coef_total_number_of_tours_is_0_no_prior_tours,-999,T +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,-5.759,F +coef_total_number_of_tours_is_2,-11.517,F +coef_total_number_of_tours_is_3,-17.276,F +coef_total_number_of_tours_is_4,-23.035,F +coef_total_number_of_tours_is_5,-999,T +coef_total_number_of_tours_is_6_plus,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_1,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_2,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_3,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_4,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,0,T +coef_number_of_joint_tours_and_tour_frequency_is_0,0,T +coef_number_of_joint_tours_and_tour_frequency_is_1,0,T +coef_number_of_joint_tours_and_tour_frequency_is_2,0,T +coef_number_of_joint_tours_and_tour_frequency_is_3,0,T +coef_number_of_joint_tours_and_tour_frequency_is_4,0,T +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,0,T +coef_number_of_joint_shopping_tours,0,T +coef_number_of_joint_maintenance_tours,0,T +coef_number_of_joint_eating_out_tours,0,T +coef_number_of_joint_visit_tours,0,T +coef_number_of_joint_discretionary_tours,0,T +coef_logged_maximum_residual_window_tour_frequency_is_0,0,T +coef_logged_maximum_residual_window_tour_frequency_is_1,0,T +coef_logged_maximum_residual_window_tour_frequency_is_2,0,T +coef_logged_maximum_residual_window_tour_frequency_is_3,0,T +coef_logged_maximum_residual_window_tour_frequency_is_4,0,T +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,0,T +coef_mediumlow_income_group_and_tour_frequency_is_1,0,T +coef_mediumlow_income_group_and_tour_frequency_is_2,0,T +coef_mediumlow_income_group_and_tour_frequency_is_3,0,T +coef_mediumlow_income_group_and_tour_frequency_is_4,0,T +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_1,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_2,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_3,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_4,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0,T +coef_high_income_group_and_tour_frequency_is_1,0,T +coef_high_income_group_and_tour_frequency_is_2,0,T +coef_high_income_group_and_tour_frequency_is_3,0,T +coef_high_income_group_and_tour_frequency_is_4,0,T +coef_high_income_group_and_tour_frequency_is_5_plus,0,T +coef_mediumlow_income_group_and_shopping_tour,0,T +coef_mediumhigh_income_group_and_shopping_tour,0,T +coef_high_income_group_and_shopping_tour,0,T +coef_mediumlow_income_group_and_maintenance_tour,0,T +coef_mediumhigh_income_group_and_maintenance_tour,0,T +coef_high_income_group_and_maintenance_tour,0,T +coef_mediumlow_income_group_and_eating_out_tour,0,T +coef_mediumhigh_income_group_and_eating_out_tour,0,T +coef_high_income_group_and_eating_out_tour,0,T +coef_mediumlow_income_group_and_discretionary_tour,0,T +coef_mediumhigh_income_group_and_discretionary_tour,0,T +coef_high_income_group_and_discretionary_tour,0,T +coef_mediumlow_income_group_and_visiting_tour,0,T +coef_mediumhigh_income_group_and_visiting_tour,0,T +coef_high_income_group_and_visiting_tour,0,T +coef_female_and_tour_frequency_is_1,0,T +coef_female_and_tour_frequency_is_2,0,T +coef_female_and_tour_frequency_is_3,0,T +coef_female_and_tour_frequency_is_4,0,T +coef_female_and_tour_frequency_is_5,0,T +coef_female_and_escorting_tour,0,T +coef_female_and_shopping_tour,0,T +coef_female_and_maintenance_tour,0,T +coef_female_and_eatingout_tour,0,T +coef_female_and_discretionary_tour,0,T +coef_zero_car_ownership_and_tour_frequency_is_1,0,T +coef_zero_car_ownership_and_tour_frequency_is_2,0,T +coef_zero_car_ownership_and_tour_frequency_is_3,0,T +coef_zero_car_ownership_and_tour_frequency_is_4,0,T +coef_zero_car_ownership_and_tour_frequency_is_5_plus,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T +coef_presence_of_non_worker_and_tour_frequency_is_1,0,T +coef_presence_of_non_worker_and_tour_frequency_is_2,0,T +coef_presence_of_non_worker_and_tour_frequency_is_3,0,T +coef_presence_of_non_worker_and_tour_frequency_is_4,0,T +coef_presence_of_non_worker_and_tour_frequency_is_5,0,T +coef_presence_of_retiree_and_tour_frequency_is_1,0,T +coef_presence_of_retiree_and_tour_frequency_is_2,0,T +coef_presence_of_retiree_and_tour_frequency_is_3,0,T +coef_presence_of_retiree_and_tour_frequency_is_4,0,T +coef_presence_of_retiree_and_tour_frequency_is_5,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_full_time_worker_and_escorting_tour,-0.893,F +coef_presence_of_part_time_worker_and_escorting_tour,0,T +coef_presence_of_non_worker_and_escorting_tour,0.89,F +coef_presence_of_retiree_and_escorting_tour,0,T +coef_presence_of_university_student_and_escorting_tour,0,T +coef_presence_of_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_school_kid_and_escorting_tour,0,T +coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T +coef_at_home_pre_school_kid_and_escorting_tour,0,T +coef_presence_of_full_time_worker_and_shopping_tour,0,T +coef_presence_of_part_time_worker_and_shopping_tour,1.155,F +coef_presence_of_non_worker_and_shopping_tour,0.808,F +coef_presence_of_retiree_and_shopping_tour,0,T +coef_presence_of_university_student_and_shopping_tour,0,T +coef_presence_of_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_school_kid_and_shopping_tour,0,T +coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T +coef_at_home_pre_school_kid_and_shopping_tour,0,T +coef_presence_of_full_time_worker_and_maintenance_tour,0,T +coef_presence_of_part_time_worker_and_maintenance_tour,0,T +coef_presence_of_non_worker_and_maintenance_tour,0,T +coef_presence_of_retiree_and_maintenance_tour,0,T +coef_presence_of_university_student_and_maintenance_tour,0,T +coef_presence_of_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_school_kid_and_maintenance_tour,0,T +coef_presence_of_full_time_worker_and_eating_out_tour,0,T +coef_presence_of_part_time_worker_and_eating_out_tour,1.037,F +coef_presence_of_non_worker_and_eating_out_tour,1.157,F +coef_presence_of_retiree_and_eating_out_tour,0,T +coef_presence_of_university_student_and_eating_out_tour,0,T +coef_presence_of_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_school_kid_and_eating_out_tour,0,T +coef_presence_of_full_time_worker_and_discretionary_tour,0,T +coef_presence_of_part_time_worker_and_discretionary_tour,0,T +coef_presence_of_non_worker_and_discretionary_tour,0.791,F +coef_presence_of_retiree_and_discretionary_tour,0,T +coef_presence_of_university_student_and_discretionary_tour,0,T +coef_presence_of_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_school_kid_and_discretionary_tour,0,T +coef_walk_access_to_retail_and_tour_frequency_is_1,0,T +coef_walk_access_to_retail_and_tour_frequency_is_2,0,T +coef_walk_access_to_retail_and_tour_frequency_is_3,0,T +coef_walk_access_to_retail_and_tour_frequency_is_4,0,T +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_transit_access_to_retail_and_tour_frequency_is_1,0,T +coef_transit_access_to_retail_and_tour_frequency_is_2,0,T +coef_transit_access_to_retail_and_tour_frequency_is_3,0,T +coef_transit_access_to_retail_and_tour_frequency_is_4,0,T +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_auto_access_to_retail_and_tour_frequency_is_1,0,T +coef_auto_access_to_retail_and_tour_frequency_is_2,0,T +coef_auto_access_to_retail_and_tour_frequency_is_3,0,T +coef_auto_access_to_retail_and_tour_frequency_is_4,0,T +coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_walk_access_to_retail_and_escorting,0,T +coef_transit_access_to_retail_and_escorting,0,T +coef_auto_access_to_retail_and_escorting,0,T +coef_walk_access_to_retail_and_shopping,0,T +coef_transit_access_to_retail_and_shopping,0,T +coef_auto_access_to_retail_and_shopping,0,T +coef_walk_access_to_retail_and_maintenance,0,T +coef_transit_access_to_retail_and_maintenance,0,T +coef_auto_access_to_retail_and_maintenance,0,T +coef_walk_access_to_retail_and_eating_out,0,T +coef_transit_access_to_retail_and_eating_out,0,T +coef_auto_access_to_retail_and_eating_out,0,T +coef_walk_access_to_retail_and_discretionary,0,T +coef_transit_access_to_retail_and_discretionary,0,T +coef_auto_access_to_retail_and_discretionary,0,T +coef_urban_and_tour_frequency_is_1,0,T +coef_urban_and_tour_frequency_is_2,0,T +coef_urban_and_tour_frequency_is_3,0,T +coef_urban_and_tour_frequency_is_4,0,T +coef_urban_and_tour_frequency_is_5_plus,0,T +coef_urban_and_escorting_tour,0,T +coef_urban_and_shopping_tour,0,T +coef_urban_and_maintenance_tour,0,T +coef_urban_and_eatingout_tour,0,T +coef_urban_and_discretionary_tour,0,T +coef_1_escort_tour_constant,0.3622,F +coef_2_plus_escort_tours_constant,2.2219,F +coef_1_plus_shopping_tours_constant,1.6919,F +coef_1_plus_maintenance_tours_constant,0.6788,F +coef_1_plus_eating_out_tours_constant,0.9612,F +coef_1_plus_visting_tours_constant,0.4424,F +coef_1_plus_other_discretionary_tours_constant,1.4935,F coef_0_auto_household_and_escorting_tour,-2,T \ No newline at end of file diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv index ea64f3ca34..dc76652a0b 100755 --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_RETIRED.csv @@ -1,211 +1,211 @@ -coefficient_name,value,constrain -coef_escorting_tour,0,T -coef_discretionary_tour,0,T -coef_shopping_tour,0,T -coef_maintenance_tour,0,T -coef_visiting_or_social_tour,0,T -coef_eating_out_tour,0,T -coef_total_number_of_tours_is_0_no_prior_tours,-999,T -coef_total_number_of_tours_is_0_prior_tours,0,T -coef_total_number_of_tours_is_1,-8.5684,F -coef_total_number_of_tours_is_2,-12.7416,F -coef_total_number_of_tours_is_3,-15.0978,F -coef_total_number_of_tours_is_4,-19.5439,F -coef_total_number_of_tours_is_5,-20.7897,F -coef_total_number_of_tours_is_6_plus,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_1,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_2,-5.0196,F -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-5.0196,F -coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_0,0,T -coef_number_of_joint_tours_and_tour_frequency_is_1,0,T -coef_number_of_joint_tours_and_tour_frequency_is_2,-0.95,F -coef_number_of_joint_tours_and_tour_frequency_is_3,-7.143,F -coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_shopping_tours,-0.8072,F -coef_number_of_joint_maintenance_tours,0,T -coef_number_of_joint_eating_out_tours,0,T -coef_number_of_joint_visit_tours,0,T -coef_number_of_joint_discretionary_tours,0,T -coef_logged_maximum_residual_window_tour_frequency_is_0,0,T -coef_logged_maximum_residual_window_tour_frequency_is_1,1.8357,F -coef_logged_maximum_residual_window_tour_frequency_is_2,2.2707,F -coef_logged_maximum_residual_window_tour_frequency_is_3,4.4023,F -coef_logged_maximum_residual_window_tour_frequency_is_4,4.4023,F -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,4.4023,F -coef_mediumlow_income_group_and_tour_frequency_is_1,0,T -coef_mediumlow_income_group_and_tour_frequency_is_2,0,T -coef_mediumlow_income_group_and_tour_frequency_is_3,0,T -coef_mediumlow_income_group_and_tour_frequency_is_4,0,T -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_1,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_2,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_3,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_4,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0,T -coef_high_income_group_and_tour_frequency_is_1,0,T -coef_high_income_group_and_tour_frequency_is_2,0,T -coef_high_income_group_and_tour_frequency_is_3,0,T -coef_high_income_group_and_tour_frequency_is_4,0,T -coef_high_income_group_and_tour_frequency_is_5_plus,0,T -coef_mediumlow_income_group_and_shopping_tour,1.0949,F -coef_mediumhigh_income_group_and_shopping_tour,1.0949,F -coef_high_income_group_and_shopping_tour,1.0949,F -coef_mediumlow_income_group_and_maintenance_tour,0.7648,F -coef_mediumhigh_income_group_and_maintenance_tour,0.7648,F -coef_high_income_group_and_maintenance_tour,1.3795,F -coef_mediumlow_income_group_and_eating_out_tour,0.9769,F -coef_mediumhigh_income_group_and_eating_out_tour,1.181,F -coef_high_income_group_and_eating_out_tour,1.4842,F -coef_mediumlow_income_group_and_discretionary_tour,1.0095,F -coef_mediumhigh_income_group_and_discretionary_tour,1.0095,F -coef_high_income_group_and_discretionary_tour,1.0095,F -coef_mediumlow_income_group_and_visiting_tour,0,T -coef_mediumhigh_income_group_and_visiting_tour,-0.4368,F -coef_high_income_group_and_visiting_tour,-0.5137,F -coef_female_and_tour_frequency_is_1,-0.9348,F -coef_female_and_tour_frequency_is_2,-1.3028,F -coef_female_and_tour_frequency_is_3,-2.266,F -coef_female_and_tour_frequency_is_4,-2.266,F -coef_female_and_tour_frequency_is_5,-2.266,F -coef_female_and_escorting_tour,0,T -coef_female_and_shopping_tour,0.9688,F -coef_female_and_maintenance_tour,0.7424,F -coef_female_and_eatingout_tour,0,T -coef_female_and_discretionary_tour,0.4954,F -coef_zero_car_ownership_and_tour_frequency_is_1,0,T -coef_zero_car_ownership_and_tour_frequency_is_2,0,T -coef_zero_car_ownership_and_tour_frequency_is_3,0,T -coef_zero_car_ownership_and_tour_frequency_is_4,0,T -coef_zero_car_ownership_and_tour_frequency_is_5_plus,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.7965,F -coef_car_surplus_vs_workers_and_tour_frequency_is_2,2.1302,F -coef_car_surplus_vs_workers_and_tour_frequency_is_3,2.1302,F -coef_car_surplus_vs_workers_and_tour_frequency_is_4,2.1302,F -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,2.1302,F -coef_presence_of_non_worker_and_tour_frequency_is_1,0.224,F -coef_presence_of_non_worker_and_tour_frequency_is_2,0.2436,F -coef_presence_of_non_worker_and_tour_frequency_is_3,0.62,F -coef_presence_of_non_worker_and_tour_frequency_is_4,3.3742,F -coef_presence_of_non_worker_and_tour_frequency_is_5,3.3742,F -coef_presence_of_retiree_and_tour_frequency_is_1,-0.4458,F -coef_presence_of_retiree_and_tour_frequency_is_2,-0.5315,F -coef_presence_of_retiree_and_tour_frequency_is_3,-0.5315,F -coef_presence_of_retiree_and_tour_frequency_is_4,-0.5315,F -coef_presence_of_retiree_and_tour_frequency_is_5,-0.5315,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_full_time_worker_and_escorting_tour,0,T -coef_presence_of_part_time_worker_and_escorting_tour,0,T -coef_presence_of_non_worker_and_escorting_tour,0,T -coef_presence_of_retiree_and_escorting_tour,0,T -coef_presence_of_university_student_and_escorting_tour,0,T -coef_presence_of_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.4903,F -coef_presence_of_pre_school_kid_and_escorting_tour,0.5027,F -coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T -coef_at_home_pre_school_kid_and_escorting_tour,0,T -coef_presence_of_full_time_worker_and_shopping_tour,-0.3609,F -coef_presence_of_part_time_worker_and_shopping_tour,0,T -coef_presence_of_non_worker_and_shopping_tour,0,T -coef_presence_of_retiree_and_shopping_tour,0,T -coef_presence_of_university_student_and_shopping_tour,0,T -coef_presence_of_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_school_kid_and_shopping_tour,0,T -coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T -coef_at_home_pre_school_kid_and_shopping_tour,0,T -coef_presence_of_full_time_worker_and_maintenance_tour,0,T -coef_presence_of_part_time_worker_and_maintenance_tour,0,T -coef_presence_of_non_worker_and_maintenance_tour,0,T -coef_presence_of_retiree_and_maintenance_tour,0,T -coef_presence_of_university_student_and_maintenance_tour,0,T -coef_presence_of_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_school_kid_and_maintenance_tour,0,T -coef_presence_of_full_time_worker_and_eating_out_tour,-0.788,F -coef_presence_of_part_time_worker_and_eating_out_tour,-0.788,F -coef_presence_of_non_worker_and_eating_out_tour,-0.788,F -coef_presence_of_retiree_and_eating_out_tour,-0.9282,F -coef_presence_of_university_student_and_eating_out_tour,0,T -coef_presence_of_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_school_kid_and_eating_out_tour,0,T -coef_presence_of_full_time_worker_and_discretionary_tour,-0.4835,F -coef_presence_of_part_time_worker_and_discretionary_tour,0,T -coef_presence_of_non_worker_and_discretionary_tour,-0.5603,F -coef_presence_of_retiree_and_discretionary_tour,0,T -coef_presence_of_university_student_and_discretionary_tour,0,T -coef_presence_of_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_school_kid_and_discretionary_tour,0,T -coef_walk_access_to_retail_and_tour_frequency_is_1,0.0616,F -coef_walk_access_to_retail_and_tour_frequency_is_2,0.0616,F -coef_walk_access_to_retail_and_tour_frequency_is_3,0.0616,F -coef_walk_access_to_retail_and_tour_frequency_is_4,0.0616,F -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.0616,F -coef_transit_access_to_retail_and_tour_frequency_is_1,0,T -coef_transit_access_to_retail_and_tour_frequency_is_2,0,T -coef_transit_access_to_retail_and_tour_frequency_is_3,0,T -coef_transit_access_to_retail_and_tour_frequency_is_4,0,T -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_auto_access_to_retail_and_tour_frequency_is_1,0,T -coef_auto_access_to_retail_and_tour_frequency_is_2,0,T -coef_auto_access_to_retail_and_tour_frequency_is_3,0,T -coef_auto_access_to_retail_and_tour_frequency_is_4,0,T -coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_walk_access_to_retail_and_escorting,0,T -coef_transit_access_to_retail_and_escorting,0,T -coef_auto_access_to_retail_and_escorting,0,T -coef_walk_access_to_retail_and_shopping,0,T -coef_transit_access_to_retail_and_shopping,0,T -coef_auto_access_to_retail_and_shopping,0,T -coef_walk_access_to_retail_and_maintenance,0,T -coef_transit_access_to_retail_and_maintenance,0,T -coef_auto_access_to_retail_and_maintenance,0,T -coef_walk_access_to_retail_and_eating_out,0,T -coef_transit_access_to_retail_and_eating_out,0,T -coef_auto_access_to_retail_and_eating_out,0,T -coef_walk_access_to_retail_and_discretionary,0,T -coef_transit_access_to_retail_and_discretionary,0,T -coef_auto_access_to_retail_and_discretionary,0,T -coef_urban_and_tour_frequency_is_1,0,T -coef_urban_and_tour_frequency_is_2,0,T -coef_urban_and_tour_frequency_is_3,0,T -coef_urban_and_tour_frequency_is_4,0,T -coef_urban_and_tour_frequency_is_5_plus,0,T -coef_urban_and_escorting_tour,0,T -coef_urban_and_shopping_tour,0,T -coef_urban_and_maintenance_tour,0,T -coef_urban_and_eatingout_tour,0,T -coef_urban_and_discretionary_tour,0,T -coef_1_escort_tour_constant,-0.3992,F -coef_2_plus_escort_tours_constant,0.5175,F -coef_1_plus_shopping_tours_constant,0.5947,F -coef_1_plus_maintenance_tours_constant,0.1046,F -coef_1_plus_eating_out_tours_constant,0.0245,F -coef_1_plus_visting_tours_constant,0.2789,F -coef_1_plus_other_discretionary_tours_constant,0.4282,F +coefficient_name,value,constrain +coef_escorting_tour,0,T +coef_discretionary_tour,0,T +coef_shopping_tour,0,T +coef_maintenance_tour,0,T +coef_visiting_or_social_tour,0,T +coef_eating_out_tour,0,T +coef_total_number_of_tours_is_0_no_prior_tours,-999,T +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,-8.5684,F +coef_total_number_of_tours_is_2,-12.7416,F +coef_total_number_of_tours_is_3,-15.0978,F +coef_total_number_of_tours_is_4,-19.5439,F +coef_total_number_of_tours_is_5,-20.7897,F +coef_total_number_of_tours_is_6_plus,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_1,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_2,-5.0196,F +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-5.0196,F +coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_0,0,T +coef_number_of_joint_tours_and_tour_frequency_is_1,0,T +coef_number_of_joint_tours_and_tour_frequency_is_2,-0.95,F +coef_number_of_joint_tours_and_tour_frequency_is_3,-7.143,F +coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_shopping_tours,-0.8072,F +coef_number_of_joint_maintenance_tours,0,T +coef_number_of_joint_eating_out_tours,0,T +coef_number_of_joint_visit_tours,0,T +coef_number_of_joint_discretionary_tours,0,T +coef_logged_maximum_residual_window_tour_frequency_is_0,0,T +coef_logged_maximum_residual_window_tour_frequency_is_1,1.8357,F +coef_logged_maximum_residual_window_tour_frequency_is_2,2.2707,F +coef_logged_maximum_residual_window_tour_frequency_is_3,4.4023,F +coef_logged_maximum_residual_window_tour_frequency_is_4,4.4023,F +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,4.4023,F +coef_mediumlow_income_group_and_tour_frequency_is_1,0,T +coef_mediumlow_income_group_and_tour_frequency_is_2,0,T +coef_mediumlow_income_group_and_tour_frequency_is_3,0,T +coef_mediumlow_income_group_and_tour_frequency_is_4,0,T +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_1,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_2,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_3,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_4,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0,T +coef_high_income_group_and_tour_frequency_is_1,0,T +coef_high_income_group_and_tour_frequency_is_2,0,T +coef_high_income_group_and_tour_frequency_is_3,0,T +coef_high_income_group_and_tour_frequency_is_4,0,T +coef_high_income_group_and_tour_frequency_is_5_plus,0,T +coef_mediumlow_income_group_and_shopping_tour,1.0949,F +coef_mediumhigh_income_group_and_shopping_tour,1.0949,F +coef_high_income_group_and_shopping_tour,1.0949,F +coef_mediumlow_income_group_and_maintenance_tour,0.7648,F +coef_mediumhigh_income_group_and_maintenance_tour,0.7648,F +coef_high_income_group_and_maintenance_tour,1.3795,F +coef_mediumlow_income_group_and_eating_out_tour,0.9769,F +coef_mediumhigh_income_group_and_eating_out_tour,1.181,F +coef_high_income_group_and_eating_out_tour,1.4842,F +coef_mediumlow_income_group_and_discretionary_tour,1.0095,F +coef_mediumhigh_income_group_and_discretionary_tour,1.0095,F +coef_high_income_group_and_discretionary_tour,1.0095,F +coef_mediumlow_income_group_and_visiting_tour,0,T +coef_mediumhigh_income_group_and_visiting_tour,-0.4368,F +coef_high_income_group_and_visiting_tour,-0.5137,F +coef_female_and_tour_frequency_is_1,-0.9348,F +coef_female_and_tour_frequency_is_2,-1.3028,F +coef_female_and_tour_frequency_is_3,-2.266,F +coef_female_and_tour_frequency_is_4,-2.266,F +coef_female_and_tour_frequency_is_5,-2.266,F +coef_female_and_escorting_tour,0,T +coef_female_and_shopping_tour,0.9688,F +coef_female_and_maintenance_tour,0.7424,F +coef_female_and_eatingout_tour,0,T +coef_female_and_discretionary_tour,0.4954,F +coef_zero_car_ownership_and_tour_frequency_is_1,0,T +coef_zero_car_ownership_and_tour_frequency_is_2,0,T +coef_zero_car_ownership_and_tour_frequency_is_3,0,T +coef_zero_car_ownership_and_tour_frequency_is_4,0,T +coef_zero_car_ownership_and_tour_frequency_is_5_plus,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_1,0.7965,F +coef_car_surplus_vs_workers_and_tour_frequency_is_2,2.1302,F +coef_car_surplus_vs_workers_and_tour_frequency_is_3,2.1302,F +coef_car_surplus_vs_workers_and_tour_frequency_is_4,2.1302,F +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,2.1302,F +coef_presence_of_non_worker_and_tour_frequency_is_1,0.224,F +coef_presence_of_non_worker_and_tour_frequency_is_2,0.2436,F +coef_presence_of_non_worker_and_tour_frequency_is_3,0.62,F +coef_presence_of_non_worker_and_tour_frequency_is_4,3.3742,F +coef_presence_of_non_worker_and_tour_frequency_is_5,3.3742,F +coef_presence_of_retiree_and_tour_frequency_is_1,-0.4458,F +coef_presence_of_retiree_and_tour_frequency_is_2,-0.5315,F +coef_presence_of_retiree_and_tour_frequency_is_3,-0.5315,F +coef_presence_of_retiree_and_tour_frequency_is_4,-0.5315,F +coef_presence_of_retiree_and_tour_frequency_is_5,-0.5315,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_full_time_worker_and_escorting_tour,0,T +coef_presence_of_part_time_worker_and_escorting_tour,0,T +coef_presence_of_non_worker_and_escorting_tour,0,T +coef_presence_of_retiree_and_escorting_tour,0,T +coef_presence_of_university_student_and_escorting_tour,0,T +coef_presence_of_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_driving_school_kid_and_escorting_tour,1.4903,F +coef_presence_of_pre_school_kid_and_escorting_tour,0.5027,F +coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T +coef_at_home_pre_school_kid_and_escorting_tour,0,T +coef_presence_of_full_time_worker_and_shopping_tour,-0.3609,F +coef_presence_of_part_time_worker_and_shopping_tour,0,T +coef_presence_of_non_worker_and_shopping_tour,0,T +coef_presence_of_retiree_and_shopping_tour,0,T +coef_presence_of_university_student_and_shopping_tour,0,T +coef_presence_of_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_school_kid_and_shopping_tour,0,T +coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T +coef_at_home_pre_school_kid_and_shopping_tour,0,T +coef_presence_of_full_time_worker_and_maintenance_tour,0,T +coef_presence_of_part_time_worker_and_maintenance_tour,0,T +coef_presence_of_non_worker_and_maintenance_tour,0,T +coef_presence_of_retiree_and_maintenance_tour,0,T +coef_presence_of_university_student_and_maintenance_tour,0,T +coef_presence_of_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_school_kid_and_maintenance_tour,0,T +coef_presence_of_full_time_worker_and_eating_out_tour,-0.788,F +coef_presence_of_part_time_worker_and_eating_out_tour,-0.788,F +coef_presence_of_non_worker_and_eating_out_tour,-0.788,F +coef_presence_of_retiree_and_eating_out_tour,-0.9282,F +coef_presence_of_university_student_and_eating_out_tour,0,T +coef_presence_of_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_school_kid_and_eating_out_tour,0,T +coef_presence_of_full_time_worker_and_discretionary_tour,-0.4835,F +coef_presence_of_part_time_worker_and_discretionary_tour,0,T +coef_presence_of_non_worker_and_discretionary_tour,-0.5603,F +coef_presence_of_retiree_and_discretionary_tour,0,T +coef_presence_of_university_student_and_discretionary_tour,0,T +coef_presence_of_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_school_kid_and_discretionary_tour,0,T +coef_walk_access_to_retail_and_tour_frequency_is_1,0.0616,F +coef_walk_access_to_retail_and_tour_frequency_is_2,0.0616,F +coef_walk_access_to_retail_and_tour_frequency_is_3,0.0616,F +coef_walk_access_to_retail_and_tour_frequency_is_4,0.0616,F +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0.0616,F +coef_transit_access_to_retail_and_tour_frequency_is_1,0,T +coef_transit_access_to_retail_and_tour_frequency_is_2,0,T +coef_transit_access_to_retail_and_tour_frequency_is_3,0,T +coef_transit_access_to_retail_and_tour_frequency_is_4,0,T +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_auto_access_to_retail_and_tour_frequency_is_1,0,T +coef_auto_access_to_retail_and_tour_frequency_is_2,0,T +coef_auto_access_to_retail_and_tour_frequency_is_3,0,T +coef_auto_access_to_retail_and_tour_frequency_is_4,0,T +coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_walk_access_to_retail_and_escorting,0,T +coef_transit_access_to_retail_and_escorting,0,T +coef_auto_access_to_retail_and_escorting,0,T +coef_walk_access_to_retail_and_shopping,0,T +coef_transit_access_to_retail_and_shopping,0,T +coef_auto_access_to_retail_and_shopping,0,T +coef_walk_access_to_retail_and_maintenance,0,T +coef_transit_access_to_retail_and_maintenance,0,T +coef_auto_access_to_retail_and_maintenance,0,T +coef_walk_access_to_retail_and_eating_out,0,T +coef_transit_access_to_retail_and_eating_out,0,T +coef_auto_access_to_retail_and_eating_out,0,T +coef_walk_access_to_retail_and_discretionary,0,T +coef_transit_access_to_retail_and_discretionary,0,T +coef_auto_access_to_retail_and_discretionary,0,T +coef_urban_and_tour_frequency_is_1,0,T +coef_urban_and_tour_frequency_is_2,0,T +coef_urban_and_tour_frequency_is_3,0,T +coef_urban_and_tour_frequency_is_4,0,T +coef_urban_and_tour_frequency_is_5_plus,0,T +coef_urban_and_escorting_tour,0,T +coef_urban_and_shopping_tour,0,T +coef_urban_and_maintenance_tour,0,T +coef_urban_and_eatingout_tour,0,T +coef_urban_and_discretionary_tour,0,T +coef_1_escort_tour_constant,-0.3992,F +coef_2_plus_escort_tours_constant,0.5175,F +coef_1_plus_shopping_tours_constant,0.5947,F +coef_1_plus_maintenance_tours_constant,0.1046,F +coef_1_plus_eating_out_tours_constant,0.0245,F +coef_1_plus_visting_tours_constant,0.2789,F +coef_1_plus_other_discretionary_tours_constant,0.4282,F coef_0_auto_household_and_escorting_tour,-2,T \ No newline at end of file diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv similarity index 98% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv index 8f392d20ea..fae10e51ff 100755 --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_SCHOOL.csv @@ -1,211 +1,211 @@ -coefficient_name,value,constrain -coef_escorting_tour,0,T -coef_discretionary_tour,0,T -coef_shopping_tour,0,T -coef_maintenance_tour,0,T -coef_visiting_or_social_tour,0,T -coef_eating_out_tour,0,T -coef_total_number_of_tours_is_0_no_prior_tours,-999,T -coef_total_number_of_tours_is_0_prior_tours,0,T -coef_total_number_of_tours_is_1,-7.4863,F -coef_total_number_of_tours_is_2,-10.718,F -coef_total_number_of_tours_is_3,-13.7884,F -coef_total_number_of_tours_is_4,-999,T -coef_total_number_of_tours_is_5,-999,T -coef_total_number_of_tours_is_6_plus,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_1,-1.0331,F -coef_number_of_mandatory_tours_and_tour_frequency_is_2,-2.7445,F -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-2.7445,F -coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_0,0,T -coef_number_of_joint_tours_and_tour_frequency_is_1,-0.6149,F -coef_number_of_joint_tours_and_tour_frequency_is_2,-0.6149,F -coef_number_of_joint_tours_and_tour_frequency_is_3,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_shopping_tours,0,T -coef_number_of_joint_maintenance_tours,-1.3476,F -coef_number_of_joint_eating_out_tours,0,T -coef_number_of_joint_visit_tours,0,T -coef_number_of_joint_discretionary_tours,0,T -coef_logged_maximum_residual_window_tour_frequency_is_0,0,T -coef_logged_maximum_residual_window_tour_frequency_is_1,1.5603,F -coef_logged_maximum_residual_window_tour_frequency_is_2,1.5603,F -coef_logged_maximum_residual_window_tour_frequency_is_3,1.5603,F -coef_logged_maximum_residual_window_tour_frequency_is_4,1.5603,F -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.5603,F -coef_mediumlow_income_group_and_tour_frequency_is_1,1.0873,F -coef_mediumlow_income_group_and_tour_frequency_is_2,1.0873,F -coef_mediumlow_income_group_and_tour_frequency_is_3,1.0873,F -coef_mediumlow_income_group_and_tour_frequency_is_4,1.0873,F -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,1.0873,F -coef_mediumhigh_income_group_and_tour_frequency_is_1,1.5197,F -coef_mediumhigh_income_group_and_tour_frequency_is_2,1.5197,F -coef_mediumhigh_income_group_and_tour_frequency_is_3,1.5197,F -coef_mediumhigh_income_group_and_tour_frequency_is_4,1.5197,F -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.5197,F -coef_high_income_group_and_tour_frequency_is_1,2.0175,F -coef_high_income_group_and_tour_frequency_is_2,2.0175,F -coef_high_income_group_and_tour_frequency_is_3,2.0175,F -coef_high_income_group_and_tour_frequency_is_4,2.0175,F -coef_high_income_group_and_tour_frequency_is_5_plus,2.0175,F -coef_mediumlow_income_group_and_shopping_tour,-0.6506,F -coef_mediumhigh_income_group_and_shopping_tour,-0.6506,F -coef_high_income_group_and_shopping_tour,-0.6506,F -coef_mediumlow_income_group_and_maintenance_tour,0,T -coef_mediumhigh_income_group_and_maintenance_tour,0,T -coef_high_income_group_and_maintenance_tour,0,T -coef_mediumlow_income_group_and_eating_out_tour,-0.701,F -coef_mediumhigh_income_group_and_eating_out_tour,-0.701,F -coef_high_income_group_and_eating_out_tour,-0.701,F -coef_mediumlow_income_group_and_discretionary_tour,0,T -coef_mediumhigh_income_group_and_discretionary_tour,0,T -coef_high_income_group_and_discretionary_tour,0,T -coef_mediumlow_income_group_and_visiting_tour,0,T -coef_mediumhigh_income_group_and_visiting_tour,0,T -coef_high_income_group_and_visiting_tour,0,T -coef_female_and_tour_frequency_is_1,0,T -coef_female_and_tour_frequency_is_2,0,T -coef_female_and_tour_frequency_is_3,0,T -coef_female_and_tour_frequency_is_4,0,T -coef_female_and_tour_frequency_is_5,0,T -coef_female_and_escorting_tour,0,T -coef_female_and_shopping_tour,0,T -coef_female_and_maintenance_tour,0,T -coef_female_and_eatingout_tour,0,T -coef_female_and_discretionary_tour,0,T -coef_zero_car_ownership_and_tour_frequency_is_1,0,T -coef_zero_car_ownership_and_tour_frequency_is_2,0,T -coef_zero_car_ownership_and_tour_frequency_is_3,0,T -coef_zero_car_ownership_and_tour_frequency_is_4,0,T -coef_zero_car_ownership_and_tour_frequency_is_5_plus,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T -coef_presence_of_non_worker_and_tour_frequency_is_1,0.2177,F -coef_presence_of_non_worker_and_tour_frequency_is_2,0.2177,F -coef_presence_of_non_worker_and_tour_frequency_is_3,0.2177,F -coef_presence_of_non_worker_and_tour_frequency_is_4,0.2177,F -coef_presence_of_non_worker_and_tour_frequency_is_5,0.2177,F -coef_presence_of_retiree_and_tour_frequency_is_1,0,T -coef_presence_of_retiree_and_tour_frequency_is_2,0,T -coef_presence_of_retiree_and_tour_frequency_is_3,0,T -coef_presence_of_retiree_and_tour_frequency_is_4,0,T -coef_presence_of_retiree_and_tour_frequency_is_5,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.4439,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-0.4439,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-0.4439,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-0.4439,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.4439,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,-0.2264,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,-0.2264,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,-0.2264,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,-0.2264,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,-0.2264,F -coef_presence_of_full_time_worker_and_escorting_tour,0,T -coef_presence_of_part_time_worker_and_escorting_tour,0,T -coef_presence_of_non_worker_and_escorting_tour,0,T -coef_presence_of_retiree_and_escorting_tour,0,T -coef_presence_of_university_student_and_escorting_tour,0,T -coef_presence_of_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_school_kid_and_escorting_tour,0,T -coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T -coef_at_home_pre_school_kid_and_escorting_tour,0,T -coef_presence_of_full_time_worker_and_shopping_tour,0,T -coef_presence_of_part_time_worker_and_shopping_tour,0,T -coef_presence_of_non_worker_and_shopping_tour,-0.645,F -coef_presence_of_retiree_and_shopping_tour,0,T -coef_presence_of_university_student_and_shopping_tour,0,T -coef_presence_of_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_driving_school_kid_and_shopping_tour,0.9365,F -coef_presence_of_pre_school_kid_and_shopping_tour,0,T -coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T -coef_at_home_pre_school_kid_and_shopping_tour,0,T -coef_presence_of_full_time_worker_and_maintenance_tour,0,T -coef_presence_of_part_time_worker_and_maintenance_tour,0,T -coef_presence_of_non_worker_and_maintenance_tour,0,T -coef_presence_of_retiree_and_maintenance_tour,0,T -coef_presence_of_university_student_and_maintenance_tour,0,T -coef_presence_of_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_school_kid_and_maintenance_tour,0,T -coef_presence_of_full_time_worker_and_eating_out_tour,0,T -coef_presence_of_part_time_worker_and_eating_out_tour,0,T -coef_presence_of_non_worker_and_eating_out_tour,-1.3074,F -coef_presence_of_retiree_and_eating_out_tour,0,T -coef_presence_of_university_student_and_eating_out_tour,0,T -coef_presence_of_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_school_kid_and_eating_out_tour,0,T -coef_presence_of_full_time_worker_and_discretionary_tour,0.7526,F -coef_presence_of_part_time_worker_and_discretionary_tour,0.3721,F -coef_presence_of_non_worker_and_discretionary_tour,0,T -coef_presence_of_retiree_and_discretionary_tour,0,T -coef_presence_of_university_student_and_discretionary_tour,0,T -coef_presence_of_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_school_kid_and_discretionary_tour,0,T -coef_walk_access_to_retail_and_tour_frequency_is_1,0,T -coef_walk_access_to_retail_and_tour_frequency_is_2,0,T -coef_walk_access_to_retail_and_tour_frequency_is_3,0,T -coef_walk_access_to_retail_and_tour_frequency_is_4,0,T -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_transit_access_to_retail_and_tour_frequency_is_1,0,T -coef_transit_access_to_retail_and_tour_frequency_is_2,0,T -coef_transit_access_to_retail_and_tour_frequency_is_3,0,T -coef_transit_access_to_retail_and_tour_frequency_is_4,0,T -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_auto_access_to_retail_and_tour_frequency_is_1,0,T -coef_auto_access_to_retail_and_tour_frequency_is_2,0,T -coef_auto_access_to_retail_and_tour_frequency_is_3,0,T -coef_auto_access_to_retail_and_tour_frequency_is_4,0,T -coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_walk_access_to_retail_and_escorting,0,T -coef_transit_access_to_retail_and_escorting,0,T -coef_auto_access_to_retail_and_escorting,0.0629,F -coef_walk_access_to_retail_and_shopping,0,T -coef_transit_access_to_retail_and_shopping,0,T -coef_auto_access_to_retail_and_shopping,0,T -coef_walk_access_to_retail_and_maintenance,0,T -coef_transit_access_to_retail_and_maintenance,0,T -coef_auto_access_to_retail_and_maintenance,0,T -coef_walk_access_to_retail_and_eating_out,0.0738,F -coef_transit_access_to_retail_and_eating_out,0,T -coef_auto_access_to_retail_and_eating_out,0,T -coef_walk_access_to_retail_and_discretionary,0,T -coef_transit_access_to_retail_and_discretionary,0,T -coef_auto_access_to_retail_and_discretionary,0,T -coef_urban_and_tour_frequency_is_1,0,T -coef_urban_and_tour_frequency_is_2,0,T -coef_urban_and_tour_frequency_is_3,0,T -coef_urban_and_tour_frequency_is_4,0,T -coef_urban_and_tour_frequency_is_5_plus,0,T -coef_urban_and_escorting_tour,0.4352,F -coef_urban_and_shopping_tour,0,T -coef_urban_and_maintenance_tour,0,T -coef_urban_and_eatingout_tour,0,T -coef_urban_and_discretionary_tour,0,T -coef_1_escort_tour_constant,-0.7551,F -coef_2_plus_escort_tours_constant,-0.0086,F -coef_1_plus_shopping_tours_constant,0.4783,F -coef_1_plus_maintenance_tours_constant,-0.506,F -coef_1_plus_eating_out_tours_constant,1.1145,F -coef_1_plus_visting_tours_constant,-0.4006,F -coef_1_plus_other_discretionary_tours_constant,0.4634,F +coefficient_name,value,constrain +coef_escorting_tour,0,T +coef_discretionary_tour,0,T +coef_shopping_tour,0,T +coef_maintenance_tour,0,T +coef_visiting_or_social_tour,0,T +coef_eating_out_tour,0,T +coef_total_number_of_tours_is_0_no_prior_tours,-999,T +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,-7.4863,F +coef_total_number_of_tours_is_2,-10.718,F +coef_total_number_of_tours_is_3,-13.7884,F +coef_total_number_of_tours_is_4,-999,T +coef_total_number_of_tours_is_5,-999,T +coef_total_number_of_tours_is_6_plus,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_1,-1.0331,F +coef_number_of_mandatory_tours_and_tour_frequency_is_2,-2.7445,F +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-2.7445,F +coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_0,0,T +coef_number_of_joint_tours_and_tour_frequency_is_1,-0.6149,F +coef_number_of_joint_tours_and_tour_frequency_is_2,-0.6149,F +coef_number_of_joint_tours_and_tour_frequency_is_3,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_shopping_tours,0,T +coef_number_of_joint_maintenance_tours,-1.3476,F +coef_number_of_joint_eating_out_tours,0,T +coef_number_of_joint_visit_tours,0,T +coef_number_of_joint_discretionary_tours,0,T +coef_logged_maximum_residual_window_tour_frequency_is_0,0,T +coef_logged_maximum_residual_window_tour_frequency_is_1,1.5603,F +coef_logged_maximum_residual_window_tour_frequency_is_2,1.5603,F +coef_logged_maximum_residual_window_tour_frequency_is_3,1.5603,F +coef_logged_maximum_residual_window_tour_frequency_is_4,1.5603,F +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.5603,F +coef_mediumlow_income_group_and_tour_frequency_is_1,1.0873,F +coef_mediumlow_income_group_and_tour_frequency_is_2,1.0873,F +coef_mediumlow_income_group_and_tour_frequency_is_3,1.0873,F +coef_mediumlow_income_group_and_tour_frequency_is_4,1.0873,F +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,1.0873,F +coef_mediumhigh_income_group_and_tour_frequency_is_1,1.5197,F +coef_mediumhigh_income_group_and_tour_frequency_is_2,1.5197,F +coef_mediumhigh_income_group_and_tour_frequency_is_3,1.5197,F +coef_mediumhigh_income_group_and_tour_frequency_is_4,1.5197,F +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,1.5197,F +coef_high_income_group_and_tour_frequency_is_1,2.0175,F +coef_high_income_group_and_tour_frequency_is_2,2.0175,F +coef_high_income_group_and_tour_frequency_is_3,2.0175,F +coef_high_income_group_and_tour_frequency_is_4,2.0175,F +coef_high_income_group_and_tour_frequency_is_5_plus,2.0175,F +coef_mediumlow_income_group_and_shopping_tour,-0.6506,F +coef_mediumhigh_income_group_and_shopping_tour,-0.6506,F +coef_high_income_group_and_shopping_tour,-0.6506,F +coef_mediumlow_income_group_and_maintenance_tour,0,T +coef_mediumhigh_income_group_and_maintenance_tour,0,T +coef_high_income_group_and_maintenance_tour,0,T +coef_mediumlow_income_group_and_eating_out_tour,-0.701,F +coef_mediumhigh_income_group_and_eating_out_tour,-0.701,F +coef_high_income_group_and_eating_out_tour,-0.701,F +coef_mediumlow_income_group_and_discretionary_tour,0,T +coef_mediumhigh_income_group_and_discretionary_tour,0,T +coef_high_income_group_and_discretionary_tour,0,T +coef_mediumlow_income_group_and_visiting_tour,0,T +coef_mediumhigh_income_group_and_visiting_tour,0,T +coef_high_income_group_and_visiting_tour,0,T +coef_female_and_tour_frequency_is_1,0,T +coef_female_and_tour_frequency_is_2,0,T +coef_female_and_tour_frequency_is_3,0,T +coef_female_and_tour_frequency_is_4,0,T +coef_female_and_tour_frequency_is_5,0,T +coef_female_and_escorting_tour,0,T +coef_female_and_shopping_tour,0,T +coef_female_and_maintenance_tour,0,T +coef_female_and_eatingout_tour,0,T +coef_female_and_discretionary_tour,0,T +coef_zero_car_ownership_and_tour_frequency_is_1,0,T +coef_zero_car_ownership_and_tour_frequency_is_2,0,T +coef_zero_car_ownership_and_tour_frequency_is_3,0,T +coef_zero_car_ownership_and_tour_frequency_is_4,0,T +coef_zero_car_ownership_and_tour_frequency_is_5_plus,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_1,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_2,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_3,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_4,0,T +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T +coef_presence_of_non_worker_and_tour_frequency_is_1,0.2177,F +coef_presence_of_non_worker_and_tour_frequency_is_2,0.2177,F +coef_presence_of_non_worker_and_tour_frequency_is_3,0.2177,F +coef_presence_of_non_worker_and_tour_frequency_is_4,0.2177,F +coef_presence_of_non_worker_and_tour_frequency_is_5,0.2177,F +coef_presence_of_retiree_and_tour_frequency_is_1,0,T +coef_presence_of_retiree_and_tour_frequency_is_2,0,T +coef_presence_of_retiree_and_tour_frequency_is_3,0,T +coef_presence_of_retiree_and_tour_frequency_is_4,0,T +coef_presence_of_retiree_and_tour_frequency_is_5,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.4439,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-0.4439,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-0.4439,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-0.4439,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-0.4439,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,-0.2264,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,-0.2264,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,-0.2264,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,-0.2264,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,-0.2264,F +coef_presence_of_full_time_worker_and_escorting_tour,0,T +coef_presence_of_part_time_worker_and_escorting_tour,0,T +coef_presence_of_non_worker_and_escorting_tour,0,T +coef_presence_of_retiree_and_escorting_tour,0,T +coef_presence_of_university_student_and_escorting_tour,0,T +coef_presence_of_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_school_kid_and_escorting_tour,0,T +coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T +coef_at_home_pre_school_kid_and_escorting_tour,0,T +coef_presence_of_full_time_worker_and_shopping_tour,0,T +coef_presence_of_part_time_worker_and_shopping_tour,0,T +coef_presence_of_non_worker_and_shopping_tour,-0.645,F +coef_presence_of_retiree_and_shopping_tour,0,T +coef_presence_of_university_student_and_shopping_tour,0,T +coef_presence_of_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_driving_school_kid_and_shopping_tour,0.9365,F +coef_presence_of_pre_school_kid_and_shopping_tour,0,T +coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T +coef_at_home_pre_school_kid_and_shopping_tour,0,T +coef_presence_of_full_time_worker_and_maintenance_tour,0,T +coef_presence_of_part_time_worker_and_maintenance_tour,0,T +coef_presence_of_non_worker_and_maintenance_tour,0,T +coef_presence_of_retiree_and_maintenance_tour,0,T +coef_presence_of_university_student_and_maintenance_tour,0,T +coef_presence_of_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_school_kid_and_maintenance_tour,0,T +coef_presence_of_full_time_worker_and_eating_out_tour,0,T +coef_presence_of_part_time_worker_and_eating_out_tour,0,T +coef_presence_of_non_worker_and_eating_out_tour,-1.3074,F +coef_presence_of_retiree_and_eating_out_tour,0,T +coef_presence_of_university_student_and_eating_out_tour,0,T +coef_presence_of_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_school_kid_and_eating_out_tour,0,T +coef_presence_of_full_time_worker_and_discretionary_tour,0.7526,F +coef_presence_of_part_time_worker_and_discretionary_tour,0.3721,F +coef_presence_of_non_worker_and_discretionary_tour,0,T +coef_presence_of_retiree_and_discretionary_tour,0,T +coef_presence_of_university_student_and_discretionary_tour,0,T +coef_presence_of_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_school_kid_and_discretionary_tour,0,T +coef_walk_access_to_retail_and_tour_frequency_is_1,0,T +coef_walk_access_to_retail_and_tour_frequency_is_2,0,T +coef_walk_access_to_retail_and_tour_frequency_is_3,0,T +coef_walk_access_to_retail_and_tour_frequency_is_4,0,T +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_transit_access_to_retail_and_tour_frequency_is_1,0,T +coef_transit_access_to_retail_and_tour_frequency_is_2,0,T +coef_transit_access_to_retail_and_tour_frequency_is_3,0,T +coef_transit_access_to_retail_and_tour_frequency_is_4,0,T +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_auto_access_to_retail_and_tour_frequency_is_1,0,T +coef_auto_access_to_retail_and_tour_frequency_is_2,0,T +coef_auto_access_to_retail_and_tour_frequency_is_3,0,T +coef_auto_access_to_retail_and_tour_frequency_is_4,0,T +coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_walk_access_to_retail_and_escorting,0,T +coef_transit_access_to_retail_and_escorting,0,T +coef_auto_access_to_retail_and_escorting,0.0629,F +coef_walk_access_to_retail_and_shopping,0,T +coef_transit_access_to_retail_and_shopping,0,T +coef_auto_access_to_retail_and_shopping,0,T +coef_walk_access_to_retail_and_maintenance,0,T +coef_transit_access_to_retail_and_maintenance,0,T +coef_auto_access_to_retail_and_maintenance,0,T +coef_walk_access_to_retail_and_eating_out,0.0738,F +coef_transit_access_to_retail_and_eating_out,0,T +coef_auto_access_to_retail_and_eating_out,0,T +coef_walk_access_to_retail_and_discretionary,0,T +coef_transit_access_to_retail_and_discretionary,0,T +coef_auto_access_to_retail_and_discretionary,0,T +coef_urban_and_tour_frequency_is_1,0,T +coef_urban_and_tour_frequency_is_2,0,T +coef_urban_and_tour_frequency_is_3,0,T +coef_urban_and_tour_frequency_is_4,0,T +coef_urban_and_tour_frequency_is_5_plus,0,T +coef_urban_and_escorting_tour,0.4352,F +coef_urban_and_shopping_tour,0,T +coef_urban_and_maintenance_tour,0,T +coef_urban_and_eatingout_tour,0,T +coef_urban_and_discretionary_tour,0,T +coef_1_escort_tour_constant,-0.7551,F +coef_2_plus_escort_tours_constant,-0.0086,F +coef_1_plus_shopping_tours_constant,0.4783,F +coef_1_plus_maintenance_tours_constant,-0.506,F +coef_1_plus_eating_out_tours_constant,1.1145,F +coef_1_plus_visting_tours_constant,-0.4006,F +coef_1_plus_other_discretionary_tours_constant,0.4634,F coef_0_auto_household_and_escorting_tour,-2,T \ No newline at end of file diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv index 1080f36b4d..7799e3e5d2 100755 --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_coeffs_PTYPE_UNIVERSITY.csv @@ -1,211 +1,211 @@ -coefficient_name,value,constrain -coef_escorting_tour,0,T -coef_discretionary_tour,0,T -coef_shopping_tour,0,T -coef_maintenance_tour,0,T -coef_visiting_or_social_tour,0,T -coef_eating_out_tour,0,T -coef_total_number_of_tours_is_0_no_prior_tours,-999,T -coef_total_number_of_tours_is_0_prior_tours,0,T -coef_total_number_of_tours_is_1,-6.2138,F -coef_total_number_of_tours_is_2,-8.908,F -coef_total_number_of_tours_is_3,-12.3261,F -coef_total_number_of_tours_is_4,-15.8114,F -coef_total_number_of_tours_is_5,-999,T -coef_total_number_of_tours_is_6_plus,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T -coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.1852,F -coef_number_of_mandatory_tours_and_tour_frequency_is_2,-0.8753,F -coef_number_of_mandatory_tours_and_tour_frequency_is_3,-1.6158,F -coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T -coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_0,0,T -coef_number_of_joint_tours_and_tour_frequency_is_1,0,T -coef_number_of_joint_tours_and_tour_frequency_is_2,-0.3153,F -coef_number_of_joint_tours_and_tour_frequency_is_3,-0.7351,F -coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T -coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T -coef_number_of_joint_shopping_tours,-0.713,F -coef_number_of_joint_maintenance_tours,0,T -coef_number_of_joint_eating_out_tours,0,T -coef_number_of_joint_visit_tours,0,T -coef_number_of_joint_discretionary_tours,0.6713,F -coef_logged_maximum_residual_window_tour_frequency_is_0,1.1858,F -coef_logged_maximum_residual_window_tour_frequency_is_1,1.4842,F -coef_logged_maximum_residual_window_tour_frequency_is_2,1.4842,F -coef_logged_maximum_residual_window_tour_frequency_is_3,1.4842,F -coef_logged_maximum_residual_window_tour_frequency_is_4,1.4842,F -coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.4842,F -coef_mediumlow_income_group_and_tour_frequency_is_1,0,T -coef_mediumlow_income_group_and_tour_frequency_is_2,0,T -coef_mediumlow_income_group_and_tour_frequency_is_3,0,T -coef_mediumlow_income_group_and_tour_frequency_is_4,0,T -coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T -coef_mediumhigh_income_group_and_tour_frequency_is_1,0.1109,F -coef_mediumhigh_income_group_and_tour_frequency_is_2,0.3914,F -coef_mediumhigh_income_group_and_tour_frequency_is_3,0.6137,F -coef_mediumhigh_income_group_and_tour_frequency_is_4,0.6137,F -coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0.6137,F -coef_high_income_group_and_tour_frequency_is_1,0.3986,F -coef_high_income_group_and_tour_frequency_is_2,0.8009,F -coef_high_income_group_and_tour_frequency_is_3,0.8254,F -coef_high_income_group_and_tour_frequency_is_4,0.8254,F -coef_high_income_group_and_tour_frequency_is_5_plus,0.8254,F -coef_mediumlow_income_group_and_shopping_tour,0.5693,F -coef_mediumhigh_income_group_and_shopping_tour,0.5693,F -coef_high_income_group_and_shopping_tour,0.5693,F -coef_mediumlow_income_group_and_maintenance_tour,0,T -coef_mediumhigh_income_group_and_maintenance_tour,0,T -coef_high_income_group_and_maintenance_tour,0,T -coef_mediumlow_income_group_and_eating_out_tour,0,T -coef_mediumhigh_income_group_and_eating_out_tour,-0.7207,F -coef_high_income_group_and_eating_out_tour,-0.7207,F -coef_mediumlow_income_group_and_discretionary_tour,0,T -coef_mediumhigh_income_group_and_discretionary_tour,0,T -coef_high_income_group_and_discretionary_tour,0,T -coef_mediumlow_income_group_and_visiting_tour,0,T -coef_mediumhigh_income_group_and_visiting_tour,-0.3694,F -coef_high_income_group_and_visiting_tour,-0.3694,F -coef_female_and_tour_frequency_is_1,0.0973,F -coef_female_and_tour_frequency_is_2,0.2361,F -coef_female_and_tour_frequency_is_3,1.9002,F -coef_female_and_tour_frequency_is_4,1.9002,F -coef_female_and_tour_frequency_is_5,1.9002,F -coef_female_and_escorting_tour,0,T -coef_female_and_shopping_tour,0,T -coef_female_and_maintenance_tour,0,T -coef_female_and_eatingout_tour,-0.6568,F -coef_female_and_discretionary_tour,-0.3266,F -coef_zero_car_ownership_and_tour_frequency_is_1,-0.581,F -coef_zero_car_ownership_and_tour_frequency_is_2,-0.581,F -coef_zero_car_ownership_and_tour_frequency_is_3,-0.581,F -coef_zero_car_ownership_and_tour_frequency_is_4,-0.581,F -coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.581,F -coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.581,F -coef_car_shortage_vs_workers_and_tour_frequency_is_2,-0.581,F -coef_car_shortage_vs_workers_and_tour_frequency_is_3,-0.581,F -coef_car_shortage_vs_workers_and_tour_frequency_is_4,-0.581,F -coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-0.581,F -coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T -coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T -coef_presence_of_non_worker_and_tour_frequency_is_1,-0.8506,F -coef_presence_of_non_worker_and_tour_frequency_is_2,-1.1804,F -coef_presence_of_non_worker_and_tour_frequency_is_3,-1.1804,F -coef_presence_of_non_worker_and_tour_frequency_is_4,-1.1804,F -coef_presence_of_non_worker_and_tour_frequency_is_5,-1.1804,F -coef_presence_of_retiree_and_tour_frequency_is_1,0,T -coef_presence_of_retiree_and_tour_frequency_is_2,0,T -coef_presence_of_retiree_and_tour_frequency_is_3,0,T -coef_presence_of_retiree_and_tour_frequency_is_4,0,T -coef_presence_of_retiree_and_tour_frequency_is_5,0,T -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.9961,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-1.9096,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-2.8469,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-2.8469,F -coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-2.8469,F -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T -coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T -coef_presence_of_full_time_worker_and_escorting_tour,0,T -coef_presence_of_part_time_worker_and_escorting_tour,-1.8213,F -coef_presence_of_non_worker_and_escorting_tour,0,T -coef_presence_of_retiree_and_escorting_tour,0,T -coef_presence_of_university_student_and_escorting_tour,0,T -coef_presence_of_driving_school_kid_and_escorting_tour,0,T -coef_presence_of_pre_driving_school_kid_and_escorting_tour,0.9489,F -coef_presence_of_pre_school_kid_and_escorting_tour,2.1465,F -coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T -coef_at_home_pre_school_kid_and_escorting_tour,0,T -coef_presence_of_full_time_worker_and_shopping_tour,-0.7728,F -coef_presence_of_part_time_worker_and_shopping_tour,-0.5199,F -coef_presence_of_non_worker_and_shopping_tour,0,T -coef_presence_of_retiree_and_shopping_tour,0,T -coef_presence_of_university_student_and_shopping_tour,0,T -coef_presence_of_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T -coef_presence_of_pre_school_kid_and_shopping_tour,1.3135,F -coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T -coef_at_home_pre_school_kid_and_shopping_tour,0,T -coef_presence_of_full_time_worker_and_maintenance_tour,0,T -coef_presence_of_part_time_worker_and_maintenance_tour,0,T -coef_presence_of_non_worker_and_maintenance_tour,0,T -coef_presence_of_retiree_and_maintenance_tour,0,T -coef_presence_of_university_student_and_maintenance_tour,0,T -coef_presence_of_driving_school_kid_and_maintenance_tour,0,T -coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0.3863,F -coef_presence_of_pre_school_kid_and_maintenance_tour,0.9694,F -coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T -coef_at_home_pre_school_kid_and_maintenance_tour,0,T -coef_presence_of_full_time_worker_and_eating_out_tour,-0.5251,F -coef_presence_of_part_time_worker_and_eating_out_tour,-1.9795,F -coef_presence_of_non_worker_and_eating_out_tour,0,T -coef_presence_of_retiree_and_eating_out_tour,0,T -coef_presence_of_university_student_and_eating_out_tour,-0.6529,F -coef_presence_of_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T -coef_presence_of_pre_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T -coef_at_home_pre_school_kid_and_eating_out_tour,0,T -coef_presence_of_full_time_worker_and_discretionary_tour,-0.4833,F -coef_presence_of_part_time_worker_and_discretionary_tour,0,T -coef_presence_of_non_worker_and_discretionary_tour,0.9781,F -coef_presence_of_retiree_and_discretionary_tour,0,T -coef_presence_of_university_student_and_discretionary_tour,-0.6542,F -coef_presence_of_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T -coef_presence_of_pre_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T -coef_at_home_pre_school_kid_and_discretionary_tour,0,T -coef_walk_access_to_retail_and_tour_frequency_is_1,0,T -coef_walk_access_to_retail_and_tour_frequency_is_2,0,T -coef_walk_access_to_retail_and_tour_frequency_is_3,0,T -coef_walk_access_to_retail_and_tour_frequency_is_4,0,T -coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_transit_access_to_retail_and_tour_frequency_is_1,0.0664,F -coef_transit_access_to_retail_and_tour_frequency_is_2,0.0664,F -coef_transit_access_to_retail_and_tour_frequency_is_3,0.0664,F -coef_transit_access_to_retail_and_tour_frequency_is_4,0.0664,F -coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0.0664,F -coef_auto_access_to_retail_and_tour_frequency_is_1,0,T -coef_auto_access_to_retail_and_tour_frequency_is_2,0,T -coef_auto_access_to_retail_and_tour_frequency_is_3,0,T -coef_auto_access_to_retail_and_tour_frequency_is_4,0,T -coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T -coef_walk_access_to_retail_and_escorting,0,T -coef_transit_access_to_retail_and_escorting,0,T -coef_auto_access_to_retail_and_escorting,0,T -coef_walk_access_to_retail_and_shopping,0.0972,F -coef_transit_access_to_retail_and_shopping,0,T -coef_auto_access_to_retail_and_shopping,0,T -coef_walk_access_to_retail_and_maintenance,0,T -coef_transit_access_to_retail_and_maintenance,0.0314,F -coef_auto_access_to_retail_and_maintenance,0,T -coef_walk_access_to_retail_and_eating_out,0,T -coef_transit_access_to_retail_and_eating_out,0,T -coef_auto_access_to_retail_and_eating_out,0.1018,F -coef_walk_access_to_retail_and_discretionary,0,T -coef_transit_access_to_retail_and_discretionary,0,T -coef_auto_access_to_retail_and_discretionary,0.094,F -coef_urban_and_tour_frequency_is_1,-1.1648,F -coef_urban_and_tour_frequency_is_2,-2.3177,F -coef_urban_and_tour_frequency_is_3,-2.5027,F -coef_urban_and_tour_frequency_is_4,-2.5027,F -coef_urban_and_tour_frequency_is_5_plus,-2.5027,F -coef_urban_and_escorting_tour,0.8516,F -coef_urban_and_shopping_tour,0.533,F -coef_urban_and_maintenance_tour,1.0316,F -coef_urban_and_eatingout_tour,0.68,F -coef_urban_and_discretionary_tour,0.9563,F -coef_1_escort_tour_constant,1.7028,F -coef_2_plus_escort_tours_constant,2.8379,F -coef_1_plus_shopping_tours_constant,1.8403,F -coef_1_plus_maintenance_tours_constant,0.3348,F -coef_1_plus_eating_out_tours_constant,2.0723,F -coef_1_plus_visting_tours_constant,1.2172,F -coef_1_plus_other_discretionary_tours_constant,1.3389,F +coefficient_name,value,constrain +coef_escorting_tour,0,T +coef_discretionary_tour,0,T +coef_shopping_tour,0,T +coef_maintenance_tour,0,T +coef_visiting_or_social_tour,0,T +coef_eating_out_tour,0,T +coef_total_number_of_tours_is_0_no_prior_tours,-999,T +coef_total_number_of_tours_is_0_prior_tours,0,T +coef_total_number_of_tours_is_1,-6.2138,F +coef_total_number_of_tours_is_2,-8.908,F +coef_total_number_of_tours_is_3,-12.3261,F +coef_total_number_of_tours_is_4,-15.8114,F +coef_total_number_of_tours_is_5,-999,T +coef_total_number_of_tours_is_6_plus,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_0,0,T +coef_number_of_mandatory_tours_and_tour_frequency_is_1,-0.1852,F +coef_number_of_mandatory_tours_and_tour_frequency_is_2,-0.8753,F +coef_number_of_mandatory_tours_and_tour_frequency_is_3,-1.6158,F +coef_number_of_mandatory_tours_and_tour_frequency_is_4,-999,T +coef_number_of_mandatory_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_0,0,T +coef_number_of_joint_tours_and_tour_frequency_is_1,0,T +coef_number_of_joint_tours_and_tour_frequency_is_2,-0.3153,F +coef_number_of_joint_tours_and_tour_frequency_is_3,-0.7351,F +coef_number_of_joint_tours_and_tour_frequency_is_4,-999,T +coef_number_of_joint_tours_and_tour_frequency_is_5_plus,-999,T +coef_number_of_joint_shopping_tours,-0.713,F +coef_number_of_joint_maintenance_tours,0,T +coef_number_of_joint_eating_out_tours,0,T +coef_number_of_joint_visit_tours,0,T +coef_number_of_joint_discretionary_tours,0.6713,F +coef_logged_maximum_residual_window_tour_frequency_is_0,1.1858,F +coef_logged_maximum_residual_window_tour_frequency_is_1,1.4842,F +coef_logged_maximum_residual_window_tour_frequency_is_2,1.4842,F +coef_logged_maximum_residual_window_tour_frequency_is_3,1.4842,F +coef_logged_maximum_residual_window_tour_frequency_is_4,1.4842,F +coef_logged_maximum_residual_window_tour_frequency_is_5_plus,1.4842,F +coef_mediumlow_income_group_and_tour_frequency_is_1,0,T +coef_mediumlow_income_group_and_tour_frequency_is_2,0,T +coef_mediumlow_income_group_and_tour_frequency_is_3,0,T +coef_mediumlow_income_group_and_tour_frequency_is_4,0,T +coef_mediumlow_income_group_and_tour_frequency_is_5_plus,0,T +coef_mediumhigh_income_group_and_tour_frequency_is_1,0.1109,F +coef_mediumhigh_income_group_and_tour_frequency_is_2,0.3914,F +coef_mediumhigh_income_group_and_tour_frequency_is_3,0.6137,F +coef_mediumhigh_income_group_and_tour_frequency_is_4,0.6137,F +coef_mediumhigh_income_group_and_tour_frequency_is_5_plus,0.6137,F +coef_high_income_group_and_tour_frequency_is_1,0.3986,F +coef_high_income_group_and_tour_frequency_is_2,0.8009,F +coef_high_income_group_and_tour_frequency_is_3,0.8254,F +coef_high_income_group_and_tour_frequency_is_4,0.8254,F +coef_high_income_group_and_tour_frequency_is_5_plus,0.8254,F +coef_mediumlow_income_group_and_shopping_tour,0.5693,F +coef_mediumhigh_income_group_and_shopping_tour,0.5693,F +coef_high_income_group_and_shopping_tour,0.5693,F +coef_mediumlow_income_group_and_maintenance_tour,0,T +coef_mediumhigh_income_group_and_maintenance_tour,0,T +coef_high_income_group_and_maintenance_tour,0,T +coef_mediumlow_income_group_and_eating_out_tour,0,T +coef_mediumhigh_income_group_and_eating_out_tour,-0.7207,F +coef_high_income_group_and_eating_out_tour,-0.7207,F +coef_mediumlow_income_group_and_discretionary_tour,0,T +coef_mediumhigh_income_group_and_discretionary_tour,0,T +coef_high_income_group_and_discretionary_tour,0,T +coef_mediumlow_income_group_and_visiting_tour,0,T +coef_mediumhigh_income_group_and_visiting_tour,-0.3694,F +coef_high_income_group_and_visiting_tour,-0.3694,F +coef_female_and_tour_frequency_is_1,0.0973,F +coef_female_and_tour_frequency_is_2,0.2361,F +coef_female_and_tour_frequency_is_3,1.9002,F +coef_female_and_tour_frequency_is_4,1.9002,F +coef_female_and_tour_frequency_is_5,1.9002,F +coef_female_and_escorting_tour,0,T +coef_female_and_shopping_tour,0,T +coef_female_and_maintenance_tour,0,T +coef_female_and_eatingout_tour,-0.6568,F +coef_female_and_discretionary_tour,-0.3266,F +coef_zero_car_ownership_and_tour_frequency_is_1,-0.581,F +coef_zero_car_ownership_and_tour_frequency_is_2,-0.581,F +coef_zero_car_ownership_and_tour_frequency_is_3,-0.581,F +coef_zero_car_ownership_and_tour_frequency_is_4,-0.581,F +coef_zero_car_ownership_and_tour_frequency_is_5_plus,-0.581,F +coef_car_shortage_vs_workers_and_tour_frequency_is_1,-0.581,F +coef_car_shortage_vs_workers_and_tour_frequency_is_2,-0.581,F +coef_car_shortage_vs_workers_and_tour_frequency_is_3,-0.581,F +coef_car_shortage_vs_workers_and_tour_frequency_is_4,-0.581,F +coef_car_shortage_vs_workers_and_tour_frequency_is_5_plus,-0.581,F +coef_car_surplus_vs_workers_and_tour_frequency_is_1,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_2,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_3,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_4,0,T +coef_car_surplus_vs_workers_and_tour_frequency_is_5_plus,0,T +coef_presence_of_non_worker_and_tour_frequency_is_1,-0.8506,F +coef_presence_of_non_worker_and_tour_frequency_is_2,-1.1804,F +coef_presence_of_non_worker_and_tour_frequency_is_3,-1.1804,F +coef_presence_of_non_worker_and_tour_frequency_is_4,-1.1804,F +coef_presence_of_non_worker_and_tour_frequency_is_5,-1.1804,F +coef_presence_of_retiree_and_tour_frequency_is_1,0,T +coef_presence_of_retiree_and_tour_frequency_is_2,0,T +coef_presence_of_retiree_and_tour_frequency_is_3,0,T +coef_presence_of_retiree_and_tour_frequency_is_4,0,T +coef_presence_of_retiree_and_tour_frequency_is_5,0,T +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_1,-0.9961,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_2,-1.9096,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_3,-2.8469,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_4,-2.8469,F +coef_presence_of_preschool_kid_in_household_and_tour_frequency_is_5,-2.8469,F +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_1,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_2,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_3,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_4,0,T +coef_presence_of_predriving_school_kid_in_household_and_tour_frequency_is_5,0,T +coef_presence_of_full_time_worker_and_escorting_tour,0,T +coef_presence_of_part_time_worker_and_escorting_tour,-1.8213,F +coef_presence_of_non_worker_and_escorting_tour,0,T +coef_presence_of_retiree_and_escorting_tour,0,T +coef_presence_of_university_student_and_escorting_tour,0,T +coef_presence_of_driving_school_kid_and_escorting_tour,0,T +coef_presence_of_pre_driving_school_kid_and_escorting_tour,0.9489,F +coef_presence_of_pre_school_kid_and_escorting_tour,2.1465,F +coef_at_home_pre_driving_school_kid_and_escorting_tour,0,T +coef_at_home_pre_school_kid_and_escorting_tour,0,T +coef_presence_of_full_time_worker_and_shopping_tour,-0.7728,F +coef_presence_of_part_time_worker_and_shopping_tour,-0.5199,F +coef_presence_of_non_worker_and_shopping_tour,0,T +coef_presence_of_retiree_and_shopping_tour,0,T +coef_presence_of_university_student_and_shopping_tour,0,T +coef_presence_of_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_driving_school_kid_and_shopping_tour,0,T +coef_presence_of_pre_school_kid_and_shopping_tour,1.3135,F +coef_at_home_pre_driving_school_kid_and_shopping_tour,0,T +coef_at_home_pre_school_kid_and_shopping_tour,0,T +coef_presence_of_full_time_worker_and_maintenance_tour,0,T +coef_presence_of_part_time_worker_and_maintenance_tour,0,T +coef_presence_of_non_worker_and_maintenance_tour,0,T +coef_presence_of_retiree_and_maintenance_tour,0,T +coef_presence_of_university_student_and_maintenance_tour,0,T +coef_presence_of_driving_school_kid_and_maintenance_tour,0,T +coef_presence_of_pre_driving_school_kid_and_maintenance_tour,0.3863,F +coef_presence_of_pre_school_kid_and_maintenance_tour,0.9694,F +coef_at_home_pre_driving_school_kid_and_maintenance_tour,0,T +coef_at_home_pre_school_kid_and_maintenance_tour,0,T +coef_presence_of_full_time_worker_and_eating_out_tour,-0.5251,F +coef_presence_of_part_time_worker_and_eating_out_tour,-1.9795,F +coef_presence_of_non_worker_and_eating_out_tour,0,T +coef_presence_of_retiree_and_eating_out_tour,0,T +coef_presence_of_university_student_and_eating_out_tour,-0.6529,F +coef_presence_of_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_driving_school_kid_and_eating_out_tour,0,T +coef_presence_of_pre_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_driving_school_kid_and_eating_out_tour,0,T +coef_at_home_pre_school_kid_and_eating_out_tour,0,T +coef_presence_of_full_time_worker_and_discretionary_tour,-0.4833,F +coef_presence_of_part_time_worker_and_discretionary_tour,0,T +coef_presence_of_non_worker_and_discretionary_tour,0.9781,F +coef_presence_of_retiree_and_discretionary_tour,0,T +coef_presence_of_university_student_and_discretionary_tour,-0.6542,F +coef_presence_of_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_driving_school_kid_and_discretionary_tour,0,T +coef_presence_of_pre_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_driving_school_kid_and_discretionary_tour,0,T +coef_at_home_pre_school_kid_and_discretionary_tour,0,T +coef_walk_access_to_retail_and_tour_frequency_is_1,0,T +coef_walk_access_to_retail_and_tour_frequency_is_2,0,T +coef_walk_access_to_retail_and_tour_frequency_is_3,0,T +coef_walk_access_to_retail_and_tour_frequency_is_4,0,T +coef_walk_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_transit_access_to_retail_and_tour_frequency_is_1,0.0664,F +coef_transit_access_to_retail_and_tour_frequency_is_2,0.0664,F +coef_transit_access_to_retail_and_tour_frequency_is_3,0.0664,F +coef_transit_access_to_retail_and_tour_frequency_is_4,0.0664,F +coef_transit_access_to_retail_and_tour_frequency_is_5_plus,0.0664,F +coef_auto_access_to_retail_and_tour_frequency_is_1,0,T +coef_auto_access_to_retail_and_tour_frequency_is_2,0,T +coef_auto_access_to_retail_and_tour_frequency_is_3,0,T +coef_auto_access_to_retail_and_tour_frequency_is_4,0,T +coef_auto_access_to_retail_and_tour_frequency_is_5_plus,0,T +coef_walk_access_to_retail_and_escorting,0,T +coef_transit_access_to_retail_and_escorting,0,T +coef_auto_access_to_retail_and_escorting,0,T +coef_walk_access_to_retail_and_shopping,0.0972,F +coef_transit_access_to_retail_and_shopping,0,T +coef_auto_access_to_retail_and_shopping,0,T +coef_walk_access_to_retail_and_maintenance,0,T +coef_transit_access_to_retail_and_maintenance,0.0314,F +coef_auto_access_to_retail_and_maintenance,0,T +coef_walk_access_to_retail_and_eating_out,0,T +coef_transit_access_to_retail_and_eating_out,0,T +coef_auto_access_to_retail_and_eating_out,0.1018,F +coef_walk_access_to_retail_and_discretionary,0,T +coef_transit_access_to_retail_and_discretionary,0,T +coef_auto_access_to_retail_and_discretionary,0.094,F +coef_urban_and_tour_frequency_is_1,-1.1648,F +coef_urban_and_tour_frequency_is_2,-2.3177,F +coef_urban_and_tour_frequency_is_3,-2.5027,F +coef_urban_and_tour_frequency_is_4,-2.5027,F +coef_urban_and_tour_frequency_is_5_plus,-2.5027,F +coef_urban_and_escorting_tour,0.8516,F +coef_urban_and_shopping_tour,0.533,F +coef_urban_and_maintenance_tour,1.0316,F +coef_urban_and_eatingout_tour,0.68,F +coef_urban_and_discretionary_tour,0.9563,F +coef_1_escort_tour_constant,1.7028,F +coef_2_plus_escort_tours_constant,2.8379,F +coef_1_plus_shopping_tours_constant,1.8403,F +coef_1_plus_maintenance_tours_constant,0.3348,F +coef_1_plus_eating_out_tours_constant,2.0723,F +coef_1_plus_visting_tours_constant,1.2172,F +coef_1_plus_other_discretionary_tours_constant,1.3389,F coef_0_auto_household_and_escorting_tour,-2,T \ No newline at end of file diff --git a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_extension_probs.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_extension_probs.csv similarity index 95% rename from activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_extension_probs.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_extension_probs.csv index 632f453ce0..ec78c4c8e7 100755 --- a/activitysim/examples/example_psrc/configs/non_mandatory_tour_frequency_extension_probs.csv +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_frequency_extension_probs.csv @@ -1,193 +1,193 @@ -ptype,has_mandatory_tour,has_joint_tour,nonmandatory_tour_type,0_tours,1_tours,2_tours -1,0,0,1,0.829545455,1,1 -2,0,0,1,0.769230769,1,1 -3,0,0,1,0.893939394,1,1 -4,0,0,1,0.75,1,1 -5,0,0,1,0.842105263,1,1 -6,0,0,1,0.714285714,1,1 -7,0,0,1,0.814814815,1,1 -8,0,0,1,0.75,1,1 -1,1,0,1,0.789473684,1,1 -2,1,0,1,0.6,1,1 -3,1,0,1,1,1,1 -4,1,0,1,1,1,1 -5,1,0,1,0.825910931,1,1 -6,1,0,1,0.837209302,1,1 -7,1,0,1,0.6,1,1 -8,1,0,1,1,1,1 -1,0,1,1,0.842105263,1,1 -2,0,1,1,1,1,1 -3,0,1,1,1,1,1 -4,0,1,1,1,1,1 -5,0,1,1,1,1,1 -6,0,1,1,1,1,1 -7,0,1,1,1,1,1 -8,0,1,1,1,1,1 -1,1,1,1,1,1,1 -2,1,1,1,1,1,1 -3,1,1,1,1,1,1 -4,1,1,1,1,1,1 -5,1,1,1,0.777777778,1,1 -6,1,1,1,1,1,1 -7,1,1,1,1,1,1 -8,1,1,1,1,1,1 -1,0,0,2,0.892694064,0.99086758,1 -2,0,0,2,0.84057971,0.992753623,1 -3,0,0,2,0.971014493,1,1 -4,0,0,2,0.96969697,1,1 -5,0,0,2,0.870056497,0.994350282,1 -6,0,0,2,0.866666667,1,1 -7,0,0,2,0.971014493,1,1 -8,0,0,2,0.931034483,1,1 -1,1,0,2,0.885057471,1,1 -2,1,0,2,0.727272727,1,1 -3,1,0,2,0.971428571,1,1 -4,1,0,2,1,1,1 -5,1,0,2,0.895977809,0.993065187,1 -6,1,0,2,0.885185185,1,1 -7,1,0,2,1,1,1 -8,1,0,2,1,1,1 -1,0,1,2,0.910087719,0.993421053,1 -2,0,1,2,0.88,1,1 -3,0,1,2,0.8,1,1 -4,0,1,2,1,1,1 -5,0,1,2,1,1,1 -6,0,1,2,1,1,1 -7,0,1,2,1,1,1 -8,0,1,2,1,1,1 -1,1,1,2,1,1,1 -2,1,1,2,1,1,1 -3,1,1,2,1,1,1 -4,1,1,2,1,1,1 -5,1,1,2,1,1,1 -6,1,1,2,0.964912281,1,1 -7,1,1,2,1,1,1 -8,1,1,2,0.888888889,1,1 -1,0,0,3,0.935643564,0.997524752,1 -2,0,0,3,0.905660377,1,1 -3,0,0,3,0.978813559,1,1 -4,0,0,3,0.928571429,1,1 -5,0,0,3,0.901515152,0.992424242,1 -6,0,0,3,0.863636364,1,1 -7,0,0,3,0.947368421,1,1 -8,0,0,3,0.913043478,1,1 -1,1,0,3,0.893333333,0.986666667,1 -2,1,0,3,1,1,1 -3,1,0,3,1,1,1 -4,1,0,3,0.857142857,1,1 -5,1,0,3,0.916071429,0.996428571,1 -6,1,0,3,0.856382979,0.984042553,1 -7,1,0,3,1,1,1 -8,1,0,3,1,1,1 -1,0,1,3,0.916201117,0.991620112,1 -2,0,1,3,0.912280702,0.98245614,1 -3,0,1,3,1,1,1 -4,0,1,3,1,1,1 -5,0,1,3,1,1,1 -6,0,1,3,0.833333333,1,1 -7,0,1,3,0.961538462,1,1 -8,0,1,3,1,1,1 -1,1,1,3,0.97826087,0.989130435,1 -2,1,1,3,0.97260274,1,1 -3,1,1,3,1,1,1 -4,1,1,3,1,1,1 -5,1,1,3,0.995762712,1,1 -6,1,1,3,0.921568627,0.980392157,1 -7,1,1,3,1,1,1 -8,1,1,3,1,1,1 -1,0,0,4,0.9218107,0.995884774,1 -2,0,0,4,0.900900901,1,1 -3,0,0,4,0.997354497,1,1 -4,0,0,4,0.991176471,1,1 -5,0,0,4,0.921568627,0.980392157,1 -6,0,0,4,0.954545455,1,1 -7,0,0,4,1,1,1 -8,0,0,4,0.954545455,1,1 -1,1,0,4,0.941176471,0.970588235,1 -2,1,0,4,0.925925926,1,1 -3,1,0,4,1,1,1 -4,1,0,4,0.875,1,1 -5,1,0,4,0.915322581,1,1 -6,1,0,4,0.947674419,0.994186047,1 -7,1,0,4,0.666666667,1,1 -8,1,0,4,1,1,1 -1,0,1,4,0.925925926,0.987654321,1 -2,0,1,4,0.903703704,1,1 -3,0,1,4,1,1,1 -4,0,1,4,1,1,1 -5,0,1,4,1,1,1 -6,0,1,4,1,1,1 -7,0,1,4,1,1,1 -8,0,1,4,1,1,1 -1,1,1,4,1,1,1 -2,1,1,4,0.911111111,1,1 -3,1,1,4,1,1,1 -4,1,1,4,1,1,1 -5,1,1,4,1,1,1 -6,1,1,4,0.962962963,1,1 -7,1,1,4,1,1,1 -8,1,1,4,1,1,1 -1,0,0,5,0.976744186,1,1 -2,0,0,5,0.981818182,1,1 -3,0,0,5,0.985915493,1,1 -4,0,0,5,1,1,1 -5,0,0,5,1,1,1 -6,0,0,5,1,1,1 -7,0,0,5,1,1,1 -8,0,0,5,0.875,1,1 -1,1,0,5,1,1,1 -2,1,0,5,1,1,1 -3,1,0,5,0.964285714,1,1 -4,1,0,5,1,1,1 -5,1,0,5,0.985714286,1,1 -6,1,0,5,0.951807229,1,1 -7,1,0,5,1,1,1 -8,1,0,5,1,1,1 -1,0,1,5,0.926605505,1,1 -2,0,1,5,0.941176471,1,1 -3,0,1,5,1,1,1 -4,0,1,5,1,1,1 -5,0,1,5,1,1,1 -6,0,1,5,1,1,1 -7,0,1,5,1,1,1 -8,0,1,5,1,1,1 -1,1,1,5,1,1,1 -2,1,1,5,1,1,1 -3,1,1,5,0.972972973,1,1 -4,1,1,5,1,1,1 -5,1,1,5,1,1,1 -6,1,1,5,0.933333333,1,1 -7,1,1,5,1,1,1 -8,1,1,5,1,1,1 -1,0,0,6,0.93837535,0.988795518,1 -2,0,0,6,0.888888889,1,1 -3,0,0,6,0.966832504,0.998341625,1 -4,0,0,6,0.942028986,1,1 -5,0,0,6,0.88034188,1,1 -6,0,0,6,0.925925926,1,1 -7,0,0,6,0.967741935,1,1 -8,0,0,6,0.90625,1,1 -1,1,0,6,0.85915493,1,1 -2,1,0,6,0.818181818,0.96969697,1 -3,1,0,6,1,1,1 -4,1,0,6,0.952380952,1,1 -5,1,0,6,0.879237288,0.997881356,1 -6,1,0,6,0.862944162,0.984771574,1 -7,1,0,6,0.9,1,1 -8,1,0,6,1,1,1 -1,0,1,6,0.927835052,0.996563574,1 -2,0,1,6,0.859375,0.9921875,1 -3,0,1,6,1,1,1 -4,0,1,6,1,1,1 -5,0,1,6,0.92,1,1 -6,0,1,6,1,1,1 -7,0,1,6,0.904761905,1,1 -8,0,1,6,1,1,1 -1,1,1,6,0.982758621,1,1 -2,1,1,6,0.927710843,0.987951807,1 -3,1,1,6,0.982954545,1,1 -4,1,1,6,0.938679245,1,1 -5,1,1,6,1,1,1 -6,1,1,6,0.9375,1,1 -7,1,1,6,1,1,1 -8,1,1,6,1,1,1 +ptype,has_mandatory_tour,has_joint_tour,nonmandatory_tour_type,0_tours,1_tours,2_tours +1,0,0,1,0.829545455,1,1 +2,0,0,1,0.769230769,1,1 +3,0,0,1,0.893939394,1,1 +4,0,0,1,0.75,1,1 +5,0,0,1,0.842105263,1,1 +6,0,0,1,0.714285714,1,1 +7,0,0,1,0.814814815,1,1 +8,0,0,1,0.75,1,1 +1,1,0,1,0.789473684,1,1 +2,1,0,1,0.6,1,1 +3,1,0,1,1,1,1 +4,1,0,1,1,1,1 +5,1,0,1,0.825910931,1,1 +6,1,0,1,0.837209302,1,1 +7,1,0,1,0.6,1,1 +8,1,0,1,1,1,1 +1,0,1,1,0.842105263,1,1 +2,0,1,1,1,1,1 +3,0,1,1,1,1,1 +4,0,1,1,1,1,1 +5,0,1,1,1,1,1 +6,0,1,1,1,1,1 +7,0,1,1,1,1,1 +8,0,1,1,1,1,1 +1,1,1,1,1,1,1 +2,1,1,1,1,1,1 +3,1,1,1,1,1,1 +4,1,1,1,1,1,1 +5,1,1,1,0.777777778,1,1 +6,1,1,1,1,1,1 +7,1,1,1,1,1,1 +8,1,1,1,1,1,1 +1,0,0,2,0.892694064,0.99086758,1 +2,0,0,2,0.84057971,0.992753623,1 +3,0,0,2,0.971014493,1,1 +4,0,0,2,0.96969697,1,1 +5,0,0,2,0.870056497,0.994350282,1 +6,0,0,2,0.866666667,1,1 +7,0,0,2,0.971014493,1,1 +8,0,0,2,0.931034483,1,1 +1,1,0,2,0.885057471,1,1 +2,1,0,2,0.727272727,1,1 +3,1,0,2,0.971428571,1,1 +4,1,0,2,1,1,1 +5,1,0,2,0.895977809,0.993065187,1 +6,1,0,2,0.885185185,1,1 +7,1,0,2,1,1,1 +8,1,0,2,1,1,1 +1,0,1,2,0.910087719,0.993421053,1 +2,0,1,2,0.88,1,1 +3,0,1,2,0.8,1,1 +4,0,1,2,1,1,1 +5,0,1,2,1,1,1 +6,0,1,2,1,1,1 +7,0,1,2,1,1,1 +8,0,1,2,1,1,1 +1,1,1,2,1,1,1 +2,1,1,2,1,1,1 +3,1,1,2,1,1,1 +4,1,1,2,1,1,1 +5,1,1,2,1,1,1 +6,1,1,2,0.964912281,1,1 +7,1,1,2,1,1,1 +8,1,1,2,0.888888889,1,1 +1,0,0,3,0.935643564,0.997524752,1 +2,0,0,3,0.905660377,1,1 +3,0,0,3,0.978813559,1,1 +4,0,0,3,0.928571429,1,1 +5,0,0,3,0.901515152,0.992424242,1 +6,0,0,3,0.863636364,1,1 +7,0,0,3,0.947368421,1,1 +8,0,0,3,0.913043478,1,1 +1,1,0,3,0.893333333,0.986666667,1 +2,1,0,3,1,1,1 +3,1,0,3,1,1,1 +4,1,0,3,0.857142857,1,1 +5,1,0,3,0.916071429,0.996428571,1 +6,1,0,3,0.856382979,0.984042553,1 +7,1,0,3,1,1,1 +8,1,0,3,1,1,1 +1,0,1,3,0.916201117,0.991620112,1 +2,0,1,3,0.912280702,0.98245614,1 +3,0,1,3,1,1,1 +4,0,1,3,1,1,1 +5,0,1,3,1,1,1 +6,0,1,3,0.833333333,1,1 +7,0,1,3,0.961538462,1,1 +8,0,1,3,1,1,1 +1,1,1,3,0.97826087,0.989130435,1 +2,1,1,3,0.97260274,1,1 +3,1,1,3,1,1,1 +4,1,1,3,1,1,1 +5,1,1,3,0.995762712,1,1 +6,1,1,3,0.921568627,0.980392157,1 +7,1,1,3,1,1,1 +8,1,1,3,1,1,1 +1,0,0,4,0.9218107,0.995884774,1 +2,0,0,4,0.900900901,1,1 +3,0,0,4,0.997354497,1,1 +4,0,0,4,0.991176471,1,1 +5,0,0,4,0.921568627,0.980392157,1 +6,0,0,4,0.954545455,1,1 +7,0,0,4,1,1,1 +8,0,0,4,0.954545455,1,1 +1,1,0,4,0.941176471,0.970588235,1 +2,1,0,4,0.925925926,1,1 +3,1,0,4,1,1,1 +4,1,0,4,0.875,1,1 +5,1,0,4,0.915322581,1,1 +6,1,0,4,0.947674419,0.994186047,1 +7,1,0,4,0.666666667,1,1 +8,1,0,4,1,1,1 +1,0,1,4,0.925925926,0.987654321,1 +2,0,1,4,0.903703704,1,1 +3,0,1,4,1,1,1 +4,0,1,4,1,1,1 +5,0,1,4,1,1,1 +6,0,1,4,1,1,1 +7,0,1,4,1,1,1 +8,0,1,4,1,1,1 +1,1,1,4,1,1,1 +2,1,1,4,0.911111111,1,1 +3,1,1,4,1,1,1 +4,1,1,4,1,1,1 +5,1,1,4,1,1,1 +6,1,1,4,0.962962963,1,1 +7,1,1,4,1,1,1 +8,1,1,4,1,1,1 +1,0,0,5,0.976744186,1,1 +2,0,0,5,0.981818182,1,1 +3,0,0,5,0.985915493,1,1 +4,0,0,5,1,1,1 +5,0,0,5,1,1,1 +6,0,0,5,1,1,1 +7,0,0,5,1,1,1 +8,0,0,5,0.875,1,1 +1,1,0,5,1,1,1 +2,1,0,5,1,1,1 +3,1,0,5,0.964285714,1,1 +4,1,0,5,1,1,1 +5,1,0,5,0.985714286,1,1 +6,1,0,5,0.951807229,1,1 +7,1,0,5,1,1,1 +8,1,0,5,1,1,1 +1,0,1,5,0.926605505,1,1 +2,0,1,5,0.941176471,1,1 +3,0,1,5,1,1,1 +4,0,1,5,1,1,1 +5,0,1,5,1,1,1 +6,0,1,5,1,1,1 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a/activitysim/examples/example_semcog/configs/non_mandatory_tour_scheduling.yaml b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling.yaml similarity index 100% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_scheduling.yaml rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling.yaml diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_alts_preprocessor.csv diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_scheduling_annotate_shifts_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_shifts_preprocessor.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_scheduling_annotate_shifts_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_shifts_preprocessor.csv index c0b407c9d5..26f1403782 100755 --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_scheduling_annotate_shifts_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_shifts_preprocessor.csv @@ -1,15 +1,15 @@ -Description,Target,Expression -#,, -departure_shift_escort,departureLinearShift1_escort,"(alt_tdd.start<=10) * np.minimum(10-alt_tdd.start,7) + (alt_tdd.start>10) * np.minimum(alt_tdd.start-10,35)" -arrival_shift_escort,arrivalLinearShift1_escort,"(alt_tdd.end<=11) * np.minimum(11-alt_tdd.end,7) + (alt_tdd.end>11) * np.minimum(alt_tdd.end-11,35)" -departure_shift2_escort,departureLinearShift2_escort,"(alt_tdd.start<=24) * np.minimum(24-alt_tdd.start,3) + (alt_tdd.start>24) * np.minimum(alt_tdd.start-24,9)" -arrival_shift2_escort,arrivalLinearShift2_escort,"(alt_tdd.end<=25) * np.minimum(25-alt_tdd.end,3) + (alt_tdd.end>25) * np.minimum(alt_tdd.end-25,9)" -duration_shift_escort,durationShift_escort,"(alt_tdd.duration<=1) * np.minimum(1-alt_tdd.duration, 0) + (alt_tdd.duration>1) * np.minimum(alt_tdd.duration-1,47)" -#,, -depConstShift1_escort,depConstShift1_escort,"(alt_tdd.start<8) * np.minimum(8-alt_tdd.start,4) + (alt_tdd.start>13) * np.minimum(alt_tdd.start-13,28)" -depConstShift2_escort,depConstShift2_escort,"(alt_tdd.start<0) * np.minimum(0-alt_tdd.start,48) + (alt_tdd.start>26) * np.minimum(alt_tdd.start-26,15)" -arrConstShift1_escort,arrConstShift1_escort,"(alt_tdd.end<8) * np.minimum(8-alt_tdd.end,2) + (alt_tdd.end>13) * np.minimum(alt_tdd.end-13,30)" -arrConstShift2_escort,arrConstShift2_escort,"(alt_tdd.end<0) * np.minimum(0-alt_tdd.end,48) + (alt_tdd.start>28) * np.minimum(alt_tdd.end-28,15)" -,, -,origin_to_destination_distance,1 -,num_person_joint_tours,1 +Description,Target,Expression +#,, +departure_shift_escort,departureLinearShift1_escort,"(alt_tdd.start<=10) * np.minimum(10-alt_tdd.start,7) + (alt_tdd.start>10) * np.minimum(alt_tdd.start-10,35)" +arrival_shift_escort,arrivalLinearShift1_escort,"(alt_tdd.end<=11) * np.minimum(11-alt_tdd.end,7) + (alt_tdd.end>11) * np.minimum(alt_tdd.end-11,35)" +departure_shift2_escort,departureLinearShift2_escort,"(alt_tdd.start<=24) * np.minimum(24-alt_tdd.start,3) + (alt_tdd.start>24) * np.minimum(alt_tdd.start-24,9)" +arrival_shift2_escort,arrivalLinearShift2_escort,"(alt_tdd.end<=25) * np.minimum(25-alt_tdd.end,3) + (alt_tdd.end>25) * np.minimum(alt_tdd.end-25,9)" +duration_shift_escort,durationShift_escort,"(alt_tdd.duration<=1) * np.minimum(1-alt_tdd.duration, 0) + (alt_tdd.duration>1) * np.minimum(alt_tdd.duration-1,47)" +#,, +depConstShift1_escort,depConstShift1_escort,"(alt_tdd.start<8) * np.minimum(8-alt_tdd.start,4) + (alt_tdd.start>13) * np.minimum(alt_tdd.start-13,28)" +depConstShift2_escort,depConstShift2_escort,"(alt_tdd.start<0) * np.minimum(0-alt_tdd.start,48) + (alt_tdd.start>26) * np.minimum(alt_tdd.start-26,15)" +arrConstShift1_escort,arrConstShift1_escort,"(alt_tdd.end<8) * np.minimum(8-alt_tdd.end,2) + (alt_tdd.end>13) * np.minimum(alt_tdd.end-13,30)" +arrConstShift2_escort,arrConstShift2_escort,"(alt_tdd.end<0) * np.minimum(0-alt_tdd.end,48) + (alt_tdd.start>28) * np.minimum(alt_tdd.end-28,15)" +,, +,origin_to_destination_distance,1 +,num_person_joint_tours,1 diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv index fa76277995..616a627d22 100755 --- a/activitysim/examples/example_semcog/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_annotate_tours_preprocessor.csv @@ -1,5 +1,5 @@ -Description,Target,Expression -#,, -number of person joint tours,num_person_joint_tours,"reindex_i(joint_tour_participants.groupby('person_id').size(), non_mandatory_tours.person_id)" -#,, -,origin_to_destination_distance,"skim_dict.lookup(non_mandatory_tours.origin, non_mandatory_tours.destination, ('SOV_DIST', 'MD'))" +Description,Target,Expression +#,, +number of person joint tours,num_person_joint_tours,"reindex_i(joint_tour_participants.groupby('person_id').size(), non_mandatory_tours.person_id)" +#,, +,origin_to_destination_distance,"skim_dict.lookup(non_mandatory_tours.origin, non_mandatory_tours.destination, ('SOV_DIST', 'MD'))" diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_scheduling_escort_annotate_alts_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_escort_annotate_alts_preprocessor.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_scheduling_escort_annotate_alts_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_escort_annotate_alts_preprocessor.csv diff --git a/activitysim/examples/example_semcog/configs/non_mandatory_tour_scheduling_shopping_annotate_alts_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_shopping_annotate_alts_preprocessor.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/non_mandatory_tour_scheduling_shopping_annotate_alts_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/non_mandatory_tour_scheduling_shopping_annotate_alts_preprocessor.csv diff --git a/activitysim/examples/example_semcog/configs/school_location.csv b/activitysim/examples/prototype_semcog/configs/school_location.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/school_location.csv rename to activitysim/examples/prototype_semcog/configs/school_location.csv index ea1682a123..808e1eb216 100755 --- a/activitysim/examples/example_semcog/configs/school_location.csv +++ b/activitysim/examples/prototype_semcog/configs/school_location.csv @@ -1,19 +1,19 @@ -Label,Description,Expression,university,highschool,gradeschool -local_dist,,_DIST@skims['DIST'],1,1,1 -util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_univ_dist_0_1,0,0 -util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_univ_dist_1_2,0,0 -util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_univ_dist_2_5,0,0 -util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_univ_dist_5_15,0,0 -util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_univ_dist_15_up,0,0 -util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1,1,1 -util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1 -util_no_attractions,No attractions,@df['size_term']==0,-999,-999,-999 -util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum_uni,coef_mode_logsum,coef_mode_logsum -util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1 -util_dist,Distance,@_DIST,0,coef_dist,coef_dist -util_dist_squared,"Distance squared, capped at 20 miles","@(_DIST).clip(0,20)**2",0,coef_dist_squared,coef_dist_squared -util_dist_cubed,"Distance cubed, capped at 20 miles","@(_DIST).clip(0,20)**3",0,coef_dist_cubed,coef_dist_cubed -util_dist_logged,Distance logged,@(_DIST).apply(np.log1p),0,coef_dist_logged,coef_dist_logged -util_dist_part_time,"Distance,part time",@(df['pemploy']==2) * _DIST,0,coef_dist_part_time,coef_dist_part_time -util_dist_child_0_5,"Distance,child 0 to 5",@(df['age_0_to_5']==True) * _DIST,0,coef_dist_child_0_5,coef_dist_child_0_5 -util_dist_child_6_12,"Distance,child 6 to 12",@(df['age_6_to_12']==True) * _DIST,0,coef_dist_child_6_12,coef_dist_child_6_12 +Label,Description,Expression,university,highschool,gradeschool +local_dist,,_DIST@skims['DIST'],1,1,1 +util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_univ_dist_0_1,0,0 +util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_univ_dist_1_2,0,0 +util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_univ_dist_2_5,0,0 +util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_univ_dist_5_15,0,0 +util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_univ_dist_15_up,0,0 +util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1,1,1 +util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1 +util_no_attractions,No attractions,@df['size_term']==0,-999,-999,-999 +util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum_uni,coef_mode_logsum,coef_mode_logsum +util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1 +util_dist,Distance,@_DIST,0,coef_dist,coef_dist +util_dist_squared,"Distance squared, capped at 20 miles","@(_DIST).clip(0,20)**2",0,coef_dist_squared,coef_dist_squared +util_dist_cubed,"Distance cubed, capped at 20 miles","@(_DIST).clip(0,20)**3",0,coef_dist_cubed,coef_dist_cubed +util_dist_logged,Distance logged,@(_DIST).apply(np.log1p),0,coef_dist_logged,coef_dist_logged +util_dist_part_time,"Distance,part time",@(df['pemploy']==2) * _DIST,0,coef_dist_part_time,coef_dist_part_time +util_dist_child_0_5,"Distance,child 0 to 5",@(df['age_0_to_5']==True) * _DIST,0,coef_dist_child_0_5,coef_dist_child_0_5 +util_dist_child_6_12,"Distance,child 6 to 12",@(df['age_6_to_12']==True) * _DIST,0,coef_dist_child_6_12,coef_dist_child_6_12 diff --git a/activitysim/examples/example_semcog/configs/school_location.yaml b/activitysim/examples/prototype_semcog/configs/school_location.yaml similarity index 95% rename from activitysim/examples/example_semcog/configs/school_location.yaml rename to activitysim/examples/prototype_semcog/configs/school_location.yaml index df4f09c9f6..e16a80dde0 100755 --- a/activitysim/examples/example_semcog/configs/school_location.yaml +++ b/activitysim/examples/prototype_semcog/configs/school_location.yaml @@ -1,62 +1,62 @@ -SAMPLE_SIZE: 30 - -SIMULATE_CHOOSER_COLUMNS: - - home_zone_id - - school_segment - - household_id - - is_student - - age_0_to_5 - - age_6_to_12 - - pemploy - -# model-specific logsum-related settings -CHOOSER_ORIG_COL_NAME: home_zone_id -ALT_DEST_COL_NAME: alt_dest -IN_PERIOD: 14 -OUT_PERIOD: 8 - -DEST_CHOICE_COLUMN_NAME: school_zone_id - -SAMPLE_SPEC: school_location_sample.csv -SPEC: school_location.csv -COEFFICIENTS: school_location_coeffs.csv - -LOGSUM_SETTINGS: tour_mode_choice.yaml -LOGSUM_PREPROCESSOR: nontour_preprocessor - -LOGSUM_TOUR_PURPOSE: - university: univ - highschool: school - gradeschool: school - -annotate_persons: - SPEC: annotate_persons_school - DF: persons - -# - shadow pricing - -# required by initialize_households when creating school_destination_size table -CHOOSER_TABLE_NAME: persons - -# size_terms model_selector -MODEL_SELECTOR: school - -# chooser column with segment_id for this segment type -CHOOSER_SEGMENT_COLUMN_NAME: school_segment - -# boolean column to filter choosers (True means keep) -CHOOSER_FILTER_COLUMN_NAME: is_student - - -# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this -SEGMENT_IDS: - university: 3 - highschool: 2 - gradeschool: 1 - -# model adds these tables (informational - not added if commented out) -SHADOW_PRICE_TABLE: school_shadow_prices -MODELED_SIZE_TABLE: school_modeled_size - -# not loaded if commented out -SAVED_SHADOW_PRICE_TABLE_NAME: final_school_shadow_prices.csv +SAMPLE_SIZE: 30 + +SIMULATE_CHOOSER_COLUMNS: + - home_zone_id + - school_segment + - household_id + - is_student + - age_0_to_5 + - age_6_to_12 + - pemploy + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 14 +OUT_PERIOD: 8 + +DEST_CHOICE_COLUMN_NAME: school_zone_id + +SAMPLE_SPEC: school_location_sample.csv +SPEC: school_location.csv +COEFFICIENTS: school_location_coeffs.csv + +LOGSUM_SETTINGS: tour_mode_choice.yaml +LOGSUM_PREPROCESSOR: nontour_preprocessor + +LOGSUM_TOUR_PURPOSE: + university: univ + highschool: school + gradeschool: school + +annotate_persons: + SPEC: annotate_persons_school + DF: persons + +# - shadow pricing + +# required by initialize_households when creating school_destination_size table +CHOOSER_TABLE_NAME: persons + +# size_terms model_selector +MODEL_SELECTOR: school + +# chooser column with segment_id for this segment type +CHOOSER_SEGMENT_COLUMN_NAME: school_segment + +# boolean column to filter choosers (True means keep) +CHOOSER_FILTER_COLUMN_NAME: is_student + + +# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this +SEGMENT_IDS: + university: 3 + highschool: 2 + gradeschool: 1 + +# model adds these tables (informational - not added if commented out) +SHADOW_PRICE_TABLE: school_shadow_prices +MODELED_SIZE_TABLE: school_modeled_size + +# not loaded if commented out +SAVED_SHADOW_PRICE_TABLE_NAME: final_school_shadow_prices.csv diff --git a/activitysim/examples/example_semcog/configs/school_location_coeffs.csv b/activitysim/examples/prototype_semcog/configs/school_location_coeffs.csv similarity index 96% rename from activitysim/examples/example_semcog/configs/school_location_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/school_location_coeffs.csv index 55c5e6be1b..ce7f4a60e5 100755 --- a/activitysim/examples/example_semcog/configs/school_location_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/school_location_coeffs.csv @@ -1,15 +1,15 @@ -coefficient_name,value,constrain -coef_univ_dist_0_1,-3.2451,F -coef_univ_dist_1_2,-2.7011,F -coef_univ_dist_2_5,-0.5707,F -coef_univ_dist_5_15,-0.5002,F -coef_univ_dist_15_up,-0.073,F -coef_mode_logsum_uni,0.5358,F -coef_dist,-0.1560,F -coef_dist_squared,-0.0116,F -coef_dist_cubed,0.0005,F -coef_dist_logged,-0.9316,F -coef_dist_part_time,-0.0985,F -coef_dist_child_0_5,0.0236,F -coef_dist_child_6_12,-0.0657,F -coef_mode_logsum,0.4,F +coefficient_name,value,constrain +coef_univ_dist_0_1,-3.2451,F +coef_univ_dist_1_2,-2.7011,F +coef_univ_dist_2_5,-0.5707,F +coef_univ_dist_5_15,-0.5002,F +coef_univ_dist_15_up,-0.073,F +coef_mode_logsum_uni,0.5358,F +coef_dist,-0.1560,F +coef_dist_squared,-0.0116,F +coef_dist_cubed,0.0005,F +coef_dist_logged,-0.9316,F +coef_dist_part_time,-0.0985,F +coef_dist_child_0_5,0.0236,F +coef_dist_child_6_12,-0.0657,F +coef_mode_logsum,0.4,F diff --git a/activitysim/examples/example_semcog/configs/school_location_sample.csv b/activitysim/examples/prototype_semcog/configs/school_location_sample.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/school_location_sample.csv rename to activitysim/examples/prototype_semcog/configs/school_location_sample.csv index ed8188b8be..a7d0120ab8 100755 --- a/activitysim/examples/example_semcog/configs/school_location_sample.csv +++ b/activitysim/examples/prototype_semcog/configs/school_location_sample.csv @@ -1,17 +1,17 @@ -Label,Description,Expression,university,highschool,gradeschool -local_dist,,_DIST@skims['DIST'],1,1,1 -util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_univ_dist_0_1,0,0 -util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_univ_dist_1_2,0,0 -util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_univ_dist_2_5,0,0 -util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_univ_dist_5_15,0,0 -util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_univ_dist_15_up,0,0 -util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1,1,1 -util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1 -util_no_attractions,No attractions,@df['size_term']==0,-999,-999,-999 -util_dist,Distance,@_DIST,0,coef_dist,coef_dist -util_dist_squared,"Distance squared, capped at 20 miles","@(_DIST).clip(0,20)**2",0,coef_dist_squared,coef_dist_squared -util_dist_cubed,"Distance cubed, capped at 20 miles","@(_DIST).clip(0,20)**3",0,coef_dist_cubed,coef_dist_cubed -util_dist_logged,Distance logged,@(_DIST).apply(np.log1p),0,coef_dist_logged,coef_dist_logged -util_dist_part_time,"Distance,part time",@(df['pemploy']==2) * _DIST,0,coef_dist_part_time,coef_dist_part_time -util_dist_child_0_5,"Distance,child 0 to 5",@(df['age_0_to_5']==True) * _DIST,0,coef_dist_child_0_5,coef_dist_child_0_5 -util_dist_child_6_12,"Distance,child 6 to 12",@(df['age_6_to_12']==True) * _DIST,0,coef_dist_child_6_12,coef_dist_child_6_12 +Label,Description,Expression,university,highschool,gradeschool +local_dist,,_DIST@skims['DIST'],1,1,1 +util_dist_0_1,"Distance, piecewise linear from 0 to 1 miles","@_DIST.clip(0,1)",coef_univ_dist_0_1,0,0 +util_dist_1_2,"Distance, piecewise linear from 1 to 2 miles","@(_DIST-1).clip(0,1)",coef_univ_dist_1_2,0,0 +util_dist_2_5,"Distance, piecewise linear from 2 to 5 miles","@(_DIST-2).clip(0,3)",coef_univ_dist_2_5,0,0 +util_dist_5_15,"Distance, piecewise linear from 5 to 15 miles","@(_DIST-5).clip(0,10)",coef_univ_dist_5_15,0,0 +util_dist_15_up,"Distance, piecewise linear for 15+ miles",@(_DIST-15.0).clip(0),coef_univ_dist_15_up,0,0 +util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1,1,1 +util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1 +util_no_attractions,No attractions,@df['size_term']==0,-999,-999,-999 +util_dist,Distance,@_DIST,0,coef_dist,coef_dist +util_dist_squared,"Distance squared, capped at 20 miles","@(_DIST).clip(0,20)**2",0,coef_dist_squared,coef_dist_squared +util_dist_cubed,"Distance cubed, capped at 20 miles","@(_DIST).clip(0,20)**3",0,coef_dist_cubed,coef_dist_cubed +util_dist_logged,Distance logged,@(_DIST).apply(np.log1p),0,coef_dist_logged,coef_dist_logged +util_dist_part_time,"Distance,part time",@(df['pemploy']==2) * _DIST,0,coef_dist_part_time,coef_dist_part_time +util_dist_child_0_5,"Distance,child 0 to 5",@(df['age_0_to_5']==True) * _DIST,0,coef_dist_child_0_5,coef_dist_child_0_5 +util_dist_child_6_12,"Distance,child 6 to 12",@(df['age_6_to_12']==True) * _DIST,0,coef_dist_child_6_12,coef_dist_child_6_12 diff --git a/activitysim/examples/example_semcog/configs/settings.yaml b/activitysim/examples/prototype_semcog/configs/settings.yaml similarity index 96% rename from activitysim/examples/example_semcog/configs/settings.yaml rename to activitysim/examples/prototype_semcog/configs/settings.yaml index 2f679c5483..3d1bb870fc 100755 --- a/activitysim/examples/example_semcog/configs/settings.yaml +++ b/activitysim/examples/prototype_semcog/configs/settings.yaml @@ -1,156 +1,156 @@ -# input tables -# -# activitysim uses "well-known" index and foreign key names for imported tables (e.g. households, persons, land_use) -# as well as for created tables (tours, joint_tour_participants, trips) -# e.g. the households table must have an index column 'household_id' and the foreign key to households in the -# persons table is also household_id. This naming convention allows activitysim to intuit the relationship -# between tables - for instance, to ensure that multiprocess slicing includes all the persons, tours, and trips -# in the same subprocess pipeline. The same strategy is also when chunking choosers, and to support tracing by -# household_id. -# -# the input_table_list index_col directive instructs activitysim to set the imported table index to zone_id -# you cannot change the well-known name of the index by modifying this directive. However, if your input file -# has a different id column name, you can rename it to the required index name with the rename_columns directive. -# In the settings below, the 'TAZ' column in the imported table is renamed 'zone_id' in the rename_columns settings. -# -# input tables -input_table_list: - # - # households (table index 'household_id') - # - - tablename: households - filename: households.csv - index_col: household_id - rename_columns: - persons: hhsize - cars: auto_ownership - zone_id: home_zone_id - # - # persons (table index 'person_id') - # - - tablename: persons - filename: persons.csv - index_col: person_id - # - # land_use (table index 'zone_id') - # - - tablename: land_use - filename: land_use.csv - index_col: zone_id - rename_columns: - ZONE: zone_id - -# convert input CSVs to HDF5 format and save to outputs directory -# create_input_store: True - -# number of households to simulate -households_sample_size: 100 -# simulate all households -#households_sample_size: 0 - -#hh_ids: override_hh_ids.csv - -chunk_size: 0 - -# assume enough RAM to not chunk -chunk_training_mode: disabled - -# set false to disable variability check in simple_simulate and interaction_simulate -check_for_variability: False - -# - shadow pricing global switches - -# turn shadow_pricing on and off for all models (e.g. school and work) -# shadow pricing is deprecated for less than full samples -# see shadow_pricing.yaml for additional settings -use_shadow_pricing: False - - -# - tracing - -# trace household id; comment out or leave empty for no trace -# households with all tour types -# [ 728370 1234067 1402924 1594625 1595333 1747572 1896849 1931818 2222690 2344951 2677154] -trace_hh_id: 1311364 - -# trace origin, destination in accessibility calculation; comment out or leave empty for no trace -# trace_od: [5, 11] -trace_od: - - -models: - - initialize_landuse - - initialize_households - - compute_accessibility - - work_from_home - - add_size_tables - - school_location - - workplace_location - - transit_pass_subsidy - - transit_pass_ownership - - auto_ownership_simulate - - free_parking - - telecommute_frequency - - cdap_simulate - - mandatory_tour_frequency - - mandatory_tour_scheduling - - joint_tour_frequency - - joint_tour_composition - - joint_tour_participation - - joint_tour_destination - - joint_tour_scheduling - - non_mandatory_tour_frequency - - non_mandatory_tour_destination - - non_mandatory_tour_scheduling - - tour_mode_choice_simulate - - atwork_subtour_frequency - - atwork_subtour_destination - - atwork_subtour_scheduling - - atwork_subtour_mode_choice - - stop_frequency - - trip_purpose - - trip_destination - - trip_purpose_and_destination - - trip_scheduling - - trip_mode_choice - - write_data_dictionary - - track_skim_usage - - write_tables - - write_trip_matrices - -# to resume after last successful checkpoint, specify resume_after: _ -resume_after: - -output_tables: - h5_store: False - action: include - prefix: final_ - tables: - - checkpoints - - accessibility - - land_use - - households - - persons - - tours - - trips - - joint_tour_participants - -# area_types less than this are considered urban -urban_threshold: 4 -cbd_threshold: 2 -rural_threshold: 6 - -# - value of time - -# value_of_time = lognormal(np.log(median_value_of_time * mu), sigma).clip(min_vot, max_vot) - -min_value_of_time: 1 -max_value_of_time: 50 -distributed_vot_mu: 0.684 -distributed_vot_sigma: 0.85 - -household_median_value_of_time: - 1: 6.01 - 2: 8.81 - 3: 10.44 - 4: 12.86 +# input tables +# +# activitysim uses "well-known" index and foreign key names for imported tables (e.g. households, persons, land_use) +# as well as for created tables (tours, joint_tour_participants, trips) +# e.g. the households table must have an index column 'household_id' and the foreign key to households in the +# persons table is also household_id. This naming convention allows activitysim to intuit the relationship +# between tables - for instance, to ensure that multiprocess slicing includes all the persons, tours, and trips +# in the same subprocess pipeline. The same strategy is also when chunking choosers, and to support tracing by +# household_id. +# +# the input_table_list index_col directive instructs activitysim to set the imported table index to zone_id +# you cannot change the well-known name of the index by modifying this directive. However, if your input file +# has a different id column name, you can rename it to the required index name with the rename_columns directive. +# In the settings below, the 'TAZ' column in the imported table is renamed 'zone_id' in the rename_columns settings. +# +# input tables +input_table_list: + # + # households (table index 'household_id') + # + - tablename: households + filename: households.csv + index_col: household_id + rename_columns: + persons: hhsize + cars: auto_ownership + zone_id: home_zone_id + # + # persons (table index 'person_id') + # + - tablename: persons + filename: persons.csv + index_col: person_id + # + # land_use (table index 'zone_id') + # + - tablename: land_use + filename: land_use.csv + index_col: zone_id + rename_columns: + ZONE: zone_id + +# convert input CSVs to HDF5 format and save to outputs directory +# create_input_store: True + +# number of households to simulate +households_sample_size: 100 +# simulate all households +#households_sample_size: 0 + +#hh_ids: override_hh_ids.csv + +chunk_size: 0 + +# assume enough RAM to not chunk +chunk_training_mode: disabled + +# set false to disable variability check in simple_simulate and interaction_simulate +check_for_variability: False + +# - shadow pricing global switches + +# turn shadow_pricing on and off for all models (e.g. school and work) +# shadow pricing is deprecated for less than full samples +# see shadow_pricing.yaml for additional settings +use_shadow_pricing: False + + +# - tracing + +# trace household id; comment out or leave empty for no trace +# households with all tour types +# [ 728370 1234067 1402924 1594625 1595333 1747572 1896849 1931818 2222690 2344951 2677154] +trace_hh_id: 1311364 + +# trace origin, destination in accessibility calculation; comment out or leave empty for no trace +# trace_od: [5, 11] +trace_od: + + +models: + - initialize_landuse + - initialize_households + - compute_accessibility + - work_from_home + - add_size_tables + - school_location + - workplace_location + - transit_pass_subsidy + - transit_pass_ownership + - auto_ownership_simulate + - free_parking + - telecommute_frequency + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + - write_data_dictionary + - track_skim_usage + - write_tables + - write_trip_matrices + +# to resume after last successful checkpoint, specify resume_after: _ +resume_after: + +output_tables: + h5_store: False + action: include + prefix: final_ + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + - joint_tour_participants + +# area_types less than this are considered urban +urban_threshold: 4 +cbd_threshold: 2 +rural_threshold: 6 + +# - value of time + +# value_of_time = lognormal(np.log(median_value_of_time * mu), sigma).clip(min_vot, max_vot) + +min_value_of_time: 1 +max_value_of_time: 50 +distributed_vot_mu: 0.684 +distributed_vot_sigma: 0.85 + +household_median_value_of_time: + 1: 6.01 + 2: 8.81 + 3: 10.44 + 4: 12.86 diff --git a/activitysim/examples/example_semcog/configs/shadow_pricing.yaml b/activitysim/examples/prototype_semcog/configs/shadow_pricing.yaml similarity index 96% rename from activitysim/examples/example_semcog/configs/shadow_pricing.yaml rename to activitysim/examples/prototype_semcog/configs/shadow_pricing.yaml index b8ee4e9afd..b61ec41928 100755 --- a/activitysim/examples/example_semcog/configs/shadow_pricing.yaml +++ b/activitysim/examples/prototype_semcog/configs/shadow_pricing.yaml @@ -1,34 +1,34 @@ -shadow_pricing_models: - school: school_location - workplace: workplace_location - -# global switch to enable/disable loading of saved shadow prices -# (ignored if global use_shadow_pricing switch is False) -LOAD_SAVED_SHADOW_PRICES: True - -# number of shadow price iterations for cold start -MAX_ITERATIONS: 10 - -# number of shadow price iterations for warm start (after loading saved shadow_prices) -MAX_ITERATIONS_SAVED: 1 - -# ignore criteria for zones smaller than size_threshold -SIZE_THRESHOLD: 10 - -# zone passes if modeled is within percent_tolerance of predicted_size -PERCENT_TOLERANCE: 5 - -# max percentage of zones allowed to fail -FAIL_THRESHOLD: 10 - -# CTRAMP or daysim -SHADOW_PRICE_METHOD: ctramp -#SHADOW_PRICE_METHOD: daysim - -# ctramp-style shadow_pricing_method parameters -DAMPING_FACTOR: 1 - -# daysim-style shadow_pricing_method parameters -# FIXME should these be the same as PERCENT_TOLERANCE and FAIL_THRESHOLD above? -DAYSIM_ABSOLUTE_TOLERANCE: 50 -DAYSIM_PERCENT_TOLERANCE: 10 +shadow_pricing_models: + school: school_location + workplace: workplace_location + +# global switch to enable/disable loading of saved shadow prices +# (ignored if global use_shadow_pricing switch is False) +LOAD_SAVED_SHADOW_PRICES: True + +# number of shadow price iterations for cold start +MAX_ITERATIONS: 10 + +# number of shadow price iterations for warm start (after loading saved shadow_prices) +MAX_ITERATIONS_SAVED: 1 + +# ignore criteria for zones smaller than size_threshold +SIZE_THRESHOLD: 10 + +# zone passes if modeled is within percent_tolerance of predicted_size +PERCENT_TOLERANCE: 5 + +# max percentage of zones allowed to fail +FAIL_THRESHOLD: 10 + +# CTRAMP or daysim +SHADOW_PRICE_METHOD: ctramp +#SHADOW_PRICE_METHOD: daysim + +# ctramp-style shadow_pricing_method parameters +DAMPING_FACTOR: 1 + +# daysim-style shadow_pricing_method parameters +# FIXME should these be the same as PERCENT_TOLERANCE and FAIL_THRESHOLD above? +DAYSIM_ABSOLUTE_TOLERANCE: 50 +DAYSIM_PERCENT_TOLERANCE: 10 diff --git a/activitysim/examples/example_semcog/configs/stop_frequency.yaml b/activitysim/examples/prototype_semcog/configs/stop_frequency.yaml similarity index 95% rename from activitysim/examples/example_semcog/configs/stop_frequency.yaml rename to activitysim/examples/prototype_semcog/configs/stop_frequency.yaml index a32b6cc93d..5831f72e72 100755 --- a/activitysim/examples/example_semcog/configs/stop_frequency.yaml +++ b/activitysim/examples/prototype_semcog/configs/stop_frequency.yaml @@ -1,78 +1,78 @@ -LOGIT_TYPE: MNL - -preprocessor: - SPEC: stop_frequency_annotate_tours_preprocessor - DF: tours_merged - TABLES: - - persons - - land_use - - accessibility - -SEGMENT_COL: primary_purpose - -SPEC_SEGMENTS: - - primary_purpose: work - SPEC: stop_frequency_work.csv - COEFFICIENTS: _dummy_coefficients.csv - - primary_purpose: school - SPEC: stop_frequency_school.csv - COEFFICIENTS: _dummy_coefficients.csv - - primary_purpose: univ - SPEC: stop_frequency_univ.csv - COEFFICIENTS: _dummy_coefficients.csv - - primary_purpose: social - SPEC: stop_frequency_social.csv - COEFFICIENTS: _dummy_coefficients.csv - - primary_purpose: shopping - SPEC: stop_frequency_shopping.csv - COEFFICIENTS: _dummy_coefficients.csv - - primary_purpose: eatout - SPEC: stop_frequency_eatout.csv - COEFFICIENTS: _dummy_coefficients.csv - - primary_purpose: escort - SPEC: stop_frequency_escort.csv - COEFFICIENTS: _dummy_coefficients.csv - - primary_purpose: othmaint - SPEC: stop_frequency_othmaint.csv - COEFFICIENTS: _dummy_coefficients.csv - - primary_purpose: othdiscr - SPEC: stop_frequency_othdiscr.csv - COEFFICIENTS: _dummy_coefficients.csv - - primary_purpose: atwork - SPEC: stop_frequency_atwork.csv - COEFFICIENTS: _dummy_coefficients.csv - -CONSTANTS: - TRANSIT_MODES: - - WALK_LOC - - WALK_PRM - - WALK_MIX - - PNR_LOC - - PNR_PRM - - PNR_MIX - - KNR_LOC - - KNR_PRM - - KNR_MIX - - DRIVE_TO_TRANSIT_MODES: - - PNR_LOC - - PNR_PRM - - PNR_MIX - - KNR_LOC - - KNR_PRM - - KNR_MIX - NONMOTORIZED_MODES: - - WALK - - BIKE - SHOP_TOUR: shopping - MAINT_TOUR: othmaint - SCHOOL_TOUR: school - EATOUT_TOUR: eatout - SOCIAL_TOUR: social - num_atwork_subtours_map: - no_subtours: 0 - eat: 1 - business1: 1 - maint: 1 - business2: 2 - eat_business: 2 +LOGIT_TYPE: MNL + +preprocessor: + SPEC: stop_frequency_annotate_tours_preprocessor + DF: tours_merged + TABLES: + - persons + - land_use + - accessibility + +SEGMENT_COL: primary_purpose + +SPEC_SEGMENTS: + - primary_purpose: work + SPEC: stop_frequency_work.csv + COEFFICIENTS: _dummy_coefficients.csv + - primary_purpose: school + SPEC: stop_frequency_school.csv + COEFFICIENTS: _dummy_coefficients.csv + - primary_purpose: univ + SPEC: stop_frequency_univ.csv + COEFFICIENTS: _dummy_coefficients.csv + - primary_purpose: social + SPEC: stop_frequency_social.csv + COEFFICIENTS: _dummy_coefficients.csv + - primary_purpose: shopping + SPEC: stop_frequency_shopping.csv + COEFFICIENTS: _dummy_coefficients.csv + - primary_purpose: eatout + SPEC: stop_frequency_eatout.csv + COEFFICIENTS: _dummy_coefficients.csv + - primary_purpose: escort + SPEC: stop_frequency_escort.csv + COEFFICIENTS: _dummy_coefficients.csv + - primary_purpose: othmaint + SPEC: stop_frequency_othmaint.csv + COEFFICIENTS: _dummy_coefficients.csv + - primary_purpose: othdiscr + SPEC: stop_frequency_othdiscr.csv + COEFFICIENTS: _dummy_coefficients.csv + - primary_purpose: atwork + SPEC: stop_frequency_atwork.csv + COEFFICIENTS: _dummy_coefficients.csv + +CONSTANTS: + TRANSIT_MODES: + - WALK_LOC + - WALK_PRM + - WALK_MIX + - PNR_LOC + - PNR_PRM + - PNR_MIX + - KNR_LOC + - KNR_PRM + - KNR_MIX + + DRIVE_TO_TRANSIT_MODES: + - PNR_LOC + - PNR_PRM + - PNR_MIX + - KNR_LOC + - KNR_PRM + - KNR_MIX + NONMOTORIZED_MODES: + - WALK + - BIKE + SHOP_TOUR: shopping + MAINT_TOUR: othmaint + SCHOOL_TOUR: school + EATOUT_TOUR: eatout + SOCIAL_TOUR: social + num_atwork_subtours_map: + no_subtours: 0 + eat: 1 + business1: 1 + maint: 1 + business2: 2 + eat_business: 2 diff --git a/activitysim/examples/example_semcog/configs/stop_frequency_alternatives.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_alternatives.csv similarity index 94% rename from activitysim/examples/example_semcog/configs/stop_frequency_alternatives.csv rename to activitysim/examples/prototype_semcog/configs/stop_frequency_alternatives.csv index 0153cb399b..72f49a7c77 100755 --- a/activitysim/examples/example_semcog/configs/stop_frequency_alternatives.csv +++ b/activitysim/examples/prototype_semcog/configs/stop_frequency_alternatives.csv @@ -1,18 +1,18 @@ -#,,alt file for building tours even though simulation is simple_simulate not interaction_simulate -alt,out,in -0out_0in,0,0 -0out_1in,0,1 -0out_2in,0,2 -0out_3in,0,3 -1out_0in,1,0 -1out_1in,1,1 -1out_2in,1,2 -1out_3in,1,3 -2out_0in,2,0 -2out_1in,2,1 -2out_2in,2,2 -2out_3in,2,3 -3out_0in,3,0 -3out_1in,3,1 -3out_2in,3,2 -3out_3in,3,3 +#,,alt file for building tours even though simulation is simple_simulate not interaction_simulate +alt,out,in +0out_0in,0,0 +0out_1in,0,1 +0out_2in,0,2 +0out_3in,0,3 +1out_0in,1,0 +1out_1in,1,1 +1out_2in,1,2 +1out_3in,1,3 +2out_0in,2,0 +2out_1in,2,1 +2out_2in,2,2 +2out_3in,2,3 +3out_0in,3,0 +3out_1in,3,1 +3out_2in,3,2 +3out_3in,3,3 diff --git a/activitysim/examples/example_semcog/configs/stop_frequency_annotate_tours_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_annotate_tours_preprocessor.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/stop_frequency_annotate_tours_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/stop_frequency_annotate_tours_preprocessor.csv index c7693b2d22..989f3abe8e 100755 --- a/activitysim/examples/example_semcog/configs/stop_frequency_annotate_tours_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/stop_frequency_annotate_tours_preprocessor.csv @@ -1,47 +1,47 @@ -Description,Target,Expression -#,, -# define primary_purpose to use for slicing choosers with a value that identifies the spec to be used ,, -# e.g. univ segment means there will be a spec called stop_frequency_univ.csv,, -# so the 'school' tour_type can treat univ and non-univ school tours differently,, -,primary_purpose,"df.tour_type.where((df.tour_type != 'school') | ~df.is_university, 'univ')" -,primary_purpose,"primary_purpose.where(df.tour_category!='atwork', 'atwork')" -#,, -,distance_in_miles,od_skims['DIST'] -#,, -,is_joint,df.tour_category=='joint' -,_HH_PERSON_COUNT,"lambda exp, persons: persons.query(exp).groupby('household_id').size()" -,num_full,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_FULL, persons), df.household_id)" -,num_part,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_PART, persons), df.household_id)" -,num_student,"reindex_i(_HH_PERSON_COUNT('pstudent != %s' % PSTUDENT_NOT, persons), df.household_id)" -Num Kids between 0 and 4 (including) years old,num_age_0_4,"reindex_i(_HH_PERSON_COUNT('age < 5', persons), df.household_id)" -Num kids between 4 and 15 (including) years old,num_age_5_15,"reindex_i(_HH_PERSON_COUNT('(age >= 5) & (age <16)', persons), df.household_id)" -Number of Adults (>= 16 years old),num_adult,"reindex_i(_HH_PERSON_COUNT('age >= 16', persons), df.household_id)" -,more_cars_than_workers,df.auto_ownership >= (num_full + num_part) -,tour_mode_is_transit,df.tour_mode.isin(TRANSIT_MODES) -,tour_mode_is_drive_transit,df.tour_mode.isin(DRIVE_TO_TRANSIT_MODES) -,tour_mode_is_non_motorized,df.tour_mode.isin(NONMOTORIZED_MODES) -#,, -#num_work_tours already defined,, -school but not university,num_school_tours,"reindex_i(df[primary_purpose==SCHOOL_TOUR].groupby('person_id').size(), df.person_id)" -,num_univ_tours,(df.is_university) * num_school_tours -#num_escort_tours already defined,, -# indiv tour counts should not include joint tours by point_person,, -,num_shop_tours,"reindex_i(df[~is_joint & (df.tour_type==SHOP_TOUR)].groupby('person_id').size(), df.person_id)" -,num_maint_tours,"reindex_i(df[~is_joint & (df.tour_type==MAINT_TOUR)].groupby('person_id').size(), df.person_id)" -,num_eatout_tours,"reindex_i(df[~is_joint & (df.tour_type==EATOUT_TOUR)].groupby('person_id').size(), df.person_id)" -,num_social_tours,"reindex_i(df[~is_joint & (df.tour_type==SOCIAL_TOUR)].groupby('person_id').size(), df.person_id)" -#,, -Number of subtours in the tour,num_atwork_subtours,"df.atwork_subtour_frequency.map(num_atwork_subtours_map, na_action='ignore').fillna(0).astype(np.int8)" -#,, -Number of hh shop tours including joint,num_hh_shop_tours,"reindex_i(df[df.tour_type==SHOP_TOUR].groupby('household_id').size(), df.person_id)" -Number of hh maint tours including joint,num_hh_maint_tours,"reindex_i(df[df.tour_type==MAINT_TOUR].groupby('household_id').size(), df.person_id)" -tourStartsInPeakPeriod,_tour_starts_in_peak,(network_los.skim_time_period_label(df.start) == 'AM') | (network_los.skim_time_period_label(df.start) == 'PM') -AccesibilityAtOrigin fallback,hhacc,0 -AccesibilityAtOrigin if transit,hhacc,"hhacc.where(~tour_mode_is_transit, df.trPkRetail.where(_tour_starts_in_peak, df.trOpRetail))" -AccesibilityAtOrigin if non_motorized,hhacc,"hhacc.where(~tour_mode_is_non_motorized, df.nmRetail)" -AccesibilityADestination fallback,pracc,0 -AccesibilityADestination peak transit,_dest_trPkRetail,"reindex(accessibility.trPkRetail, df.destination)" -AccesibilityADestination off-peak transit,_dest_trOpRetail,"reindex(accessibility.trOpRetail, df.destination)" -AccesibilityAtDestination if transit,pracc,"pracc.where(~tour_mode_is_transit, _dest_trPkRetail.where(_tour_starts_in_peak, _dest_trOpRetail))" -AccesibilityAtDestination if non_motorized,pracc,"pracc.where(~tour_mode_is_non_motorized, reindex(accessibility.nmRetail, df.destination))" -,destination_area_type,"reindex(land_use.AreaType, df.destination)" +Description,Target,Expression +#,, +# define primary_purpose to use for slicing choosers with a value that identifies the spec to be used ,, +# e.g. univ segment means there will be a spec called stop_frequency_univ.csv,, +# so the 'school' tour_type can treat univ and non-univ school tours differently,, +,primary_purpose,"df.tour_type.where((df.tour_type != 'school') | ~df.is_university, 'univ')" +,primary_purpose,"primary_purpose.where(df.tour_category!='atwork', 'atwork')" +#,, +,distance_in_miles,od_skims['DIST'] +#,, +,is_joint,df.tour_category=='joint' +,_HH_PERSON_COUNT,"lambda exp, persons: persons.query(exp).groupby('household_id').size()" +,num_full,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_FULL, persons), df.household_id)" +,num_part,"reindex_i(_HH_PERSON_COUNT('ptype == %s' % PEMPLOY_PART, persons), df.household_id)" +,num_student,"reindex_i(_HH_PERSON_COUNT('pstudent != %s' % PSTUDENT_NOT, persons), df.household_id)" +Num Kids between 0 and 4 (including) years old,num_age_0_4,"reindex_i(_HH_PERSON_COUNT('age < 5', persons), df.household_id)" +Num kids between 4 and 15 (including) years old,num_age_5_15,"reindex_i(_HH_PERSON_COUNT('(age >= 5) & (age <16)', persons), df.household_id)" +Number of Adults (>= 16 years old),num_adult,"reindex_i(_HH_PERSON_COUNT('age >= 16', persons), df.household_id)" +,more_cars_than_workers,df.auto_ownership >= (num_full + num_part) +,tour_mode_is_transit,df.tour_mode.isin(TRANSIT_MODES) +,tour_mode_is_drive_transit,df.tour_mode.isin(DRIVE_TO_TRANSIT_MODES) +,tour_mode_is_non_motorized,df.tour_mode.isin(NONMOTORIZED_MODES) +#,, +#num_work_tours already defined,, +school but not university,num_school_tours,"reindex_i(df[primary_purpose==SCHOOL_TOUR].groupby('person_id').size(), df.person_id)" +,num_univ_tours,(df.is_university) * num_school_tours +#num_escort_tours already defined,, +# indiv tour counts should not include joint tours by point_person,, +,num_shop_tours,"reindex_i(df[~is_joint & (df.tour_type==SHOP_TOUR)].groupby('person_id').size(), df.person_id)" +,num_maint_tours,"reindex_i(df[~is_joint & (df.tour_type==MAINT_TOUR)].groupby('person_id').size(), df.person_id)" +,num_eatout_tours,"reindex_i(df[~is_joint & (df.tour_type==EATOUT_TOUR)].groupby('person_id').size(), df.person_id)" +,num_social_tours,"reindex_i(df[~is_joint & (df.tour_type==SOCIAL_TOUR)].groupby('person_id').size(), df.person_id)" +#,, +Number of subtours in the tour,num_atwork_subtours,"df.atwork_subtour_frequency.map(num_atwork_subtours_map, na_action='ignore').fillna(0).astype(np.int8)" +#,, +Number of hh shop tours including joint,num_hh_shop_tours,"reindex_i(df[df.tour_type==SHOP_TOUR].groupby('household_id').size(), df.person_id)" +Number of hh maint tours including joint,num_hh_maint_tours,"reindex_i(df[df.tour_type==MAINT_TOUR].groupby('household_id').size(), df.person_id)" +tourStartsInPeakPeriod,_tour_starts_in_peak,(network_los.skim_time_period_label(df.start) == 'AM') | (network_los.skim_time_period_label(df.start) == 'PM') +AccesibilityAtOrigin fallback,hhacc,0 +AccesibilityAtOrigin if transit,hhacc,"hhacc.where(~tour_mode_is_transit, df.trPkRetail.where(_tour_starts_in_peak, df.trOpRetail))" +AccesibilityAtOrigin if non_motorized,hhacc,"hhacc.where(~tour_mode_is_non_motorized, df.nmRetail)" +AccesibilityADestination fallback,pracc,0 +AccesibilityADestination peak transit,_dest_trPkRetail,"reindex(accessibility.trPkRetail, df.destination)" +AccesibilityADestination off-peak transit,_dest_trOpRetail,"reindex(accessibility.trOpRetail, df.destination)" +AccesibilityAtDestination if transit,pracc,"pracc.where(~tour_mode_is_transit, _dest_trPkRetail.where(_tour_starts_in_peak, _dest_trOpRetail))" +AccesibilityAtDestination if non_motorized,pracc,"pracc.where(~tour_mode_is_non_motorized, reindex(accessibility.nmRetail, df.destination))" +,destination_area_type,"reindex(land_use.AreaType, df.destination)" diff --git a/activitysim/examples/example_semcog/configs/stop_frequency_atwork.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_atwork.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/stop_frequency_atwork.csv rename to activitysim/examples/prototype_semcog/configs/stop_frequency_atwork.csv index 47baa8adcc..8f9709d4bb 100755 --- a/activitysim/examples/example_semcog/configs/stop_frequency_atwork.csv +++ b/activitysim/examples/prototype_semcog/configs/stop_frequency_atwork.csv @@ -1,13 +1,13 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH ,income_in_thousands<50000,,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45 -Number of eating tours tours undertaken by the person,num_eatout_tours,,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28 -Subtour departure less than or equal to 11AM,start<19,,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31 -Subtour return time greater or equal to 2PM,end>24,,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34 -Subtour duration in hours (integer),end-start,,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56 -dummy for subtour origin (tour destination) at Exurban or Rual (AreaTypes = 6 or 7),destination_area_type >5,,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27 -Primary Destination Accessibility (LOG of it),pracc,,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18 -subtour distance in miles (from tour destination to subtour primary destination one way),distance_in_miles,,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02 -Alternative specific constant for outbound stops,1,,,,,-3.896,-3.896,-3.896,-3.896,-5.709,-5.709,-5.709,-5.709,-7.361,-7.361,-7.361,-7.361 -Alternative specific constant for return stops,1,,-3.671,-5.388,-6.21,,-3.671,-5.388,-6.21,,-3.671,-5.388,-6.21,,-3.671,-5.388,-6.21 -Alternative specific constant for the total number of stops,1,,,0,0,,0,0,2.127,0,0,2.127,2.127,0,2.127,2.127,2.127 -Phase1_Calibration,1,,-0.214873516,-0.08829587,-0.021076701,-0.583908707,1.759594513,1.768617768,0.015908847,-0.496833561,0.742087614,0.721158833,-0.038026423,1.210255745,0.858858431,2.537970801,3.355148354 +Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +Middle to Low Income HH ,income_in_thousands<50000,,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45,0.45 +Number of eating tours tours undertaken by the person,num_eatout_tours,,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28 +Subtour departure less than or equal to 11AM,start<19,,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31 +Subtour return time greater or equal to 2PM,end>24,,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34,0.34 +Subtour duration in hours (integer),end-start,,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.56 +dummy for subtour origin (tour destination) at Exurban or Rual (AreaTypes = 6 or 7),destination_area_type >5,,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27,0.27 +Primary Destination Accessibility (LOG of it),pracc,,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18,0.18 +subtour distance in miles (from tour destination to subtour primary destination one way),distance_in_miles,,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02 +Alternative specific constant for outbound stops,1,,,,,-3.896,-3.896,-3.896,-3.896,-5.709,-5.709,-5.709,-5.709,-7.361,-7.361,-7.361,-7.361 +Alternative specific constant for return stops,1,,-3.671,-5.388,-6.21,,-3.671,-5.388,-6.21,,-3.671,-5.388,-6.21,,-3.671,-5.388,-6.21 +Alternative specific constant for the total number of stops,1,,,0,0,,0,0,2.127,0,0,2.127,2.127,0,2.127,2.127,2.127 +Phase1_Calibration,1,,-0.214873516,-0.08829587,-0.021076701,-0.583908707,1.759594513,1.768617768,0.015908847,-0.496833561,0.742087614,0.721158833,-0.038026423,1.210255745,0.858858431,2.537970801,3.355148354 diff --git a/activitysim/examples/example_semcog/configs/stop_frequency_eatout.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_eatout.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/stop_frequency_eatout.csv rename to activitysim/examples/prototype_semcog/configs/stop_frequency_eatout.csv index 81fb521ef9..c6dc0b83b4 100755 --- a/activitysim/examples/example_semcog/configs/stop_frequency_eatout.csv +++ b/activitysim/examples/prototype_semcog/configs/stop_frequency_eatout.csv @@ -1,54 +1,54 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, -High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, -Number of HH Persons,hhsize,,,,,,,,,,,,,,,, -Number of full time workes in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,,,,,,,,,,,,,,, -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, -Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, -Number of Vehicles,auto_ownership,,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19 -Dummy for female,~is_joint & female,,,,,,,,,,,,,,,, -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73 -Number of work tours undertaken by the person,~is_joint * num_work_tours,,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28 -Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,,,,,,,,,,,,,,, -Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,,,,,,,,,,,,,,, -Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24 -Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,,,,,,,,,,,,,,, -Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, -Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46 -Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, -At least one kid and one adult participate in the tour,composition=='mixed',,0.37,0.37,0.37,,0.37,0.37,0.37,,0.37,0.37,0.37,,0.37,0.37,0.37 -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, -Arrival later than 17:00.,(end > 28),,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45 -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31 -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 20 Miles ,(distance_in_miles < 20),,,,,,,,,,,,,,,, -dummy for distance in miles,distance_in_miles,,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01 -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,~is_joint,,,,,-2.19,-2.19,-2.19,-2.19,-4.516,-4.516,-4.516,-4.516,-5.255,-5.255,-5.255,-5.255 -Alternative specific constant for return stops,~is_joint,,-1.761,-3.697,-4.717,,-1.761,-3.697,-4.717,,-1.761,-3.697,-4.717,,-1.761,-3.697,-4.717 -Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0.94,0,0,0.94,2.026,0,0.94,2.026,2.026 -Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 -Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 -Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 -Dummy for an outbound visiting tour,primary_purpose == 'social',,,,,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69 -Dummy for a return visiting tour,primary_purpose == 'social',,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64 -Dummy for a visiting tour with both outbound and return leg,primary_purpose == 'social',,,,,,0.44,0.44,0.44,,0.44,0.44,0.44,,0.44,0.44,0.44 -Phase1_Calibration,~is_joint,,0.402777606,0.769044419,1.547375234,0.17724742,1.523737838,1.704085866,1.219450496,1.50935493,2.583402672,2.411844468,0.759903236,1.610165394,2.324582352,2.016696053,2.783482815 +Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, +Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, +High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, +Number of HH Persons,hhsize,,,,,,,,,,,,,,,, +Number of full time workes in HH,num_full,,,,,,,,,,,,,,,, +Number of Students in HH,num_student,,,,,,,,,,,,,,,, +Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, +Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, +Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, +Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, +Number of Vehicles,auto_ownership,,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19 +Dummy for female,~is_joint & female,,,,,,,,,,,,,,,, +Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 +Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73 +Number of work tours undertaken by the person,~is_joint * num_work_tours,,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28 +Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,,,,,,,,,,,,,,, +Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,,,,,,,,,,,,,,, +Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, +Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24 +Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,,,,,,,,,,,,,,, +Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, +Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, +Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, +Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46 +Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, +At least one kid and one adult participate in the tour,composition=='mixed',,0.37,0.37,0.37,,0.37,0.37,0.37,,0.37,0.37,0.37,,0.37,0.37,0.37 +AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, +Arrival later than 17:00.,(end > 28),,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45 +Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31 +HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +dummy for distance less than 20 Miles ,(distance_in_miles < 20),,,,,,,,,,,,,,,, +dummy for distance in miles,distance_in_miles,,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01 +#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, +No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Alternative specific constant for outbound stops,~is_joint,,,,,-2.19,-2.19,-2.19,-2.19,-4.516,-4.516,-4.516,-4.516,-5.255,-5.255,-5.255,-5.255 +Alternative specific constant for return stops,~is_joint,,-1.761,-3.697,-4.717,,-1.761,-3.697,-4.717,,-1.761,-3.697,-4.717,,-1.761,-3.697,-4.717 +Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0.94,0,0,0.94,2.026,0,0.94,2.026,2.026 +Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 +Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 +Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 +Dummy for an outbound visiting tour,primary_purpose == 'social',,,,,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69 +Dummy for a return visiting tour,primary_purpose == 'social',,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64 +Dummy for a visiting tour with both outbound and return leg,primary_purpose == 'social',,,,,,0.44,0.44,0.44,,0.44,0.44,0.44,,0.44,0.44,0.44 +Phase1_Calibration,~is_joint,,0.402777606,0.769044419,1.547375234,0.17724742,1.523737838,1.704085866,1.219450496,1.50935493,2.583402672,2.411844468,0.759903236,1.610165394,2.324582352,2.016696053,2.783482815 diff --git a/activitysim/examples/example_semcog/configs/stop_frequency_escort.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_escort.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/stop_frequency_escort.csv rename to activitysim/examples/prototype_semcog/configs/stop_frequency_escort.csv index f0924ab209..c38df6d575 100755 --- a/activitysim/examples/example_semcog/configs/stop_frequency_escort.csv +++ b/activitysim/examples/prototype_semcog/configs/stop_frequency_escort.csv @@ -1,48 +1,48 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, -High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, -Number of HH Persons,hhsize,,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24 -Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19 -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, -Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, -Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, -Dummy for female,female,,,,,,,,,,,,,,,, -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91 -Number of work tours undertaken by the person,num_work_tours,,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29 -Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,, -Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,, -Number of escort tours tours undertaken by the person,num_escort_tours,,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15 -Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, -Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, -Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, -Arrival later than 17:00.,(end > 28),,,,,,,,,,,,,,,, -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59 -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 5 Miles,(distance_in_miles < 5),,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32 -dummy for distance in miles,distance_in_miles,,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01 -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,~is_joint,,,,,-2.173,-2.173,-2.173,-2.173,-4.294,-4.294,-4.294,-4.294,-4.758,-4.758,-4.758,-4.758 -Alternative specific constant for return stops,~is_joint,,-0.968,-2.41,-3.024,,-0.968,-2.41,-3.024,,-0.968,-2.41,-3.024,,-0.968,-2.41,-3.024 -Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0,0,0,0,-1.807,0,0,-1.807,-1.807 -Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 -Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 -Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 -Phase1_Calibration,~is_joint,,-0.281471428,-0.016095822,0.633091127,0.54790928,0.034142435,0.648536014,1.179253849,1.588769694,1.014803541,1.362137736,4.47587972,1.689728522,1.349848675,3.365045965,4.461548606 +Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, +Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, +High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, +Number of HH Persons,hhsize,,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24 +Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, +Number of Students in HH,num_student,,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19 +Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, +Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, +Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, +Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, +Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, +Dummy for female,female,,,,,,,,,,,,,,,, +Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 +Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91,-1.91 +Number of work tours undertaken by the person,num_work_tours,,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29,-0.29 +Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,, +Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,, +Number of escort tours tours undertaken by the person,num_escort_tours,,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15 +Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, +Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, +Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, +Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, +Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, +AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, +Arrival later than 17:00.,(end > 28),,,,,,,,,,,,,,,, +Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59,0.59 +Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, +HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +dummy for distance less than 5 Miles,(distance_in_miles < 5),,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32,0.32 +dummy for distance in miles,distance_in_miles,,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01 +#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, +No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Alternative specific constant for outbound stops,~is_joint,,,,,-2.173,-2.173,-2.173,-2.173,-4.294,-4.294,-4.294,-4.294,-4.758,-4.758,-4.758,-4.758 +Alternative specific constant for return stops,~is_joint,,-0.968,-2.41,-3.024,,-0.968,-2.41,-3.024,,-0.968,-2.41,-3.024,,-0.968,-2.41,-3.024 +Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0,0,0,0,-1.807,0,0,-1.807,-1.807 +Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 +Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 +Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 +Phase1_Calibration,~is_joint,,-0.281471428,-0.016095822,0.633091127,0.54790928,0.034142435,0.648536014,1.179253849,1.588769694,1.014803541,1.362137736,4.47587972,1.689728522,1.349848675,3.365045965,4.461548606 diff --git a/activitysim/examples/example_semcog/configs/stop_frequency_othdiscr.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_othdiscr.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/stop_frequency_othdiscr.csv rename to activitysim/examples/prototype_semcog/configs/stop_frequency_othdiscr.csv index 6384dcfdf9..9dc631ceef 100755 --- a/activitysim/examples/example_semcog/configs/stop_frequency_othdiscr.csv +++ b/activitysim/examples/prototype_semcog/configs/stop_frequency_othdiscr.csv @@ -1,50 +1,50 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, -High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, -Number of HH Persons,hhsize,,,,,,,,,,,,,,,, -Number of full time workes in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,,,,,,,,,,,,,,, -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, -Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, -Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, -Dummy for female,~is_joint & female,,,,,,,,,,,,,,,, -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578 -Number of work tours undertaken by the person,~is_joint * num_work_tours,,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153 -Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,,,,,,,,,,,,,,, -Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176 -Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629 -Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715 -Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, -Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, -Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, -Arrival later than 17:00.,(end > 28),,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383 -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335 -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 10 Miles ,(distance_in_miles < 10),,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756 -dummy for distance in miles,distance_in_miles,,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225 -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,~is_joint,,,,,-1.581,-1.581,-1.581,-1.581,-3.323,-3.323,-3.323,-3.323,-4.623,-4.623,-4.623,-4.623 -Alternative specific constant for return stops,~is_joint,,-0.921,-2.336,-2.927,,-0.921,-2.336,-2.927,,-0.921,-2.336,-2.927,,-0.921,-2.336,-2.927 -Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0.863,0,0,0.863,0.939,0,0.863,0.939,0.939 -Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 -Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 -Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 -Phase1_Calibration,~is_joint,,0.187263987,0.670000983,0.992092361,0.283823906,0.461537002,0.958158373,-0.156485013,0.806611419,0.872137091,0.343003767,0.7657564,2.063744949,1.13525473,1.376716611,2.113754844 +Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, +Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, +High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, +Number of HH Persons,hhsize,,,,,,,,,,,,,,,, +Number of full time workes in HH,num_full,,,,,,,,,,,,,,,, +Number of Students in HH,num_student,,,,,,,,,,,,,,,, +Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, +Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, +Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, +Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, +Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, +Dummy for female,~is_joint & female,,,,,,,,,,,,,,,, +Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 +Dummy for walking to all stops,tour_mode_is_non_motorized,,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578,-2.4578 +Number of work tours undertaken by the person,~is_joint * num_work_tours,,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153,-0.6153 +Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,,,,,,,,,,,,,,, +Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176,-0.8176 +Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, +Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629,-0.629 +Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715,-0.3715 +Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, +Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, +Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, +Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, +Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, +AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, +Arrival later than 17:00.,(end > 28),,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383,-0.6383 +Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335,0.8335 +Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, +HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +dummy for distance less than 10 Miles ,(distance_in_miles < 10),,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756,0.3756 +dummy for distance in miles,distance_in_miles,,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225,-0.0225 +#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, +No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Alternative specific constant for outbound stops,~is_joint,,,,,-1.581,-1.581,-1.581,-1.581,-3.323,-3.323,-3.323,-3.323,-4.623,-4.623,-4.623,-4.623 +Alternative specific constant for return stops,~is_joint,,-0.921,-2.336,-2.927,,-0.921,-2.336,-2.927,,-0.921,-2.336,-2.927,,-0.921,-2.336,-2.927 +Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0.863,0,0,0.863,0.939,0,0.863,0.939,0.939 +Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 +Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 +Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 +Phase1_Calibration,~is_joint,,0.187263987,0.670000983,0.992092361,0.283823906,0.461537002,0.958158373,-0.156485013,0.806611419,0.872137091,0.343003767,0.7657564,2.063744949,1.13525473,1.376716611,2.113754844 diff --git a/activitysim/examples/example_semcog/configs/stop_frequency_othmaint.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_othmaint.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/stop_frequency_othmaint.csv rename to activitysim/examples/prototype_semcog/configs/stop_frequency_othmaint.csv index b29838cb53..d8f4a60fc0 100755 --- a/activitysim/examples/example_semcog/configs/stop_frequency_othmaint.csv +++ b/activitysim/examples/prototype_semcog/configs/stop_frequency_othmaint.csv @@ -1,51 +1,51 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17 -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23 -High Income HH,(income_in_thousands>=100000),,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24 -Number of HH Persons,hhsize,,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31 -Number of full time workes in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21 -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74 -Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, -Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, -Dummy for female,~is_joint & female,,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012 -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329 -Number of work tours undertaken by the person,~is_joint * num_work_tours,,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364 -Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252 -Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135 -Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428 -Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,,,,,,,,,,,,,,, -Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, -Number of maintenace tours undertaken by the houshold,num_hh_maint_tours,,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468 -Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, -Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,0.4904,0.4904,0.4904,,0.4904,0.4904,0.4904,,0.4904,0.4904,0.4904,,0.4904,0.4904,0.4904 -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, -Arrival later than 17:00.,(end > 28),,,,,,,,,,,,,,,, -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134 -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 20 Miles ,(distance_in_miles < 20),,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408 -dummy for distance in miles,distance_in_miles,,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273 -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,~is_joint,,,,,-1.761,-1.761,-1.761,-1.761,-3.661,-3.661,-3.661,-3.661,-5.426,-5.426,-5.426,-5.426 -Alternative specific constant for return stops,~is_joint,,-0.585,-1.48,-2.462,,-0.585,-1.48,-2.462,,-0.585,-1.48,-2.462,,-0.585,-1.48,-2.462 -Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0.414,0,0,0.414,0.488,0,0.414,0.488,0.488 -Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 -Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 -Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 -Phase1_Calibration,~is_joint,,0.475190752,0.530588025,1.389497026,0.960593185,0.775245847,1.122815367,1.226361438,1.683900584,1.594911845,1.101961687,2.302278146,3.35277309,2.734475828,2.746850954,3.93916549 +Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17 +Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23 +High Income HH,(income_in_thousands>=100000),,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24 +Number of HH Persons,hhsize,,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31 +Number of full time workes in HH,num_full,,,,,,,,,,,,,,,, +Number of Students in HH,num_student,,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21 +Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74 +Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, +Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, +Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, +Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, +Dummy for female,~is_joint & female,,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012,0.3012 +Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 +Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329,-1.4329 +Number of work tours undertaken by the person,~is_joint * num_work_tours,,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364,-0.364 +Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252,-0.6252 +Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135,-1.4135 +Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, +Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428,-0.1428 +Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,,,,,,,,,,,,,,, +Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, +Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, +Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, +Number of maintenace tours undertaken by the houshold,num_hh_maint_tours,,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468,-0.0468 +Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, +Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,0.4904,0.4904,0.4904,,0.4904,0.4904,0.4904,,0.4904,0.4904,0.4904,,0.4904,0.4904,0.4904 +AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, +Arrival later than 17:00.,(end > 28),,,,,,,,,,,,,,,, +Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134,0.5134 +Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, +HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +dummy for distance less than 20 Miles ,(distance_in_miles < 20),,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408,-0.408 +dummy for distance in miles,distance_in_miles,,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273,0.0273 +#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, +No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Alternative specific constant for outbound stops,~is_joint,,,,,-1.761,-1.761,-1.761,-1.761,-3.661,-3.661,-3.661,-3.661,-5.426,-5.426,-5.426,-5.426 +Alternative specific constant for return stops,~is_joint,,-0.585,-1.48,-2.462,,-0.585,-1.48,-2.462,,-0.585,-1.48,-2.462,,-0.585,-1.48,-2.462 +Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0.414,0,0,0.414,0.488,0,0.414,0.488,0.488 +Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 +Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 +Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 +Phase1_Calibration,~is_joint,,0.475190752,0.530588025,1.389497026,0.960593185,0.775245847,1.122815367,1.226361438,1.683900584,1.594911845,1.101961687,2.302278146,3.35277309,2.734475828,2.746850954,3.93916549 diff --git a/activitysim/examples/example_semcog/configs/stop_frequency_school.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_school.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/stop_frequency_school.csv rename to activitysim/examples/prototype_semcog/configs/stop_frequency_school.csv index da2963b96f..d48e0f739c 100755 --- a/activitysim/examples/example_semcog/configs/stop_frequency_school.csv +++ b/activitysim/examples/prototype_semcog/configs/stop_frequency_school.csv @@ -1,45 +1,45 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, -High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, -Number of HH Persons,hhsize,,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506 -Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,,,,,,,,,,,,,,, -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, -Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299 -Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331 -Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, -Dummy for female,female,,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099 -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163 -Number of work tours undertaken by the person,num_work_tours,,,,,,,,,,,,,,,, -Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,, -Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,, -Number of escort tours tours undertaken by the person,num_escort_tours,,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365 -Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, -Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, -Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, -Arrival later than 17:00.,(end > 28),,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377 -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549 -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 15 Miles,(distance_in_miles < 15),,,,,,,,,,,,,,,, -dummy for distance in miles,distance_in_miles,,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438 -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,1,,,,,-2.123,-2.123,-2.123,-2.123,-3.798,-3.798,-3.798,-3.798,-5.85,-5.85,-5.85,-5.85 -Alternative specific constant for return stops,1,,-1.206,-2.672,-3.364,,-1.206,-2.672,-3.364,,-1.206,-2.672,-3.364,,-1.206,-2.672,-3.364 -Alternative specific constant for the total number of stops,1,,,0,0,,0,0,0.701,0,0,0.701,1.135,0,0.701,1.135,1.135 -Phase1_Calibration,1,,-0.462185603,0.034598859,0.143710811,-0.107820253,0.247416913,0.525399532,0.065742876,-0.264881606,-0.844194184,-0.191651123,-0.295391166,1.390171934,-1.256414914,0.034312538,0.662067578 +Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, +Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, +High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, +Number of HH Persons,hhsize,,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506,-0.506 +Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, +Number of Students in HH,num_student,,,,,,,,,,,,,,,, +Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, +Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, +Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299,0.3299 +Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +Number of Cars > Number of Workers,more_cars_than_workers,,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331,0.5331 +Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, +Dummy for female,female,,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099,0.4099 +Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 +Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163,-1.8163 +Number of work tours undertaken by the person,num_work_tours,,,,,,,,,,,,,,,, +Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,, +Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,, +Number of escort tours tours undertaken by the person,num_escort_tours,,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365,1.2365 +Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, +Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, +Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, +Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, +Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, +AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, +Arrival later than 17:00.,(end > 28),,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377,1.8377 +Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549,0.9549 +Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, +HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +dummy for distance less than 15 Miles,(distance_in_miles < 15),,,,,,,,,,,,,,,, +dummy for distance in miles,distance_in_miles,,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438,0.0438 +#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, +No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Alternative specific constant for outbound stops,1,,,,,-2.123,-2.123,-2.123,-2.123,-3.798,-3.798,-3.798,-3.798,-5.85,-5.85,-5.85,-5.85 +Alternative specific constant for return stops,1,,-1.206,-2.672,-3.364,,-1.206,-2.672,-3.364,,-1.206,-2.672,-3.364,,-1.206,-2.672,-3.364 +Alternative specific constant for the total number of stops,1,,,0,0,,0,0,0.701,0,0,0.701,1.135,0,0.701,1.135,1.135 +Phase1_Calibration,1,,-0.462185603,0.034598859,0.143710811,-0.107820253,0.247416913,0.525399532,0.065742876,-0.264881606,-0.844194184,-0.191651123,-0.295391166,1.390171934,-1.256414914,0.034312538,0.662067578 diff --git a/activitysim/examples/example_semcog/configs/stop_frequency_shopping.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_shopping.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/stop_frequency_shopping.csv rename to activitysim/examples/prototype_semcog/configs/stop_frequency_shopping.csv index eeb66016c4..909e706109 100755 --- a/activitysim/examples/example_semcog/configs/stop_frequency_shopping.csv +++ b/activitysim/examples/prototype_semcog/configs/stop_frequency_shopping.csv @@ -1,51 +1,51 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, -High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, -Number of HH Persons,hhsize,,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522 -Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,,,,,,,,,,,,,,, -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, -Num kids between 5 and 15 (including) years old,num_age_5_15,,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482 -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, -Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, -Dummy for female,~is_joint & female,,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721 -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908 -Number of work tours undertaken by the person,~is_joint * num_work_tours,,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548 -Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709 -Number of school tours tours undertaken by the person,~is_joint * num_school_tours,,,,,,,,,,,,,,,, -Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,,,,,,,,,,,,,,, -Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977 -Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733 -Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, -Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, -Dummy for only adults participate in the tour,composition=='adults',,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902 -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, -Arrival later than 17:00.,(end > 28),,,,,,,,,,,,,,,, -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056 -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 5 Miles,(distance_in_miles < 5),,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768 -dummy for distance in miles,distance_in_miles,,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289 -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,~is_joint,,,,,-1.339,-1.339,-1.339,-1.339,-3.11,-3.11,-3.11,-3.11,-4.487,-4.487,-4.487,-4.487 -Alternative specific constant for return stops,~is_joint,,-1.179,-2.305,-3.024,,-1.179,-2.305,-3.024,,-1.179,-2.305,-3.024,,-1.179,-2.305,-3.024 -Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0.252,0,0,0.252,0.514,0,0.252,0.514,0.514 -Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 -Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 -Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 -Phase1_Calibration,~is_joint,,0.061908526,0.211807872,0.269959028,0.159052037,0.386325269,0.209133272,0.563685874,1.002319475,1.182476858,1.537241286,1.004078003,2.038636069,1.69019331,2.060858768,2.809711477 +Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, +Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, +High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, +Number of HH Persons,hhsize,,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522,-0.1522 +Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, +Number of Students in HH,num_student,,,,,,,,,,,,,,,, +Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, +Num kids between 5 and 15 (including) years old,num_age_5_15,,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482,0.0482 +Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, +Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, +Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, +Dummy for female,~is_joint & female,,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721,0.1721 +Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 +Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908,-1.4908 +Number of work tours undertaken by the person,~is_joint * num_work_tours,,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548,-0.548 +Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709,-0.6709 +Number of school tours tours undertaken by the person,~is_joint * num_school_tours,,,,,,,,,,,,,,,, +Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, +Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,,,,,,,,,,,,,,, +Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977,-0.1977 +Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, +Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, +Number of shop tours undertaken by the houshold,num_hh_shop_tours,,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733,-0.0733 +Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, +Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, +Dummy for only adults participate in the tour,composition=='adults',,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902,0.1902 +AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, +Arrival later than 17:00.,(end > 28),,,,,,,,,,,,,,,, +Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056,0.9056 +Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, +HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +dummy for distance less than 5 Miles,(distance_in_miles < 5),,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768,0.3768 +dummy for distance in miles,distance_in_miles,,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289,0.0289 +#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, +No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Alternative specific constant for outbound stops,~is_joint,,,,,-1.339,-1.339,-1.339,-1.339,-3.11,-3.11,-3.11,-3.11,-4.487,-4.487,-4.487,-4.487 +Alternative specific constant for return stops,~is_joint,,-1.179,-2.305,-3.024,,-1.179,-2.305,-3.024,,-1.179,-2.305,-3.024,,-1.179,-2.305,-3.024 +Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0.252,0,0,0.252,0.514,0,0.252,0.514,0.514 +Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 +Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 +Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 +Phase1_Calibration,~is_joint,,0.061908526,0.211807872,0.269959028,0.159052037,0.386325269,0.209133272,0.563685874,1.002319475,1.182476858,1.537241286,1.004078003,2.038636069,1.69019331,2.060858768,2.809711477 diff --git a/activitysim/examples/example_semcog/configs/stop_frequency_social.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_social.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/stop_frequency_social.csv rename to activitysim/examples/prototype_semcog/configs/stop_frequency_social.csv index 82031d49b4..c04a51f48a 100755 --- a/activitysim/examples/example_semcog/configs/stop_frequency_social.csv +++ b/activitysim/examples/prototype_semcog/configs/stop_frequency_social.csv @@ -1,54 +1,54 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, -High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, -Number of HH Persons,hhsize,,,,,,,,,,,,,,,, -Number of full time workes in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,,,,,,,,,,,,,,, -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, -Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, -Number of Vehicles,auto_ownership,,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19 -Dummy for female,~is_joint & female,,,,,,,,,,,,,,,, -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73 -Number of work tours undertaken by the person,~is_joint * num_work_tours,,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28 -Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,,,,,,,,,,,,,,, -Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,,,,,,,,,,,,,,, -Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24 -Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,,,,,,,,,,,,,,, -Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, -Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46 -Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, -At least one kid and one adult participate in the tour,composition=='mixed',,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37 -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, -Arrival later than 17:00.,(end > 28),,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45 -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31 -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 20 Miles ,(distance_in_miles < 20),,,,,,,,,,,,,,,, -dummy for distance in miles,distance_in_miles,,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01 -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,~is_joint,,,,,-1.081,-1.081,-1.081,-1.081,-2.874,-2.874,-2.874,-2.874,-4.552,-4.552,-4.552,-4.552 -Alternative specific constant for return stops,~is_joint,,-1.12,-2.764,-3.451,,-1.12,-2.764,-3.451,,-1.12,-2.764,-3.451,,-1.12,-2.764,-3.451 -Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0.496,0,0,0.496,0.882,0,0.496,0.882,0.882 -Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 -Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 -Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 -Dummy for an outbound visiting tour,primary_purpose == 'social',,,,,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69 -Dummy for a return visiting tour,primary_purpose == 'social',,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64 -Dummy for a visiting tour with both outbound and return leg,primary_purpose == 'social',,,,,,0.44,0.44,0.44,,0.44,0.44,0.44,,0.44,0.44,0.44 -Phase1_Calibration,~is_joint,,0.38246443,0.839705649,1.208071759,0.577945215,0.470848758,0.683396345,0.561543342,1.335624596,0.972779572,1.949517699,0.318404726,3.210420697,0.539522475,0.864775287,3.503722118 +Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +Middle to Low Income HH ,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, +Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, +High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, +Number of HH Persons,hhsize,,,,,,,,,,,,,,,, +Number of full time workes in HH,num_full,,,,,,,,,,,,,,,, +Number of Students in HH,num_student,,,,,,,,,,,,,,,, +Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, +Num kids between 4 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, +Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,,,,,,,,,,,,,,, +Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, +Number of Vehicles,auto_ownership,,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19,-0.19 +Dummy for female,~is_joint & female,,,,,,,,,,,,,,,, +Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 +Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73,-1.73 +Number of work tours undertaken by the person,~is_joint * num_work_tours,,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28,-0.28 +Number of university tours tours undertaken by the person,~is_joint * num_univ_tours,,,,,,,,,,,,,,,, +Number of shool tours tours undertaken by the person,~is_joint * num_school_tours,,,,,,,,,,,,,,,, +Number of escort tours tours undertaken by the person,~is_joint * num_escort_tours,,,,,,,,,,,,,,,, +Number of shop tours undertaken by the person,~is_joint * num_shop_tours,,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24,-0.24 +Number of maintenace tours tours undertaken by the person,~is_joint * num_maint_tours,,,,,,,,,,,,,,,, +Number of eating tours tours undertaken by the person,~is_joint * num_eatout_tours,,,,,,,,,,,,,,,, +Number of visit tours tours undertaken by the person,~is_joint * num_social_tours,,,,,,,,,,,,,,,, +Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, +Number of persons participating in the tour.Outgoing stops interaction,is_joint * number_of_participants,,,,,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46,-0.46 +Number of persons participating in the tour.Return stops interaction,is_joint * number_of_participants,,,,,,,,,,,,,,,, +At least one kid and one adult participate in the tour,composition=='mixed',,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37,0.37 +AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, +Arrival later than 17:00.,(end > 28),,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45,-0.45 +Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31,1.31 +HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +dummy for distance less than 20 Miles ,(distance_in_miles < 20),,,,,,,,,,,,,,,, +dummy for distance in miles,distance_in_miles,,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01,-0.01 +#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, +No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Alternative specific constant for outbound stops,~is_joint,,,,,-1.081,-1.081,-1.081,-1.081,-2.874,-2.874,-2.874,-2.874,-4.552,-4.552,-4.552,-4.552 +Alternative specific constant for return stops,~is_joint,,-1.12,-2.764,-3.451,,-1.12,-2.764,-3.451,,-1.12,-2.764,-3.451,,-1.12,-2.764,-3.451 +Alternative specific constant for the total number of stops,~is_joint,,,0,0,,0,0,0.496,0,0,0.496,0.882,0,0.496,0.882,0.882 +Alternative specific constant for outbound stops on joint tours,is_joint,,,,,-1.783,-1.783,-1.783,-1.783,-4.067,-4.067,-4.067,-4.067,-4.998,-4.998,-4.998,-4.998 +Alternative specific constant for return stops on joint tours,is_joint,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379,,-1.329,-2.796,-3.379 +Alternative specific constant for the total number of stops on joint tours,is_joint,,,0,0,,0,0,0.518,0,0,0.518,1.497,0,0.518,1.497,1.497 +Dummy for an outbound visiting tour,primary_purpose == 'social',,,,,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69,-0.69 +Dummy for a return visiting tour,primary_purpose == 'social',,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64,,-0.64,-0.64,-0.64 +Dummy for a visiting tour with both outbound and return leg,primary_purpose == 'social',,,,,,0.44,0.44,0.44,,0.44,0.44,0.44,,0.44,0.44,0.44 +Phase1_Calibration,~is_joint,,0.38246443,0.839705649,1.208071759,0.577945215,0.470848758,0.683396345,0.561543342,1.335624596,0.972779572,1.949517699,0.318404726,3.210420697,0.539522475,0.864775287,3.503722118 diff --git a/activitysim/examples/example_semcog/configs/stop_frequency_univ.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_univ.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/stop_frequency_univ.csv rename to activitysim/examples/prototype_semcog/configs/stop_frequency_univ.csv index 725233c680..9c9ba1af06 100755 --- a/activitysim/examples/example_semcog/configs/stop_frequency_univ.csv +++ b/activitysim/examples/prototype_semcog/configs/stop_frequency_univ.csv @@ -1,45 +1,45 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, -High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, -Number of HH Persons,hhsize,,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827 -Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,,,,,,,,,,,,,,, -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, -Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823 -Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, -Number of Vehicles,auto_ownership,,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703 -Dummy for female,female,,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349 -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,,,,,,,,,,,,,,, -Number of work tours undertaken by the person,num_work_tours,,,,,,,,,,,,,,,, -Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,, -Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,, -Number of escort tours tours undertaken by the person,num_escort_tours,,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018 -Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, -Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, -Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, -Arrival later than 17:00.,(end > 28),,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389 -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434 -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481 -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 20 Miles,(distance_in_miles < 20),,,,,,,,,,,,,,,, -dummy for distance in miles,distance_in_miles,,,,,,,,,,,,,,,, -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,1,,,,,-2.628,-2.628,-2.628,-2.628,-3.741,-3.741,-3.741,-3.741,-4.981,-4.981,-4.981,-4.981 -Alternative specific constant for return stops,1,,-2.003,-3.51,-3.677,,-2.003,-3.51,-3.677,,-2.003,-3.51,-3.677,,-2.003,-3.51,-3.677 -Alternative specific constant for the total number of stops,1,,,0,0,,0,0,1.272,0,0,1.272,1.871,0,1.272,1.871,1.871 -Phase1_Calibration,1,,0.420011539,0.781344394,0.733748807,0.712121201,1.545847839,1.973538669,0.675841505,0.652388889,0.802044327,0.807649045,0.537484229,1.764449963,1.356930627,0.668833391,1.667362221 +Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,,,,,,,,,,,,,,, +Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,,,,,,,,,,,,,,, +High Income HH,(income_in_thousands>=100000),,,,,,,,,,,,,,,, +Number of HH Persons,hhsize,,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827,-0.2827 +Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, +Number of Students in HH,num_student,,,,,,,,,,,,,,,, +Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,,,,,,,,,,,,,,, +Num kids between 5 and 15 (including) years old,num_age_5_15,,,,,,,,,,,,,,,, +Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823,0.6823 +Number of Adults (>= 16 years old),num_adult,,,,,,,,,,,,,,,, +Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +Number of Cars > Number of Workers,more_cars_than_workers,,,,,,,,,,,,,,,, +Number of Vehicles,auto_ownership,,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703,0.1703 +Dummy for female,female,,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349,0.7349 +Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 +Dummy for walking to all stops,tour_mode_is_non_motorized,,,,,,,,,,,,,,,, +Number of work tours undertaken by the person,num_work_tours,,,,,,,,,,,,,,,, +Number of university tours tours undertaken by the person,num_univ_tours,,,,,,,,,,,,,,,, +Number of school tours tours undertaken by the person,num_school_tours,,,,,,,,,,,,,,,, +Number of escort tours tours undertaken by the person,num_escort_tours,,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018,0.9018 +Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, +Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, +Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, +Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, +Number of shop tours undertaken by the houshold,num_hh_shop_tours,,,,,,,,,,,,,,,, +AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,,,,,,,,,,,,,,, +Arrival later than 17:00.,(end > 28),,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389,0.389 +Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434,0.8434 +Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, +HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +HH accesibility for inbound tours. Interaction,hhacc,,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481,0.2481 +Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +dummy for distance less than 20 Miles,(distance_in_miles < 20),,,,,,,,,,,,,,,, +dummy for distance in miles,distance_in_miles,,,,,,,,,,,,,,,, +#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, +No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Alternative specific constant for outbound stops,1,,,,,-2.628,-2.628,-2.628,-2.628,-3.741,-3.741,-3.741,-3.741,-4.981,-4.981,-4.981,-4.981 +Alternative specific constant for return stops,1,,-2.003,-3.51,-3.677,,-2.003,-3.51,-3.677,,-2.003,-3.51,-3.677,,-2.003,-3.51,-3.677 +Alternative specific constant for the total number of stops,1,,,0,0,,0,0,1.272,0,0,1.272,1.871,0,1.272,1.871,1.871 +Phase1_Calibration,1,,0.420011539,0.781344394,0.733748807,0.712121201,1.545847839,1.973538669,0.675841505,0.652388889,0.802044327,0.807649045,0.537484229,1.764449963,1.356930627,0.668833391,1.667362221 diff --git a/activitysim/examples/example_semcog/configs/stop_frequency_work.csv b/activitysim/examples/prototype_semcog/configs/stop_frequency_work.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/stop_frequency_work.csv rename to activitysim/examples/prototype_semcog/configs/stop_frequency_work.csv index f10d875a1b..89389766d4 100755 --- a/activitysim/examples/example_semcog/configs/stop_frequency_work.csv +++ b/activitysim/examples/prototype_semcog/configs/stop_frequency_work.csv @@ -1,46 +1,46 @@ -Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in -Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17 -Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23 -High Income HH,(income_in_thousands>=100000),,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24 -Number of HH Persons,hhsize,,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31 -Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, -Number of Students in HH,num_student,,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21 -Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, -Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74 -Num kids between 5 and 15 (including) years old,num_age_5_15,,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08 -Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26 -Number of Adults (>= 16 years old),num_adult,,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03 -Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, -Number of Cars > Number of Workers,more_cars_than_workers,,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16 -Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, -Dummy for female,female,,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22 -Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 -Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54 -Number of work tours undertaken by the person,num_work_tours,,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15 -Number of university tours tours undertaken by the person,num_univ_tours,,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48 -Number of school tours tours undertaken by the person,num_school_tours,,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55 -Number of escort tours tours undertaken by the person,num_escort_tours,,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2 -Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, -Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, -Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, -Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, -Number of shop tours undertaken by the houshold,num_hh_shop_tours,,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05 -AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93 -Arrival later than 17:00.,(end > 28),,,,,,,,,,,,,,,, -Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31 -Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6 -Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,,,,,,,,,,,,,,, -Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, -HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, -Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, -dummy for distance less than 20 Miles,(distance_in_miles < 20),,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22 -dummy for distance in miles,distance_in_miles,,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01 -#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, -No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Alternative specific constant for outbound stops,1,,,,,-0.833,-0.833,-0.833,-0.833,-2.613,-2.613,-2.613,-2.613,-3.934,-3.934,-3.934,-3.934 -Alternative specific constant for return stops,1,,-0.445,-1.775,-2.139,,-0.445,-1.775,-2.139,,-0.445,-1.775,-2.139,,-0.445,-1.775,-2.139 -Alternative specific constant for the total number of stops,1,,,0,0,,0,0,0,0,0,0,0.695,0,0,0.695,0.695 -Number of subtours in the tour,num_atwork_subtours,,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19 -Phase1_Calibration,1,,0.223196473,0.325510562,0.203854333,-0.303375802,-0.391244072,-0.093964049,-0.096568307,-0.097190886,-0.494486563,0.334444913,-0.145593481,0.40493641,-0.229067675,0.390561162,0.076806162 +Description,Expression,0out_0in,0out_1in,0out_2in,0out_3in,1out_0in,1out_1in,1out_2in,1out_3in,2out_0in,2out_1in,2out_2in,2out_3in,3out_0in,3out_1in,3out_2in,3out_3in +Middle to Low Income HH,(income_in_thousands>19999) & (income_in_thousands<50000),,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17,0.17 +Mid to High Income HH,(income_in_thousands>=50000) & (income_in_thousands<100000),,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23,0.23 +High Income HH,(income_in_thousands>=100000),,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24,0.24 +Number of HH Persons,hhsize,,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31,-0.31 +Number of full time workers in HH,num_full,,,,,,,,,,,,,,,, +Number of Students in HH,num_student,,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21,0.21 +Num Kids between 0 and 4 (including) years old,num_age_0_4,,,,,,,,,,,,,,,, +Presence of Kids between 0 and 4 (including) years old,(num_age_0_4 > 0),,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74,0.74 +Num kids between 5 and 15 (including) years old,num_age_5_15,,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08,0.08 +Presence of kids between 5 and 15 (including) years old,(num_age_5_15 > 0),,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26,0.26 +Number of Adults (>= 16 years old),num_adult,,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03 +Dummy for single parent household,(num_adult == 1) & (num_age_0_4 + num_age_5_15 > 0),,,,,,,,,,,,,,,, +Number of Cars > Number of Workers,more_cars_than_workers,,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16,0.16 +Number of Vehicles,auto_ownership,,,,,,,,,,,,,,,, +Dummy for female,female,,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22,0.22 +Dummy for all stops made by transit,tour_mode_is_transit,,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7,-0.7 +Dummy for walking to all stops,tour_mode_is_non_motorized,,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54,-1.54 +Number of work tours undertaken by the person,num_work_tours,,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15,-0.15 +Number of university tours tours undertaken by the person,num_univ_tours,,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48,-0.48 +Number of school tours tours undertaken by the person,num_school_tours,,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55,-1.55 +Number of escort tours tours undertaken by the person,num_escort_tours,,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2 +Number of shop tours undertaken by the person,num_shop_tours,,,,,,,,,,,,,,,, +Number of maintenace tours tours undertaken by the person,num_maint_tours,,,,,,,,,,,,,,,, +Number of eating tours tours undertaken by the person,num_eatout_tours,,,,,,,,,,,,,,,, +Number of visit tours tours undertaken by the person,num_social_tours,,,,,,,,,,,,,,,, +Number of shop tours undertaken by the houshold,num_hh_shop_tours,,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05 +AM Peak departure between 6AM and 7 AM (including) Interacted with outbound tours,(start>6) & (start<11),,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93,-1.93 +Arrival later than 17:00.,(end > 28),,,,,,,,,,,,,,,, +Evening Arrival (>=19:00) Interacted with return tours,(end > 32),,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31,0.31 +Dummy for the duration of the tour being equal or greater than or equal to 11 hours,(duration > 21),,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6,0.6 +Dummy for the duration of the tour being equal or greater than or equal to 9 hours ,(duration > 17),,,,,,,,,,,,,,,, +Dummy for the duration of the tour being equal or greater than or equal to 3 hours ,(duration > 5),,,,,,,,,,,,,,,, +HH accesibility for outbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +HH accesibility for inbound tours. Interaction,hhacc,,,,,,,,,,,,,,,, +Primary Destination Accessibility for outbound tours. Interaction,pracc,,,,,,,,,,,,,,,, +Primary Destination Accessibility for return tours. Interaction,pracc,,,,,,,,,,,,,,,, +dummy for distance less than 20 Miles,(distance_in_miles < 20),,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22,-0.22 +dummy for distance in miles,distance_in_miles,,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01 +#distance in miles * Number of stops,distance_in_miles * @@numStopsAlt,,,,,,,,,,,,,,,, +No stops if tour mode is driveTransit,tour_mode_is_drive_transit,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Alternative specific constant for outbound stops,1,,,,,-0.833,-0.833,-0.833,-0.833,-2.613,-2.613,-2.613,-2.613,-3.934,-3.934,-3.934,-3.934 +Alternative specific constant for return stops,1,,-0.445,-1.775,-2.139,,-0.445,-1.775,-2.139,,-0.445,-1.775,-2.139,,-0.445,-1.775,-2.139 +Alternative specific constant for the total number of stops,1,,,0,0,,0,0,0,0,0,0,0.695,0,0,0.695,0.695 +Number of subtours in the tour,num_atwork_subtours,,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19,0.19 +Phase1_Calibration,1,,0.223196473,0.325510562,0.203854333,-0.303375802,-0.391244072,-0.093964049,-0.096568307,-0.097190886,-0.494486563,0.334444913,-0.145593481,0.40493641,-0.229067675,0.390561162,0.076806162 diff --git a/activitysim/examples/example_semcog/configs/telecommute_frequency.csv b/activitysim/examples/prototype_semcog/configs/telecommute_frequency.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/telecommute_frequency.csv rename to activitysim/examples/prototype_semcog/configs/telecommute_frequency.csv index 450a6c6e84..265be1b506 100755 --- a/activitysim/examples/example_semcog/configs/telecommute_frequency.csv +++ b/activitysim/examples/prototype_semcog/configs/telecommute_frequency.csv @@ -1,21 +1,21 @@ -Label,Description,Expression,No_Telecommute,1_day_week,2_3_days_week,4_days_week -#util_Services,Services,@df.occup==52,0,coef_Services_1day,coef_Services_23day,coef_Services_4day -#util_SalesOffice,SalesOffice,@df.occup==53,0,coef_SalesOffice_1day,coef_SalesOffice_23day,coef_SalesOffice_4day -#util_ResourceConstruct,ResourceConstruct,@df.occup==54,0,coef_ResourceConstruct_1day,coef_ResourceConstruct_23day,coef_ResourceConstruct_4day -#util_TransportMat,TransportMat,@df.occup==55,0,coef_TransportMat_1day,coef_TransportMat_23day,coef_TransportMat_4day -util_HasChildren0to5,Has children 0 to 5 years old,@df.has_young_children,0,coef_HasChildren0to5_1day,coef_HasChildren0to5_23day,coef_HasChildren0to5_4day -util_HasChildren6to12,Has children 6 to 12 years old,@df.has_children_6_to_12,0,coef_HasChildren6to12_1day,coef_HasChildren6to12_23day,coef_HasChildren6to12_4day -util_OneAdultInHH,One adult in hh,@df.num_adults==1,0,coef_OneAdultInHH_1day,coef_OneAdultInHH_23day,coef_OneAdultInHH_4day -util_2plusAdultsInHH,2 or more adults in hh,@df.num_adults==2,0,coef_2plusAdultsInHH_1day,coef_2plusAdultsInHH_23day,coef_2plusAdultsInHH_4day -util_Female,female,@df.sex==2,0,coef_Female_1day,coef_Female_23day,coef_Female_4day -util_PartTimeWorker,Part-time worker,@df.pemploy==PEMPLOY_PART,0,coef_PartTimeWorker_1day,coef_PartTimeWorker_23day,coef_PartTimeWorker_4day -util_CollegeStudent,College student,@df.ptype==PTYPE_UNIVERSITY,0,coef_CollegeStudent_1day,coef_CollegeStudent_23day,coef_CollegeStudent_4day -util_PaysToPark,Pays to park,@~df.free_parking_at_work,0,coef_PaysToPark_1day,coef_PaysToPark_23day,coef_PaysToPark_4day -util_Income60to100k,Income 60-100k,"@df.income.between(60000, 100000)",0,coef_Income60to100k_1day,coef_Income60to100k_23day,coef_Income60to100k_4day -util_Income100to150k,Income 100-150k,"@df.income.between(100000, 150000)",0,coef_Income100to150k_1day,coef_Income100to150k_23day,coef_Income100to150k_4day -util_Income150kplus,Income 150k+,@df.income > 150000,0,coef_Income150kplus_1day,coef_Income150kplus_23day,coef_Income150kplus_4day -util_0Autos,0 Autos,@df.auto_ownership==0,0,coef_0Autos_1day,coef_0Autos_23day,coef_0Autos_4day -util_1Auto,1 Auto,@df.auto_ownership==1,0,coef_1Auto_1day,coef_1Auto_23day,coef_1Auto_4day -util_3plusAutos,3+ Autos,@df.auto_ownership>=3,0,coef_3plusAutos_1day,coef_3plusAutos_23day,coef_3plusAutos_4day -util_DistanceToWork,Distance to work,@df.distance_to_work,0,coef_DistanceToWork_1day,coef_DistanceToWork_23day,coef_DistanceToWork_4day -util_temp,temp_calibration,1,0.00,-4.00,-4.25,-6.00 +Label,Description,Expression,No_Telecommute,1_day_week,2_3_days_week,4_days_week +#util_Services,Services,@df.occup==52,0,coef_Services_1day,coef_Services_23day,coef_Services_4day +#util_SalesOffice,SalesOffice,@df.occup==53,0,coef_SalesOffice_1day,coef_SalesOffice_23day,coef_SalesOffice_4day +#util_ResourceConstruct,ResourceConstruct,@df.occup==54,0,coef_ResourceConstruct_1day,coef_ResourceConstruct_23day,coef_ResourceConstruct_4day +#util_TransportMat,TransportMat,@df.occup==55,0,coef_TransportMat_1day,coef_TransportMat_23day,coef_TransportMat_4day +util_HasChildren0to5,Has children 0 to 5 years old,@df.has_young_children,0,coef_HasChildren0to5_1day,coef_HasChildren0to5_23day,coef_HasChildren0to5_4day +util_HasChildren6to12,Has children 6 to 12 years old,@df.has_children_6_to_12,0,coef_HasChildren6to12_1day,coef_HasChildren6to12_23day,coef_HasChildren6to12_4day +util_OneAdultInHH,One adult in hh,@df.num_adults==1,0,coef_OneAdultInHH_1day,coef_OneAdultInHH_23day,coef_OneAdultInHH_4day +util_2plusAdultsInHH,2 or more adults in hh,@df.num_adults==2,0,coef_2plusAdultsInHH_1day,coef_2plusAdultsInHH_23day,coef_2plusAdultsInHH_4day +util_Female,female,@df.sex==2,0,coef_Female_1day,coef_Female_23day,coef_Female_4day +util_PartTimeWorker,Part-time worker,@df.pemploy==PEMPLOY_PART,0,coef_PartTimeWorker_1day,coef_PartTimeWorker_23day,coef_PartTimeWorker_4day +util_CollegeStudent,College student,@df.ptype==PTYPE_UNIVERSITY,0,coef_CollegeStudent_1day,coef_CollegeStudent_23day,coef_CollegeStudent_4day +util_PaysToPark,Pays to park,@~df.free_parking_at_work,0,coef_PaysToPark_1day,coef_PaysToPark_23day,coef_PaysToPark_4day +util_Income60to100k,Income 60-100k,"@df.income.between(60000, 100000)",0,coef_Income60to100k_1day,coef_Income60to100k_23day,coef_Income60to100k_4day +util_Income100to150k,Income 100-150k,"@df.income.between(100000, 150000)",0,coef_Income100to150k_1day,coef_Income100to150k_23day,coef_Income100to150k_4day +util_Income150kplus,Income 150k+,@df.income > 150000,0,coef_Income150kplus_1day,coef_Income150kplus_23day,coef_Income150kplus_4day +util_0Autos,0 Autos,@df.auto_ownership==0,0,coef_0Autos_1day,coef_0Autos_23day,coef_0Autos_4day +util_1Auto,1 Auto,@df.auto_ownership==1,0,coef_1Auto_1day,coef_1Auto_23day,coef_1Auto_4day +util_3plusAutos,3+ Autos,@df.auto_ownership>=3,0,coef_3plusAutos_1day,coef_3plusAutos_23day,coef_3plusAutos_4day +util_DistanceToWork,Distance to work,@df.distance_to_work,0,coef_DistanceToWork_1day,coef_DistanceToWork_23day,coef_DistanceToWork_4day +util_temp,temp_calibration,1,0.00,-4.00,-4.25,-6.00 diff --git a/activitysim/examples/example_semcog/configs/telecommute_frequency.yaml b/activitysim/examples/prototype_semcog/configs/telecommute_frequency.yaml similarity index 94% rename from activitysim/examples/example_semcog/configs/telecommute_frequency.yaml rename to activitysim/examples/prototype_semcog/configs/telecommute_frequency.yaml index 7b69e136e3..162066ef6b 100755 --- a/activitysim/examples/example_semcog/configs/telecommute_frequency.yaml +++ b/activitysim/examples/prototype_semcog/configs/telecommute_frequency.yaml @@ -1,9 +1,9 @@ - -# borrowed from free parking model - -SPEC: telecommute_frequency.csv -COEFFICIENTS: telecommute_frequency_coeffs.csv - -#LOGIT_TYPE: NL -LOGIT_TYPE: MNL - + +# borrowed from free parking model + +SPEC: telecommute_frequency.csv +COEFFICIENTS: telecommute_frequency_coeffs.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + diff --git a/activitysim/examples/example_semcog/configs/telecommute_frequency_coeffs.csv b/activitysim/examples/prototype_semcog/configs/telecommute_frequency_coeffs.csv similarity index 97% rename from activitysim/examples/example_semcog/configs/telecommute_frequency_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/telecommute_frequency_coeffs.csv index f07ec2e373..e4aeff94da 100755 --- a/activitysim/examples/example_semcog/configs/telecommute_frequency_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/telecommute_frequency_coeffs.csv @@ -1,58 +1,58 @@ -coefficient_name,coefficient_name,value,constrain -coef_Services_1day,Services,-1.624,F -coef_SalesOffice_1day,SalesOffice,-0.62,F -coef_ResourceConstruct_1day,ResourceConstruct,-1.57,F -coef_TransportMat_1day,TransportMat,-14.747,F -coef_HasChildren0to5_1day,HasChildren0to5,0,F -coef_HasChildren6to12_1day,HasChildren6to12,0,F -coef_OneAdultInHH_1day,OneAdultInHH,0.177,F -coef_2plusAdultsInHH_1day,2plusAdultsInHH,0,F -coef_Female_1day,Female,0,F -coef_PartTimeWorker_1day,PartTimeWorker,0,F -coef_CollegeStudent_1day,CollegeStudent,0,F -coef_PaysToPark_1day,PaysToPark,0.457,F -coef_Income60to100k_1day,Income60to100k,0.56,F -coef_Income100to150k_1day,Income100to150k,0.644,F -coef_Income150kplus_1day,Income150kplus,0.92,F -coef_0Autos_1day,0Autos,0,F -coef_1Auto_1day,1Auto,0,F -coef_3plusAutos_1day,3plusAutos,0,F -coef_DistanceToWork_1day,DistanceToWork,0.016,F -coef_Services_23day,Services,-0.651,F -coef_SalesOffice_23day,SalesOffice,-0.738,F -coef_ResourceConstruct_23day,ResourceConstruct,0,F -coef_TransportMat_23day,TransportMat,0,F -coef_HasChildren0to5_23day,HasChildren0to5,0,F -coef_HasChildren6to12_23day,HasChildren6to12,0.517,F -coef_OneAdultInHH_23day,OneAdultInHH,0,F -coef_2plusAdultsInHH_23day,2plusAdultsInHH,0,F -coef_Female_23day,Female,0,F -coef_PartTimeWorker_23day,PartTimeWorker,0.425,F -coef_CollegeStudent_23day,CollegeStudent,0.6,F -coef_PaysToPark_23day,PaysToPark,0,F -coef_Income60to100k_23day,Income60to100k,0.389,F -coef_Income100to150k_23day,Income100to150k,0.193,F -coef_Income150kplus_23day,Income150kplus,0.765,F -coef_0Autos_23day,0Autos,0.407,F -coef_1Auto_23day,1Auto,0,F -coef_3plusAutos_23day,3plusAutos,-0.73,F -coef_DistanceToWork_23day,DistanceToWork,0,F -coef_Services_4day,Services,0,F -coef_SalesOffice_4day,SalesOffice,-0.894,F -coef_ResourceConstruct_4day,ResourceConstruct,0,F -coef_TransportMat_4day,TransportMat,0,F -coef_HasChildren0to5_4day,HasChildren0to5,-0.864,F -coef_HasChildren6to12_4day,HasChildren6to12,-0.81,F -coef_OneAdultInHH_4day,OneAdultInHH,-0.043,F -coef_2plusAdultsInHH_4day,2plusAdultsInHH,0,F -coef_Female_4day,Female,0,F -coef_PartTimeWorker_4day,PartTimeWorker,1.112,F -coef_CollegeStudent_4day,CollegeStudent,0,F -coef_PaysToPark_4day,PaysToPark,0,F -coef_Income60to100k_4day,Income60to100k,0,F -coef_Income100to150k_4day,Income100to150k,0,F -coef_Income150kplus_4day,Income150kplus,0,F -coef_0Autos_4day,0Autos,0,F -coef_1Auto_4day,1Auto,0,F -coef_3plusAutos_4day,3plusAutos,0,F -coef_DistanceToWork_4day,DistanceToWork,0,F +coefficient_name,coefficient_name,value,constrain +coef_Services_1day,Services,-1.624,F +coef_SalesOffice_1day,SalesOffice,-0.62,F +coef_ResourceConstruct_1day,ResourceConstruct,-1.57,F +coef_TransportMat_1day,TransportMat,-14.747,F +coef_HasChildren0to5_1day,HasChildren0to5,0,F +coef_HasChildren6to12_1day,HasChildren6to12,0,F +coef_OneAdultInHH_1day,OneAdultInHH,0.177,F +coef_2plusAdultsInHH_1day,2plusAdultsInHH,0,F +coef_Female_1day,Female,0,F +coef_PartTimeWorker_1day,PartTimeWorker,0,F +coef_CollegeStudent_1day,CollegeStudent,0,F +coef_PaysToPark_1day,PaysToPark,0.457,F +coef_Income60to100k_1day,Income60to100k,0.56,F +coef_Income100to150k_1day,Income100to150k,0.644,F +coef_Income150kplus_1day,Income150kplus,0.92,F +coef_0Autos_1day,0Autos,0,F +coef_1Auto_1day,1Auto,0,F +coef_3plusAutos_1day,3plusAutos,0,F +coef_DistanceToWork_1day,DistanceToWork,0.016,F +coef_Services_23day,Services,-0.651,F +coef_SalesOffice_23day,SalesOffice,-0.738,F +coef_ResourceConstruct_23day,ResourceConstruct,0,F +coef_TransportMat_23day,TransportMat,0,F +coef_HasChildren0to5_23day,HasChildren0to5,0,F +coef_HasChildren6to12_23day,HasChildren6to12,0.517,F +coef_OneAdultInHH_23day,OneAdultInHH,0,F +coef_2plusAdultsInHH_23day,2plusAdultsInHH,0,F +coef_Female_23day,Female,0,F +coef_PartTimeWorker_23day,PartTimeWorker,0.425,F +coef_CollegeStudent_23day,CollegeStudent,0.6,F +coef_PaysToPark_23day,PaysToPark,0,F +coef_Income60to100k_23day,Income60to100k,0.389,F +coef_Income100to150k_23day,Income100to150k,0.193,F +coef_Income150kplus_23day,Income150kplus,0.765,F +coef_0Autos_23day,0Autos,0.407,F +coef_1Auto_23day,1Auto,0,F +coef_3plusAutos_23day,3plusAutos,-0.73,F +coef_DistanceToWork_23day,DistanceToWork,0,F +coef_Services_4day,Services,0,F +coef_SalesOffice_4day,SalesOffice,-0.894,F +coef_ResourceConstruct_4day,ResourceConstruct,0,F +coef_TransportMat_4day,TransportMat,0,F +coef_HasChildren0to5_4day,HasChildren0to5,-0.864,F +coef_HasChildren6to12_4day,HasChildren6to12,-0.81,F +coef_OneAdultInHH_4day,OneAdultInHH,-0.043,F +coef_2plusAdultsInHH_4day,2plusAdultsInHH,0,F +coef_Female_4day,Female,0,F +coef_PartTimeWorker_4day,PartTimeWorker,1.112,F +coef_CollegeStudent_4day,CollegeStudent,0,F +coef_PaysToPark_4day,PaysToPark,0,F +coef_Income60to100k_4day,Income60to100k,0,F +coef_Income100to150k_4day,Income100to150k,0,F +coef_Income150kplus_4day,Income150kplus,0,F +coef_0Autos_4day,0Autos,0,F +coef_1Auto_4day,1Auto,0,F +coef_3plusAutos_4day,3plusAutos,0,F +coef_DistanceToWork_4day,DistanceToWork,0,F diff --git a/activitysim/examples/example_semcog/configs/tour_departure_and_duration_alternatives.csv b/activitysim/examples/prototype_semcog/configs/tour_departure_and_duration_alternatives.csv similarity index 84% rename from activitysim/examples/example_semcog/configs/tour_departure_and_duration_alternatives.csv rename to activitysim/examples/prototype_semcog/configs/tour_departure_and_duration_alternatives.csv index ca2b4d789a..30ff417ca9 100755 --- a/activitysim/examples/example_semcog/configs/tour_departure_and_duration_alternatives.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_departure_and_duration_alternatives.csv @@ -1,1177 +1,1177 @@ -start,end -1,1 -1,2 -1,3 -1,4 -1,5 -1,6 -1,7 -1,8 -1,9 -1,10 -1,11 -1,12 -1,13 -1,14 -1,15 -1,16 -1,17 -1,18 -1,19 -1,20 -1,21 -1,22 -1,23 -1,24 -1,25 -1,26 -1,27 -1,28 -1,29 -1,30 -1,31 -1,32 -1,33 -1,34 -1,35 -1,36 -1,37 -1,38 -1,39 -1,40 -1,41 -1,42 -1,43 -1,44 -1,45 -1,46 -1,47 -1,48 -2,2 -2,3 -2,4 -2,5 -2,6 -2,7 -2,8 -2,9 -2,10 -2,11 -2,12 -2,13 -2,14 -2,15 -2,16 -2,17 -2,18 -2,19 -2,20 -2,21 -2,22 -2,23 -2,24 -2,25 -2,26 -2,27 -2,28 -2,29 -2,30 -2,31 -2,32 -2,33 -2,34 -2,35 -2,36 -2,37 -2,38 -2,39 -2,40 -2,41 -2,42 -2,43 -2,44 -2,45 -2,46 -2,47 -2,48 -3,3 -3,4 -3,5 -3,6 -3,7 -3,8 -3,9 -3,10 -3,11 -3,12 -3,13 -3,14 -3,15 -3,16 -3,17 -3,18 -3,19 -3,20 -3,21 -3,22 -3,23 -3,24 -3,25 -3,26 -3,27 -3,28 -3,29 -3,30 -3,31 -3,32 -3,33 -3,34 -3,35 -3,36 -3,37 -3,38 -3,39 -3,40 -3,41 -3,42 -3,43 -3,44 -3,45 -3,46 -3,47 -3,48 -4,4 -4,5 -4,6 -4,7 -4,8 -4,9 -4,10 -4,11 -4,12 -4,13 -4,14 -4,15 -4,16 -4,17 -4,18 -4,19 -4,20 -4,21 -4,22 -4,23 -4,24 -4,25 -4,26 -4,27 -4,28 -4,29 -4,30 -4,31 -4,32 -4,33 -4,34 -4,35 -4,36 -4,37 -4,38 -4,39 -4,40 -4,41 -4,42 -4,43 -4,44 -4,45 -4,46 -4,47 -4,48 -5,5 -5,6 -5,7 -5,8 -5,9 -5,10 -5,11 -5,12 -5,13 -5,14 -5,15 -5,16 -5,17 -5,18 -5,19 -5,20 -5,21 -5,22 -5,23 -5,24 -5,25 -5,26 -5,27 -5,28 -5,29 -5,30 -5,31 -5,32 -5,33 -5,34 -5,35 -5,36 -5,37 -5,38 -5,39 -5,40 -5,41 -5,42 -5,43 -5,44 -5,45 -5,46 -5,47 -5,48 -6,6 -6,7 -6,8 -6,9 -6,10 -6,11 -6,12 -6,13 -6,14 -6,15 -6,16 -6,17 -6,18 -6,19 -6,20 -6,21 -6,22 -6,23 -6,24 -6,25 -6,26 -6,27 -6,28 -6,29 -6,30 -6,31 -6,32 -6,33 -6,34 -6,35 -6,36 -6,37 -6,38 -6,39 -6,40 -6,41 -6,42 -6,43 -6,44 -6,45 -6,46 -6,47 -6,48 -7,7 -7,8 -7,9 -7,10 -7,11 -7,12 -7,13 -7,14 -7,15 -7,16 -7,17 -7,18 -7,19 -7,20 -7,21 -7,22 -7,23 -7,24 -7,25 -7,26 -7,27 -7,28 -7,29 -7,30 -7,31 -7,32 -7,33 -7,34 -7,35 -7,36 -7,37 -7,38 -7,39 -7,40 -7,41 -7,42 -7,43 -7,44 -7,45 -7,46 -7,47 -7,48 -8,8 -8,9 -8,10 -8,11 -8,12 -8,13 -8,14 -8,15 -8,16 -8,17 -8,18 -8,19 -8,20 -8,21 -8,22 -8,23 -8,24 -8,25 -8,26 -8,27 -8,28 -8,29 -8,30 -8,31 -8,32 -8,33 -8,34 -8,35 -8,36 -8,37 -8,38 -8,39 -8,40 -8,41 -8,42 -8,43 -8,44 -8,45 -8,46 -8,47 -8,48 -9,9 -9,10 -9,11 -9,12 -9,13 -9,14 -9,15 -9,16 -9,17 -9,18 -9,19 -9,20 -9,21 -9,22 -9,23 -9,24 -9,25 -9,26 -9,27 -9,28 -9,29 -9,30 -9,31 -9,32 -9,33 -9,34 -9,35 -9,36 -9,37 -9,38 -9,39 -9,40 -9,41 -9,42 -9,43 -9,44 -9,45 -9,46 -9,47 -9,48 -10,10 -10,11 -10,12 -10,13 -10,14 -10,15 -10,16 -10,17 -10,18 -10,19 -10,20 -10,21 -10,22 -10,23 -10,24 -10,25 -10,26 -10,27 -10,28 -10,29 -10,30 -10,31 -10,32 -10,33 -10,34 -10,35 -10,36 -10,37 -10,38 -10,39 -10,40 -10,41 -10,42 -10,43 -10,44 -10,45 -10,46 -10,47 -10,48 -11,11 -11,12 -11,13 -11,14 -11,15 -11,16 -11,17 -11,18 -11,19 -11,20 -11,21 -11,22 -11,23 -11,24 -11,25 -11,26 -11,27 -11,28 -11,29 -11,30 -11,31 -11,32 -11,33 -11,34 -11,35 -11,36 -11,37 -11,38 -11,39 -11,40 -11,41 -11,42 -11,43 -11,44 -11,45 -11,46 -11,47 -11,48 -12,12 -12,13 -12,14 -12,15 -12,16 -12,17 -12,18 -12,19 -12,20 -12,21 -12,22 -12,23 -12,24 -12,25 -12,26 -12,27 -12,28 -12,29 -12,30 -12,31 -12,32 -12,33 -12,34 -12,35 -12,36 -12,37 -12,38 -12,39 -12,40 -12,41 -12,42 -12,43 -12,44 -12,45 -12,46 -12,47 -12,48 -13,13 -13,14 -13,15 -13,16 -13,17 -13,18 -13,19 -13,20 -13,21 -13,22 -13,23 -13,24 -13,25 -13,26 -13,27 -13,28 -13,29 -13,30 -13,31 -13,32 -13,33 -13,34 -13,35 -13,36 -13,37 -13,38 -13,39 -13,40 -13,41 -13,42 -13,43 -13,44 -13,45 -13,46 -13,47 -13,48 -14,14 -14,15 -14,16 -14,17 -14,18 -14,19 -14,20 -14,21 -14,22 -14,23 -14,24 -14,25 -14,26 -14,27 -14,28 -14,29 -14,30 -14,31 -14,32 -14,33 -14,34 -14,35 -14,36 -14,37 -14,38 -14,39 -14,40 -14,41 -14,42 -14,43 -14,44 -14,45 -14,46 -14,47 -14,48 -15,15 -15,16 -15,17 -15,18 -15,19 -15,20 -15,21 -15,22 -15,23 -15,24 -15,25 -15,26 -15,27 -15,28 -15,29 -15,30 -15,31 -15,32 -15,33 -15,34 -15,35 -15,36 -15,37 -15,38 -15,39 -15,40 -15,41 -15,42 -15,43 -15,44 -15,45 -15,46 -15,47 -15,48 -16,16 -16,17 -16,18 -16,19 -16,20 -16,21 -16,22 -16,23 -16,24 -16,25 -16,26 -16,27 -16,28 -16,29 -16,30 -16,31 -16,32 -16,33 -16,34 -16,35 -16,36 -16,37 -16,38 -16,39 -16,40 -16,41 -16,42 -16,43 -16,44 -16,45 -16,46 -16,47 -16,48 -17,17 -17,18 -17,19 -17,20 -17,21 -17,22 -17,23 -17,24 -17,25 -17,26 -17,27 -17,28 -17,29 -17,30 -17,31 -17,32 -17,33 -17,34 -17,35 -17,36 -17,37 -17,38 -17,39 -17,40 -17,41 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activitysim/examples/prototype_semcog/configs/tour_mode_choice.csv index 412e803441..14e13a802f 100755 --- a/activitysim/examples/example_semcog/configs/tour_mode_choice.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_mode_choice.csv @@ -1,323 +1,323 @@ -Label,Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK,BIKE,WALK_LOC,WALK_PRM,WALK_MIX,PNR_LOC,PNR_PRM,PNR_MIX,KNR_LOC,KNR_PRM,KNR_MIX,SCHOOLBUS,TAXI,TNC_SINGLE,TNC_SHARED -#Drive_alone_no_toll,#Drive alone no toll,,,,,,,,,,,,,,,,,,, -util_DRIVEALONE_Unavailable,DRIVEALONE - Unavailable,sov_available == False,-999,,,,,,,,,,,,,,,,, -util_DRIVEALONE_Unavailable_for_persons_less_than_16,DRIVEALONE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,,,,,,, -util_DRIVEALONE_Unavailable_forJoint_tours,DRIVEALONE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,,,,,,, -util_DRIVEALONE_Unavailable_if_didn't_drive_to_work,DRIVEALONE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,,,,,,, -util_DRIVEALONE_In_vehicle_time,DRIVEALONE - In-vehicle time,@odt_skims['SOV_TIME'] + dot_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,,,,,,, -util_DRIVEALONE_Terminal_time,DRIVEALONE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,0,,,,,,,,,,,,,,,,, -util_DRIVEALONE_Operating_cost_,DRIVEALONE - Operating cost ,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']),coef_ivt,,,,,,,,,,,,,,,,, -util_DRIVEALONE_Parking_cost_,DRIVEALONE - Parking cost ,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,coef_ivt,,,,,,,,,,,,,,,,, -util_DRIVEALONE_Bridge_toll_,DRIVEALONE - Bridge toll ,@ivt_cost_multiplier * df.ivot * (odt_skims['SOV_BTOLL'] + dot_skims['SOV_BTOLL']),0,,,,,,,,,,,,,,,,, -util_DRIVEALONE_Person_is_between_16_and_19_years_old,DRIVEALONE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da_multiplier,,,,,,,,,,,,,,,,, -#Shared_ride_2,#Shared ride 2,,,,,,,,,,,,,,,,,,, -util_SHARED2_Unavailable,SHARED2 - Unavailable,hov2_available == False,,-999,,,,,,,,,,,,,,,, -util_SHARED2_Unavailable_based_on_party_size,SHARED2 - Unavailable based on party size,is_joint & (number_of_participants > 2),,-999,,,,,,,,,,,,,,,, -util_SHARED2_In_vehicle_time,SHARED2 - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED2_Terminal_time,SHARED2 - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,0,,,,,,,,,,,,,,,, -util_SHARED2_Operating_cost,SHARED2 - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']),,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED2_Parking_cost,SHARED2 - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,coef_ivt,,,,,,,,,,,,,,,, -util_SHARED2_Bridge_toll,SHARED2 - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']) / costShareSr2,,0,,,,,,,,,,,,,,,, -util_SHARED2_One_person_household,SHARED2 - One person household,@(df.hhsize == 1),,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,,, -util_SHARED2_Two_person_household,SHARED2 - Two person household,@(df.hhsize == 2),,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,,, -util_SHARED2_Person_is_16_years_old_or_older,SHARED2 - Person is 16 years old or older,@(df.age >= 16),,coef_age16p_sr_multiplier,,,,,,,,,,,,,,,, -#Shared_ride_3+,#Shared ride 3+,,,,,,,,,,,,,,,,,,, -util_SHARED3_Unavailable,SHARED3 - Unavailable,hov3_available == False,,,-999,,,,,,,,,,,,,,, -util_SHARED3_In_vehicle_time,SHARED3 - In-vehicle time,@(odt_skims['HOV3_TIME'] + dot_skims['HOV3_TIME']),,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3_Terminal_time,SHARED3 - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,0,,,,,,,,,,,,,,, -util_SHARED3_Operating_cost,SHARED3 - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3_DIST'] + dot_skims['HOV3_DIST']),,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3_Parking_cost,SHARED3 - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,coef_ivt,,,,,,,,,,,,,,, -util_SHARED3_Bridge_toll,SHARED3 - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3_BTOLL'] + dot_skims['HOV3_BTOLL']) / costShareSr3,,,0,,,,,,,,,,,,,,, -util_SHARED3_One_person_household,SHARED3 - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,, -util_SHARED3_Two_person_household,SHARED3 - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,, -util_SHARED3_Person_is_16_years_old_or_older,SHARED3 - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,, -#util_WALK,#Walk,,,,,,,,,,,,,,,,,,, -#FIXME_skims_aren't_symmetrical,#FIXME - skims aren't symmetrical, so we have to make sure they can get back,,,,,,,,,,,,,,,,,, -util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@walktimeshort_multiplier * (od_skims['DISTWALK'].clip(upper=walkThresh) + od_skims.reverse('DISTWALK').clip(upper=walkThresh))*60/walkSpeed,,,,coef_ivt,,,,,,,,,,,,,, -util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * ((od_skims['DISTWALK'] - walkThresh).clip(lower=0) + (od_skims.reverse('DISTWALK') - walkThresh).clip(lower=0))*60/walkSpeed,,,,coef_ivt,,,,,,,,,,,,,, -util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,coef_ivt,,,,,,,,,,,,,, -util_WALK_Topology,WALK - Topology,@coef_topology_walk_multiplier * df.dest_topology,,,,coef_ivt,,,,,,,,,,,,,, -#util_BIKE,#Bike,,,,,,,,,,,,,,,,,,, -#FIXME_skims_aren't_symmetrical,#FIXME - skims aren't symmetrical, so we have to make sure they can get back,,,,,,,,,,,,,,,,,, -util_BIKE_Unavailable_if_didn't_BIKE_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,-999,,,,,,,,,,,,, -util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@biketimeshort_multiplier * (od_skims['DISTBIKE'].clip(upper=bikeThresh) + od_skims.reverse('DISTBIKE').clip(upper=bikeThresh))*60/bikeSpeed,,,,,coef_ivt,,,,,,,,,,,,, -util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@biketimelong_multiplier * ((od_skims['DISTBIKE']-bikeThresh).clip(lower=0) + (od_skims.reverse('DISTBIKE')-bikeThresh).clip(lower=0))*60/bikeSpeed,,,,,coef_ivt,,,,,,,,,,,,, -util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,coef_ivt,,,,,,,,,,,,, -util_BIKE_Topology,BIKE - Topology,@coef_topology_bike_multiplier * df.dest_topology,,,,,coef_ivt,,,,,,,,,,,,, -#util_WALK_to_Local,#Walk to Local,,,,,,,,,,,,,,,,,,, -util_WALK_LOC_Unavailable,WALK_LOC - Unavailable,walk_local_available == False,,,,,,-999,,,,,,,,,,,, -util_WALK_LOC_In_vehicle_time,WALK_LOC - In-vehicle time,@(odt_skims['WLK_LOC_IVT'] + dot_skims['WLK_LOC_IVT']),,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Short_iwait_time,WALK_LOC - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_LOC_IWAIT']).clip(upper=waitThresh) + (dot_skims['WLK_LOC_IWAIT']).clip(upper=waitThresh)),,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Long_iwait_time,WALK_LOC - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_LOC_IWAIT']-waitThresh).clip(0) + (dot_skims['WLK_LOC_IWAIT']-waitThresh).clip(0)),,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_transfer_wait_time,WALK_LOC - transfer wait time,@xwait_multiplier * (odt_skims['WLK_LOC_XWAIT'] + dot_skims['WLK_LOC_XWAIT']),,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_number_of_transfers,WALK_LOC - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_LOC_NT']).clip(0) + (dot_skims['WLK_LOC_NT']).clip(0)),,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access time,@wacc_multiplier * 2 * df.origin_walk_time,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Walk_egress_time,WALK_LOC - Walk egress time,@wegr_multiplier * 2 * df.destination_walk_time,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Walk_other_time,WALK_LOC - Walk other time,@waux_multiplier * (odt_skims['WLK_LOC_WAUX'] + dot_skims['WLK_LOC_WAUX']),,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Fare,WALK_LOC - Fare,@ivt_cost_multiplier * df.fare_discount_percent * df.ivot * (odt_skims['WLK_LOC_FARE'] + dot_skims['WLK_LOC_FARE']),,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Destination_zone_densityIndex,WALK_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Topology,WALK_LOC - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,coef_ivt,,,,,,,,,,,, -util_WALK_LOC_Person_is_less_than_10_years_old,WALK_LOC - Person is less than 10 years old,@(df.age <= 10),,,,,,coef_age010_trn_multiplier,,,,,,,,,,,, -#util_WALK_to_Premium,#Walk to Premium,,,,,,,,,,,,,,,,,,, -util_WALK_PRM_Unavailable,WALK_PRM - Unavailable,walk_premium_available == False,,,,,,,-999,,,,,,,,,,, -util_WALK_PRM_In_vehicle_time,WALK_PRM - In-vehicle time,@(odt_skims['WLK_PRM_IVT'] + dot_skims['WLK_PRM_IVT']),,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_In_vehicle_time_on_PMov,WALK_PRM - In-vehicle time on PMov,@(ivt_pmov_multiplier - 1) * (odt_skims['WLK_PRM_IVTT_PMov'] + dot_skims['WLK_PRM_IVTT_PMov']),,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_In_vehicle_time_on_StCar,WALK_PRM - In-vehicle time on StCar,@(ivt_stcar_multiplier - 1) * (odt_skims['WLK_PRM_IVTT_StCar'] + dot_skims['WLK_PRM_IVTT_StCar']),,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_In_vehicle_time_on_BRT,WALK_PRM - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['WLK_PRM_IVTT_Brt'] + dot_skims['WLK_PRM_IVTT_Brt']),,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_In_vehicle_time_on_URB,WALK_PRM - In-vehicle time on URB,@(ivt_urb_multiplier - 1) * (odt_skims['WLK_PRM_IVTT_UrbRail'] + dot_skims['WLK_PRM_IVTT_UrbRail']),,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_In_vehicle_time_on_COM,WALK_PRM - In-vehicle time on COM,@(ivt_com_multiplier - 1) * (odt_skims['WLK_PRM_IVTT_ComRail'] + dot_skims['WLK_PRM_IVTT_ComRail']),,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_Short_iwait_time,WALK_PRM - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_PRM_IWAIT']).clip(upper=waitThresh) + (dot_skims['WLK_PRM_IWAIT']).clip(upper=waitThresh)),,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_Long_iwait_time,WALK_PRM - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_PRM_IWAIT']-waitThresh).clip(0) + (dot_skims['WLK_PRM_IWAIT']-waitThresh).clip(0)),,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_transfer_wait_time,WALK_PRM - transfer wait time,@xwait_multiplier * (odt_skims['WLK_PRM_XWAIT'] + dot_skims['WLK_PRM_XWAIT']),,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_number_of_transfers,WALK_PRM - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_PRM_NT']).clip(0) + (dot_skims['WLK_PRM_NT']).clip(0)),,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_Walk_access_time,WALK_PRM - Walk access time,@wacc_multiplier * 2 * df.origin_walk_time,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_Walk_egress_time,WALK_PRM - Walk egress time,@wegr_multiplier * 2 * df.destination_walk_time,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_Walk_other_time,WALK_PRM - Walk other time,@waux_multiplier * (odt_skims['WLK_PRM_WAUX'] + dot_skims['WLK_PRM_WAUX']),,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_Fare,WALK_PRM - Fare,@ivt_cost_multiplier * df.fare_discount_percent * df.ivot * (odt_skims['WLK_PRM_FARE'] + dot_skims['WLK_PRM_FARE']),,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_Destination_zone_densityIndex,WALK_PRM - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_Topology,WALK_PRM - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,coef_ivt,,,,,,,,,,, -util_WALK_PRM_Person_is_less_than_10_years_old,WALK_PRM - Person is less than 10 years old,@(df.age <= 10),,,,,,,coef_age010_trn_multiplier,,,,,,,,,,, -#util_WALK_to_MIX,#Walk to MIX,,,,,,,,,,,,,,,,,,, -util_WALK_MIX_Unavailable,WALK_MIX - Unavailable,walk_mix_available == False,,,,,,,,-999,,,,,,,,,, -util_WALK_MIX_In_vehicle_time,WALK_MIX - In-vehicle time,@(odt_skims['WLK_MIX_IVT'] + dot_skims['WLK_MIX_IVT']),,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_In_vehicle_time_on_Bus,WALK_MIX - In-vehicle time on Bus,@(odt_skims['WLK_MIX_IVTT_Bus'] + dot_skims['WLK_MIX_IVTT_Bus']),,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_In_vehicle_time_on_PMov,WALK_MIX - In-vehicle time on PMov,@(ivt_pmov_multiplier - 1) * (odt_skims['WLK_MIX_IVTT_PMov'] + dot_skims['WLK_MIX_IVTT_PMov']),,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_In_vehicle_time_on_StCar,WALK_MIX - In-vehicle time on StCar,@(ivt_stcar_multiplier - 1) * (odt_skims['WLK_MIX_IVTT_StCar'] + dot_skims['WLK_MIX_IVTT_StCar']),,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_In_vehicle_time_on_BRT,WALK_MIX - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['WLK_MIX_IVTT_Brt'] + dot_skims['WLK_MIX_IVTT_Brt']),,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_In_vehicle_time_on_URB,WALK_MIX - In-vehicle time on URB,@(ivt_urb_multiplier - 1) * (odt_skims['WLK_MIX_IVTT_UrbRail'] + dot_skims['WLK_MIX_IVTT_UrbRail']),,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_In_vehicle_time_on_COM,WALK_MIX - In-vehicle time on COM,@(ivt_com_multiplier - 1) * (odt_skims['WLK_MIX_IVTT_ComRail'] + dot_skims['WLK_MIX_IVTT_ComRail']),,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_Short_iwait_time,WALK_MIX - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_MIX_IWAIT']).clip(upper=waitThresh) + (dot_skims['WLK_MIX_IWAIT']).clip(upper=waitThresh)),,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_Long_iwait_time,WALK_MIX - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_MIX_IWAIT']-waitThresh).clip(0) + (dot_skims['WLK_MIX_IWAIT']-waitThresh).clip(0)),,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_transfer_wait_time,WALK_MIX - transfer wait time,@xwait_multiplier * (odt_skims['WLK_MIX_XWAIT'] + dot_skims['WLK_MIX_XWAIT']),,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_number_of_transfers,WALK_MIX - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_MIX_NT']).clip(0) + (dot_skims['WLK_MIX_NT']).clip(0)),,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_Walk_access_time,WALK_MIX - Walk access time,@wacc_multiplier * 2 * df.origin_walk_time,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_Walk_egress_time,WALK_MIX - Walk egress time,@wegr_multiplier * 2 * df.destination_walk_time,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_Walk_other_time,WALK_MIX - Walk other time,@waux_multiplier * (odt_skims['WLK_MIX_WAUX'] + dot_skims['WLK_MIX_WAUX']),,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_Fare,WALK_MIX - Fare,@ivt_cost_multiplier * df.fare_discount_percent * df.ivot * (odt_skims['WLK_MIX_FARE'] + dot_skims['WLK_MIX_FARE']),,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_Destination_zone_densityIndex,WALK_MIX - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,coef_ivt,,,,,,,,,, -util_WALK_MIX_Topology,WALK_MIX - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,coef_age010_trn_multiplier,,,,,,,,,, -util_WALK_MIX_Person_is_less_than_10_years_old,WALK_MIX - Person is less than 10 years old,@(df.age <= 10),,,,,,,,1,,,,,,,,,, -#util_PNR_to_Local,#PNR to Local,,,,,,,,,,,,,,,,,,, -util_PNR_LOC_Unavailable,PNR_LOC - Unavailable,pnr_local_available == False,,,,,,,,,-999,,,,,,,,, -util_PNR_LOC_Unavailable_for_persons_less_than_16,PNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,-999,,,,,,,,, -util_PNR_LOC_In_vehicle_time,PNR_LOC - In-vehicle time,@(odt_skims['PNR_LOC_IVT'] + dot_skims['PNRE_LOC_IVT']),,,,,,,,,coef_ivt,,,,,,,,, -util_PNR_LOC_Short_iwait_time,PNR_LOC - Short iwait time,@short_i_wait_multiplier * ((odt_skims['PNR_LOC_IWAIT']).clip(upper=waitThresh) + (dot_skims['PNRE_LOC_IWAIT']).clip(upper=waitThresh)),,,,,,,,,coef_ivt,,,,,,,,, -util_PNR_LOC_Long_iwait_time,PNR_LOC - Long iwait time,@long_i_wait_multiplier * ((odt_skims['PNR_LOC_IWAIT']-waitThresh).clip(0) + (dot_skims['PNRE_LOC_IWAIT']-waitThresh).clip(0)),,,,,,,,,coef_ivt,,,,,,,,, -util_PNR_LOC_transfer_wait_time,PNR_LOC - transfer wait time,@xwait_multiplier * (odt_skims['PNR_LOC_XWAIT'] + dot_skims['PNRE_LOC_XWAIT']),,,,,,,,,coef_ivt,,,,,,,,, -util_PNR_LOC_number_of_transfers,PNR_LOC - number of transfers,@xfers_wlk_multiplier * ((odt_skims['PNR_LOC_NT']).clip(0) + (dot_skims['PNRE_LOC_NT']).clip(0)),,,,,,,,,coef_ivt,,,,,,,,, -util_PNR_LOC_PNR_time,PNR_LOC - PNR time,@dtim_multiplier * (odt_skims['PNR_LOC_DTIME'] + dot_skims['PNRE_LOC_DTIME']),,,,,,,,,coef_ivt,,,,,,,,, -util_PNR_LOC_Walk_access_time,PNR_LOC - Walk access time,@wacc_multiplier * df.destination_walk_time,,,,,,,,,coef_ivt,,,,,,,,, -util_PNR_LOC_Walk_egress_time_(at_attraction_end),PNR_LOC - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,coef_ivt,,,,,,,,, -util_PNR_LOC_Walk_other_time,PNR_LOC - Walk other time,@waux_multiplier * (odt_skims['PNR_LOC_WAUX'] + dot_skims['PNRE_LOC_WAUX']),,,,,,,,,coef_ivt,,,,,,,,, -util_PNR_LOC_Fare_and_operating_cost,PNR_LOC - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['PNR_LOC_FARE'] + dot_skims['PNRE_LOC_FARE']) + ((odt_skims['PNR_LOC_DDIST']+dot_skims['PNRE_LOC_DDIST']) * costPerMile)),,,,,,,,,coef_ivt,,,,,,,,, -util_PNR_LOC_Ratio_of_PNR_access_distance_to_OD_distance,PNR_LOC - Ratio of PNR access distance to OD distance,@dacc_ratio * ((odt_skims['PNR_LOC_DDIST']+ dot_skims['PNRE_LOC_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,coef_ivt,,,,,,,,, -util_PNR_LOC_Destination_zone_densityIndex,PNR_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,coef_ivt,,,,,,,,, -util_PNR_LOC_Topology,PNR_LOC - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,coef_ivt,,,,,,,,, -util_PNR_LOC_Person_is_less_than_10_years_old,PNR_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,coef_age010_trn_multiplier,,,,,,,,, -#util_PNR_to_Premium,#PNR to Premium,,,,,,,,,,,,,,,,,,, -util_PNR_PRM_Unavailable,PNR_PRM - Unavailable,pnr_premium_available == False,,,,,,,,,,-999,,,,,,,, -util_PNR_PRM_Unavailable_for_persons_less_than_16,PNR_PRM - Unavailable for persons less than 16,age < 16,,,,,,,,,,-999,,,,,,,, -util_PNR_PRM_In_vehicle_time,PNR_PRM - In-vehicle time,@(odt_skims['PNR_PRM_IVT'] + dot_skims['PNRE_PRM_IVT']),,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_In_vehicle_time_on_PMov,PNR_PRM - In-vehicle time on PMov,@(ivt_pmov_multiplier - 1) * (odt_skims['PNR_PRM_IVTT_PMov'] + dot_skims['PNRE_PRM_IVTT_PMov']),,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_In_vehicle_time_on_StCar,PNR_PRM - In-vehicle time on StCar,@(ivt_stcar_multiplier - 1) * (odt_skims['PNR_PRM_IVTT_StCar'] + dot_skims['PNRE_PRM_IVTT_StCar']),,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_In_vehicle_time_on_BRT,PNR_PRM - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['PNR_PRM_IVTT_Brt'] + dot_skims['PNRE_PRM_IVTT_Brt']),,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_In_vehicle_time_on_URB,PNR_PRM - In-vehicle time on URB,@(ivt_urb_multiplier - 1) * (odt_skims['PNR_PRM_IVTT_UrbRail'] + dot_skims['PNRE_PRM_IVTT_UrbRail']),,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_In_vehicle_time_on_COM,PNR_PRM - In-vehicle time on COM,@(ivt_com_multiplier - 1) * (odt_skims['PNR_PRM_IVTT_ComRail'] + dot_skims['PNRE_PRM_IVTT_ComRail']),,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_Short_iwait_time,PNR_PRM - Short iwait time,@short_i_wait_multiplier * ((odt_skims['PNR_PRM_IWAIT']).clip(upper=waitThresh) + (dot_skims['PNRE_PRM_IWAIT']).clip(upper=waitThresh)),,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_Long_iwait_time,PNR_PRM - Long iwait time,@long_i_wait_multiplier * ((odt_skims['PNR_PRM_IWAIT']-waitThresh).clip(0) + (dot_skims['PNRE_PRM_IWAIT']-waitThresh).clip(0)) ,,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_transfer_wait_time,PNR_PRM - transfer wait time,@xwait_multiplier * (odt_skims['PNR_PRM_XWAIT'] + dot_skims['PNRE_PRM_XWAIT']),,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_number_of_transfers,PNR_PRM - number of transfers,@xfers_drv_multiplier * ((odt_skims['PNR_PRM_NT']).clip(0) + (dot_skims['PNRE_PRM_NT']).clip(0)),,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_PNR_time,PNR_PRM - PNR time,@dtim_multiplier * (odt_skims['PNR_PRM_DTIME'] + dot_skims['PNRE_PRM_DTIME']),,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_Walk_access_time_(at_attraction_end),PNR_PRM - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_Walk_egress_time_(at_attraction_end),PNR_PRM - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_Walk_other_time,PNR_PRM - Walk other time,@waux_multiplier * (odt_skims['PNR_PRM_WAUX'] + dot_skims['PNRE_PRM_WAUX']),,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_Fare_and_operating_cost,PNR_PRM - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['PNR_PRM_FARE']+dot_skims['PNRE_PRM_FARE']) + ((odt_skims['PNR_PRM_DDIST']+dot_skims['PNRE_PRM_DDIST']) *costPerMile)),,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_Ratio_of_PNR_access_distance_to_OD_distance,PNR_PRM - Ratio of PNR access distance to OD distance,@dacc_ratio * ((odt_skims['PNR_PRM_DDIST']+ dot_skims['PNRE_PRM_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_Destination_zone_densityIndex,PNR_PRM - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_Topology,PNR_PRM - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,coef_ivt,,,,,,,, -util_PNR_PRM_Person_is_less_than_10_years_old,PNR_PRM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,coef_age010_trn_multiplier,,,,,,,, -#util_PNR_to_MIX,#PNR to MIX,,,,,,,,,,,,,,,,,,, -util_PNR_MIX_Unavailable,PNR_MIX - Unavailable,pnr_mix_available == False,,,,,,,,,,,-999,,,,,,, -util_PNR_MIX_Unavailable_for_persons_less_than_16,PNR_MIX - Unavailable for persons less than 16,age < 16,,,,,,,,,,,-999,,,,,,, -util_PNR_MIX_In_vehicle_time,PNR_MIX - In-vehicle time,@(odt_skims['PNR_MIX_IVT'] + dot_skims['PNRE_MIX_IVT']),,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_In_vehicle_time_on_Bus,PNR_MIX - In-vehicle time on Bus,@(odt_skims['PNR_MIX_IVTT_Bus'] + dot_skims['PNRE_MIX_IVTT_Bus']),,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_In_vehicle_time_on_PMov,PNR_MIX - In-vehicle time on PMov,@(ivt_pmov_multiplier - 1) * (odt_skims['PNR_MIX_IVTT_PMov'] + dot_skims['PNRE_MIX_IVTT_PMov']),,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_In_vehicle_time_on_StCar,PNR_MIX - In-vehicle time on StCar,@(ivt_stcar_multiplier - 1) * (odt_skims['PNR_MIX_IVTT_StCar'] + dot_skims['PNRE_MIX_IVTT_StCar']),,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_In_vehicle_time_on_BRT,PNR_MIX - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['PNR_MIX_IVTT_Brt'] + dot_skims['PNRE_MIX_IVTT_Brt']),,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_In_vehicle_time_on_URB,PNR_MIX - In-vehicle time on URB,@(ivt_urb_multiplier - 1) * (odt_skims['PNR_MIX_IVTT_UrbRail'] + dot_skims['PNRE_MIX_IVTT_UrbRail']),,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_In_vehicle_time_on_COM,PNR_MIX - In-vehicle time on COM,@(ivt_com_multiplier - 1) * (odt_skims['PNR_MIX_IVTT_ComRail'] + dot_skims['PNRE_MIX_IVTT_ComRail']),,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_Short_iwait_time,PNR_MIX - Short iwait time,@short_i_wait_multiplier * ((odt_skims['PNR_MIX_IWAIT']).clip(upper=waitThresh) + (dot_skims['PNRE_MIX_IWAIT']).clip(upper=waitThresh)),,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_Long_iwait_time,PNR_MIX - Long iwait time,@long_i_wait_multiplier * ((odt_skims['PNR_MIX_IWAIT']-waitThresh).clip(0) + (dot_skims['PNRE_MIX_IWAIT']-waitThresh).clip(0)),,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_transfer_wait_time,PNR_MIX - transfer wait time,@xwait_multiplier * (odt_skims['PNR_MIX_XWAIT'] + dot_skims['PNRE_MIX_XWAIT']),,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_number_of_transfers,PNR_MIX - number of transfers,@xfers_drv_multiplier * ((odt_skims['PNR_MIX_NT']).clip(0) + (dot_skims['PNRE_MIX_NT']).clip(0)),,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_PNR_time,PNR_MIX - PNR time,@dtim_multiplier * (odt_skims['PNR_MIX_DTIME'] + dot_skims['PNRE_MIX_DTIME']),,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_Walk_access_time_(at_attraction_end),PNR_MIX - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_Walk_egress_ime_(at_attraction_end),PNR_MIX - Walk egress ime (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_Walk_other_time,PNR_MIX - Walk other time,@waux_multiplier * (odt_skims['PNR_MIX_WAUX'] + dot_skims['PNRE_MIX_WAUX']),,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_Fare_and_operating_cost,PNR_MIX - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['PNR_MIX_FARE']+dot_skims['PNRE_MIX_FARE']) + ((odt_skims['PNR_MIX_DDIST']+dot_skims['PNRE_MIX_DDIST']) *costPerMile)),,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_Ratio_of_PNR_access_distance_to_OD_distance,PNR_MIX - Ratio of PNR access distance to OD distance,@dacc_ratio * ((odt_skims['PNR_MIX_DDIST']+ dot_skims['PNRE_MIX_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_Destination_zone_densityIndex,PNR_MIX - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_Topology,PNR_MIX - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,coef_ivt,,,,,,, -util_PNR_MIX_Person_is_less_than_10_years_old,PNR_MIX - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,coef_age010_trn_multiplier,,,,,,, -#util_KNR_to_Local,#KNR to Local,,,,,,,,,,,,,,,,,,, -util_KNR_LOC_Unavailable,KNR_LOC - Unavailable,knr_local_available == False,,,,,,,,,,,,-999,,,,,, -util_KNR_LOC_Unavailable_for_persons_less_than_16,KNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,-999,,,,,, -util_KNR_LOC_In_vehicle_time,KNR_LOC - In-vehicle time,@(odt_skims['KNR_LOC_IVT'] + dot_skims['KNRE_LOC_IVT']),,,,,,,,,,,,coef_ivt,,,,,, -util_KNR_LOC_Short_iwait_time,KNR_LOC - Short iwait time,@short_i_wait_multiplier * ((odt_skims['KNR_LOC_IWAIT']).clip(upper=waitThresh) + (dot_skims['KNRE_LOC_IWAIT']).clip(upper=waitThresh)),,,,,,,,,,,,coef_ivt,,,,,, -util_KNR_LOC_Long_iwait_time,KNR_LOC - Long iwait time,@long_i_wait_multiplier * ((odt_skims['KNR_LOC_IWAIT']-waitThresh).clip(0) + (dot_skims['KNRE_LOC_IWAIT']-waitThresh).clip(0)),,,,,,,,,,,,coef_ivt,,,,,, -util_KNR_LOC_transfer_wait_time,KNR_LOC - transfer wait time,@xwait_multiplier * (odt_skims['KNR_LOC_XWAIT'] + dot_skims['KNRE_LOC_XWAIT']),,,,,,,,,,,,coef_ivt,,,,,, -util_KNR_LOC_number_of_transfers,KNR_LOC - number of transfers,@xfers_wlk_multiplier * ((odt_skims['KNR_LOC_NT']).clip(0) + (dot_skims['KNRE_LOC_NT']).clip(0)),,,,,,,,,,,,coef_ivt,,,,,, -util_KNR_LOC_KNR_time,KNR_LOC - KNR time,@dtim_multiplier * (odt_skims['KNR_LOC_DTIME'] + dot_skims['KNRE_LOC_DTIME']),,,,,,,,,,,,coef_ivt,,,,,, -util_KNR_LOC_Walk_access_time,KNR_LOC - Walk access time,@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,coef_ivt,,,,,, -util_KNR_LOC_Walk_egress_time_(at_attraction_end),KNR_LOC - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,coef_ivt,,,,,, -util_KNR_LOC_Walk_other_time,KNR_LOC - Walk other time,@waux_multiplier * (odt_skims['KNR_LOC_WAUX'] + dot_skims['KNRE_LOC_WAUX']),,,,,,,,,,,,coef_ivt,,,,,, -util_KNR_LOC_Fare_and_operating_cost,KNR_LOC - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['KNR_LOC_FARE'] + dot_skims['KNRE_LOC_FARE']) + ((odt_skims['KNR_LOC_DDIST']+dot_skims['KNRE_LOC_DDIST']) * costPerMile)),,,,,,,,,,,,coef_ivt,,,,,, -util_KNR_LOC_Ratio_of_KNR_access_distance_to_OD_distance,KNR_LOC - Ratio of KNR access distance to OD distance,@dacc_ratio * ((odt_skims['KNR_LOC_DDIST']+ dot_skims['KNRE_LOC_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,,coef_ivt,,,,,, -util_KNR_LOC_Destination_zone_densityIndex,KNR_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,coef_ivt,,,,,, -util_KNR_LOC_Topology,KNR_LOC - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,coef_ivt,,,,,, -util_KNR_LOC_Person_is_less_than_10_years_old,KNR_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,coef_age010_trn_multiplier,,,,,, -#util_KNR_to_Premium,#KNR to Premium,,,,,,,,,,,,,,,,,,, -util_KNR_PRM_Unavailable,KNR_PRM - Unavailable,knr_premium_available == False,,,,,,,,,,,,,-999,,,,, -util_KNR_PRM_Unavailable_for_persons_less_than_16,KNR_PRM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,-999,,,,, -util_KNR_PRM_In_vehicle_time,KNR_PRM - In-vehicle time,@(odt_skims['KNR_PRM_IVT'] + dot_skims['KNRE_PRM_IVT']),,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_In_vehicle_time_on_PMov,KNR_PRM - In-vehicle time on PMov,@(ivt_pmov_multiplier - 1) * (odt_skims['KNR_PRM_IVTT_PMov'] + dot_skims['KNRE_PRM_IVTT_PMov']),,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_In_vehicle_time_on_StCar,KNR_PRM - In-vehicle time on StCar,@(ivt_stcar_multiplier - 1) * (odt_skims['KNR_PRM_IVTT_StCar'] + dot_skims['KNRE_PRM_IVTT_StCar']),,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_In_vehicle_time_on_BRT,KNR_PRM - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['KNR_PRM_IVTT_Brt'] + dot_skims['KNRE_PRM_IVTT_Brt']),,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_In_vehicle_time_on_URB,KNR_PRM - In-vehicle time on URB,@(ivt_urb_multiplier - 1) * (odt_skims['KNR_PRM_IVTT_UrbRail'] + dot_skims['KNRE_PRM_IVTT_UrbRail']),,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_In_vehicle_time_on_COM,KNR_PRM - In-vehicle time on COM,@(ivt_com_multiplier - 1) * (odt_skims['KNR_PRM_IVTT_ComRail'] + dot_skims['KNRE_PRM_IVTT_ComRail']),,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_Short_iwait_time,KNR_PRM - Short iwait time,@short_i_wait_multiplier * ((odt_skims['KNR_PRM_IWAIT']).clip(upper=waitThresh) + (dot_skims['KNRE_PRM_IWAIT']).clip(upper=waitThresh)),,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_Long_iwait_time,KNR_PRM - Long iwait time,@long_i_wait_multiplier * ((odt_skims['KNR_PRM_IWAIT']-waitThresh).clip(0) + (dot_skims['KNRE_PRM_IWAIT']-waitThresh).clip(0)) ,,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_transfer_wait_time,KNR_PRM - transfer wait time,@xwait_multiplier * (odt_skims['KNR_PRM_XWAIT'] + dot_skims['KNRE_PRM_XWAIT']),,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_number_of_transfers,KNR_PRM - number of transfers,@xfers_drv_multiplier *((odt_skims['KNR_PRM_NT']).clip(0) + (dot_skims['KNRE_PRM_NT']).clip(0)),,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_KNR_time,KNR_PRM - KNR time,@dtim_multiplier * (odt_skims['KNR_PRM_DTIME'] + dot_skims['KNRE_PRM_DTIME']),,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_Walk_access_time_(at_attraction_end),KNR_PRM - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_Walk_egress_time_(at_attraction_end),KNR_PRM - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_Walk_other_time,KNR_PRM - Walk other time,@waux_multiplier * (odt_skims['KNR_PRM_WAUX'] + dot_skims['KNRE_PRM_WAUX']),,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_Fare_and_operating_cost,KNR_PRM - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['KNR_PRM_FARE']+dot_skims['KNRE_PRM_FARE']) + ((odt_skims['KNR_PRM_DDIST']+dot_skims['KNRE_PRM_DDIST']) *costPerMile)),,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_Ratio_of_KNR_access_distance_to_OD_distance,KNR_PRM - Ratio of KNR access distance to OD distance,@dacc_ratio * ((odt_skims['KNR_PRM_DDIST']+ dot_skims['KNRE_PRM_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_Destination_zone_densityIndex,KNR_PRM - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_Topology,KNR_PRM - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,coef_ivt,,,,, -util_KNR_PRM_Person_is_less_than_10_years_old,KNR_PRM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,coef_age010_trn_multiplier,,,,, -#util_KNR_to_MIX,#KNR to MIX,,,,,,,,,,,,,,,,,,, -util_KNR_MIX_Unavailable,KNR_MIX - Unavailable,knr_mix_available == False,,,,,,,,,,,,,,-999,,,, -util_KNR_MIX_Unavailable_for_persons_less_than_16,KNR_MIX - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,-999,,,, -util_KNR_MIX_In_vehicle_time,KNR_MIX - In-vehicle time,@(odt_skims['KNR_MIX_IVT'] + dot_skims['KNRE_MIX_IVT']),,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_In_vehicle_time_on_Bus,KNR_MIX - In-vehicle time on Bus,@(odt_skims['KNR_MIX_IVTT_Bus'] + dot_skims['KNRE_MIX_IVTT_Bus']),,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_In_vehicle_time_on_PMov,KNR_MIX - In-vehicle time on PMov,@(ivt_pmov_multiplier - 1) * (odt_skims['KNR_MIX_IVTT_PMov'] + dot_skims['KNRE_MIX_IVTT_PMov']),,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_In_vehicle_time_on_StCar,KNR_MIX - In-vehicle time on StCar,@(ivt_stcar_multiplier - 1) * (odt_skims['KNR_MIX_IVTT_StCar'] + dot_skims['KNRE_MIX_IVTT_StCar']),,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_In_vehicle_time_on_BRT,KNR_MIX - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['KNR_MIX_IVTT_Brt'] + dot_skims['KNRE_MIX_IVTT_Brt']),,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_In_vehicle_time_on_URB,KNR_MIX - In-vehicle time on URB,@(ivt_urb_multiplier - 1) * (odt_skims['KNR_MIX_IVTT_UrbRail'] + dot_skims['KNRE_MIX_IVTT_UrbRail']),,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_In_vehicle_time_on_COM,KNR_MIX - In-vehicle time on COM,@(ivt_com_multiplier - 1) * (odt_skims['KNR_MIX_IVTT_ComRail'] + dot_skims['KNRE_MIX_IVTT_ComRail']),,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_Short_iwait_time,KNR_MIX - Short iwait time,@short_i_wait_multiplier * ((odt_skims['KNR_MIX_IWAIT']).clip(upper=waitThresh) + (dot_skims['KNRE_MIX_IWAIT']).clip(upper=waitThresh)),,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_Long_iwait_time,KNR_MIX - Long iwait time,@long_i_wait_multiplier * ((odt_skims['KNR_MIX_IWAIT']-waitThresh).clip(0) + (dot_skims['KNRE_MIX_IWAIT']-waitThresh).clip(0)),,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_transfer_wait_time,KNR_MIX - transfer wait time,@xwait_multiplier * (odt_skims['KNR_MIX_XWAIT'] + dot_skims['KNRE_MIX_XWAIT']),,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_number_of_transfers,KNR_MIX - number of transfers,@xfers_drv_multiplier * ((odt_skims['KNR_MIX_NT']).clip(0) + (dot_skims['KNRE_MIX_NT']).clip(0)),,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_KNR_time,KNR_MIX - KNR time,@dtim_multiplier * (odt_skims['KNR_MIX_DTIME'] + dot_skims['KNRE_MIX_DTIME']),,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_Walk_access_time_(at_attraction_end),KNR_MIX - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_Walk_egress_ime_(at_attraction_end),KNR_MIX - Walk egress ime (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_Walk_other_time,KNR_MIX - Walk other time,@waux_multiplier * (odt_skims['KNR_MIX_WAUX'] + dot_skims['KNRE_MIX_WAUX']),,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_Fare_and_operating_cost,KNR_MIX - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['KNR_MIX_FARE']+dot_skims['KNRE_MIX_FARE']) + ((odt_skims['KNR_MIX_DDIST']+dot_skims['KNRE_MIX_DDIST']) *costPerMile)),,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_Ratio_of_KNR_access_distance_to_OD_distance,KNR_MIX - Ratio of KNR access distance to OD distance,@dacc_ratio * ((odt_skims['KNR_MIX_DDIST']+ dot_skims['KNRE_MIX_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_Destination_zone_densityIndex,KNR_MIX - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_Topology,KNR_MIX - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,coef_ivt,,,, -util_KNR_MIX_Person_is_less_than_10_years_old,KNR_MIX - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,coef_age010_trn_multiplier,,,, -#,Taxi,,,,,,,,,,,,,,,,,,, -util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,,,,,,,,,,,,,,,coef_ivt,, -#, FIXME magic constant 1.5,,,,,,,,,,,,,,,,,,, -util_Taxi_Wait_time,Taxi - Wait time,@1.5 * df.totalWaitTaxi,,,,,,,,,,,,,,,,coef_ivt,, -#util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,coef_ivt,, -#util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,coef_ivt,, -util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare * 2 + (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']) * Taxi_costPerMile +(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']) * Taxi_costPerMinute)*100,,,,,,,,,,,,,,,,coef_ivt,, -#,TNC Single,,,,,,,,,,,,,,,,,,, -util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Wait_time,TNC Single - Wait time,@1.5 * df.totalWaitSingleTNC,,,,,,,,,,,,,,,,,coef_ivt, -#util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,,coef_ivt, -#util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,coef_ivt, -util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare * 2 + (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']) * TNC_single_costPerMile + (odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']) * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,,,,,,coef_ivt, -#,TNC Shared,,,,,,,,,,,,,,,,,,, -util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']) * TNC_shared_IVTFactor,,,,,,,,,,,,,,,,,,coef_ivt -#, FIXME magic constant 1.5,,,,,,,,,,,,,,,,,,, -util_TNC_Shared_Wait_time,TNC Shared - Wait time,@1.5 * df.totalWaitSharedTNC,,,,,,,,,,,,,,,,,,coef_ivt -#util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,,,coef_ivt -#util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,coef_ivt -util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare * 2 + (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']) * TNC_shared_costPerMile + (odt_skims['HOV2_TIME'] + dot_skims['HOV3_TIME']) * TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,,,,,,coef_ivt -#indiv_tour_ASCs,#indiv tour ASCs,,,,,,,,,,,,,,,,,,, -util_WALK_ASC_Zero_auto,Walk ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,walk_ASC_no_auto,,,,,,,,,,,,,, -util_WALK_ASC_Auto_deficient,Walk ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,walk_ASC_auto_deficient,,,,,,,,,,,,,, -util_WALK_ASC_Auto_sufficient,Walk ASC - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,walk_ASC_auto_sufficient,,,,,,,,,,,,,, -util_BIKE_ASC_Zero_auto,Bike ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,bike_ASC_no_auto,,,,,,,,,,,,, -util_BIKE_ASC_Auto_deficient,Bike ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,bike_ASC_auto_deficient,,,,,,,,,,,,, -util_BIKE_ASC_Auto_sufficient,Bike ASC - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,bike_ASC_auto_sufficient,,,,,,,,,,,,, -util_Shared_ride_2_ASC_Zero_auto,Shared ride 2 ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,sr2_ASC_no_auto,,,,,,,,,,,,,,,, -util_Shared_ride_2_ASC_Auto_deficient,Shared ride 2 ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,sr2_ASC_auto_deficient,,,,,,,,,,,,,,,, -util_Shared_ride_2_ASC_Auto_sufficient,Shared ride 2 ASC - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,sr2_ASC_auto_sufficient,,,,,,,,,,,,,,,, -util_Shared_ride_3+_Zero_auto,Shared ride 3+ - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,sr3p_ASC_no_auto,,,,,,,,,,,,,,, -util_Shared_ride_3+_Auto_deficient,Shared ride 3+ - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,sr3p_ASC_auto_deficient,,,,,,,,,,,,,,, -util_Shared_ride_3+_Auto_sufficient,Shared ride 3+ - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,sr3p_ASC_auto_sufficient,,,,,,,,,,,,,,, -util_WALK_to_Transit_Zero_auto,Walk to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,,,,,,,,,, -util_WALK_to_Transit_Auto_deficient,Walk to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,,,,,,,,,, -util_WALK_to_Transit_Auto_sufficient,Walk to Transit - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,,,,,,,,,, -util_PNR_to_Transit_Zero_auto,PNR to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,pnr_transit_ASC_no_auto,pnr_transit_ASC_no_auto,pnr_transit_ASC_no_auto,,,,,,, -util_PNR_to_Transit_Auto_deficient,PNR to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,pnr_transit_ASC_auto_deficient,pnr_transit_ASC_auto_deficient,pnr_transit_ASC_auto_deficient,,,,,,, -util_PNR_to_Transit_Auto_sufficient,PNR to Transit - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,pnr_transit_ASC_auto_sufficient,pnr_transit_ASC_auto_sufficient,pnr_transit_ASC_auto_sufficient,,,,,,, -util_KNR_to_Transit_Zero_auto,KNR to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,knr_transit_ASC_no_auto,knr_transit_ASC_no_auto,knr_transit_ASC_no_auto,,,, -util_KNR_to_Transit_Auto_deficient,KNR to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,knr_transit_ASC_auto_deficient,knr_transit_ASC_auto_deficient,knr_transit_ASC_auto_deficient,,,, -util_KNR_to_Transit_Auto_sufficient,KNR to Transit - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,knr_transit_ASC_auto_sufficient,knr_transit_ASC_auto_sufficient,knr_transit_ASC_auto_sufficient,,,, -util_Taxi_Zero_auto,Taxi - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,taxi_ASC_no_auto,, -util_Taxi_Auto_deficient,Taxi - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,taxi_ASC_auto_deficient,, -util_Taxi_Auto_sufficient,Taxi - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,taxi_ASC_auto_sufficient,, -util_TNC_Single_Zero_auto,TNC Single - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,tnc_single_ASC_no_auto, -util_TNC_Single_Auto_deficient,TNC Single - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,tnc_single_ASC_auto_deficient, -util_TNC_Single_Auto_sufficient,TNC Single - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,tnc_single_ASC_auto_sufficient, -util_TNC_Shared_Zero_auto,TNC Shared - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,tnc_shared_ASC_no_auto -util_TNC_Shared_Auto_deficient,TNC Shared - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,tnc_shared_ASC_auto_deficient -util_TNC_Shared_Auto_sufficient,TNC Shared - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,tnc_shared_ASC_auto_sufficient -#util_Joint_tour_ASCs,#joint tour ASCs,,,,,,,,,,,,,,,,,,, -util_Joint_WALK_ASC_Zero_auto,Joint - Walk ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,joint_walk_ASC_no_auto,,,,,,,,,,,,,, -util_Joint_WALK_ASC_Auto_deficient,Joint - Walk ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,joint_walk_ASC_auto_deficient,,,,,,,,,,,,,, -util_Joint_WALK_ASC_Auto_sufficient,Joint - Walk ASC - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,joint_walk_ASC_auto_sufficient,,,,,,,,,,,,,, -util_Joint_BIKE_ASC_Zero_auto,Joint - Bike ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,joint_bike_ASC_no_auto,,,,,,,,,,,,, -util_Joint_BIKE_ASC_Auto_deficient,Joint - Bike ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,joint_bike_ASC_auto_deficient,,,,,,,,,,,,, -util_Joint_BIKE_ASC_Auto_sufficient,Joint - Bike ASC - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,joint_bike_ASC_auto_sufficient,,,,,,,,,,,,, -util_Joint_Shared_ride_2_ASC_Zero_auto,Joint - Shared ride 2 ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,joint_sr2_ASC_no_auto,,,,,,,,,,,,,,,, -util_Joint_Shared_ride_2_ASC_Auto_deficient,Joint - Shared ride 2 ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,joint_sr2_ASC_auto_deficient,,,,,,,,,,,,,,,, -util_Joint_Shared_ride_2_ASC_Auto_sufficient,Joint - Shared ride 2 ASC - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,joint_sr2_ASC_auto_sufficient,,,,,,,,,,,,,,,, -util_Joint_Shared_ride_3+_Zero_auto,Joint - Shared ride 3+ - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,joint_sr3p_ASC_no_auto,,,,,,,,,,,,,,, -util_Joint_Shared_ride_3+_Auto_deficient,Joint - Shared ride 3+ - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,joint_sr3p_ASC_auto_deficient,,,,,,,,,,,,,,, -util_Joint_Shared_ride_3+_Auto_sufficient,Joint - Shared ride 3+ - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,joint_sr3p_ASC_auto_sufficient,,,,,,,,,,,,,,, -util_Joint_WALK_to_Transit_Zero_auto,Joint - Walk to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,,,,,,,,,, -util_Joint_WALK_to_Transit_Auto_deficient,Joint - Walk to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,,,,,,,,,, -util_Joint_WALK_to_Transit_Auto_sufficient,Joint - Walk to Transit - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,,,,,,,,,, -util_Joint_PNR_to_Transit_Zero_auto,Joint - PNR to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,joint_pnr_transit_ASC_no_auto,joint_pnr_transit_ASC_no_auto,joint_pnr_transit_ASC_no_auto,,,,,,, -util_Joint_PNR_to_Transit_Auto_deficient,Joint - PNR to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,joint_pnr_transit_ASC_auto_deficient,joint_pnr_transit_ASC_auto_deficient,joint_pnr_transit_ASC_auto_deficient,,,,,,, -util_Joint_PNR_to_Transit_Auto_sufficient,Joint - PNR to Transit - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,joint_pnr_transit_ASC_auto_sufficient,joint_pnr_transit_ASC_auto_sufficient,joint_pnr_transit_ASC_auto_sufficient,,,,,,, -util_Joint_KNR_to_Transit_Zero_auto,Joint - KNR to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,joint_knr_transit_ASC_no_auto,joint_knr_transit_ASC_no_auto,joint_knr_transit_ASC_no_auto,,,, -util_Joint_KNR_to_Transit_Auto_deficient,Joint - KNR to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,joint_knr_transit_ASC_auto_deficient,joint_knr_transit_ASC_auto_deficient,joint_knr_transit_ASC_auto_deficient,,,, -util_Joint_KNR_to_Transit_Auto_sufficient,Joint - KNR to Transit - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,joint_knr_transit_ASC_auto_sufficient,joint_knr_transit_ASC_auto_sufficient,joint_knr_transit_ASC_auto_sufficient,,,, -util_Joint_Taxi_Zero_auto,Joint - Taxi - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,joint_taxi_ASC_no_auto,, -util_Joint_Taxi_Auto_deficient,Joint - Taxi - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,joint_taxi_ASC_auto_deficient,, -util_Joint_Taxi_Auto_sufficient,Joint - Taxi - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,joint_taxi_ASC_auto_sufficient,, -util_Joint_TNC_Single_Zero_auto,Joint - TNC Single - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,joint_tnc_single_ASC_no_auto, -util_Joint_TNC_Single_Auto_deficient,Joint - TNC Single - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,joint_tnc_single_ASC_auto_deficient, -util_Joint_TNC_Single_Auto_sufficient,Joint - TNC Single - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,joint_tnc_single_ASC_auto_sufficient, -util_Joint_TNC_Shared_Zero_auto,Joint - TNC Shared - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_no_auto -util_Joint_TNC_Shared_Auto_deficient,Joint - TNC Shared - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_auto_deficient -util_Joint_TNC_Shared_Auto_sufficient,Joint - TNC Shared - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_auto_sufficient -util_local_asc,Local ASC,1,,,,,,local_ASC,,,local_ASC,,,local_ASC,,,,,, -util_premium_asc,Premium ASC,1,,,,,,,premium_ASC,,,premium_ASC,,,premium_ASC,,,,, -util_mix_asc,Mix ASC,1,,,,,,,,mix_ASC,,,mix_ASC,,,mix_ASC,,,, -util_WALK_to_Transit_dest_CBD,Walk to Transit dest CBD,@df.destination_in_cbd,,,,,,walk_transit_CBD_ASC,walk_transit_CBD_ASC,walk_transit_CBD_ASC,,,,,,,,,, -Drive_to_Transit_dest_CBD,Drive to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,,,, -Drive_to_Transit_distance_penalty,Drive to Transit - distance penalty,@drvtrn_distpen_0_multiplier * (1-od_skims['DIST']/drvtrn_distpen_max).clip(lower=0),,,,,,,,,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,,,, -#_FIXME_skims_aren't_symmetrical,# FIXME - skims aren't symmetrical, so we have to make sure they can get back,,,,,,,,,,,,,,,,,, -util_WALK_not_available_for_long_distances,Walk not available for long distances,@od_skims.max('DISTWALK') > 3,,,,-999,,,,,,,,,,,,,, -util_BIKE_not_available_for_long_distances,Bike not available for long distances,@od_skims.max('DISTBIKE') > 8,,,,,-999,,,,,,,,,,,,, -Drive_alone_not_available_for_escort_tours,Drive alone not available for escort tours,is_escort,-999,,,,,,,,,,,,,,,,, -#School_Bus,#School Bus,,,,,,,,,,,,,,,,,,, -School_Bus_Unavailable_if_NOT_school_tour,School Bus - Unavailable if NOT school tour,~is_school,,,,,,,,,,,,,,,-999,,, -School_Bus_In_vehicle_time_(HOV3+_skims)_20_mph,School Bus - In-vehicle time (HOV3+ skims) - 20 mph,@(odt_skims['HOV3_DIST'] + dot_skims['HOV3_DIST'])*3,,,,,,,,,,,,,,,coef_ivt,,, -School_Bus_WALK_Time,School Bus - Walk Time,@wacc_multiplier*10,,,,,,,,,,,,,,,coef_ivt,,, -School_Bus_Wait_Time,School Bus - Wait Time,@short_i_wait_multiplier*10,,,,,,,,,,,,,,,coef_ivt,,, -util_schoolbus_ASC_no_auto,School Bus No Auto ASC,@(df.auto_ownership == 0),,,,,,,,,,,,,,,schoolbus_ASC_no_auto,,, -util_schoolbus_ASC_auto_deficient,School Bus Auto Deficient ASC,@((df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,schoolbus_ASC_auto_deficient,,, -util_schoolbus_ASC_auto_sufficient,School Bus Auto Sufficient ASC,@((df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,schoolbus_ASC_auto_sufficient,,, +Label,Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK,BIKE,WALK_LOC,WALK_PRM,WALK_MIX,PNR_LOC,PNR_PRM,PNR_MIX,KNR_LOC,KNR_PRM,KNR_MIX,SCHOOLBUS,TAXI,TNC_SINGLE,TNC_SHARED +#Drive_alone_no_toll,#Drive alone no toll,,,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Unavailable,DRIVEALONE - Unavailable,sov_available == False,-999,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Unavailable_for_persons_less_than_16,DRIVEALONE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Unavailable_forJoint_tours,DRIVEALONE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Unavailable_if_didn't_drive_to_work,DRIVEALONE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,,,,,,, +util_DRIVEALONE_In_vehicle_time,DRIVEALONE - In-vehicle time,@odt_skims['SOV_TIME'] + dot_skims['SOV_TIME'],coef_ivt,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Terminal_time,DRIVEALONE - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,0,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Operating_cost_,DRIVEALONE - Operating cost ,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']),coef_ivt,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Parking_cost_,DRIVEALONE - Parking cost ,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost,coef_ivt,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Bridge_toll_,DRIVEALONE - Bridge toll ,@ivt_cost_multiplier * df.ivot * (odt_skims['SOV_BTOLL'] + dot_skims['SOV_BTOLL']),0,,,,,,,,,,,,,,,,, +util_DRIVEALONE_Person_is_between_16_and_19_years_old,DRIVEALONE - Person is between 16 and 19 years old,@(df.age >= 16) & (df.age <= 19),coef_age1619_da_multiplier,,,,,,,,,,,,,,,,, +#Shared_ride_2,#Shared ride 2,,,,,,,,,,,,,,,,,,, +util_SHARED2_Unavailable,SHARED2 - Unavailable,hov2_available == False,,-999,,,,,,,,,,,,,,,, +util_SHARED2_Unavailable_based_on_party_size,SHARED2 - Unavailable based on party size,is_joint & (number_of_participants > 2),,-999,,,,,,,,,,,,,,,, +util_SHARED2_In_vehicle_time,SHARED2 - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED2_Terminal_time,SHARED2 - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,0,,,,,,,,,,,,,,,, +util_SHARED2_Operating_cost,SHARED2 - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']),,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED2_Parking_cost,SHARED2 - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr2,,coef_ivt,,,,,,,,,,,,,,,, +util_SHARED2_Bridge_toll,SHARED2 - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']) / costShareSr2,,0,,,,,,,,,,,,,,,, +util_SHARED2_One_person_household,SHARED2 - One person household,@(df.hhsize == 1),,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,,, +util_SHARED2_Two_person_household,SHARED2 - Two person household,@(df.hhsize == 2),,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,,, +util_SHARED2_Person_is_16_years_old_or_older,SHARED2 - Person is 16 years old or older,@(df.age >= 16),,coef_age16p_sr_multiplier,,,,,,,,,,,,,,,, +#Shared_ride_3+,#Shared ride 3+,,,,,,,,,,,,,,,,,,, +util_SHARED3_Unavailable,SHARED3 - Unavailable,hov3_available == False,,,-999,,,,,,,,,,,,,,, +util_SHARED3_In_vehicle_time,SHARED3 - In-vehicle time,@(odt_skims['HOV3_TIME'] + dot_skims['HOV3_TIME']),,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3_Terminal_time,SHARED3 - Terminal time,@2 * walktimeshort_multiplier * df.terminal_time,,,0,,,,,,,,,,,,,,, +util_SHARED3_Operating_cost,SHARED3 - Operating cost,@ivt_cost_multiplier * df.ivot * costPerMile * (odt_skims['HOV3_DIST'] + dot_skims['HOV3_DIST']),,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3_Parking_cost,SHARED3 - Parking cost,@ivt_cost_multiplier * df.ivot * df.daily_parking_cost / costShareSr3,,,coef_ivt,,,,,,,,,,,,,,, +util_SHARED3_Bridge_toll,SHARED3 - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV3_BTOLL'] + dot_skims['HOV3_BTOLL']) / costShareSr3,,,0,,,,,,,,,,,,,,, +util_SHARED3_One_person_household,SHARED3 - One person household,@(df.hhsize == 1),,,coef_hhsize1_sr_multiplier,,,,,,,,,,,,,,, +util_SHARED3_Two_person_household,SHARED3 - Two person household,@(df.hhsize == 2),,,coef_hhsize2_sr_multiplier,,,,,,,,,,,,,,, +util_SHARED3_Person_is_16_years_old_or_older,SHARED3 - Person is 16 years old or older,@(df.age >= 16),,,coef_age16p_sr_multiplier,,,,,,,,,,,,,,, +#util_WALK,#Walk,,,,,,,,,,,,,,,,,,, +#FIXME_skims_aren't_symmetrical,#FIXME - skims aren't symmetrical, so we have to make sure they can get back,,,,,,,,,,,,,,,,,, +util_WALK_Time_up_to_2_miles,WALK - Time up to 2 miles,@walktimeshort_multiplier * (od_skims['DISTWALK'].clip(upper=walkThresh) + od_skims.reverse('DISTWALK').clip(upper=walkThresh))*60/walkSpeed,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Time_beyond_2_of_a_miles,WALK - Time beyond 2 of a miles,@walktimelong_multiplier * ((od_skims['DISTWALK'] - walkThresh).clip(lower=0) + (od_skims.reverse('DISTWALK') - walkThresh).clip(lower=0))*60/walkSpeed,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Destination_zone_densityIndex,WALK - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,coef_ivt,,,,,,,,,,,,,, +util_WALK_Topology,WALK - Topology,@coef_topology_walk_multiplier * df.dest_topology,,,,coef_ivt,,,,,,,,,,,,,, +#util_BIKE,#Bike,,,,,,,,,,,,,,,,,,, +#FIXME_skims_aren't_symmetrical,#FIXME - skims aren't symmetrical, so we have to make sure they can get back,,,,,,,,,,,,,,,,,, +util_BIKE_Unavailable_if_didn't_BIKE_to_work,BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,-999,,,,,,,,,,,,, +util_BIKE_Time_up_to_6_miles,BIKE - Time up to 6 miles,@biketimeshort_multiplier * (od_skims['DISTBIKE'].clip(upper=bikeThresh) + od_skims.reverse('DISTBIKE').clip(upper=bikeThresh))*60/bikeSpeed,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Time_beyond_6_of_a_miles,BIKE - Time beyond 6 of a miles,@biketimelong_multiplier * ((od_skims['DISTBIKE']-bikeThresh).clip(lower=0) + (od_skims.reverse('DISTBIKE')-bikeThresh).clip(lower=0))*60/bikeSpeed,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Destination_zone_densityIndex,BIKE - Destination zone densityIndex,@density_index_multiplier * df.density_index,,,,,coef_ivt,,,,,,,,,,,,, +util_BIKE_Topology,BIKE - Topology,@coef_topology_bike_multiplier * df.dest_topology,,,,,coef_ivt,,,,,,,,,,,,, +#util_WALK_to_Local,#Walk to Local,,,,,,,,,,,,,,,,,,, +util_WALK_LOC_Unavailable,WALK_LOC - Unavailable,walk_local_available == False,,,,,,-999,,,,,,,,,,,, +util_WALK_LOC_In_vehicle_time,WALK_LOC - In-vehicle time,@(odt_skims['WLK_LOC_IVT'] + dot_skims['WLK_LOC_IVT']),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Short_iwait_time,WALK_LOC - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_LOC_IWAIT']).clip(upper=waitThresh) + (dot_skims['WLK_LOC_IWAIT']).clip(upper=waitThresh)),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Long_iwait_time,WALK_LOC - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_LOC_IWAIT']-waitThresh).clip(0) + (dot_skims['WLK_LOC_IWAIT']-waitThresh).clip(0)),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_transfer_wait_time,WALK_LOC - transfer wait time,@xwait_multiplier * (odt_skims['WLK_LOC_XWAIT'] + dot_skims['WLK_LOC_XWAIT']),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_number_of_transfers,WALK_LOC - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_LOC_NT']).clip(0) + (dot_skims['WLK_LOC_NT']).clip(0)),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_access_time,WALK_LOC - Walk access time,@wacc_multiplier * 2 * df.origin_walk_time,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_egress_time,WALK_LOC - Walk egress time,@wegr_multiplier * 2 * df.destination_walk_time,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Walk_other_time,WALK_LOC - Walk other time,@waux_multiplier * (odt_skims['WLK_LOC_WAUX'] + dot_skims['WLK_LOC_WAUX']),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Fare,WALK_LOC - Fare,@ivt_cost_multiplier * df.fare_discount_percent * df.ivot * (odt_skims['WLK_LOC_FARE'] + dot_skims['WLK_LOC_FARE']),,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Destination_zone_densityIndex,WALK_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Topology,WALK_LOC - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,coef_ivt,,,,,,,,,,,, +util_WALK_LOC_Person_is_less_than_10_years_old,WALK_LOC - Person is less than 10 years old,@(df.age <= 10),,,,,,coef_age010_trn_multiplier,,,,,,,,,,,, +#util_WALK_to_Premium,#Walk to Premium,,,,,,,,,,,,,,,,,,, +util_WALK_PRM_Unavailable,WALK_PRM - Unavailable,walk_premium_available == False,,,,,,,-999,,,,,,,,,,, +util_WALK_PRM_In_vehicle_time,WALK_PRM - In-vehicle time,@(odt_skims['WLK_PRM_IVT'] + dot_skims['WLK_PRM_IVT']),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_In_vehicle_time_on_PMov,WALK_PRM - In-vehicle time on PMov,@(ivt_pmov_multiplier - 1) * (odt_skims['WLK_PRM_IVTT_PMov'] + dot_skims['WLK_PRM_IVTT_PMov']),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_In_vehicle_time_on_StCar,WALK_PRM - In-vehicle time on StCar,@(ivt_stcar_multiplier - 1) * (odt_skims['WLK_PRM_IVTT_StCar'] + dot_skims['WLK_PRM_IVTT_StCar']),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_In_vehicle_time_on_BRT,WALK_PRM - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['WLK_PRM_IVTT_Brt'] + dot_skims['WLK_PRM_IVTT_Brt']),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_In_vehicle_time_on_URB,WALK_PRM - In-vehicle time on URB,@(ivt_urb_multiplier - 1) * (odt_skims['WLK_PRM_IVTT_UrbRail'] + dot_skims['WLK_PRM_IVTT_UrbRail']),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_In_vehicle_time_on_COM,WALK_PRM - In-vehicle time on COM,@(ivt_com_multiplier - 1) * (odt_skims['WLK_PRM_IVTT_ComRail'] + dot_skims['WLK_PRM_IVTT_ComRail']),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Short_iwait_time,WALK_PRM - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_PRM_IWAIT']).clip(upper=waitThresh) + (dot_skims['WLK_PRM_IWAIT']).clip(upper=waitThresh)),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Long_iwait_time,WALK_PRM - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_PRM_IWAIT']-waitThresh).clip(0) + (dot_skims['WLK_PRM_IWAIT']-waitThresh).clip(0)),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_transfer_wait_time,WALK_PRM - transfer wait time,@xwait_multiplier * (odt_skims['WLK_PRM_XWAIT'] + dot_skims['WLK_PRM_XWAIT']),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_number_of_transfers,WALK_PRM - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_PRM_NT']).clip(0) + (dot_skims['WLK_PRM_NT']).clip(0)),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Walk_access_time,WALK_PRM - Walk access time,@wacc_multiplier * 2 * df.origin_walk_time,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Walk_egress_time,WALK_PRM - Walk egress time,@wegr_multiplier * 2 * df.destination_walk_time,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Walk_other_time,WALK_PRM - Walk other time,@waux_multiplier * (odt_skims['WLK_PRM_WAUX'] + dot_skims['WLK_PRM_WAUX']),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Fare,WALK_PRM - Fare,@ivt_cost_multiplier * df.fare_discount_percent * df.ivot * (odt_skims['WLK_PRM_FARE'] + dot_skims['WLK_PRM_FARE']),,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Destination_zone_densityIndex,WALK_PRM - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Topology,WALK_PRM - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,coef_ivt,,,,,,,,,,, +util_WALK_PRM_Person_is_less_than_10_years_old,WALK_PRM - Person is less than 10 years old,@(df.age <= 10),,,,,,,coef_age010_trn_multiplier,,,,,,,,,,, +#util_WALK_to_MIX,#Walk to MIX,,,,,,,,,,,,,,,,,,, +util_WALK_MIX_Unavailable,WALK_MIX - Unavailable,walk_mix_available == False,,,,,,,,-999,,,,,,,,,, +util_WALK_MIX_In_vehicle_time,WALK_MIX - In-vehicle time,@(odt_skims['WLK_MIX_IVT'] + dot_skims['WLK_MIX_IVT']),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_In_vehicle_time_on_Bus,WALK_MIX - In-vehicle time on Bus,@(odt_skims['WLK_MIX_IVTT_Bus'] + dot_skims['WLK_MIX_IVTT_Bus']),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_In_vehicle_time_on_PMov,WALK_MIX - In-vehicle time on PMov,@(ivt_pmov_multiplier - 1) * (odt_skims['WLK_MIX_IVTT_PMov'] + dot_skims['WLK_MIX_IVTT_PMov']),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_In_vehicle_time_on_StCar,WALK_MIX - In-vehicle time on StCar,@(ivt_stcar_multiplier - 1) * (odt_skims['WLK_MIX_IVTT_StCar'] + dot_skims['WLK_MIX_IVTT_StCar']),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_In_vehicle_time_on_BRT,WALK_MIX - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['WLK_MIX_IVTT_Brt'] + dot_skims['WLK_MIX_IVTT_Brt']),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_In_vehicle_time_on_URB,WALK_MIX - In-vehicle time on URB,@(ivt_urb_multiplier - 1) * (odt_skims['WLK_MIX_IVTT_UrbRail'] + dot_skims['WLK_MIX_IVTT_UrbRail']),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_In_vehicle_time_on_COM,WALK_MIX - In-vehicle time on COM,@(ivt_com_multiplier - 1) * (odt_skims['WLK_MIX_IVTT_ComRail'] + dot_skims['WLK_MIX_IVTT_ComRail']),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Short_iwait_time,WALK_MIX - Short iwait time,@short_i_wait_multiplier * ((odt_skims['WLK_MIX_IWAIT']).clip(upper=waitThresh) + (dot_skims['WLK_MIX_IWAIT']).clip(upper=waitThresh)),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Long_iwait_time,WALK_MIX - Long iwait time,@long_i_wait_multiplier * ((odt_skims['WLK_MIX_IWAIT']-waitThresh).clip(0) + (dot_skims['WLK_MIX_IWAIT']-waitThresh).clip(0)),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_transfer_wait_time,WALK_MIX - transfer wait time,@xwait_multiplier * (odt_skims['WLK_MIX_XWAIT'] + dot_skims['WLK_MIX_XWAIT']),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_number_of_transfers,WALK_MIX - number of transfers,@xfers_wlk_multiplier * ((odt_skims['WLK_MIX_NT']).clip(0) + (dot_skims['WLK_MIX_NT']).clip(0)),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Walk_access_time,WALK_MIX - Walk access time,@wacc_multiplier * 2 * df.origin_walk_time,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Walk_egress_time,WALK_MIX - Walk egress time,@wegr_multiplier * 2 * df.destination_walk_time,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Walk_other_time,WALK_MIX - Walk other time,@waux_multiplier * (odt_skims['WLK_MIX_WAUX'] + dot_skims['WLK_MIX_WAUX']),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Fare,WALK_MIX - Fare,@ivt_cost_multiplier * df.fare_discount_percent * df.ivot * (odt_skims['WLK_MIX_FARE'] + dot_skims['WLK_MIX_FARE']),,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Destination_zone_densityIndex,WALK_MIX - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,coef_ivt,,,,,,,,,, +util_WALK_MIX_Topology,WALK_MIX - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,coef_age010_trn_multiplier,,,,,,,,,, +util_WALK_MIX_Person_is_less_than_10_years_old,WALK_MIX - Person is less than 10 years old,@(df.age <= 10),,,,,,,,1,,,,,,,,,, +#util_PNR_to_Local,#PNR to Local,,,,,,,,,,,,,,,,,,, +util_PNR_LOC_Unavailable,PNR_LOC - Unavailable,pnr_local_available == False,,,,,,,,,-999,,,,,,,,, +util_PNR_LOC_Unavailable_for_persons_less_than_16,PNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,-999,,,,,,,,, +util_PNR_LOC_In_vehicle_time,PNR_LOC - In-vehicle time,@(odt_skims['PNR_LOC_IVT'] + dot_skims['PNRE_LOC_IVT']),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_Short_iwait_time,PNR_LOC - Short iwait time,@short_i_wait_multiplier * ((odt_skims['PNR_LOC_IWAIT']).clip(upper=waitThresh) + (dot_skims['PNRE_LOC_IWAIT']).clip(upper=waitThresh)),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_Long_iwait_time,PNR_LOC - Long iwait time,@long_i_wait_multiplier * ((odt_skims['PNR_LOC_IWAIT']-waitThresh).clip(0) + (dot_skims['PNRE_LOC_IWAIT']-waitThresh).clip(0)),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_transfer_wait_time,PNR_LOC - transfer wait time,@xwait_multiplier * (odt_skims['PNR_LOC_XWAIT'] + dot_skims['PNRE_LOC_XWAIT']),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_number_of_transfers,PNR_LOC - number of transfers,@xfers_wlk_multiplier * ((odt_skims['PNR_LOC_NT']).clip(0) + (dot_skims['PNRE_LOC_NT']).clip(0)),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_PNR_time,PNR_LOC - PNR time,@dtim_multiplier * (odt_skims['PNR_LOC_DTIME'] + dot_skims['PNRE_LOC_DTIME']),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_Walk_access_time,PNR_LOC - Walk access time,@wacc_multiplier * df.destination_walk_time,,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_Walk_egress_time_(at_attraction_end),PNR_LOC - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_Walk_other_time,PNR_LOC - Walk other time,@waux_multiplier * (odt_skims['PNR_LOC_WAUX'] + dot_skims['PNRE_LOC_WAUX']),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_Fare_and_operating_cost,PNR_LOC - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['PNR_LOC_FARE'] + dot_skims['PNRE_LOC_FARE']) + ((odt_skims['PNR_LOC_DDIST']+dot_skims['PNRE_LOC_DDIST']) * costPerMile)),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_Ratio_of_PNR_access_distance_to_OD_distance,PNR_LOC - Ratio of PNR access distance to OD distance,@dacc_ratio * ((odt_skims['PNR_LOC_DDIST']+ dot_skims['PNRE_LOC_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_Destination_zone_densityIndex,PNR_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_Topology,PNR_LOC - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,coef_ivt,,,,,,,,, +util_PNR_LOC_Person_is_less_than_10_years_old,PNR_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,coef_age010_trn_multiplier,,,,,,,,, +#util_PNR_to_Premium,#PNR to Premium,,,,,,,,,,,,,,,,,,, +util_PNR_PRM_Unavailable,PNR_PRM - Unavailable,pnr_premium_available == False,,,,,,,,,,-999,,,,,,,, +util_PNR_PRM_Unavailable_for_persons_less_than_16,PNR_PRM - Unavailable for persons less than 16,age < 16,,,,,,,,,,-999,,,,,,,, +util_PNR_PRM_In_vehicle_time,PNR_PRM - In-vehicle time,@(odt_skims['PNR_PRM_IVT'] + dot_skims['PNRE_PRM_IVT']),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In_vehicle_time_on_PMov,PNR_PRM - In-vehicle time on PMov,@(ivt_pmov_multiplier - 1) * (odt_skims['PNR_PRM_IVTT_PMov'] + dot_skims['PNRE_PRM_IVTT_PMov']),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In_vehicle_time_on_StCar,PNR_PRM - In-vehicle time on StCar,@(ivt_stcar_multiplier - 1) * (odt_skims['PNR_PRM_IVTT_StCar'] + dot_skims['PNRE_PRM_IVTT_StCar']),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In_vehicle_time_on_BRT,PNR_PRM - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['PNR_PRM_IVTT_Brt'] + dot_skims['PNRE_PRM_IVTT_Brt']),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In_vehicle_time_on_URB,PNR_PRM - In-vehicle time on URB,@(ivt_urb_multiplier - 1) * (odt_skims['PNR_PRM_IVTT_UrbRail'] + dot_skims['PNRE_PRM_IVTT_UrbRail']),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_In_vehicle_time_on_COM,PNR_PRM - In-vehicle time on COM,@(ivt_com_multiplier - 1) * (odt_skims['PNR_PRM_IVTT_ComRail'] + dot_skims['PNRE_PRM_IVTT_ComRail']),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_Short_iwait_time,PNR_PRM - Short iwait time,@short_i_wait_multiplier * ((odt_skims['PNR_PRM_IWAIT']).clip(upper=waitThresh) + (dot_skims['PNRE_PRM_IWAIT']).clip(upper=waitThresh)),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_Long_iwait_time,PNR_PRM - Long iwait time,@long_i_wait_multiplier * ((odt_skims['PNR_PRM_IWAIT']-waitThresh).clip(0) + (dot_skims['PNRE_PRM_IWAIT']-waitThresh).clip(0)) ,,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_transfer_wait_time,PNR_PRM - transfer wait time,@xwait_multiplier * (odt_skims['PNR_PRM_XWAIT'] + dot_skims['PNRE_PRM_XWAIT']),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_number_of_transfers,PNR_PRM - number of transfers,@xfers_drv_multiplier * ((odt_skims['PNR_PRM_NT']).clip(0) + (dot_skims['PNRE_PRM_NT']).clip(0)),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_PNR_time,PNR_PRM - PNR time,@dtim_multiplier * (odt_skims['PNR_PRM_DTIME'] + dot_skims['PNRE_PRM_DTIME']),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_Walk_access_time_(at_attraction_end),PNR_PRM - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_Walk_egress_time_(at_attraction_end),PNR_PRM - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_Walk_other_time,PNR_PRM - Walk other time,@waux_multiplier * (odt_skims['PNR_PRM_WAUX'] + dot_skims['PNRE_PRM_WAUX']),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_Fare_and_operating_cost,PNR_PRM - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['PNR_PRM_FARE']+dot_skims['PNRE_PRM_FARE']) + ((odt_skims['PNR_PRM_DDIST']+dot_skims['PNRE_PRM_DDIST']) *costPerMile)),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_Ratio_of_PNR_access_distance_to_OD_distance,PNR_PRM - Ratio of PNR access distance to OD distance,@dacc_ratio * ((odt_skims['PNR_PRM_DDIST']+ dot_skims['PNRE_PRM_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_Destination_zone_densityIndex,PNR_PRM - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_Topology,PNR_PRM - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,coef_ivt,,,,,,,, +util_PNR_PRM_Person_is_less_than_10_years_old,PNR_PRM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,coef_age010_trn_multiplier,,,,,,,, +#util_PNR_to_MIX,#PNR to MIX,,,,,,,,,,,,,,,,,,, +util_PNR_MIX_Unavailable,PNR_MIX - Unavailable,pnr_mix_available == False,,,,,,,,,,,-999,,,,,,, +util_PNR_MIX_Unavailable_for_persons_less_than_16,PNR_MIX - Unavailable for persons less than 16,age < 16,,,,,,,,,,,-999,,,,,,, +util_PNR_MIX_In_vehicle_time,PNR_MIX - In-vehicle time,@(odt_skims['PNR_MIX_IVT'] + dot_skims['PNRE_MIX_IVT']),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In_vehicle_time_on_Bus,PNR_MIX - In-vehicle time on Bus,@(odt_skims['PNR_MIX_IVTT_Bus'] + dot_skims['PNRE_MIX_IVTT_Bus']),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In_vehicle_time_on_PMov,PNR_MIX - In-vehicle time on PMov,@(ivt_pmov_multiplier - 1) * (odt_skims['PNR_MIX_IVTT_PMov'] + dot_skims['PNRE_MIX_IVTT_PMov']),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In_vehicle_time_on_StCar,PNR_MIX - In-vehicle time on StCar,@(ivt_stcar_multiplier - 1) * (odt_skims['PNR_MIX_IVTT_StCar'] + dot_skims['PNRE_MIX_IVTT_StCar']),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In_vehicle_time_on_BRT,PNR_MIX - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['PNR_MIX_IVTT_Brt'] + dot_skims['PNRE_MIX_IVTT_Brt']),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In_vehicle_time_on_URB,PNR_MIX - In-vehicle time on URB,@(ivt_urb_multiplier - 1) * (odt_skims['PNR_MIX_IVTT_UrbRail'] + dot_skims['PNRE_MIX_IVTT_UrbRail']),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_In_vehicle_time_on_COM,PNR_MIX - In-vehicle time on COM,@(ivt_com_multiplier - 1) * (odt_skims['PNR_MIX_IVTT_ComRail'] + dot_skims['PNRE_MIX_IVTT_ComRail']),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_Short_iwait_time,PNR_MIX - Short iwait time,@short_i_wait_multiplier * ((odt_skims['PNR_MIX_IWAIT']).clip(upper=waitThresh) + (dot_skims['PNRE_MIX_IWAIT']).clip(upper=waitThresh)),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_Long_iwait_time,PNR_MIX - Long iwait time,@long_i_wait_multiplier * ((odt_skims['PNR_MIX_IWAIT']-waitThresh).clip(0) + (dot_skims['PNRE_MIX_IWAIT']-waitThresh).clip(0)),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_transfer_wait_time,PNR_MIX - transfer wait time,@xwait_multiplier * (odt_skims['PNR_MIX_XWAIT'] + dot_skims['PNRE_MIX_XWAIT']),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_number_of_transfers,PNR_MIX - number of transfers,@xfers_drv_multiplier * ((odt_skims['PNR_MIX_NT']).clip(0) + (dot_skims['PNRE_MIX_NT']).clip(0)),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_PNR_time,PNR_MIX - PNR time,@dtim_multiplier * (odt_skims['PNR_MIX_DTIME'] + dot_skims['PNRE_MIX_DTIME']),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_Walk_access_time_(at_attraction_end),PNR_MIX - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_Walk_egress_ime_(at_attraction_end),PNR_MIX - Walk egress ime (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_Walk_other_time,PNR_MIX - Walk other time,@waux_multiplier * (odt_skims['PNR_MIX_WAUX'] + dot_skims['PNRE_MIX_WAUX']),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_Fare_and_operating_cost,PNR_MIX - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['PNR_MIX_FARE']+dot_skims['PNRE_MIX_FARE']) + ((odt_skims['PNR_MIX_DDIST']+dot_skims['PNRE_MIX_DDIST']) *costPerMile)),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_Ratio_of_PNR_access_distance_to_OD_distance,PNR_MIX - Ratio of PNR access distance to OD distance,@dacc_ratio * ((odt_skims['PNR_MIX_DDIST']+ dot_skims['PNRE_MIX_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_Destination_zone_densityIndex,PNR_MIX - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_Topology,PNR_MIX - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,coef_ivt,,,,,,, +util_PNR_MIX_Person_is_less_than_10_years_old,PNR_MIX - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,coef_age010_trn_multiplier,,,,,,, +#util_KNR_to_Local,#KNR to Local,,,,,,,,,,,,,,,,,,, +util_KNR_LOC_Unavailable,KNR_LOC - Unavailable,knr_local_available == False,,,,,,,,,,,,-999,,,,,, +util_KNR_LOC_Unavailable_for_persons_less_than_16,KNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,-999,,,,,, +util_KNR_LOC_In_vehicle_time,KNR_LOC - In-vehicle time,@(odt_skims['KNR_LOC_IVT'] + dot_skims['KNRE_LOC_IVT']),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_Short_iwait_time,KNR_LOC - Short iwait time,@short_i_wait_multiplier * ((odt_skims['KNR_LOC_IWAIT']).clip(upper=waitThresh) + (dot_skims['KNRE_LOC_IWAIT']).clip(upper=waitThresh)),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_Long_iwait_time,KNR_LOC - Long iwait time,@long_i_wait_multiplier * ((odt_skims['KNR_LOC_IWAIT']-waitThresh).clip(0) + (dot_skims['KNRE_LOC_IWAIT']-waitThresh).clip(0)),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_transfer_wait_time,KNR_LOC - transfer wait time,@xwait_multiplier * (odt_skims['KNR_LOC_XWAIT'] + dot_skims['KNRE_LOC_XWAIT']),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_number_of_transfers,KNR_LOC - number of transfers,@xfers_wlk_multiplier * ((odt_skims['KNR_LOC_NT']).clip(0) + (dot_skims['KNRE_LOC_NT']).clip(0)),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_KNR_time,KNR_LOC - KNR time,@dtim_multiplier * (odt_skims['KNR_LOC_DTIME'] + dot_skims['KNRE_LOC_DTIME']),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_Walk_access_time,KNR_LOC - Walk access time,@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_Walk_egress_time_(at_attraction_end),KNR_LOC - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_Walk_other_time,KNR_LOC - Walk other time,@waux_multiplier * (odt_skims['KNR_LOC_WAUX'] + dot_skims['KNRE_LOC_WAUX']),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_Fare_and_operating_cost,KNR_LOC - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['KNR_LOC_FARE'] + dot_skims['KNRE_LOC_FARE']) + ((odt_skims['KNR_LOC_DDIST']+dot_skims['KNRE_LOC_DDIST']) * costPerMile)),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_Ratio_of_KNR_access_distance_to_OD_distance,KNR_LOC - Ratio of KNR access distance to OD distance,@dacc_ratio * ((odt_skims['KNR_LOC_DDIST']+ dot_skims['KNRE_LOC_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_Destination_zone_densityIndex,KNR_LOC - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_Topology,KNR_LOC - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,coef_ivt,,,,,, +util_KNR_LOC_Person_is_less_than_10_years_old,KNR_LOC - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,coef_age010_trn_multiplier,,,,,, +#util_KNR_to_Premium,#KNR to Premium,,,,,,,,,,,,,,,,,,, +util_KNR_PRM_Unavailable,KNR_PRM - Unavailable,knr_premium_available == False,,,,,,,,,,,,,-999,,,,, +util_KNR_PRM_Unavailable_for_persons_less_than_16,KNR_PRM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,-999,,,,, +util_KNR_PRM_In_vehicle_time,KNR_PRM - In-vehicle time,@(odt_skims['KNR_PRM_IVT'] + dot_skims['KNRE_PRM_IVT']),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In_vehicle_time_on_PMov,KNR_PRM - In-vehicle time on PMov,@(ivt_pmov_multiplier - 1) * (odt_skims['KNR_PRM_IVTT_PMov'] + dot_skims['KNRE_PRM_IVTT_PMov']),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In_vehicle_time_on_StCar,KNR_PRM - In-vehicle time on StCar,@(ivt_stcar_multiplier - 1) * (odt_skims['KNR_PRM_IVTT_StCar'] + dot_skims['KNRE_PRM_IVTT_StCar']),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In_vehicle_time_on_BRT,KNR_PRM - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['KNR_PRM_IVTT_Brt'] + dot_skims['KNRE_PRM_IVTT_Brt']),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In_vehicle_time_on_URB,KNR_PRM - In-vehicle time on URB,@(ivt_urb_multiplier - 1) * (odt_skims['KNR_PRM_IVTT_UrbRail'] + dot_skims['KNRE_PRM_IVTT_UrbRail']),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_In_vehicle_time_on_COM,KNR_PRM - In-vehicle time on COM,@(ivt_com_multiplier - 1) * (odt_skims['KNR_PRM_IVTT_ComRail'] + dot_skims['KNRE_PRM_IVTT_ComRail']),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_Short_iwait_time,KNR_PRM - Short iwait time,@short_i_wait_multiplier * ((odt_skims['KNR_PRM_IWAIT']).clip(upper=waitThresh) + (dot_skims['KNRE_PRM_IWAIT']).clip(upper=waitThresh)),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_Long_iwait_time,KNR_PRM - Long iwait time,@long_i_wait_multiplier * ((odt_skims['KNR_PRM_IWAIT']-waitThresh).clip(0) + (dot_skims['KNRE_PRM_IWAIT']-waitThresh).clip(0)) ,,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_transfer_wait_time,KNR_PRM - transfer wait time,@xwait_multiplier * (odt_skims['KNR_PRM_XWAIT'] + dot_skims['KNRE_PRM_XWAIT']),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_number_of_transfers,KNR_PRM - number of transfers,@xfers_drv_multiplier *((odt_skims['KNR_PRM_NT']).clip(0) + (dot_skims['KNRE_PRM_NT']).clip(0)),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_KNR_time,KNR_PRM - KNR time,@dtim_multiplier * (odt_skims['KNR_PRM_DTIME'] + dot_skims['KNRE_PRM_DTIME']),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_Walk_access_time_(at_attraction_end),KNR_PRM - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_Walk_egress_time_(at_attraction_end),KNR_PRM - Walk egress time (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_Walk_other_time,KNR_PRM - Walk other time,@waux_multiplier * (odt_skims['KNR_PRM_WAUX'] + dot_skims['KNRE_PRM_WAUX']),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_Fare_and_operating_cost,KNR_PRM - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['KNR_PRM_FARE']+dot_skims['KNRE_PRM_FARE']) + ((odt_skims['KNR_PRM_DDIST']+dot_skims['KNRE_PRM_DDIST']) *costPerMile)),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_Ratio_of_KNR_access_distance_to_OD_distance,KNR_PRM - Ratio of KNR access distance to OD distance,@dacc_ratio * ((odt_skims['KNR_PRM_DDIST']+ dot_skims['KNRE_PRM_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_Destination_zone_densityIndex,KNR_PRM - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_Topology,KNR_PRM - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,coef_ivt,,,,, +util_KNR_PRM_Person_is_less_than_10_years_old,KNR_PRM - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,coef_age010_trn_multiplier,,,,, +#util_KNR_to_MIX,#KNR to MIX,,,,,,,,,,,,,,,,,,, +util_KNR_MIX_Unavailable,KNR_MIX - Unavailable,knr_mix_available == False,,,,,,,,,,,,,,-999,,,, +util_KNR_MIX_Unavailable_for_persons_less_than_16,KNR_MIX - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,-999,,,, +util_KNR_MIX_In_vehicle_time,KNR_MIX - In-vehicle time,@(odt_skims['KNR_MIX_IVT'] + dot_skims['KNRE_MIX_IVT']),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In_vehicle_time_on_Bus,KNR_MIX - In-vehicle time on Bus,@(odt_skims['KNR_MIX_IVTT_Bus'] + dot_skims['KNRE_MIX_IVTT_Bus']),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In_vehicle_time_on_PMov,KNR_MIX - In-vehicle time on PMov,@(ivt_pmov_multiplier - 1) * (odt_skims['KNR_MIX_IVTT_PMov'] + dot_skims['KNRE_MIX_IVTT_PMov']),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In_vehicle_time_on_StCar,KNR_MIX - In-vehicle time on StCar,@(ivt_stcar_multiplier - 1) * (odt_skims['KNR_MIX_IVTT_StCar'] + dot_skims['KNRE_MIX_IVTT_StCar']),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In_vehicle_time_on_BRT,KNR_MIX - In-vehicle time on BRT,@(ivt_brt_multiplier - 1) * (odt_skims['KNR_MIX_IVTT_Brt'] + dot_skims['KNRE_MIX_IVTT_Brt']),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In_vehicle_time_on_URB,KNR_MIX - In-vehicle time on URB,@(ivt_urb_multiplier - 1) * (odt_skims['KNR_MIX_IVTT_UrbRail'] + dot_skims['KNRE_MIX_IVTT_UrbRail']),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_In_vehicle_time_on_COM,KNR_MIX - In-vehicle time on COM,@(ivt_com_multiplier - 1) * (odt_skims['KNR_MIX_IVTT_ComRail'] + dot_skims['KNRE_MIX_IVTT_ComRail']),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_Short_iwait_time,KNR_MIX - Short iwait time,@short_i_wait_multiplier * ((odt_skims['KNR_MIX_IWAIT']).clip(upper=waitThresh) + (dot_skims['KNRE_MIX_IWAIT']).clip(upper=waitThresh)),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_Long_iwait_time,KNR_MIX - Long iwait time,@long_i_wait_multiplier * ((odt_skims['KNR_MIX_IWAIT']-waitThresh).clip(0) + (dot_skims['KNRE_MIX_IWAIT']-waitThresh).clip(0)),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_transfer_wait_time,KNR_MIX - transfer wait time,@xwait_multiplier * (odt_skims['KNR_MIX_XWAIT'] + dot_skims['KNRE_MIX_XWAIT']),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_number_of_transfers,KNR_MIX - number of transfers,@xfers_drv_multiplier * ((odt_skims['KNR_MIX_NT']).clip(0) + (dot_skims['KNRE_MIX_NT']).clip(0)),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_KNR_time,KNR_MIX - KNR time,@dtim_multiplier * (odt_skims['KNR_MIX_DTIME'] + dot_skims['KNRE_MIX_DTIME']),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_Walk_access_time_(at_attraction_end),KNR_MIX - Walk access time (at attraction end),@wacc_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_Walk_egress_ime_(at_attraction_end),KNR_MIX - Walk egress ime (at attraction end),@wegr_multiplier * df.destination_walk_time,,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_Walk_other_time,KNR_MIX - Walk other time,@waux_multiplier * (odt_skims['KNR_MIX_WAUX'] + dot_skims['KNRE_MIX_WAUX']),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_Fare_and_operating_cost,KNR_MIX - Fare and operating cost,@ivt_cost_multiplier * df.ivot * (df.fare_discount_percent * (odt_skims['KNR_MIX_FARE']+dot_skims['KNRE_MIX_FARE']) + ((odt_skims['KNR_MIX_DDIST']+dot_skims['KNRE_MIX_DDIST']) *costPerMile)),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_Ratio_of_KNR_access_distance_to_OD_distance,KNR_MIX - Ratio of KNR access distance to OD distance,@dacc_ratio * ((odt_skims['KNR_MIX_DDIST']+ dot_skims['KNRE_MIX_DDIST'])/ (od_skims['DIST']*2)),,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_Destination_zone_densityIndex,KNR_MIX - Destination zone densityIndex,@density_index_multiplier * df.dest_density_index,,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_Topology,KNR_MIX - Topology,@coef_topology_trn_multiplier * df.dest_topology,,,,,,,,,,,,,,coef_ivt,,,, +util_KNR_MIX_Person_is_less_than_10_years_old,KNR_MIX - Person is less than 10 years old,@(df.age < 10),,,,,,,,,,,,,,coef_age010_trn_multiplier,,,, +#,Taxi,,,,,,,,,,,,,,,,,,, +util_Taxi_In_vehicle_time,Taxi - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,,,,,,,,,,,,,,,coef_ivt,, +#, FIXME magic constant 1.5,,,,,,,,,,,,,,,,,,, +util_Taxi_Wait_time,Taxi - Wait time,@1.5 * df.totalWaitTaxi,,,,,,,,,,,,,,,,coef_ivt,, +#util_Taxi_Tolls,Taxi - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,coef_ivt,, +#util_Taxi_Bridge_toll,Taxi - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,coef_ivt,, +util_Taxi_Fare,Taxi - Fare,@ivt_cost_multiplier * df.ivot * (Taxi_baseFare * 2 + (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']) * Taxi_costPerMile +(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']) * Taxi_costPerMinute)*100,,,,,,,,,,,,,,,,coef_ivt,, +#,TNC Single,,,,,,,,,,,,,,,,,,, +util_TNC_Single_In_vehicle_time,TNC Single - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']),,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Wait_time,TNC Single - Wait time,@1.5 * df.totalWaitSingleTNC,,,,,,,,,,,,,,,,,coef_ivt, +#util_TNC_Single_Tolls,TNC Single - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,,coef_ivt, +#util_TNC_Single_Bridge_toll,TNC Single - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,coef_ivt, +util_TNC_Single_Cost,TNC Single - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_single_baseFare * 2 + (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']) * TNC_single_costPerMile + (odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']) * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,,,,,,coef_ivt, +#,TNC Shared,,,,,,,,,,,,,,,,,,, +util_TNC_Shared_In_vehicle_time,TNC Shared - In-vehicle time,@(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME']) * TNC_shared_IVTFactor,,,,,,,,,,,,,,,,,,coef_ivt +#, FIXME magic constant 1.5,,,,,,,,,,,,,,,,,,, +util_TNC_Shared_Wait_time,TNC Shared - Wait time,@1.5 * df.totalWaitSharedTNC,,,,,,,,,,,,,,,,,,coef_ivt +#util_TNC_Shared_Tolls,TNC Shared - Tolls,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_VTOLL'] + dot_skims['HOV2TOLL_VTOLL']),,,,,,,,,,,,,,,,,,coef_ivt +#util_TNC_Shared_Bridge_toll,TNC Shared - Bridge toll,@ivt_cost_multiplier * df.ivot * (odt_skims['HOV2TOLL_BTOLL'] + odr_skims['HOV2TOLL_BTOLL'] + dot_skims['HOV2TOLL_BTOLL'] + dor_skims['HOV2TOLL_BTOLL']),,,,,,,,,,,,,,,,,,coef_ivt +util_TNC_Shared_Cost,TNC Shared - Cost,"@ivt_cost_multiplier * df.ivot * np.maximum(TNC_shared_baseFare * 2 + (odt_skims['HOV2_DIST'] + dot_skims['HOV2_DIST']) * TNC_shared_costPerMile + (odt_skims['HOV2_TIME'] + dot_skims['HOV3_TIME']) * TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,,,,,,coef_ivt +#indiv_tour_ASCs,#indiv tour ASCs,,,,,,,,,,,,,,,,,,, +util_WALK_ASC_Zero_auto,Walk ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,walk_ASC_no_auto,,,,,,,,,,,,,, +util_WALK_ASC_Auto_deficient,Walk ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,walk_ASC_auto_deficient,,,,,,,,,,,,,, +util_WALK_ASC_Auto_sufficient,Walk ASC - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,walk_ASC_auto_sufficient,,,,,,,,,,,,,, +util_BIKE_ASC_Zero_auto,Bike ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,bike_ASC_no_auto,,,,,,,,,,,,, +util_BIKE_ASC_Auto_deficient,Bike ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,bike_ASC_auto_deficient,,,,,,,,,,,,, +util_BIKE_ASC_Auto_sufficient,Bike ASC - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,bike_ASC_auto_sufficient,,,,,,,,,,,,, +util_Shared_ride_2_ASC_Zero_auto,Shared ride 2 ASC - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,sr2_ASC_no_auto,,,,,,,,,,,,,,,, +util_Shared_ride_2_ASC_Auto_deficient,Shared ride 2 ASC - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,sr2_ASC_auto_deficient,,,,,,,,,,,,,,,, +util_Shared_ride_2_ASC_Auto_sufficient,Shared ride 2 ASC - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,sr2_ASC_auto_sufficient,,,,,,,,,,,,,,,, +util_Shared_ride_3+_Zero_auto,Shared ride 3+ - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,sr3p_ASC_no_auto,,,,,,,,,,,,,,, +util_Shared_ride_3+_Auto_deficient,Shared ride 3+ - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,sr3p_ASC_auto_deficient,,,,,,,,,,,,,,, +util_Shared_ride_3+_Auto_sufficient,Shared ride 3+ - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,sr3p_ASC_auto_sufficient,,,,,,,,,,,,,,, +util_WALK_to_Transit_Zero_auto,Walk to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,walk_transit_ASC_no_auto,,,,,,,,,, +util_WALK_to_Transit_Auto_deficient,Walk to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient,,,,,,,,,, +util_WALK_to_Transit_Auto_sufficient,Walk to Transit - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient,,,,,,,,,, +util_PNR_to_Transit_Zero_auto,PNR to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,pnr_transit_ASC_no_auto,pnr_transit_ASC_no_auto,pnr_transit_ASC_no_auto,,,,,,, +util_PNR_to_Transit_Auto_deficient,PNR to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,pnr_transit_ASC_auto_deficient,pnr_transit_ASC_auto_deficient,pnr_transit_ASC_auto_deficient,,,,,,, +util_PNR_to_Transit_Auto_sufficient,PNR to Transit - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,pnr_transit_ASC_auto_sufficient,pnr_transit_ASC_auto_sufficient,pnr_transit_ASC_auto_sufficient,,,,,,, +util_KNR_to_Transit_Zero_auto,KNR to Transit - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,knr_transit_ASC_no_auto,knr_transit_ASC_no_auto,knr_transit_ASC_no_auto,,,, +util_KNR_to_Transit_Auto_deficient,KNR to Transit - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,knr_transit_ASC_auto_deficient,knr_transit_ASC_auto_deficient,knr_transit_ASC_auto_deficient,,,, +util_KNR_to_Transit_Auto_sufficient,KNR to Transit - Auto Sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,knr_transit_ASC_auto_sufficient,knr_transit_ASC_auto_sufficient,knr_transit_ASC_auto_sufficient,,,, +util_Taxi_Zero_auto,Taxi - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,taxi_ASC_no_auto,, +util_Taxi_Auto_deficient,Taxi - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,taxi_ASC_auto_deficient,, +util_Taxi_Auto_sufficient,Taxi - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,taxi_ASC_auto_sufficient,, +util_TNC_Single_Zero_auto,TNC Single - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,tnc_single_ASC_no_auto, +util_TNC_Single_Auto_deficient,TNC Single - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,tnc_single_ASC_auto_deficient, +util_TNC_Single_Auto_sufficient,TNC Single - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,tnc_single_ASC_auto_sufficient, +util_TNC_Shared_Zero_auto,TNC Shared - Zero auto,@(df.is_indiv & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,tnc_shared_ASC_no_auto +util_TNC_Shared_Auto_deficient,TNC Shared - Auto deficient,@(df.is_indiv & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,tnc_shared_ASC_auto_deficient +util_TNC_Shared_Auto_sufficient,TNC Shared - Auto sufficient,@(df.is_indiv & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,tnc_shared_ASC_auto_sufficient +#util_Joint_tour_ASCs,#joint tour ASCs,,,,,,,,,,,,,,,,,,, +util_Joint_WALK_ASC_Zero_auto,Joint - Walk ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,joint_walk_ASC_no_auto,,,,,,,,,,,,,, +util_Joint_WALK_ASC_Auto_deficient,Joint - Walk ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,joint_walk_ASC_auto_deficient,,,,,,,,,,,,,, +util_Joint_WALK_ASC_Auto_sufficient,Joint - Walk ASC - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,joint_walk_ASC_auto_sufficient,,,,,,,,,,,,,, +util_Joint_BIKE_ASC_Zero_auto,Joint - Bike ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,joint_bike_ASC_no_auto,,,,,,,,,,,,, +util_Joint_BIKE_ASC_Auto_deficient,Joint - Bike ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,joint_bike_ASC_auto_deficient,,,,,,,,,,,,, +util_Joint_BIKE_ASC_Auto_sufficient,Joint - Bike ASC - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,joint_bike_ASC_auto_sufficient,,,,,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Zero_auto,Joint - Shared ride 2 ASC - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,joint_sr2_ASC_no_auto,,,,,,,,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Auto_deficient,Joint - Shared ride 2 ASC - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,joint_sr2_ASC_auto_deficient,,,,,,,,,,,,,,,, +util_Joint_Shared_ride_2_ASC_Auto_sufficient,Joint - Shared ride 2 ASC - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,joint_sr2_ASC_auto_sufficient,,,,,,,,,,,,,,,, +util_Joint_Shared_ride_3+_Zero_auto,Joint - Shared ride 3+ - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,joint_sr3p_ASC_no_auto,,,,,,,,,,,,,,, +util_Joint_Shared_ride_3+_Auto_deficient,Joint - Shared ride 3+ - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,joint_sr3p_ASC_auto_deficient,,,,,,,,,,,,,,, +util_Joint_Shared_ride_3+_Auto_sufficient,Joint - Shared ride 3+ - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,joint_sr3p_ASC_auto_sufficient,,,,,,,,,,,,,,, +util_Joint_WALK_to_Transit_Zero_auto,Joint - Walk to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto,,,,,,,,,, +util_Joint_WALK_to_Transit_Auto_deficient,Joint - Walk to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient,,,,,,,,,, +util_Joint_WALK_to_Transit_Auto_sufficient,Joint - Walk to Transit - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient,,,,,,,,,, +util_Joint_PNR_to_Transit_Zero_auto,Joint - PNR to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,joint_pnr_transit_ASC_no_auto,joint_pnr_transit_ASC_no_auto,joint_pnr_transit_ASC_no_auto,,,,,,, +util_Joint_PNR_to_Transit_Auto_deficient,Joint - PNR to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,joint_pnr_transit_ASC_auto_deficient,joint_pnr_transit_ASC_auto_deficient,joint_pnr_transit_ASC_auto_deficient,,,,,,, +util_Joint_PNR_to_Transit_Auto_sufficient,Joint - PNR to Transit - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,joint_pnr_transit_ASC_auto_sufficient,joint_pnr_transit_ASC_auto_sufficient,joint_pnr_transit_ASC_auto_sufficient,,,,,,, +util_Joint_KNR_to_Transit_Zero_auto,Joint - KNR to Transit - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,joint_knr_transit_ASC_no_auto,joint_knr_transit_ASC_no_auto,joint_knr_transit_ASC_no_auto,,,, +util_Joint_KNR_to_Transit_Auto_deficient,Joint - KNR to Transit - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,joint_knr_transit_ASC_auto_deficient,joint_knr_transit_ASC_auto_deficient,joint_knr_transit_ASC_auto_deficient,,,, +util_Joint_KNR_to_Transit_Auto_sufficient,Joint - KNR to Transit - Auto Sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,joint_knr_transit_ASC_auto_sufficient,joint_knr_transit_ASC_auto_sufficient,joint_knr_transit_ASC_auto_sufficient,,,, +util_Joint_Taxi_Zero_auto,Joint - Taxi - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,joint_taxi_ASC_no_auto,, +util_Joint_Taxi_Auto_deficient,Joint - Taxi - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,joint_taxi_ASC_auto_deficient,, +util_Joint_Taxi_Auto_sufficient,Joint - Taxi - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,joint_taxi_ASC_auto_sufficient,, +util_Joint_TNC_Single_Zero_auto,Joint - TNC Single - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,joint_tnc_single_ASC_no_auto, +util_Joint_TNC_Single_Auto_deficient,Joint - TNC Single - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,joint_tnc_single_ASC_auto_deficient, +util_Joint_TNC_Single_Auto_sufficient,Joint - TNC Single - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,joint_tnc_single_ASC_auto_sufficient, +util_Joint_TNC_Shared_Zero_auto,Joint - TNC Shared - Zero auto,@(df.is_joint & (df.auto_ownership == 0)),,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_no_auto +util_Joint_TNC_Shared_Auto_deficient,Joint - TNC Shared - Auto deficient,@(df.is_joint & (df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_auto_deficient +util_Joint_TNC_Shared_Auto_sufficient,Joint - TNC Shared - Auto sufficient,@(df.is_joint & (df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,,,,joint_tnc_shared_ASC_auto_sufficient +util_local_asc,Local ASC,1,,,,,,local_ASC,,,local_ASC,,,local_ASC,,,,,, +util_premium_asc,Premium ASC,1,,,,,,,premium_ASC,,,premium_ASC,,,premium_ASC,,,,, +util_mix_asc,Mix ASC,1,,,,,,,,mix_ASC,,,mix_ASC,,,mix_ASC,,,, +util_WALK_to_Transit_dest_CBD,Walk to Transit dest CBD,@df.destination_in_cbd,,,,,,walk_transit_CBD_ASC,walk_transit_CBD_ASC,walk_transit_CBD_ASC,,,,,,,,,, +Drive_to_Transit_dest_CBD,Drive to Transit dest CBD,@df.destination_in_cbd,,,,,,,,,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,drive_transit_CBD_ASC,,,, +Drive_to_Transit_distance_penalty,Drive to Transit - distance penalty,@drvtrn_distpen_0_multiplier * (1-od_skims['DIST']/drvtrn_distpen_max).clip(lower=0),,,,,,,,,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,coef_ivt,,,, +#_FIXME_skims_aren't_symmetrical,# FIXME - skims aren't symmetrical, so we have to make sure they can get back,,,,,,,,,,,,,,,,,, +util_WALK_not_available_for_long_distances,Walk not available for long distances,@od_skims.max('DISTWALK') > 3,,,,-999,,,,,,,,,,,,,, +util_BIKE_not_available_for_long_distances,Bike not available for long distances,@od_skims.max('DISTBIKE') > 8,,,,,-999,,,,,,,,,,,,, +Drive_alone_not_available_for_escort_tours,Drive alone not available for escort tours,is_escort,-999,,,,,,,,,,,,,,,,, +#School_Bus,#School Bus,,,,,,,,,,,,,,,,,,, +School_Bus_Unavailable_if_NOT_school_tour,School Bus - Unavailable if NOT school tour,~is_school,,,,,,,,,,,,,,,-999,,, +School_Bus_In_vehicle_time_(HOV3+_skims)_20_mph,School Bus - In-vehicle time (HOV3+ skims) - 20 mph,@(odt_skims['HOV3_DIST'] + dot_skims['HOV3_DIST'])*3,,,,,,,,,,,,,,,coef_ivt,,, +School_Bus_WALK_Time,School Bus - Walk Time,@wacc_multiplier*10,,,,,,,,,,,,,,,coef_ivt,,, +School_Bus_Wait_Time,School Bus - Wait Time,@short_i_wait_multiplier*10,,,,,,,,,,,,,,,coef_ivt,,, +util_schoolbus_ASC_no_auto,School Bus No Auto ASC,@(df.auto_ownership == 0),,,,,,,,,,,,,,,schoolbus_ASC_no_auto,,, +util_schoolbus_ASC_auto_deficient,School Bus Auto Deficient ASC,@((df.auto_ownership < df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,schoolbus_ASC_auto_deficient,,, +util_schoolbus_ASC_auto_sufficient,School Bus Auto Sufficient ASC,@((df.auto_ownership >= df.num_workers) & (df.auto_ownership > 0)),,,,,,,,,,,,,,,schoolbus_ASC_auto_sufficient,,, diff --git a/activitysim/examples/example_semcog/configs/tour_mode_choice.yaml b/activitysim/examples/prototype_semcog/configs/tour_mode_choice.yaml similarity index 95% rename from activitysim/examples/example_semcog/configs/tour_mode_choice.yaml rename to activitysim/examples/prototype_semcog/configs/tour_mode_choice.yaml index 179aaf5066..59f4067bf2 100755 --- a/activitysim/examples/example_semcog/configs/tour_mode_choice.yaml +++ b/activitysim/examples/prototype_semcog/configs/tour_mode_choice.yaml @@ -1,204 +1,204 @@ -LOGIT_TYPE: NL -#LOGIT_TYPE: MNL - -NESTS: - name: root - coefficient: coef_nest_root - alternatives: - - name: AUTO - coefficient: coef_nest_AUTO - alternatives: - - DRIVEALONE - - SHARED2 - - SHARED3 - - name: NONMOTORIZED - coefficient: coef_nest_NONMOTORIZED - alternatives: - - WALK - - BIKE - - name: TRANSIT - coefficient: coef_nest_TRANSIT - alternatives: - - name: WALKACCESS - coefficient: coef_nest_TRANSIT_WALKACCESS - alternatives: - - WALK_LOC - - WALK_PRM - - WALK_MIX - - name: PNRACCESS - coefficient: coef_nest_TRANSIT_PNRACCESS - alternatives: - - PNR_LOC - - PNR_PRM - - PNR_MIX - - name: KNRACCESS - coefficient: coef_nest_TRANSIT_KNRACCESS - alternatives: - - KNR_LOC - - KNR_PRM - - KNR_MIX - - name: SCHOOL_BUS - coefficient: coef_nest_SCHOOL_BUS - alternatives: - - SCHOOLBUS - - name: RIDEHAIL - coefficient: coef_nest_RIDEHAIL - alternatives: - - TAXI - - TNC_SINGLE - - TNC_SHARED - -SPEC: tour_mode_choice.csv -COEFFICIENTS: tour_mode_choice_coeffs.csv -COEFFICIENT_TEMPLATE: tour_mode_choice_coeffs_template.csv - -CONSTANTS: - #valueOfTime: 8.00 - costPerMile: 18.29 - costShareSr2: 1.75 - costShareSr3: 2.50 - waitThresh: 10.00 - walkThresh: 1.50 - shortWalk: 0.333 - longWalk: 0.667 - walkSpeed: 3.00 - bikeThresh: 6.00 - bikeSpeed: 12.00 - maxCbdAreaTypeThresh: 2 - indivTour: 1.00000 - upperEA: 5 - upperAM: 10 - upperMD: 15 - upperPM: 19 - # RIDEHAIL Settings - Taxi_baseFare: 2.20 - Taxi_costPerMile: 2.30 - Taxi_costPerMinute: 0.10 - Taxi_waitTime_mean: - 1: 5.5 - 2: 9.5 - 3: 13.3 - 4: 17.3 - 5: 26.5 - Taxi_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - TNC_single_baseFare: 2.20 - TNC_single_costPerMile: 1.33 - TNC_single_costPerMinute: 0.24 - TNC_single_costMinimum: 7.20 - TNC_single_waitTime_mean: - 1: 3.0 - 2: 6.3 - 3: 8.4 - 4: 8.5 - 5: 10.3 - TNC_single_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - TNC_shared_baseFare: 2.20 - TNC_shared_costPerMile: 0.53 - TNC_shared_costPerMinute: 0.10 - TNC_shared_costMinimum: 3.00 - TNC_shared_IVTFactor: 1.5 - TNC_shared_waitTime_mean: - 1: 5.0 - 2: 8.0 - 3: 11.0 - 4: 15.0 - 5: 15.0 - TNC_shared_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - min_waitTime: 0 - max_waitTime: 50 - - ivt_cost_multiplier: 0.6 - ivt_pmov_multiplier: 1.0 - ivt_stcar_multiplier: 0.9 - ivt_brt_multiplier: 0.9 - ivt_urb_multiplier: 0.8 - ivt_com_multiplier: 0.7 - walktimeshort_multiplier: 2 - walktimelong_multiplier: 10 - biketimeshort_multiplier: 4 - biketimelong_multiplier: 20 - short_i_wait_multiplier: 2 - long_i_wait_multiplier: 1 - wacc_multiplier: 2 - wegr_multiplier: 2 - waux_multiplier: 2 - dtim_multiplier: 2 - xwait_multiplier: 2 - dacc_ratio: 0 - xfers_wlk_multiplier: 10 - xfers_drv_multiplier: 20 - drvtrn_distpen_0_multiplier: 270 - drvtrn_distpen_max: 15 - density_index_multiplier: -0.2 - - transit_nopass_fare_discount_percent: - 1: 1.0 - 2: 1.0 - 3: 0.62 - 4: 1.0 - 5: 0.54 - 6: 0.62 - 7: 0.62 - 8: 1.0 - transit_pass_fare_discount_percent: - 1: 0.32 - 2: 0.49 - 3: 0.03 - 4: 0.54 - 5: 0.17 - 6: 0.37 - 7: 0.36 - 8: 1.0 - transit_subsidy_fare_discount_percent_worktour: 0.9 - -# joint_sr2_ASC_no_auto: 0 -# joint_sr2_ASC_auto_deficient: 0 -# joint_sr2_ASC_auto_sufficient: 0 -# joint_drive_transit_ASC_no_auto: 0 - -# so far, we can use the same spec as for non-joint tours -preprocessor: - SPEC: tour_mode_choice_annotate_choosers_preprocessor - DF: choosers - TABLES: - - land_use - - tours - -nontour_preprocessor: - SPEC: tour_mode_choice_annotate_choosers_preprocessor - DF: choosers - TABLES: - - land_use - -# to reduce memory needs filter chooser table to these fields -LOGSUM_CHOOSER_COLUMNS: - - tour_type - - hhsize - - density_index - - age - - age_16_p - - age_16_to_19 - - auto_ownership - - number_of_participants - - tour_category - - num_workers - - value_of_time - - free_parking_at_work - - transit_pass_ownership - - transit_pass_subsidy +LOGIT_TYPE: NL +#LOGIT_TYPE: MNL + +NESTS: + name: root + coefficient: coef_nest_root + alternatives: + - name: AUTO + coefficient: coef_nest_AUTO + alternatives: + - DRIVEALONE + - SHARED2 + - SHARED3 + - name: NONMOTORIZED + coefficient: coef_nest_NONMOTORIZED + alternatives: + - WALK + - BIKE + - name: TRANSIT + coefficient: coef_nest_TRANSIT + alternatives: + - name: WALKACCESS + coefficient: coef_nest_TRANSIT_WALKACCESS + alternatives: + - WALK_LOC + - WALK_PRM + - WALK_MIX + - name: PNRACCESS + coefficient: coef_nest_TRANSIT_PNRACCESS + alternatives: + - PNR_LOC + - PNR_PRM + - PNR_MIX + - name: KNRACCESS + coefficient: coef_nest_TRANSIT_KNRACCESS + alternatives: + - KNR_LOC + - KNR_PRM + - KNR_MIX + - name: SCHOOL_BUS + coefficient: coef_nest_SCHOOL_BUS + alternatives: + - SCHOOLBUS + - name: RIDEHAIL + coefficient: coef_nest_RIDEHAIL + alternatives: + - TAXI + - TNC_SINGLE + - TNC_SHARED + +SPEC: tour_mode_choice.csv +COEFFICIENTS: tour_mode_choice_coeffs.csv +COEFFICIENT_TEMPLATE: tour_mode_choice_coeffs_template.csv + +CONSTANTS: + #valueOfTime: 8.00 + costPerMile: 18.29 + costShareSr2: 1.75 + costShareSr3: 2.50 + waitThresh: 10.00 + walkThresh: 1.50 + shortWalk: 0.333 + longWalk: 0.667 + walkSpeed: 3.00 + bikeThresh: 6.00 + bikeSpeed: 12.00 + maxCbdAreaTypeThresh: 2 + indivTour: 1.00000 + upperEA: 5 + upperAM: 10 + upperMD: 15 + upperPM: 19 + # RIDEHAIL Settings + Taxi_baseFare: 2.20 + Taxi_costPerMile: 2.30 + Taxi_costPerMinute: 0.10 + Taxi_waitTime_mean: + 1: 5.5 + 2: 9.5 + 3: 13.3 + 4: 17.3 + 5: 26.5 + Taxi_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_single_baseFare: 2.20 + TNC_single_costPerMile: 1.33 + TNC_single_costPerMinute: 0.24 + TNC_single_costMinimum: 7.20 + TNC_single_waitTime_mean: + 1: 3.0 + 2: 6.3 + 3: 8.4 + 4: 8.5 + 5: 10.3 + TNC_single_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_shared_baseFare: 2.20 + TNC_shared_costPerMile: 0.53 + TNC_shared_costPerMinute: 0.10 + TNC_shared_costMinimum: 3.00 + TNC_shared_IVTFactor: 1.5 + TNC_shared_waitTime_mean: + 1: 5.0 + 2: 8.0 + 3: 11.0 + 4: 15.0 + 5: 15.0 + TNC_shared_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + min_waitTime: 0 + max_waitTime: 50 + + ivt_cost_multiplier: 0.6 + ivt_pmov_multiplier: 1.0 + ivt_stcar_multiplier: 0.9 + ivt_brt_multiplier: 0.9 + ivt_urb_multiplier: 0.8 + ivt_com_multiplier: 0.7 + walktimeshort_multiplier: 2 + walktimelong_multiplier: 10 + biketimeshort_multiplier: 4 + biketimelong_multiplier: 20 + short_i_wait_multiplier: 2 + long_i_wait_multiplier: 1 + wacc_multiplier: 2 + wegr_multiplier: 2 + waux_multiplier: 2 + dtim_multiplier: 2 + xwait_multiplier: 2 + dacc_ratio: 0 + xfers_wlk_multiplier: 10 + xfers_drv_multiplier: 20 + drvtrn_distpen_0_multiplier: 270 + drvtrn_distpen_max: 15 + density_index_multiplier: -0.2 + + transit_nopass_fare_discount_percent: + 1: 1.0 + 2: 1.0 + 3: 0.62 + 4: 1.0 + 5: 0.54 + 6: 0.62 + 7: 0.62 + 8: 1.0 + transit_pass_fare_discount_percent: + 1: 0.32 + 2: 0.49 + 3: 0.03 + 4: 0.54 + 5: 0.17 + 6: 0.37 + 7: 0.36 + 8: 1.0 + transit_subsidy_fare_discount_percent_worktour: 0.9 + +# joint_sr2_ASC_no_auto: 0 +# joint_sr2_ASC_auto_deficient: 0 +# joint_sr2_ASC_auto_sufficient: 0 +# joint_drive_transit_ASC_no_auto: 0 + +# so far, we can use the same spec as for non-joint tours +preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + - tours + +nontour_preprocessor: + SPEC: tour_mode_choice_annotate_choosers_preprocessor + DF: choosers + TABLES: + - land_use + +# to reduce memory needs filter chooser table to these fields +LOGSUM_CHOOSER_COLUMNS: + - tour_type + - hhsize + - density_index + - age + - age_16_p + - age_16_to_19 + - auto_ownership + - number_of_participants + - tour_category + - num_workers + - value_of_time + - free_parking_at_work + - transit_pass_ownership + - transit_pass_subsidy MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum \ No newline at end of file diff --git a/activitysim/examples/example_semcog/configs/tour_mode_choice_annotate_choosers_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/tour_mode_choice_annotate_choosers_preprocessor.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/tour_mode_choice_annotate_choosers_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/tour_mode_choice_annotate_choosers_preprocessor.csv index d2d31869fb..d437d0b105 100755 --- a/activitysim/examples/example_semcog/configs/tour_mode_choice_annotate_choosers_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_mode_choice_annotate_choosers_preprocessor.csv @@ -1,98 +1,98 @@ -Description,Target,Expression -#,, -local,_DF_IS_TOUR,'tour_type' in df.columns -,number_of_participants,df.number_of_participants if _DF_IS_TOUR else 1 -,is_joint,(df.tour_category=='joint') if _DF_IS_TOUR else False -#,, - local,_HAVE_PARENT_TOURS,'parent_tour_id' in df.columns -,_parent_tour_mode,"reindex(tours.tour_mode, df.parent_tour_id) if _HAVE_PARENT_TOURS else ''" -,work_tour_is_drive,_parent_tour_mode.isin(['DRIVEALONE']) -,work_tour_is_bike,_parent_tour_mode=='BIKE' -,work_tour_is_SOV,_parent_tour_mode.isin(['DRIVEALONE']) -#,, -,is_mandatory,(df.tour_category=='mandatory') if 'tour_category' in df.columns else False -,is_joint,(df.tour_category=='joint') if 'tour_category' in df.columns else False -,is_indiv,~is_joint -,is_atwork_subtour,(df.tour_category=='atwork') if 'tour_category' in df.columns else False -,is_escort,(df.tour_type == 'escort') if _DF_IS_TOUR else False -,is_school,(df.tour_type=='school') & (df.is_university==False) if _DF_IS_TOUR else False -#,, -#,c_cost,(0.60 * c_ivt) / df.value_of_time -#,, -,ivot,1.0/df.value_of_time -,dest_topology,"reindex(land_use.TOPOLOGY, df[dest_col_name])" -,terminal_time,"reindex(land_use.TERMINAL, df[dest_col_name])" -,dest_density_index,"reindex(land_use.density_index, df[dest_col_name])" -# FIXME no transit subzones so all zones short walk to transit,, -,_walk_transit_origin,True -,_walk_transit_destination,True -,walk_transit_available,_walk_transit_origin & _walk_transit_destination -,pnr_transit_available,_walk_transit_destination & (df.auto_ownership > 0) -,knr_transit_available,_walk_transit_origin & _walk_transit_destination -,origin_walk_time,shortWalk*60/walkSpeed -,destination_walk_time,shortWalk*60/walkSpeed -# RIDEHAIL,, -,origin_density_measure,"(reindex(land_use.tot_pop, df[orig_col_name]) + reindex(land_use.tot_emp, df[orig_col_name])) / (reindex(land_use.tot_acres, df[orig_col_name]) / 640)" -,dest_density_measure,"(reindex(land_use.tot_pop, df[dest_col_name]) + reindex(land_use.tot_emp, df[dest_col_name])) / (reindex(land_use.tot_acres, df[dest_col_name]) / 640)" -,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" -,dest_density,"pd.cut(dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" -,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" -,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" -,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" -,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" -# ,, Note that the mean and standard deviation are not the values for the distribution itself but of the underlying normal distribution it is derived from -,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" -,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" -,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" -,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" -,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" -,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" -,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" -,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" -,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" -,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime -,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime -,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime -#,, -,_free_parking_available,(df.tour_type == 'work') & df.free_parking_at_work if _DF_IS_TOUR else False -,_dest_hourly_peak_parking_cost,"reindex(land_use.PRKCST, df[dest_col_name])" -,_dest_hourly_offpeak_parking_cost,"reindex(land_use.OPRKCST, df[dest_col_name])" -,_hourly_peak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_peak_parking_cost)" -,_hourly_offpeak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_offpeak_parking_cost)" -,daily_parking_cost,"np.where(is_mandatory, _hourly_peak_parking_cost * df.duration/2, _hourly_offpeak_parking_cost * df.duration/2)" -#,, -,distance,od_skims['DIST'] -,sov_available,(odt_skims['SOV_TIME']>0) & (dot_skims['SOV_TIME']>0) -,hov2_available,(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME'])>0 -,hov3_available,(odt_skims['HOV3_TIME']>0) & (dot_skims['HOV3_TIME']>0) -,walk_local_available,walk_transit_available & (odt_skims['WLK_LOC_IVT']>0) & (dot_skims['WLK_LOC_IVT']>0) -,walk_premium_available,walk_transit_available & (odt_skims['WLK_PRM_IVT']>0) & (dot_skims['WLK_PRM_IVT']>0) -,walk_mix_available,walk_transit_available & (odt_skims['WLK_MIX_IVT']>0) & (dot_skims['WLK_MIX_IVT']>0) -,pnr_local_available,pnr_transit_available & (odt_skims['PNR_LOC_IVT']>0) & (dot_skims['PNRE_LOC_IVT']>0) -,pnr_premium_available,pnr_transit_available & (odt_skims['PNR_PRM_IVT']>0) & (dot_skims['PNRE_PRM_IVT']>0) -,pnr_mix_available,pnr_transit_available & (odt_skims['PNR_MIX_IVT']>0) & (dot_skims['PNRE_MIX_IVT']>0) -,knr_local_available,knr_transit_available & (odt_skims['KNR_LOC_IVT']>0) & (dot_skims['KNRE_LOC_IVT']>0) -,knr_premium_available,knr_transit_available & (odt_skims['KNR_PRM_IVT']>0) & (dot_skims['KNRE_PRM_IVT']>0) -,knr_mix_available,knr_transit_available & (odt_skims['KNR_MIX_IVT']>0) & (dot_skims['KNRE_MIX_IVT']>0) -#,, -destination in central business district,destination_in_cbd,"(reindex(land_use.AreaType, df[dest_col_name]) < setting('cbd_threshold')) * 1" -#,,FIXME diagnostic -#,sov_dist_rt,(odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']) -#,,fare discounts (no discount for use in logsums) -,ptype,df.ptype if _DF_IS_TOUR else 1 -,transit_pass_ownership,df.transit_pass_ownership if _DF_IS_TOUR else 0 -,transit_pass_subsidy,df.transit_pass_subsidy if _DF_IS_TOUR else 0 -,tour_type,df.tour_type if _DF_IS_TOUR else 'other' -,fare_nopass_discount_percent,"ptype.map({k: v for k, v in transit_nopass_fare_discount_percent.items()})" -,fare_pass_discount_percent,"ptype.map({k: v for k, v in transit_pass_fare_discount_percent.items()})" -,fare_subsidy_discount_percent,"np.where(tour_type=='work', transit_subsidy_fare_discount_percent_worktour, 1)" -,fare_discount_percent_ff,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), fare_nopass_discount_percent, 1)" -,fare_discount_percent_tf,"np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), fare_pass_discount_percent, 1)" -,fare_discount_percent_ft,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), fare_nopass_discount_percent * (1-fare_subsidy_discount_percent), 1)" -,fare_discount_percent_tt,"np.where((transit_pass_ownership == True ) & (transit_pass_subsidy == True), fare_pass_discount_percent * (1-fare_subsidy_discount_percent), 1)" +Description,Target,Expression +#,, +local,_DF_IS_TOUR,'tour_type' in df.columns +,number_of_participants,df.number_of_participants if _DF_IS_TOUR else 1 +,is_joint,(df.tour_category=='joint') if _DF_IS_TOUR else False +#,, + local,_HAVE_PARENT_TOURS,'parent_tour_id' in df.columns +,_parent_tour_mode,"reindex(tours.tour_mode, df.parent_tour_id) if _HAVE_PARENT_TOURS else ''" +,work_tour_is_drive,_parent_tour_mode.isin(['DRIVEALONE']) +,work_tour_is_bike,_parent_tour_mode=='BIKE' +,work_tour_is_SOV,_parent_tour_mode.isin(['DRIVEALONE']) +#,, +,is_mandatory,(df.tour_category=='mandatory') if 'tour_category' in df.columns else False +,is_joint,(df.tour_category=='joint') if 'tour_category' in df.columns else False +,is_indiv,~is_joint +,is_atwork_subtour,(df.tour_category=='atwork') if 'tour_category' in df.columns else False +,is_escort,(df.tour_type == 'escort') if _DF_IS_TOUR else False +,is_school,(df.tour_type=='school') & (df.is_university==False) if _DF_IS_TOUR else False +#,, +#,c_cost,(0.60 * c_ivt) / df.value_of_time +#,, +,ivot,1.0/df.value_of_time +,dest_topology,"reindex(land_use.TOPOLOGY, df[dest_col_name])" +,terminal_time,"reindex(land_use.TERMINAL, df[dest_col_name])" +,dest_density_index,"reindex(land_use.density_index, df[dest_col_name])" +# FIXME no transit subzones so all zones short walk to transit,, +,_walk_transit_origin,True +,_walk_transit_destination,True +,walk_transit_available,_walk_transit_origin & _walk_transit_destination +,pnr_transit_available,_walk_transit_destination & (df.auto_ownership > 0) +,knr_transit_available,_walk_transit_origin & _walk_transit_destination +,origin_walk_time,shortWalk*60/walkSpeed +,destination_walk_time,shortWalk*60/walkSpeed +# RIDEHAIL,, +,origin_density_measure,"(reindex(land_use.tot_pop, df[orig_col_name]) + reindex(land_use.tot_emp, df[orig_col_name])) / (reindex(land_use.tot_acres, df[orig_col_name]) / 640)" +,dest_density_measure,"(reindex(land_use.tot_pop, df[dest_col_name]) + reindex(land_use.tot_emp, df[dest_col_name])) / (reindex(land_use.tot_acres, df[dest_col_name]) / 640)" +,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" +,dest_density,"pd.cut(dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)" +,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" +,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" +,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})" +,dest_zone_taxi_wait_time_sd,"dest_density.map({k: v for k, v in Taxi_waitTime_sd.items()})" +# ,, Note that the mean and standard deviation are not the values for the distribution itself but of the underlying normal distribution it is derived from +,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destTaxiWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_taxi_wait_time_mean, sigma=dest_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" +,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" +,dest_zone_singleTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})" +,dest_zone_singleTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})" +,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_singleTNC_wait_time_mean, sigma=dest_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" +,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" +,dest_zone_sharedTNC_wait_time_mean,"dest_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})" +,dest_zone_sharedTNC_wait_time_sd,"dest_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})" +,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,destSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=dest_zone_sharedTNC_wait_time_mean, sigma=dest_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)" +,totalWaitTaxi,origTaxiWaitTime + destTaxiWaitTime +,totalWaitSingleTNC,origSingleTNCWaitTime + destSingleTNCWaitTime +,totalWaitSharedTNC,origSharedTNCWaitTime + destSharedTNCWaitTime +#,, +,_free_parking_available,(df.tour_type == 'work') & df.free_parking_at_work if _DF_IS_TOUR else False +,_dest_hourly_peak_parking_cost,"reindex(land_use.PRKCST, df[dest_col_name])" +,_dest_hourly_offpeak_parking_cost,"reindex(land_use.OPRKCST, df[dest_col_name])" +,_hourly_peak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_peak_parking_cost)" +,_hourly_offpeak_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_offpeak_parking_cost)" +,daily_parking_cost,"np.where(is_mandatory, _hourly_peak_parking_cost * df.duration/2, _hourly_offpeak_parking_cost * df.duration/2)" +#,, +,distance,od_skims['DIST'] +,sov_available,(odt_skims['SOV_TIME']>0) & (dot_skims['SOV_TIME']>0) +,hov2_available,(odt_skims['HOV2_TIME'] + dot_skims['HOV2_TIME'])>0 +,hov3_available,(odt_skims['HOV3_TIME']>0) & (dot_skims['HOV3_TIME']>0) +,walk_local_available,walk_transit_available & (odt_skims['WLK_LOC_IVT']>0) & (dot_skims['WLK_LOC_IVT']>0) +,walk_premium_available,walk_transit_available & (odt_skims['WLK_PRM_IVT']>0) & (dot_skims['WLK_PRM_IVT']>0) +,walk_mix_available,walk_transit_available & (odt_skims['WLK_MIX_IVT']>0) & (dot_skims['WLK_MIX_IVT']>0) +,pnr_local_available,pnr_transit_available & (odt_skims['PNR_LOC_IVT']>0) & (dot_skims['PNRE_LOC_IVT']>0) +,pnr_premium_available,pnr_transit_available & (odt_skims['PNR_PRM_IVT']>0) & (dot_skims['PNRE_PRM_IVT']>0) +,pnr_mix_available,pnr_transit_available & (odt_skims['PNR_MIX_IVT']>0) & (dot_skims['PNRE_MIX_IVT']>0) +,knr_local_available,knr_transit_available & (odt_skims['KNR_LOC_IVT']>0) & (dot_skims['KNRE_LOC_IVT']>0) +,knr_premium_available,knr_transit_available & (odt_skims['KNR_PRM_IVT']>0) & (dot_skims['KNRE_PRM_IVT']>0) +,knr_mix_available,knr_transit_available & (odt_skims['KNR_MIX_IVT']>0) & (dot_skims['KNRE_MIX_IVT']>0) +#,, +destination in central business district,destination_in_cbd,"(reindex(land_use.AreaType, df[dest_col_name]) < setting('cbd_threshold')) * 1" +#,,FIXME diagnostic +#,sov_dist_rt,(odt_skims['SOV_DIST'] + dot_skims['SOV_DIST']) +#,,fare discounts (no discount for use in logsums) +,ptype,df.ptype if _DF_IS_TOUR else 1 +,transit_pass_ownership,df.transit_pass_ownership if _DF_IS_TOUR else 0 +,transit_pass_subsidy,df.transit_pass_subsidy if _DF_IS_TOUR else 0 +,tour_type,df.tour_type if _DF_IS_TOUR else 'other' +,fare_nopass_discount_percent,"ptype.map({k: v for k, v in transit_nopass_fare_discount_percent.items()})" +,fare_pass_discount_percent,"ptype.map({k: v for k, v in transit_pass_fare_discount_percent.items()})" +,fare_subsidy_discount_percent,"np.where(tour_type=='work', transit_subsidy_fare_discount_percent_worktour, 1)" +,fare_discount_percent_ff,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == False), fare_nopass_discount_percent, 1)" +,fare_discount_percent_tf,"np.where((transit_pass_ownership == True) & (transit_pass_subsidy == False), fare_pass_discount_percent, 1)" +,fare_discount_percent_ft,"np.where((transit_pass_ownership == False) & (transit_pass_subsidy == True), fare_nopass_discount_percent * (1-fare_subsidy_discount_percent), 1)" +,fare_discount_percent_tt,"np.where((transit_pass_ownership == True ) & (transit_pass_subsidy == True), fare_pass_discount_percent * (1-fare_subsidy_discount_percent), 1)" ,fare_discount_percent,fare_discount_percent_ff * fare_discount_percent_tf * fare_discount_percent_ft * fare_discount_percent_tt \ No newline at end of file diff --git a/activitysim/examples/example_semcog/configs/tour_mode_choice_coeffs.csv b/activitysim/examples/prototype_semcog/configs/tour_mode_choice_coeffs.csv similarity index 97% rename from activitysim/examples/example_semcog/configs/tour_mode_choice_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/tour_mode_choice_coeffs.csv index 9f7f8f44e3..d21e4c81cc 100755 --- a/activitysim/examples/example_semcog/configs/tour_mode_choice_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_mode_choice_coeffs.csv @@ -1,349 +1,349 @@ -coefficient_name,value,constrain -coef_nest_root,1,T -coef_nest_AUTO,0.72,T -coef_nest_AUTO_DRIVEALONE,0.35,T -coef_nest_AUTO_SHAREDRIDE2,0.35,T -coef_nest_AUTO_SHAREDRIDE3,0.35,T -coef_nest_NONMOTORIZED,0.72,T -coef_nest_TRANSIT,0.72,T -coef_nest_TRANSIT_WALKACCESS,0.5,T -coef_nest_TRANSIT_KNRACCESS,0.5,T -coef_nest_TRANSIT_PNRACCESS,0.5,T -coef_nest_SCHOOL_BUS,0.72,T -coef_nest_RIDEHAIL,0.36,T -coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,-0.0175,F -coef_ivt_school_univ,-0.0224,F -coef_ivt_work,-0.0134,F -coef_ivt_atwork,-0.0188,F -coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,15,F -coef_topology_walk_multiplier_atwork,7.5,F -coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,20,F -coef_topology_bike_multiplier_atwork,10,F -coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,2.2,F -coef_topology_trn_multiplier_atwork,2,F -coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F -coef_age1619_da_multiplier_school_univ,-1.3813,F -coef_age1619_da_multiplier_atwork,0.0032336,F -coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,0,F -coef_age010_trn_multiplier_school_univ,-1.5548,F -coef_age010_trn_multiplier_atwork,0.000722,F -coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,-1.366,F -coef_age16p_sr_multiplier_school_univ_work_atwork,0,F -coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,0,F -coef_hhsize1_sr_multiplier_work,-0.734588,F -coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,0,F -coef_hhsize2_sr_multiplier_school_univ,-0.6359,F -walk_ASC_no_auto_eatout,6.589427019,F -walk_ASC_no_auto_escort,4.880440839,F -walk_ASC_no_auto_othdiscr,4.730904319,F -walk_ASC_no_auto_othmaint,3.366533039,F -walk_ASC_no_auto_school,12.00708155,F -walk_ASC_no_auto_shopping,4.456111339,F -walk_ASC_no_auto_social,3.332401219,F -walk_ASC_no_auto_univ,3.063248695,F -walk_ASC_no_auto_work,4.43703301,F -walk_ASC_no_auto_atwork,0.421704944,F -walk_ASC_auto_deficient_eatout,2.887675213,F -walk_ASC_auto_deficient_escort,-1.356970662,F -walk_ASC_auto_deficient_othdiscr,1.862477713,F -walk_ASC_auto_deficient_othmaint,0.914116298,F -walk_ASC_auto_deficient_school,1.097961688,F -walk_ASC_auto_deficient_shopping,1.815249198,F -walk_ASC_auto_deficient_social,2.483254213,F -walk_ASC_auto_deficient_univ,2.850042543,F -walk_ASC_auto_deficient_work,1.637904594,F -walk_ASC_auto_deficient_atwork,-17.85938696,F -walk_ASC_auto_sufficient_eatout,1.896049479,F -walk_ASC_auto_sufficient_escort,-1.53483226,F -walk_ASC_auto_sufficient_othdiscr,1.607706779,F -walk_ASC_auto_sufficient_othmaint,0.07673774,F -walk_ASC_auto_sufficient_school,-1.384129543,F -walk_ASC_auto_sufficient_shopping,0.00804064,F -walk_ASC_auto_sufficient_social,2.051577779,F -walk_ASC_auto_sufficient_univ,2.131895451,F -walk_ASC_auto_sufficient_work,-0.633007823,F -walk_ASC_auto_sufficient_atwork,0.1438828,F -bike_ASC_no_auto_eatout,1.477690443,F -bike_ASC_no_auto_escort,-2.060060749,F -bike_ASC_no_auto_othdiscr,0.233196283,F -bike_ASC_no_auto_othmaint,0.195584651,F -bike_ASC_no_auto_school,0,F -bike_ASC_no_auto_shopping,-0.509693249,F -bike_ASC_no_auto_social,0.630202693,F -bike_ASC_no_auto_univ,1.297297637,F -bike_ASC_no_auto_work,2.338049977,F -bike_ASC_no_auto_atwork,18.45901812,F -bike_ASC_auto_deficient_eatout,-0.743790882,F -bike_ASC_auto_deficient_escort,-3.511021918,F -bike_ASC_auto_deficient_othdiscr,0.732851378,F -bike_ASC_auto_deficient_othmaint,-0.501558818,F -bike_ASC_auto_deficient_school,-0.465024971,F -bike_ASC_auto_deficient_shopping,0.141061422,F -bike_ASC_auto_deficient_social,1.459841118,F -bike_ASC_auto_deficient_univ,0.950303703,F -bike_ASC_auto_deficient_work,0.224440057,F -bike_ASC_auto_deficient_atwork,-3.8074083,F -bike_ASC_auto_sufficient_eatout,-0.885432566,F -bike_ASC_auto_sufficient_escort,-4.318656917,F -bike_ASC_auto_sufficient_othdiscr,-0.756545166,F -bike_ASC_auto_sufficient_othmaint,-2.063850917,F -bike_ASC_auto_sufficient_school,-2.878621083,F -bike_ASC_auto_sufficient_shopping,-1.821758817,F -bike_ASC_auto_sufficient_social,-1.053156466,F -bike_ASC_auto_sufficient_univ,-0.119499054,F -bike_ASC_auto_sufficient_work,-1.806805644,F -bike_ASC_auto_sufficient_atwork,-0.106470168,F -sr2_ASC_auto_deficient_eatout,0.413083337,F -sr2_ASC_auto_deficient_escort,0.664157519,F -sr2_ASC_auto_deficient_othdiscr,0.485000137,F -sr2_ASC_auto_deficient_othmaint,0.926310219,F -sr2_ASC_auto_deficient_school,0.100573545,F -sr2_ASC_auto_deficient_shopping,0.908255079,F -sr2_ASC_auto_deficient_social,1.680701637,F -sr2_ASC_auto_deficient_univ,0.078542755,F -sr2_ASC_auto_deficient_work,-0.304765262,F -sr2_ASC_auto_deficient_atwork,-20.27957549,F -sr2_ASC_auto_sufficient_eatout,0.899117605,F -sr2_ASC_auto_sufficient_escort,0.075825052,F -sr2_ASC_auto_sufficient_othdiscr,0.533158225,F -sr2_ASC_auto_sufficient_othmaint,0.334003882,F -sr2_ASC_auto_sufficient_school,-1.897992866,F -sr2_ASC_auto_sufficient_shopping,0.273532122,F -sr2_ASC_auto_sufficient_social,0.559914555,F -sr2_ASC_auto_sufficient_univ,-0.53703344,F -sr2_ASC_auto_sufficient_work,-0.766476766,F -sr2_ASC_auto_sufficient_atwork,-1.621794475,F -sr3p_ASC_no_auto_eatout,2.190553045,F -sr3p_ASC_no_auto_escort,0.209802522,F -sr3p_ASC_no_auto_othdiscr,2.140722265,F -sr3p_ASC_no_auto_othmaint,1.219543822,F -sr3p_ASC_no_auto_school,9.926556957,F -sr3p_ASC_no_auto_shopping,1.742939742,F -sr3p_ASC_no_auto_social,0.464863045,F -sr3p_ASC_no_auto_univ,-0.087322435,F -sr3p_ASC_no_auto_work,-1.064082,F -sr3p_ASC_no_auto_atwork,-0.940776326,F -sr3p_ASC_auto_deficient_eatout,0.140990485,F -sr3p_ASC_auto_deficient_escort,0.409243052,F -sr3p_ASC_auto_deficient_othdiscr,1.142034725,F -sr3p_ASC_auto_deficient_othmaint,-0.531961788,F -sr3p_ASC_auto_deficient_school,0.587171252,F -sr3p_ASC_auto_deficient_shopping,0.744060546,F -sr3p_ASC_auto_deficient_social,1.595662425,F -sr3p_ASC_auto_deficient_univ,0.335943236,F -sr3p_ASC_auto_deficient_work,-0.800161676,F -sr3p_ASC_auto_deficient_atwork,-20.24587122,F -sr3p_ASC_auto_sufficient_eatout,0.665638218,F -sr3p_ASC_auto_sufficient_escort,0.017050265,F -sr3p_ASC_auto_sufficient_othdiscr,0.407280668,F -sr3p_ASC_auto_sufficient_othmaint,-0.001035875,F -sr3p_ASC_auto_sufficient_school,-1.294501505,F -sr3p_ASC_auto_sufficient_shopping,-0.0031085,F -sr3p_ASC_auto_sufficient_social,0.324957478,F -sr3p_ASC_auto_sufficient_univ,-0.702604136,F -sr3p_ASC_auto_sufficient_work,-1.404899513,F -sr3p_ASC_auto_sufficient_atwork,-2.062496761,F -walk_transit_ASC_no_auto_eatout,1.388223363,F -walk_transit_ASC_no_auto_escort,-3.242571633,F -walk_transit_ASC_no_auto_othdiscr,1.038365063,F -walk_transit_ASC_no_auto_othmaint,1.538982067,F -walk_transit_ASC_no_auto_school,9.278859746,F -walk_transit_ASC_no_auto_shopping,1.081384067,F -walk_transit_ASC_no_auto_social,0.176051663,F -walk_transit_ASC_no_auto_univ,3.321119594,F -walk_transit_ASC_no_auto_work,3.061409806,F -walk_transit_ASC_no_auto_atwork,-2.75530458,F -walk_transit_ASC_auto_deficient_eatout,-0.710721656,F -walk_transit_ASC_auto_deficient_escort,-3.220750388,F -walk_transit_ASC_auto_deficient_othdiscr,0.281329984,F -walk_transit_ASC_auto_deficient_othmaint,-1.319772188,F -walk_transit_ASC_auto_deficient_school,1.023632747,F -walk_transit_ASC_auto_deficient_shopping,0.892296712,F -walk_transit_ASC_auto_deficient_social,0.302686454,F -walk_transit_ASC_auto_deficient_univ,2.231442966,F -walk_transit_ASC_auto_deficient_work,0.186141441,F -walk_transit_ASC_auto_deficient_atwork,-22.64172131,F -walk_transit_ASC_auto_sufficient_eatout,-3.537966973,F -walk_transit_ASC_auto_sufficient_escort,-6.313884394,F -walk_transit_ASC_auto_sufficient_othdiscr,-3.231644303,F -walk_transit_ASC_auto_sufficient_othmaint,-2.926154591,F -walk_transit_ASC_auto_sufficient_school,-3.421589964,F -walk_transit_ASC_auto_sufficient_shopping,-3.582717191,F -walk_transit_ASC_auto_sufficient_social,-2.770652273,F -walk_transit_ASC_auto_sufficient_univ,-0.179769816,F -walk_transit_ASC_auto_sufficient_work,-2.912845227,F -walk_transit_ASC_auto_sufficient_atwork,-6.453181068,F -joint_walk_ASC_no_auto_all,-0.039874578,F -joint_walk_ASC_auto_deficient_all,0.010741394,F -joint_walk_ASC_auto_sufficient_all,-1.975332928,F -joint_bike_ASC_no_auto_all,-2.221523576,F -joint_bike_ASC_auto_deficient_all,-2.104028545,F -joint_bike_ASC_auto_sufficient_all,-5.415064076,F -joint_sr2_ASC_no_auto_all,0,T -joint_sr2_ASC_auto_deficient_all,0,T -joint_sr2_ASC_auto_sufficient_all,0,T -joint_sr3p_ASC_no_auto_all,-1.057362954,F -joint_sr3p_ASC_auto_deficient_all,-1.358121204,F -joint_sr3p_ASC_auto_sufficient_all,-2.71447634,F -joint_walk_transit_ASC_no_auto_all,-2.489686956,F -joint_walk_transit_ASC_auto_deficient_all,-3.485773675,F -joint_walk_transit_ASC_auto_sufficient_all,-15.264534,F -walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F -walk_transit_CBD_ASC_school_univ,0.672,F -walk_transit_CBD_ASC_work,0.804,F -walk_transit_CBD_ASC_atwork,0.564,F -drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,0.525,F -drive_transit_CBD_ASC_school_univ,0.672,F -drive_transit_CBD_ASC_work,1.1,F -drive_transit_CBD_ASC_atwork,0.564,F -joint_knr_transit_ASC_auto_deficient_all,-3.534648371,F -joint_knr_transit_ASC_auto_sufficient_all,-8.843788241,F -joint_knr_transit_ASC_no_auto_all,-3.987873766,F -joint_pnr_transit_ASC_auto_deficient_all,-5.146470604,F -joint_pnr_transit_ASC_auto_sufficient_all,-8.812715084,F -joint_pnr_transit_ASC_no_auto_all,0,T -knr_transit_ASC_auto_deficient_atwork,-23.01220348,F -knr_transit_ASC_auto_deficient_eatout,-1.841465519,F -knr_transit_ASC_auto_deficient_escort,-3.212214291,F -knr_transit_ASC_auto_deficient_othdiscr,-0.849413879,F -knr_transit_ASC_auto_deficient_othmaint,-1.311236091,F -knr_transit_ASC_auto_deficient_school,2.329981301,F -knr_transit_ASC_auto_deficient_shopping,0.900832809,F -knr_transit_ASC_auto_deficient_social,-0.828057409,F -knr_transit_ASC_auto_deficient_univ,0.781396375,F -knr_transit_ASC_auto_deficient_work,-1.44770466,F -knr_transit_ASC_auto_sufficient_atwork,-4.401027,F -knr_transit_ASC_auto_sufficient_eatout,-4.103602632,F -knr_transit_ASC_auto_sufficient_escort,-6.720817468,F -knr_transit_ASC_auto_sufficient_othdiscr,-3.797279962,F -knr_transit_ASC_auto_sufficient_othmaint,-3.333087668,F -knr_transit_ASC_auto_sufficient_school,-2.273708088,F -knr_transit_ASC_auto_sufficient_shopping,-3.989650268,F -knr_transit_ASC_auto_sufficient_social,-3.336287932,F -knr_transit_ASC_auto_sufficient_univ,-1.714133165,F -knr_transit_ASC_auto_sufficient_work,-4.698155028,F -knr_transit_ASC_no_auto_atwork,-0.580585596,F -knr_transit_ASC_no_auto_eatout,0.219095046,F -knr_transit_ASC_no_auto_escort,0.32982881,F -knr_transit_ASC_no_auto_othdiscr,0.219095046,F -knr_transit_ASC_no_auto_othmaint,0.32982881,F -knr_transit_ASC_no_auto_school,0.38894965,F -knr_transit_ASC_no_auto_shopping,0.32982881,F -knr_transit_ASC_no_auto_social,0.219095046,F -knr_transit_ASC_no_auto_univ,2.400389489,F -knr_transit_ASC_no_auto_work,1.821801152,F -local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0,F -local_ASC_school_univ,0,F -local_ASC_work,0,F -mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0,F -mix_ASC_school_univ,0,F -mix_ASC_work,0,F -pnr_transit_ASC_auto_deficient_atwork,-998.8196,F -pnr_transit_ASC_auto_deficient_eatout,-0.430359164,F -pnr_transit_ASC_auto_deficient_escort,-1.239230126,F -pnr_transit_ASC_auto_deficient_othdiscr,-0.710234464,F -pnr_transit_ASC_auto_deficient_othmaint,-0.384955706,F -pnr_transit_ASC_auto_deficient_school,1.14553143,F -pnr_transit_ASC_auto_deficient_shopping,-0.504015206,F -pnr_transit_ASC_auto_deficient_social,0.532554236,F -pnr_transit_ASC_auto_deficient_univ,1.899678681,F -pnr_transit_ASC_auto_deficient_work,-0.99603153,F -pnr_transit_ASC_auto_sufficient_atwork,-999.21466,F -pnr_transit_ASC_auto_sufficient_eatout,-7.21057228,F -pnr_transit_ASC_auto_sufficient_escort,-6.200504711,F -pnr_transit_ASC_auto_sufficient_othdiscr,-6.61964812,F -pnr_transit_ASC_auto_sufficient_othmaint,-4.224027811,F -pnr_transit_ASC_auto_sufficient_school,-3.608979199,F -pnr_transit_ASC_auto_sufficient_shopping,-3.770973811,F -pnr_transit_ASC_auto_sufficient_social,-6.85691217,F -pnr_transit_ASC_auto_sufficient_univ,0.143230071,F -pnr_transit_ASC_auto_sufficient_work,-2.571689178,F -pnr_transit_ASC_no_auto_atwork,0,F -pnr_transit_ASC_no_auto_eatout,0,F -pnr_transit_ASC_no_auto_escort,0,F -pnr_transit_ASC_no_auto_othdiscr,0,F -pnr_transit_ASC_no_auto_othmaint,0,F -pnr_transit_ASC_no_auto_school,0,F -pnr_transit_ASC_no_auto_shopping,0,F -pnr_transit_ASC_no_auto_social,0,F -pnr_transit_ASC_no_auto_univ,0,F -pnr_transit_ASC_no_auto_work,0,F -premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,0,F -premium_ASC_school_univ,0,F -premium_ASC_work,0,F -schoolbus_ASC_auto_deficient_school,0.835683653,F -schoolbus_ASC_auto_sufficient_school,-1.200290369,F -schoolbus_ASC_no_auto_school,10.43589235,F -schoolbus_ASC_not_school,0,T -sr2_ASC_no_auto_atwork,-0.963211851,F -sr2_ASC_no_auto_eatout,1.502329299,F -sr2_ASC_no_auto_escort,1.796584262,F -sr2_ASC_no_auto_othdiscr,1.502329299,F -sr2_ASC_no_auto_othmaint,1.796584262,F -sr2_ASC_no_auto_school,8.462111263,F -sr2_ASC_no_auto_shopping,1.796584262,F -sr2_ASC_no_auto_social,1.502329299,F -sr2_ASC_no_auto_univ,0.438511398,F -sr2_ASC_no_auto_work,0.768381772,F -taxi_ASC_no_auto_eatout_othdiscr_social,0.9923,F -taxi_ASC_no_auto_escort_othmaint_shopping,1.8939,F -taxi_ASC_no_auto_school_univ,-7,T -taxi_ASC_no_auto_work,4.7291,F -taxi_ASC_no_auto_atwork,4.1021,F -taxi_ASC_auto_deficient_eatout_othdiscr_social,-3.1317,F -taxi_ASC_auto_deficient_escort_othmaint_shopping,0.1766,F -taxi_ASC_auto_deficient_school,-0.3338,F -taxi_ASC_auto_deficient_univ,4.2492,F -taxi_ASC_auto_deficient_work,-1.4766,F -taxi_ASC_auto_deficient_atwork,-4.4046,F -taxi_ASC_auto_sufficient_eatout_othdiscr_social,-3.0374,F -taxi_ASC_auto_sufficient_escort_othmaint_shopping,-1.8055,F -taxi_ASC_auto_sufficient_school,-2.4294,F -taxi_ASC_auto_sufficient_univ,-0.3131,F -taxi_ASC_auto_sufficient_work,-4.8509,F -taxi_ASC_auto_sufficient_atwork,-2.8804,F -tnc_single_ASC_no_auto_eatout_othdiscr_social,1.6852,F -tnc_single_ASC_no_auto_escort_othmaint_shopping,1.8605,F -tnc_single_ASC_no_auto_school,-7,T -tnc_single_ASC_no_auto_univ,-2.519,F -tnc_single_ASC_no_auto_work,5.7855,F -tnc_single_ASC_no_auto_atwork,4.4982,F -tnc_single_ASC_auto_deficient_eatout_othdiscr_social,-2.9623,F -tnc_single_ASC_auto_deficient_escort_othmaint_shopping,0.6748,F -tnc_single_ASC_auto_deficient_school,-0.5524,F -tnc_single_ASC_auto_deficient_univ,1.0221,F -tnc_single_ASC_auto_deficient_work,-0.8013,F -tnc_single_ASC_auto_deficient_atwork,-3.7626,F -tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,-2.3239,F -tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,-1.45,F -tnc_single_ASC_auto_sufficient_school,-2.8375,F -tnc_single_ASC_auto_sufficient_univ,0.2088,F -tnc_single_ASC_auto_sufficient_work,-4.1946,F -tnc_single_ASC_auto_sufficient_atwork,-2.7988,F -tnc_shared_ASC_no_auto_eatout_othdiscr_social,0.6464,F -tnc_shared_ASC_no_auto_escort_othmaint_shopping,0.9361,F -tnc_shared_ASC_no_auto_school,-7,T -tnc_shared_ASC_no_auto_univ,-5.8116,F -tnc_shared_ASC_no_auto_work,3.2429,F -tnc_shared_ASC_no_auto_atwork,3.3672,F -tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,-4.3576,F -tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,-0.3863,F -tnc_shared_ASC_auto_deficient_school,-1.4746,F -tnc_shared_ASC_auto_deficient_univ,3.25,F -tnc_shared_ASC_auto_deficient_work,-2.1435,F -tnc_shared_ASC_auto_deficient_atwork,-4.5089,F -tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,-3.6638,F -tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,-2.4365,F -tnc_shared_ASC_auto_sufficient_school,-3.7219,F -tnc_shared_ASC_auto_sufficient_univ,-0.9068,F -tnc_shared_ASC_auto_sufficient_work,-5.3575,F -tnc_shared_ASC_auto_sufficient_atwork,-3.5397,F -joint_taxi_ASC_no_auto_all,-4.5792,F -joint_taxi_ASC_auto_deficient_all,-9.8157,F -joint_taxi_ASC_auto_sufficient_all,-11.7099,T -joint_tnc_single_ASC_no_auto_all,-4.4917,F -joint_tnc_single_ASC_auto_deficient_all,-9.8961,F -joint_tnc_single_ASC_auto_sufficient_all,-14.0159,T -joint_tnc_shared_ASC_no_auto_all,-4.3002,F 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+taxi_ASC_no_auto_eatout_othdiscr_social,0.9923,F +taxi_ASC_no_auto_escort_othmaint_shopping,1.8939,F +taxi_ASC_no_auto_school_univ,-7,T +taxi_ASC_no_auto_work,4.7291,F +taxi_ASC_no_auto_atwork,4.1021,F +taxi_ASC_auto_deficient_eatout_othdiscr_social,-3.1317,F +taxi_ASC_auto_deficient_escort_othmaint_shopping,0.1766,F +taxi_ASC_auto_deficient_school,-0.3338,F +taxi_ASC_auto_deficient_univ,4.2492,F +taxi_ASC_auto_deficient_work,-1.4766,F +taxi_ASC_auto_deficient_atwork,-4.4046,F +taxi_ASC_auto_sufficient_eatout_othdiscr_social,-3.0374,F +taxi_ASC_auto_sufficient_escort_othmaint_shopping,-1.8055,F +taxi_ASC_auto_sufficient_school,-2.4294,F +taxi_ASC_auto_sufficient_univ,-0.3131,F +taxi_ASC_auto_sufficient_work,-4.8509,F +taxi_ASC_auto_sufficient_atwork,-2.8804,F +tnc_single_ASC_no_auto_eatout_othdiscr_social,1.6852,F +tnc_single_ASC_no_auto_escort_othmaint_shopping,1.8605,F +tnc_single_ASC_no_auto_school,-7,T +tnc_single_ASC_no_auto_univ,-2.519,F +tnc_single_ASC_no_auto_work,5.7855,F +tnc_single_ASC_no_auto_atwork,4.4982,F +tnc_single_ASC_auto_deficient_eatout_othdiscr_social,-2.9623,F +tnc_single_ASC_auto_deficient_escort_othmaint_shopping,0.6748,F +tnc_single_ASC_auto_deficient_school,-0.5524,F +tnc_single_ASC_auto_deficient_univ,1.0221,F +tnc_single_ASC_auto_deficient_work,-0.8013,F +tnc_single_ASC_auto_deficient_atwork,-3.7626,F +tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,-2.3239,F +tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,-1.45,F +tnc_single_ASC_auto_sufficient_school,-2.8375,F +tnc_single_ASC_auto_sufficient_univ,0.2088,F +tnc_single_ASC_auto_sufficient_work,-4.1946,F +tnc_single_ASC_auto_sufficient_atwork,-2.7988,F +tnc_shared_ASC_no_auto_eatout_othdiscr_social,0.6464,F +tnc_shared_ASC_no_auto_escort_othmaint_shopping,0.9361,F +tnc_shared_ASC_no_auto_school,-7,T +tnc_shared_ASC_no_auto_univ,-5.8116,F +tnc_shared_ASC_no_auto_work,3.2429,F +tnc_shared_ASC_no_auto_atwork,3.3672,F +tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,-4.3576,F +tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,-0.3863,F +tnc_shared_ASC_auto_deficient_school,-1.4746,F +tnc_shared_ASC_auto_deficient_univ,3.25,F +tnc_shared_ASC_auto_deficient_work,-2.1435,F +tnc_shared_ASC_auto_deficient_atwork,-4.5089,F +tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,-3.6638,F +tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,-2.4365,F +tnc_shared_ASC_auto_sufficient_school,-3.7219,F +tnc_shared_ASC_auto_sufficient_univ,-0.9068,F +tnc_shared_ASC_auto_sufficient_work,-5.3575,F +tnc_shared_ASC_auto_sufficient_atwork,-3.5397,F +joint_taxi_ASC_no_auto_all,-4.5792,F +joint_taxi_ASC_auto_deficient_all,-9.8157,F +joint_taxi_ASC_auto_sufficient_all,-11.7099,T +joint_tnc_single_ASC_no_auto_all,-4.4917,F +joint_tnc_single_ASC_auto_deficient_all,-9.8961,F +joint_tnc_single_ASC_auto_sufficient_all,-14.0159,T +joint_tnc_shared_ASC_no_auto_all,-4.3002,F +joint_tnc_shared_ASC_auto_deficient_all,-11.1572,F +joint_tnc_shared_ASC_auto_sufficient_all,-13.205,T diff --git a/activitysim/examples/example_semcog/configs/tour_mode_choice_coeffs_template.csv b/activitysim/examples/prototype_semcog/configs/tour_mode_choice_coeffs_template.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/tour_mode_choice_coeffs_template.csv rename to activitysim/examples/prototype_semcog/configs/tour_mode_choice_coeffs_template.csv index e1204a1567..347e23e8f0 100755 --- a/activitysim/examples/example_semcog/configs/tour_mode_choice_coeffs_template.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_mode_choice_coeffs_template.csv @@ -1,97 +1,97 @@ -coefficient_name,eatout,escort,othdiscr,othmaint,school,shopping,social,univ,work,atwork -coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root -coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO -coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE -coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2 -coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3 -coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED -coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT -coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS -coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS -coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS -coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS -coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL -coef_ivt,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_work,coef_ivt_atwork -coef_topology_walk_multiplier,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_atwork -coef_topology_bike_multiplier,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_atwork -coef_topology_trn_multiplier,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_atwork -coef_age1619_da_multiplier,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_atwork -coef_age010_trn_multiplier,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_atwork -coef_age16p_sr_multiplier,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork -coef_hhsize1_sr_multiplier,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_work,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork -coef_hhsize2_sr_multiplier,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork -walk_ASC_no_auto,walk_ASC_no_auto_eatout,walk_ASC_no_auto_escort,walk_ASC_no_auto_othdiscr,walk_ASC_no_auto_othmaint,walk_ASC_no_auto_school,walk_ASC_no_auto_shopping,walk_ASC_no_auto_social,walk_ASC_no_auto_univ,walk_ASC_no_auto_work,walk_ASC_no_auto_atwork -walk_ASC_auto_deficient,walk_ASC_auto_deficient_eatout,walk_ASC_auto_deficient_escort,walk_ASC_auto_deficient_othdiscr,walk_ASC_auto_deficient_othmaint,walk_ASC_auto_deficient_school,walk_ASC_auto_deficient_shopping,walk_ASC_auto_deficient_social,walk_ASC_auto_deficient_univ,walk_ASC_auto_deficient_work,walk_ASC_auto_deficient_atwork -walk_ASC_auto_sufficient,walk_ASC_auto_sufficient_eatout,walk_ASC_auto_sufficient_escort,walk_ASC_auto_sufficient_othdiscr,walk_ASC_auto_sufficient_othmaint,walk_ASC_auto_sufficient_school,walk_ASC_auto_sufficient_shopping,walk_ASC_auto_sufficient_social,walk_ASC_auto_sufficient_univ,walk_ASC_auto_sufficient_work,walk_ASC_auto_sufficient_atwork -bike_ASC_no_auto,bike_ASC_no_auto_eatout,bike_ASC_no_auto_escort,bike_ASC_no_auto_othdiscr,bike_ASC_no_auto_othmaint,bike_ASC_no_auto_school,bike_ASC_no_auto_shopping,bike_ASC_no_auto_social,bike_ASC_no_auto_univ,bike_ASC_no_auto_work,bike_ASC_no_auto_atwork -bike_ASC_auto_deficient,bike_ASC_auto_deficient_eatout,bike_ASC_auto_deficient_escort,bike_ASC_auto_deficient_othdiscr,bike_ASC_auto_deficient_othmaint,bike_ASC_auto_deficient_school,bike_ASC_auto_deficient_shopping,bike_ASC_auto_deficient_social,bike_ASC_auto_deficient_univ,bike_ASC_auto_deficient_work,bike_ASC_auto_deficient_atwork -bike_ASC_auto_sufficient,bike_ASC_auto_sufficient_eatout,bike_ASC_auto_sufficient_escort,bike_ASC_auto_sufficient_othdiscr,bike_ASC_auto_sufficient_othmaint,bike_ASC_auto_sufficient_school,bike_ASC_auto_sufficient_shopping,bike_ASC_auto_sufficient_social,bike_ASC_auto_sufficient_univ,bike_ASC_auto_sufficient_work,bike_ASC_auto_sufficient_atwork -sr2_ASC_no_auto,sr2_ASC_no_auto_eatout,sr2_ASC_no_auto_escort,sr2_ASC_no_auto_othdiscr,sr2_ASC_no_auto_othmaint,sr2_ASC_no_auto_school,sr2_ASC_no_auto_shopping,sr2_ASC_no_auto_social,sr2_ASC_no_auto_univ,sr2_ASC_no_auto_work,sr2_ASC_no_auto_atwork -sr2_ASC_auto_deficient,sr2_ASC_auto_deficient_eatout,sr2_ASC_auto_deficient_escort,sr2_ASC_auto_deficient_othdiscr,sr2_ASC_auto_deficient_othmaint,sr2_ASC_auto_deficient_school,sr2_ASC_auto_deficient_shopping,sr2_ASC_auto_deficient_social,sr2_ASC_auto_deficient_univ,sr2_ASC_auto_deficient_work,sr2_ASC_auto_deficient_atwork -sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient_eatout,sr2_ASC_auto_sufficient_escort,sr2_ASC_auto_sufficient_othdiscr,sr2_ASC_auto_sufficient_othmaint,sr2_ASC_auto_sufficient_school,sr2_ASC_auto_sufficient_shopping,sr2_ASC_auto_sufficient_social,sr2_ASC_auto_sufficient_univ,sr2_ASC_auto_sufficient_work,sr2_ASC_auto_sufficient_atwork -sr3p_ASC_no_auto,sr3p_ASC_no_auto_eatout,sr3p_ASC_no_auto_escort,sr3p_ASC_no_auto_othdiscr,sr3p_ASC_no_auto_othmaint,sr3p_ASC_no_auto_school,sr3p_ASC_no_auto_shopping,sr3p_ASC_no_auto_social,sr3p_ASC_no_auto_univ,sr3p_ASC_no_auto_work,sr3p_ASC_no_auto_atwork -sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient_eatout,sr3p_ASC_auto_deficient_escort,sr3p_ASC_auto_deficient_othdiscr,sr3p_ASC_auto_deficient_othmaint,sr3p_ASC_auto_deficient_school,sr3p_ASC_auto_deficient_shopping,sr3p_ASC_auto_deficient_social,sr3p_ASC_auto_deficient_univ,sr3p_ASC_auto_deficient_work,sr3p_ASC_auto_deficient_atwork -sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient_eatout,sr3p_ASC_auto_sufficient_escort,sr3p_ASC_auto_sufficient_othdiscr,sr3p_ASC_auto_sufficient_othmaint,sr3p_ASC_auto_sufficient_school,sr3p_ASC_auto_sufficient_shopping,sr3p_ASC_auto_sufficient_social,sr3p_ASC_auto_sufficient_univ,sr3p_ASC_auto_sufficient_work,sr3p_ASC_auto_sufficient_atwork -walk_transit_ASC_no_auto,walk_transit_ASC_no_auto_eatout,walk_transit_ASC_no_auto_escort,walk_transit_ASC_no_auto_othdiscr,walk_transit_ASC_no_auto_othmaint,walk_transit_ASC_no_auto_school,walk_transit_ASC_no_auto_shopping,walk_transit_ASC_no_auto_social,walk_transit_ASC_no_auto_univ,walk_transit_ASC_no_auto_work,walk_transit_ASC_no_auto_atwork -walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient_eatout,walk_transit_ASC_auto_deficient_escort,walk_transit_ASC_auto_deficient_othdiscr,walk_transit_ASC_auto_deficient_othmaint,walk_transit_ASC_auto_deficient_school,walk_transit_ASC_auto_deficient_shopping,walk_transit_ASC_auto_deficient_social,walk_transit_ASC_auto_deficient_univ,walk_transit_ASC_auto_deficient_work,walk_transit_ASC_auto_deficient_atwork -walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient_eatout,walk_transit_ASC_auto_sufficient_escort,walk_transit_ASC_auto_sufficient_othdiscr,walk_transit_ASC_auto_sufficient_othmaint,walk_transit_ASC_auto_sufficient_school,walk_transit_ASC_auto_sufficient_shopping,walk_transit_ASC_auto_sufficient_social,walk_transit_ASC_auto_sufficient_univ,walk_transit_ASC_auto_sufficient_work,walk_transit_ASC_auto_sufficient_atwork -pnr_transit_ASC_no_auto,pnr_transit_ASC_no_auto_eatout,pnr_transit_ASC_no_auto_escort,pnr_transit_ASC_no_auto_othdiscr,pnr_transit_ASC_no_auto_othmaint,pnr_transit_ASC_no_auto_school,pnr_transit_ASC_no_auto_shopping,pnr_transit_ASC_no_auto_social,pnr_transit_ASC_no_auto_univ,pnr_transit_ASC_no_auto_work,pnr_transit_ASC_no_auto_atwork -pnr_transit_ASC_auto_deficient,pnr_transit_ASC_auto_deficient_eatout,pnr_transit_ASC_auto_deficient_escort,pnr_transit_ASC_auto_deficient_othdiscr,pnr_transit_ASC_auto_deficient_othmaint,pnr_transit_ASC_auto_deficient_school,pnr_transit_ASC_auto_deficient_shopping,pnr_transit_ASC_auto_deficient_social,pnr_transit_ASC_auto_deficient_univ,pnr_transit_ASC_auto_deficient_work,pnr_transit_ASC_auto_deficient_atwork -pnr_transit_ASC_auto_sufficient,pnr_transit_ASC_auto_sufficient_eatout,pnr_transit_ASC_auto_sufficient_escort,pnr_transit_ASC_auto_sufficient_othdiscr,pnr_transit_ASC_auto_sufficient_othmaint,pnr_transit_ASC_auto_sufficient_school,pnr_transit_ASC_auto_sufficient_shopping,pnr_transit_ASC_auto_sufficient_social,pnr_transit_ASC_auto_sufficient_univ,pnr_transit_ASC_auto_sufficient_work,pnr_transit_ASC_auto_sufficient_atwork -knr_transit_ASC_no_auto,knr_transit_ASC_no_auto_eatout,knr_transit_ASC_no_auto_escort,knr_transit_ASC_no_auto_othdiscr,knr_transit_ASC_no_auto_othmaint,knr_transit_ASC_no_auto_school,knr_transit_ASC_no_auto_shopping,knr_transit_ASC_no_auto_social,knr_transit_ASC_no_auto_univ,knr_transit_ASC_no_auto_work,knr_transit_ASC_no_auto_atwork -knr_transit_ASC_auto_deficient,knr_transit_ASC_auto_deficient_eatout,knr_transit_ASC_auto_deficient_escort,knr_transit_ASC_auto_deficient_othdiscr,knr_transit_ASC_auto_deficient_othmaint,knr_transit_ASC_auto_deficient_school,knr_transit_ASC_auto_deficient_shopping,knr_transit_ASC_auto_deficient_social,knr_transit_ASC_auto_deficient_univ,knr_transit_ASC_auto_deficient_work,knr_transit_ASC_auto_deficient_atwork -knr_transit_ASC_auto_sufficient,knr_transit_ASC_auto_sufficient_eatout,knr_transit_ASC_auto_sufficient_escort,knr_transit_ASC_auto_sufficient_othdiscr,knr_transit_ASC_auto_sufficient_othmaint,knr_transit_ASC_auto_sufficient_school,knr_transit_ASC_auto_sufficient_shopping,knr_transit_ASC_auto_sufficient_social,knr_transit_ASC_auto_sufficient_univ,knr_transit_ASC_auto_sufficient_work,knr_transit_ASC_auto_sufficient_atwork -taxi_ASC_no_auto,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_work,taxi_ASC_no_auto_atwork -taxi_ASC_auto_deficient,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_school,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_univ,taxi_ASC_auto_deficient_work,taxi_ASC_auto_deficient_atwork -taxi_ASC_auto_sufficient,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_school,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_univ,taxi_ASC_auto_sufficient_work,taxi_ASC_auto_sufficient_atwork -tnc_single_ASC_no_auto,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_school,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_univ,tnc_single_ASC_no_auto_work,tnc_single_ASC_no_auto_atwork -tnc_single_ASC_auto_deficient,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_school,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_univ,tnc_single_ASC_auto_deficient_work,tnc_single_ASC_auto_deficient_atwork -tnc_single_ASC_auto_sufficient,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_school,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_univ,tnc_single_ASC_auto_sufficient_work,tnc_single_ASC_auto_sufficient_atwork -tnc_shared_ASC_no_auto,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_school,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_univ,tnc_shared_ASC_no_auto_work,tnc_shared_ASC_no_auto_atwork -tnc_shared_ASC_auto_deficient,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_school,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_univ,tnc_shared_ASC_auto_deficient_work,tnc_shared_ASC_auto_deficient_atwork -tnc_shared_ASC_auto_sufficient,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_school,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_univ,tnc_shared_ASC_auto_sufficient_work,tnc_shared_ASC_auto_sufficient_atwork -joint_walk_ASC_no_auto,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all -joint_walk_ASC_auto_deficient,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all -joint_walk_ASC_auto_sufficient,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all -joint_bike_ASC_no_auto,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all -joint_bike_ASC_auto_deficient,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all -joint_bike_ASC_auto_sufficient,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all -joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all -joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all -joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all -joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all -joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all -joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all -joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all -joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all -joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all -joint_pnr_transit_ASC_no_auto,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all -joint_pnr_transit_ASC_auto_deficient,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all -joint_pnr_transit_ASC_auto_sufficient,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all -joint_knr_transit_ASC_no_auto,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all -joint_knr_transit_ASC_auto_deficient,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all -joint_knr_transit_ASC_auto_sufficient,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all -joint_taxi_ASC_no_auto,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all -joint_taxi_ASC_auto_deficient,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all -joint_taxi_ASC_auto_sufficient,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all -joint_tnc_single_ASC_no_auto,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all -joint_tnc_single_ASC_auto_deficient,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all -joint_tnc_single_ASC_auto_sufficient,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all -joint_tnc_shared_ASC_no_auto,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all -joint_tnc_shared_ASC_auto_deficient,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all -joint_tnc_shared_ASC_auto_sufficient,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all -local_ASC,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_school_univ,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_school_univ,local_ASC_work,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -premium_ASC,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_school_univ,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_school_univ,premium_ASC_work,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -mix_ASC,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_school_univ,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_school_univ,mix_ASC_work,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# walk_light_rail_ASC,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_work,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# drive_light_rail_ASC,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_work,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# walk_ferry_ASC,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_work,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# drive_ferry_ASC,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_work,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# express_bus_ASC,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_work,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# heavy_rail_ASC,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_work,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -# commuter_rail_ASC,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_work,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork -walk_transit_CBD_ASC,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_work,walk_transit_CBD_ASC_atwork -drive_transit_CBD_ASC,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_work,drive_transit_CBD_ASC_atwork -schoolbus_ASC_no_auto,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_no_auto_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school -schoolbus_ASC_auto_deficient,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_auto_deficient_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school -schoolbus_ASC_auto_sufficient,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_auto_sufficient_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school +coefficient_name,eatout,escort,othdiscr,othmaint,school,shopping,social,univ,work,atwork +coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root,coef_nest_root +coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO,coef_nest_AUTO +coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE,coef_nest_AUTO_DRIVEALONE +coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2,coef_nest_AUTO_SHAREDRIDE2 +coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3,coef_nest_AUTO_SHAREDRIDE3 +coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED,coef_nest_NONMOTORIZED +coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT,coef_nest_TRANSIT +coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS,coef_nest_TRANSIT_WALKACCESS +coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS,coef_nest_TRANSIT_PNRACCESS +coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS,coef_nest_TRANSIT_KNRACCESS +coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS,coef_nest_SCHOOL_BUS +coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL,coef_nest_RIDEHAIL +coef_ivt,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_eatout_escort_othdiscr_othmaint_shopping_social,coef_ivt_school_univ,coef_ivt_work,coef_ivt_atwork +coef_topology_walk_multiplier,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_walk_multiplier_atwork +coef_topology_bike_multiplier,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_bike_multiplier_atwork +coef_topology_trn_multiplier,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_work,coef_topology_trn_multiplier_atwork +coef_age1619_da_multiplier,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_school_univ,coef_age1619_da_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age1619_da_multiplier_atwork +coef_age010_trn_multiplier,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_school_univ,coef_age010_trn_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work,coef_age010_trn_multiplier_atwork +coef_age16p_sr_multiplier,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork,coef_age16p_sr_multiplier_school_univ_work_atwork +coef_hhsize1_sr_multiplier,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork,coef_hhsize1_sr_multiplier_work,coef_hhsize1_sr_multiplier_eatout_escort_othdiscr_othmaint_school_shopping_social_univ_atwork +coef_hhsize2_sr_multiplier,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_school_univ,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork,coef_hhsize2_sr_multiplier_eatout_escort_othdiscr_othmaint_shopping_social_work_atwork +walk_ASC_no_auto,walk_ASC_no_auto_eatout,walk_ASC_no_auto_escort,walk_ASC_no_auto_othdiscr,walk_ASC_no_auto_othmaint,walk_ASC_no_auto_school,walk_ASC_no_auto_shopping,walk_ASC_no_auto_social,walk_ASC_no_auto_univ,walk_ASC_no_auto_work,walk_ASC_no_auto_atwork +walk_ASC_auto_deficient,walk_ASC_auto_deficient_eatout,walk_ASC_auto_deficient_escort,walk_ASC_auto_deficient_othdiscr,walk_ASC_auto_deficient_othmaint,walk_ASC_auto_deficient_school,walk_ASC_auto_deficient_shopping,walk_ASC_auto_deficient_social,walk_ASC_auto_deficient_univ,walk_ASC_auto_deficient_work,walk_ASC_auto_deficient_atwork +walk_ASC_auto_sufficient,walk_ASC_auto_sufficient_eatout,walk_ASC_auto_sufficient_escort,walk_ASC_auto_sufficient_othdiscr,walk_ASC_auto_sufficient_othmaint,walk_ASC_auto_sufficient_school,walk_ASC_auto_sufficient_shopping,walk_ASC_auto_sufficient_social,walk_ASC_auto_sufficient_univ,walk_ASC_auto_sufficient_work,walk_ASC_auto_sufficient_atwork +bike_ASC_no_auto,bike_ASC_no_auto_eatout,bike_ASC_no_auto_escort,bike_ASC_no_auto_othdiscr,bike_ASC_no_auto_othmaint,bike_ASC_no_auto_school,bike_ASC_no_auto_shopping,bike_ASC_no_auto_social,bike_ASC_no_auto_univ,bike_ASC_no_auto_work,bike_ASC_no_auto_atwork +bike_ASC_auto_deficient,bike_ASC_auto_deficient_eatout,bike_ASC_auto_deficient_escort,bike_ASC_auto_deficient_othdiscr,bike_ASC_auto_deficient_othmaint,bike_ASC_auto_deficient_school,bike_ASC_auto_deficient_shopping,bike_ASC_auto_deficient_social,bike_ASC_auto_deficient_univ,bike_ASC_auto_deficient_work,bike_ASC_auto_deficient_atwork +bike_ASC_auto_sufficient,bike_ASC_auto_sufficient_eatout,bike_ASC_auto_sufficient_escort,bike_ASC_auto_sufficient_othdiscr,bike_ASC_auto_sufficient_othmaint,bike_ASC_auto_sufficient_school,bike_ASC_auto_sufficient_shopping,bike_ASC_auto_sufficient_social,bike_ASC_auto_sufficient_univ,bike_ASC_auto_sufficient_work,bike_ASC_auto_sufficient_atwork +sr2_ASC_no_auto,sr2_ASC_no_auto_eatout,sr2_ASC_no_auto_escort,sr2_ASC_no_auto_othdiscr,sr2_ASC_no_auto_othmaint,sr2_ASC_no_auto_school,sr2_ASC_no_auto_shopping,sr2_ASC_no_auto_social,sr2_ASC_no_auto_univ,sr2_ASC_no_auto_work,sr2_ASC_no_auto_atwork +sr2_ASC_auto_deficient,sr2_ASC_auto_deficient_eatout,sr2_ASC_auto_deficient_escort,sr2_ASC_auto_deficient_othdiscr,sr2_ASC_auto_deficient_othmaint,sr2_ASC_auto_deficient_school,sr2_ASC_auto_deficient_shopping,sr2_ASC_auto_deficient_social,sr2_ASC_auto_deficient_univ,sr2_ASC_auto_deficient_work,sr2_ASC_auto_deficient_atwork +sr2_ASC_auto_sufficient,sr2_ASC_auto_sufficient_eatout,sr2_ASC_auto_sufficient_escort,sr2_ASC_auto_sufficient_othdiscr,sr2_ASC_auto_sufficient_othmaint,sr2_ASC_auto_sufficient_school,sr2_ASC_auto_sufficient_shopping,sr2_ASC_auto_sufficient_social,sr2_ASC_auto_sufficient_univ,sr2_ASC_auto_sufficient_work,sr2_ASC_auto_sufficient_atwork +sr3p_ASC_no_auto,sr3p_ASC_no_auto_eatout,sr3p_ASC_no_auto_escort,sr3p_ASC_no_auto_othdiscr,sr3p_ASC_no_auto_othmaint,sr3p_ASC_no_auto_school,sr3p_ASC_no_auto_shopping,sr3p_ASC_no_auto_social,sr3p_ASC_no_auto_univ,sr3p_ASC_no_auto_work,sr3p_ASC_no_auto_atwork +sr3p_ASC_auto_deficient,sr3p_ASC_auto_deficient_eatout,sr3p_ASC_auto_deficient_escort,sr3p_ASC_auto_deficient_othdiscr,sr3p_ASC_auto_deficient_othmaint,sr3p_ASC_auto_deficient_school,sr3p_ASC_auto_deficient_shopping,sr3p_ASC_auto_deficient_social,sr3p_ASC_auto_deficient_univ,sr3p_ASC_auto_deficient_work,sr3p_ASC_auto_deficient_atwork +sr3p_ASC_auto_sufficient,sr3p_ASC_auto_sufficient_eatout,sr3p_ASC_auto_sufficient_escort,sr3p_ASC_auto_sufficient_othdiscr,sr3p_ASC_auto_sufficient_othmaint,sr3p_ASC_auto_sufficient_school,sr3p_ASC_auto_sufficient_shopping,sr3p_ASC_auto_sufficient_social,sr3p_ASC_auto_sufficient_univ,sr3p_ASC_auto_sufficient_work,sr3p_ASC_auto_sufficient_atwork +walk_transit_ASC_no_auto,walk_transit_ASC_no_auto_eatout,walk_transit_ASC_no_auto_escort,walk_transit_ASC_no_auto_othdiscr,walk_transit_ASC_no_auto_othmaint,walk_transit_ASC_no_auto_school,walk_transit_ASC_no_auto_shopping,walk_transit_ASC_no_auto_social,walk_transit_ASC_no_auto_univ,walk_transit_ASC_no_auto_work,walk_transit_ASC_no_auto_atwork +walk_transit_ASC_auto_deficient,walk_transit_ASC_auto_deficient_eatout,walk_transit_ASC_auto_deficient_escort,walk_transit_ASC_auto_deficient_othdiscr,walk_transit_ASC_auto_deficient_othmaint,walk_transit_ASC_auto_deficient_school,walk_transit_ASC_auto_deficient_shopping,walk_transit_ASC_auto_deficient_social,walk_transit_ASC_auto_deficient_univ,walk_transit_ASC_auto_deficient_work,walk_transit_ASC_auto_deficient_atwork +walk_transit_ASC_auto_sufficient,walk_transit_ASC_auto_sufficient_eatout,walk_transit_ASC_auto_sufficient_escort,walk_transit_ASC_auto_sufficient_othdiscr,walk_transit_ASC_auto_sufficient_othmaint,walk_transit_ASC_auto_sufficient_school,walk_transit_ASC_auto_sufficient_shopping,walk_transit_ASC_auto_sufficient_social,walk_transit_ASC_auto_sufficient_univ,walk_transit_ASC_auto_sufficient_work,walk_transit_ASC_auto_sufficient_atwork +pnr_transit_ASC_no_auto,pnr_transit_ASC_no_auto_eatout,pnr_transit_ASC_no_auto_escort,pnr_transit_ASC_no_auto_othdiscr,pnr_transit_ASC_no_auto_othmaint,pnr_transit_ASC_no_auto_school,pnr_transit_ASC_no_auto_shopping,pnr_transit_ASC_no_auto_social,pnr_transit_ASC_no_auto_univ,pnr_transit_ASC_no_auto_work,pnr_transit_ASC_no_auto_atwork +pnr_transit_ASC_auto_deficient,pnr_transit_ASC_auto_deficient_eatout,pnr_transit_ASC_auto_deficient_escort,pnr_transit_ASC_auto_deficient_othdiscr,pnr_transit_ASC_auto_deficient_othmaint,pnr_transit_ASC_auto_deficient_school,pnr_transit_ASC_auto_deficient_shopping,pnr_transit_ASC_auto_deficient_social,pnr_transit_ASC_auto_deficient_univ,pnr_transit_ASC_auto_deficient_work,pnr_transit_ASC_auto_deficient_atwork +pnr_transit_ASC_auto_sufficient,pnr_transit_ASC_auto_sufficient_eatout,pnr_transit_ASC_auto_sufficient_escort,pnr_transit_ASC_auto_sufficient_othdiscr,pnr_transit_ASC_auto_sufficient_othmaint,pnr_transit_ASC_auto_sufficient_school,pnr_transit_ASC_auto_sufficient_shopping,pnr_transit_ASC_auto_sufficient_social,pnr_transit_ASC_auto_sufficient_univ,pnr_transit_ASC_auto_sufficient_work,pnr_transit_ASC_auto_sufficient_atwork +knr_transit_ASC_no_auto,knr_transit_ASC_no_auto_eatout,knr_transit_ASC_no_auto_escort,knr_transit_ASC_no_auto_othdiscr,knr_transit_ASC_no_auto_othmaint,knr_transit_ASC_no_auto_school,knr_transit_ASC_no_auto_shopping,knr_transit_ASC_no_auto_social,knr_transit_ASC_no_auto_univ,knr_transit_ASC_no_auto_work,knr_transit_ASC_no_auto_atwork +knr_transit_ASC_auto_deficient,knr_transit_ASC_auto_deficient_eatout,knr_transit_ASC_auto_deficient_escort,knr_transit_ASC_auto_deficient_othdiscr,knr_transit_ASC_auto_deficient_othmaint,knr_transit_ASC_auto_deficient_school,knr_transit_ASC_auto_deficient_shopping,knr_transit_ASC_auto_deficient_social,knr_transit_ASC_auto_deficient_univ,knr_transit_ASC_auto_deficient_work,knr_transit_ASC_auto_deficient_atwork +knr_transit_ASC_auto_sufficient,knr_transit_ASC_auto_sufficient_eatout,knr_transit_ASC_auto_sufficient_escort,knr_transit_ASC_auto_sufficient_othdiscr,knr_transit_ASC_auto_sufficient_othmaint,knr_transit_ASC_auto_sufficient_school,knr_transit_ASC_auto_sufficient_shopping,knr_transit_ASC_auto_sufficient_social,knr_transit_ASC_auto_sufficient_univ,knr_transit_ASC_auto_sufficient_work,knr_transit_ASC_auto_sufficient_atwork +taxi_ASC_no_auto,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_escort_othmaint_shopping,taxi_ASC_no_auto_eatout_othdiscr_social,taxi_ASC_no_auto_school_univ,taxi_ASC_no_auto_work,taxi_ASC_no_auto_atwork +taxi_ASC_auto_deficient,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_school,taxi_ASC_auto_deficient_escort_othmaint_shopping,taxi_ASC_auto_deficient_eatout_othdiscr_social,taxi_ASC_auto_deficient_univ,taxi_ASC_auto_deficient_work,taxi_ASC_auto_deficient_atwork +taxi_ASC_auto_sufficient,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_school,taxi_ASC_auto_sufficient_escort_othmaint_shopping,taxi_ASC_auto_sufficient_eatout_othdiscr_social,taxi_ASC_auto_sufficient_univ,taxi_ASC_auto_sufficient_work,taxi_ASC_auto_sufficient_atwork +tnc_single_ASC_no_auto,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_school,tnc_single_ASC_no_auto_escort_othmaint_shopping,tnc_single_ASC_no_auto_eatout_othdiscr_social,tnc_single_ASC_no_auto_univ,tnc_single_ASC_no_auto_work,tnc_single_ASC_no_auto_atwork +tnc_single_ASC_auto_deficient,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_school,tnc_single_ASC_auto_deficient_escort_othmaint_shopping,tnc_single_ASC_auto_deficient_eatout_othdiscr_social,tnc_single_ASC_auto_deficient_univ,tnc_single_ASC_auto_deficient_work,tnc_single_ASC_auto_deficient_atwork +tnc_single_ASC_auto_sufficient,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_school,tnc_single_ASC_auto_sufficient_escort_othmaint_shopping,tnc_single_ASC_auto_sufficient_eatout_othdiscr_social,tnc_single_ASC_auto_sufficient_univ,tnc_single_ASC_auto_sufficient_work,tnc_single_ASC_auto_sufficient_atwork +tnc_shared_ASC_no_auto,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_school,tnc_shared_ASC_no_auto_escort_othmaint_shopping,tnc_shared_ASC_no_auto_eatout_othdiscr_social,tnc_shared_ASC_no_auto_univ,tnc_shared_ASC_no_auto_work,tnc_shared_ASC_no_auto_atwork +tnc_shared_ASC_auto_deficient,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_school,tnc_shared_ASC_auto_deficient_escort_othmaint_shopping,tnc_shared_ASC_auto_deficient_eatout_othdiscr_social,tnc_shared_ASC_auto_deficient_univ,tnc_shared_ASC_auto_deficient_work,tnc_shared_ASC_auto_deficient_atwork +tnc_shared_ASC_auto_sufficient,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_school,tnc_shared_ASC_auto_sufficient_escort_othmaint_shopping,tnc_shared_ASC_auto_sufficient_eatout_othdiscr_social,tnc_shared_ASC_auto_sufficient_univ,tnc_shared_ASC_auto_sufficient_work,tnc_shared_ASC_auto_sufficient_atwork +joint_walk_ASC_no_auto,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all,joint_walk_ASC_no_auto_all +joint_walk_ASC_auto_deficient,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all,joint_walk_ASC_auto_deficient_all +joint_walk_ASC_auto_sufficient,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all,joint_walk_ASC_auto_sufficient_all +joint_bike_ASC_no_auto,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all,joint_bike_ASC_no_auto_all +joint_bike_ASC_auto_deficient,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all,joint_bike_ASC_auto_deficient_all +joint_bike_ASC_auto_sufficient,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all,joint_bike_ASC_auto_sufficient_all +joint_sr2_ASC_no_auto,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all,joint_sr2_ASC_no_auto_all +joint_sr2_ASC_auto_deficient,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all,joint_sr2_ASC_auto_deficient_all +joint_sr2_ASC_auto_sufficient,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all,joint_sr2_ASC_auto_sufficient_all +joint_sr3p_ASC_no_auto,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all,joint_sr3p_ASC_no_auto_all +joint_sr3p_ASC_auto_deficient,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all,joint_sr3p_ASC_auto_deficient_all +joint_sr3p_ASC_auto_sufficient,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all,joint_sr3p_ASC_auto_sufficient_all +joint_walk_transit_ASC_no_auto,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all,joint_walk_transit_ASC_no_auto_all +joint_walk_transit_ASC_auto_deficient,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all,joint_walk_transit_ASC_auto_deficient_all +joint_walk_transit_ASC_auto_sufficient,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all,joint_walk_transit_ASC_auto_sufficient_all +joint_pnr_transit_ASC_no_auto,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all,joint_pnr_transit_ASC_no_auto_all +joint_pnr_transit_ASC_auto_deficient,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all,joint_pnr_transit_ASC_auto_deficient_all +joint_pnr_transit_ASC_auto_sufficient,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all,joint_pnr_transit_ASC_auto_sufficient_all +joint_knr_transit_ASC_no_auto,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all,joint_knr_transit_ASC_no_auto_all +joint_knr_transit_ASC_auto_deficient,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all,joint_knr_transit_ASC_auto_deficient_all +joint_knr_transit_ASC_auto_sufficient,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all,joint_knr_transit_ASC_auto_sufficient_all +joint_taxi_ASC_no_auto,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all,joint_taxi_ASC_no_auto_all +joint_taxi_ASC_auto_deficient,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all,joint_taxi_ASC_auto_deficient_all +joint_taxi_ASC_auto_sufficient,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all,joint_taxi_ASC_auto_sufficient_all +joint_tnc_single_ASC_no_auto,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all,joint_tnc_single_ASC_no_auto_all +joint_tnc_single_ASC_auto_deficient,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all,joint_tnc_single_ASC_auto_deficient_all +joint_tnc_single_ASC_auto_sufficient,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all,joint_tnc_single_ASC_auto_sufficient_all +joint_tnc_shared_ASC_no_auto,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all,joint_tnc_shared_ASC_no_auto_all +joint_tnc_shared_ASC_auto_deficient,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all,joint_tnc_shared_ASC_auto_deficient_all +joint_tnc_shared_ASC_auto_sufficient,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all,joint_tnc_shared_ASC_auto_sufficient_all +local_ASC,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_school_univ,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,local_ASC_school_univ,local_ASC_work,local_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +premium_ASC,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_school_univ,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,premium_ASC_school_univ,premium_ASC_work,premium_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +mix_ASC,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_school_univ,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,mix_ASC_school_univ,mix_ASC_work,mix_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +# walk_light_rail_ASC,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_light_rail_ASC_school_univ,walk_light_rail_ASC_work,walk_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +# drive_light_rail_ASC,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_light_rail_ASC_school_univ,drive_light_rail_ASC_work,drive_light_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +# walk_ferry_ASC,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,walk_ferry_ASC_school_univ,walk_ferry_ASC_work,walk_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +# drive_ferry_ASC,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,drive_ferry_ASC_school_univ,drive_ferry_ASC_work,drive_ferry_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +# express_bus_ASC,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,express_bus_ASC_school_univ,express_bus_ASC_work,express_bus_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +# heavy_rail_ASC,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,heavy_rail_ASC_school_univ,heavy_rail_ASC_work,heavy_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +# commuter_rail_ASC,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork,commuter_rail_ASC_school_univ,commuter_rail_ASC_work,commuter_rail_ASC_eatout_escort_othdiscr_othmaint_shopping_social_atwork +walk_transit_CBD_ASC,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,walk_transit_CBD_ASC_school_univ,walk_transit_CBD_ASC_work,walk_transit_CBD_ASC_atwork +drive_transit_CBD_ASC,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_eatout_escort_othdiscr_othmaint_shopping_social,drive_transit_CBD_ASC_school_univ,drive_transit_CBD_ASC_work,drive_transit_CBD_ASC_atwork +schoolbus_ASC_no_auto,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_no_auto_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school +schoolbus_ASC_auto_deficient,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_auto_deficient_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school +schoolbus_ASC_auto_sufficient,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_auto_sufficient_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school,schoolbus_ASC_not_school diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_atwork.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/tour_scheduling_atwork.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork.csv index 1b1be59ad0..c0166ffa5e 100755 --- a/activitysim/examples/example_semcog/configs/tour_scheduling_atwork.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork.csv @@ -1,50 +1,50 @@ -Label,Description,Expression,Coefficient -util_Female_Departure_after_1230_pm_Linear,Female - Departure after 12:30 pm - Linear,"@np.where(((df.female) & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Female_Departure_after_1230_pm_Linear -util_Parttime_worker_Departure_after_1230_pm__Linear,Part-time worker - Departure after 12:30 pm - Linear,"@np.where(((df.ptype == 2) & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Parttime_worker_Departure_after_1230_pm__Linear -util_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period,Part-time worker - Duration greater than 0.5 hours (depart and arrive in the same period),"@np.where(((df.ptype == 2) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period -util_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Low income (<25000) - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.is_income_less25K) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period -util_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Med income (25k to 60k) - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.is_income_25K_to_60K) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period -util_Med_income_25k_to_60k_Duration_greater_than_0p5_hours,Med income (25k to 60k) - Duration greater than 0.5 hours,"@np.where(((df.is_income_25K_to_60K) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Med_income_25k_to_60k_Duration_greater_than_0p5_hours -util_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours,Med-high income (60k to 120k) - Duration greater than 0.5 hours,"@np.where(((df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours -#util_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Blue collar - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.work_segment == 5) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period -#util_Blue_collar_Duration_greater_than_0p5_hours,Blue collar - Duration greater than 0.5 hours,"@np.where(((df.work_segment == 5) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Blue_collar_Duration_greater_than_0p5_hours -#util_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Health - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.work_segment == 3) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period -util_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Distance to destination - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where((df.duration<1), ((np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0))) * df.od_distance, 0)",coef_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period -util_Distance_to_destination_Duration_greater_than_0p5_hours,Distance to destination - Duration greater than 0.5 hours,"@np.where((df.duration>1), ((np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0))) * df.od_distance, 0)",coef_Distance_to_destination_Duration_greater_than_0p5_hours -util_Subtour_purpose_Business_Departure_before_1200_pm__Linear,Subtour purpose: Business - Departure before 12:00 pm - Linear,"@np.where(((df.tour_type == 'business') & (df.start<19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Business_Departure_before_1200_pm__Linear -util_Subtour_purpose_Business_Departure_after_1230_pm_Linear,Subtour purpose: Business - Departure after 12:30 pm - Linear,"@np.where(((df.tour_type == 'business') & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Business_Departure_after_1230_pm_Linear -util_Subtour_purpose_Business_Duration_greater_than_0p5_hours,Subtour purpose: Business - Duration greater than 0.5 hours,"@np.where(((df.tour_type == 'business') & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Subtour_purpose_Business_Duration_greater_than_0p5_hours -util_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear,Subtour purpose: Eat-out - Departure before 12:00 pm - Linear,"@np.where(((df.tour_type == 'eat') & (df.start<19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear -util_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear,Subtour purpose: Eat-out - Departure after 12:30 pm - Linear,"@np.where(((df.tour_type == 'eat') & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear -util_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Subtour purpose: Eat-out - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.tour_type == 'eat') & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period -util__Departure_constants,# Departure constants,,coef__Departure_constants -util_Shift_for_every_30_minutes_before_1030_am_Linear,Shift for every 30 minutes before 10:30 am - Linear,"@np.where((df.start<16), (np.where((df.start< 16), np.minimum(16 - df.start, 9), 0) + np.where((df.start> 21), np.minimum(df.start - 21, 11), 0)), 0)",coef_Shift_for_every_30_minutes_before_1030_am_Linear -util_Before_1100_AM,Before 11:00 AM,@(df.start<17),coef_Before_1100_AM -util_1100_AM_1130_AM,11:00 AM - 11:30 AM,@(df.start==17),coef_1100_AM_1130_AM -util_1130_AM_1200_PM,11:30 AM - 12:00 PM,@(df.start==18),coef_1130_AM_1200_PM -util_1200_AM_1230_PM,12:00 AM - 12:30 PM,@(df.start==19),coef_1200_AM_1230_PM -util_1230_PM_0100_PM,12:30 PM - 01:00 PM,@(df.start==20),coef_1230_PM_0100_PM -util_After_0100_PM,After 01:00 PM,@(df.start>20),coef_After_0100_PM -util_Shift_for_every_30_minutes_after_130_pm_Square_Root,Shift for every 30 minutes after 1:30 pm - Square Root,"@np.where((df.start>21), ((np.where((df.start < 16), np.minimum(16 - df.start, 9), 0) + np.where((df.start > 21), np.minimum(df.start - 21, 11), 0))** 0.5), 0)",coef_Shift_for_every_30_minutes_after_130_pm_Square_Root -util__Arrival_constants,# Arrival constants,,coef__Arrival_constants -util_Shift_for_every_30_minutes_before_1130_am_Linear,Shift for every 30 minutes before 11:30 am - Linear,"@np.where((df.end<18), (np.where((df.end < 14), np.minimum(14 - df.end, 9), 0) + np.where((df.end > 24), np.minimum(df.end - 24, 10), 0)), 0)",coef_Shift_for_every_30_minutes_before_1130_am_Linear -util_Before_1200_PM,Before 12:00 PM,@(df.end<19),coef_Before_1200_PM -util_1200_AM_1230_PM,12:00 AM - 12:30 PM,@(df.end==19),coef_1200_AM_1230_PM -util_1230_PM_0100_PM,12:30 PM - 01:00 PM,@(df.end==20),coef_1230_PM_0100_PM -util_0100_PM_0130_PM,01:00 PM - 01:30 PM,@(df.end==21),coef_0100_PM_0130_PM -util_0130_PM_0200_PM,01:30 PM - 02:00 PM,@(df.end==22),coef_0130_PM_0200_PM -util_0200_PM_0230_PM,02:00 PM - 02:30 PM,@(df.end==23),coef_0200_PM_0230_PM -util_After_0230_PM,After 02:30 PM,@(df.end>23),coef_After_0230_PM -util_Shift_for_every_30_minutes_after_300_pm_Linear,Shift for every 30 minutes after 3:00 pm - Linear,"@np.where((df.end>24), (np.where((df.end < 14), np.minimum(14 - df.end, 9), 0) + np.where((df.end > 24), np.minimum(df.end - 24, 10), 0)), 0)",coef_Shift_for_every_30_minutes_after_300_pm_Linear -util__Duration_constants,# Duration constants,,coef__Duration_constants -util_0_hrs,0 hrs,@(df.duration==0),coef_0_hrs -util_0p5_hrs,0.5 hrs,@(df.duration==1),coef_0p5_hrs -util_1_hrs,1 hrs,@(df.duration==2),coef_1_hrs -util_1p5hrs,1.5hrs,@(df.duration==3),coef_1p5hrs -util_2_hrs,2 hrs,@(df.duration==4),coef_2_hrs -util_Longer_than_2_hrs,Longer than 2 hrs,@(df.duration>4),coef_Longer_than_2_hrs -util_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root,Shift for every 30 minutes more than 2.5 hrs - Square Root,"@np.where((df.duration>5), ((np.where((df.duration < 0), np.minimum(0 - df.duration, 47), 0) + np.where((df.duration > 5), np.minimum(df.duration - 5, 13), 0)) ** 0.5), 0)",coef_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root -util_Calibration_Constant_Departure_eq_18,Calibration Constant - Departure = 18,@(df.start==18),coef_Calibration_Constant_Departure_eq_18 -util_Calibration_Constant_Departure_eq_19,Calibration Constant - Departure = 19,@(df.start==19),coef_Calibration_Constant_Departure_eq_19 -util_Calibration_Constant_Arrival_eq_20,Calibration Constant - Arrival = 20,@(df.end==20),coef_Calibration_Constant_Arrival_eq_20 -util_Calibration_Constant_Arrival_eq_21,Calibration Constant - Arrival = 21,@(df.end==21),coef_Calibration_Constant_Arrival_eq_21 +Label,Description,Expression,Coefficient +util_Female_Departure_after_1230_pm_Linear,Female - Departure after 12:30 pm - Linear,"@np.where(((df.female) & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Female_Departure_after_1230_pm_Linear +util_Parttime_worker_Departure_after_1230_pm__Linear,Part-time worker - Departure after 12:30 pm - Linear,"@np.where(((df.ptype == 2) & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Parttime_worker_Departure_after_1230_pm__Linear +util_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period,Part-time worker - Duration greater than 0.5 hours (depart and arrive in the same period),"@np.where(((df.ptype == 2) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Low income (<25000) - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.is_income_less25K) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Med income (25k to 60k) - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.is_income_25K_to_60K) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Med_income_25k_to_60k_Duration_greater_than_0p5_hours,Med income (25k to 60k) - Duration greater than 0.5 hours,"@np.where(((df.is_income_25K_to_60K) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Med_income_25k_to_60k_Duration_greater_than_0p5_hours +util_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours,Med-high income (60k to 120k) - Duration greater than 0.5 hours,"@np.where(((df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours +#util_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Blue collar - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.work_segment == 5) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +#util_Blue_collar_Duration_greater_than_0p5_hours,Blue collar - Duration greater than 0.5 hours,"@np.where(((df.work_segment == 5) & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Blue_collar_Duration_greater_than_0p5_hours +#util_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Health - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.work_segment == 3) & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Distance to destination - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where((df.duration<1), ((np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0))) * df.od_distance, 0)",coef_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util_Distance_to_destination_Duration_greater_than_0p5_hours,Distance to destination - Duration greater than 0.5 hours,"@np.where((df.duration>1), ((np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0))) * df.od_distance, 0)",coef_Distance_to_destination_Duration_greater_than_0p5_hours +util_Subtour_purpose_Business_Departure_before_1200_pm__Linear,Subtour purpose: Business - Departure before 12:00 pm - Linear,"@np.where(((df.tour_type == 'business') & (df.start<19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Business_Departure_before_1200_pm__Linear +util_Subtour_purpose_Business_Departure_after_1230_pm_Linear,Subtour purpose: Business - Departure after 12:30 pm - Linear,"@np.where(((df.tour_type == 'business') & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Business_Departure_after_1230_pm_Linear +util_Subtour_purpose_Business_Duration_greater_than_0p5_hours,Subtour purpose: Business - Duration greater than 0.5 hours,"@np.where(((df.tour_type == 'business') & (df.duration>1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Subtour_purpose_Business_Duration_greater_than_0p5_hours +util_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear,Subtour purpose: Eat-out - Departure before 12:00 pm - Linear,"@np.where(((df.tour_type == 'eat') & (df.start<19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear +util_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear,Subtour purpose: Eat-out - Departure after 12:30 pm - Linear,"@np.where(((df.tour_type == 'eat') & (df.start>19)), (np.where((df.start<= 19), np.minimum(19 - df.start, 48), 0) + np.where((df.start > 19), np.minimum(df.start - 19, 48), 0)), 0)",coef_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear +util_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,Subtour purpose: Eat-out - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.tour_type == 'eat') & (df.duration<1)), (np.where((df.duration <= 1), np.minimum(1 - df.duration, 47), 0) + np.where((df.duration > 1), np.minimum(df.duration - 1, 47), 0)), 0)",coef_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util__Departure_constants,# Departure constants,,coef__Departure_constants +util_Shift_for_every_30_minutes_before_1030_am_Linear,Shift for every 30 minutes before 10:30 am - Linear,"@np.where((df.start<16), (np.where((df.start< 16), np.minimum(16 - df.start, 9), 0) + np.where((df.start> 21), np.minimum(df.start - 21, 11), 0)), 0)",coef_Shift_for_every_30_minutes_before_1030_am_Linear +util_Before_1100_AM,Before 11:00 AM,@(df.start<17),coef_Before_1100_AM +util_1100_AM_1130_AM,11:00 AM - 11:30 AM,@(df.start==17),coef_1100_AM_1130_AM +util_1130_AM_1200_PM,11:30 AM - 12:00 PM,@(df.start==18),coef_1130_AM_1200_PM +util_1200_AM_1230_PM,12:00 AM - 12:30 PM,@(df.start==19),coef_1200_AM_1230_PM +util_1230_PM_0100_PM,12:30 PM - 01:00 PM,@(df.start==20),coef_1230_PM_0100_PM +util_After_0100_PM,After 01:00 PM,@(df.start>20),coef_After_0100_PM +util_Shift_for_every_30_minutes_after_130_pm_Square_Root,Shift for every 30 minutes after 1:30 pm - Square Root,"@np.where((df.start>21), ((np.where((df.start < 16), np.minimum(16 - df.start, 9), 0) + np.where((df.start > 21), np.minimum(df.start - 21, 11), 0))** 0.5), 0)",coef_Shift_for_every_30_minutes_after_130_pm_Square_Root +util__Arrival_constants,# Arrival constants,,coef__Arrival_constants +util_Shift_for_every_30_minutes_before_1130_am_Linear,Shift for every 30 minutes before 11:30 am - Linear,"@np.where((df.end<18), (np.where((df.end < 14), np.minimum(14 - df.end, 9), 0) + np.where((df.end > 24), np.minimum(df.end - 24, 10), 0)), 0)",coef_Shift_for_every_30_minutes_before_1130_am_Linear +util_Before_1200_PM,Before 12:00 PM,@(df.end<19),coef_Before_1200_PM +util_1200_AM_1230_PM,12:00 AM - 12:30 PM,@(df.end==19),coef_1200_AM_1230_PM +util_1230_PM_0100_PM,12:30 PM - 01:00 PM,@(df.end==20),coef_1230_PM_0100_PM +util_0100_PM_0130_PM,01:00 PM - 01:30 PM,@(df.end==21),coef_0100_PM_0130_PM +util_0130_PM_0200_PM,01:30 PM - 02:00 PM,@(df.end==22),coef_0130_PM_0200_PM +util_0200_PM_0230_PM,02:00 PM - 02:30 PM,@(df.end==23),coef_0200_PM_0230_PM +util_After_0230_PM,After 02:30 PM,@(df.end>23),coef_After_0230_PM +util_Shift_for_every_30_minutes_after_300_pm_Linear,Shift for every 30 minutes after 3:00 pm - Linear,"@np.where((df.end>24), (np.where((df.end < 14), np.minimum(14 - df.end, 9), 0) + np.where((df.end > 24), np.minimum(df.end - 24, 10), 0)), 0)",coef_Shift_for_every_30_minutes_after_300_pm_Linear +util__Duration_constants,# Duration constants,,coef__Duration_constants +util_0_hrs,0 hrs,@(df.duration==0),coef_0_hrs +util_0p5_hrs,0.5 hrs,@(df.duration==1),coef_0p5_hrs +util_1_hrs,1 hrs,@(df.duration==2),coef_1_hrs +util_1p5hrs,1.5hrs,@(df.duration==3),coef_1p5hrs +util_2_hrs,2 hrs,@(df.duration==4),coef_2_hrs +util_Longer_than_2_hrs,Longer than 2 hrs,@(df.duration>4),coef_Longer_than_2_hrs +util_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root,Shift for every 30 minutes more than 2.5 hrs - Square Root,"@np.where((df.duration>5), ((np.where((df.duration < 0), np.minimum(0 - df.duration, 47), 0) + np.where((df.duration > 5), np.minimum(df.duration - 5, 13), 0)) ** 0.5), 0)",coef_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root +util_Calibration_Constant_Departure_eq_18,Calibration Constant - Departure = 18,@(df.start==18),coef_Calibration_Constant_Departure_eq_18 +util_Calibration_Constant_Departure_eq_19,Calibration Constant - Departure = 19,@(df.start==19),coef_Calibration_Constant_Departure_eq_19 +util_Calibration_Constant_Arrival_eq_20,Calibration Constant - Arrival = 20,@(df.end==20),coef_Calibration_Constant_Arrival_eq_20 +util_Calibration_Constant_Arrival_eq_21,Calibration Constant - Arrival = 21,@(df.end==21),coef_Calibration_Constant_Arrival_eq_21 diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_atwork.yaml b/activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork.yaml similarity index 94% rename from activitysim/examples/example_semcog/configs/tour_scheduling_atwork.yaml rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork.yaml index e1702e6e67..f04f93b8e5 100755 --- a/activitysim/examples/example_semcog/configs/tour_scheduling_atwork.yaml +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork.yaml @@ -1,16 +1,16 @@ - -SPEC: tour_scheduling_atwork.csv -COEFFICIENTS: tour_scheduling_atwork_coeffs.csv - -preprocessor: - SPEC: tour_scheduling_atwork_preprocessor - DF: df -# TABLES: -# - land_use -# - tours - -SIMULATE_CHOOSER_COLUMNS: - - od_distance - -CONSTANTS: + +SPEC: tour_scheduling_atwork.csv +COEFFICIENTS: tour_scheduling_atwork_coeffs.csv + +preprocessor: + SPEC: tour_scheduling_atwork_preprocessor + DF: df +# TABLES: +# - land_use +# - tours + +SIMULATE_CHOOSER_COLUMNS: + - od_distance + +CONSTANTS: time_cap: 30 \ No newline at end of file diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_atwork_coeffs.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork_coeffs.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/tour_scheduling_atwork_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork_coeffs.csv index 3d27bcdc86..457c9e4b8a 100755 --- a/activitysim/examples/example_semcog/configs/tour_scheduling_atwork_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork_coeffs.csv @@ -1,47 +1,47 @@ -coefficient_name,value,constrain -coef_Female_Departure_after_1230_pm_Linear,0.05574558,F -coef_Parttime_worker_Departure_after_1230_pm__Linear,0.129291333,F -coef_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period,0.162008704,F -coef_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.885322446,F -coef_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.526935246,F -coef_Med_income_25k_to_60k_Duration_greater_than_0p5_hours,-0.081917021,F -coef_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours,-0.068358924,F -coef_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,1.191378628,F -coef_Blue_collar_Duration_greater_than_0p5_hours,0.123072852,F -coef_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.791205377,F -coef_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.292363361,F -coef_Distance_to_destination_Duration_greater_than_0p5_hours,0.006885922,F -coef_Subtour_purpose_Business_Departure_before_1200_pm__Linear,0.268963895,F -coef_Subtour_purpose_Business_Departure_after_1230_pm_Linear,0.17631122,F -coef_Subtour_purpose_Business_Duration_greater_than_0p5_hours,0.362189199,F -coef_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear,-0.250770206,F -coef_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear,-0.169861029,F -coef_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.678939929,F -coef_Shift_for_every_30_minutes_before_1030_am_Linear,-0.731880037,F -coef_Before_1100_AM,-2.176744062,F -coef_1100_AM_1130_AM,-1.190017952,F -coef_1130_AM_1200_PM,-0.198229872,F -#coef_1200_AM_1230_PM,0,T -#coef_1230_PM_0100_PM,-0.084950396,F -coef_After_0100_PM,-0.205562723,F -coef_Shift_for_every_30_minutes_after_130_pm_Square_Root,0.539088697,F -coef_Shift_for_every_30_minutes_before_1130_am_Linear,0.414546555,F -coef_Before_1200_PM,0.279351638,F -coef_1200_AM_1230_PM,-0.045281832,F -coef_1230_PM_0100_PM,0.214070736,F -coef_0100_PM_0130_PM,0,T -coef_0130_PM_0200_PM,-0.69742748,F -coef_0200_PM_0230_PM,-1.284283533,F -coef_After_0230_PM,-2.119733896,F -coef_Shift_for_every_30_minutes_after_300_pm_Linear,-0.508006414,F -coef_0_hrs,-0.969734874,F -coef_0p5_hrs,0,T -coef_1_hrs,0.177457256,F -coef_1p5hrs,-0.171124657,F -coef_2_hrs,-0.4678094,F -coef_Longer_than_2_hrs,-0.523935526,F -coef_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root,-0.424301372,F -coef_Calibration_Constant_Departure_eq_18,-0.045958531,F -coef_Calibration_Constant_Departure_eq_19,-0.099009925,F -coef_Calibration_Constant_Arrival_eq_20,-0.0698094,F -coef_Calibration_Constant_Arrival_eq_21,-0.064355276,F +coefficient_name,value,constrain +coef_Female_Departure_after_1230_pm_Linear,0.05574558,F +coef_Parttime_worker_Departure_after_1230_pm__Linear,0.129291333,F +coef_Parttime_worker_Duration_greater_than_0p5_hours_depart_and_arrive_in_the_same_period,0.162008704,F +coef_Low_income_lt25000_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.885322446,F +coef_Med_income_25k_to_60k_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.526935246,F +coef_Med_income_25k_to_60k_Duration_greater_than_0p5_hours,-0.081917021,F +coef_Medhigh_income_60k_to_120k_Duration_greater_than_0p5_hours,-0.068358924,F +coef_Blue_collar_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,1.191378628,F +coef_Blue_collar_Duration_greater_than_0p5_hours,0.123072852,F +coef_Health_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,0.791205377,F +coef_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.292363361,F +coef_Distance_to_destination_Duration_greater_than_0p5_hours,0.006885922,F +coef_Subtour_purpose_Business_Departure_before_1200_pm__Linear,0.268963895,F +coef_Subtour_purpose_Business_Departure_after_1230_pm_Linear,0.17631122,F +coef_Subtour_purpose_Business_Duration_greater_than_0p5_hours,0.362189199,F +coef_Subtour_purpose_Eatout_Departure_before_1200_pm__Linear,-0.250770206,F +coef_Subtour_purpose_Eatout_Departure_after_1230_pm_Linear,-0.169861029,F +coef_Subtour_purpose_Eatout_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.678939929,F +coef_Shift_for_every_30_minutes_before_1030_am_Linear,-0.731880037,F +coef_Before_1100_AM,-2.176744062,F +coef_1100_AM_1130_AM,-1.190017952,F +coef_1130_AM_1200_PM,-0.198229872,F +#coef_1200_AM_1230_PM,0,T +#coef_1230_PM_0100_PM,-0.084950396,F +coef_After_0100_PM,-0.205562723,F +coef_Shift_for_every_30_minutes_after_130_pm_Square_Root,0.539088697,F +coef_Shift_for_every_30_minutes_before_1130_am_Linear,0.414546555,F +coef_Before_1200_PM,0.279351638,F +coef_1200_AM_1230_PM,-0.045281832,F +coef_1230_PM_0100_PM,0.214070736,F +coef_0100_PM_0130_PM,0,T +coef_0130_PM_0200_PM,-0.69742748,F +coef_0200_PM_0230_PM,-1.284283533,F +coef_After_0230_PM,-2.119733896,F +coef_Shift_for_every_30_minutes_after_300_pm_Linear,-0.508006414,F +coef_0_hrs,-0.969734874,F +coef_0p5_hrs,0,T +coef_1_hrs,0.177457256,F +coef_1p5hrs,-0.171124657,F +coef_2_hrs,-0.4678094,F +coef_Longer_than_2_hrs,-0.523935526,F +coef_Shift_for_every_30_minutes_more_than_2p5_hrs_Square_Root,-0.424301372,F +coef_Calibration_Constant_Departure_eq_18,-0.045958531,F +coef_Calibration_Constant_Departure_eq_19,-0.099009925,F +coef_Calibration_Constant_Arrival_eq_20,-0.0698094,F +coef_Calibration_Constant_Arrival_eq_21,-0.064355276,F diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_atwork_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork_preprocessor.csv similarity index 97% rename from activitysim/examples/example_semcog/configs/tour_scheduling_atwork_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork_preprocessor.csv index 7a009186f4..26ac917ae5 100755 --- a/activitysim/examples/example_semcog/configs/tour_scheduling_atwork_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_atwork_preprocessor.csv @@ -1,3 +1,3 @@ -Description,Target,Expression -#,, -local scalar distance skim,od_distance,"od_skims[('SOV_DIST', 'MD')]" +Description,Target,Expression +#,, +local scalar distance skim,od_distance,"od_skims[('SOV_DIST', 'MD')]" diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_joint.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_joint.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/tour_scheduling_joint.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_joint.csv index 0a2409ad80..a9f0a51860 100755 --- a/activitysim/examples/example_semcog/configs/tour_scheduling_joint.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_joint.csv @@ -1,315 +1,315 @@ -Label,Description,Expression,Coefficient -,,, -#ESCORT,#ESCORT,,ESCORT -util_escort_Mode_Choice_Logsum,ESCORT - Mode Choice Logsum,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort')), df.mode_choice_logsum, 0)",coef_escort_Mode_Choice_Logsum -util_escort_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,ESCORT - Distance to destination - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration<1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0))),0)",coef_escort_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period -util_escort_Distance_to_destination_Duration_greater_than_0p5_hours,ESCORT - Distance to destination - Duration greater than 0.5 hours,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration>1)), ((df.origin_to_destination_distance) * (np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_Distance_to_destination_Duration_greater_than_0p5_hours -util_escort_Fulltime_worker_Departure_after_8_00_am_Linear,ESCORT - Full-time worker - Departure after 8:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 1) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)),0)",coef_escort_Fulltime_worker_Departure_after_8_00_am_Linear -util_escort_Fulltime_worker_Departure_after_3_00_am_Linear,ESCORT - Full-time worker - Departure after 3:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 1) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_Fulltime_worker_Departure_after_3_00_am_Linear -util_escort_Fulltime_worker_Duration_lt_0p5_hrs,ESCORT - Full-time worker - Duration < 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 1) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0)), 0)",coef_escort_Fulltime_worker_Duration_lt_0p5_hrs -util_escort_Fulltime_worker_Duration_gt_0p5_hrs,ESCORT - Full-time worker - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 1) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Fulltime_worker_Duration_gt_0p5_hrs -util_escort_University_student_Duration_lt_0p5_hrs,ESCORT - University student - Duration < 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 3) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0)), 0)",coef_escort_University_student_Duration_lt_0p5_hrs -util_escort_Nondriving_age_student_Duration_gt_0p5_hrs,ESCORT - Non-driving age student - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & ((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Nondriving_age_student_Duration_gt_0p5_hrs -util_escort_Driving_age_student_Duration_lt_0p5_hrs,ESCORT - Driving age student - Duration < 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 6) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0)), 0)",coef_escort_Driving_age_student_Duration_lt_0p5_hrs -util_escort_Driving_age_student_Duration_gt_0p5_hrs,ESCORT - Driving age student - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 6) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Driving_age_student_Duration_gt_0p5_hrs -util_escort_Preschool_kid_Duration_gt_0p5_hrs,ESCORT - Pre-school kid - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 8) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0)), 0)",coef_escort_Preschool_kid_Duration_gt_0p5_hrs -util_escort_Medhigh_income_60k_to_120k_Duration_gt_0p5_hrs,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Medhigh_income_60k_to_120k_Duration_gt_0p5_hrs -util_escort_Households_with_no_kids_Dummy_1_0_Departure_before_7_30_AM,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.start<10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_7_30_AM -util_escort_Households_with_no_kids_Dummy_1_0_Departure_after_8_00_AM,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_8_00_AM -util_escort_Households_with_no_kids_Dummy_1_0_Departure_before_2_30_PM,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.start<24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_2_30_PM -util_escort_Households_with_no_kids_Dummy_1_0_Departure_after_3_00_PM,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where ((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_3_00_PM -util_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_8_00_AM,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_8_00_AM -util_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_8_30_AM,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_8_30_AM -util_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_3_00_PM,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_3_00_PM -util_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_3_30_PM,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_3_30_PM -util_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start >10)), (np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM -util_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_3_00_PM,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), (np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_3_00_PM -util_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_8_00_AM,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0)), 0)",coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_8_00_AM -util_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_3_00_PM,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0)), 0)",coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_3_00_PM -util_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), (np.where ((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM -util_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_8_30_AM,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), (np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_8_30_AM -util_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_3_30_PM,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) &( df.end>25)), (np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_3_30_PM -util_escort_Number_of_autos_greater_than_number_of_adults_Duration_gt_0p5_hrs,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Number_of_autos_greater_than_number_of_adults_Duration_gt_0p5_hrs -#FIXME_Number_of_nonescort_tours_is_not_known_until_the_nonmandatory_frequency_model_is_run,#FIXME - Number of non-escort tours is not known until the non-mandatory frequency model is run,,FIXME_Number_of_nonescort_tours_is_not_known_until_the_nonmandatory_frequency_model_is_run -#util_escort_Number_of_Individual_Tours_excluding_escorting_Duration_gt_0p5_hrs,#ESCORT - Number of Individual Tours (excluding escorting) - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type != 'escort') & (df.num_non_escort_tours > 0) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Number_of_Individual_Tours_excluding_escorting_Duration_gt_0p5_hrs -util_escort_Number_of_joint_tours_Duration_gt_0p5_hrs,ESCORT - Number of joint tours - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) *(df.num_joint_tours), 0)",coef_escort_Number_of_joint_tours_Duration_gt_0p5_hrs -util_escort_Departure_Constant_Shift_for_every_30_minutes_before_06_30_am_Linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start<8)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + np.where((df.start>13), np.minimum(df.start-13,28), 0)), 0)",coef_escort_Departure_Constant_Shift_for_every_30_minutes_before_06_30_am_Linear -util_escort_Departure_Constant_Before_07_00_AM,ESCORT - Departure Constant: Before 07:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start<9)),coef_escort_Departure_Constant_Before_07_00_AM -util_escort_Departure_Constant_07_00_AM_07_30_AM,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==9)),coef_escort_Departure_Constant_07_00_AM_07_30_AM -util_escort_Departure_Constant_07_30_AM_08_00_AM,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==10)),coef_escort_Departure_Constant_07_30_AM_08_00_AM -util_escort_Departure_Constant_08_00_AM_08_30_AM,ESCORT - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==11)),coef_escort_Departure_Constant_08_00_AM_08_30_AM -util_escort_Departure_Constant_08_30_AM_09_00_AM,ESCORT - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==12)),coef_escort_Departure_Constant_08_30_AM_09_00_AM -util_escort_Departure_Constant_After_09_00_AM,ESCORT - Departure Constant: After 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start>12)),coef_escort_Departure_Constant_After_09_00_AM -util_escort_Departure_Constant_01_30_PM_02_00_PM,ESCORT - Departure Constant: 01:30 PM - 02:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==22)),coef_escort_Departure_Constant_01_30_PM_02_00_PM -util_escort_Departure_Constant_02_00_PM_02_30_PM,ESCORT - Departure Constant: 02:00 PM - 02:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==23)),coef_escort_Departure_Constant_02_00_PM_02_30_PM -util_escort_Departure_Constant_02_30_PM_03_00_PM,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==24)),coef_escort_Departure_Constant_02_30_PM_03_00_PM -util_escort_Departure_Constant_03_00_PM_03_30_PM,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==25)),coef_escort_Departure_Constant_03_00_PM_03_30_PM -util_escort_Departure_Constant_After_03_30_PM,ESCORT - Departure Constant: After 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start>25)),coef_escort_Departure_Constant_After_03_30_PM -util_escort_Departure_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start>13)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + (np.where((df.start>13), np.minimum(df.start-13,28), 0))), 0)",coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear -util_escort_Departure_Constant_Shift_for_every_30_minutes_after_4_00_pm_Linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start>26)), (np.where((df.start<0), np.minimum(0-df.start,48), 0) + np.where((df.start>26), np.minimum(df.start-26,15),0)), 0)",coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_4_00_pm_Linear -util_escort_Arrival_Constant_Shift_for_every_30_minutes_before_6_30_am_Linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end<8)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_Arrival_Constant_Shift_for_every_30_minutes_before_6_30_am_Linear -util_escort_Arrival_Constant_Before_07_00_AM,ESCORT - Arrival Constant: Before 07:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end<9)),coef_escort_Arrival_Constant_Before_07_00_AM -util_escort_Arrival_Constant_07_00_AM_07_30_AM,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==9)),coef_escort_Arrival_Constant_07_00_AM_07_30_AM -util_escort_Arrival_Constant_07_30_AM_08_00_AM,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==10)),coef_escort_Arrival_Constant_07_30_AM_08_00_AM -util_escort_Arrival_Constant_08_00_AM_08_30_AM,ESCORT - Arrival Constant: 08:00 AM - 08:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==11)),coef_escort_Arrival_Constant_08_00_AM_08_30_AM -util_escort_Arrival_Constant_08_30_AM_09_00_AM,ESCORT - Arrival Constant: 08:30 AM - 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==12)),coef_escort_Arrival_Constant_08_30_AM_09_00_AM -util_escort_Arrival_Constant_After_09_00_AM,ESCORT - Arrival Constant: After 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end>12)),coef_escort_Arrival_Constant_After_09_00_AM -util_escort_Arrival_Constant_02_30_PM_03_00_PM,ESCORT - Arrival Constant: 02:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==24)),coef_escort_Arrival_Constant_02_30_PM_03_00_PM -util_escort_Arrival_Constant_03_00_PM_03_30_PM,ESCORT - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==25)),coef_escort_Arrival_Constant_03_00_PM_03_30_PM -util_escort_Arrival_Constant_03_30_PM_04_00_PM,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==26)),coef_escort_Arrival_Constant_03_30_PM_04_00_PM -util_escort_Arrival_Constant_04_00_PM_04_30_PM,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==27)),coef_escort_Arrival_Constant_04_00_PM_04_30_PM -util_escort_Arrival_Constant_After_04_30_PM,ESCORT - Arrival Constant: After 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end>27)),coef_escort_Arrival_Constant_After_04_30_PM -util_escort_Arrival_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end>13)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear -util_escort_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end>28)), (np.where((df.end<0), np.minimum(0-df.end,48), 0) + np.where((df.start>28), np.minimum(df.end-28,15), 0)), 0)",coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear -util_escort_Duration_Constant_0_hrs,ESCORT - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==0)),coef_escort_Duration_Constant_0_hrs -util_escort_Duration_Constant_0p5_hrs,ESCORT - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==1)),coef_escort_Duration_Constant_0p5_hrs -util_escort_Duration_Constant_1_hrs,ESCORT - Duration Constant: 1 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==2)),coef_escort_Duration_Constant_1_hrs -util_escort_Duration_Constant_1p5hrs,ESCORT - Duration Constant: 1.5hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==3)),coef_escort_Duration_Constant_1p5hrs -util_escort_Duration_Constant_2_hrs,ESCORT - Duration Constant: 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==4)),coef_escort_Duration_Constant_2_hrs -util_escort_Duration_Constant_Longer_than_2_hrs,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration>4)),coef_escort_Duration_Constant_Longer_than_2_hrs -util_escort_Calibration_Constant_Duration_eq_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==0)),coef_escort_Calibration_Constant_Duration_eq_1 -util_escort_Calibration_Constant_Duration_eq_2,ESCORT - Calibration Constant - Duration = 2,@(((df.tour_category == 'joint') & (df.tour_type == 'escort') & df.duration==1)),coef_escort_Calibration_Constant_Duration_eq_2 -util_escort_Calibration_Constant_Departure_eq_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==9)),coef_escort_Calibration_Constant_Departure_eq_9 -util_escort_Calibration_Constant_Departure_eq_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==10)),coef_escort_Calibration_Constant_Departure_eq_10 -util_escort_Calibration_Constant_Departure_eq_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==23)),coef_escort_Calibration_Constant_Departure_eq_23 -util_escort_Calibration_Constant_Departure_eq_24,ESCORT - Calibration Constant - Departure = 24,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==24)),coef_escort_Calibration_Constant_Departure_eq_24 -#SHOPPING,#SHOPPING,,SHOPPING -util_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear,SHOPPING - Joint Shopping tours dummy: Departure before 10:00 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<15)), (np.where((df.start<=15), np.minimum(15-df.start,7), 0) + np.where((df.start>15), np.minimum(df.start-15,24), 0)), 0)",coef_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear -util_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear,SHOPPING - Joint Shopping tours dummy: Departure after 10:30 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>15)), (np.where((df.start<=15), np.minimum(15-df.start,7), 0) + np.where((df.start>15), np.minimum(df.start-15,24), 0)), 0)",coef_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear -util_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,SHOPPING - Joint Tours Party Size > 2: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.number_of_participants > 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs -util_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,SHOPPING - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.number_of_participants > 2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr -util_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,SHOPPING - Joint Tour with only adults: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.composition=='adults')), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs -util_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,SHOPPING - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs -util_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr,SHOPPING - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr -util_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_less25K) & (df.duration>3)), (np.where((df.duration>3),np.minimum(df.duration-3,27), 0)), 0)",coef_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr -util_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_25K_to_60K) & (df.duration<3)), (np.where((df.duration>3),np.minimum(df.duration-3,27), 0)), 0)",coef_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs -util_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_60K_to_120K) & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,27), 0)), 0)",coef_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr -util_shop_Distance_Duration_lt_1p5_hrs,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)) * (df.origin_to_destination_distance), 0)",coef_shop_Distance_Duration_lt_1p5_hrs -util_shop_Distance_Duration_gt_1p5_hr,SHOPPING - Distance: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>2)), ((np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance)), 0)",coef_shop_Distance_Duration_gt_1p5_hr -util_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear -util_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0))**0.5, 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root -util_shop_Departure_Constant_Before_09_00_AM,SHOPPING - Departure Constant: Before 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<13)),coef_shop_Departure_Constant_Before_09_00_AM -util_shop_Departure_Constant_09_00_AM_09_30_AM,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==13)),coef_shop_Departure_Constant_09_00_AM_09_30_AM -util_shop_Departure_Constant_09_30_AM_10_00_AM,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==14)),coef_shop_Departure_Constant_09_30_AM_10_00_AM -util_shop_Departure_Constant_10_00_AM_10_30_AM,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==15)),coef_shop_Departure_Constant_10_00_AM_10_30_AM -util_shop_Departure_Constant_10_30_AM_11_00_AM,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==16)),coef_shop_Departure_Constant_10_30_AM_11_00_AM -util_shop_Departure_Constant_After_11_00_AM,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>16)),coef_shop_Departure_Constant_After_11_00_AM -util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear -util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared -util_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear -util_shop_Arrival_Constant_Before_12_30_PM,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<20)),coef_shop_Arrival_Constant_Before_12_30_PM -util_shop_Arrival_Constant_12_30_PM_03_00_PM,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shop_Arrival_Constant_12_30_PM_03_00_PM -util_shop_Arrival_Constant_03_00_PM_03_30_PM,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==25)),coef_shop_Arrival_Constant_03_00_PM_03_30_PM -util_shop_Arrival_Constant_03_30_PM_04_00_PM,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==26)),coef_shop_Arrival_Constant_03_30_PM_04_00_PM -util_shop_Arrival_Constant_04_00_PM_04_30_PM,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==27)),coef_shop_Arrival_Constant_04_00_PM_04_30_PM -util_shop_Arrival_Constant_04_30_PM_05_00_PM,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==28)),coef_shop_Arrival_Constant_04_30_PM_05_00_PM -util_shop_Arrival_Constant_05_00_PM_05_30_PM,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==29)),coef_shop_Arrival_Constant_05_00_PM_05_30_PM -util_shop_Arrival_Constant_05_30_PM_07_00_PM,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>=30) & (df.end<=32)),coef_shop_Arrival_Constant_05_30_PM_07_00_PM -util_shop_Arrival_Constant_07_00_PM_09_30_PM,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>=33) & (df.end<=37)),coef_shop_Arrival_Constant_07_00_PM_09_30_PM -util_shop_Arrival_Constant_After_09_30_PM,SHOPPING - Arrival Constant: After 09:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>37)),coef_shop_Arrival_Constant_After_09_30_PM -util_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>38)), (np.where((df.end<19), np.minimum(19-df.end,10), 0) + np.where ((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear -util_shop_Duration_Constant_0_hrs,SHOPPING - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==0)),coef_shop_Duration_Constant_0_hrs -util_shop_Duration_Constant_0p5_hrs,SHOPPING - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==1)),coef_shop_Duration_Constant_0p5_hrs -util_shop_Duration_Constant_1_hrs,SHOPPING - Duration Constant: 1 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==2)),coef_shop_Duration_Constant_1_hrs -util_shop_Duration_Constant_1p5hrs,SHOPPING - Duration Constant: 1.5hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==3)),coef_shop_Duration_Constant_1p5hrs -util_shop_Duration_Constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==4)),coef_shop_Duration_Constant_2_hrs -util_shop_Duration_Constant_Longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>4)),coef_shop_Duration_Constant_Longer_than_2_hrs -util_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)), 0)",coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear -util_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>5)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)) ** 0.5), 0)",coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root -util_shop_Calibration_Constant_Duration_eq_1,SHOPPING - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==0)),coef_shop_Calibration_Constant_Duration_eq_1 -util_shop_Calibration_Constant_Duration_eq_2,SHOPPING - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==1)),coef_shop_Calibration_Constant_Duration_eq_2 -util_shop_Calibration_Constant_Duration_eq_3,SHOPPING - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==2)),coef_shop_Calibration_Constant_Duration_eq_3 -#MAINTENANCE,#MAINTENANCE,,MAINTENANCE -util_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear,MAINTENANCE - Joint Maintenance tours dummy: Departure before 10:00 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<15)), (np.where((df.start<=15), np.minimum(15-df.start,7), 0) + np.where((df.start>15), np.minimum(df.start-15,24), 0)), 0)",coef_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear -util_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear,MAINTENANCE - Joint Maintenance tours dummy: Departure after 10:30 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>15)), (np.where((df.start<=15), np.minimum(15-df.start,7), 0) + np.where((df.start>15), np.minimum(df.start-15,24), 0)), 0)",coef_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear -util_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,MAINTENANCE - Joint Tours Party Size > 2: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.number_of_participants > 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs -util_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,MAINTENANCE - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.number_of_participants > 2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr -util_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,MAINTENANCE - Joint Tour with only adults: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.composition=='adults')), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs -util_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,MAINTENANCE - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs -util_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr,MAINTENANCE - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr -util_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr,"MAINTENANCE - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr -util_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs -util_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration>2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr -util_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr -util_maint_Distance_Duration_lt_1p5_hrs,MAINTENANCE - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)) *(df.origin_to_destination_distance), 0)",coef_maint_Distance_Duration_lt_1p5_hrs -util_maint_Distance_Duration_gt_1p5_hr,MAINTENANCE - Distance: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_maint_Distance_Duration_gt_1p5_hr -util_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<10)), (np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear -util_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<10)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + (np.where((df.start>17), np.minimum(df.start-17,24), 0)))** 0.5), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root -util_maint_Departure_Constant_Before_08_00_AM,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<11)),coef_maint_Departure_Constant_Before_08_00_AM -util_maint_Departure_Constant_08_00_AM_08_30_AM,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==11)),coef_maint_Departure_Constant_08_00_AM_08_30_AM -util_maint_Departure_Constant_08_30_AM_09_00_AM,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==12)),coef_maint_Departure_Constant_08_30_AM_09_00_AM -util_maint_Departure_Constant_09_00_AM_09_30_AM,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==13)),coef_maint_Departure_Constant_09_00_AM_09_30_AM -util_maint_Departure_Constant_09_30_AM_10_00_AM,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==14)),coef_maint_Departure_Constant_09_30_AM_10_00_AM -util_maint_Departure_Constant_10_00_AM_10_30_AM,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==15)),coef_maint_Departure_Constant_10_00_AM_10_30_AM -util_maint_Departure_Constant_10_30_AM_11_00_AM,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==16)),coef_maint_Departure_Constant_10_30_AM_11_00_AM -util_maint_Departure_Constant_After_11_00_AM,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>16)),coef_maint_Departure_Constant_After_11_00_AM -util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear -util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared -util_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear -util_maint_Arrival_Constant_Before_10_30_AM,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<16)),coef_maint_Arrival_Constant_Before_10_30_AM -util_maint_Arrival_Constant_10_30_AM_11_00_AM,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==16)),coef_maint_Arrival_Constant_10_30_AM_11_00_AM -util_maint_Arrival_Constant_11_00_AM_11_30_AM,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==17)),coef_maint_Arrival_Constant_11_00_AM_11_30_AM -util_maint_Arrival_Constant_11_30_AM_01_30_PM,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=18) & (df.end<=21)),coef_maint_Arrival_Constant_11_30_AM_01_30_PM -util_maint_Arrival_Constant_01_30_PM_02_30_PM,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=22) & (df.end<=23)),coef_maint_Arrival_Constant_01_30_PM_02_30_PM -util_maint_Arrival_Constant_02_30_PM_04_00_PM,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=24) & (df.end<=26)),coef_maint_Arrival_Constant_02_30_PM_04_00_PM -util_maint_Arrival_Constant_04_00_PM_04_30_PM,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==27)),coef_maint_Arrival_Constant_04_00_PM_04_30_PM -util_maint_Arrival_Constant_After_04_30_PM,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>27)),coef_maint_Arrival_Constant_After_04_30_PM -util_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>28)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear -util_maint_Duration_Constant_0_hrs,MAINTENANCE - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==0)),coef_maint_Duration_Constant_0_hrs -util_maint_Duration_Constant_0p5_hrs,MAINTENANCE - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==1)),coef_maint_Duration_Constant_0p5_hrs -util_maint_Duration_Constant_Longer_than_0p5_hrs,MAINTENANCE - Duration Constant: Longer than 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>1)),coef_maint_Duration_Constant_Longer_than_0p5_hrs -util_maint_Duration_Constant_Duration_gt_1_hrs_Linear,MAINTENANCE - Duration Constant: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Duration_Constant_Duration_gt_1_hrs_Linear -util_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root,MAINTENANCE - Duration Constant: Duration > 1 hrs - Square Root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0))** 0.5), 0)",coef_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root -util_maint_Calibration_Constant_Duration_eq_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==0)),coef_maint_Calibration_Constant_Duration_eq_1 -util_maint_Calibration_Constant_Duration_eq_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==1)),coef_maint_Calibration_Constant_Duration_eq_2 -util_maint_Calibration_Constant_Duration_eq_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==2)),coef_maint_Calibration_Constant_Duration_eq_3 -util_maint_Calibration_Constant_Duration_eq_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==3)),coef_maint_Calibration_Constant_Duration_eq_4 -util_maint_Calibration_Constant_Duration_eq_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==4)),coef_maint_Calibration_Constant_Duration_eq_5 -#EATOUT,#EAT-OUT,,EATOUT -util_eatout_Distance_to_destination_Duration_lt_1_hrs,EAT-OUT - Distance to destination - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)) * (df.origin_to_destination_distance), 0)",coef_eatout_Distance_to_destination_Duration_lt_1_hrs -util_eatout_Distance_to_destination_Duration_gt_1_hrs,EAT-OUT - Distance to destination - Duration > 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>2)), (np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) *(df.origin_to_destination_distance), 0)",coef_eatout_Distance_to_destination_Duration_gt_1_hrs -util_eatout_Low_income_lt25000_Duration_lt_1_hrs,EAT-OUT - Low income (<25000) - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.is_income_less25K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)), 0)",coef_eatout_Low_income_lt25000_Duration_lt_1_hrs -util_eatout_Medium_25k_to_60k_Duration_lt_1_hrs,EAT-OUT - Medium (25k to 60k) - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)), 0)",coef_eatout_Medium_25k_to_60k_Duration_lt_1_hrs -util_eatout_Zero_auto_HH_Duration_gt_1_hrs,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.auto_ownership == 0) & (df.duration>2)), (np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_Zero_auto_HH_Duration_gt_1_hrs -util_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs,EAT-OUT - Kids in Joint tour - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)), 0)",coef_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs -util_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs,EAT-OUT - Joint Tours Party Size greater than 2 - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & (df.number_of_participants > 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)), 0)",coef_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs -util_eatout_Departure_Constant_11_00_AM_12_00_PM,EAT-OUT - Departure Constant: 11:00 AM - 12:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>=17) & (df.start<=18)),coef_eatout_Departure_Constant_11_00_AM_12_00_PM -util_eatout_Departure_Constant_12_00_PM_12_30_PM,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==19),coef_eatout_Departure_Constant_12_00_PM_12_30_PM -util_eatout_Departure_Constant_12_30_PM_to_01_00_PM,EAT-OUT - Departure Constant: 12:30 PM to 01:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==20),coef_eatout_Departure_Constant_12_30_PM_to_01_00_PM -util_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start<29)), (np.where((df.start<29), np.minimum(29-df.start,20), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear -util_eatout_Departure_Constant_Before_05_30_PM,EAT-OUT - Departure Constant: Before 05:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start<30),coef_eatout_Departure_Constant_Before_05_30_PM -util_eatout_Departure_Constant_05_30_PM_06_00_PM,EAT-OUT - Departure Constant: 05:30 PM - 06:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==30),coef_eatout_Departure_Constant_05_30_PM_06_00_PM -util_eatout_Departure_Constant_06_00_PM_06_30_PM,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==31),coef_eatout_Departure_Constant_06_00_PM_06_30_PM -util_eatout_Departure_Constant_06_30_PM_07_00_PM,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==32),coef_eatout_Departure_Constant_06_30_PM_07_00_PM -util_eatout_Departure_Constant_07_00_PM_07_30_PM,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==33),coef_eatout_Departure_Constant_07_00_PM_07_30_PM -util_eatout_Departure_Constant_After_07_30_PM,EAT-OUT - Departure Constant: After 07:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>33),coef_eatout_Departure_Constant_After_07_30_PM -util_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>34)), (np.where((df.start<29), np.minimum(29-df.start,20), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear -util_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==20),coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM -util_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==21),coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM -util_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==22),coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM -util_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==23),coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM -util_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end<32)), (np.where((df.end<32), np.minimum(32-df.end,21), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear -util_eatout_Arrival_Constant_Before_7_00_PM,EAT-OUT - Arrival Constant: Before 7:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end<33),coef_eatout_Arrival_Constant_Before_7_00_PM -util_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@(df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.end==33),coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM -util_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==34),coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM -util_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==35),coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM -util_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==36),coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM -util_eatout_Arrival_Constant_After_09_00_PM,EAT-OUT - Arrival Constant: After 09:00 PM,@(df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.end>36),coef_eatout_Arrival_Constant_After_09_00_PM -util_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_type== 'eatout') & (df.end>37) & (df.tour_category == 'joint')), (np.where((df.end<32), np.minimum(32-df.end,21), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear -util_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>6)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>6), np.minimum(df.duration-6,12), 0)), 0)",coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear -util_eatout_Duration_Constant_0_hours,EAT-OUT - Duration Constant: 0 hours,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_Duration_Constant_0_hours -util_eatout_Duration_Constant_0p5_hous,EAT-OUT - Duration Constant: 0.5 hous,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_Duration_Constant_0p5_hous -util_eatout_Duration_Constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_Duration_Constant_1_hour -util_eatout_Duration_Constant_1p5_hours,EAT-OUT - Duration Constant: 1.5 hours,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_Duration_Constant_1p5_hours -util_eatout_Duration_Constant_2_hours_or_more,EAT-OUT - Duration Constant: 2 hours or more,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>4),coef_eatout_Duration_Constant_2_hours_or_more -util_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 2.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,13), 0)), 0)",coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear -util_eatout_Calibration_Constant_Duration_eq_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_Calibration_Constant_Duration_eq_1 -util_eatout_Calibration_Constant_Duration_eq_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_Calibration_Constant_Duration_eq_2 -util_eatout_Calibration_Constant_Duration_eq_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_Calibration_Constant_Duration_eq_3 -util_eatout_Calibration_Constant_Duration_eq_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_Calibration_Constant_Duration_eq_4 -util_eatout_Calibration_Constant_Departure_eq_1,EAT-OUT - Calibration Constant - Departure = 1,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start == 1)),coef_eatout_Calibration_Constant_Departure_eq_1 -util_eatout_Calibration_Constant_Departure_eq_2,EAT-OUT - Calibration Constant - Departure = 2,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start == 2)),coef_eatout_Calibration_Constant_Departure_eq_2 -util_eatout_Calibration_Constant_Departure_eq_3,EAT-OUT - Calibration Constant - Departure = 3,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.start == 3)),coef_eatout_Calibration_Constant_Departure_eq_3 -util_eatout_Calibration_Constant_Departure_eq_17,EAT-OUT - Calibration Constant - Departure = 17,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==17)),coef_eatout_Calibration_Constant_Departure_eq_17 -util_eatout_Calibration_Constant_Departure_eq_18,EAT-OUT - Calibration Constant - Departure = 18,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.start ==18)),coef_eatout_Calibration_Constant_Departure_eq_18 -util_eatout_Calibration_Constant_Departure_eq_19,EAT-OUT - Calibration Constant - Departure = 19,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==19)),coef_eatout_Calibration_Constant_Departure_eq_19 -util_eatout_Calibration_Constant_Departure_eq_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==20)),coef_eatout_Calibration_Constant_Departure_eq_20 -util_eatout_Calibration_Constant_Departure_eq_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==21)),coef_eatout_Calibration_Constant_Departure_eq_21 -#SOCIAL,#SOCIAL,,SOCIAL -util_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.retired_adults_only_hh) & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear -util_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear,SOCIAL - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear -util_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear,SOCIAL - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear -util_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,SOCIAL - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint')&(df.tour_type == 'social') & (df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear -util_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,SOCIAL - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear -util_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,SOCIAL - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<3) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs -util_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr,SOCIAL - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>3) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr -util_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,SOCIAL - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>3) & (df.number_of_participants > 2)), (np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr -util_social_Auto_Distance_Duration_lt_1_hrs_Linear,SOCIAL - Auto Distance: Duration < 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) * (df.origin_to_destination_distance)), 0) ",coef_social_Auto_Distance_Duration_lt_1_hrs_Linear -util_social_Auto_Distance_Duration_gt_1_hrs_Linear,SOCIAL - Auto Distance: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0) * (df.origin_to_destination_distance)), 0)",coef_social_Auto_Distance_Duration_gt_1_hrs_Linear -util_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48), 0)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear -util_social_Departure_Constant_Before_09_00_AM,SOCIAL - Departure Constant: Before 09:00 AM,@(df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<13),coef_social_Departure_Constant_Before_09_00_AM -util_social_Departure_Constant_09_00_AM_to_09_30_AM,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==13),coef_social_Departure_Constant_09_00_AM_to_09_30_AM -util_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<29), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear -util_social_Departure_Constant_Before_05_30_PM,SOCIAL - Departure Constant: Before 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<30)),coef_social_Departure_Constant_Before_05_30_PM -util_social_Departure_Constant_05_30_PM_06_00_PM,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==30)),coef_social_Departure_Constant_05_30_PM_06_00_PM -util_social_Departure_Constant_06_00_PM_06_30_PM,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==31)),coef_social_Departure_Constant_06_00_PM_06_30_PM -util_social_Departure_Constant_06_30_PM_07_00_PM,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==32)),coef_social_Departure_Constant_06_30_PM_07_00_PM -util_social_Departure_Constant_07_00_PM_07_30_PM,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==33)),coef_social_Departure_Constant_07_00_PM_07_30_PM -util_social_Departure_Constant_After_07_30_PM,SOCIAL - Departure Constant: After 07:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start>33)),coef_social_Departure_Constant_After_07_30_PM -util_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start>34), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear -util_social_Arrival_Constant_03_00_PM_to_03_30_PM,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==25)),coef_social_Arrival_Constant_03_00_PM_to_03_30_PM -util_social_Arrival_Constant_03_30_PM_to_04_00_PM,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==26)),coef_social_Arrival_Constant_03_30_PM_to_04_00_PM -util_social_Arrival_Constant_04_00_PM_to_04_30_PM,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==27)),coef_social_Arrival_Constant_04_00_PM_to_04_30_PM -util_social_Arrival_Constant_05_00_PM_to_06_00_PM,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>=29) & (df.end<=30)),coef_social_Arrival_Constant_05_00_PM_to_06_00_PM -util_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end<35)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) + np.where((df.end>40), np.minimum(df.end-40,48), 0)), 0)",coef_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear -util_social_Arrival_Constant_Before_8_30_PM,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end<36)),coef_social_Arrival_Constant_Before_8_30_PM -util_social_Arrival_Constant_8_30_PM_to_9_00_PM,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==36)),coef_social_Arrival_Constant_8_30_PM_to_9_00_PM -util_social_Arrival_Constant_9_00_PM_to_9_30_PM,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==37)),coef_social_Arrival_Constant_9_00_PM_to_9_30_PM -util_social_Arrival_Constant_9_30_PM_to10_00_PM,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==38)),coef_social_Arrival_Constant_9_30_PM_to10_00_PM -util_social_Arrival_Constant_10_00_PM_to_10_30_PM,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==39)),coef_social_Arrival_Constant_10_00_PM_to_10_30_PM -util_social_Arrival_Constant_After_10_30_PM,SOCIAL - Arrival Constant: After 10:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>39)),coef_social_Arrival_Constant_After_10_30_PM -util_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>40)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) +np.where((df.end>40),np.minimum(df.end-40,48),0)), 0)",coef_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear -util_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 1.5 hrs - Linear,"@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<3)) * ((np.minimum(3-df.duration,47)*(df.duration<3)) + (np.minimum(df.duration-6,47)*(df.duration>6)))",coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear -util_social_Duration_Constant_Less_than_2_hours,SOCIAL - Duration Constant: Less than 2 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<4)),coef_social_Duration_Constant_Less_than_2_hours -util_social_Duration_Constant_2_hours,SOCIAL - Duration Constant: 2 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration==4)),coef_social_Duration_Constant_2_hours -util_social_Duration_Constant_2p5_hours,SOCIAL - Duration Constant: 2.5 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration==5)),coef_social_Duration_Constant_2p5_hours -util_social_Duration_Constant_3_hours_or_more,SOCIAL - Duration Constant: 3 hours or more,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>5)),coef_social_Duration_Constant_3_hours_or_more -util_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 3.5 hrs - Linear,"@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>6)) * ((np.minimum(3-df.duration,47)*(df.duration<3)) + (np.minimum(df.duration-6,47)*(df.duration>6)))",coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear -util_social_Calibration_Constant_Duration_eq_1,SOCIAL - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==0)),coef_social_Calibration_Constant_Duration_eq_1 -util_social_Calibration_Constant_Duration_eq_2,SOCIAL - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration == 1)),coef_social_Calibration_Constant_Duration_eq_2 -util_social_Calibration_Constant_Duration_eq_3,SOCIAL - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==2)),coef_social_Calibration_Constant_Duration_eq_3 -util_social_Calibration_Constant_Duration_eq_4,SOCIAL - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==3)),coef_social_Calibration_Constant_Duration_eq_4 -util_social_Calibration_Constant_Duration_eq_5,SOCIAL - Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==4)),coef_social_Calibration_Constant_Duration_eq_5 -util_social_Calibration_Constant_Duration_eq_6,SOCIAL - Calibration Constant - Duration = 6,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==5)),coef_social_Calibration_Constant_Duration_eq_6 -util_social_Calibration_Constant_Duration_eq_7,SOCIAL - Calibration Constant - Duration = 7,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==6)),coef_social_Calibration_Constant_Duration_eq_7 -util_social_Calibration_Constant_Duration_eq_8,SOCIAL - Calibration Constant - Duration = 8,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==7)),coef_social_Calibration_Constant_Duration_eq_8 -util_social_Calibration_Constant_Duration_eq_9,SOCIAL - Calibration Constant - Duration = 9,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==8)),coef_social_Calibration_Constant_Duration_eq_9 -#DISCRETIONARY,#DISCRETIONARY,,DISCRETIONARY -util_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.retired_adults_only_hh) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear -util_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear -util_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear -util_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)),0)",coef_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear -util_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear -util_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,DISCRETIONARY - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs -util_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr,DISCRETIONARY - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr -util_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,DISCRETIONARY - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.number_of_participants > 2)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr -util_disc_Auto_Distance_Duration_lt_1_hrs_Linear,DISCRETIONARY - Auto Distance: Duration < 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)) * (df.origin_to_destination_distance), 0)",coef_disc_Auto_Distance_Duration_lt_1_hrs_Linear -util_disc_Auto_Distance_Duration_gt_1_hrs_Linear,DISCRETIONARY - Auto Distance: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_disc_Auto_Distance_Duration_gt_1_hrs_Linear -util_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<9)), (np.where((df.start<9), np.minimum(9-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48),0)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear -util_disc_Departure_Constant_Before_7_30_AM_,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<10)),coef_disc_Departure_Constant_Before_7_30_AM_ -util_disc_Departure_Constant_7_30_AM_to_8_00_AM,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==10)),coef_disc_Departure_Constant_7_30_AM_to_8_00_AM -util_disc_Departure_Constant_8_00_AM_to_8_30_AM,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==11)),coef_disc_Departure_Constant_8_00_AM_to_8_30_AM -util_disc_Departure_Constant_8_30_AM_to_9_00_AM,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==12)),coef_disc_Departure_Constant_8_30_AM_to_9_00_AM -util_disc_Departure_Constant_9_00_AM_to_9_30_AM,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==13)),coef_disc_Departure_Constant_9_00_AM_to_9_30_AM -util_disc_Departure_Constant_9_30_AM_to_10_00_AM,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==14)),coef_disc_Departure_Constant_9_30_AM_to_10_00_AM -util_disc_Departure_Constant_10_00_AM_to_10_30_AM,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==15)),coef_disc_Departure_Constant_10_00_AM_to_10_30_AM -util_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<28)), (np.where((df.start<28), np.minimum(28-df.start,8),0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear -util_disc_Departure_Constant_Before_05_00_PM,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<29)),coef_disc_Departure_Constant_Before_05_00_PM -util_disc_Departure_Constant_05_00_PM_05_30_PM,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==29)),coef_disc_Departure_Constant_05_00_PM_05_30_PM -util_disc_Departure_Constant_05_30_PM_06_00_PM,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==30)),coef_disc_Departure_Constant_05_30_PM_06_00_PM -util_disc_Departure_Constant_06_00_PM_06_30_PM,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==31)),coef_disc_Departure_Constant_06_00_PM_06_30_PM -util_disc_Departure_Constant_06_30_PM_07_00_PM,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==32)),coef_disc_Departure_Constant_06_30_PM_07_00_PM -util_disc_Departure_Constant_After_07_00_PM,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start>32)),coef_disc_Departure_Constant_After_07_00_PM -util_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start>33)), (np.where((df.start<28), np.minimum(28-df.start,8), 0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear -util_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end<31)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear -util_disc_Arrival_Constant_Before_6_30_PM,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end<32)),coef_disc_Arrival_Constant_Before_6_30_PM -util_disc_Arrival_Constant_6_30_PM_to_7_00_PM,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==32)),coef_disc_Arrival_Constant_6_30_PM_to_7_00_PM -util_disc_Arrival_Constant_7_00_PM_to_7_30_PM,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==33)),coef_disc_Arrival_Constant_7_00_PM_to_7_30_PM -util_disc_Arrival_Constant_7_30_PM_to_8_00_PM,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==34)),coef_disc_Arrival_Constant_7_30_PM_to_8_00_PM -util_disc_Arrival_Constant_8_00_PM_to_8_30_PM,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==35)),coef_disc_Arrival_Constant_8_00_PM_to_8_30_PM -util_disc_Arrival_Constant_8_30_PM_to_9_00_PM,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==36)),coef_disc_Arrival_Constant_8_30_PM_to_9_00_PM -util_disc_Arrival_Constant_After_9_00_PM,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end>36)),coef_disc_Arrival_Constant_After_9_00_PM -util_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48),0)), 0)",coef_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear -util_disc_Duration_Constant_0_hours,DISCRETIONARY - Duration Constant: 0 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==0),coef_disc_Duration_Constant_0_hours -util_disc_Duration_Constant_0p5_hous,DISCRETIONARY - Duration Constant: 0.5 hous,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==1),coef_disc_Duration_Constant_0p5_hous -util_disc_Duration_Constant_1_hour,DISCRETIONARY - Duration Constant: 1 hour,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==2),coef_disc_Duration_Constant_1_hour -util_disc_Duration_Constant_1p5_hours,DISCRETIONARY - Duration Constant: 1.5 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3),coef_disc_Duration_Constant_1p5_hours -util_disc_Duration_Constant_2_hours,DISCRETIONARY - Duration Constant: 2 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4),coef_disc_Duration_Constant_2_hours -util_disc_Duration_Constant_2p5_hours_or_more,DISCRETIONARY - Duration Constant: 2.5 hours or more,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>4),coef_disc_Duration_Constant_2p5_hours_or_more -util_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,DISCRETIONARY - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,47), 0)), 0)",coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear -util_disc_Calibration_Constant_Duration_eq_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3)),coef_disc_Calibration_Constant_Duration_eq_4 -util_disc_Calibration_Constant_Duration_eq_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4)),coef_disc_Calibration_Constant_Duration_eq_5 -util_disc_Calibration_Constant_Departure_eq_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==29)),coef_disc_Calibration_Constant_Departure_eq_29 -util_disc_Calibration_Constant_Departure_eq_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==30)),coef_disc_Calibration_Constant_Departure_eq_30 -util_disc_Calibration_Constant_Departure_eq_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==31)),coef_disc_Calibration_Constant_Departure_eq_31 -util_disc_Calibration_Constant_Departure_eq_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==32)),coef_disc_Calibration_Constant_Departure_eq_32 +Label,Description,Expression,Coefficient +,,, +#ESCORT,#ESCORT,,ESCORT +util_escort_Mode_Choice_Logsum,ESCORT - Mode Choice Logsum,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort')), df.mode_choice_logsum, 0)",coef_escort_Mode_Choice_Logsum +util_escort_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,ESCORT - Distance to destination - Duration less than 0.5 hours (depart and arrive in the same period),"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration<1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0))),0)",coef_escort_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period +util_escort_Distance_to_destination_Duration_greater_than_0p5_hours,ESCORT - Distance to destination - Duration greater than 0.5 hours,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration>1)), ((df.origin_to_destination_distance) * (np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_Distance_to_destination_Duration_greater_than_0p5_hours +util_escort_Fulltime_worker_Departure_after_8_00_am_Linear,ESCORT - Full-time worker - Departure after 8:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 1) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)),0)",coef_escort_Fulltime_worker_Departure_after_8_00_am_Linear +util_escort_Fulltime_worker_Departure_after_3_00_am_Linear,ESCORT - Full-time worker - Departure after 3:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 1) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_Fulltime_worker_Departure_after_3_00_am_Linear +util_escort_Fulltime_worker_Duration_lt_0p5_hrs,ESCORT - Full-time worker - Duration < 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 1) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0)), 0)",coef_escort_Fulltime_worker_Duration_lt_0p5_hrs +util_escort_Fulltime_worker_Duration_gt_0p5_hrs,ESCORT - Full-time worker - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 1) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Fulltime_worker_Duration_gt_0p5_hrs +util_escort_University_student_Duration_lt_0p5_hrs,ESCORT - University student - Duration < 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 3) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0)), 0)",coef_escort_University_student_Duration_lt_0p5_hrs +util_escort_Nondriving_age_student_Duration_gt_0p5_hrs,ESCORT - Non-driving age student - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & ((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Nondriving_age_student_Duration_gt_0p5_hrs +util_escort_Driving_age_student_Duration_lt_0p5_hrs,ESCORT - Driving age student - Duration < 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 6) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0)), 0)",coef_escort_Driving_age_student_Duration_lt_0p5_hrs +util_escort_Driving_age_student_Duration_gt_0p5_hrs,ESCORT - Driving age student - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 6) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Driving_age_student_Duration_gt_0p5_hrs +util_escort_Preschool_kid_Duration_gt_0p5_hrs,ESCORT - Pre-school kid - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.ptype == 8) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0)), 0)",coef_escort_Preschool_kid_Duration_gt_0p5_hrs +util_escort_Medhigh_income_60k_to_120k_Duration_gt_0p5_hrs,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Medhigh_income_60k_to_120k_Duration_gt_0p5_hrs +util_escort_Households_with_no_kids_Dummy_1_0_Departure_before_7_30_AM,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.start<10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_7_30_AM +util_escort_Households_with_no_kids_Dummy_1_0_Departure_after_8_00_AM,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_8_00_AM +util_escort_Households_with_no_kids_Dummy_1_0_Departure_before_2_30_PM,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.start<24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_2_30_PM +util_escort_Households_with_no_kids_Dummy_1_0_Departure_after_3_00_PM,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where ((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_3_00_PM +util_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_8_00_AM,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_8_00_AM +util_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_8_30_AM,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_8_30_AM +util_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_3_00_PM,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_3_00_PM +util_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_3_30_PM,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.num_children == 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_3_30_PM +util_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start >10)), (np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM +util_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_3_00_PM,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), (np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_3_00_PM +util_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_8_00_AM,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0)), 0)",coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_8_00_AM +util_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_3_00_PM,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0)), 0)",coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_3_00_PM +util_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), (np.where ((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM +util_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_8_30_AM,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), (np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_8_30_AM +util_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_3_30_PM,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) &( df.end>25)), (np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_3_30_PM +util_escort_Number_of_autos_greater_than_number_of_adults_Duration_gt_0p5_hrs,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Number_of_autos_greater_than_number_of_adults_Duration_gt_0p5_hrs +#FIXME_Number_of_nonescort_tours_is_not_known_until_the_nonmandatory_frequency_model_is_run,#FIXME - Number of non-escort tours is not known until the non-mandatory frequency model is run,,FIXME_Number_of_nonescort_tours_is_not_known_until_the_nonmandatory_frequency_model_is_run +#util_escort_Number_of_Individual_Tours_excluding_escorting_Duration_gt_0p5_hrs,#ESCORT - Number of Individual Tours (excluding escorting) - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type != 'escort') & (df.num_non_escort_tours > 0) & (df.duration>1)), (np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_Number_of_Individual_Tours_excluding_escorting_Duration_gt_0p5_hrs +util_escort_Number_of_joint_tours_Duration_gt_0p5_hrs,ESCORT - Number of joint tours - Duration > 0.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) *(df.num_joint_tours), 0)",coef_escort_Number_of_joint_tours_Duration_gt_0p5_hrs +util_escort_Departure_Constant_Shift_for_every_30_minutes_before_06_30_am_Linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start<8)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + np.where((df.start>13), np.minimum(df.start-13,28), 0)), 0)",coef_escort_Departure_Constant_Shift_for_every_30_minutes_before_06_30_am_Linear +util_escort_Departure_Constant_Before_07_00_AM,ESCORT - Departure Constant: Before 07:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start<9)),coef_escort_Departure_Constant_Before_07_00_AM +util_escort_Departure_Constant_07_00_AM_07_30_AM,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==9)),coef_escort_Departure_Constant_07_00_AM_07_30_AM +util_escort_Departure_Constant_07_30_AM_08_00_AM,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==10)),coef_escort_Departure_Constant_07_30_AM_08_00_AM +util_escort_Departure_Constant_08_00_AM_08_30_AM,ESCORT - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==11)),coef_escort_Departure_Constant_08_00_AM_08_30_AM +util_escort_Departure_Constant_08_30_AM_09_00_AM,ESCORT - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==12)),coef_escort_Departure_Constant_08_30_AM_09_00_AM +util_escort_Departure_Constant_After_09_00_AM,ESCORT - Departure Constant: After 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start>12)),coef_escort_Departure_Constant_After_09_00_AM +util_escort_Departure_Constant_01_30_PM_02_00_PM,ESCORT - Departure Constant: 01:30 PM - 02:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==22)),coef_escort_Departure_Constant_01_30_PM_02_00_PM +util_escort_Departure_Constant_02_00_PM_02_30_PM,ESCORT - Departure Constant: 02:00 PM - 02:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==23)),coef_escort_Departure_Constant_02_00_PM_02_30_PM +util_escort_Departure_Constant_02_30_PM_03_00_PM,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==24)),coef_escort_Departure_Constant_02_30_PM_03_00_PM +util_escort_Departure_Constant_03_00_PM_03_30_PM,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==25)),coef_escort_Departure_Constant_03_00_PM_03_30_PM +util_escort_Departure_Constant_After_03_30_PM,ESCORT - Departure Constant: After 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start>25)),coef_escort_Departure_Constant_After_03_30_PM +util_escort_Departure_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start>13)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + (np.where((df.start>13), np.minimum(df.start-13,28), 0))), 0)",coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear +util_escort_Departure_Constant_Shift_for_every_30_minutes_after_4_00_pm_Linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start>26)), (np.where((df.start<0), np.minimum(0-df.start,48), 0) + np.where((df.start>26), np.minimum(df.start-26,15),0)), 0)",coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_4_00_pm_Linear +util_escort_Arrival_Constant_Shift_for_every_30_minutes_before_6_30_am_Linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end<8)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_Arrival_Constant_Shift_for_every_30_minutes_before_6_30_am_Linear +util_escort_Arrival_Constant_Before_07_00_AM,ESCORT - Arrival Constant: Before 07:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end<9)),coef_escort_Arrival_Constant_Before_07_00_AM +util_escort_Arrival_Constant_07_00_AM_07_30_AM,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==9)),coef_escort_Arrival_Constant_07_00_AM_07_30_AM +util_escort_Arrival_Constant_07_30_AM_08_00_AM,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==10)),coef_escort_Arrival_Constant_07_30_AM_08_00_AM +util_escort_Arrival_Constant_08_00_AM_08_30_AM,ESCORT - Arrival Constant: 08:00 AM - 08:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==11)),coef_escort_Arrival_Constant_08_00_AM_08_30_AM +util_escort_Arrival_Constant_08_30_AM_09_00_AM,ESCORT - Arrival Constant: 08:30 AM - 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==12)),coef_escort_Arrival_Constant_08_30_AM_09_00_AM +util_escort_Arrival_Constant_After_09_00_AM,ESCORT - Arrival Constant: After 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end>12)),coef_escort_Arrival_Constant_After_09_00_AM +util_escort_Arrival_Constant_02_30_PM_03_00_PM,ESCORT - Arrival Constant: 02:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==24)),coef_escort_Arrival_Constant_02_30_PM_03_00_PM +util_escort_Arrival_Constant_03_00_PM_03_30_PM,ESCORT - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==25)),coef_escort_Arrival_Constant_03_00_PM_03_30_PM +util_escort_Arrival_Constant_03_30_PM_04_00_PM,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==26)),coef_escort_Arrival_Constant_03_30_PM_04_00_PM +util_escort_Arrival_Constant_04_00_PM_04_30_PM,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end==27)),coef_escort_Arrival_Constant_04_00_PM_04_30_PM +util_escort_Arrival_Constant_After_04_30_PM,ESCORT - Arrival Constant: After 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end>27)),coef_escort_Arrival_Constant_After_04_30_PM +util_escort_Arrival_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end>13)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear +util_escort_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.end>28)), (np.where((df.end<0), np.minimum(0-df.end,48), 0) + np.where((df.start>28), np.minimum(df.end-28,15), 0)), 0)",coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear +util_escort_Duration_Constant_0_hrs,ESCORT - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==0)),coef_escort_Duration_Constant_0_hrs +util_escort_Duration_Constant_0p5_hrs,ESCORT - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==1)),coef_escort_Duration_Constant_0p5_hrs +util_escort_Duration_Constant_1_hrs,ESCORT - Duration Constant: 1 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==2)),coef_escort_Duration_Constant_1_hrs +util_escort_Duration_Constant_1p5hrs,ESCORT - Duration Constant: 1.5hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==3)),coef_escort_Duration_Constant_1p5hrs +util_escort_Duration_Constant_2_hrs,ESCORT - Duration Constant: 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==4)),coef_escort_Duration_Constant_2_hrs +util_escort_Duration_Constant_Longer_than_2_hrs,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration>4)),coef_escort_Duration_Constant_Longer_than_2_hrs +util_escort_Calibration_Constant_Duration_eq_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.duration==0)),coef_escort_Calibration_Constant_Duration_eq_1 +util_escort_Calibration_Constant_Duration_eq_2,ESCORT - Calibration Constant - Duration = 2,@(((df.tour_category == 'joint') & (df.tour_type == 'escort') & df.duration==1)),coef_escort_Calibration_Constant_Duration_eq_2 +util_escort_Calibration_Constant_Departure_eq_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==9)),coef_escort_Calibration_Constant_Departure_eq_9 +util_escort_Calibration_Constant_Departure_eq_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==10)),coef_escort_Calibration_Constant_Departure_eq_10 +util_escort_Calibration_Constant_Departure_eq_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==23)),coef_escort_Calibration_Constant_Departure_eq_23 +util_escort_Calibration_Constant_Departure_eq_24,ESCORT - Calibration Constant - Departure = 24,@((df.tour_category == 'joint') & (df.tour_type == 'escort') & (df.start==24)),coef_escort_Calibration_Constant_Departure_eq_24 +#SHOPPING,#SHOPPING,,SHOPPING +util_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear,SHOPPING - Joint Shopping tours dummy: Departure before 10:00 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<15)), (np.where((df.start<=15), np.minimum(15-df.start,7), 0) + np.where((df.start>15), np.minimum(df.start-15,24), 0)), 0)",coef_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear +util_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear,SHOPPING - Joint Shopping tours dummy: Departure after 10:30 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>15)), (np.where((df.start<=15), np.minimum(15-df.start,7), 0) + np.where((df.start>15), np.minimum(df.start-15,24), 0)), 0)",coef_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear +util_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,SHOPPING - Joint Tours Party Size > 2: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.number_of_participants > 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs +util_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,SHOPPING - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.number_of_participants > 2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,SHOPPING - Joint Tour with only adults: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.composition=='adults')), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs +util_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,SHOPPING - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr,SHOPPING - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_less25K) & (df.duration>3)), (np.where((df.duration>3),np.minimum(df.duration-3,27), 0)), 0)",coef_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr +util_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_25K_to_60K) & (df.duration<3)), (np.where((df.duration>3),np.minimum(df.duration-3,27), 0)), 0)",coef_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs +util_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.is_income_60K_to_120K) & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,27), 0)), 0)",coef_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr +util_shop_Distance_Duration_lt_1p5_hrs,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)) * (df.origin_to_destination_distance), 0)",coef_shop_Distance_Duration_lt_1p5_hrs +util_shop_Distance_Duration_gt_1p5_hr,SHOPPING - Distance: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>2)), ((np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance)), 0)",coef_shop_Distance_Duration_gt_1p5_hr +util_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear +util_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0))**0.5, 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root +util_shop_Departure_Constant_Before_09_00_AM,SHOPPING - Departure Constant: Before 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start<13)),coef_shop_Departure_Constant_Before_09_00_AM +util_shop_Departure_Constant_09_00_AM_09_30_AM,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==13)),coef_shop_Departure_Constant_09_00_AM_09_30_AM +util_shop_Departure_Constant_09_30_AM_10_00_AM,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==14)),coef_shop_Departure_Constant_09_30_AM_10_00_AM +util_shop_Departure_Constant_10_00_AM_10_30_AM,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==15)),coef_shop_Departure_Constant_10_00_AM_10_30_AM +util_shop_Departure_Constant_10_30_AM_11_00_AM,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start==16)),coef_shop_Departure_Constant_10_30_AM_11_00_AM +util_shop_Departure_Constant_After_11_00_AM,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>16)),coef_shop_Departure_Constant_After_11_00_AM +util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear +util_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared +util_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear +util_shop_Arrival_Constant_Before_12_30_PM,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end<20)),coef_shop_Arrival_Constant_Before_12_30_PM +util_shop_Arrival_Constant_12_30_PM_03_00_PM,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shop_Arrival_Constant_12_30_PM_03_00_PM +util_shop_Arrival_Constant_03_00_PM_03_30_PM,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==25)),coef_shop_Arrival_Constant_03_00_PM_03_30_PM +util_shop_Arrival_Constant_03_30_PM_04_00_PM,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==26)),coef_shop_Arrival_Constant_03_30_PM_04_00_PM +util_shop_Arrival_Constant_04_00_PM_04_30_PM,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==27)),coef_shop_Arrival_Constant_04_00_PM_04_30_PM +util_shop_Arrival_Constant_04_30_PM_05_00_PM,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==28)),coef_shop_Arrival_Constant_04_30_PM_05_00_PM +util_shop_Arrival_Constant_05_00_PM_05_30_PM,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end==29)),coef_shop_Arrival_Constant_05_00_PM_05_30_PM +util_shop_Arrival_Constant_05_30_PM_07_00_PM,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>=30) & (df.end<=32)),coef_shop_Arrival_Constant_05_30_PM_07_00_PM +util_shop_Arrival_Constant_07_00_PM_09_30_PM,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>=33) & (df.end<=37)),coef_shop_Arrival_Constant_07_00_PM_09_30_PM +util_shop_Arrival_Constant_After_09_30_PM,SHOPPING - Arrival Constant: After 09:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>37)),coef_shop_Arrival_Constant_After_09_30_PM +util_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.end>38)), (np.where((df.end<19), np.minimum(19-df.end,10), 0) + np.where ((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear +util_shop_Duration_Constant_0_hrs,SHOPPING - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==0)),coef_shop_Duration_Constant_0_hrs +util_shop_Duration_Constant_0p5_hrs,SHOPPING - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==1)),coef_shop_Duration_Constant_0p5_hrs +util_shop_Duration_Constant_1_hrs,SHOPPING - Duration Constant: 1 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==2)),coef_shop_Duration_Constant_1_hrs +util_shop_Duration_Constant_1p5hrs,SHOPPING - Duration Constant: 1.5hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==3)),coef_shop_Duration_Constant_1p5hrs +util_shop_Duration_Constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==4)),coef_shop_Duration_Constant_2_hrs +util_shop_Duration_Constant_Longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>4)),coef_shop_Duration_Constant_Longer_than_2_hrs +util_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)), 0)",coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear +util_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration>5)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)) ** 0.5), 0)",coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root +util_shop_Calibration_Constant_Duration_eq_1,SHOPPING - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==0)),coef_shop_Calibration_Constant_Duration_eq_1 +util_shop_Calibration_Constant_Duration_eq_2,SHOPPING - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==1)),coef_shop_Calibration_Constant_Duration_eq_2 +util_shop_Calibration_Constant_Duration_eq_3,SHOPPING - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'shopping') & (df.duration==2)),coef_shop_Calibration_Constant_Duration_eq_3 +#MAINTENANCE,#MAINTENANCE,,MAINTENANCE +util_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear,MAINTENANCE - Joint Maintenance tours dummy: Departure before 10:00 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<15)), (np.where((df.start<=15), np.minimum(15-df.start,7), 0) + np.where((df.start>15), np.minimum(df.start-15,24), 0)), 0)",coef_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear +util_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear,MAINTENANCE - Joint Maintenance tours dummy: Departure after 10:30 AM - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>15)), (np.where((df.start<=15), np.minimum(15-df.start,7), 0) + np.where((df.start>15), np.minimum(df.start-15,24), 0)), 0)",coef_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear +util_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,MAINTENANCE - Joint Tours Party Size > 2: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.number_of_participants > 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs +util_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,MAINTENANCE - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.number_of_participants > 2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,MAINTENANCE - Joint Tour with only adults: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.composition=='adults')), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs +util_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,MAINTENANCE - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr,MAINTENANCE - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr,"MAINTENANCE - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr +util_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)), 0)",coef_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs +util_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration>2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr +util_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hr","@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr +util_maint_Distance_Duration_lt_1p5_hrs,MAINTENANCE - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0)) *(df.origin_to_destination_distance), 0)",coef_maint_Distance_Duration_lt_1p5_hrs +util_maint_Distance_Duration_gt_1p5_hr,MAINTENANCE - Distance: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_maint_Distance_Duration_gt_1p5_hr +util_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<10)), (np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear +util_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<10)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + (np.where((df.start>17), np.minimum(df.start-17,24), 0)))** 0.5), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root +util_maint_Departure_Constant_Before_08_00_AM,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start<11)),coef_maint_Departure_Constant_Before_08_00_AM +util_maint_Departure_Constant_08_00_AM_08_30_AM,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==11)),coef_maint_Departure_Constant_08_00_AM_08_30_AM +util_maint_Departure_Constant_08_30_AM_09_00_AM,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==12)),coef_maint_Departure_Constant_08_30_AM_09_00_AM +util_maint_Departure_Constant_09_00_AM_09_30_AM,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==13)),coef_maint_Departure_Constant_09_00_AM_09_30_AM +util_maint_Departure_Constant_09_30_AM_10_00_AM,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==14)),coef_maint_Departure_Constant_09_30_AM_10_00_AM +util_maint_Departure_Constant_10_00_AM_10_30_AM,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==15)),coef_maint_Departure_Constant_10_00_AM_10_30_AM +util_maint_Departure_Constant_10_30_AM_11_00_AM,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start==16)),coef_maint_Departure_Constant_10_30_AM_11_00_AM +util_maint_Departure_Constant_After_11_00_AM,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>16)),coef_maint_Departure_Constant_After_11_00_AM +util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear +util_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared +util_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear +util_maint_Arrival_Constant_Before_10_30_AM,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end<16)),coef_maint_Arrival_Constant_Before_10_30_AM +util_maint_Arrival_Constant_10_30_AM_11_00_AM,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==16)),coef_maint_Arrival_Constant_10_30_AM_11_00_AM +util_maint_Arrival_Constant_11_00_AM_11_30_AM,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==17)),coef_maint_Arrival_Constant_11_00_AM_11_30_AM +util_maint_Arrival_Constant_11_30_AM_01_30_PM,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=18) & (df.end<=21)),coef_maint_Arrival_Constant_11_30_AM_01_30_PM +util_maint_Arrival_Constant_01_30_PM_02_30_PM,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=22) & (df.end<=23)),coef_maint_Arrival_Constant_01_30_PM_02_30_PM +util_maint_Arrival_Constant_02_30_PM_04_00_PM,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>=24) & (df.end<=26)),coef_maint_Arrival_Constant_02_30_PM_04_00_PM +util_maint_Arrival_Constant_04_00_PM_04_30_PM,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end==27)),coef_maint_Arrival_Constant_04_00_PM_04_30_PM +util_maint_Arrival_Constant_After_04_30_PM,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>27)),coef_maint_Arrival_Constant_After_04_30_PM +util_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.end>28)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear +util_maint_Duration_Constant_0_hrs,MAINTENANCE - Duration Constant: 0 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==0)),coef_maint_Duration_Constant_0_hrs +util_maint_Duration_Constant_0p5_hrs,MAINTENANCE - Duration Constant: 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==1)),coef_maint_Duration_Constant_0p5_hrs +util_maint_Duration_Constant_Longer_than_0p5_hrs,MAINTENANCE - Duration Constant: Longer than 0.5 hrs,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>1)),coef_maint_Duration_Constant_Longer_than_0p5_hrs +util_maint_Duration_Constant_Duration_gt_1_hrs_Linear,MAINTENANCE - Duration Constant: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maint_Duration_Constant_Duration_gt_1_hrs_Linear +util_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root,MAINTENANCE - Duration Constant: Duration > 1 hrs - Square Root,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration>2)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0))** 0.5), 0)",coef_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root +util_maint_Calibration_Constant_Duration_eq_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==0)),coef_maint_Calibration_Constant_Duration_eq_1 +util_maint_Calibration_Constant_Duration_eq_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==1)),coef_maint_Calibration_Constant_Duration_eq_2 +util_maint_Calibration_Constant_Duration_eq_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==2)),coef_maint_Calibration_Constant_Duration_eq_3 +util_maint_Calibration_Constant_Duration_eq_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==3)),coef_maint_Calibration_Constant_Duration_eq_4 +util_maint_Calibration_Constant_Duration_eq_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'othmaint') & (df.duration==4)),coef_maint_Calibration_Constant_Duration_eq_5 +#EATOUT,#EAT-OUT,,EATOUT +util_eatout_Distance_to_destination_Duration_lt_1_hrs,EAT-OUT - Distance to destination - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)) * (df.origin_to_destination_distance), 0)",coef_eatout_Distance_to_destination_Duration_lt_1_hrs +util_eatout_Distance_to_destination_Duration_gt_1_hrs,EAT-OUT - Distance to destination - Duration > 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>2)), (np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) *(df.origin_to_destination_distance), 0)",coef_eatout_Distance_to_destination_Duration_gt_1_hrs +util_eatout_Low_income_lt25000_Duration_lt_1_hrs,EAT-OUT - Low income (<25000) - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.is_income_less25K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)), 0)",coef_eatout_Low_income_lt25000_Duration_lt_1_hrs +util_eatout_Medium_25k_to_60k_Duration_lt_1_hrs,EAT-OUT - Medium (25k to 60k) - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)), 0)",coef_eatout_Medium_25k_to_60k_Duration_lt_1_hrs +util_eatout_Zero_auto_HH_Duration_gt_1_hrs,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.auto_ownership == 0) & (df.duration>2)), (np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_Zero_auto_HH_Duration_gt_1_hrs +util_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs,EAT-OUT - Kids in Joint tour - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)), 0)",coef_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs +util_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs,EAT-OUT - Joint Tours Party Size greater than 2 - Duration < 1 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & (df.number_of_participants > 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0)), 0)",coef_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs +util_eatout_Departure_Constant_11_00_AM_12_00_PM,EAT-OUT - Departure Constant: 11:00 AM - 12:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>=17) & (df.start<=18)),coef_eatout_Departure_Constant_11_00_AM_12_00_PM +util_eatout_Departure_Constant_12_00_PM_12_30_PM,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==19),coef_eatout_Departure_Constant_12_00_PM_12_30_PM +util_eatout_Departure_Constant_12_30_PM_to_01_00_PM,EAT-OUT - Departure Constant: 12:30 PM to 01:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==20),coef_eatout_Departure_Constant_12_30_PM_to_01_00_PM +util_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start<29)), (np.where((df.start<29), np.minimum(29-df.start,20), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear +util_eatout_Departure_Constant_Before_05_30_PM,EAT-OUT - Departure Constant: Before 05:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start<30),coef_eatout_Departure_Constant_Before_05_30_PM +util_eatout_Departure_Constant_05_30_PM_06_00_PM,EAT-OUT - Departure Constant: 05:30 PM - 06:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==30),coef_eatout_Departure_Constant_05_30_PM_06_00_PM +util_eatout_Departure_Constant_06_00_PM_06_30_PM,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==31),coef_eatout_Departure_Constant_06_00_PM_06_30_PM +util_eatout_Departure_Constant_06_30_PM_07_00_PM,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==32),coef_eatout_Departure_Constant_06_30_PM_07_00_PM +util_eatout_Departure_Constant_07_00_PM_07_30_PM,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start==33),coef_eatout_Departure_Constant_07_00_PM_07_30_PM +util_eatout_Departure_Constant_After_07_30_PM,EAT-OUT - Departure Constant: After 07:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>33),coef_eatout_Departure_Constant_After_07_30_PM +util_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start>34)), (np.where((df.start<29), np.minimum(29-df.start,20), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear +util_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==20),coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM +util_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==21),coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM +util_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==22),coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM +util_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==23),coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM +util_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end<32)), (np.where((df.end<32), np.minimum(32-df.end,21), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear +util_eatout_Arrival_Constant_Before_7_00_PM,EAT-OUT - Arrival Constant: Before 7:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end<33),coef_eatout_Arrival_Constant_Before_7_00_PM +util_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@(df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.end==33),coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM +util_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==34),coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM +util_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==35),coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM +util_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.end==36),coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM +util_eatout_Arrival_Constant_After_09_00_PM,EAT-OUT - Arrival Constant: After 09:00 PM,@(df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.end>36),coef_eatout_Arrival_Constant_After_09_00_PM +util_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_type== 'eatout') & (df.end>37) & (df.tour_category == 'joint')), (np.where((df.end<32), np.minimum(32-df.end,21), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear +util_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>6)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>6), np.minimum(df.duration-6,12), 0)), 0)",coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear +util_eatout_Duration_Constant_0_hours,EAT-OUT - Duration Constant: 0 hours,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_Duration_Constant_0_hours +util_eatout_Duration_Constant_0p5_hous,EAT-OUT - Duration Constant: 0.5 hous,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_Duration_Constant_0p5_hous +util_eatout_Duration_Constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_Duration_Constant_1_hour +util_eatout_Duration_Constant_1p5_hours,EAT-OUT - Duration Constant: 1.5 hours,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_Duration_Constant_1p5_hours +util_eatout_Duration_Constant_2_hours_or_more,EAT-OUT - Duration Constant: 2 hours or more,@(df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>4),coef_eatout_Duration_Constant_2_hours_or_more +util_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 2.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,13), 0)), 0)",coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear +util_eatout_Calibration_Constant_Duration_eq_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_Calibration_Constant_Duration_eq_1 +util_eatout_Calibration_Constant_Duration_eq_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_Calibration_Constant_Duration_eq_2 +util_eatout_Calibration_Constant_Duration_eq_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_Calibration_Constant_Duration_eq_3 +util_eatout_Calibration_Constant_Duration_eq_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_Calibration_Constant_Duration_eq_4 +util_eatout_Calibration_Constant_Departure_eq_1,EAT-OUT - Calibration Constant - Departure = 1,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start == 1)),coef_eatout_Calibration_Constant_Departure_eq_1 +util_eatout_Calibration_Constant_Departure_eq_2,EAT-OUT - Calibration Constant - Departure = 2,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start == 2)),coef_eatout_Calibration_Constant_Departure_eq_2 +util_eatout_Calibration_Constant_Departure_eq_3,EAT-OUT - Calibration Constant - Departure = 3,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.start == 3)),coef_eatout_Calibration_Constant_Departure_eq_3 +util_eatout_Calibration_Constant_Departure_eq_17,EAT-OUT - Calibration Constant - Departure = 17,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==17)),coef_eatout_Calibration_Constant_Departure_eq_17 +util_eatout_Calibration_Constant_Departure_eq_18,EAT-OUT - Calibration Constant - Departure = 18,@((df.tour_category == 'joint') & (df.tour_type== 'eatout') & (df.start ==18)),coef_eatout_Calibration_Constant_Departure_eq_18 +util_eatout_Calibration_Constant_Departure_eq_19,EAT-OUT - Calibration Constant - Departure = 19,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==19)),coef_eatout_Calibration_Constant_Departure_eq_19 +util_eatout_Calibration_Constant_Departure_eq_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==20)),coef_eatout_Calibration_Constant_Departure_eq_20 +util_eatout_Calibration_Constant_Departure_eq_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.tour_category == 'joint') & (df.tour_type == 'eatout') & (df.start ==21)),coef_eatout_Calibration_Constant_Departure_eq_21 +#SOCIAL,#SOCIAL,,SOCIAL +util_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.retired_adults_only_hh) & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear +util_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear,SOCIAL - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear +util_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear,SOCIAL - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear +util_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,SOCIAL - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint')&(df.tour_type == 'social') & (df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear +util_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,SOCIAL - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear +util_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,SOCIAL - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<3) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr,SOCIAL - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>3) & ((df.composition=='children')|(df.composition=='mixed'))), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,SOCIAL - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>3) & (df.number_of_participants > 2)), (np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_social_Auto_Distance_Duration_lt_1_hrs_Linear,SOCIAL - Auto Distance: Duration < 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) * (df.origin_to_destination_distance)), 0) ",coef_social_Auto_Distance_Duration_lt_1_hrs_Linear +util_social_Auto_Distance_Duration_gt_1_hrs_Linear,SOCIAL - Auto Distance: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0) * (df.origin_to_destination_distance)), 0)",coef_social_Auto_Distance_Duration_gt_1_hrs_Linear +util_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48), 0)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear +util_social_Departure_Constant_Before_09_00_AM,SOCIAL - Departure Constant: Before 09:00 AM,@(df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<13),coef_social_Departure_Constant_Before_09_00_AM +util_social_Departure_Constant_09_00_AM_to_09_30_AM,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==13),coef_social_Departure_Constant_09_00_AM_to_09_30_AM +util_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<29), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear +util_social_Departure_Constant_Before_05_30_PM,SOCIAL - Departure Constant: Before 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start<30)),coef_social_Departure_Constant_Before_05_30_PM +util_social_Departure_Constant_05_30_PM_06_00_PM,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==30)),coef_social_Departure_Constant_05_30_PM_06_00_PM +util_social_Departure_Constant_06_00_PM_06_30_PM,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==31)),coef_social_Departure_Constant_06_00_PM_06_30_PM +util_social_Departure_Constant_06_30_PM_07_00_PM,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==32)),coef_social_Departure_Constant_06_30_PM_07_00_PM +util_social_Departure_Constant_07_00_PM_07_30_PM,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start==33)),coef_social_Departure_Constant_07_00_PM_07_30_PM +util_social_Departure_Constant_After_07_30_PM,SOCIAL - Departure Constant: After 07:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start>33)),coef_social_Departure_Constant_After_07_30_PM +util_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.start>34), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear +util_social_Arrival_Constant_03_00_PM_to_03_30_PM,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==25)),coef_social_Arrival_Constant_03_00_PM_to_03_30_PM +util_social_Arrival_Constant_03_30_PM_to_04_00_PM,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==26)),coef_social_Arrival_Constant_03_30_PM_to_04_00_PM +util_social_Arrival_Constant_04_00_PM_to_04_30_PM,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==27)),coef_social_Arrival_Constant_04_00_PM_to_04_30_PM +util_social_Arrival_Constant_05_00_PM_to_06_00_PM,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>=29) & (df.end<=30)),coef_social_Arrival_Constant_05_00_PM_to_06_00_PM +util_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end<35)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) + np.where((df.end>40), np.minimum(df.end-40,48), 0)), 0)",coef_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear +util_social_Arrival_Constant_Before_8_30_PM,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end<36)),coef_social_Arrival_Constant_Before_8_30_PM +util_social_Arrival_Constant_8_30_PM_to_9_00_PM,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==36)),coef_social_Arrival_Constant_8_30_PM_to_9_00_PM +util_social_Arrival_Constant_9_00_PM_to_9_30_PM,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==37)),coef_social_Arrival_Constant_9_00_PM_to_9_30_PM +util_social_Arrival_Constant_9_30_PM_to10_00_PM,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==38)),coef_social_Arrival_Constant_9_30_PM_to10_00_PM +util_social_Arrival_Constant_10_00_PM_to_10_30_PM,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end==39)),coef_social_Arrival_Constant_10_00_PM_to_10_30_PM +util_social_Arrival_Constant_After_10_30_PM,SOCIAL - Arrival Constant: After 10:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>39)),coef_social_Arrival_Constant_After_10_30_PM +util_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.end>40)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) +np.where((df.end>40),np.minimum(df.end-40,48),0)), 0)",coef_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear +util_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 1.5 hrs - Linear,"@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<3)) * ((np.minimum(3-df.duration,47)*(df.duration<3)) + (np.minimum(df.duration-6,47)*(df.duration>6)))",coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear +util_social_Duration_Constant_Less_than_2_hours,SOCIAL - Duration Constant: Less than 2 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration<4)),coef_social_Duration_Constant_Less_than_2_hours +util_social_Duration_Constant_2_hours,SOCIAL - Duration Constant: 2 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration==4)),coef_social_Duration_Constant_2_hours +util_social_Duration_Constant_2p5_hours,SOCIAL - Duration Constant: 2.5 hours,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration==5)),coef_social_Duration_Constant_2p5_hours +util_social_Duration_Constant_3_hours_or_more,SOCIAL - Duration Constant: 3 hours or more,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>5)),coef_social_Duration_Constant_3_hours_or_more +util_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 3.5 hrs - Linear,"@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration>6)) * ((np.minimum(3-df.duration,47)*(df.duration<3)) + (np.minimum(df.duration-6,47)*(df.duration>6)))",coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear +util_social_Calibration_Constant_Duration_eq_1,SOCIAL - Calibration Constant - Duration = 1,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==0)),coef_social_Calibration_Constant_Duration_eq_1 +util_social_Calibration_Constant_Duration_eq_2,SOCIAL - Calibration Constant - Duration = 2,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration == 1)),coef_social_Calibration_Constant_Duration_eq_2 +util_social_Calibration_Constant_Duration_eq_3,SOCIAL - Calibration Constant - Duration = 3,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==2)),coef_social_Calibration_Constant_Duration_eq_3 +util_social_Calibration_Constant_Duration_eq_4,SOCIAL - Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==3)),coef_social_Calibration_Constant_Duration_eq_4 +util_social_Calibration_Constant_Duration_eq_5,SOCIAL - Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==4)),coef_social_Calibration_Constant_Duration_eq_5 +util_social_Calibration_Constant_Duration_eq_6,SOCIAL - Calibration Constant - Duration = 6,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==5)),coef_social_Calibration_Constant_Duration_eq_6 +util_social_Calibration_Constant_Duration_eq_7,SOCIAL - Calibration Constant - Duration = 7,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==6)),coef_social_Calibration_Constant_Duration_eq_7 +util_social_Calibration_Constant_Duration_eq_8,SOCIAL - Calibration Constant - Duration = 8,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==7)),coef_social_Calibration_Constant_Duration_eq_8 +util_social_Calibration_Constant_Duration_eq_9,SOCIAL - Calibration Constant - Duration = 9,@((df.tour_category == 'joint') & (df.tour_type == 'social') & (df.duration ==8)),coef_social_Calibration_Constant_Duration_eq_9 +#DISCRETIONARY,#DISCRETIONARY,,DISCRETIONARY +util_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.retired_adults_only_hh) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear +util_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear +util_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear +util_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)),0)",coef_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear +util_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear +util_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,DISCRETIONARY - Kids in Joint Tour: Duration < 1.5 hrs,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs +util_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr,DISCRETIONARY - Kids in Joint Tour: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & ((df.ptype == 6) | (df.ptype == 7) | (df.ptype == 8))), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr +util_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,DISCRETIONARY - Joint Tours Party Size > 2: Duration > 1.5 hr,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.number_of_participants > 2)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)), 0)",coef_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr +util_disc_Auto_Distance_Duration_lt_1_hrs_Linear,DISCRETIONARY - Auto Distance: Duration < 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0)) * (df.origin_to_destination_distance), 0)",coef_disc_Auto_Distance_Duration_lt_1_hrs_Linear +util_disc_Auto_Distance_Duration_gt_1_hrs_Linear,DISCRETIONARY - Auto Distance: Duration > 1 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_disc_Auto_Distance_Duration_gt_1_hrs_Linear +util_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<9)), (np.where((df.start<9), np.minimum(9-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48),0)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear +util_disc_Departure_Constant_Before_7_30_AM_,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<10)),coef_disc_Departure_Constant_Before_7_30_AM_ +util_disc_Departure_Constant_7_30_AM_to_8_00_AM,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==10)),coef_disc_Departure_Constant_7_30_AM_to_8_00_AM +util_disc_Departure_Constant_8_00_AM_to_8_30_AM,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==11)),coef_disc_Departure_Constant_8_00_AM_to_8_30_AM +util_disc_Departure_Constant_8_30_AM_to_9_00_AM,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==12)),coef_disc_Departure_Constant_8_30_AM_to_9_00_AM +util_disc_Departure_Constant_9_00_AM_to_9_30_AM,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==13)),coef_disc_Departure_Constant_9_00_AM_to_9_30_AM +util_disc_Departure_Constant_9_30_AM_to_10_00_AM,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==14)),coef_disc_Departure_Constant_9_30_AM_to_10_00_AM +util_disc_Departure_Constant_10_00_AM_to_10_30_AM,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==15)),coef_disc_Departure_Constant_10_00_AM_to_10_30_AM +util_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<28)), (np.where((df.start<28), np.minimum(28-df.start,8),0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear +util_disc_Departure_Constant_Before_05_00_PM,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start<29)),coef_disc_Departure_Constant_Before_05_00_PM +util_disc_Departure_Constant_05_00_PM_05_30_PM,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==29)),coef_disc_Departure_Constant_05_00_PM_05_30_PM +util_disc_Departure_Constant_05_30_PM_06_00_PM,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==30)),coef_disc_Departure_Constant_05_30_PM_06_00_PM +util_disc_Departure_Constant_06_00_PM_06_30_PM,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==31)),coef_disc_Departure_Constant_06_00_PM_06_30_PM +util_disc_Departure_Constant_06_30_PM_07_00_PM,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==32)),coef_disc_Departure_Constant_06_30_PM_07_00_PM +util_disc_Departure_Constant_After_07_00_PM,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start>32)),coef_disc_Departure_Constant_After_07_00_PM +util_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start>33)), (np.where((df.start<28), np.minimum(28-df.start,8), 0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear +util_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end<31)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear +util_disc_Arrival_Constant_Before_6_30_PM,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end<32)),coef_disc_Arrival_Constant_Before_6_30_PM +util_disc_Arrival_Constant_6_30_PM_to_7_00_PM,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==32)),coef_disc_Arrival_Constant_6_30_PM_to_7_00_PM +util_disc_Arrival_Constant_7_00_PM_to_7_30_PM,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==33)),coef_disc_Arrival_Constant_7_00_PM_to_7_30_PM +util_disc_Arrival_Constant_7_30_PM_to_8_00_PM,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==34)),coef_disc_Arrival_Constant_7_30_PM_to_8_00_PM +util_disc_Arrival_Constant_8_00_PM_to_8_30_PM,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==35)),coef_disc_Arrival_Constant_8_00_PM_to_8_30_PM +util_disc_Arrival_Constant_8_30_PM_to_9_00_PM,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end==36)),coef_disc_Arrival_Constant_8_30_PM_to_9_00_PM +util_disc_Arrival_Constant_After_9_00_PM,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end>36)),coef_disc_Arrival_Constant_After_9_00_PM +util_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48),0)), 0)",coef_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear +util_disc_Duration_Constant_0_hours,DISCRETIONARY - Duration Constant: 0 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==0),coef_disc_Duration_Constant_0_hours +util_disc_Duration_Constant_0p5_hous,DISCRETIONARY - Duration Constant: 0.5 hous,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==1),coef_disc_Duration_Constant_0p5_hous +util_disc_Duration_Constant_1_hour,DISCRETIONARY - Duration Constant: 1 hour,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==2),coef_disc_Duration_Constant_1_hour +util_disc_Duration_Constant_1p5_hours,DISCRETIONARY - Duration Constant: 1.5 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3),coef_disc_Duration_Constant_1p5_hours +util_disc_Duration_Constant_2_hours,DISCRETIONARY - Duration Constant: 2 hours,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4),coef_disc_Duration_Constant_2_hours +util_disc_Duration_Constant_2p5_hours_or_more,DISCRETIONARY - Duration Constant: 2.5 hours or more,@(df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>4),coef_disc_Duration_Constant_2p5_hours_or_more +util_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,DISCRETIONARY - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,47), 0)), 0)",coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear +util_disc_Calibration_Constant_Duration_eq_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3)),coef_disc_Calibration_Constant_Duration_eq_4 +util_disc_Calibration_Constant_Duration_eq_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4)),coef_disc_Calibration_Constant_Duration_eq_5 +util_disc_Calibration_Constant_Departure_eq_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==29)),coef_disc_Calibration_Constant_Departure_eq_29 +util_disc_Calibration_Constant_Departure_eq_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==30)),coef_disc_Calibration_Constant_Departure_eq_30 +util_disc_Calibration_Constant_Departure_eq_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==31)),coef_disc_Calibration_Constant_Departure_eq_31 +util_disc_Calibration_Constant_Departure_eq_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.tour_category == 'joint') & (df.tour_type == 'othdiscr') & (df.start==32)),coef_disc_Calibration_Constant_Departure_eq_32 diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_joint_coeffs.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_joint_coeffs.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/tour_scheduling_joint_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_joint_coeffs.csv index 54d013138d..131bb53358 100755 --- a/activitysim/examples/example_semcog/configs/tour_scheduling_joint_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_joint_coeffs.csv @@ -1,400 +1,400 @@ -coefficient_name,value,constrain -coef_escort_Mode_Choice_Logsum,1.173173034,F -coef_escort_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.335017673,F -coef_escort_Distance_to_destination_Duration_greater_than_0p5_hours,0.005298165,F -coef_escort_Fulltime_worker_Departure_after_8_00_am_Linear,-0.037980109,F -coef_escort_Fulltime_worker_Departure_after_3_00_am_Linear,0.163254125,F -coef_escort_Fulltime_worker_Duration_lt_0p5_hrs,-0.275077482,F -coef_escort_Fulltime_worker_Duration_gt_0p5_hrs,0.051530545,F -coef_escort_University_student_Duration_lt_0p5_hrs,-0.426802718,F -coef_escort_Nondriving_age_student_Duration_gt_0p5_hrs,0.240582361,F -coef_escort_Driving_age_student_Duration_lt_0p5_hrs,-0.554146191,F -coef_escort_Driving_age_student_Duration_gt_0p5_hrs,0.299387708,F -coef_escort_Preschool_kid_Duration_gt_0p5_hrs,0.195482563,F -coef_escort_Medhigh_income_60k_to_120k_Duration_gt_0p5_hrs,-0.029281467,F -coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_7_30_AM,0.589083327,F -coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_8_00_AM,0.086690827,F -coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_2_30_PM,0.477582648,F -coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_3_00_PM,-0.204065502,F -coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_8_00_AM,-0.360039254,F -coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_8_30_AM,0.091614107,F -coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_3_00_PM,0.432854268,F -coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_3_30_PM,0.131037275,F -coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,0.109700265,F -coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_3_00_PM,-0.224568648,F -coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_8_00_AM,-0.357416434,F -coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_3_00_PM,0.629285298,F -coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,0.039005148,F -coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_8_30_AM,-0.06556611,F -coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_3_30_PM,0.117680977,F -coef_escort_Number_of_autos_greater_than_number_of_adults_Duration_gt_0p5_hrs,-0.057322708,F -coef_escort_Number_of_Individual_Tours_excluding_escorting_Duration_gt_0p5_hrs,-0.062899692,F -coef_escort_Number_of_joint_tours_Duration_gt_0p5_hrs,-0.048533895,F -coef_escort_Departure_Constant_Shift_for_every_30_minutes_before_06_30_am_Linear,-1.469240002,F -coef_escort_Departure_Constant_Before_07_00_AM,-2.070292862,F -coef_escort_Departure_Constant_07_00_AM_07_30_AM,-0.642734296,F -coef_escort_Departure_Constant_07_30_AM_08_00_AM,0,T -coef_escort_Departure_Constant_08_00_AM_08_30_AM,-0.214617667,F -coef_escort_Departure_Constant_08_30_AM_09_00_AM,-0.147266606,F -coef_escort_Departure_Constant_After_09_00_AM,-1.356686422,F -coef_escort_Departure_Constant_01_30_PM_02_00_PM,0.368092381,F -coef_escort_Departure_Constant_02_00_PM_02_30_PM,1.166803383,F -coef_escort_Departure_Constant_02_30_PM_03_00_PM,1.28466083,F -coef_escort_Departure_Constant_03_00_PM_03_30_PM,0.581891245,F -coef_escort_Departure_Constant_After_03_30_PM,0.834510243,F -coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,0.175257649,F -coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_4_00_pm_Linear,-0.019161202,F -coef_escort_Arrival_Constant_Shift_for_every_30_minutes_before_6_30_am_Linear,0.44978138,F -coef_escort_Arrival_Constant_Before_07_00_AM,0.549584585,F -coef_escort_Arrival_Constant_07_00_AM_07_30_AM,0.488181278,F -coef_escort_Arrival_Constant_07_30_AM_08_00_AM,0.236447651,F -coef_escort_Arrival_Constant_08_00_AM_08_30_AM,0,T -coef_escort_Arrival_Constant_08_30_AM_09_00_AM,-0.683756801,F -coef_escort_Arrival_Constant_After_09_00_AM,-1.428888485,F -coef_escort_Arrival_Constant_02_30_PM_03_00_PM,1.311480662,F -coef_escort_Arrival_Constant_03_00_PM_03_30_PM,1.316883154,F -coef_escort_Arrival_Constant_03_30_PM_04_00_PM,1.396838392,F -coef_escort_Arrival_Constant_04_00_PM_04_30_PM,1.03146139,F -coef_escort_Arrival_Constant_After_04_30_PM,0.907344583,F -coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,-0.148408887,F -coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,-0.389082896,F -coef_escort_Duration_Constant_0_hrs,-0.173757322,F -coef_escort_Duration_Constant_0p5_hrs,0,T -coef_escort_Duration_Constant_1_hrs,-0.431287743,F -coef_escort_Duration_Constant_1p5hrs,-0.700473959,F -coef_escort_Duration_Constant_2_hrs,-1.071871358,F -coef_escort_Duration_Constant_Longer_than_2_hrs,-1.691098421,F -coef_escort_Calibration_Constant_Duration_eq_1,-0.047200214,F -coef_escort_Calibration_Constant_Duration_eq_2,0.035611332,F -coef_escort_Calibration_Constant_Departure_eq_9,0.106814756,F -coef_escort_Calibration_Constant_Departure_eq_10,0.215386864,F -coef_escort_Calibration_Constant_Departure_eq_23,-0.255087318,F -coef_escort_Calibration_Constant_Departure_eq_24,-0.296870428,F -coef_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear,-0.190727375,F -coef_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear,-0.029551313,F -coef_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,-0.291965906,F -coef_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.045755784,F -coef_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,-0.571185116,F -coef_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,-0.468815184,F -coef_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr,-0.047470039,F -coef_shop_Driving_age_student_Duration_gt_1p5_hr,0.122149101,F -coef_shop_Fulltime_worker_Duration_gt_1p5_hr,-0.037886459,F -coef_shop_Nondriving_Student_Duration_gt_1p5_hr,0.10247157,F -coef_shop_Preschool_Child_Duration_lt_1p5_hrs,-1.882944033,F -coef_shop_Part_Time_Worker_Duration_lt_1p5_hrs,-0.197455071,F -coef_shop_Part_Time_Worker_Duration_gt_1p5_hr,-0.045152663,F -coef_shop_Retired_Duration_lt_1p5_hrs,-0.264728908,F -coef_shop_Retired_Duration_gt_1p5_hr,-0.042981757,F -coef_shop_University_Student_Duration_gt_1p5_hr,0.045926523,F -coef_shop_Female_Duration_lt_1p5_hrs,-0.417208254,F -coef_shop_Female_Duration_gt_1p5_hr,0.045801918,F -coef_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr,0.040776383,F -coef_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,0.108462927,F -coef_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,-0.037893416,F -coef_shop_Distance_Duration_lt_1p5_hrs,-0.214802537,F -coef_shop_Distance_Duration_gt_1p5_hr,0.007991656,F -coef_shop_Time_Pressure_Duration_gt_1p5_hrs,0.013503327,F -coef_shop_Number_of_additional_individual_shop_and_maintp_tours_Duration_lt_1p5_hrs,0.078844289,F -coef_shop_Number_of_additional_individual_shop_and_maintp_tours_Duration_gt_1p5_hrs,-0.115431492,F -coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,-0.959875456,F -coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root,1.112594898,F -coef_shop_Departure_Constant_Before_09_00_AM,-0.446394064,F -coef_shop_Departure_Constant_09_00_AM_09_30_AM,-0.021669265,F -coef_shop_Departure_Constant_09_30_AM_10_00_AM,-0.282978638,F -coef_shop_Departure_Constant_10_00_AM_10_30_AM,0,T -coef_shop_Departure_Constant_10_30_AM_11_00_AM,-0.309421311,F -coef_shop_Departure_Constant_After_11_00_AM,-0.541073357,F -coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,-0.072013428,F -coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,-0.000653398,F -coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,-0.18376635,F -coef_shop_Arrival_Constant_Before_12_30_PM,-0.716195343,F -coef_shop_Arrival_Constant_12_30_PM_03_00_PM,-0.502714001,F -coef_shop_Arrival_Constant_03_00_PM_03_30_PM,-0.167868872,F -coef_shop_Arrival_Constant_03_30_PM_04_00_PM,-0.156786941,F -coef_shop_Arrival_Constant_04_00_PM_04_30_PM,0,T -coef_shop_Arrival_Constant_04_30_PM_05_00_PM,-0.057314044,F -coef_shop_Arrival_Constant_05_00_PM_05_30_PM,-0.580040851,F -coef_shop_Arrival_Constant_05_30_PM_07_00_PM,-0.32239566,F -coef_shop_Arrival_Constant_07_00_PM_09_30_PM,-0.347828147,F -coef_shop_Arrival_Constant_After_09_30_PM,-1.123574723,F -coef_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear,-0.499770654,F -coef_shop_Duration_Constant_0_hrs,-0.131743185,F -coef_shop_Duration_Constant_0p5_hrs,0.888857137,F -coef_shop_Duration_Constant_1_hrs,0,T -coef_shop_Duration_Constant_1p5hrs,-0.333413031,F -coef_shop_Duration_Constant_2_hrs,-0.850897912,F -coef_shop_Duration_Constant_Longer_than_2_hrs,-1.203783479,F -coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear,-0.293581223,F -coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root,-0.215759138,F -coef_shop_Calibration_Constant_Duration_eq_1,-0.138450424,F -coef_shop_Calibration_Constant_Duration_eq_2,-0.092704403,F -coef_shop_Calibration_Constant_Duration_eq_3,-0.087738073,F -coef_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear,-0.139150288,F -coef_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear,-0.065786345,F -coef_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,-0.291965906,F -coef_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.045755784,F -coef_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,-0.571185116,F -coef_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,-0.468815184,F -coef_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr,-0.047470039,F -coef_maint_Fulltime_worker_Duration_gt_1p5_hr,-0.037886459,F -coef_maint_Nondriving_Student_Duration_gt_1p5_hr,0.10247157,F -coef_maint_Preschool_Child_Duration_lt_1p5_hrs,-1.882944033,F -coef_maint_Part_Time_Worker_Duration_lt_1p5_hrs,-0.197455071,F -coef_maint_Part_Time_Worker_Duration_gt_1p5_hr,-0.045152663,F -coef_maint_Retired_Duration_lt_1p5_hrs,-0.264728908,F -coef_maint_Retired_Duration_gt_1p5_hr,-0.042981757,F -coef_maint_University_Student_Duration_gt_1p5_hr,0.045926523,F -coef_maint_Female_Duration_lt_1p5_hrs,-0.417208254,F -coef_maint_Female_Duration_gt_1p5_hr,0.045801918,F -coef_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr,0.040776383,F -coef_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,0.108462927,F -coef_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr,0,T -coef_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,-0.037893416,F -coef_maint_Distance_Duration_lt_1p5_hrs,-0.214802537,F -coef_maint_Distance_Duration_gt_1p5_hr,0.007991656,F -coef_maint_Time_Pressure_Duration_gt_1p5_hrs,0.013503327,F -coef_maint_Number_of_additional_individual_shop_and_maintp_tours_Duration_lt_1p5_hrs,0.078844289,F -coef_maint_Number_of_additional_individual_shop_and_maintp_tours_Duration_gt_1p5_hrs,-0.115431492,F -coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear,-0.864112609,F -coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root,0.504598473,F -coef_maint_Departure_Constant_Before_08_00_AM,-0.383711788,F -coef_maint_Departure_Constant_08_00_AM_08_30_AM,-0.076771517,F -coef_maint_Departure_Constant_08_30_AM_09_00_AM,-0.169259979,F -coef_maint_Departure_Constant_09_00_AM_09_30_AM,-0.051785379,F -coef_maint_Departure_Constant_09_30_AM_10_00_AM,-0.214942451,F -coef_maint_Departure_Constant_10_00_AM_10_30_AM,0,T -coef_maint_Departure_Constant_10_30_AM_11_00_AM,-0.427568963,F -coef_maint_Departure_Constant_After_11_00_AM,-0.520863411,F -coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,0.042879095,F -coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,-0.003157293,F -coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,-0.226803619,F -coef_maint_Arrival_Constant_Before_10_30_AM,-0.223212258,F -coef_maint_Arrival_Constant_10_30_AM_11_00_AM,0,T -coef_maint_Arrival_Constant_11_00_AM_11_30_AM,-0.128382637,F -coef_maint_Arrival_Constant_11_30_AM_01_30_PM,0.167977332,F -coef_maint_Arrival_Constant_01_30_PM_02_30_PM,-0.149495878,F -coef_maint_Arrival_Constant_02_30_PM_04_00_PM,0.087679934,F -coef_maint_Arrival_Constant_04_00_PM_04_30_PM,0.121707557,F -coef_maint_Arrival_Constant_After_04_30_PM,0.106745013,F -coef_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,-0.232610927,F -coef_maint_Duration_Constant_0_hrs,-0.483549396,F -coef_maint_Duration_Constant_0p5_hrs,0,T -coef_maint_Duration_Constant_Longer_than_0p5_hrs,-1.450618319,F -coef_maint_Duration_Constant_Duration_gt_1_hrs_Linear,-0.275082922,F -coef_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root,0.208434683,F -coef_maint_Calibration_Constant_Duration_eq_1,-0.124602605,F -coef_maint_Calibration_Constant_Duration_eq_2,-0.103637715,F -coef_maint_Calibration_Constant_Duration_eq_3,-0.225442145,F -coef_maint_Calibration_Constant_Duration_eq_4,-0.145273012,F -coef_maint_Calibration_Constant_Duration_eq_5,-0.019241539,F -coef_eatout_Distance_to_destination_Duration_lt_1_hrs,-0.134981987,F -coef_eatout_Distance_to_destination_Duration_gt_1_hrs,0.017860742,F -coef_eatout_Low_income_lt25000_Duration_lt_1_hrs,1.002485807,F -coef_eatout_Medium_25k_to_60k_Duration_lt_1_hrs,0.499822018,F -coef_eatout_Zero_auto_HH_Duration_gt_1_hrs,0.259409942,F -coef_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs,1.785123348,F -coef_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs,-1.626003709,F -coef_eatout_University_student_Departure_after_7_00_pm_Linear,0.293827759,F -coef_eatout_Female_Duration_lt_1_hrs,-0.399414247,F -coef_eatout_Female_Duration_gt_1_hrs,0.064593482,F -coef_eatout_Time_Pressure_Departure_before_6_30_pm,0.083673557,F -coef_eatout_Time_Pressure_Duration_lt_1_hrs,1.69632588,F -coef_eatout_Departure_Constant_07_30_AM_09_00_AM,1.222417262,F -coef_eatout_Departure_Constant_10_30_AM_11_00_AM,0.519559134,F -coef_eatout_Departure_Constant_11_00_AM_11_30_AM,1.191543552,F -coef_eatout_Departure_Constant_11_30_AM_12_00_PM,1.66870995,F -#coef_eatout_Departure_Constant_12_00_PM_12_30_PM,1.164106986,F -coef_eatout_Departure_Constant_12_30_PM_01_00_PM,1.057346496,F -coef_eatout_Departure_Constant_01_00_PM_01_30_PM,0.728959087,F -coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_30_pm_Linear,-0.477439748,F -coef_eatout_Departure_Constant_Before_06_00_PM,-1.21554671,F -#coef_eatout_Departure_Constant_06_00_PM_06_30_PM,-0.425984037,F -#coef_eatout_Departure_Constant_06_30_PM_07_00_PM,0,T -#coef_eatout_Departure_Constant_07_00_PM_07_30_PM,-0.227800647,F -#coef_eatout_Departure_Constant_After_07_30_PM,-0.293904097,F -#coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,-0.55440861,F -coef_eatout_Departure_Constant_11_00_AM_12_00_PM,0.531539506,F -coef_eatout_Departure_Constant_12_00_PM_12_30_PM,0.673838195,F -coef_eatout_Departure_Constant_12_30_PM_to_01_00_PM,0.422292261,F -coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,-0.033290717,F -coef_eatout_Departure_Constant_Before_05_30_PM,-0.561079452,F -coef_eatout_Departure_Constant_05_30_PM_06_00_PM,-0.178719161,F -coef_eatout_Departure_Constant_06_00_PM_06_30_PM,0,T -coef_eatout_Departure_Constant_06_30_PM_07_00_PM,-0.282095841,F -coef_eatout_Departure_Constant_07_00_PM_07_30_PM,-0.299748613,F -coef_eatout_Departure_Constant_After_07_30_PM,-0.845300559,F -coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,-0.667843486,F -coef_eatout_Arrival_Constant_9_30_AM_to_11_00_AM,0.486337344,F -#coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,0.629299404,F -#coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,0.938528731,F -#coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,0.584420106,F -#coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,0.842550215,F -coef_eatout_Arrival_Constant_02_30_PM_to_03_00_PM,0.298486505,F -#coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,0.125034982,F -coef_eatout_Arrival_Constant_Before_6_30_PM,-0.029062996,F -coef_eatout_Arrival_Constant_6_30_PM_to_7_00_PM,-0.509075598,F -# coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,-0.124885931,F -# coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,-0.605455664,F -# coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,0,T -# coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,-0.657382835,F -coef_eatout_Arrival_Constant_After_9_00_PM,-0.813629712,F -#coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-0.204722406,F -coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,2.002032369,F -coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,2.115334472,F -coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,1.647879687,F -coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,1.525310078,F -coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear,-0.152980854,F -coef_eatout_Arrival_Constant_Before_7_00_PM,-0.41165142,F -coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,-0.384557379,F -coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,-0.044050359,F -coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,0,T -coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,-0.239939049,F -coef_eatout_Arrival_Constant_After_09_00_PM,-0.248639696,F -coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-0.204771082,F -#coef_eatout_Duration_Constant_0_hours,-11.72660422,F -#coef_eatout_Duration_Constant_0p5_hous,-5.08873115,F -#coef_eatout_Duration_Constant_1_hour,-0.125521065,F -#coef_eatout_Duration_Constant_1p5_hours,0,T -coef_eatout_Duration_Constant_2_hours,-0.124807752,F -coef_eatout_Duration_Constant_2p5_hours_or_more,-0.156019835,F -coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,-0.3357414,F -coef_eatout_Duration_Constant_0_hours,-4.268996522,F -coef_eatout_Duration_Constant_0p5_hous,-1.323297693,F -coef_eatout_Duration_Constant_1_hour,0,T -coef_eatout_Duration_Constant_1p5_hours,-0.195669185,F -coef_eatout_Duration_Constant_2_hours_or_more,-0.523723192,F -coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear,-0.649331488,F -coef_eatout_Calibration_Constant_Duration_eq_1,-0.333697861,F -coef_eatout_Calibration_Constant_Duration_eq_2,-0.245716,F -coef_eatout_Calibration_Constant_Duration_eq_3,0.052708833,F -coef_eatout_Calibration_Constant_Duration_eq_4,0.041571499,F -coef_eatout_Calibration_Constant_Departure_eq_1,-10,F -coef_eatout_Calibration_Constant_Departure_eq_2,-10,F -coef_eatout_Calibration_Constant_Departure_eq_3,-10,F -coef_eatout_Calibration_Constant_Departure_eq_17,0.706568704,F -coef_eatout_Calibration_Constant_Departure_eq_18,0.634353544,F -coef_eatout_Calibration_Constant_Departure_eq_19,0.584387268,F -coef_eatout_Calibration_Constant_Departure_eq_20,0.469777884,F -coef_eatout_Calibration_Constant_Departure_eq_21,0.39548931,F -coef_social_Personlt_18_years_old_Duration_lt_1p5_hrs_Linear,-0.262839914,F -coef_social_Personlt_18_years_old_Duration_gt_1p5_hrs_Linear,0.086919429,F -coef_social_Nonworking_senior_retiree_Duration_lt_1p5_hrs_Linear,0.468354376,F -coef_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,-0.312282762,F -coef_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear,-0.508439932,F -coef_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear,0.074190914,F -coef_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,0.127185965,F -coef_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,0.048756122,F -coef_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,-0.559947083,F -coef_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr,-0.115347031,F -coef_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.104494637,F -coef_social_Auto_Distance_Duration_lt_1_hrs_Linear,-0.162965435,F -coef_social_Auto_Distance_Duration_gt_1_hrs_Linear,0.006797399,F -coef_social_Time_Pressure_Duration_lt_1_hr,-0.229264474,F -coef_social_Time_Pressure_Duration_gt_1_hr,0.219325112,F -coef_social_Number_of_additional_individual_social_and_dicretionary_tours_Duration_lt_1_hr,0.156250451,F -coef_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,-0.529943196,F -coef_social_Departure_Constant_Before_09_00_AM,-0.198438086,F -coef_social_Departure_Constant_09_00_AM_to_09_30_AM,0.137620628,F -coef_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,-0.142078961,F -coef_social_Departure_Constant_Before_05_30_PM,-0.390965052,F -coef_social_Departure_Constant_05_30_PM_06_00_PM,-0.453580491,F -coef_social_Departure_Constant_06_00_PM_06_30_PM,0,T -coef_social_Departure_Constant_06_30_PM_07_00_PM,-0.088537991,F -coef_social_Departure_Constant_07_00_PM_07_30_PM,0.052983115,F -coef_social_Departure_Constant_After_07_30_PM,-0.649629162,F -coef_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,-0.09574499,F -coef_social_Arrival_Constant_03_00_PM_to_03_30_PM,0.37674882,F -coef_social_Arrival_Constant_03_30_PM_to_04_00_PM,0.583355461,F -coef_social_Arrival_Constant_04_00_PM_to_04_30_PM,0.727855233,F -coef_social_Arrival_Constant_05_00_PM_to_06_00_PM,0.249551955,F -coef_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear,0.053771388,F -coef_social_Arrival_Constant_Before_8_30_PM,0.308763611,F -coef_social_Arrival_Constant_8_30_PM_to_9_00_PM,-0.208797698,F -coef_social_Arrival_Constant_9_00_PM_to_9_30_PM,-0.336319511,F -coef_social_Arrival_Constant_9_30_PM_to10_00_PM,0,T -coef_social_Arrival_Constant_10_00_PM_to_10_30_PM,-0.055707591,F -coef_social_Arrival_Constant_After_10_30_PM,-0.612356296,F -coef_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear,-0.348479901,F -coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear,0.619073863,F -coef_social_Duration_Constant_Less_than_2_hours,-0.584024011,F -coef_social_Duration_Constant_2_hours,-0.271552271,F -coef_social_Duration_Constant_2p5_hours,0,T -coef_social_Duration_Constant_3_hours_or_more,0.042083404,F -coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear,-0.13049452,F -coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_2p5_hrs_Linear,0.614440191,F -coef_social_Duration_Constant_Less_than_3_hrs,0.353666691,F -coef_social_Duration_Constant_3_hours,0,T -coef_social_Duration_Constant_3p5_hours,-0.691218836,F -coef_social_Duration_Constant_4_hours_or_more,-1.344375328,F -coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_4p5_hrs_Linear,-0.786970714,F -coef_social_Calibration_Constant_Duration_eq_1,-1.346772472,F -coef_social_Calibration_Constant_Duration_eq_2,0.377121689,F -coef_social_Calibration_Constant_Duration_eq_3,0.179818928,F -coef_social_Calibration_Constant_Duration_eq_4,-0.283418619,F -coef_social_Calibration_Constant_Duration_eq_5,-0.103541313,F -coef_social_Calibration_Constant_Duration_eq_6,-0.03704707,F -coef_social_Calibration_Constant_Duration_eq_7,-0.062437167,F -coef_social_Calibration_Constant_Duration_eq_8,0.047640282,F -coef_social_Calibration_Constant_Duration_eq_9,0.284369793,F -coef_disc_Personlt_18_years_old_Duration_lt_1p5_hrs_Linear,-0.262839914,F -coef_disc_Personlt_18_years_old_Duration_gt_1p5_hrs_Linear,0.086919429,F -coef_disc_Nonworking_senior_retiree_Duration_lt_1p5_hrs_Linear,0.468354376,F -coef_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,-0.312282762,F -coef_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear,-0.508439932,F -coef_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear,0.074190914,F -coef_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,0.127185965,F -coef_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,0.048756122,F -coef_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,-0.559947083,F -coef_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr,-0.115347031,F -coef_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.104494637,F -coef_disc_Auto_Distance_Duration_lt_1_hrs_Linear,-0.162965435,F -coef_disc_Auto_Distance_Duration_gt_1_hrs_Linear,0.006797399,F -coef_disc_Time_Pressure_Duration_lt_1_hr,-0.229264474,F -coef_disc_Time_Pressure_Duration_gt_1_hr,0.219325112,F -coef_disc_Number_of_additional_individual_social_and_dicretionary_tours_Duration_lt_1_hr,0.156250451,F -coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear,-0.742176805,F -coef_disc_Departure_Constant_Before_7_30_AM_,-1.323901585,F -coef_disc_Departure_Constant_7_30_AM_to_8_00_AM,-0.695441631,F -coef_disc_Departure_Constant_8_00_AM_to_8_30_AM,-0.269903336,F -coef_disc_Departure_Constant_8_30_AM_to_9_00_AM,-0.093709211,F -coef_disc_Departure_Constant_9_00_AM_to_9_30_AM,0.265634082,F -coef_disc_Departure_Constant_9_30_AM_to_10_00_AM,0.287521134,F -coef_disc_Departure_Constant_10_00_AM_to_10_30_AM,0.396547817,F -coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear,-0.245885745,F -coef_disc_Departure_Constant_Before_05_00_PM,-1.344482349,F -coef_disc_Departure_Constant_05_00_PM_05_30_PM,-0.622632748,F -coef_disc_Departure_Constant_05_30_PM_06_00_PM,-0.456718676,F -coef_disc_Departure_Constant_06_00_PM_06_30_PM,-0.206896106,F -coef_disc_Departure_Constant_06_30_PM_07_00_PM,0,T -coef_disc_Departure_Constant_After_07_00_PM,-0.46439343,F -coef_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear,-0.291998986,F -coef_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,0.148649188,F -coef_disc_Arrival_Constant_Before_6_30_PM,0.668775963,F -coef_disc_Arrival_Constant_6_30_PM_to_7_00_PM,-0.053520826,F -coef_disc_Arrival_Constant_7_00_PM_to_7_30_PM,0.099726391,F -coef_disc_Arrival_Constant_7_30_PM_to_8_00_PM,0.063414092,F -coef_disc_Arrival_Constant_8_00_PM_to_8_30_PM,0,T -coef_disc_Arrival_Constant_8_30_PM_to_9_00_PM,-0.18610847,F -coef_disc_Arrival_Constant_After_9_00_PM,-0.423207857,F -coef_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-0.525545923,F -#coef_disc_Duration_Constant_0_hours,-0.944257762,F -#coef_disc_Duration_Constant_0p5_hous,-0.117695955,F -#coef_disc_Duration_Constant_1_hour,0.438403665,F -#coef_disc_Duration_Constant_1p5_hours,-0.002500048,F -#coef_disc_Duration_Constant_2_hours,0,T -coef_disc_Duration_Constant_2p5_hours_or_more,0.239192556,F -#coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,-0.108260689,F -coef_disc_Duration_Constant_0_hours,0.436988282,F -coef_disc_Duration_Constant_0p5_hous,1.35967577,F -coef_disc_Duration_Constant_1_hour,1.692672999,F -coef_disc_Duration_Constant_1p5_hours,1.118932964,F -coef_disc_Duration_Constant_2_hours,0.771255733,F -coef_disc_Duration_Constant_2p5_hours,0,T -coef_disc_Duration_Constant_3_hours_or_more,-0.631242175,F -coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,-0.700935645,F -coef_disc_Calibration_Constant_Duration_eq_4,-0.132674257,F -coef_disc_Calibration_Constant_Duration_eq_5,-0.013371871,F -coef_disc_Calibration_Constant_Departure_eq_29,0.232927977,F -coef_disc_Calibration_Constant_Departure_eq_30,0.306104612,F -coef_disc_Calibration_Constant_Departure_eq_31,0.285520678,F -coef_disc_Calibration_Constant_Departure_eq_32,0.115886631,F +coefficient_name,value,constrain +coef_escort_Mode_Choice_Logsum,1.173173034,F +coef_escort_Distance_to_destination_Duration_less_than_0p5_hours_depart_and_arrive_in_the_same_period,-0.335017673,F +coef_escort_Distance_to_destination_Duration_greater_than_0p5_hours,0.005298165,F +coef_escort_Fulltime_worker_Departure_after_8_00_am_Linear,-0.037980109,F +coef_escort_Fulltime_worker_Departure_after_3_00_am_Linear,0.163254125,F +coef_escort_Fulltime_worker_Duration_lt_0p5_hrs,-0.275077482,F +coef_escort_Fulltime_worker_Duration_gt_0p5_hrs,0.051530545,F +coef_escort_University_student_Duration_lt_0p5_hrs,-0.426802718,F +coef_escort_Nondriving_age_student_Duration_gt_0p5_hrs,0.240582361,F +coef_escort_Driving_age_student_Duration_lt_0p5_hrs,-0.554146191,F +coef_escort_Driving_age_student_Duration_gt_0p5_hrs,0.299387708,F +coef_escort_Preschool_kid_Duration_gt_0p5_hrs,0.195482563,F +coef_escort_Medhigh_income_60k_to_120k_Duration_gt_0p5_hrs,-0.029281467,F +coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_7_30_AM,0.589083327,F +coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_8_00_AM,0.086690827,F +coef_escort_Households_with_no_kids_Dummy_1_0_Departure_before_2_30_PM,0.477582648,F +coef_escort_Households_with_no_kids_Dummy_1_0_Departure_after_3_00_PM,-0.204065502,F +coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_8_00_AM,-0.360039254,F +coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_8_30_AM,0.091614107,F +coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_before_3_00_PM,0.432854268,F +coef_escort_Households_with_no_kids_Dummy_1_0_Arrival_after_3_30_PM,0.131037275,F +coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,0.109700265,F +coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_3_00_PM,-0.224568648,F +coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_8_00_AM,-0.357416434,F +coef_escort_PreSchool_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_before_3_00_PM,0.629285298,F +coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Departure_after_8_00_AM,0.039005148,F +coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_8_30_AM,-0.06556611,F +coef_escort_Driving_age_School_Child_in_HH_with_Mandatory_tour_Dummy_1_0_Arrival_after_3_30_PM,0.117680977,F +coef_escort_Number_of_autos_greater_than_number_of_adults_Duration_gt_0p5_hrs,-0.057322708,F +coef_escort_Number_of_Individual_Tours_excluding_escorting_Duration_gt_0p5_hrs,-0.062899692,F +coef_escort_Number_of_joint_tours_Duration_gt_0p5_hrs,-0.048533895,F +coef_escort_Departure_Constant_Shift_for_every_30_minutes_before_06_30_am_Linear,-1.469240002,F +coef_escort_Departure_Constant_Before_07_00_AM,-2.070292862,F +coef_escort_Departure_Constant_07_00_AM_07_30_AM,-0.642734296,F +coef_escort_Departure_Constant_07_30_AM_08_00_AM,0,T +coef_escort_Departure_Constant_08_00_AM_08_30_AM,-0.214617667,F +coef_escort_Departure_Constant_08_30_AM_09_00_AM,-0.147266606,F +coef_escort_Departure_Constant_After_09_00_AM,-1.356686422,F +coef_escort_Departure_Constant_01_30_PM_02_00_PM,0.368092381,F +coef_escort_Departure_Constant_02_00_PM_02_30_PM,1.166803383,F +coef_escort_Departure_Constant_02_30_PM_03_00_PM,1.28466083,F +coef_escort_Departure_Constant_03_00_PM_03_30_PM,0.581891245,F +coef_escort_Departure_Constant_After_03_30_PM,0.834510243,F +coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,0.175257649,F +coef_escort_Departure_Constant_Shift_for_every_30_minutes_after_4_00_pm_Linear,-0.019161202,F +coef_escort_Arrival_Constant_Shift_for_every_30_minutes_before_6_30_am_Linear,0.44978138,F +coef_escort_Arrival_Constant_Before_07_00_AM,0.549584585,F +coef_escort_Arrival_Constant_07_00_AM_07_30_AM,0.488181278,F +coef_escort_Arrival_Constant_07_30_AM_08_00_AM,0.236447651,F +coef_escort_Arrival_Constant_08_00_AM_08_30_AM,0,T +coef_escort_Arrival_Constant_08_30_AM_09_00_AM,-0.683756801,F +coef_escort_Arrival_Constant_After_09_00_AM,-1.428888485,F +coef_escort_Arrival_Constant_02_30_PM_03_00_PM,1.311480662,F +coef_escort_Arrival_Constant_03_00_PM_03_30_PM,1.316883154,F +coef_escort_Arrival_Constant_03_30_PM_04_00_PM,1.396838392,F +coef_escort_Arrival_Constant_04_00_PM_04_30_PM,1.03146139,F +coef_escort_Arrival_Constant_After_04_30_PM,0.907344583,F +coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_9_30_am_Linear,-0.148408887,F +coef_escort_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,-0.389082896,F +coef_escort_Duration_Constant_0_hrs,-0.173757322,F +coef_escort_Duration_Constant_0p5_hrs,0,T +coef_escort_Duration_Constant_1_hrs,-0.431287743,F +coef_escort_Duration_Constant_1p5hrs,-0.700473959,F +coef_escort_Duration_Constant_2_hrs,-1.071871358,F +coef_escort_Duration_Constant_Longer_than_2_hrs,-1.691098421,F +coef_escort_Calibration_Constant_Duration_eq_1,-0.047200214,F +coef_escort_Calibration_Constant_Duration_eq_2,0.035611332,F +coef_escort_Calibration_Constant_Departure_eq_9,0.106814756,F +coef_escort_Calibration_Constant_Departure_eq_10,0.215386864,F +coef_escort_Calibration_Constant_Departure_eq_23,-0.255087318,F +coef_escort_Calibration_Constant_Departure_eq_24,-0.296870428,F +coef_shop_Joint_Shopping_tours_dummy_Departure_before_10_00_AM_Linear,-0.190727375,F +coef_shop_Joint_Shopping_tours_dummy_Departure_after_10_30_AM_Linear,-0.029551313,F +coef_shop_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,-0.291965906,F +coef_shop_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.045755784,F +coef_shop_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,-0.571185116,F +coef_shop_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,-0.468815184,F +coef_shop_Kids_in_Joint_Tour_Duration_gt_1p5_hr,-0.047470039,F +coef_shop_Driving_age_student_Duration_gt_1p5_hr,0.122149101,F +coef_shop_Fulltime_worker_Duration_gt_1p5_hr,-0.037886459,F +coef_shop_Nondriving_Student_Duration_gt_1p5_hr,0.10247157,F +coef_shop_Preschool_Child_Duration_lt_1p5_hrs,-1.882944033,F +coef_shop_Part_Time_Worker_Duration_lt_1p5_hrs,-0.197455071,F +coef_shop_Part_Time_Worker_Duration_gt_1p5_hr,-0.045152663,F +coef_shop_Retired_Duration_lt_1p5_hrs,-0.264728908,F +coef_shop_Retired_Duration_gt_1p5_hr,-0.042981757,F +coef_shop_University_Student_Duration_gt_1p5_hr,0.045926523,F +coef_shop_Female_Duration_lt_1p5_hrs,-0.417208254,F +coef_shop_Female_Duration_gt_1p5_hr,0.045801918,F +coef_shop_Low_Income_lteq25_000_Duration_gt_1p5_hr,0.040776383,F +coef_shop_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,0.108462927,F +coef_shop_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,-0.037893416,F +coef_shop_Distance_Duration_lt_1p5_hrs,-0.214802537,F +coef_shop_Distance_Duration_gt_1p5_hr,0.007991656,F +coef_shop_Time_Pressure_Duration_gt_1p5_hrs,0.013503327,F +coef_shop_Number_of_additional_individual_shop_and_maintp_tours_Duration_lt_1p5_hrs,0.078844289,F +coef_shop_Number_of_additional_individual_shop_and_maintp_tours_Duration_gt_1p5_hrs,-0.115431492,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,-0.959875456,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Square_root,1.112594898,F +coef_shop_Departure_Constant_Before_09_00_AM,-0.446394064,F +coef_shop_Departure_Constant_09_00_AM_09_30_AM,-0.021669265,F +coef_shop_Departure_Constant_09_30_AM_10_00_AM,-0.282978638,F +coef_shop_Departure_Constant_10_00_AM_10_30_AM,0,T +coef_shop_Departure_Constant_10_30_AM_11_00_AM,-0.309421311,F +coef_shop_Departure_Constant_After_11_00_AM,-0.541073357,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,-0.072013428,F +coef_shop_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,-0.000653398,F +coef_shop_Arrival_Constant_Shift_for_every_30_minutes_before_12_00_pm_Linear,-0.18376635,F +coef_shop_Arrival_Constant_Before_12_30_PM,-0.716195343,F +coef_shop_Arrival_Constant_12_30_PM_03_00_PM,-0.502714001,F +coef_shop_Arrival_Constant_03_00_PM_03_30_PM,-0.167868872,F +coef_shop_Arrival_Constant_03_30_PM_04_00_PM,-0.156786941,F +coef_shop_Arrival_Constant_04_00_PM_04_30_PM,0,T +coef_shop_Arrival_Constant_04_30_PM_05_00_PM,-0.057314044,F +coef_shop_Arrival_Constant_05_00_PM_05_30_PM,-0.580040851,F +coef_shop_Arrival_Constant_05_30_PM_07_00_PM,-0.32239566,F +coef_shop_Arrival_Constant_07_00_PM_09_30_PM,-0.347828147,F +coef_shop_Arrival_Constant_After_09_30_PM,-1.123574723,F +coef_shop_Arrival_Constant_Shift_for_every_30_minutes_after_10_00_pm_Linear,-0.499770654,F +coef_shop_Duration_Constant_0_hrs,-0.131743185,F +coef_shop_Duration_Constant_0p5_hrs,0.888857137,F +coef_shop_Duration_Constant_1_hrs,0,T +coef_shop_Duration_Constant_1p5hrs,-0.333413031,F +coef_shop_Duration_Constant_2_hrs,-0.850897912,F +coef_shop_Duration_Constant_Longer_than_2_hrs,-1.203783479,F +coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Linear,-0.293581223,F +coef_shop_Duration_Constant_Duration_gt_2p5_hrs_Square_root,-0.215759138,F +coef_shop_Calibration_Constant_Duration_eq_1,-0.138450424,F +coef_shop_Calibration_Constant_Duration_eq_2,-0.092704403,F +coef_shop_Calibration_Constant_Duration_eq_3,-0.087738073,F +coef_maint_Joint_Maintenance_tours_dummy_Departure_before_10_00_AM_Linear,-0.139150288,F +coef_maint_Joint_Maintenance_tours_dummy_Departure_after_10_30_AM_Linear,-0.065786345,F +coef_maint_Joint_Tours_Party_Size_gt_2_Duration_lt_1p5_hrs,-0.291965906,F +coef_maint_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.045755784,F +coef_maint_Joint_Tour_with_only_adults_Duration_lt_1p5_hrs,-0.571185116,F +coef_maint_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,-0.468815184,F +coef_maint_Kids_in_Joint_Tour_Duration_gt_1p5_hr,-0.047470039,F +coef_maint_Fulltime_worker_Duration_gt_1p5_hr,-0.037886459,F +coef_maint_Nondriving_Student_Duration_gt_1p5_hr,0.10247157,F +coef_maint_Preschool_Child_Duration_lt_1p5_hrs,-1.882944033,F +coef_maint_Part_Time_Worker_Duration_lt_1p5_hrs,-0.197455071,F +coef_maint_Part_Time_Worker_Duration_gt_1p5_hr,-0.045152663,F +coef_maint_Retired_Duration_lt_1p5_hrs,-0.264728908,F +coef_maint_Retired_Duration_gt_1p5_hr,-0.042981757,F +coef_maint_University_Student_Duration_gt_1p5_hr,0.045926523,F +coef_maint_Female_Duration_lt_1p5_hrs,-0.417208254,F +coef_maint_Female_Duration_gt_1p5_hr,0.045801918,F +coef_maint_Low_Income_lteq25_000_Duration_gt_1p5_hr,0.040776383,F +coef_maint_Medium_Income_25_001_to_60_000_Duration_lt_1p5_hrs,0.108462927,F +coef_maint_Medium_Income_25_001_to_60_000_Duration_gt_1p5_hr,0,T +coef_maint_MediumHigh_Income_60_001_to_120_00_Duration_gt_1p5_hr,-0.037893416,F +coef_maint_Distance_Duration_lt_1p5_hrs,-0.214802537,F +coef_maint_Distance_Duration_gt_1p5_hr,0.007991656,F +coef_maint_Time_Pressure_Duration_gt_1p5_hrs,0.013503327,F +coef_maint_Number_of_additional_individual_shop_and_maintp_tours_Duration_lt_1p5_hrs,0.078844289,F +coef_maint_Number_of_additional_individual_shop_and_maintp_tours_Duration_gt_1p5_hrs,-0.115431492,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Linear,-0.864112609,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_before_07_30_am_Square_Root,0.504598473,F +coef_maint_Departure_Constant_Before_08_00_AM,-0.383711788,F +coef_maint_Departure_Constant_08_00_AM_08_30_AM,-0.076771517,F +coef_maint_Departure_Constant_08_30_AM_09_00_AM,-0.169259979,F +coef_maint_Departure_Constant_09_00_AM_09_30_AM,-0.051785379,F +coef_maint_Departure_Constant_09_30_AM_10_00_AM,-0.214942451,F +coef_maint_Departure_Constant_10_00_AM_10_30_AM,0,T +coef_maint_Departure_Constant_10_30_AM_11_00_AM,-0.427568963,F +coef_maint_Departure_Constant_After_11_00_AM,-0.520863411,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Linear,0.042879095,F +coef_maint_Departure_Constant_Shift_for_every_30_minutes_after_11_30_am_Squared,-0.003157293,F +coef_maint_Arrival_Constant_Shift_for_every_30_minutes_before_10_00_am_Linear,-0.226803619,F +coef_maint_Arrival_Constant_Before_10_30_AM,-0.223212258,F +coef_maint_Arrival_Constant_10_30_AM_11_00_AM,0,T +coef_maint_Arrival_Constant_11_00_AM_11_30_AM,-0.128382637,F +coef_maint_Arrival_Constant_11_30_AM_01_30_PM,0.167977332,F +coef_maint_Arrival_Constant_01_30_PM_02_30_PM,-0.149495878,F +coef_maint_Arrival_Constant_02_30_PM_04_00_PM,0.087679934,F +coef_maint_Arrival_Constant_04_00_PM_04_30_PM,0.121707557,F +coef_maint_Arrival_Constant_After_04_30_PM,0.106745013,F +coef_maint_Arrival_Constant_Shift_for_every_30_minutes_after_5_00_pm_Linear,-0.232610927,F +coef_maint_Duration_Constant_0_hrs,-0.483549396,F +coef_maint_Duration_Constant_0p5_hrs,0,T +coef_maint_Duration_Constant_Longer_than_0p5_hrs,-1.450618319,F +coef_maint_Duration_Constant_Duration_gt_1_hrs_Linear,-0.275082922,F +coef_maint_Duration_Constant_Duration_gt_1_hrs_Square_Root,0.208434683,F +coef_maint_Calibration_Constant_Duration_eq_1,-0.124602605,F +coef_maint_Calibration_Constant_Duration_eq_2,-0.103637715,F +coef_maint_Calibration_Constant_Duration_eq_3,-0.225442145,F +coef_maint_Calibration_Constant_Duration_eq_4,-0.145273012,F +coef_maint_Calibration_Constant_Duration_eq_5,-0.019241539,F +coef_eatout_Distance_to_destination_Duration_lt_1_hrs,-0.134981987,F +coef_eatout_Distance_to_destination_Duration_gt_1_hrs,0.017860742,F +coef_eatout_Low_income_lt25000_Duration_lt_1_hrs,1.002485807,F +coef_eatout_Medium_25k_to_60k_Duration_lt_1_hrs,0.499822018,F +coef_eatout_Zero_auto_HH_Duration_gt_1_hrs,0.259409942,F +coef_eatout_Kids_in_Joint_tour_Duration_lt_1_hrs,1.785123348,F +coef_eatout_Joint_Tours_Party_Size_greater_than_2_Duration_lt_1_hrs,-1.626003709,F +coef_eatout_University_student_Departure_after_7_00_pm_Linear,0.293827759,F +coef_eatout_Female_Duration_lt_1_hrs,-0.399414247,F +coef_eatout_Female_Duration_gt_1_hrs,0.064593482,F +coef_eatout_Time_Pressure_Departure_before_6_30_pm,0.083673557,F +coef_eatout_Time_Pressure_Duration_lt_1_hrs,1.69632588,F +coef_eatout_Departure_Constant_07_30_AM_09_00_AM,1.222417262,F +coef_eatout_Departure_Constant_10_30_AM_11_00_AM,0.519559134,F +coef_eatout_Departure_Constant_11_00_AM_11_30_AM,1.191543552,F +coef_eatout_Departure_Constant_11_30_AM_12_00_PM,1.66870995,F +#coef_eatout_Departure_Constant_12_00_PM_12_30_PM,1.164106986,F +coef_eatout_Departure_Constant_12_30_PM_01_00_PM,1.057346496,F +coef_eatout_Departure_Constant_01_00_PM_01_30_PM,0.728959087,F +coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_30_pm_Linear,-0.477439748,F +coef_eatout_Departure_Constant_Before_06_00_PM,-1.21554671,F +#coef_eatout_Departure_Constant_06_00_PM_06_30_PM,-0.425984037,F +#coef_eatout_Departure_Constant_06_30_PM_07_00_PM,0,T +#coef_eatout_Departure_Constant_07_00_PM_07_30_PM,-0.227800647,F +#coef_eatout_Departure_Constant_After_07_30_PM,-0.293904097,F +#coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,-0.55440861,F +coef_eatout_Departure_Constant_11_00_AM_12_00_PM,0.531539506,F +coef_eatout_Departure_Constant_12_00_PM_12_30_PM,0.673838195,F +coef_eatout_Departure_Constant_12_30_PM_to_01_00_PM,0.422292261,F +coef_eatout_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,-0.033290717,F +coef_eatout_Departure_Constant_Before_05_30_PM,-0.561079452,F +coef_eatout_Departure_Constant_05_30_PM_06_00_PM,-0.178719161,F +coef_eatout_Departure_Constant_06_00_PM_06_30_PM,0,T +coef_eatout_Departure_Constant_06_30_PM_07_00_PM,-0.282095841,F +coef_eatout_Departure_Constant_07_00_PM_07_30_PM,-0.299748613,F +coef_eatout_Departure_Constant_After_07_30_PM,-0.845300559,F +coef_eatout_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,-0.667843486,F +coef_eatout_Arrival_Constant_9_30_AM_to_11_00_AM,0.486337344,F +#coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,0.629299404,F +#coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,0.938528731,F +#coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,0.584420106,F +#coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,0.842550215,F +coef_eatout_Arrival_Constant_02_30_PM_to_03_00_PM,0.298486505,F +#coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,0.125034982,F +coef_eatout_Arrival_Constant_Before_6_30_PM,-0.029062996,F +coef_eatout_Arrival_Constant_6_30_PM_to_7_00_PM,-0.509075598,F +# coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,-0.124885931,F +# coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,-0.605455664,F +# coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,0,T +# coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,-0.657382835,F +coef_eatout_Arrival_Constant_After_9_00_PM,-0.813629712,F +#coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-0.204722406,F +coef_eatout_Arrival_Constant_12_30_PM_to_01_00_PM,2.002032369,F +coef_eatout_Arrival_Constant_01_00_PM_to_01_30_PM,2.115334472,F +coef_eatout_Arrival_Constant_01_30_PM_to_02_00_PM,1.647879687,F +coef_eatout_Arrival_Constant_02_00_PM_to_02_30_PM,1.525310078,F +coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_before_06_30_pm_Linear,-0.152980854,F +coef_eatout_Arrival_Constant_Before_7_00_PM,-0.41165142,F +coef_eatout_Arrival_Constant_7_00_PM_to_7_30_PM,-0.384557379,F +coef_eatout_Arrival_Constant_7_30_PM_to_8_00_PM,-0.044050359,F +coef_eatout_Arrival_Constant_8_00_PM_to_8_30_PM,0,T +coef_eatout_Arrival_Constant_8_30_PM_to_9_00_PM,-0.239939049,F +coef_eatout_Arrival_Constant_After_09_00_PM,-0.248639696,F +coef_eatout_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-0.204771082,F +#coef_eatout_Duration_Constant_0_hours,-11.72660422,F +#coef_eatout_Duration_Constant_0p5_hous,-5.08873115,F +#coef_eatout_Duration_Constant_1_hour,-0.125521065,F +#coef_eatout_Duration_Constant_1p5_hours,0,T +coef_eatout_Duration_Constant_2_hours,-0.124807752,F +coef_eatout_Duration_Constant_2p5_hours_or_more,-0.156019835,F +coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,-0.3357414,F +coef_eatout_Duration_Constant_0_hours,-4.268996522,F +coef_eatout_Duration_Constant_0p5_hous,-1.323297693,F +coef_eatout_Duration_Constant_1_hour,0,T +coef_eatout_Duration_Constant_1p5_hours,-0.195669185,F +coef_eatout_Duration_Constant_2_hours_or_more,-0.523723192,F +coef_eatout_Duration_Constant_Shift_for_every_30_minutes_more_than_2p5_hrs_Linear,-0.649331488,F +coef_eatout_Calibration_Constant_Duration_eq_1,-0.333697861,F +coef_eatout_Calibration_Constant_Duration_eq_2,-0.245716,F +coef_eatout_Calibration_Constant_Duration_eq_3,0.052708833,F +coef_eatout_Calibration_Constant_Duration_eq_4,0.041571499,F +coef_eatout_Calibration_Constant_Departure_eq_1,-10,F +coef_eatout_Calibration_Constant_Departure_eq_2,-10,F +coef_eatout_Calibration_Constant_Departure_eq_3,-10,F +coef_eatout_Calibration_Constant_Departure_eq_17,0.706568704,F +coef_eatout_Calibration_Constant_Departure_eq_18,0.634353544,F +coef_eatout_Calibration_Constant_Departure_eq_19,0.584387268,F +coef_eatout_Calibration_Constant_Departure_eq_20,0.469777884,F +coef_eatout_Calibration_Constant_Departure_eq_21,0.39548931,F +coef_social_Personlt_18_years_old_Duration_lt_1p5_hrs_Linear,-0.262839914,F +coef_social_Personlt_18_years_old_Duration_gt_1p5_hrs_Linear,0.086919429,F +coef_social_Nonworking_senior_retiree_Duration_lt_1p5_hrs_Linear,0.468354376,F +coef_social_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,-0.312282762,F +coef_social_Zero_auto_households_Duration_lt_1p5_hrs_Linear,-0.508439932,F +coef_social_Zero_auto_households_Duration_gt_1p5_hrs_Linear,0.074190914,F +coef_social_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,0.127185965,F +coef_social_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,0.048756122,F +coef_social_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,-0.559947083,F +coef_social_Kids_in_Joint_Tour_Duration_gt_1p5_hr,-0.115347031,F +coef_social_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.104494637,F +coef_social_Auto_Distance_Duration_lt_1_hrs_Linear,-0.162965435,F +coef_social_Auto_Distance_Duration_gt_1_hrs_Linear,0.006797399,F +coef_social_Time_Pressure_Duration_lt_1_hr,-0.229264474,F +coef_social_Time_Pressure_Duration_gt_1_hr,0.219325112,F +coef_social_Number_of_additional_individual_social_and_dicretionary_tours_Duration_lt_1_hr,0.156250451,F +coef_social_Departure_Constant_Shift_for_every_30_minutes_before_08_30_am_Linear,-0.529943196,F +coef_social_Departure_Constant_Before_09_00_AM,-0.198438086,F +coef_social_Departure_Constant_09_00_AM_to_09_30_AM,0.137620628,F +coef_social_Departure_Constant_Shift_for_every_30_minutes_before_05_00_pm_Linear,-0.142078961,F +coef_social_Departure_Constant_Before_05_30_PM,-0.390965052,F +coef_social_Departure_Constant_05_30_PM_06_00_PM,-0.453580491,F +coef_social_Departure_Constant_06_00_PM_06_30_PM,0,T +coef_social_Departure_Constant_06_30_PM_07_00_PM,-0.088537991,F +coef_social_Departure_Constant_07_00_PM_07_30_PM,0.052983115,F +coef_social_Departure_Constant_After_07_30_PM,-0.649629162,F +coef_social_Departure_Constant_Shift_for_every_30_minutes_after_08_00_pm_Linear,-0.09574499,F +coef_social_Arrival_Constant_03_00_PM_to_03_30_PM,0.37674882,F +coef_social_Arrival_Constant_03_30_PM_to_04_00_PM,0.583355461,F +coef_social_Arrival_Constant_04_00_PM_to_04_30_PM,0.727855233,F +coef_social_Arrival_Constant_05_00_PM_to_06_00_PM,0.249551955,F +coef_social_Arrival_Constant_Shift_for_every_30_minutes_before_08_00_pm_Linear,0.053771388,F +coef_social_Arrival_Constant_Before_8_30_PM,0.308763611,F +coef_social_Arrival_Constant_8_30_PM_to_9_00_PM,-0.208797698,F +coef_social_Arrival_Constant_9_00_PM_to_9_30_PM,-0.336319511,F +coef_social_Arrival_Constant_9_30_PM_to10_00_PM,0,T +coef_social_Arrival_Constant_10_00_PM_to_10_30_PM,-0.055707591,F +coef_social_Arrival_Constant_After_10_30_PM,-0.612356296,F +coef_social_Arrival_Constant_Shift_for_every_30_minutes_after_11_00_pm_Linear,-0.348479901,F +coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_1p5_hrs_Linear,0.619073863,F +coef_social_Duration_Constant_Less_than_2_hours,-0.584024011,F +coef_social_Duration_Constant_2_hours,-0.271552271,F +coef_social_Duration_Constant_2p5_hours,0,T +coef_social_Duration_Constant_3_hours_or_more,0.042083404,F +coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_3p5_hrs_Linear,-0.13049452,F +coef_social_Duration_Constant_Shift_for_every_30_minutes_less_than_2p5_hrs_Linear,0.614440191,F +coef_social_Duration_Constant_Less_than_3_hrs,0.353666691,F +coef_social_Duration_Constant_3_hours,0,T +coef_social_Duration_Constant_3p5_hours,-0.691218836,F +coef_social_Duration_Constant_4_hours_or_more,-1.344375328,F +coef_social_Duration_Constant_Shift_for_every_30_minutes_more_than_4p5_hrs_Linear,-0.786970714,F +coef_social_Calibration_Constant_Duration_eq_1,-1.346772472,F +coef_social_Calibration_Constant_Duration_eq_2,0.377121689,F +coef_social_Calibration_Constant_Duration_eq_3,0.179818928,F +coef_social_Calibration_Constant_Duration_eq_4,-0.283418619,F +coef_social_Calibration_Constant_Duration_eq_5,-0.103541313,F +coef_social_Calibration_Constant_Duration_eq_6,-0.03704707,F +coef_social_Calibration_Constant_Duration_eq_7,-0.062437167,F +coef_social_Calibration_Constant_Duration_eq_8,0.047640282,F +coef_social_Calibration_Constant_Duration_eq_9,0.284369793,F +coef_disc_Personlt_18_years_old_Duration_lt_1p5_hrs_Linear,-0.262839914,F +coef_disc_Personlt_18_years_old_Duration_gt_1p5_hrs_Linear,0.086919429,F +coef_disc_Nonworking_senior_retiree_Duration_lt_1p5_hrs_Linear,0.468354376,F +coef_disc_Retiree_Nonworking_senior_only_HH_Duration_lt_1p5_hrs_Linear,-0.312282762,F +coef_disc_Zero_auto_households_Duration_lt_1p5_hrs_Linear,-0.508439932,F +coef_disc_Zero_auto_households_Duration_gt_1p5_hrs_Linear,0.074190914,F +coef_disc_Number_of_auto_more_that_number_of_adults_Duration_lt_1p5_hrs_Linear,0.127185965,F +coef_disc_Number_of_auto_more_that_number_of_adults_Duration_gt_1p5_hrs_Linear,0.048756122,F +coef_disc_Kids_in_Joint_Tour_Duration_lt_1p5_hrs,-0.559947083,F +coef_disc_Kids_in_Joint_Tour_Duration_gt_1p5_hr,-0.115347031,F +coef_disc_Joint_Tours_Party_Size_gt_2_Duration_gt_1p5_hr,0.104494637,F +coef_disc_Auto_Distance_Duration_lt_1_hrs_Linear,-0.162965435,F +coef_disc_Auto_Distance_Duration_gt_1_hrs_Linear,0.006797399,F +coef_disc_Time_Pressure_Duration_lt_1_hr,-0.229264474,F +coef_disc_Time_Pressure_Duration_gt_1_hr,0.219325112,F +coef_disc_Number_of_additional_individual_social_and_dicretionary_tours_Duration_lt_1_hr,0.156250451,F +coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_07_30_pm_Linear,-0.742176805,F +coef_disc_Departure_Constant_Before_7_30_AM_,-1.323901585,F +coef_disc_Departure_Constant_7_30_AM_to_8_00_AM,-0.695441631,F +coef_disc_Departure_Constant_8_00_AM_to_8_30_AM,-0.269903336,F +coef_disc_Departure_Constant_8_30_AM_to_9_00_AM,-0.093709211,F +coef_disc_Departure_Constant_9_00_AM_to_9_30_AM,0.265634082,F +coef_disc_Departure_Constant_9_30_AM_to_10_00_AM,0.287521134,F +coef_disc_Departure_Constant_10_00_AM_to_10_30_AM,0.396547817,F +coef_disc_Departure_Constant_Shift_for_every_30_minutes_before_04_30_pm_Linear,-0.245885745,F +coef_disc_Departure_Constant_Before_05_00_PM,-1.344482349,F +coef_disc_Departure_Constant_05_00_PM_05_30_PM,-0.622632748,F +coef_disc_Departure_Constant_05_30_PM_06_00_PM,-0.456718676,F +coef_disc_Departure_Constant_06_00_PM_06_30_PM,-0.206896106,F +coef_disc_Departure_Constant_06_30_PM_07_00_PM,0,T +coef_disc_Departure_Constant_After_07_00_PM,-0.46439343,F +coef_disc_Departure_Constant_Shift_for_every_30_minutes_after_07_30_pm_Linear,-0.291998986,F +coef_disc_Arrival_Constant_Shift_for_every_30_minutes_before_06_00_pm_Linear,0.148649188,F +coef_disc_Arrival_Constant_Before_6_30_PM,0.668775963,F +coef_disc_Arrival_Constant_6_30_PM_to_7_00_PM,-0.053520826,F +coef_disc_Arrival_Constant_7_00_PM_to_7_30_PM,0.099726391,F +coef_disc_Arrival_Constant_7_30_PM_to_8_00_PM,0.063414092,F +coef_disc_Arrival_Constant_8_00_PM_to_8_30_PM,0,T +coef_disc_Arrival_Constant_8_30_PM_to_9_00_PM,-0.18610847,F +coef_disc_Arrival_Constant_After_9_00_PM,-0.423207857,F +coef_disc_Arrival_Constant_Shift_for_every_30_minutes_after_09_30_pm_Linear,-0.525545923,F +#coef_disc_Duration_Constant_0_hours,-0.944257762,F +#coef_disc_Duration_Constant_0p5_hous,-0.117695955,F +#coef_disc_Duration_Constant_1_hour,0.438403665,F +#coef_disc_Duration_Constant_1p5_hours,-0.002500048,F +#coef_disc_Duration_Constant_2_hours,0,T +coef_disc_Duration_Constant_2p5_hours_or_more,0.239192556,F +#coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,-0.108260689,F +coef_disc_Duration_Constant_0_hours,0.436988282,F +coef_disc_Duration_Constant_0p5_hous,1.35967577,F +coef_disc_Duration_Constant_1_hour,1.692672999,F +coef_disc_Duration_Constant_1p5_hours,1.118932964,F +coef_disc_Duration_Constant_2_hours,0.771255733,F +coef_disc_Duration_Constant_2p5_hours,0,T +coef_disc_Duration_Constant_3_hours_or_more,-0.631242175,F +coef_disc_Duration_Constant_Shift_for_every_30_minutes_more_than_3_hrs_Linear,-0.700935645,F +coef_disc_Calibration_Constant_Duration_eq_4,-0.132674257,F +coef_disc_Calibration_Constant_Duration_eq_5,-0.013371871,F +coef_disc_Calibration_Constant_Departure_eq_29,0.232927977,F +coef_disc_Calibration_Constant_Departure_eq_30,0.306104612,F +coef_disc_Calibration_Constant_Departure_eq_31,0.285520678,F +coef_disc_Calibration_Constant_Departure_eq_32,0.115886631,F diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory.csv index f3ae1b2170..3e2c109d90 100755 --- a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory.csv @@ -1,348 +1,348 @@ -Label,Description,Expression,Coefficient -# ESCORT,,, -util_escort_mode_choice_logsum,ESCORT - Mode Choice Logsum,"@np.where(df.tour_type == 'escort', df.mode_choice_logsum, 0) ",coef_escort_mode_choice_logsum -"#Note: In CTRAMP expressions, duration alternative is from 1 to 48 but in ActivitySim, it is from 0 to 47 since the duration alternative ID was calculated as (end - start). Therefore, duration in ActivitySim expression = CTRAMP duration expresssion - 1 ",,,# -util_escort_distance_to_destination_duration_less_than_30_minutes,ESCORT - Distance to destination - Duration less than 0.5 hour (depart and arrive in the same period),"@np.where(((df.tour_type == 'escort') & (df.duration<1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_distance_to_destination_duration_less_than_30_minutes -util_escort_distance_to_destination_duration_greater_than_30_minutes,ESCORT - Distance to destination - Duration greater than 0.5 hour,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_distance_to_destination_duration_greater_than_30_minutes -util_escort_fulltime_worker_departure_after_8_am_linear,ESCORT - Full-time worker - Departure after 8:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)),0)",coef_escort_fulltime_worker_departure_after_8_am_linear -"#Note: In CTRAMP expression file, the description below says departure is after 3 am but from the expression it seems that it would be 3 pm instead of 3 am",,, -util_escort_fulltime_worker_departure_after_3_am_linear,ESCORT - Full-time worker - Departure after 3:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_fulltime_worker_departure_after_3_am_linear -util_escort_fulltime_worker_duration_less_than_30_minutes,ESCORT - Full-time worker - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_fulltime_worker_duration_less_than_30_minutes -util_escort_fulltime_worker_duration_greater_than_30_minutes,ESCORT - Full-time worker - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_fulltime_worker_duration_greater_than_30_minutes -util_escort_university_student_duration_less_than_30_minutes,ESCORT - University student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 3) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_university_student_duration_less_than_30_minutes -util_escort_non_driving_age_student_duration_greater_than_30_minutes,ESCORT - Non-driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & ((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_non_driving_age_student_duration_greater_than_30_minutes -util_escort_driving_age_student_duration_less_than_30_minutes,ESCORT - Driving age student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_driving_age_student_duration_less_than_30_minutes -util_escort_driving_age_student_duration_greater_than_30_minutes,ESCORT - Driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_driving_age_student_duration_greater_than_30_minutes -"#Note: In CTRAMP expression file, description says the expression below is for duration > 0.5 hr but the expression says duration < 0.5 hr",,, -util_escort_pre_school_kid_duration_greater_than_30_minutes,ESCORT - Pre-school kid - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 8) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_pre_school_kid_duration_greater_than_30_minutes -util_escort_med_high_income_duration_greater_than_30_minutes,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_med_high_income_duration_greater_than_30_minutes -util_escort_households_with_no_kids_departure_before_7_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_households_with_no_kids_departure_before_7_30_am -util_escort_households_with_no_kids_departure_after_8_00_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_households_with_no_kids_departure_after_8_00_am -util_escort_households_with_no_kids_departure_before_2_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_households_with_no_kids_departure_before_2_30_pm -util_escort_households_with_no_kids_departure_after_3_00_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where ((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_households_with_no_kids_departure_after_3_00_pm -util_escort_households_with_no_kids_arrival_before_8_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_households_with_no_kids_arrival_before_8_am -util_escort_households_with_no_kids_arrival_after_8_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_households_with_no_kids_arrival_after_8_30_am -util_escort_households_with_no_kids_arrival_before_3_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_households_with_no_kids_arrival_before_3_pm -util_escort_households_with_no_kids_arrival_after_3_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_households_with_no_kids_arrival_after_3_30_pm -util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am -util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm -util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am -util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where ((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm -util_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes -util_escort_number_of_individual_tours_duration_greater_than_30_minutes,ESCORT -Number of Individual Tours (excluding escorting) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.num_non_escort_tours > 0) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) * (df.num_non_escort_tours), 0)",coef_escort_number_of_individual_tours_duration_greater_than_30_minutes -util_escort_number_of_joint_tours_duration_greater_than_30_minutes,ESCORT - Number of joint tours - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) *(df.num_joint_tours), 0)",coef_escort_number_of_joint_tours_duration_greater_than_30_minutes -util_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start<8)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + np.where((df.start>13), np.minimum(df.start-13,28), 0)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear -util_escort_departure_constant_before_7_am,ESCORT - Departure Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.start<9)),coef_escort_departure_constant_before_7_am -util_escort_departure_constant_7_am_to_7_30_am,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_departure_constant_7_am_to_7_30_am -util_escort_departure_constant_7_30_am_to_8_am,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_departure_constant_7_30_am_to_8_am -util_escort_departure_constant_8_am_to_8_30_am,ESCORT - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.start==11)),coef_escort_departure_constant_8_am_to_8_30_am -util_escort_departure_constant_8_30_am_to_9_am,ESCORT - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.start==12)),coef_escort_departure_constant_8_30_am_to_9_am -util_escort_departure_constant_after_9_am,ESCORT - Departure Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.start>12)),coef_escort_departure_constant_after_9_am -util_escort_departure_constant_1_30_pm_to_2_pm,ESCORT - Departure Constant: 01:30 PM - 02:00 PM,@((df.tour_type == 'escort') & (df.start==22)),coef_escort_departure_constant_1_30_pm_to_2_pm -util_escort_departure_constant_2_pm_to_2_30_pm,ESCORT - Departure Constant: 02:00 PM - 02:30 PM,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_departure_constant_2_pm_to_2_30_pm -util_escort_departure_constant_2_30_pm_to_3_pm,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_departure_constant_2_30_pm_to_3_pm -util_escort_departure_constant_3_pm_to_3_30_pm,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.start==25)),coef_escort_departure_constant_3_pm_to_3_30_pm -util_escort_departure_constant_after_3_30_pm,ESCORT - Departure Constant: After 03:30 PM,@((df.tour_type == 'escort') & (df.start>25)),coef_escort_departure_constant_after_3_30_pm -util_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>13)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + (np.where((df.start>13), np.minimum(df.start-13,28), 0))), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear -util_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>26)), (np.where((df.start<0), np.minimum(0-df.start,48), 0) + np.where((df.start>26), np.minimum(df.start-26,15),0)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear -util_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end<8)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear -util_escort_arrival_constant_before_7_am,ESCORT - Arrival Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.end<9)),coef_escort_arrival_constant_before_7_am -util_escort_arrival_constant_7_am_to_7_30_am,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.end==9)),coef_escort_arrival_constant_7_am_to_7_30_am -util_escort_arrival_constant_7_30_am_to_8_am,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.end==10)),coef_escort_arrival_constant_7_30_am_to_8_am -util_escort_arrival_constant_8_am_to_8_30_am,ESCORT - Arrival Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.end==11)),coef_escort_arrival_constant_8_am_to_8_30_am -util_escort_arrival_constant_8_30_am_to_9_am,ESCORT - Arrival Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.end==12)),coef_escort_arrival_constant_8_30_am_to_9_am -util_escort_arrival_constant_after_9_am,ESCORT - Arrival Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.end>12)),coef_escort_arrival_constant_after_9_am -util_escort_arrival_constant_2_30_pm_to_3_pm,ESCORT - Arrival Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.end==24)),coef_escort_arrival_constant_2_30_pm_to_3_pm -util_escort_arrival_constant_3_pm_to_3_30_pm,ESCORT - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.end==25)),coef_escort_arrival_constant_3_pm_to_3_30_pm -util_escort_arrival_constant_3_30_pm_to_4_pm,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'escort') & (df.end==26)),coef_escort_arrival_constant_3_30_pm_to_4_pm -util_escort_arrival_constant_4_pm_to_4_30_pm,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'escort') & (df.end==27)),coef_escort_arrival_constant_4_pm_to_4_30_pm -util_escort_arrival_constant_after_4_30_pm,ESCORT - Arrival Constant: After 04:30 PM,@((df.tour_type == 'escort') & (df.end>27)),coef_escort_arrival_constant_after_4_30_pm -util_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>13)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear -util_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>28)), (np.where((df.end<0), np.minimum(0-df.end,48), 0) + np.where((df.start>28), np.minimum(df.end-28,15), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear -util_escort_duration_constant_0_hour,ESCORT - Duration Constant: 0 hr,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_duration_constant_0_hour -util_escort_duration_constant_30_minutes,ESCORT - Duration Constant: 0.5 hr,@((df.tour_type == 'escort') & (df.duration==1)),coef_escort_duration_constant_30_minutes -util_escort_duration_constant_1_hour,ESCORT - Duration Constant: 1 hr,@((df.tour_type == 'escort') & (df.duration==2)),coef_escort_duration_constant_1_hour -util_escort_duration_constant_1_hour_30_minutes,ESCORT - Duration Constant: 1.5hrs,@((df.tour_type == 'escort') & (df.duration==3)),coef_escort_duration_constant_1_hour_30_minutes -util_escort_duration_constant_2_hours,ESCORT - Duration Constant: 2 hrs,@((df.tour_type == 'escort') & (df.duration==4)),coef_escort_duration_constant_2_hours -util_escort_duration_constant_longer_than_2_hours,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'escort') & (df.duration>4)),coef_escort_duration_constant_longer_than_2_hours -util_escort_calibration_constant_duration_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_calibration_constant_duration_1 -util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@(((df.tour_type == 'escort') & df.duration==1)),coef_escort_calibration_constant_duration_2 -util_escort_calibration_constant_departure_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_calibration_constant_departure_9 -util_escort_calibration_constant_departure_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_calibration_constant_departure_10 -util_escort_calibration_constant_departure_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_calibration_constant_departure_23 -util_escort_calibration_constant_departure_24,ESCORT - Calibration Constant - Departure = 24,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_calibration_constant_departure_24 -#SHOPPING,,,#SHOPPING -util_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes -util_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Full-time worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes -util_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Non-driving Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes -util_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes,SHOPPING - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes -util_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes -util_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes -util_shopping_retired_duration_less_than_1_hour_30_minutes,SHOPPING - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_less_than_1_hour_30_minutes -util_shopping_retired_duration_greater_than_1_hour_30_minutes,SHOPPING - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_greater_than_1_hour_30_minutes -util_shopping_university_student_duration_greater_than_1_hour_30_minutes,SHOPPING - University Student: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_university_student_duration_greater_than_1_hour_30_minutes -util_shopping_female_duration_less_than_1_hour_30_minutes,SHOPPING - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_less_than_1_hour_30_minutes -util_shopping_female_duration_greater_than_1_hour_30_minutes,SHOPPING - Female: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_greater_than_1_hour_30_minutes -util_shopping_low_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_type == 'shopping') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_low_income_duration_greater_than_1_hour_30_minutes -util_shopping_medium_income_duration_less_than_1_hour_30_minutes,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_income_duration_less_than_1_hour_30_minutes -util_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes -util_shopping_distance_duration_less_than_1_hour_30_minutes,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_less_than_1_hour_30_minutes -util_shopping_distance_duration_greater_than_1_hour_30_minutes,SHOPPING - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_greater_than_1_hour_30_minutes -util_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,SHOPPING - Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes -util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes -util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') &(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes -util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear -util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0))**0.5, 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root -util_shopping_departure_constant_before_9_am,SHOPPING - Departure Constant: Before 09:00 AM,@((df.tour_type == 'shopping') & (df.start<13)),coef_shopping_departure_constant_before_9_am -util_shopping_departure_constant_9_am_to_9_30_am,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'shopping') & (df.start==13)),coef_shopping_departure_constant_9_am_to_9_30_am -util_shopping_departure_constant_9_30_am_to_10_am,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'shopping') & (df.start==14)),coef_shopping_departure_constant_9_30_am_to_10_am -util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'shopping') & (df.start==15)),coef_shopping_departure_constant_10_am_to_10_30_am -util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'shopping') & (df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am -util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_type == 'shopping') & (df.start>16)),coef_shopping_departure_constant_after_11_am -util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared -util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear -util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_type == 'shopping') & (df.end<20)),coef_shopping_arrival_constant_before_12_30_pm -util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm -util_shopping_arrival_constant_3_pm_to_3_30_pm,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'shopping') & (df.end==25)),coef_shopping_arrival_constant_3_pm_to_3_30_pm -util_shopping_arrival_constant_3_30_pm_to_4_pm,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'shopping') & (df.end==26)),coef_shopping_arrival_constant_3_30_pm_to_4_pm -util_shopping_arrival_constant_4_pm_to_4_30_pm,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'shopping') & (df.end==27)),coef_shopping_arrival_constant_4_pm_to_4_30_pm -util_shopping_arrival_constant_4_30_pm_to_5_pm,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.tour_type == 'shopping') & (df.end==28)),coef_shopping_arrival_constant_4_30_pm_to_5_pm -util_shopping_arrival_constant_5_pm_to_5_30_pm,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'shopping') & (df.end==29)),coef_shopping_arrival_constant_5_pm_to_5_30_pm -util_shopping_arrival_constant_5_30_pm_to_7_pm,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.tour_type == 'shopping') & (df.end>=30) & (df.end<=32)),coef_shopping_arrival_constant_5_30_pm_to_7_pm -util_shopping_arrival_constant_7_pm_to_9_30_pm,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.tour_type == 'shopping') & (df.end>=33) & (df.end<=37)),coef_shopping_arrival_constant_7_pm_to_9_30_pm -util_shopping_arrival_constant_after_9_30_pm,SHOPPING - Arrival Constant: After 09:30 PM,@((df.tour_type == 'shopping') & (df.end>37)),coef_shopping_arrival_constant_after_9_30_pm -util_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end>38)), (np.where((df.end<19), np.minimum(19-df.end,10), 0) + np.where ((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear -util_shopping_duration_constant_0_hr,SHOPPING - Duration Constant: 0 hr,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_duration_constant_0_hr -util_shopping_duration_constant_30_minutes,SHOPPING - Duration Constant: 0.5 hr,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_duration_constant_30_minutes -util_shopping_duration_constant_1_hr,SHOPPING - Duration Constant: 1 hr,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_duration_constant_1_hr -util_shopping_duration_constant_1_hour_30_minutes,SHOPPING - Duration Constant: 1.5hrs,@(df.tour_type == 'shopping') & (df.duration==3),coef_shopping_duration_constant_1_hour_30_minutes -util_shopping_duration_constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.tour_type == 'shopping') & (df.duration==4)),coef_shopping_duration_constant_2_hrs -util_shopping_duration_constant_longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'shopping') & (df.duration>4)),coef_shopping_duration_constant_longer_than_2_hrs -util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear -util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)) ** 0.5), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root -util_shopping_calibration_constant_duration_1,SHOPPING - Calibration Constant - Duration = 1,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_calibration_constant_duration_1 -util_shopping_calibration_constant_duration_2,SHOPPING - Calibration Constant - Duration = 2,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_calibration_constant_duration_2 -util_shopping_calibration_constant_duration_3,SHOPPING - Calibration Constant - Duration = 3,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_calibration_constant_duration_3 -#MAINTENANCE,#MAINTENANCE,,#MAINTENANCE -util_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes -util_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Full-time worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes -util_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Non-driving Student: Duration > 1.5 hrs,"@np.where (((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes -util_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes,MAINTENANCE - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes -util_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes -util_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes -util_maintenance_retired_duration_less_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_less_than_1_hour_30_minutes -util_maintenance_retired_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_greater_than_1_hour_30_minutes -util_maintenance_university_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - University Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_university_student_duration_greater_than_1_hour_30_minutes -util_maintenance_female_duration_less_than_1_hour_30_minutes,MAINTENANCE - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_less_than_1_hour_30_minutes -util_maintenance_female_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Female: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_greater_than_1_hour_30_minutes -util_maintenance_low_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Low Income (<=$25,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_low_income_duration_greater_than_1_hour_30_minutes -util_maintenance_medium_income_duration_less_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_less_than_1_hour_30_minutes -util_maintenance_medium_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_greater_than_1_hour_30_minutes -util_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes -util_maintenance_distance_duration_less_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_less_than_1_hour_30_minutes -util_maintenance_distance_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_greater_than_1_hour_30_minutes -util_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes,Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), np.minimum(df.duration-2,26) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes -util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes -util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes -util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), (np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear -util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + (np.where((df.start>17), np.minimum(df.start-17,24), 0)))** 0.5), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root -util_maintenance_departure_constant_before_8_am,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.tour_type == 'othmaint') & (df.start<11)),coef_maintenance_departure_constant_before_8_am -util_maintenance_departure_constant_8_am_to_8_30_am,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'othmaint') & (df.start==11)),coef_maintenance_departure_constant_8_am_to_8_30_am -util_maintenance_departure_constant_8_30_am_to_9_00_am,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'othmaint') & (df.start==12)),coef_maintenance_departure_constant_8_30_am_to_9_00_am -util_maintenance_departure_constant_9_am_to_9_30_am,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'othmaint') & (df.start==13)),coef_maintenance_departure_constant_9_am_to_9_30_am -util_maintenance_departure_constant_9_30_am_to_10_am,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'othmaint') & (df.start==14)),coef_maintenance_departure_constant_9_30_am_to_10_am -util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'othmaint') & (df.start==15)),coef_maintenance_departure_constant_10_am_to_10_30_am -util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am -util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_type == 'othmaint') & (df.start>16)),coef_maintenance_departure_constant_after_11_am -util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared -util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear -util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_type == 'othmaint') & (df.end<16)),coef_maintenance_arrival_constant_before_10_30_am -util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am -util_maintenance_arrival_constant_11_am_to_11_30_am,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.tour_type == 'othmaint') & (df.end==17)),coef_maintenance_arrival_constant_11_am_to_11_30_am -util_maintenance_arrival_constant_11_30_am_to_1_30_pm,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.tour_type == 'othmaint') & (df.end>=18) & (df.end<=21)),coef_maintenance_arrival_constant_11_30_am_to_1_30_pm -util_maintenance_arrival_constant_1_30_pm_to_2_30_pm,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.tour_type == 'othmaint') & (df.end>=22) & (df.end<=23)),coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm -util_maintenance_arrival_constant_2_30_pm_to_4_pm,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.tour_type == 'othmaint') & (df.end>=24) & (df.end<=26)),coef_maintenance_arrival_constant_2_30_pm_to_4_pm -util_maintenance_arrival_constant_4_pm_to_4_30_pm,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'othmaint') & (df.end==27)),coef_maintenance_arrival_constant_4_pm_to_4_30_pm -util_maintenance_arrival_constant_after_4_30_pm,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.tour_type == 'othmaint') & (df.end>27)),coef_maintenance_arrival_constant_after_4_30_pm -util_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end>28)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear -util_maintenance_duration_constant_0_hr,MAINTENANCE - Duration Constant: 0 hr,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_duration_constant_0_hr -util_maintenance_duration_constant_30_minutes,MAINTENANCE - Duration Constant: 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_duration_constant_30_minutes -util_maintenance_duration_constant_longer_than_30_minutes,MAINTENANCE - Duration Constant: Longer than 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration>1)),coef_maintenance_duration_constant_longer_than_30_minutes -util_maintenance_duration_constant_duration_greater_than_1_hr_linear,MAINTENANCE - Duration Constant: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_linear -util_maintenance_duration_constant_duration_greater_than_1_hr_square_root,MAINTENANCE - Duration Constant: Duration > 1 hr - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0))** 0.5), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root -util_maintenance_calibration_constant_duration_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_calibration_constant_duration_1 -util_maintenance_calibration_constant_duration_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_calibration_constant_duration_2 -util_maintenance_calibration_constant_duration_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.tour_type == 'othmaint') & (df.duration==2)),coef_maintenance_calibration_constant_duration_3 -util_maintenance_calibration_constant_duration_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.tour_type == 'othmaint') & (df.duration==3)),coef_maintenance_calibration_constant_duration_4 -util_maintenance_calibration_constant_duration_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.tour_type == 'othmaint') & (df.duration==4)),coef_maintenance_calibration_constant_duration_5 -#EAT-OUT,#EAT-OUT,,#EAT-OUT -util_eatout_distance_to_destination_duration_less_than_1_hr,EAT-OUT - Distance to destination - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_less_than_1_hr -util_eatout_distance_to_destination_duration_greater_than_1_hr,EAT-OUT - Distance to destination - Duration > 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) *(df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_greater_than_1_hr -util_eatout_low_income_duration_less_than_1_hr,EAT-OUT - Low income (<25000) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_less25K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_low_income_duration_less_than_1_hr -util_eatout_medium_income_duration_less_than_1_hr,EAT-OUT - Medium (25k to 60k) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_medium_income_duration_less_than_1_hr -util_eatout_zeroauto_HH_duration_greater_than_1_hr,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.tour_type == 'eatout') & (df.auto_ownership == 0) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_zeroauto_HH_duration_greater_than_1_hr -util_eatout_university_student_departure_after_7_pm_linear,EAT-OUT - University student - Departure after 7:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start>32) & (df.ptype == 3)), (np.where((df.start<=32), np.minimum(32-df.start,29), 0) + np.where((df.start>32), np.minimum(df.start-32,8), 0)), 0)",coef_eatout_university_student_departure_after_7_pm_linear -util_eatout_female_duration_less_than_1_hr,EAT-OUT - Female - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_less_than_1_hr -util_eatout_female_duration_greater_than_1_hr,EAT-OUT - Female - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_greater_than_1_hr -util_eatout_time_pressure_departure_before_6_30_pm,EAT-OUT - Time Pressure - Departure before 6:30 pm,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<32)), (np.minimum(32-df.start,29)) * (np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_departure_before_6_30_pm -util_eatout_time_pressure_duration_less_than_1_hr,EAT-OUT - Time Pressure - Duration < 1 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration<2)), np.minimum(2-df.duration,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_duration_less_than_1_hr -util_eatout_departure_constant_7_30_am_to_9_am,EAT-OUT - Departure Constant: 07:30 AM - 09:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>=10) & (df.start<=12)),coef_eatout_departure_constant_7_30_am_to_9_am -util_eatout_departure_constant_10_30_am_to_11_am,EAT-OUT - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==16)),coef_eatout_departure_constant_10_30_am_to_11_am -util_eatout_departure_constant_11_am_to_11_30_am,EAT-OUT - Departure Constant: 11:00 AM - 11:30 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==17)),coef_eatout_departure_constant_11_am_to_11_30_am -util_eatout_departure_constant_11_30_am_to_12_pm,EAT-OUT - Departure Constant: 11:30 AM - 12:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==18)),coef_eatout_departure_constant_11_30_am_to_12_pm -util_eatout_departure_constant_12_pm_to_12_30_pm,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start==19)),coef_eatout_departure_constant_12_pm_to_12_30_pm -util_eatout_departure_constant_12_30_pm_to_1_pm,EAT-OUT - Departure Constant: 12:30 PM - 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==20)),coef_eatout_departure_constant_12_30_pm_to_1_pm -util_eatout_departure_constant_1_pm_to_1_30_pm,EAT-OUT - Departure Constant: 01:00 PM - 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==21)),coef_eatout_departure_constant_1_pm_to_1_30_pm -util_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear -util_eatout_departure_constant_before_6_pm,EAT-OUT - Departure Constant: Before 06:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)),coef_eatout_departure_constant_before_6_pm -util_eatout_departure_constant_6_pm_to_6_30_pm,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==31)),coef_eatout_departure_constant_6_pm_to_6_30_pm -util_eatout_departure_constant_6_30_pm_to_7_pm,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==32)),coef_eatout_departure_constant_6_30_pm_to_7_pm -util_eatout_departure_constant_7_pm_to_7_30_pm,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==33)),coef_eatout_departure_constant_7_pm_to_7_30_pm -util_eatout_departure_constant_after_7_30_pm,EAT-OUT - Departure Constant: After 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>33)),coef_eatout_departure_constant_after_7_30_pm -util_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>34)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear -util_eatout_arrival_constant_9_30_am_to_11_am,EAT-OUT - Arrival Constant: 9:30 AM to 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>=14) & (df.end<=16)),coef_eatout_arrival_constant_9_30_am_to_11_am -util_eatout_arrival_constant_12_30_pm_to_1_pm,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==20)),coef_eatout_arrival_constant_12_30_pm_to_1_pm -util_eatout_arrival_constant_1_pm_to_1_30_pm,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==21)),coef_eatout_arrival_constant_1_pm_to_1_30_pm -util_eatout_arrival_constant_1_30_pm_to_2_pm,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==22)),coef_eatout_arrival_constant_1_30_pm_to_2_pm -util_eatout_arrival_constant_2_pm_to_2_30_pm,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==23)),coef_eatout_arrival_constant_2_pm_to_2_30_pm -util_eatout_arrival_constant_2_30_pm_to_3_pm,EAT-OUT - Arrival Constant: 02:30 PM to 03:00 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==24)),coef_eatout_arrival_constant_2_30_pm_to_3_pm -util_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<31)),coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear -util_eatout_arrival_constant_before_6_30_pm,EAT-OUT - Arrival Constant: Before 6:30 PM,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<32)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_before_6_30_pm -util_eatout_arrival_constant_6_30_pm_to_7_pm,EAT-OUT - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==32)),coef_eatout_arrival_constant_6_30_pm_to_7_pm -util_eatout_arrival_constant_7_pm_to_7_30_pm,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==33)),coef_eatout_arrival_constant_7_pm_to_7_30_pm -util_eatout_arrival_constant_7_30_pm_to_8_pm,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==34)),coef_eatout_arrival_constant_7_30_pm_to_8_pm -util_eatout_arrival_constant_8_pm_to_8_30_pm,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_category != 'joint') &(df.tour_type == 'eatout') & (df.end==35)),coef_eatout_arrival_constant_8_pm_to_8_30_pm -util_eatout_arrival_constant_8_30_pm_to_9_pm,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==36)),coef_eatout_arrival_constant_8_30_pm_to_9_pm -util_eatout_arrival_constant_after_9_pm,EAT-OUT - Arrival Constant: After 9:00 PM,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.end>36)),coef_eatout_arrival_constant_after_9_pm -util_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear -util_eatout_duration_constant_0_hour,EAT-OUT - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration==0)),coef_eatout_duration_constant_0_hour -util_eatout_duration_constant_30_minutes,EAT-OUT - Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_duration_constant_30_minutes -util_eatout_duration_constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==2)),coef_eatout_duration_constant_1_hour -util_eatout_duration_constant_1_hour_30_minutes,EAT-OUT - Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_duration_constant_1_hour_30_minutes -util_eatout_duration_constant_2_hours,EAT-OUT - Duration Constant: 2 hours,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.duration==4)),coef_eatout_duration_constant_2_hours -util_eatout_duration_constant_2_hour_30_minutes_or_more,EAT-OUT - Duration Constant: 2.5 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>4)),coef_eatout_duration_constant_2_hour_30_minutes_or_more -util_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,11), 0)), 0)",coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear -util_eatout_calibration_constant_duration_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_calibration_constant_duration_1 -util_eatout_calibration_constant_duration_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_calibration_constant_duration_2 -util_eatout_calibration_constant_duration_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_calibration_constant_duration_3 -util_eatout_calibration_constant_duration_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_calibration_constant_duration_4 -util_eatout_calibration_constant_departure_1,EAT-OUT - Calibration Constant - Departure = 1,@((df.tour_type == 'eatout') & (df.start == 1)),coef_eatout_calibration_constant_departure_1 -util_eatout_calibration_constant_departure_2,EAT-OUT - Calibration Constant - Departure = 2,@((df.tour_type == 'eatout') & (df.start == 2)),coef_eatout_calibration_constant_departure_2 -util_eatout_calibration_constant_departure_3,EAT-OUT - Calibration Constant - Departure = 3,@((df.tour_type== 'eatout') & (df.start == 3)),coef_eatout_calibration_constant_departure_3 -util_eatout_calibration_constant_departure_17,EAT-OUT - Calibration Constant - Departure = 17,@((df.tour_type == 'eatout') & (df.start ==17)),coef_eatout_calibration_constant_departure_17 -util_eatout_calibration_constant_departure_18,EAT-OUT - Calibration Constant - Departure = 18,@((df.tour_type== 'eatout') & (df.start ==18)),coef_eatout_calibration_constant_departure_18 -util_eatout_calibration_constant_departure_19,EAT-OUT - Calibration Constant - Departure = 19,@((df.tour_type == 'eatout') & (df.start ==19)),coef_eatout_calibration_constant_departure_19 -util_eatout_calibration_constant_departure_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.tour_type == 'eatout') & (df.start ==20)),coef_eatout_calibration_constant_departure_20 -util_eatout_calibration_constant_departure_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.tour_type == 'eatout') & (df.start ==21)),coef_eatout_calibration_constant_departure_21 -#SOCIAL,#SOCIAL,,#SOCIAL -util_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear -util_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear -util_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear -util_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear -util_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear -util_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear -util_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear -util_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear -"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, -util_social_auto_distance_duration_less_than_1_hr_linear,SOCIAL - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_less_than_1_hr_linear -util_social_auto_distance_duration_greater_than_1_hr_linear,SOCIAL - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_greater_than_1_hr_linear -util_social_time_pressure_duration_less_than_1_hr,SOCIAL - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), np.minimum(3-df.duration,47), 0)* (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_less_than_1_hr -util_social_time_pressure_duration_greater_than_1_hr,SOCIAL - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3)), np.minimum(df.duration-3,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_greater_than_1_hr -util_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,SOCIAL - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.num_add_soc_discr_tours), 0)",coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr -util_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@((df.tour_type == 'social') & (df.start<12)) * ((np.minimum(12-df.start,48)*(df.start<12)) + (np.minimum(df.start-48,48)*(df.start>48)))",coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear -util_social_departure_constant_before_9_am,SOCIAL - Departure Constant: Before 09:00 AM,@(df.tour_type == 'social') & (df.start<13),coef_social_departure_constant_before_9_am -util_social_departure_constant_9_am_to_9_30_am,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.tour_type == 'social') & (df.start==13),coef_social_departure_constant_9_am_to_9_30_am -util_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.tour_type == 'social') & (df.start<29), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear -util_social_departure_constant_before_5_30_pm,SOCIAL - Departure Constant: Before 05:30 PM,@((df.tour_type == 'social') & (df.start<30)),coef_social_departure_constant_before_5_30_pm -util_social_departure_constant_5_30_pm_to_6_pm,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'social') & (df.start==30)),coef_social_departure_constant_5_30_pm_to_6_pm -util_social_departure_constant_6_pm_to_6_30_pm,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'social') & (df.start==31)),coef_social_departure_constant_6_pm_to_6_30_pm -util_social_departure_constant_6_30_pm_to_7_pm,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'social') & (df.start==32)),coef_social_departure_constant_6_30_pm_to_7_pm -util_social_departure_constant_7_pm_to_7_30_pm,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_type == 'social') & (df.start==33)),coef_social_departure_constant_7_pm_to_7_30_pm -util_social_departure_constant_after_7_30_pm,SOCIAL - Departure Constant: After 07:30 PM,@((df.tour_type == 'social') & (df.start>33)),coef_social_departure_constant_after_7_30_pm -util_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.tour_type == 'social') & (df.start>34), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear -util_social_arrival_constant_3_pm_to_3_30_pm,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.tour_type == 'social') & (df.end==25)),coef_social_arrival_constant_3_pm_to_3_30_pm -util_social_arrival_constant_3_30_pm_to_4_pm,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.tour_type == 'social') & (df.end==26)),coef_social_arrival_constant_3_30_pm_to_4_pm -util_social_arrival_constant_4_pm_to_4_30_pm,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.tour_type == 'social') & (df.end==27)),coef_social_arrival_constant_4_pm_to_4_30_pm -util_social_arrival_constant_5_pm_to_6_pm,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.tour_type == 'social') & (df.end>=29) & (df.end<=30)),coef_social_arrival_constant_5_pm_to_6_pm -util_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end<35)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) + np.where((df.end>40), np.minimum(df.end-40,48), 0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear -util_social_arrival_constant_before_8_30_pm,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.tour_type == 'social') & (df.end<36)),coef_social_arrival_constant_before_8_30_pm -util_social_arrival_constant_8_30_pm_to_9_pm,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'social') & (df.end==36)),coef_social_arrival_constant_8_30_pm_to_9_pm -util_social_arrival_constant_9_pm_to_9_30_pm,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.tour_type == 'social') & (df.end==37)),coef_social_arrival_constant_9_pm_to_9_30_pm -util_social_arrival_constant_9_30_pm_to_10_pm,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.tour_type == 'social') & (df.end==38)),coef_social_arrival_constant_9_30_pm_to_10_pm -util_social_arrival_constant_10_pm_to_10_30_pm,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.tour_type == 'social') & (df.end==39)),coef_social_arrival_constant_10_pm_to_10_30_pm -util_social_arrival_constant_after_10_30_pm,SOCIAL - Arrival Constant: After 10:30 PM,@((df.tour_type == 'social') & (df.end>39)),coef_social_arrival_constant_after_10_30_pm -util_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end>40)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) +np.where((df.end>40),np.minimum(df.end-40,48),0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear -util_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 2.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<5)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>7), np.minimum(df.duration-7,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear -util_social_duration_constant_less_than_3_hours,SOCIAL - Duration Constant: Less than 3 hrs,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<6)),coef_social_duration_constant_less_than_3_hours -util_social_duration_constant_3_hours,SOCIAL - Duration Constant: 3 hours,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration==6)),coef_social_duration_constant_3_hours -util_social_duration_constant_3_hrs_30_minutes,SOCIAL - Duration Constant: 3.5 hours,@((df.tour_category != 'joint')& (df.tour_type == 'social') & (df.duration==7)),coef_social_duration_constant_3_hrs_30_minutes -util_social_duration_constant_4_hours_or_more,SOCIAL - Duration Constant: 4 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>7)),coef_social_duration_constant_4_hours_or_more -util_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 4.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>8)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>8), np.minimum(df.duration-8,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear -util_social_calibration_constant_duration_1,SOCIAL - Calibration Constant - Duration = 1,@((df.tour_type == 'social') & (df.duration ==0)),coef_social_calibration_constant_duration_1 -util_social_calibration_constant_duration_2,SOCIAL - Calibration Constant - Duration = 2,@((df.tour_type == 'social') & (df.duration == 1)),coef_social_calibration_constant_duration_2 -util_social_calibration_constant_duration_3,SOCIAL - Calibration Constant - Duration = 3,@((df.tour_type == 'social') & (df.duration ==2)),coef_social_calibration_constant_duration_3 -util_social_calibration_constant_duration_4,SOCIAL - Calibration Constant - Duration = 4,@((df.tour_type == 'social') & (df.duration ==3)),coef_social_calibration_constant_duration_4 -util_social_calibration_constant_duration_5,SOCIAL - Calibration Constant - Duration = 5,@((df.tour_type == 'social') & (df.duration ==4)),coef_social_calibration_constant_duration_5 -util_social_calibration_constant_duration_6,SOCIAL - Calibration Constant - Duration = 6,@((df.tour_type == 'social') & (df.duration ==5)),coef_social_calibration_constant_duration_6 -util_social_calibration_constant_duration_7,SOCIAL - Calibration Constant - Duration = 7,@((df.tour_type == 'social') & (df.duration ==6)),coef_social_calibration_constant_duration_7 -util_social_calibration_constant_duration_8,SOCIAL - Calibration Constant - Duration = 8,@((df.tour_type == 'social') & (df.duration ==7)),coef_social_calibration_constant_duration_8 -util_social_calibration_constant_duration_9,SOCIAL - Calibration Constant - Duration = 9,@((df.tour_type == 'social') & (df.duration ==8)),coef_social_calibration_constant_duration_9 -#DISCRETIONARY,#DISCRETIONARY,,#DISCRETIONARY -util_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear -util_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear -util_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear -util_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear -util_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear -util_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear -util_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)),0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear -util_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear -"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, -util_discretionary_auto_distance_duration_less_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_less_than_1_hr_linear -util_discretionary_auto_distance_duration_greater_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_greater_than_1_hr_linear -util_discretionary_time_pressure_duration_less_than_1_hr,DISCRETIONARY - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), np.minimum(3-df.duration,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_discretionary_time_pressure_duration_less_than_1_hr -util_discretionary_time_pressure_duration_greater_than_1_hr,DISCRETIONARY - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), np.minimum(df.duration-3,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_greater_than_1_hr -util_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,DISCRETIONARY - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0))*(df.num_add_soc_discr_tours),0)",coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr -util_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<9)), (np.where((df.start<9), np.minimum(9-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48),0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear -util_discretionary_departure_constant_before_7_30_am,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.tour_type == 'othdiscr') & (df.start<10)),coef_discretionary_departure_constant_before_7_30_am -util_discretionary_departure_constant_7_30_am_to_8_am,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.tour_type == 'othdiscr') & (df.start==10)),coef_discretionary_departure_constant_7_30_am_to_8_am -util_discretionary_departure_constant_8_am_to_8_30_am,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.tour_type == 'othdiscr') & (df.start==11)),coef_discretionary_departure_constant_8_am_to_8_30_am -util_discretionary_departure_constant_8_30_am_to_9_am,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.tour_type == 'othdiscr') & (df.start==12)),coef_discretionary_departure_constant_8_30_am_to_9_am -util_discretionary_departure_constant_9_am_to_9_30_am,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.tour_type == 'othdiscr') & (df.start==13)),coef_discretionary_departure_constant_9_am_to_9_30_am -util_discretionary_departure_constant_9_30_am_to_10_am,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.tour_type == 'othdiscr') & (df.start==14)),coef_discretionary_departure_constant_9_30_am_to_10_am -util_discretionary_departure_constant_10_am_to_10_30_am,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.tour_type == 'othdiscr') & (df.start==15)),coef_discretionary_departure_constant_10_am_to_10_30_am -util_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<28)), (np.where((df.start<28), np.minimum(28-df.start,8),0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear -util_discretionary_departure_constant_before_5_pm,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.tour_type == 'othdiscr') & (df.start<29)),coef_discretionary_departure_constant_before_5_pm -util_discretionary_departure_constant_5_pm_to_5_30_pm,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_departure_constant_5_pm_to_5_30_pm -util_discretionary_departure_constant_5_30_pm_to_6_pm,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_departure_constant_5_30_pm_to_6_pm -util_discretionary_departure_constant_6_pm_to_6_30_pm,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_departure_constant_6_pm_to_6_30_pm -util_discretionary_departure_constant_6_30_pm_to_7_pm,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_departure_constant_6_30_pm_to_7_pm -util_discretionary_departure_constant_after_7_pm,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start>32)),coef_discretionary_departure_constant_after_7_pm -util_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start>33)), (np.where((df.start<28), np.minimum(28-df.start,8), 0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear -util_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end<31)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear -util_discretionary_arrival_constant_before_6_30_pm,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.tour_type == 'othdiscr') & (df.end<32)),coef_discretionary_arrival_constant_before_6_30_pm -util_discretionary_arrival_constant_6_30_pm_to_7_pm,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_type == 'othdiscr') & (df.end==32)),coef_discretionary_arrival_constant_6_30_pm_to_7_pm -util_discretionary_arrival_constant_7_pm_to_7_30_pm,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_type == 'othdiscr') & (df.end==33)),coef_discretionary_arrival_constant_7_pm_to_7_30_pm -util_discretionary_arrival_constant_7_30_pm_to_8_pm,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_type == 'othdiscr') & (df.end==34)),coef_discretionary_arrival_constant_7_30_pm_to_8_pm -util_discretionary_arrival_constant_8_pm_to_8_30_pm,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_type == 'othdiscr') & (df.end==35)),coef_discretionary_arrival_constant_8_pm_to_8_30_pm -util_discretionary_arrival_constant_8_30_pm_to_9_pm,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end==36)),coef_discretionary_arrival_constant_8_30_pm_to_9_pm -util_discretionary_arrival_constant_after_9_pm,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end>36)),coef_discretionary_arrival_constant_after_9_pm -util_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48),0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear -util_discretionary_duration_constant_0_hour,DISCRETIONARY - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==0)),coef_discretionary_duration_constant_0_hour -util_discretionary_duration_constant_30_minutes,DISCRETIONARY -Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==1)),coef_discretionary_duration_constant_30_minutes -util_discretionary_duration_constant_1_hour,DISCRETIONARY -Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==2)),coef_discretionary_duration_constant_1_hour -util_discretionary_duration_constant_1_hr_30_minutes,DISCRETIONARY -Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_duration_constant_1_hr_30_minutes -util_discretionary_duration_constant_2_hours,DISCRETIONARY -Duration Constant: 2 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_duration_constant_2_hours -util_discretionary_duration_constant_2_hr_30_minutes,DISCRETIONARY -Duration Constant: 2.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==5)),coef_discretionary_duration_constant_2_hr_30_minutes -util_discretionary_duration_constant_3_hours_or_more,DISCRETIONARY -Duration Constant: 3 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>5)),coef_discretionary_duration_constant_3_hours_or_more -util_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,DISCRETIONARY -Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>6)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>6), np.minimum(df.duration-6,47), 0)), 0)",coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear -util_discretionary_calibration_constant_duration_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_calibration_constant_duration_4 -util_discretionary_calibration_constant_duration_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_calibration_constant_duration_5 -util_discretionary_calibration_constant_departure_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_calibration_constant_departure_29 -util_discretionary_calibration_constant_departure_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_calibration_constant_departure_30 -util_discretionary_calibration_constant_departure_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_calibration_constant_departure_31 -util_discretionary_calibration_constant_departure_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_calibration_constant_departure_32 +Label,Description,Expression,Coefficient +# ESCORT,,, +util_escort_mode_choice_logsum,ESCORT - Mode Choice Logsum,"@np.where(df.tour_type == 'escort', df.mode_choice_logsum, 0) ",coef_escort_mode_choice_logsum +"#Note: In CTRAMP expressions, duration alternative is from 1 to 48 but in ActivitySim, it is from 0 to 47 since the duration alternative ID was calculated as (end - start). Therefore, duration in ActivitySim expression = CTRAMP duration expresssion - 1 ",,,# +util_escort_distance_to_destination_duration_less_than_30_minutes,ESCORT - Distance to destination - Duration less than 0.5 hour (depart and arrive in the same period),"@np.where(((df.tour_type == 'escort') & (df.duration<1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_distance_to_destination_duration_less_than_30_minutes +util_escort_distance_to_destination_duration_greater_than_30_minutes,ESCORT - Distance to destination - Duration greater than 0.5 hour,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_distance_to_destination_duration_greater_than_30_minutes +util_escort_fulltime_worker_departure_after_8_am_linear,ESCORT - Full-time worker - Departure after 8:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)),0)",coef_escort_fulltime_worker_departure_after_8_am_linear +"#Note: In CTRAMP expression file, the description below says departure is after 3 am but from the expression it seems that it would be 3 pm instead of 3 am",,, +util_escort_fulltime_worker_departure_after_3_am_linear,ESCORT - Full-time worker - Departure after 3:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_fulltime_worker_departure_after_3_am_linear +util_escort_fulltime_worker_duration_less_than_30_minutes,ESCORT - Full-time worker - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_fulltime_worker_duration_less_than_30_minutes +util_escort_fulltime_worker_duration_greater_than_30_minutes,ESCORT - Full-time worker - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_fulltime_worker_duration_greater_than_30_minutes +util_escort_university_student_duration_less_than_30_minutes,ESCORT - University student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 3) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_university_student_duration_less_than_30_minutes +util_escort_non_driving_age_student_duration_greater_than_30_minutes,ESCORT - Non-driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & ((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_non_driving_age_student_duration_greater_than_30_minutes +util_escort_driving_age_student_duration_less_than_30_minutes,ESCORT - Driving age student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_driving_age_student_duration_less_than_30_minutes +util_escort_driving_age_student_duration_greater_than_30_minutes,ESCORT - Driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_driving_age_student_duration_greater_than_30_minutes +"#Note: In CTRAMP expression file, description says the expression below is for duration > 0.5 hr but the expression says duration < 0.5 hr",,, +util_escort_pre_school_kid_duration_greater_than_30_minutes,ESCORT - Pre-school kid - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 8) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_pre_school_kid_duration_greater_than_30_minutes +util_escort_med_high_income_duration_greater_than_30_minutes,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_med_high_income_duration_greater_than_30_minutes +util_escort_households_with_no_kids_departure_before_7_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_households_with_no_kids_departure_before_7_30_am +util_escort_households_with_no_kids_departure_after_8_00_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_households_with_no_kids_departure_after_8_00_am +util_escort_households_with_no_kids_departure_before_2_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_households_with_no_kids_departure_before_2_30_pm +util_escort_households_with_no_kids_departure_after_3_00_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where ((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_households_with_no_kids_departure_after_3_00_pm +util_escort_households_with_no_kids_arrival_before_8_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_households_with_no_kids_arrival_before_8_am +util_escort_households_with_no_kids_arrival_after_8_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_households_with_no_kids_arrival_after_8_30_am +util_escort_households_with_no_kids_arrival_before_3_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_households_with_no_kids_arrival_before_3_pm +util_escort_households_with_no_kids_arrival_after_3_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_households_with_no_kids_arrival_after_3_30_pm +util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am +util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm +util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am +util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where ((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm +util_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes +util_escort_number_of_individual_tours_duration_greater_than_30_minutes,ESCORT -Number of Individual Tours (excluding escorting) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.num_non_escort_tours > 0) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) * (df.num_non_escort_tours), 0)",coef_escort_number_of_individual_tours_duration_greater_than_30_minutes +util_escort_number_of_joint_tours_duration_greater_than_30_minutes,ESCORT - Number of joint tours - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) *(df.num_joint_tours), 0)",coef_escort_number_of_joint_tours_duration_greater_than_30_minutes +util_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start<8)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + np.where((df.start>13), np.minimum(df.start-13,28), 0)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear +util_escort_departure_constant_before_7_am,ESCORT - Departure Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.start<9)),coef_escort_departure_constant_before_7_am +util_escort_departure_constant_7_am_to_7_30_am,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_departure_constant_7_am_to_7_30_am +util_escort_departure_constant_7_30_am_to_8_am,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_departure_constant_7_30_am_to_8_am +util_escort_departure_constant_8_am_to_8_30_am,ESCORT - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.start==11)),coef_escort_departure_constant_8_am_to_8_30_am +util_escort_departure_constant_8_30_am_to_9_am,ESCORT - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.start==12)),coef_escort_departure_constant_8_30_am_to_9_am +util_escort_departure_constant_after_9_am,ESCORT - Departure Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.start>12)),coef_escort_departure_constant_after_9_am +util_escort_departure_constant_1_30_pm_to_2_pm,ESCORT - Departure Constant: 01:30 PM - 02:00 PM,@((df.tour_type == 'escort') & (df.start==22)),coef_escort_departure_constant_1_30_pm_to_2_pm +util_escort_departure_constant_2_pm_to_2_30_pm,ESCORT - Departure Constant: 02:00 PM - 02:30 PM,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_departure_constant_2_pm_to_2_30_pm +util_escort_departure_constant_2_30_pm_to_3_pm,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_departure_constant_2_30_pm_to_3_pm +util_escort_departure_constant_3_pm_to_3_30_pm,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.start==25)),coef_escort_departure_constant_3_pm_to_3_30_pm +util_escort_departure_constant_after_3_30_pm,ESCORT - Departure Constant: After 03:30 PM,@((df.tour_type == 'escort') & (df.start>25)),coef_escort_departure_constant_after_3_30_pm +util_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>13)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + (np.where((df.start>13), np.minimum(df.start-13,28), 0))), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear +util_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>26)), (np.where((df.start<0), np.minimum(0-df.start,48), 0) + np.where((df.start>26), np.minimum(df.start-26,15),0)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear +util_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end<8)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear +util_escort_arrival_constant_before_7_am,ESCORT - Arrival Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.end<9)),coef_escort_arrival_constant_before_7_am +util_escort_arrival_constant_7_am_to_7_30_am,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.end==9)),coef_escort_arrival_constant_7_am_to_7_30_am +util_escort_arrival_constant_7_30_am_to_8_am,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.end==10)),coef_escort_arrival_constant_7_30_am_to_8_am +util_escort_arrival_constant_8_am_to_8_30_am,ESCORT - Arrival Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.end==11)),coef_escort_arrival_constant_8_am_to_8_30_am +util_escort_arrival_constant_8_30_am_to_9_am,ESCORT - Arrival Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.end==12)),coef_escort_arrival_constant_8_30_am_to_9_am +util_escort_arrival_constant_after_9_am,ESCORT - Arrival Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.end>12)),coef_escort_arrival_constant_after_9_am +util_escort_arrival_constant_2_30_pm_to_3_pm,ESCORT - Arrival Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.end==24)),coef_escort_arrival_constant_2_30_pm_to_3_pm +util_escort_arrival_constant_3_pm_to_3_30_pm,ESCORT - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.end==25)),coef_escort_arrival_constant_3_pm_to_3_30_pm +util_escort_arrival_constant_3_30_pm_to_4_pm,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'escort') & (df.end==26)),coef_escort_arrival_constant_3_30_pm_to_4_pm +util_escort_arrival_constant_4_pm_to_4_30_pm,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'escort') & (df.end==27)),coef_escort_arrival_constant_4_pm_to_4_30_pm +util_escort_arrival_constant_after_4_30_pm,ESCORT - Arrival Constant: After 04:30 PM,@((df.tour_type == 'escort') & (df.end>27)),coef_escort_arrival_constant_after_4_30_pm +util_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>13)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear +util_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>28)), (np.where((df.end<0), np.minimum(0-df.end,48), 0) + np.where((df.start>28), np.minimum(df.end-28,15), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear +util_escort_duration_constant_0_hour,ESCORT - Duration Constant: 0 hr,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_duration_constant_0_hour +util_escort_duration_constant_30_minutes,ESCORT - Duration Constant: 0.5 hr,@((df.tour_type == 'escort') & (df.duration==1)),coef_escort_duration_constant_30_minutes +util_escort_duration_constant_1_hour,ESCORT - Duration Constant: 1 hr,@((df.tour_type == 'escort') & (df.duration==2)),coef_escort_duration_constant_1_hour +util_escort_duration_constant_1_hour_30_minutes,ESCORT - Duration Constant: 1.5hrs,@((df.tour_type == 'escort') & (df.duration==3)),coef_escort_duration_constant_1_hour_30_minutes +util_escort_duration_constant_2_hours,ESCORT - Duration Constant: 2 hrs,@((df.tour_type == 'escort') & (df.duration==4)),coef_escort_duration_constant_2_hours +util_escort_duration_constant_longer_than_2_hours,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'escort') & (df.duration>4)),coef_escort_duration_constant_longer_than_2_hours +util_escort_calibration_constant_duration_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_calibration_constant_duration_1 +util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@(((df.tour_type == 'escort') & df.duration==1)),coef_escort_calibration_constant_duration_2 +util_escort_calibration_constant_departure_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_calibration_constant_departure_9 +util_escort_calibration_constant_departure_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_calibration_constant_departure_10 +util_escort_calibration_constant_departure_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_calibration_constant_departure_23 +util_escort_calibration_constant_departure_24,ESCORT - Calibration Constant - Departure = 24,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_calibration_constant_departure_24 +#SHOPPING,,,#SHOPPING +util_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes +util_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Full-time worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes +util_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Non-driving Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes +util_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes,SHOPPING - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes +util_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes +util_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes +util_shopping_retired_duration_less_than_1_hour_30_minutes,SHOPPING - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_less_than_1_hour_30_minutes +util_shopping_retired_duration_greater_than_1_hour_30_minutes,SHOPPING - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_greater_than_1_hour_30_minutes +util_shopping_university_student_duration_greater_than_1_hour_30_minutes,SHOPPING - University Student: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_university_student_duration_greater_than_1_hour_30_minutes +util_shopping_female_duration_less_than_1_hour_30_minutes,SHOPPING - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_less_than_1_hour_30_minutes +util_shopping_female_duration_greater_than_1_hour_30_minutes,SHOPPING - Female: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_greater_than_1_hour_30_minutes +util_shopping_low_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_type == 'shopping') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_low_income_duration_greater_than_1_hour_30_minutes +util_shopping_medium_income_duration_less_than_1_hour_30_minutes,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_income_duration_less_than_1_hour_30_minutes +util_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes +util_shopping_distance_duration_less_than_1_hour_30_minutes,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_less_than_1_hour_30_minutes +util_shopping_distance_duration_greater_than_1_hour_30_minutes,SHOPPING - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_greater_than_1_hour_30_minutes +util_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,SHOPPING - Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes +util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes +util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') &(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes +util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear +util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0))**0.5, 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root +util_shopping_departure_constant_before_9_am,SHOPPING - Departure Constant: Before 09:00 AM,@((df.tour_type == 'shopping') & (df.start<13)),coef_shopping_departure_constant_before_9_am +util_shopping_departure_constant_9_am_to_9_30_am,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'shopping') & (df.start==13)),coef_shopping_departure_constant_9_am_to_9_30_am +util_shopping_departure_constant_9_30_am_to_10_am,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'shopping') & (df.start==14)),coef_shopping_departure_constant_9_30_am_to_10_am +util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'shopping') & (df.start==15)),coef_shopping_departure_constant_10_am_to_10_30_am +util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'shopping') & (df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am +util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_type == 'shopping') & (df.start>16)),coef_shopping_departure_constant_after_11_am +util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear +util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared +util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear +util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_type == 'shopping') & (df.end<20)),coef_shopping_arrival_constant_before_12_30_pm +util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm +util_shopping_arrival_constant_3_pm_to_3_30_pm,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'shopping') & (df.end==25)),coef_shopping_arrival_constant_3_pm_to_3_30_pm +util_shopping_arrival_constant_3_30_pm_to_4_pm,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'shopping') & (df.end==26)),coef_shopping_arrival_constant_3_30_pm_to_4_pm +util_shopping_arrival_constant_4_pm_to_4_30_pm,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'shopping') & (df.end==27)),coef_shopping_arrival_constant_4_pm_to_4_30_pm +util_shopping_arrival_constant_4_30_pm_to_5_pm,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.tour_type == 'shopping') & (df.end==28)),coef_shopping_arrival_constant_4_30_pm_to_5_pm +util_shopping_arrival_constant_5_pm_to_5_30_pm,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'shopping') & (df.end==29)),coef_shopping_arrival_constant_5_pm_to_5_30_pm +util_shopping_arrival_constant_5_30_pm_to_7_pm,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.tour_type == 'shopping') & (df.end>=30) & (df.end<=32)),coef_shopping_arrival_constant_5_30_pm_to_7_pm +util_shopping_arrival_constant_7_pm_to_9_30_pm,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.tour_type == 'shopping') & (df.end>=33) & (df.end<=37)),coef_shopping_arrival_constant_7_pm_to_9_30_pm +util_shopping_arrival_constant_after_9_30_pm,SHOPPING - Arrival Constant: After 09:30 PM,@((df.tour_type == 'shopping') & (df.end>37)),coef_shopping_arrival_constant_after_9_30_pm +util_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end>38)), (np.where((df.end<19), np.minimum(19-df.end,10), 0) + np.where ((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear +util_shopping_duration_constant_0_hr,SHOPPING - Duration Constant: 0 hr,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_duration_constant_0_hr +util_shopping_duration_constant_30_minutes,SHOPPING - Duration Constant: 0.5 hr,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_duration_constant_30_minutes +util_shopping_duration_constant_1_hr,SHOPPING - Duration Constant: 1 hr,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_duration_constant_1_hr +util_shopping_duration_constant_1_hour_30_minutes,SHOPPING - Duration Constant: 1.5hrs,@(df.tour_type == 'shopping') & (df.duration==3),coef_shopping_duration_constant_1_hour_30_minutes +util_shopping_duration_constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.tour_type == 'shopping') & (df.duration==4)),coef_shopping_duration_constant_2_hrs +util_shopping_duration_constant_longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'shopping') & (df.duration>4)),coef_shopping_duration_constant_longer_than_2_hrs +util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear +util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)) ** 0.5), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root +util_shopping_calibration_constant_duration_1,SHOPPING - Calibration Constant - Duration = 1,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_calibration_constant_duration_1 +util_shopping_calibration_constant_duration_2,SHOPPING - Calibration Constant - Duration = 2,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_calibration_constant_duration_2 +util_shopping_calibration_constant_duration_3,SHOPPING - Calibration Constant - Duration = 3,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_calibration_constant_duration_3 +#MAINTENANCE,#MAINTENANCE,,#MAINTENANCE +util_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes +util_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Full-time worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes +util_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Non-driving Student: Duration > 1.5 hrs,"@np.where (((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes +util_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes,MAINTENANCE - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes +util_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes +util_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes +util_maintenance_retired_duration_less_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_less_than_1_hour_30_minutes +util_maintenance_retired_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_greater_than_1_hour_30_minutes +util_maintenance_university_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - University Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_university_student_duration_greater_than_1_hour_30_minutes +util_maintenance_female_duration_less_than_1_hour_30_minutes,MAINTENANCE - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_less_than_1_hour_30_minutes +util_maintenance_female_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Female: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_greater_than_1_hour_30_minutes +util_maintenance_low_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Low Income (<=$25,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_low_income_duration_greater_than_1_hour_30_minutes +util_maintenance_medium_income_duration_less_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_less_than_1_hour_30_minutes +util_maintenance_medium_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_greater_than_1_hour_30_minutes +util_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes +util_maintenance_distance_duration_less_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_less_than_1_hour_30_minutes +util_maintenance_distance_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_greater_than_1_hour_30_minutes +util_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes,Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), np.minimum(df.duration-2,26) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes +util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes +util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes +util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), (np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear +util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + (np.where((df.start>17), np.minimum(df.start-17,24), 0)))** 0.5), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root +util_maintenance_departure_constant_before_8_am,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.tour_type == 'othmaint') & (df.start<11)),coef_maintenance_departure_constant_before_8_am +util_maintenance_departure_constant_8_am_to_8_30_am,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'othmaint') & (df.start==11)),coef_maintenance_departure_constant_8_am_to_8_30_am +util_maintenance_departure_constant_8_30_am_to_9_00_am,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'othmaint') & (df.start==12)),coef_maintenance_departure_constant_8_30_am_to_9_00_am +util_maintenance_departure_constant_9_am_to_9_30_am,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'othmaint') & (df.start==13)),coef_maintenance_departure_constant_9_am_to_9_30_am +util_maintenance_departure_constant_9_30_am_to_10_am,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'othmaint') & (df.start==14)),coef_maintenance_departure_constant_9_30_am_to_10_am +util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'othmaint') & (df.start==15)),coef_maintenance_departure_constant_10_am_to_10_30_am +util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am +util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_type == 'othmaint') & (df.start>16)),coef_maintenance_departure_constant_after_11_am +util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear +util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared +util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear +util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_type == 'othmaint') & (df.end<16)),coef_maintenance_arrival_constant_before_10_30_am +util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am +util_maintenance_arrival_constant_11_am_to_11_30_am,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.tour_type == 'othmaint') & (df.end==17)),coef_maintenance_arrival_constant_11_am_to_11_30_am +util_maintenance_arrival_constant_11_30_am_to_1_30_pm,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.tour_type == 'othmaint') & (df.end>=18) & (df.end<=21)),coef_maintenance_arrival_constant_11_30_am_to_1_30_pm +util_maintenance_arrival_constant_1_30_pm_to_2_30_pm,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.tour_type == 'othmaint') & (df.end>=22) & (df.end<=23)),coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm +util_maintenance_arrival_constant_2_30_pm_to_4_pm,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.tour_type == 'othmaint') & (df.end>=24) & (df.end<=26)),coef_maintenance_arrival_constant_2_30_pm_to_4_pm +util_maintenance_arrival_constant_4_pm_to_4_30_pm,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'othmaint') & (df.end==27)),coef_maintenance_arrival_constant_4_pm_to_4_30_pm +util_maintenance_arrival_constant_after_4_30_pm,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.tour_type == 'othmaint') & (df.end>27)),coef_maintenance_arrival_constant_after_4_30_pm +util_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end>28)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear +util_maintenance_duration_constant_0_hr,MAINTENANCE - Duration Constant: 0 hr,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_duration_constant_0_hr +util_maintenance_duration_constant_30_minutes,MAINTENANCE - Duration Constant: 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_duration_constant_30_minutes +util_maintenance_duration_constant_longer_than_30_minutes,MAINTENANCE - Duration Constant: Longer than 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration>1)),coef_maintenance_duration_constant_longer_than_30_minutes +util_maintenance_duration_constant_duration_greater_than_1_hr_linear,MAINTENANCE - Duration Constant: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_linear +util_maintenance_duration_constant_duration_greater_than_1_hr_square_root,MAINTENANCE - Duration Constant: Duration > 1 hr - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0))** 0.5), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root +util_maintenance_calibration_constant_duration_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_calibration_constant_duration_1 +util_maintenance_calibration_constant_duration_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_calibration_constant_duration_2 +util_maintenance_calibration_constant_duration_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.tour_type == 'othmaint') & (df.duration==2)),coef_maintenance_calibration_constant_duration_3 +util_maintenance_calibration_constant_duration_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.tour_type == 'othmaint') & (df.duration==3)),coef_maintenance_calibration_constant_duration_4 +util_maintenance_calibration_constant_duration_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.tour_type == 'othmaint') & (df.duration==4)),coef_maintenance_calibration_constant_duration_5 +#EAT-OUT,#EAT-OUT,,#EAT-OUT +util_eatout_distance_to_destination_duration_less_than_1_hr,EAT-OUT - Distance to destination - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_less_than_1_hr +util_eatout_distance_to_destination_duration_greater_than_1_hr,EAT-OUT - Distance to destination - Duration > 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) *(df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_greater_than_1_hr +util_eatout_low_income_duration_less_than_1_hr,EAT-OUT - Low income (<25000) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_less25K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_low_income_duration_less_than_1_hr +util_eatout_medium_income_duration_less_than_1_hr,EAT-OUT - Medium (25k to 60k) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_medium_income_duration_less_than_1_hr +util_eatout_zeroauto_HH_duration_greater_than_1_hr,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.tour_type == 'eatout') & (df.auto_ownership == 0) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_zeroauto_HH_duration_greater_than_1_hr +util_eatout_university_student_departure_after_7_pm_linear,EAT-OUT - University student - Departure after 7:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start>32) & (df.ptype == 3)), (np.where((df.start<=32), np.minimum(32-df.start,29), 0) + np.where((df.start>32), np.minimum(df.start-32,8), 0)), 0)",coef_eatout_university_student_departure_after_7_pm_linear +util_eatout_female_duration_less_than_1_hr,EAT-OUT - Female - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_less_than_1_hr +util_eatout_female_duration_greater_than_1_hr,EAT-OUT - Female - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_greater_than_1_hr +util_eatout_time_pressure_departure_before_6_30_pm,EAT-OUT - Time Pressure - Departure before 6:30 pm,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<32)), (np.minimum(32-df.start,29)) * (np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_departure_before_6_30_pm +util_eatout_time_pressure_duration_less_than_1_hr,EAT-OUT - Time Pressure - Duration < 1 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration<2)), np.minimum(2-df.duration,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_duration_less_than_1_hr +util_eatout_departure_constant_7_30_am_to_9_am,EAT-OUT - Departure Constant: 07:30 AM - 09:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>=10) & (df.start<=12)),coef_eatout_departure_constant_7_30_am_to_9_am +util_eatout_departure_constant_10_30_am_to_11_am,EAT-OUT - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==16)),coef_eatout_departure_constant_10_30_am_to_11_am +util_eatout_departure_constant_11_am_to_11_30_am,EAT-OUT - Departure Constant: 11:00 AM - 11:30 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==17)),coef_eatout_departure_constant_11_am_to_11_30_am +util_eatout_departure_constant_11_30_am_to_12_pm,EAT-OUT - Departure Constant: 11:30 AM - 12:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==18)),coef_eatout_departure_constant_11_30_am_to_12_pm +util_eatout_departure_constant_12_pm_to_12_30_pm,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start==19)),coef_eatout_departure_constant_12_pm_to_12_30_pm +util_eatout_departure_constant_12_30_pm_to_1_pm,EAT-OUT - Departure Constant: 12:30 PM - 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==20)),coef_eatout_departure_constant_12_30_pm_to_1_pm +util_eatout_departure_constant_1_pm_to_1_30_pm,EAT-OUT - Departure Constant: 01:00 PM - 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==21)),coef_eatout_departure_constant_1_pm_to_1_30_pm +util_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear +util_eatout_departure_constant_before_6_pm,EAT-OUT - Departure Constant: Before 06:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)),coef_eatout_departure_constant_before_6_pm +util_eatout_departure_constant_6_pm_to_6_30_pm,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==31)),coef_eatout_departure_constant_6_pm_to_6_30_pm +util_eatout_departure_constant_6_30_pm_to_7_pm,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==32)),coef_eatout_departure_constant_6_30_pm_to_7_pm +util_eatout_departure_constant_7_pm_to_7_30_pm,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==33)),coef_eatout_departure_constant_7_pm_to_7_30_pm +util_eatout_departure_constant_after_7_30_pm,EAT-OUT - Departure Constant: After 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>33)),coef_eatout_departure_constant_after_7_30_pm +util_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>34)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear +util_eatout_arrival_constant_9_30_am_to_11_am,EAT-OUT - Arrival Constant: 9:30 AM to 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>=14) & (df.end<=16)),coef_eatout_arrival_constant_9_30_am_to_11_am +util_eatout_arrival_constant_12_30_pm_to_1_pm,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==20)),coef_eatout_arrival_constant_12_30_pm_to_1_pm +util_eatout_arrival_constant_1_pm_to_1_30_pm,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==21)),coef_eatout_arrival_constant_1_pm_to_1_30_pm +util_eatout_arrival_constant_1_30_pm_to_2_pm,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==22)),coef_eatout_arrival_constant_1_30_pm_to_2_pm +util_eatout_arrival_constant_2_pm_to_2_30_pm,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==23)),coef_eatout_arrival_constant_2_pm_to_2_30_pm +util_eatout_arrival_constant_2_30_pm_to_3_pm,EAT-OUT - Arrival Constant: 02:30 PM to 03:00 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==24)),coef_eatout_arrival_constant_2_30_pm_to_3_pm +util_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<31)),coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear +util_eatout_arrival_constant_before_6_30_pm,EAT-OUT - Arrival Constant: Before 6:30 PM,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<32)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_before_6_30_pm +util_eatout_arrival_constant_6_30_pm_to_7_pm,EAT-OUT - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==32)),coef_eatout_arrival_constant_6_30_pm_to_7_pm +util_eatout_arrival_constant_7_pm_to_7_30_pm,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==33)),coef_eatout_arrival_constant_7_pm_to_7_30_pm +util_eatout_arrival_constant_7_30_pm_to_8_pm,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==34)),coef_eatout_arrival_constant_7_30_pm_to_8_pm +util_eatout_arrival_constant_8_pm_to_8_30_pm,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_category != 'joint') &(df.tour_type == 'eatout') & (df.end==35)),coef_eatout_arrival_constant_8_pm_to_8_30_pm +util_eatout_arrival_constant_8_30_pm_to_9_pm,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==36)),coef_eatout_arrival_constant_8_30_pm_to_9_pm +util_eatout_arrival_constant_after_9_pm,EAT-OUT - Arrival Constant: After 9:00 PM,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.end>36)),coef_eatout_arrival_constant_after_9_pm +util_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear +util_eatout_duration_constant_0_hour,EAT-OUT - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration==0)),coef_eatout_duration_constant_0_hour +util_eatout_duration_constant_30_minutes,EAT-OUT - Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_duration_constant_30_minutes +util_eatout_duration_constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==2)),coef_eatout_duration_constant_1_hour +util_eatout_duration_constant_1_hour_30_minutes,EAT-OUT - Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_duration_constant_1_hour_30_minutes +util_eatout_duration_constant_2_hours,EAT-OUT - Duration Constant: 2 hours,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.duration==4)),coef_eatout_duration_constant_2_hours +util_eatout_duration_constant_2_hour_30_minutes_or_more,EAT-OUT - Duration Constant: 2.5 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>4)),coef_eatout_duration_constant_2_hour_30_minutes_or_more +util_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,11), 0)), 0)",coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear +util_eatout_calibration_constant_duration_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_calibration_constant_duration_1 +util_eatout_calibration_constant_duration_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_calibration_constant_duration_2 +util_eatout_calibration_constant_duration_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_calibration_constant_duration_3 +util_eatout_calibration_constant_duration_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_calibration_constant_duration_4 +util_eatout_calibration_constant_departure_1,EAT-OUT - Calibration Constant - Departure = 1,@((df.tour_type == 'eatout') & (df.start == 1)),coef_eatout_calibration_constant_departure_1 +util_eatout_calibration_constant_departure_2,EAT-OUT - Calibration Constant - Departure = 2,@((df.tour_type == 'eatout') & (df.start == 2)),coef_eatout_calibration_constant_departure_2 +util_eatout_calibration_constant_departure_3,EAT-OUT - Calibration Constant - Departure = 3,@((df.tour_type== 'eatout') & (df.start == 3)),coef_eatout_calibration_constant_departure_3 +util_eatout_calibration_constant_departure_17,EAT-OUT - Calibration Constant - Departure = 17,@((df.tour_type == 'eatout') & (df.start ==17)),coef_eatout_calibration_constant_departure_17 +util_eatout_calibration_constant_departure_18,EAT-OUT - Calibration Constant - Departure = 18,@((df.tour_type== 'eatout') & (df.start ==18)),coef_eatout_calibration_constant_departure_18 +util_eatout_calibration_constant_departure_19,EAT-OUT - Calibration Constant - Departure = 19,@((df.tour_type == 'eatout') & (df.start ==19)),coef_eatout_calibration_constant_departure_19 +util_eatout_calibration_constant_departure_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.tour_type == 'eatout') & (df.start ==20)),coef_eatout_calibration_constant_departure_20 +util_eatout_calibration_constant_departure_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.tour_type == 'eatout') & (df.start ==21)),coef_eatout_calibration_constant_departure_21 +#SOCIAL,#SOCIAL,,#SOCIAL +util_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear +util_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear +util_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear +util_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear +util_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear +util_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear +util_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear +util_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear +"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, +util_social_auto_distance_duration_less_than_1_hr_linear,SOCIAL - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_less_than_1_hr_linear +util_social_auto_distance_duration_greater_than_1_hr_linear,SOCIAL - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_social_auto_distance_duration_greater_than_1_hr_linear +util_social_time_pressure_duration_less_than_1_hr,SOCIAL - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), np.minimum(3-df.duration,47), 0)* (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_less_than_1_hr +util_social_time_pressure_duration_greater_than_1_hr,SOCIAL - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3)), np.minimum(df.duration-3,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_greater_than_1_hr +util_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,SOCIAL - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.num_add_soc_discr_tours), 0)",coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr +util_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@((df.tour_type == 'social') & (df.start<12)) * ((np.minimum(12-df.start,48)*(df.start<12)) + (np.minimum(df.start-48,48)*(df.start>48)))",coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear +util_social_departure_constant_before_9_am,SOCIAL - Departure Constant: Before 09:00 AM,@(df.tour_type == 'social') & (df.start<13),coef_social_departure_constant_before_9_am +util_social_departure_constant_9_am_to_9_30_am,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.tour_type == 'social') & (df.start==13),coef_social_departure_constant_9_am_to_9_30_am +util_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.tour_type == 'social') & (df.start<29), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear +util_social_departure_constant_before_5_30_pm,SOCIAL - Departure Constant: Before 05:30 PM,@((df.tour_type == 'social') & (df.start<30)),coef_social_departure_constant_before_5_30_pm +util_social_departure_constant_5_30_pm_to_6_pm,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'social') & (df.start==30)),coef_social_departure_constant_5_30_pm_to_6_pm +util_social_departure_constant_6_pm_to_6_30_pm,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'social') & (df.start==31)),coef_social_departure_constant_6_pm_to_6_30_pm +util_social_departure_constant_6_30_pm_to_7_pm,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'social') & (df.start==32)),coef_social_departure_constant_6_30_pm_to_7_pm +util_social_departure_constant_7_pm_to_7_30_pm,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_type == 'social') & (df.start==33)),coef_social_departure_constant_7_pm_to_7_30_pm +util_social_departure_constant_after_7_30_pm,SOCIAL - Departure Constant: After 07:30 PM,@((df.tour_type == 'social') & (df.start>33)),coef_social_departure_constant_after_7_30_pm +util_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.tour_type == 'social') & (df.start>34), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear +util_social_arrival_constant_3_pm_to_3_30_pm,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.tour_type == 'social') & (df.end==25)),coef_social_arrival_constant_3_pm_to_3_30_pm +util_social_arrival_constant_3_30_pm_to_4_pm,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.tour_type == 'social') & (df.end==26)),coef_social_arrival_constant_3_30_pm_to_4_pm +util_social_arrival_constant_4_pm_to_4_30_pm,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.tour_type == 'social') & (df.end==27)),coef_social_arrival_constant_4_pm_to_4_30_pm +util_social_arrival_constant_5_pm_to_6_pm,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.tour_type == 'social') & (df.end>=29) & (df.end<=30)),coef_social_arrival_constant_5_pm_to_6_pm +util_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end<35)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) + np.where((df.end>40), np.minimum(df.end-40,48), 0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear +util_social_arrival_constant_before_8_30_pm,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.tour_type == 'social') & (df.end<36)),coef_social_arrival_constant_before_8_30_pm +util_social_arrival_constant_8_30_pm_to_9_pm,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'social') & (df.end==36)),coef_social_arrival_constant_8_30_pm_to_9_pm +util_social_arrival_constant_9_pm_to_9_30_pm,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.tour_type == 'social') & (df.end==37)),coef_social_arrival_constant_9_pm_to_9_30_pm +util_social_arrival_constant_9_30_pm_to_10_pm,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.tour_type == 'social') & (df.end==38)),coef_social_arrival_constant_9_30_pm_to_10_pm +util_social_arrival_constant_10_pm_to_10_30_pm,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.tour_type == 'social') & (df.end==39)),coef_social_arrival_constant_10_pm_to_10_30_pm +util_social_arrival_constant_after_10_30_pm,SOCIAL - Arrival Constant: After 10:30 PM,@((df.tour_type == 'social') & (df.end>39)),coef_social_arrival_constant_after_10_30_pm +util_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end>40)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) +np.where((df.end>40),np.minimum(df.end-40,48),0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear +util_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 2.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<5)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>7), np.minimum(df.duration-7,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear +util_social_duration_constant_less_than_3_hours,SOCIAL - Duration Constant: Less than 3 hrs,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<6)),coef_social_duration_constant_less_than_3_hours +util_social_duration_constant_3_hours,SOCIAL - Duration Constant: 3 hours,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration==6)),coef_social_duration_constant_3_hours +util_social_duration_constant_3_hrs_30_minutes,SOCIAL - Duration Constant: 3.5 hours,@((df.tour_category != 'joint')& (df.tour_type == 'social') & (df.duration==7)),coef_social_duration_constant_3_hrs_30_minutes +util_social_duration_constant_4_hours_or_more,SOCIAL - Duration Constant: 4 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>7)),coef_social_duration_constant_4_hours_or_more +util_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 4.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>8)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>8), np.minimum(df.duration-8,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear +util_social_calibration_constant_duration_1,SOCIAL - Calibration Constant - Duration = 1,@((df.tour_type == 'social') & (df.duration ==0)),coef_social_calibration_constant_duration_1 +util_social_calibration_constant_duration_2,SOCIAL - Calibration Constant - Duration = 2,@((df.tour_type == 'social') & (df.duration == 1)),coef_social_calibration_constant_duration_2 +util_social_calibration_constant_duration_3,SOCIAL - Calibration Constant - Duration = 3,@((df.tour_type == 'social') & (df.duration ==2)),coef_social_calibration_constant_duration_3 +util_social_calibration_constant_duration_4,SOCIAL - Calibration Constant - Duration = 4,@((df.tour_type == 'social') & (df.duration ==3)),coef_social_calibration_constant_duration_4 +util_social_calibration_constant_duration_5,SOCIAL - Calibration Constant - Duration = 5,@((df.tour_type == 'social') & (df.duration ==4)),coef_social_calibration_constant_duration_5 +util_social_calibration_constant_duration_6,SOCIAL - Calibration Constant - Duration = 6,@((df.tour_type == 'social') & (df.duration ==5)),coef_social_calibration_constant_duration_6 +util_social_calibration_constant_duration_7,SOCIAL - Calibration Constant - Duration = 7,@((df.tour_type == 'social') & (df.duration ==6)),coef_social_calibration_constant_duration_7 +util_social_calibration_constant_duration_8,SOCIAL - Calibration Constant - Duration = 8,@((df.tour_type == 'social') & (df.duration ==7)),coef_social_calibration_constant_duration_8 +util_social_calibration_constant_duration_9,SOCIAL - Calibration Constant - Duration = 9,@((df.tour_type == 'social') & (df.duration ==8)),coef_social_calibration_constant_duration_9 +#DISCRETIONARY,#DISCRETIONARY,,#DISCRETIONARY +util_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear +util_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear +util_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear +util_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear +util_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear +util_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear +util_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)),0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear +util_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear +"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, +util_discretionary_auto_distance_duration_less_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_less_than_1_hr_linear +util_discretionary_auto_distance_duration_greater_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_greater_than_1_hr_linear +util_discretionary_time_pressure_duration_less_than_1_hr,DISCRETIONARY - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), np.minimum(3-df.duration,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_discretionary_time_pressure_duration_less_than_1_hr +util_discretionary_time_pressure_duration_greater_than_1_hr,DISCRETIONARY - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), np.minimum(df.duration-3,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_greater_than_1_hr +util_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,DISCRETIONARY - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0))*(df.num_add_soc_discr_tours),0)",coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr +util_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<9)), (np.where((df.start<9), np.minimum(9-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48),0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear +util_discretionary_departure_constant_before_7_30_am,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.tour_type == 'othdiscr') & (df.start<10)),coef_discretionary_departure_constant_before_7_30_am +util_discretionary_departure_constant_7_30_am_to_8_am,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.tour_type == 'othdiscr') & (df.start==10)),coef_discretionary_departure_constant_7_30_am_to_8_am +util_discretionary_departure_constant_8_am_to_8_30_am,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.tour_type == 'othdiscr') & (df.start==11)),coef_discretionary_departure_constant_8_am_to_8_30_am +util_discretionary_departure_constant_8_30_am_to_9_am,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.tour_type == 'othdiscr') & (df.start==12)),coef_discretionary_departure_constant_8_30_am_to_9_am +util_discretionary_departure_constant_9_am_to_9_30_am,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.tour_type == 'othdiscr') & (df.start==13)),coef_discretionary_departure_constant_9_am_to_9_30_am +util_discretionary_departure_constant_9_30_am_to_10_am,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.tour_type == 'othdiscr') & (df.start==14)),coef_discretionary_departure_constant_9_30_am_to_10_am +util_discretionary_departure_constant_10_am_to_10_30_am,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.tour_type == 'othdiscr') & (df.start==15)),coef_discretionary_departure_constant_10_am_to_10_30_am +util_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<28)), (np.where((df.start<28), np.minimum(28-df.start,8),0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear +util_discretionary_departure_constant_before_5_pm,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.tour_type == 'othdiscr') & (df.start<29)),coef_discretionary_departure_constant_before_5_pm +util_discretionary_departure_constant_5_pm_to_5_30_pm,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_departure_constant_5_pm_to_5_30_pm +util_discretionary_departure_constant_5_30_pm_to_6_pm,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_departure_constant_5_30_pm_to_6_pm +util_discretionary_departure_constant_6_pm_to_6_30_pm,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_departure_constant_6_pm_to_6_30_pm +util_discretionary_departure_constant_6_30_pm_to_7_pm,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_departure_constant_6_30_pm_to_7_pm +util_discretionary_departure_constant_after_7_pm,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start>32)),coef_discretionary_departure_constant_after_7_pm +util_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start>33)), (np.where((df.start<28), np.minimum(28-df.start,8), 0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear +util_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end<31)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear +util_discretionary_arrival_constant_before_6_30_pm,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.tour_type == 'othdiscr') & (df.end<32)),coef_discretionary_arrival_constant_before_6_30_pm +util_discretionary_arrival_constant_6_30_pm_to_7_pm,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_type == 'othdiscr') & (df.end==32)),coef_discretionary_arrival_constant_6_30_pm_to_7_pm +util_discretionary_arrival_constant_7_pm_to_7_30_pm,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_type == 'othdiscr') & (df.end==33)),coef_discretionary_arrival_constant_7_pm_to_7_30_pm +util_discretionary_arrival_constant_7_30_pm_to_8_pm,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_type == 'othdiscr') & (df.end==34)),coef_discretionary_arrival_constant_7_30_pm_to_8_pm +util_discretionary_arrival_constant_8_pm_to_8_30_pm,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_type == 'othdiscr') & (df.end==35)),coef_discretionary_arrival_constant_8_pm_to_8_30_pm +util_discretionary_arrival_constant_8_30_pm_to_9_pm,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end==36)),coef_discretionary_arrival_constant_8_30_pm_to_9_pm +util_discretionary_arrival_constant_after_9_pm,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end>36)),coef_discretionary_arrival_constant_after_9_pm +util_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48),0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear +util_discretionary_duration_constant_0_hour,DISCRETIONARY - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==0)),coef_discretionary_duration_constant_0_hour +util_discretionary_duration_constant_30_minutes,DISCRETIONARY -Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==1)),coef_discretionary_duration_constant_30_minutes +util_discretionary_duration_constant_1_hour,DISCRETIONARY -Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==2)),coef_discretionary_duration_constant_1_hour +util_discretionary_duration_constant_1_hr_30_minutes,DISCRETIONARY -Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_duration_constant_1_hr_30_minutes +util_discretionary_duration_constant_2_hours,DISCRETIONARY -Duration Constant: 2 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_duration_constant_2_hours +util_discretionary_duration_constant_2_hr_30_minutes,DISCRETIONARY -Duration Constant: 2.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==5)),coef_discretionary_duration_constant_2_hr_30_minutes +util_discretionary_duration_constant_3_hours_or_more,DISCRETIONARY -Duration Constant: 3 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>5)),coef_discretionary_duration_constant_3_hours_or_more +util_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,DISCRETIONARY -Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>6)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>6), np.minimum(df.duration-6,47), 0)), 0)",coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear +util_discretionary_calibration_constant_duration_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_calibration_constant_duration_4 +util_discretionary_calibration_constant_duration_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_calibration_constant_duration_5 +util_discretionary_calibration_constant_departure_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_calibration_constant_departure_29 +util_discretionary_calibration_constant_departure_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_calibration_constant_departure_30 +util_discretionary_calibration_constant_departure_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_calibration_constant_departure_31 +util_discretionary_calibration_constant_departure_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_calibration_constant_departure_32 diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_coeffs.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_coeffs.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_coeffs.csv index 9c3b2e8f05..42cb13d767 100755 --- a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_coeffs.csv @@ -1,337 +1,337 @@ -coefficient_name,value,constrain -coef_escort_mode_choice_logsum,1.173173034,F -coef_escort_distance_to_destination_duration_less_than_30_minutes,-0.335017673,F -coef_escort_distance_to_destination_duration_greater_than_30_minutes,0.005298165,F -coef_escort_fulltime_worker_departure_after_8_am_linear,-0.037980109,F -coef_escort_fulltime_worker_departure_after_3_am_linear,0.163254125,F -coef_escort_fulltime_worker_duration_less_than_30_minutes,-0.275077482,F -coef_escort_fulltime_worker_duration_greater_than_30_minutes,0.051530545,F -coef_escort_university_student_duration_less_than_30_minutes,-0.426802718,F -coef_escort_non_driving_age_student_duration_greater_than_30_minutes,0.240582361,F -coef_escort_driving_age_student_duration_less_than_30_minutes,-0.554146191,F -coef_escort_driving_age_student_duration_greater_than_30_minutes,0.299387708,F -coef_escort_pre_school_kid_duration_greater_than_30_minutes,0.195482563,F -coef_escort_med_high_income_duration_greater_than_30_minutes,-0.029281467,F -coef_escort_households_with_no_kids_departure_before_7_30_am,0.589083327,F -coef_escort_households_with_no_kids_departure_after_8_00_am,0.086690827,F -coef_escort_households_with_no_kids_departure_before_2_30_pm,0.477582648,F -coef_escort_households_with_no_kids_departure_after_3_00_pm,-0.204065502,F -coef_escort_households_with_no_kids_arrival_before_8_am,-0.360039254,F -coef_escort_households_with_no_kids_arrival_after_8_30_am,0.091614107,F -coef_escort_households_with_no_kids_arrival_before_3_pm,0.432854268,F -coef_escort_households_with_no_kids_arrival_after_3_30_pm,0.131037275,F -coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.109700265,F -coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,-0.224568648,F -coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,-0.357416434,F -coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,0.629285298,F -coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.039005148,F -coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,-0.06556611,F -coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,0.117680977,F -coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,-0.057322708,F -coef_escort_number_of_individual_tours_duration_greater_than_30_minutes,-0.062899692,F -coef_escort_number_of_joint_tours_duration_greater_than_30_minutes,-0.048533895,F -coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,-1.469240002,F -coef_escort_departure_constant_before_7_am,-2.070292862,F -coef_escort_departure_constant_7_am_to_7_30_am,-0.642734296,F -coef_escort_departure_constant_7_30_am_to_8_am,0,T -coef_escort_departure_constant_8_am_to_8_30_am,-0.214617667,F -coef_escort_departure_constant_8_30_am_to_9_am,-0.147266606,F -coef_escort_departure_constant_after_9_am,-1.356686422,F -coef_escort_departure_constant_1_30_pm_to_2_pm,0.368092381,F -coef_escort_departure_constant_2_pm_to_2_30_pm,1.166803383,F -coef_escort_departure_constant_2_30_pm_to_3_pm,1.28466083,F -coef_escort_departure_constant_3_pm_to_3_30_pm,0.581891245,F -coef_escort_departure_constant_after_3_30_pm,0.834510243,F -coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,0.175257649,F -coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,-0.019161202,F -coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,0.44978138,F -coef_escort_arrival_constant_before_7_am,0.549584585,F -coef_escort_arrival_constant_7_am_to_7_30_am,0.488181278,F -coef_escort_arrival_constant_7_30_am_to_8_am,0.236447651,F -coef_escort_arrival_constant_8_am_to_8_30_am,0,T -coef_escort_arrival_constant_8_30_am_to_9_am,-0.683756801,F -coef_escort_arrival_constant_after_9_am,-1.428888485,F -coef_escort_arrival_constant_2_30_pm_to_3_pm,1.311480662,F -coef_escort_arrival_constant_3_pm_to_3_30_pm,1.316883154,F -coef_escort_arrival_constant_3_30_pm_to_4_pm,1.396838392,F -coef_escort_arrival_constant_4_pm_to_4_30_pm,1.03146139,F -coef_escort_arrival_constant_after_4_30_pm,0.907344583,F -coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,-0.148408887,F -coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.389082896,F -coef_escort_duration_constant_0_hour,-0.173757322,F -coef_escort_duration_constant_30_minutes,0,T -coef_escort_duration_constant_1_hour,-0.431287743,F -coef_escort_duration_constant_1_hour_30_minutes,-0.700473959,F -coef_escort_duration_constant_2_hours,-1.071871358,F -coef_escort_duration_constant_longer_than_2_hours,-1.691098421,F -coef_escort_calibration_constant_duration_1,-0.047200214,F -coef_escort_calibration_constant_duration_2,0.035611332,F -coef_escort_calibration_constant_departure_9,0.106814756,F -coef_escort_calibration_constant_departure_10,0.215386864,F -coef_escort_calibration_constant_departure_23,-0.255087318,F -coef_escort_calibration_constant_departure_24,-0.296870428,F -coef_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes,0.122149101,F -coef_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes,-0.037886459,F -coef_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes,0.10247157,F -coef_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes,-1.882944033,F -coef_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes,-0.197455071,F -coef_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes,-0.045152663,F -coef_shopping_retired_duration_less_than_1_hour_30_minutes,-0.264728908,F -coef_shopping_retired_duration_greater_than_1_hour_30_minutes,-0.042981757,F -coef_shopping_university_student_duration_greater_than_1_hour_30_minutes,0.045926523,F -coef_shopping_female_duration_less_than_1_hour_30_minutes,-0.417208254,F -coef_shopping_female_duration_greater_than_1_hour_30_minutes,0.045801918,F -coef_shopping_low_income_duration_greater_than_1_hour_30_minutes,0.040776383,F -coef_shopping_medium_income_duration_less_than_1_hour_30_minutes,0.108462927,F -coef_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes,-0.037893416,F -coef_shopping_distance_duration_less_than_1_hour_30_minutes,-0.214802537,F -coef_shopping_distance_duration_greater_than_1_hour_30_minutes,0.007991656,F -coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,0.013503327,F -coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,0.078844289,F -coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,-0.115431492,F -coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-0.959875456,F -coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,1.112594898,F -coef_shopping_departure_constant_before_9_am,-0.446394064,F -coef_shopping_departure_constant_9_am_to_9_30_am,-0.021669265,F -coef_shopping_departure_constant_9_30_am_to_10_am,-0.282978638,F -coef_shopping_departure_constant_10_am_to_10_30_am,0,T -coef_shopping_departure_constant_10_30_am_to_11_00_am,-0.309421311,F -coef_shopping_departure_constant_after_11_am,-0.541073357,F -coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,-0.072013428,F -coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,-0.000653398,F -coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,-0.18376635,F -coef_shopping_arrival_constant_before_12_30_pm,-0.716195343,F -coef_shopping_arrival_constant_12_30_pm_to_3_pm,-0.502714001,F -coef_shopping_arrival_constant_3_pm_to_3_30_pm,-0.167868872,F -coef_shopping_arrival_constant_3_30_pm_to_4_pm,-0.156786941,F -coef_shopping_arrival_constant_4_pm_to_4_30_pm,0,T -coef_shopping_arrival_constant_4_30_pm_to_5_pm,-0.057314044,F -coef_shopping_arrival_constant_5_pm_to_5_30_pm,-0.580040851,F -coef_shopping_arrival_constant_5_30_pm_to_7_pm,-0.32239566,F -coef_shopping_arrival_constant_7_pm_to_9_30_pm,-0.347828147,F -coef_shopping_arrival_constant_after_9_30_pm,-1.123574723,F -coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,-0.499770654,F -coef_shopping_duration_constant_0_hr,-0.131743185,F -coef_shopping_duration_constant_30_minutes,0.888857137,F -coef_shopping_duration_constant_1_hr,0,T -coef_shopping_duration_constant_1_hour_30_minutes,-0.333413031,F -coef_shopping_duration_constant_2_hrs,-0.850897912,F -coef_shopping_duration_constant_longer_than_2_hrs,-1.203783479,F -coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,-0.293581223,F -coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,-0.215759138,F -coef_shopping_calibration_constant_duration_1,-0.138450424,F -coef_shopping_calibration_constant_duration_2,-0.092704403,F -coef_shopping_calibration_constant_duration_3,-0.087738073,F -coef_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes,0.122149101,F -coef_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes,-0.037886459,F -coef_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes,0.10247157,F -coef_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes,-1.882944033,F -coef_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes,-0.197455071,F -coef_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes,-0.045152663,F -coef_maintenance_retired_duration_less_than_1_hour_30_minutes,-0.264728908,F -coef_maintenance_retired_duration_greater_than_1_hour_30_minutes,-0.042981757,F -coef_maintenance_university_student_duration_greater_than_1_hour_30_minutes,0.045926523,F -coef_maintenance_female_duration_less_than_1_hour_30_minutes,-0.417208254,F -coef_maintenance_female_duration_greater_than_1_hour_30_minutes,0.045801918,F -coef_maintenance_low_income_duration_greater_than_1_hour_30_minutes,0.040776383,F -coef_maintenance_medium_income_duration_less_than_1_hour_30_minutes,0.108462927,F -coef_maintenance_medium_income_duration_greater_than_1_hour_30_minutes,0,T -coef_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes,-0.037893416,F -coef_maintenance_distance_duration_less_than_1_hour_30_minutes,-0.214802537,F -coef_maintenance_distance_duration_greater_than_1_hour_30_minutes,0.007991656,F -coef_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes,0.013503327,F -coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,0.078844289,F -coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,-0.115431492,F -coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,-0.864112609,F -coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,0.504598473,F -coef_maintenance_departure_constant_before_8_am,-0.383711788,F -coef_maintenance_departure_constant_8_am_to_8_30_am,-0.076771517,F -coef_maintenance_departure_constant_8_30_am_to_9_00_am,-0.169259979,F -coef_maintenance_departure_constant_9_am_to_9_30_am,-0.051785379,F -coef_maintenance_departure_constant_9_30_am_to_10_am,-0.214942451,F -coef_maintenance_departure_constant_10_am_to_10_30_am,0,T -coef_maintenance_departure_constant_10_30_am_to_11_am,-0.427568963,F -coef_maintenance_departure_constant_after_11_am,-0.520863411,F -coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,0.042879095,F -coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,-0.003157293,F -coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,-0.226803619,F -coef_maintenance_arrival_constant_before_10_30_am,-0.223212258,F -coef_maintenance_arrival_constant_10_30_am_to_11_am,0,T -coef_maintenance_arrival_constant_11_am_to_11_30_am,-0.128382637,F -coef_maintenance_arrival_constant_11_30_am_to_1_30_pm,0.167977332,F -coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm,-0.149495878,F -coef_maintenance_arrival_constant_2_30_pm_to_4_pm,0.087679934,F -coef_maintenance_arrival_constant_4_pm_to_4_30_pm,0.121707557,F -coef_maintenance_arrival_constant_after_4_30_pm,0.106745013,F -coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.232610927,F -coef_maintenance_duration_constant_0_hr,-0.483549396,F -coef_maintenance_duration_constant_30_minutes,0,T -coef_maintenance_duration_constant_longer_than_30_minutes,-1.450618319,F -coef_maintenance_duration_constant_duration_greater_than_1_hr_linear,-0.275082922,F -coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root,0.208434683,F -coef_maintenance_calibration_constant_duration_1,-0.124602605,F -coef_maintenance_calibration_constant_duration_2,-0.103637715,F -coef_maintenance_calibration_constant_duration_3,-0.225442145,F -coef_maintenance_calibration_constant_duration_4,-0.145273012,F -coef_maintenance_calibration_constant_duration_5,-0.019241539,F -coef_eatout_distance_to_destination_duration_less_than_1_hr,-0.134981987,F -coef_eatout_distance_to_destination_duration_greater_than_1_hr,0.017860742,F -coef_eatout_low_income_duration_less_than_1_hr,1.002485807,F -coef_eatout_medium_income_duration_less_than_1_hr,0.499822018,F -coef_eatout_zeroauto_HH_duration_greater_than_1_hr,0.259409942,F -coef_eatout_university_student_departure_after_7_pm_linear,0.293827759,F -coef_eatout_female_duration_less_than_1_hr,-0.399414247,F -coef_eatout_female_duration_greater_than_1_hr,0.064593482,F -coef_eatout_time_pressure_departure_before_6_30_pm,0.083673557,F -coef_eatout_time_pressure_duration_less_than_1_hr,1.69632588,F -coef_eatout_departure_constant_7_30_am_to_9_am,1.222417262,F -coef_eatout_departure_constant_10_30_am_to_11_am,0.519559134,F -coef_eatout_departure_constant_11_am_to_11_30_am,1.191543552,F -coef_eatout_departure_constant_11_30_am_to_12_pm,1.66870995,F -coef_eatout_departure_constant_12_pm_to_12_30_pm,1.164106986,F -coef_eatout_departure_constant_12_30_pm_to_1_pm,1.057346496,F -coef_eatout_departure_constant_1_pm_to_1_30_pm,0.728959087,F -coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,-0.477439748,F -coef_eatout_departure_constant_before_6_pm,-1.21554671,F -coef_eatout_departure_constant_6_pm_to_6_30_pm,-0.425984037,F -coef_eatout_departure_constant_6_30_pm_to_7_pm,0,T -coef_eatout_departure_constant_7_pm_to_7_30_pm,-0.227800647,F -coef_eatout_departure_constant_after_7_30_pm,-0.293904097,F -coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,-0.55440861,F -coef_eatout_arrival_constant_9_30_am_to_11_am,0.486337344,F -coef_eatout_arrival_constant_12_30_pm_to_1_pm,0.629299404,F -coef_eatout_arrival_constant_1_pm_to_1_30_pm,0.938528731,F -coef_eatout_arrival_constant_1_30_pm_to_2_pm,0.584420106,F -coef_eatout_arrival_constant_2_pm_to_2_30_pm,0.842550215,F -coef_eatout_arrival_constant_2_30_pm_to_3_pm,0.298486505,F -coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,0.125034982,F -coef_eatout_arrival_constant_before_6_30_pm,-0.029062996,F -coef_eatout_arrival_constant_6_30_pm_to_7_pm,-0.509075598,F -coef_eatout_arrival_constant_7_pm_to_7_30_pm,-0.124885931,F -coef_eatout_arrival_constant_7_30_pm_to_8_pm,-0.605455664,F -coef_eatout_arrival_constant_8_pm_to_8_30_pm,0,T -coef_eatout_arrival_constant_8_30_pm_to_9_pm,-0.657382835,F -coef_eatout_arrival_constant_after_9_pm,-0.813629712,F -coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,-0.204722406,F -coef_eatout_duration_constant_0_hour,-11.72660422,F -coef_eatout_duration_constant_30_minutes,-5.08873115,F -coef_eatout_duration_constant_1_hour,-0.125521065,F -coef_eatout_duration_constant_1_hour_30_minutes,0,T -coef_eatout_duration_constant_2_hours,-0.124807752,F -coef_eatout_duration_constant_2_hour_30_minutes_or_more,-0.156019835,F -coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,-0.3357414,F -coef_eatout_calibration_constant_duration_1,-0.333697861,F -coef_eatout_calibration_constant_duration_2,-0.245716,F -coef_eatout_calibration_constant_duration_3,0.052708833,F -coef_eatout_calibration_constant_duration_4,0.041571499,F -coef_eatout_calibration_constant_departure_1,-10,F -coef_eatout_calibration_constant_departure_2,-10,F -coef_eatout_calibration_constant_departure_3,-10,F -coef_eatout_calibration_constant_departure_17,0.706568704,F -coef_eatout_calibration_constant_departure_18,0.634353544,F -coef_eatout_calibration_constant_departure_19,0.584387268,F -coef_eatout_calibration_constant_departure_20,0.469777884,F -coef_eatout_calibration_constant_departure_21,0.39548931,F -coef_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,-0.262839914,F -coef_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,0.086919429,F -coef_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear,0.468354376,F -coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear,-0.312282762,F -coef_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,-0.508439932,F -coef_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,0.074190914,F -coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,0.127185965,F -coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,0.048756122,F -coef_social_auto_distance_duration_less_than_1_hr_linear,-0.162965435,F -coef_social_auto_distance_duration_greater_than_1_hr_linear,0.006797399,F -coef_social_time_pressure_duration_less_than_1_hr,-0.229264474,F -coef_social_time_pressure_duration_greater_than_1_hr,0.219325112,F -coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,0.156250451,F -coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-0.529943196,F -coef_social_departure_constant_before_9_am,-0.198438086,F -coef_social_departure_constant_9_am_to_9_30_am,0.137620628,F -coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,-0.142078961,F -coef_social_departure_constant_before_5_30_pm,-0.390965052,F -coef_social_departure_constant_5_30_pm_to_6_pm,-0.453580491,F -coef_social_departure_constant_6_pm_to_6_30_pm,0,T -coef_social_departure_constant_6_30_pm_to_7_pm,-0.088537991,F -coef_social_departure_constant_7_pm_to_7_30_pm,0.052983115,F -coef_social_departure_constant_after_7_30_pm,-0.649629162,F -coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,-0.09574499,F -coef_social_arrival_constant_3_pm_to_3_30_pm,0.37674882,F -coef_social_arrival_constant_3_30_pm_to_4_pm,0.583355461,F -coef_social_arrival_constant_4_pm_to_4_30_pm,0.727855233,F -coef_social_arrival_constant_5_pm_to_6_pm,0.249551955,F -coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,0.053771388,F -coef_social_arrival_constant_before_8_30_pm,0.308763611,F -coef_social_arrival_constant_8_30_pm_to_9_pm,-0.208797698,F -coef_social_arrival_constant_9_pm_to_9_30_pm,-0.336319511,F -coef_social_arrival_constant_9_30_pm_to_10_pm,0,T -coef_social_arrival_constant_10_pm_to_10_30_pm,-0.055707591,F -coef_social_arrival_constant_after_10_30_pm,-0.612356296,F -coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,-0.348479901,F -coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,0.614440191,F -coef_social_duration_constant_less_than_3_hours,0.353666691,F -coef_social_duration_constant_3_hours,0,T -coef_social_duration_constant_3_hrs_30_minutes,-0.691218836,F -coef_social_duration_constant_4_hours_or_more,-1.344375328,F -coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,-0.786970714,F -coef_social_calibration_constant_duration_1,-1.346772472,F -coef_social_calibration_constant_duration_2,0.377121689,F -coef_social_calibration_constant_duration_3,0.179818928,F -coef_social_calibration_constant_duration_4,-0.283418619,F -coef_social_calibration_constant_duration_5,-0.103541313,F -coef_social_calibration_constant_duration_6,-0.03704707,F -coef_social_calibration_constant_duration_7,-0.062437167,F -coef_social_calibration_constant_duration_8,0.047640282,F -coef_social_calibration_constant_duration_9,0.284369793,F -coef_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,-0.262839914,F -coef_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,0.086919429,F -coef_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear,0.468354376,F -coef_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear,-0.312282762,F -coef_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,-0.508439932,F -coef_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,0.074190914,F -coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,0.127185965,F -coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,0.048756122,F -coef_discretionary_auto_distance_duration_less_than_1_hr_linear,-0.162965435,F -coef_discretionary_auto_distance_duration_greater_than_1_hr_linear,0.006797399,F -coef_discretionary_time_pressure_duration_less_than_1_hr,-0.229264474,F -coef_discretionary_time_pressure_duration_greater_than_1_hr,0.219325112,F -coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,0.156250451,F -coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,-0.742176805,F -coef_discretionary_departure_constant_before_7_30_am,-1.323901585,F -coef_discretionary_departure_constant_7_30_am_to_8_am,-0.695441631,F -coef_discretionary_departure_constant_8_am_to_8_30_am,-0.269903336,F -coef_discretionary_departure_constant_8_30_am_to_9_am,-0.093709211,F -coef_discretionary_departure_constant_9_am_to_9_30_am,0.265634082,F -coef_discretionary_departure_constant_9_30_am_to_10_am,0.287521134,F -coef_discretionary_departure_constant_10_am_to_10_30_am,0.396547817,F -coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,-0.245885745,F -coef_discretionary_departure_constant_before_5_pm,-1.344482349,F -coef_discretionary_departure_constant_5_pm_to_5_30_pm,-0.622632748,F -coef_discretionary_departure_constant_5_30_pm_to_6_pm,-0.456718676,F -coef_discretionary_departure_constant_6_pm_to_6_30_pm,-0.206896106,F -coef_discretionary_departure_constant_6_30_pm_to_7_pm,0,T -coef_discretionary_departure_constant_after_7_pm,-0.46439343,F -coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,-0.291998986,F -coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,0.148649188,F -coef_discretionary_arrival_constant_before_6_30_pm,0.668775963,F -coef_discretionary_arrival_constant_6_30_pm_to_7_pm,-0.053520826,F -coef_discretionary_arrival_constant_7_pm_to_7_30_pm,0.099726391,F -coef_discretionary_arrival_constant_7_30_pm_to_8_pm,0.063414092,F -coef_discretionary_arrival_constant_8_pm_to_8_30_pm,0,T -coef_discretionary_arrival_constant_8_30_pm_to_9_pm,-0.18610847,F -coef_discretionary_arrival_constant_after_9_pm,-0.423207857,F -coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,-0.525545923,F -coef_discretionary_duration_constant_0_hour,0.436988282,F -coef_discretionary_duration_constant_30_minutes,1.35967577,F -coef_discretionary_duration_constant_1_hour,1.692672999,F -coef_discretionary_duration_constant_1_hr_30_minutes,1.118932964,F -coef_discretionary_duration_constant_2_hours,0.771255733,F -coef_discretionary_duration_constant_2_hr_30_minutes,0,T -coef_discretionary_duration_constant_3_hours_or_more,-0.631242175,F -coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,-0.700935645,F -coef_discretionary_calibration_constant_duration_4,-0.132674257,F -coef_discretionary_calibration_constant_duration_5,-0.013371871,F -coef_discretionary_calibration_constant_departure_29,0.232927977,F -coef_discretionary_calibration_constant_departure_30,0.306104612,F -coef_discretionary_calibration_constant_departure_31,0.285520678,F -coef_discretionary_calibration_constant_departure_32,0.115886631,F +coefficient_name,value,constrain +coef_escort_mode_choice_logsum,1.173173034,F +coef_escort_distance_to_destination_duration_less_than_30_minutes,-0.335017673,F +coef_escort_distance_to_destination_duration_greater_than_30_minutes,0.005298165,F +coef_escort_fulltime_worker_departure_after_8_am_linear,-0.037980109,F +coef_escort_fulltime_worker_departure_after_3_am_linear,0.163254125,F +coef_escort_fulltime_worker_duration_less_than_30_minutes,-0.275077482,F +coef_escort_fulltime_worker_duration_greater_than_30_minutes,0.051530545,F +coef_escort_university_student_duration_less_than_30_minutes,-0.426802718,F +coef_escort_non_driving_age_student_duration_greater_than_30_minutes,0.240582361,F +coef_escort_driving_age_student_duration_less_than_30_minutes,-0.554146191,F +coef_escort_driving_age_student_duration_greater_than_30_minutes,0.299387708,F +coef_escort_pre_school_kid_duration_greater_than_30_minutes,0.195482563,F +coef_escort_med_high_income_duration_greater_than_30_minutes,-0.029281467,F +coef_escort_households_with_no_kids_departure_before_7_30_am,0.589083327,F +coef_escort_households_with_no_kids_departure_after_8_00_am,0.086690827,F +coef_escort_households_with_no_kids_departure_before_2_30_pm,0.477582648,F +coef_escort_households_with_no_kids_departure_after_3_00_pm,-0.204065502,F +coef_escort_households_with_no_kids_arrival_before_8_am,-0.360039254,F +coef_escort_households_with_no_kids_arrival_after_8_30_am,0.091614107,F +coef_escort_households_with_no_kids_arrival_before_3_pm,0.432854268,F +coef_escort_households_with_no_kids_arrival_after_3_30_pm,0.131037275,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.109700265,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,-0.224568648,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,-0.357416434,F +coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,0.629285298,F +coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,0.039005148,F +coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,-0.06556611,F +coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,0.117680977,F +coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,-0.057322708,F +coef_escort_number_of_individual_tours_duration_greater_than_30_minutes,-0.062899692,F +coef_escort_number_of_joint_tours_duration_greater_than_30_minutes,-0.048533895,F +coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,-1.469240002,F +coef_escort_departure_constant_before_7_am,-2.070292862,F +coef_escort_departure_constant_7_am_to_7_30_am,-0.642734296,F +coef_escort_departure_constant_7_30_am_to_8_am,0,T +coef_escort_departure_constant_8_am_to_8_30_am,-0.214617667,F +coef_escort_departure_constant_8_30_am_to_9_am,-0.147266606,F +coef_escort_departure_constant_after_9_am,-1.356686422,F +coef_escort_departure_constant_1_30_pm_to_2_pm,0.368092381,F +coef_escort_departure_constant_2_pm_to_2_30_pm,1.166803383,F +coef_escort_departure_constant_2_30_pm_to_3_pm,1.28466083,F +coef_escort_departure_constant_3_pm_to_3_30_pm,0.581891245,F +coef_escort_departure_constant_after_3_30_pm,0.834510243,F +coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,0.175257649,F +coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,-0.019161202,F +coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,0.44978138,F +coef_escort_arrival_constant_before_7_am,0.549584585,F +coef_escort_arrival_constant_7_am_to_7_30_am,0.488181278,F +coef_escort_arrival_constant_7_30_am_to_8_am,0.236447651,F +coef_escort_arrival_constant_8_am_to_8_30_am,0,T +coef_escort_arrival_constant_8_30_am_to_9_am,-0.683756801,F +coef_escort_arrival_constant_after_9_am,-1.428888485,F +coef_escort_arrival_constant_2_30_pm_to_3_pm,1.311480662,F +coef_escort_arrival_constant_3_pm_to_3_30_pm,1.316883154,F +coef_escort_arrival_constant_3_30_pm_to_4_pm,1.396838392,F +coef_escort_arrival_constant_4_pm_to_4_30_pm,1.03146139,F +coef_escort_arrival_constant_after_4_30_pm,0.907344583,F +coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,-0.148408887,F +coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.389082896,F +coef_escort_duration_constant_0_hour,-0.173757322,F +coef_escort_duration_constant_30_minutes,0,T +coef_escort_duration_constant_1_hour,-0.431287743,F +coef_escort_duration_constant_1_hour_30_minutes,-0.700473959,F +coef_escort_duration_constant_2_hours,-1.071871358,F +coef_escort_duration_constant_longer_than_2_hours,-1.691098421,F +coef_escort_calibration_constant_duration_1,-0.047200214,F +coef_escort_calibration_constant_duration_2,0.035611332,F +coef_escort_calibration_constant_departure_9,0.106814756,F +coef_escort_calibration_constant_departure_10,0.215386864,F +coef_escort_calibration_constant_departure_23,-0.255087318,F +coef_escort_calibration_constant_departure_24,-0.296870428,F +coef_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes,0.122149101,F +coef_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes,-0.037886459,F +coef_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes,0.10247157,F +coef_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes,-1.882944033,F +coef_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes,-0.197455071,F +coef_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes,-0.045152663,F +coef_shopping_retired_duration_less_than_1_hour_30_minutes,-0.264728908,F +coef_shopping_retired_duration_greater_than_1_hour_30_minutes,-0.042981757,F +coef_shopping_university_student_duration_greater_than_1_hour_30_minutes,0.045926523,F +coef_shopping_female_duration_less_than_1_hour_30_minutes,-0.417208254,F +coef_shopping_female_duration_greater_than_1_hour_30_minutes,0.045801918,F +coef_shopping_low_income_duration_greater_than_1_hour_30_minutes,0.040776383,F +coef_shopping_medium_income_duration_less_than_1_hour_30_minutes,0.108462927,F +coef_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes,-0.037893416,F +coef_shopping_distance_duration_less_than_1_hour_30_minutes,-0.214802537,F +coef_shopping_distance_duration_greater_than_1_hour_30_minutes,0.007991656,F +coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,0.013503327,F +coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,0.078844289,F +coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,-0.115431492,F +coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-0.959875456,F +coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,1.112594898,F +coef_shopping_departure_constant_before_9_am,-0.446394064,F +coef_shopping_departure_constant_9_am_to_9_30_am,-0.021669265,F +coef_shopping_departure_constant_9_30_am_to_10_am,-0.282978638,F +coef_shopping_departure_constant_10_am_to_10_30_am,0,T +coef_shopping_departure_constant_10_30_am_to_11_00_am,-0.309421311,F +coef_shopping_departure_constant_after_11_am,-0.541073357,F +coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,-0.072013428,F +coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,-0.000653398,F +coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,-0.18376635,F +coef_shopping_arrival_constant_before_12_30_pm,-0.716195343,F +coef_shopping_arrival_constant_12_30_pm_to_3_pm,-0.502714001,F +coef_shopping_arrival_constant_3_pm_to_3_30_pm,-0.167868872,F +coef_shopping_arrival_constant_3_30_pm_to_4_pm,-0.156786941,F +coef_shopping_arrival_constant_4_pm_to_4_30_pm,0,T +coef_shopping_arrival_constant_4_30_pm_to_5_pm,-0.057314044,F +coef_shopping_arrival_constant_5_pm_to_5_30_pm,-0.580040851,F +coef_shopping_arrival_constant_5_30_pm_to_7_pm,-0.32239566,F +coef_shopping_arrival_constant_7_pm_to_9_30_pm,-0.347828147,F +coef_shopping_arrival_constant_after_9_30_pm,-1.123574723,F +coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,-0.499770654,F +coef_shopping_duration_constant_0_hr,-0.131743185,F +coef_shopping_duration_constant_30_minutes,0.888857137,F +coef_shopping_duration_constant_1_hr,0,T +coef_shopping_duration_constant_1_hour_30_minutes,-0.333413031,F +coef_shopping_duration_constant_2_hrs,-0.850897912,F +coef_shopping_duration_constant_longer_than_2_hrs,-1.203783479,F +coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,-0.293581223,F +coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,-0.215759138,F +coef_shopping_calibration_constant_duration_1,-0.138450424,F +coef_shopping_calibration_constant_duration_2,-0.092704403,F +coef_shopping_calibration_constant_duration_3,-0.087738073,F +coef_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes,0.122149101,F +coef_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes,-0.037886459,F +coef_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes,0.10247157,F +coef_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes,-1.882944033,F +coef_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes,-0.197455071,F +coef_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes,-0.045152663,F +coef_maintenance_retired_duration_less_than_1_hour_30_minutes,-0.264728908,F +coef_maintenance_retired_duration_greater_than_1_hour_30_minutes,-0.042981757,F +coef_maintenance_university_student_duration_greater_than_1_hour_30_minutes,0.045926523,F +coef_maintenance_female_duration_less_than_1_hour_30_minutes,-0.417208254,F +coef_maintenance_female_duration_greater_than_1_hour_30_minutes,0.045801918,F +coef_maintenance_low_income_duration_greater_than_1_hour_30_minutes,0.040776383,F +coef_maintenance_medium_income_duration_less_than_1_hour_30_minutes,0.108462927,F +coef_maintenance_medium_income_duration_greater_than_1_hour_30_minutes,0,T +coef_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes,-0.037893416,F +coef_maintenance_distance_duration_less_than_1_hour_30_minutes,-0.214802537,F +coef_maintenance_distance_duration_greater_than_1_hour_30_minutes,0.007991656,F +coef_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes,0.013503327,F +coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,0.078844289,F +coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,-0.115431492,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,-0.864112609,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,0.504598473,F +coef_maintenance_departure_constant_before_8_am,-0.383711788,F +coef_maintenance_departure_constant_8_am_to_8_30_am,-0.076771517,F +coef_maintenance_departure_constant_8_30_am_to_9_00_am,-0.169259979,F +coef_maintenance_departure_constant_9_am_to_9_30_am,-0.051785379,F +coef_maintenance_departure_constant_9_30_am_to_10_am,-0.214942451,F +coef_maintenance_departure_constant_10_am_to_10_30_am,0,T +coef_maintenance_departure_constant_10_30_am_to_11_am,-0.427568963,F +coef_maintenance_departure_constant_after_11_am,-0.520863411,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,0.042879095,F +coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,-0.003157293,F +coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,-0.226803619,F +coef_maintenance_arrival_constant_before_10_30_am,-0.223212258,F +coef_maintenance_arrival_constant_10_30_am_to_11_am,0,T +coef_maintenance_arrival_constant_11_am_to_11_30_am,-0.128382637,F +coef_maintenance_arrival_constant_11_30_am_to_1_30_pm,0.167977332,F +coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm,-0.149495878,F +coef_maintenance_arrival_constant_2_30_pm_to_4_pm,0.087679934,F +coef_maintenance_arrival_constant_4_pm_to_4_30_pm,0.121707557,F +coef_maintenance_arrival_constant_after_4_30_pm,0.106745013,F +coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,-0.232610927,F +coef_maintenance_duration_constant_0_hr,-0.483549396,F +coef_maintenance_duration_constant_30_minutes,0,T +coef_maintenance_duration_constant_longer_than_30_minutes,-1.450618319,F +coef_maintenance_duration_constant_duration_greater_than_1_hr_linear,-0.275082922,F +coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root,0.208434683,F +coef_maintenance_calibration_constant_duration_1,-0.124602605,F +coef_maintenance_calibration_constant_duration_2,-0.103637715,F +coef_maintenance_calibration_constant_duration_3,-0.225442145,F +coef_maintenance_calibration_constant_duration_4,-0.145273012,F +coef_maintenance_calibration_constant_duration_5,-0.019241539,F +coef_eatout_distance_to_destination_duration_less_than_1_hr,-0.134981987,F +coef_eatout_distance_to_destination_duration_greater_than_1_hr,0.017860742,F +coef_eatout_low_income_duration_less_than_1_hr,1.002485807,F +coef_eatout_medium_income_duration_less_than_1_hr,0.499822018,F +coef_eatout_zeroauto_HH_duration_greater_than_1_hr,0.259409942,F +coef_eatout_university_student_departure_after_7_pm_linear,0.293827759,F +coef_eatout_female_duration_less_than_1_hr,-0.399414247,F +coef_eatout_female_duration_greater_than_1_hr,0.064593482,F +coef_eatout_time_pressure_departure_before_6_30_pm,0.083673557,F +coef_eatout_time_pressure_duration_less_than_1_hr,1.69632588,F +coef_eatout_departure_constant_7_30_am_to_9_am,1.222417262,F +coef_eatout_departure_constant_10_30_am_to_11_am,0.519559134,F +coef_eatout_departure_constant_11_am_to_11_30_am,1.191543552,F +coef_eatout_departure_constant_11_30_am_to_12_pm,1.66870995,F +coef_eatout_departure_constant_12_pm_to_12_30_pm,1.164106986,F +coef_eatout_departure_constant_12_30_pm_to_1_pm,1.057346496,F +coef_eatout_departure_constant_1_pm_to_1_30_pm,0.728959087,F +coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,-0.477439748,F +coef_eatout_departure_constant_before_6_pm,-1.21554671,F +coef_eatout_departure_constant_6_pm_to_6_30_pm,-0.425984037,F +coef_eatout_departure_constant_6_30_pm_to_7_pm,0,T +coef_eatout_departure_constant_7_pm_to_7_30_pm,-0.227800647,F +coef_eatout_departure_constant_after_7_30_pm,-0.293904097,F +coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,-0.55440861,F +coef_eatout_arrival_constant_9_30_am_to_11_am,0.486337344,F +coef_eatout_arrival_constant_12_30_pm_to_1_pm,0.629299404,F +coef_eatout_arrival_constant_1_pm_to_1_30_pm,0.938528731,F +coef_eatout_arrival_constant_1_30_pm_to_2_pm,0.584420106,F +coef_eatout_arrival_constant_2_pm_to_2_30_pm,0.842550215,F +coef_eatout_arrival_constant_2_30_pm_to_3_pm,0.298486505,F +coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,0.125034982,F +coef_eatout_arrival_constant_before_6_30_pm,-0.029062996,F +coef_eatout_arrival_constant_6_30_pm_to_7_pm,-0.509075598,F +coef_eatout_arrival_constant_7_pm_to_7_30_pm,-0.124885931,F +coef_eatout_arrival_constant_7_30_pm_to_8_pm,-0.605455664,F +coef_eatout_arrival_constant_8_pm_to_8_30_pm,0,T +coef_eatout_arrival_constant_8_30_pm_to_9_pm,-0.657382835,F +coef_eatout_arrival_constant_after_9_pm,-0.813629712,F +coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,-0.204722406,F +coef_eatout_duration_constant_0_hour,-11.72660422,F +coef_eatout_duration_constant_30_minutes,-5.08873115,F +coef_eatout_duration_constant_1_hour,-0.125521065,F +coef_eatout_duration_constant_1_hour_30_minutes,0,T +coef_eatout_duration_constant_2_hours,-0.124807752,F +coef_eatout_duration_constant_2_hour_30_minutes_or_more,-0.156019835,F +coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,-0.3357414,F +coef_eatout_calibration_constant_duration_1,-0.333697861,F +coef_eatout_calibration_constant_duration_2,-0.245716,F +coef_eatout_calibration_constant_duration_3,0.052708833,F +coef_eatout_calibration_constant_duration_4,0.041571499,F +coef_eatout_calibration_constant_departure_1,-10,F +coef_eatout_calibration_constant_departure_2,-10,F +coef_eatout_calibration_constant_departure_3,-10,F +coef_eatout_calibration_constant_departure_17,0.706568704,F +coef_eatout_calibration_constant_departure_18,0.634353544,F +coef_eatout_calibration_constant_departure_19,0.584387268,F +coef_eatout_calibration_constant_departure_20,0.469777884,F +coef_eatout_calibration_constant_departure_21,0.39548931,F +coef_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,-0.262839914,F +coef_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,0.086919429,F +coef_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear,0.468354376,F +coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear,-0.312282762,F +coef_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,-0.508439932,F +coef_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,0.074190914,F +coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,0.127185965,F +coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,0.048756122,F +coef_social_auto_distance_duration_less_than_1_hr_linear,-0.162965435,F +coef_social_auto_distance_duration_greater_than_1_hr_linear,0.006797399,F +coef_social_time_pressure_duration_less_than_1_hr,-0.229264474,F +coef_social_time_pressure_duration_greater_than_1_hr,0.219325112,F +coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,0.156250451,F +coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,-0.529943196,F +coef_social_departure_constant_before_9_am,-0.198438086,F +coef_social_departure_constant_9_am_to_9_30_am,0.137620628,F +coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,-0.142078961,F +coef_social_departure_constant_before_5_30_pm,-0.390965052,F +coef_social_departure_constant_5_30_pm_to_6_pm,-0.453580491,F +coef_social_departure_constant_6_pm_to_6_30_pm,0,T +coef_social_departure_constant_6_30_pm_to_7_pm,-0.088537991,F +coef_social_departure_constant_7_pm_to_7_30_pm,0.052983115,F +coef_social_departure_constant_after_7_30_pm,-0.649629162,F +coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,-0.09574499,F +coef_social_arrival_constant_3_pm_to_3_30_pm,0.37674882,F +coef_social_arrival_constant_3_30_pm_to_4_pm,0.583355461,F +coef_social_arrival_constant_4_pm_to_4_30_pm,0.727855233,F +coef_social_arrival_constant_5_pm_to_6_pm,0.249551955,F +coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,0.053771388,F +coef_social_arrival_constant_before_8_30_pm,0.308763611,F +coef_social_arrival_constant_8_30_pm_to_9_pm,-0.208797698,F +coef_social_arrival_constant_9_pm_to_9_30_pm,-0.336319511,F +coef_social_arrival_constant_9_30_pm_to_10_pm,0,T +coef_social_arrival_constant_10_pm_to_10_30_pm,-0.055707591,F +coef_social_arrival_constant_after_10_30_pm,-0.612356296,F +coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,-0.348479901,F +coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,0.614440191,F +coef_social_duration_constant_less_than_3_hours,0.353666691,F +coef_social_duration_constant_3_hours,0,T +coef_social_duration_constant_3_hrs_30_minutes,-0.691218836,F +coef_social_duration_constant_4_hours_or_more,-1.344375328,F +coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,-0.786970714,F +coef_social_calibration_constant_duration_1,-1.346772472,F +coef_social_calibration_constant_duration_2,0.377121689,F +coef_social_calibration_constant_duration_3,0.179818928,F +coef_social_calibration_constant_duration_4,-0.283418619,F +coef_social_calibration_constant_duration_5,-0.103541313,F +coef_social_calibration_constant_duration_6,-0.03704707,F +coef_social_calibration_constant_duration_7,-0.062437167,F +coef_social_calibration_constant_duration_8,0.047640282,F +coef_social_calibration_constant_duration_9,0.284369793,F +coef_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,-0.262839914,F +coef_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,0.086919429,F +coef_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear,0.468354376,F +coef_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear,-0.312282762,F +coef_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,-0.508439932,F +coef_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,0.074190914,F +coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,0.127185965,F +coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,0.048756122,F +coef_discretionary_auto_distance_duration_less_than_1_hr_linear,-0.162965435,F +coef_discretionary_auto_distance_duration_greater_than_1_hr_linear,0.006797399,F +coef_discretionary_time_pressure_duration_less_than_1_hr,-0.229264474,F +coef_discretionary_time_pressure_duration_greater_than_1_hr,0.219325112,F +coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,0.156250451,F +coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,-0.742176805,F +coef_discretionary_departure_constant_before_7_30_am,-1.323901585,F +coef_discretionary_departure_constant_7_30_am_to_8_am,-0.695441631,F +coef_discretionary_departure_constant_8_am_to_8_30_am,-0.269903336,F +coef_discretionary_departure_constant_8_30_am_to_9_am,-0.093709211,F +coef_discretionary_departure_constant_9_am_to_9_30_am,0.265634082,F +coef_discretionary_departure_constant_9_30_am_to_10_am,0.287521134,F +coef_discretionary_departure_constant_10_am_to_10_30_am,0.396547817,F +coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,-0.245885745,F +coef_discretionary_departure_constant_before_5_pm,-1.344482349,F +coef_discretionary_departure_constant_5_pm_to_5_30_pm,-0.622632748,F +coef_discretionary_departure_constant_5_30_pm_to_6_pm,-0.456718676,F +coef_discretionary_departure_constant_6_pm_to_6_30_pm,-0.206896106,F +coef_discretionary_departure_constant_6_30_pm_to_7_pm,0,T +coef_discretionary_departure_constant_after_7_pm,-0.46439343,F +coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,-0.291998986,F +coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,0.148649188,F +coef_discretionary_arrival_constant_before_6_30_pm,0.668775963,F +coef_discretionary_arrival_constant_6_30_pm_to_7_pm,-0.053520826,F +coef_discretionary_arrival_constant_7_pm_to_7_30_pm,0.099726391,F +coef_discretionary_arrival_constant_7_30_pm_to_8_pm,0.063414092,F +coef_discretionary_arrival_constant_8_pm_to_8_30_pm,0,T +coef_discretionary_arrival_constant_8_30_pm_to_9_pm,-0.18610847,F +coef_discretionary_arrival_constant_after_9_pm,-0.423207857,F +coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,-0.525545923,F +coef_discretionary_duration_constant_0_hour,0.436988282,F +coef_discretionary_duration_constant_30_minutes,1.35967577,F +coef_discretionary_duration_constant_1_hour,1.692672999,F +coef_discretionary_duration_constant_1_hr_30_minutes,1.118932964,F +coef_discretionary_duration_constant_2_hours,0.771255733,F +coef_discretionary_duration_constant_2_hr_30_minutes,0,T +coef_discretionary_duration_constant_3_hours_or_more,-0.631242175,F +coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,-0.700935645,F +coef_discretionary_calibration_constant_duration_4,-0.132674257,F +coef_discretionary_calibration_constant_duration_5,-0.013371871,F +coef_discretionary_calibration_constant_departure_29,0.232927977,F +coef_discretionary_calibration_constant_departure_30,0.306104612,F +coef_discretionary_calibration_constant_departure_31,0.285520678,F +coef_discretionary_calibration_constant_departure_32,0.115886631,F diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_eatout.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_eatout.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_eatout.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_eatout.csv diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_eatout_coefficients.csv diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_escort.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_escort.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_escort.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_escort.csv diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_escort_coefficients.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_escort_coefficients.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_escort_coefficients.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_escort_coefficients.csv diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_othdiscr.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othdiscr.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_othdiscr.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othdiscr.csv diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othdiscr_coefficients.csv diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_othmaint.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othmaint.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_othmaint.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othmaint.csv diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_othmaint_coefficients.csv diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_shift_in.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_in.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_shift_in.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_in.csv index 3ea664ef8a..c8eaf1bbd8 100755 --- a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_shift_in.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_in.csv @@ -1,348 +1,348 @@ -Label,Description,Expression,Coefficient -# ESCORT,,, -util_escort_mode_choice_logsum,ESCORT - Mode Choice Logsum,"@np.where(df.tour_type == 'escort', df.mode_choice_logsum, 0) ",coef_escort_mode_choice_logsum -"#Note: In CTRAMP expressions, duration alternative is from 1 to 48 but in ActivitySim, it is from 0 to 47 since the duration alternative ID was calculated as (end - start). Therefore, duration in ActivitySim expression = CTRAMP duration expresssion - 1 ",,,# -util_escort_distance_to_destination_duration_less_than_30_minutes,ESCORT - Distance to destination - Duration less than 0.5 hour (depart and arrive in the same period),"@np.where(((df.tour_type == 'escort') & (df.duration<1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_distance_to_destination_duration_less_than_30_minutes -util_escort_distance_to_destination_duration_greater_than_30_minutes,ESCORT - Distance to destination - Duration greater than 0.5 hour,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_distance_to_destination_duration_greater_than_30_minutes -util_escort_fulltime_worker_departure_after_8_am_linear,ESCORT - Full-time worker - Departure after 8:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)),0)",coef_escort_fulltime_worker_departure_after_8_am_linear -"#Note: In CTRAMP expression file, the description below says departure is after 3 am but from the expression it seems that it would be 3 pm instead of 3 am",,, -util_escort_fulltime_worker_departure_after_3_am_linear,ESCORT - Full-time worker - Departure after 3:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_fulltime_worker_departure_after_3_am_linear -util_escort_fulltime_worker_duration_less_than_30_minutes,ESCORT - Full-time worker - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_fulltime_worker_duration_less_than_30_minutes -util_escort_fulltime_worker_duration_greater_than_30_minutes,ESCORT - Full-time worker - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_fulltime_worker_duration_greater_than_30_minutes -util_escort_university_student_duration_less_than_30_minutes,ESCORT - University student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 3) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_university_student_duration_less_than_30_minutes -util_escort_non_driving_age_student_duration_greater_than_30_minutes,ESCORT - Non-driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & ((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_non_driving_age_student_duration_greater_than_30_minutes -util_escort_driving_age_student_duration_less_than_30_minutes,ESCORT - Driving age student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_driving_age_student_duration_less_than_30_minutes -util_escort_driving_age_student_duration_greater_than_30_minutes,ESCORT - Driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_driving_age_student_duration_greater_than_30_minutes -"#Note: In CTRAMP expression file, description says the expression below is for duration > 0.5 hr but the expression says duration < 0.5 hr",,, -util_escort_pre_school_kid_duration_greater_than_30_minutes,ESCORT - Pre-school kid - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 8) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_pre_school_kid_duration_greater_than_30_minutes -util_escort_med_high_income_duration_greater_than_30_minutes,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_med_high_income_duration_greater_than_30_minutes -util_escort_households_with_no_kids_departure_before_7_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_households_with_no_kids_departure_before_7_30_am -util_escort_households_with_no_kids_departure_after_8_00_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_households_with_no_kids_departure_after_8_00_am -util_escort_households_with_no_kids_departure_before_2_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_households_with_no_kids_departure_before_2_30_pm -util_escort_households_with_no_kids_departure_after_3_00_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where ((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_households_with_no_kids_departure_after_3_00_pm -util_escort_households_with_no_kids_arrival_before_8_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_households_with_no_kids_arrival_before_8_am -util_escort_households_with_no_kids_arrival_after_8_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_households_with_no_kids_arrival_after_8_30_am -util_escort_households_with_no_kids_arrival_before_3_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_households_with_no_kids_arrival_before_3_pm -util_escort_households_with_no_kids_arrival_after_3_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_households_with_no_kids_arrival_after_3_30_pm -util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am -util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm -util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am -util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where ((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm -util_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes -util_escort_number_of_individual_tours_duration_greater_than_30_minutes,ESCORT -Number of Individual Tours (excluding escorting) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.num_non_escort_tours > 0) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) * (df.num_non_escort_tours), 0)",coef_escort_number_of_individual_tours_duration_greater_than_30_minutes -util_escort_number_of_joint_tours_duration_greater_than_30_minutes,ESCORT - Number of joint tours - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) *(df.num_joint_tours), 0)",coef_escort_number_of_joint_tours_duration_greater_than_30_minutes -util_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start<8)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + np.where((df.start>13), np.minimum(df.start-13,28), 0)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear -util_escort_departure_constant_before_7_am,ESCORT - Departure Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.start<9)),coef_escort_departure_constant_before_7_am -util_escort_departure_constant_7_am_to_7_30_am,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_departure_constant_7_am_to_7_30_am -util_escort_departure_constant_7_30_am_to_8_am,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_departure_constant_7_30_am_to_8_am -util_escort_departure_constant_8_am_to_8_30_am,ESCORT - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.start==11)),coef_escort_departure_constant_8_am_to_8_30_am -util_escort_departure_constant_8_30_am_to_9_am,ESCORT - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.start==12)),coef_escort_departure_constant_8_30_am_to_9_am -util_escort_departure_constant_after_9_am,ESCORT - Departure Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.start>12)),coef_escort_departure_constant_after_9_am -util_escort_departure_constant_1_30_pm_to_2_pm,ESCORT - Departure Constant: 01:30 PM - 02:00 PM,@((df.tour_type == 'escort') & (df.start==22)),coef_escort_departure_constant_1_30_pm_to_2_pm -util_escort_departure_constant_2_pm_to_2_30_pm,ESCORT - Departure Constant: 02:00 PM - 02:30 PM,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_departure_constant_2_pm_to_2_30_pm -util_escort_departure_constant_2_30_pm_to_3_pm,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_departure_constant_2_30_pm_to_3_pm -util_escort_departure_constant_3_pm_to_3_30_pm,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.start==25)),coef_escort_departure_constant_3_pm_to_3_30_pm -util_escort_departure_constant_after_3_30_pm,ESCORT - Departure Constant: After 03:30 PM,@((df.tour_type == 'escort') & (df.start>25)),coef_escort_departure_constant_after_3_30_pm -util_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>13)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + (np.where((df.start>13), np.minimum(df.start-13,28), 0))), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear -util_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>26)), (np.where((df.start<0), np.minimum(0-df.start,48), 0) + np.where((df.start>26), np.minimum(df.start-26,15),0)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear -util_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end<8)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear -util_escort_arrival_constant_before_7_am,ESCORT - Arrival Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.end<9)),coef_escort_arrival_constant_before_7_am -util_escort_arrival_constant_7_am_to_7_30_am,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.end==9)),coef_escort_arrival_constant_7_am_to_7_30_am -util_escort_arrival_constant_7_30_am_to_8_am,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.end==10)),coef_escort_arrival_constant_7_30_am_to_8_am -util_escort_arrival_constant_8_am_to_8_30_am,ESCORT - Arrival Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.end==11)),coef_escort_arrival_constant_8_am_to_8_30_am -util_escort_arrival_constant_8_30_am_to_9_am,ESCORT - Arrival Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.end==12)),coef_escort_arrival_constant_8_30_am_to_9_am -util_escort_arrival_constant_after_9_am,ESCORT - Arrival Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.end>12)),coef_escort_arrival_constant_after_9_am -util_escort_arrival_constant_2_30_pm_to_3_pm,ESCORT - Arrival Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.end==24)),coef_escort_arrival_constant_2_30_pm_to_3_pm -util_escort_arrival_constant_3_pm_to_3_30_pm,ESCORT - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.end==25)),coef_escort_arrival_constant_3_pm_to_3_30_pm -util_escort_arrival_constant_3_30_pm_to_4_pm,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'escort') & (df.end==26)),coef_escort_arrival_constant_3_30_pm_to_4_pm -util_escort_arrival_constant_4_pm_to_4_30_pm,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'escort') & (df.end==27)),coef_escort_arrival_constant_4_pm_to_4_30_pm -util_escort_arrival_constant_after_4_30_pm,ESCORT - Arrival Constant: After 04:30 PM,@((df.tour_type == 'escort') & (df.end>27)),coef_escort_arrival_constant_after_4_30_pm -util_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>13)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear -util_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>28)), (np.where((df.end<0), np.minimum(0-df.end,48), 0) + np.where((df.start>28), np.minimum(df.end-28,15), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear -util_escort_duration_constant_0_hour,ESCORT - Duration Constant: 0 hr,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_duration_constant_0_hour -util_escort_duration_constant_30_minutes,ESCORT - Duration Constant: 0.5 hr,@((df.tour_type == 'escort') & (df.duration==1)),coef_escort_duration_constant_30_minutes -util_escort_duration_constant_1_hour,ESCORT - Duration Constant: 1 hr,@((df.tour_type == 'escort') & (df.duration==2)),coef_escort_duration_constant_1_hour -util_escort_duration_constant_1_hour_30_minutes,ESCORT - Duration Constant: 1.5hrs,@((df.tour_type == 'escort') & (df.duration==3)),coef_escort_duration_constant_1_hour_30_minutes -util_escort_duration_constant_2_hours,ESCORT - Duration Constant: 2 hrs,@((df.tour_type == 'escort') & (df.duration==4)),coef_escort_duration_constant_2_hours -util_escort_duration_constant_longer_than_2_hours,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'escort') & (df.duration>4)),coef_escort_duration_constant_longer_than_2_hours -util_escort_calibration_constant_duration_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_calibration_constant_duration_1 -util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@(((df.tour_type == 'escort') & df.duration==1)),coef_escort_calibration_constant_duration_2 -util_escort_calibration_constant_departure_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_calibration_constant_departure_9 -util_escort_calibration_constant_departure_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_calibration_constant_departure_10 -util_escort_calibration_constant_departure_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_calibration_constant_departure_23 -util_escort_calibration_constant_departure_24,ESCORT - Calibration Constant - Departure = 24,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_calibration_constant_departure_24 -#SHOPPING,,,#SHOPPING -util_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes -util_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Full-time worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes -util_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Non-driving Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes -util_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes,SHOPPING - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes -util_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes -util_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes -util_shopping_retired_duration_less_than_1_hour_30_minutes,SHOPPING - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_less_than_1_hour_30_minutes -util_shopping_retired_duration_greater_than_1_hour_30_minutes,SHOPPING - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_greater_than_1_hour_30_minutes -util_shopping_university_student_duration_greater_than_1_hour_30_minutes,SHOPPING - University Student: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_university_student_duration_greater_than_1_hour_30_minutes -util_shopping_female_duration_less_than_1_hour_30_minutes,SHOPPING - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_less_than_1_hour_30_minutes -util_shopping_female_duration_greater_than_1_hour_30_minutes,SHOPPING - Female: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_greater_than_1_hour_30_minutes -util_shopping_low_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_type == 'shopping') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_low_income_duration_greater_than_1_hour_30_minutes -util_shopping_medium_income_duration_less_than_1_hour_30_minutes,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_income_duration_less_than_1_hour_30_minutes -util_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes -util_shopping_distance_duration_less_than_1_hour_30_minutes,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_less_than_1_hour_30_minutes -util_shopping_distance_duration_greater_than_1_hour_30_minutes,SHOPPING - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_greater_than_1_hour_30_minutes -util_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,SHOPPING - Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes -util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes -util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') &(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes -util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear -util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0))**0.5, 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root -util_shopping_departure_constant_before_9_am,SHOPPING - Departure Constant: Before 09:00 AM,@((df.tour_type == 'shopping') & (df.start<13)),coef_shopping_departure_constant_before_9_am -util_shopping_departure_constant_9_am_to_9_30_am,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'shopping') & (df.start==13)),coef_shopping_departure_constant_9_am_to_9_30_am -util_shopping_departure_constant_9_30_am_to_10_am,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'shopping') & (df.start==14)),coef_shopping_departure_constant_9_30_am_to_10_am -util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'shopping') & (df.start==15)),coef_shopping_departure_constant_10_am_to_10_30_am -util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'shopping') & (df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am -util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_type == 'shopping') & (df.start>16)),coef_shopping_departure_constant_after_11_am -util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared -util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear -util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_type == 'shopping') & (df.end<20)),coef_shopping_arrival_constant_before_12_30_pm -util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm -util_shopping_arrival_constant_3_pm_to_3_30_pm,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'shopping') & (df.end==25)),coef_shopping_arrival_constant_3_pm_to_3_30_pm -util_shopping_arrival_constant_3_30_pm_to_4_pm,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'shopping') & (df.end==26)),coef_shopping_arrival_constant_3_30_pm_to_4_pm -util_shopping_arrival_constant_4_pm_to_4_30_pm,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'shopping') & (df.end==27)),coef_shopping_arrival_constant_4_pm_to_4_30_pm -util_shopping_arrival_constant_4_30_pm_to_5_pm,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.tour_type == 'shopping') & (df.end==28)),coef_shopping_arrival_constant_4_30_pm_to_5_pm -util_shopping_arrival_constant_5_pm_to_5_30_pm,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'shopping') & (df.end==29)),coef_shopping_arrival_constant_5_pm_to_5_30_pm -util_shopping_arrival_constant_5_30_pm_to_7_pm,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.tour_type == 'shopping') & (df.end>=30) & (df.end<=32)),coef_shopping_arrival_constant_5_30_pm_to_7_pm -util_shopping_arrival_constant_7_pm_to_9_30_pm,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.tour_type == 'shopping') & (df.end>=33) & (df.end<=37)),coef_shopping_arrival_constant_7_pm_to_9_30_pm -util_shopping_arrival_constant_after_9_30_pm,SHOPPING - Arrival Constant: After 09:30 PM,@((df.tour_type == 'shopping') & (df.end>37)),coef_shopping_arrival_constant_after_9_30_pm -util_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end>38)), (np.where((df.end<19), np.minimum(19-df.end,10), 0) + np.where ((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear -util_shopping_duration_constant_0_hr,SHOPPING - Duration Constant: 0 hr,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_duration_constant_0_hr -util_shopping_duration_constant_30_minutes,SHOPPING - Duration Constant: 0.5 hr,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_duration_constant_30_minutes -util_shopping_duration_constant_1_hr,SHOPPING - Duration Constant: 1 hr,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_duration_constant_1_hr -util_shopping_duration_constant_1_hour_30_minutes,SHOPPING - Duration Constant: 1.5hrs,@(df.tour_type == 'shopping') & (df.duration==3),coef_shopping_duration_constant_1_hour_30_minutes -util_shopping_duration_constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.tour_type == 'shopping') & (df.duration==4)),coef_shopping_duration_constant_2_hrs -util_shopping_duration_constant_longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'shopping') & (df.duration>4)),coef_shopping_duration_constant_longer_than_2_hrs -util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear -util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)) ** 0.5), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root -util_shopping_calibration_constant_duration_1,SHOPPING - Calibration Constant - Duration = 1,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_calibration_constant_duration_1 -util_shopping_calibration_constant_duration_2,SHOPPING - Calibration Constant - Duration = 2,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_calibration_constant_duration_2 -util_shopping_calibration_constant_duration_3,SHOPPING - Calibration Constant - Duration = 3,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_calibration_constant_duration_3 -#MAINTENANCE,#MAINTENANCE,,#MAINTENANCE -util_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes -util_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Full-time worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes -util_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Non-driving Student: Duration > 1.5 hrs,"@np.where (((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes -util_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes,MAINTENANCE - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes -util_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes -util_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes -util_maintenance_retired_duration_less_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_less_than_1_hour_30_minutes -util_maintenance_retired_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_greater_than_1_hour_30_minutes -util_maintenance_university_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - University Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_university_student_duration_greater_than_1_hour_30_minutes -util_maintenance_female_duration_less_than_1_hour_30_minutes,MAINTENANCE - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_less_than_1_hour_30_minutes -util_maintenance_female_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Female: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_greater_than_1_hour_30_minutes -util_maintenance_low_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Low Income (<=$25,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_low_income_duration_greater_than_1_hour_30_minutes -util_maintenance_medium_income_duration_less_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_less_than_1_hour_30_minutes -util_maintenance_medium_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_greater_than_1_hour_30_minutes -util_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes -util_maintenance_distance_duration_less_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_less_than_1_hour_30_minutes -util_maintenance_distance_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_greater_than_1_hour_30_minutes -util_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes,Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), np.minimum(df.duration-2,26) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes -util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes -util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes -util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), (np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear -util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + (np.where((df.start>17), np.minimum(df.start-17,24), 0)))** 0.5), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root -util_maintenance_departure_constant_before_8_am,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.tour_type == 'othmaint') & (df.start<11)),coef_maintenance_departure_constant_before_8_am -util_maintenance_departure_constant_8_am_to_8_30_am,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'othmaint') & (df.start==11)),coef_maintenance_departure_constant_8_am_to_8_30_am -util_maintenance_departure_constant_8_30_am_to_9_00_am,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'othmaint') & (df.start==12)),coef_maintenance_departure_constant_8_30_am_to_9_00_am -util_maintenance_departure_constant_9_am_to_9_30_am,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'othmaint') & (df.start==13)),coef_maintenance_departure_constant_9_am_to_9_30_am -util_maintenance_departure_constant_9_30_am_to_10_am,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'othmaint') & (df.start==14)),coef_maintenance_departure_constant_9_30_am_to_10_am -util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'othmaint') & (df.start==15)),coef_maintenance_departure_constant_10_am_to_10_30_am -util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am -util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_type == 'othmaint') & (df.start>16)),coef_maintenance_departure_constant_after_11_am -util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared -util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear -util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_type == 'othmaint') & (df.end<16)),coef_maintenance_arrival_constant_before_10_30_am -util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am -util_maintenance_arrival_constant_11_am_to_11_30_am,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.tour_type == 'othmaint') & (df.end==17)),coef_maintenance_arrival_constant_11_am_to_11_30_am -util_maintenance_arrival_constant_11_30_am_to_1_30_pm,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.tour_type == 'othmaint') & (df.end>=18) & (df.end<=21)),coef_maintenance_arrival_constant_11_30_am_to_1_30_pm -util_maintenance_arrival_constant_1_30_pm_to_2_30_pm,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.tour_type == 'othmaint') & (df.end>=22) & (df.end<=23)),coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm -util_maintenance_arrival_constant_2_30_pm_to_4_pm,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.tour_type == 'othmaint') & (df.end>=24) & (df.end<=26)),coef_maintenance_arrival_constant_2_30_pm_to_4_pm -util_maintenance_arrival_constant_4_pm_to_4_30_pm,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'othmaint') & (df.end==27)),coef_maintenance_arrival_constant_4_pm_to_4_30_pm -util_maintenance_arrival_constant_after_4_30_pm,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.tour_type == 'othmaint') & (df.end>27)),coef_maintenance_arrival_constant_after_4_30_pm -util_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end>28)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear -util_maintenance_duration_constant_0_hr,MAINTENANCE - Duration Constant: 0 hr,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_duration_constant_0_hr -util_maintenance_duration_constant_30_minutes,MAINTENANCE - Duration Constant: 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_duration_constant_30_minutes -util_maintenance_duration_constant_longer_than_30_minutes,MAINTENANCE - Duration Constant: Longer than 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration>1)),coef_maintenance_duration_constant_longer_than_30_minutes -util_maintenance_duration_constant_duration_greater_than_1_hr_linear,MAINTENANCE - Duration Constant: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_linear -util_maintenance_duration_constant_duration_greater_than_1_hr_square_root,MAINTENANCE - Duration Constant: Duration > 1 hr - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0))** 0.5), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root -util_maintenance_calibration_constant_duration_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_calibration_constant_duration_1 -util_maintenance_calibration_constant_duration_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_calibration_constant_duration_2 -util_maintenance_calibration_constant_duration_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.tour_type == 'othmaint') & (df.duration==2)),coef_maintenance_calibration_constant_duration_3 -util_maintenance_calibration_constant_duration_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.tour_type == 'othmaint') & (df.duration==3)),coef_maintenance_calibration_constant_duration_4 -util_maintenance_calibration_constant_duration_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.tour_type == 'othmaint') & (df.duration==4)),coef_maintenance_calibration_constant_duration_5 -#EAT-OUT,#EAT-OUT,,#EAT-OUT -util_eatout_distance_to_destination_duration_less_than_1_hr,EAT-OUT - Distance to destination - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_less_than_1_hr -util_eatout_distance_to_destination_duration_greater_than_1_hr,EAT-OUT - Distance to destination - Duration > 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) *(df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_greater_than_1_hr -util_eatout_low_income_duration_less_than_1_hr,EAT-OUT - Low income (<25000) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_less25K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_low_income_duration_less_than_1_hr -util_eatout_medium_income_duration_less_than_1_hr,EAT-OUT - Medium (25k to 60k) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_medium_income_duration_less_than_1_hr -util_eatout_zeroauto_HH_duration_greater_than_1_hr,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.tour_type == 'eatout') & (df.auto_ownership == 0) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_zeroauto_HH_duration_greater_than_1_hr -util_eatout_university_student_departure_after_7_pm_linear,EAT-OUT - University student - Departure after 7:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start>32) & (df.ptype == 3)), (np.where((df.start<=32), np.minimum(32-df.start,29), 0) + np.where((df.start>32), np.minimum(df.start-32,8), 0)), 0)",coef_eatout_university_student_departure_after_7_pm_linear -util_eatout_female_duration_less_than_1_hr,EAT-OUT - Female - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_less_than_1_hr -util_eatout_female_duration_greater_than_1_hr,EAT-OUT - Female - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_greater_than_1_hr -util_eatout_time_pressure_departure_before_6_30_pm,EAT-OUT - Time Pressure - Departure before 6:30 pm,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<32)), (np.minimum(32-df.start,29)) * (np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_departure_before_6_30_pm -util_eatout_time_pressure_duration_less_than_1_hr,EAT-OUT - Time Pressure - Duration < 1 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration<2)), np.minimum(2-df.duration,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_duration_less_than_1_hr -util_eatout_departure_constant_7_30_am_to_9_am,EAT-OUT - Departure Constant: 07:30 AM - 09:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>=10) & (df.start<=12)),coef_eatout_departure_constant_7_30_am_to_9_am -util_eatout_departure_constant_10_30_am_to_11_am,EAT-OUT - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==16)),coef_eatout_departure_constant_10_30_am_to_11_am -util_eatout_departure_constant_11_am_to_11_30_am,EAT-OUT - Departure Constant: 11:00 AM - 11:30 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==17)),coef_eatout_departure_constant_11_am_to_11_30_am -util_eatout_departure_constant_11_30_am_to_12_pm,EAT-OUT - Departure Constant: 11:30 AM - 12:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==18)),coef_eatout_departure_constant_11_30_am_to_12_pm -util_eatout_departure_constant_12_pm_to_12_30_pm,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start==19)),coef_eatout_departure_constant_12_pm_to_12_30_pm -util_eatout_departure_constant_12_30_pm_to_1_pm,EAT-OUT - Departure Constant: 12:30 PM - 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==20)),coef_eatout_departure_constant_12_30_pm_to_1_pm -util_eatout_departure_constant_1_pm_to_1_30_pm,EAT-OUT - Departure Constant: 01:00 PM - 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==21)),coef_eatout_departure_constant_1_pm_to_1_30_pm -util_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear -util_eatout_departure_constant_before_6_pm,EAT-OUT - Departure Constant: Before 06:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)),coef_eatout_departure_constant_before_6_pm -util_eatout_departure_constant_6_pm_to_6_30_pm,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==31)),coef_eatout_departure_constant_6_pm_to_6_30_pm -util_eatout_departure_constant_6_30_pm_to_7_pm,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==32)),coef_eatout_departure_constant_6_30_pm_to_7_pm -util_eatout_departure_constant_7_pm_to_7_30_pm,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==33)),coef_eatout_departure_constant_7_pm_to_7_30_pm -util_eatout_departure_constant_after_7_30_pm,EAT-OUT - Departure Constant: After 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>33)),coef_eatout_departure_constant_after_7_30_pm -util_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>34)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear -util_eatout_arrival_constant_9_30_am_to_11_am,EAT-OUT - Arrival Constant: 9:30 AM to 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>=14) & (df.end<=16)),coef_eatout_arrival_constant_9_30_am_to_11_am -util_eatout_arrival_constant_12_30_pm_to_1_pm,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==20)),coef_eatout_arrival_constant_12_30_pm_to_1_pm -util_eatout_arrival_constant_1_pm_to_1_30_pm,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==21)),coef_eatout_arrival_constant_1_pm_to_1_30_pm -util_eatout_arrival_constant_1_30_pm_to_2_pm,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==22)),coef_eatout_arrival_constant_1_30_pm_to_2_pm -util_eatout_arrival_constant_2_pm_to_2_30_pm,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==23)),coef_eatout_arrival_constant_2_pm_to_2_30_pm -util_eatout_arrival_constant_2_30_pm_to_3_pm,EAT-OUT - Arrival Constant: 02:30 PM to 03:00 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==24)),coef_eatout_arrival_constant_2_30_pm_to_3_pm -util_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<31)),coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear -util_eatout_arrival_constant_before_6_30_pm,EAT-OUT - Arrival Constant: Before 6:30 PM,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<32)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_before_6_30_pm -util_eatout_arrival_constant_6_30_pm_to_7_pm,EAT-OUT - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==32)),coef_eatout_arrival_constant_6_30_pm_to_7_pm -util_eatout_arrival_constant_7_pm_to_7_30_pm,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==33)),coef_eatout_arrival_constant_7_pm_to_7_30_pm -util_eatout_arrival_constant_7_30_pm_to_8_pm,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==34)),coef_eatout_arrival_constant_7_30_pm_to_8_pm -util_eatout_arrival_constant_8_pm_to_8_30_pm,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_category != 'joint') &(df.tour_type == 'eatout') & (df.end==35)),coef_eatout_arrival_constant_8_pm_to_8_30_pm -util_eatout_arrival_constant_8_30_pm_to_9_pm,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==36)),coef_eatout_arrival_constant_8_30_pm_to_9_pm -util_eatout_arrival_constant_after_9_pm,EAT-OUT - Arrival Constant: After 9:00 PM,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.end>36)),coef_eatout_arrival_constant_after_9_pm -util_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear -util_eatout_duration_constant_0_hour,EAT-OUT - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration==0)),coef_eatout_duration_constant_0_hour -util_eatout_duration_constant_30_minutes,EAT-OUT - Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_duration_constant_30_minutes -util_eatout_duration_constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==2)),coef_eatout_duration_constant_1_hour -util_eatout_duration_constant_1_hour_30_minutes,EAT-OUT - Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_duration_constant_1_hour_30_minutes -util_eatout_duration_constant_2_hours,EAT-OUT - Duration Constant: 2 hours,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.duration==4)),coef_eatout_duration_constant_2_hours -util_eatout_duration_constant_2_hour_30_minutes_or_more,EAT-OUT - Duration Constant: 2.5 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>4)),coef_eatout_duration_constant_2_hour_30_minutes_or_more -util_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,11), 0)), 0)",coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear -util_eatout_calibration_constant_duration_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_calibration_constant_duration_1 -util_eatout_calibration_constant_duration_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_calibration_constant_duration_2 -util_eatout_calibration_constant_duration_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_calibration_constant_duration_3 -util_eatout_calibration_constant_duration_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_calibration_constant_duration_4 -util_eatout_calibration_constant_departure_1,EAT-OUT - Calibration Constant - Departure = 1,@((df.tour_type == 'eatout') & (df.start == 1)),coef_eatout_calibration_constant_departure_1 -util_eatout_calibration_constant_departure_2,EAT-OUT - Calibration Constant - Departure = 2,@((df.tour_type == 'eatout') & (df.start == 2)),coef_eatout_calibration_constant_departure_2 -util_eatout_calibration_constant_departure_3,EAT-OUT - Calibration Constant - Departure = 3,@((df.tour_type== 'eatout') & (df.start == 3)),coef_eatout_calibration_constant_departure_3 -util_eatout_calibration_constant_departure_17,EAT-OUT - Calibration Constant - Departure = 17,@((df.tour_type == 'eatout') & (df.start ==17)),coef_eatout_calibration_constant_departure_17 -util_eatout_calibration_constant_departure_18,EAT-OUT - Calibration Constant - Departure = 18,@((df.tour_type== 'eatout') & (df.start ==18)),coef_eatout_calibration_constant_departure_18 -util_eatout_calibration_constant_departure_19,EAT-OUT - Calibration Constant - Departure = 19,@((df.tour_type == 'eatout') & (df.start ==19)),coef_eatout_calibration_constant_departure_19 -util_eatout_calibration_constant_departure_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.tour_type == 'eatout') & (df.start ==20)),coef_eatout_calibration_constant_departure_20 -util_eatout_calibration_constant_departure_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.tour_type == 'eatout') & (df.start ==21)),coef_eatout_calibration_constant_departure_21 -#SOCIAL,#SOCIAL,,#SOCIAL -util_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear -util_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear -util_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear -util_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear -util_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear -util_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear -util_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear -util_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear -"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, -util_social_auto_distance_duration_less_than_1_hr_linear,SOCIAL - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0) * (df.origin_to_destination_distance)",coef_social_auto_distance_duration_less_than_1_hr_linear -util_social_auto_distance_duration_greater_than_1_hr_linear,SOCIAL - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0) * (df.origin_to_destination_distance) ",coef_social_auto_distance_duration_greater_than_1_hr_linear -util_social_time_pressure_duration_less_than_1_hr,SOCIAL - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), np.minimum(3-df.duration,47), 0)* (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_less_than_1_hr -util_social_time_pressure_duration_greater_than_1_hr,SOCIAL - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3)), np.minimum(df.duration-3,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_greater_than_1_hr -util_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,SOCIAL - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.num_add_soc_discr_tours), 0)",coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr -util_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@(df.tour_type == 'social') & (df.start<12) * ((np.minimum(12-df.start,48)*(df.start<48)) + (np.minimum(df.start-48,48)*(df.start>48)))",coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear -util_social_departure_constant_before_9_am,SOCIAL - Departure Constant: Before 09:00 AM,@(df.tour_type == 'social') & (df.start<13),coef_social_departure_constant_before_9_am -util_social_departure_constant_9_am_to_9_30_am,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.tour_type == 'social') & (df.start==13),coef_social_departure_constant_9_am_to_9_30_am -util_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.start<29), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear -util_social_departure_constant_before_5_30_pm,SOCIAL - Departure Constant: Before 05:30 PM,@((df.tour_type == 'social') & (df.start<30)),coef_social_departure_constant_before_5_30_pm -util_social_departure_constant_5_30_pm_to_6_pm,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'social') & (df.start==30)),coef_social_departure_constant_5_30_pm_to_6_pm -util_social_departure_constant_6_pm_to_6_30_pm,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'social') & (df.start==31)),coef_social_departure_constant_6_pm_to_6_30_pm -util_social_departure_constant_6_30_pm_to_7_pm,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'social') & (df.start==32)),coef_social_departure_constant_6_30_pm_to_7_pm -util_social_departure_constant_7_pm_to_7_30_pm,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_type == 'social') & (df.start==33)),coef_social_departure_constant_7_pm_to_7_30_pm -util_social_departure_constant_after_7_30_pm,SOCIAL - Departure Constant: After 07:30 PM,@((df.tour_type == 'social') & (df.start>33)),coef_social_departure_constant_after_7_30_pm -util_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.start>34), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear -util_social_arrival_constant_3_pm_to_3_30_pm,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.tour_type == 'social') & (df.end==25)),coef_social_arrival_constant_3_pm_to_3_30_pm -util_social_arrival_constant_3_30_pm_to_4_pm,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.tour_type == 'social') & (df.end==26)),coef_social_arrival_constant_3_30_pm_to_4_pm -util_social_arrival_constant_4_pm_to_4_30_pm,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.tour_type == 'social') & (df.end==27)),coef_social_arrival_constant_4_pm_to_4_30_pm -util_social_arrival_constant_5_pm_to_6_pm,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.tour_type == 'social') & (df.end>=29) & (df.end<=30)),coef_social_arrival_constant_5_pm_to_6_pm -util_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end<35)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) + np.where((df.end>40), np.minimum(df.end-40,48), 0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear -util_social_arrival_constant_before_8_30_pm,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.tour_type == 'social') & (df.end<36)),coef_social_arrival_constant_before_8_30_pm -util_social_arrival_constant_8_30_pm_to_9_pm,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'social') & (df.end==36)),coef_social_arrival_constant_8_30_pm_to_9_pm -util_social_arrival_constant_9_pm_to_9_30_pm,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.tour_type == 'social') & (df.end==37)),coef_social_arrival_constant_9_pm_to_9_30_pm -util_social_arrival_constant_9_30_pm_to_10_pm,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.tour_type == 'social') & (df.end==38)),coef_social_arrival_constant_9_30_pm_to_10_pm -util_social_arrival_constant_10_pm_to_10_30_pm,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.tour_type == 'social') & (df.end==39)),coef_social_arrival_constant_10_pm_to_10_30_pm -util_social_arrival_constant_after_10_30_pm,SOCIAL - Arrival Constant: After 10:30 PM,@((df.tour_type == 'social') & (df.end>39)),coef_social_arrival_constant_after_10_30_pm -util_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end>40)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) +np.where((df.end>40),np.minimum(df.end-40,48),0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear -util_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 2.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<5)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>7), np.minimum(df.duration-7,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear -util_social_duration_constant_less_than_3_hours,SOCIAL - Duration Constant: Less than 3 hrs,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<6)),coef_social_duration_constant_less_than_3_hours -util_social_duration_constant_3_hours,SOCIAL - Duration Constant: 3 hours,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration==6)),coef_social_duration_constant_3_hours -util_social_duration_constant_3_hrs_30_minutes,SOCIAL - Duration Constant: 3.5 hours,@((df.tour_category != 'joint')& (df.tour_type == 'social') & (df.duration==7)),coef_social_duration_constant_3_hrs_30_minutes -util_social_duration_constant_4_hours_or_more,SOCIAL - Duration Constant: 4 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>7)),coef_social_duration_constant_4_hours_or_more -util_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 4.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>8)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>8), np.minimum(df.duration-8,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear -util_social_calibration_constant_duration_1,SOCIAL - Calibration Constant - Duration = 1,@((df.tour_type == 'social') & (df.duration ==0)),coef_social_calibration_constant_duration_1 -util_social_calibration_constant_duration_2,SOCIAL - Calibration Constant - Duration = 2,@((df.tour_type == 'social') & (df.duration == 1)),coef_social_calibration_constant_duration_2 -util_social_calibration_constant_duration_3,SOCIAL - Calibration Constant - Duration = 3,@((df.tour_type == 'social') & (df.duration ==2)),coef_social_calibration_constant_duration_3 -util_social_calibration_constant_duration_4,SOCIAL - Calibration Constant - Duration = 4,@((df.tour_type == 'social') & (df.duration ==3)),coef_social_calibration_constant_duration_4 -util_social_calibration_constant_duration_5,SOCIAL - Calibration Constant - Duration = 5,@((df.tour_type == 'social') & (df.duration ==4)),coef_social_calibration_constant_duration_5 -util_social_calibration_constant_duration_6,SOCIAL - Calibration Constant - Duration = 6,@((df.tour_type == 'social') & (df.duration ==5)),coef_social_calibration_constant_duration_6 -util_social_calibration_constant_duration_7,SOCIAL - Calibration Constant - Duration = 7,@((df.tour_type == 'social') & (df.duration ==6)),coef_social_calibration_constant_duration_7 -util_social_calibration_constant_duration_8,SOCIAL - Calibration Constant - Duration = 8,@((df.tour_type == 'social') & (df.duration ==7)),coef_social_calibration_constant_duration_8 -util_social_calibration_constant_duration_9,SOCIAL - Calibration Constant - Duration = 9,@((df.tour_type == 'social') & (df.duration ==8)),coef_social_calibration_constant_duration_9 -#DISCRETIONARY,#DISCRETIONARY,,#DISCRETIONARY -util_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear -util_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear -util_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear -util_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear -util_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear -util_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear -util_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)),0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear -util_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear -"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, -util_discretionary_auto_distance_duration_less_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_less_than_1_hr_linear -util_discretionary_auto_distance_duration_greater_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_greater_than_1_hr_linear -util_discretionary_time_pressure_duration_less_than_1_hr,DISCRETIONARY - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), np.minimum(3-df.duration,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_discretionary_time_pressure_duration_less_than_1_hr -util_discretionary_time_pressure_duration_greater_than_1_hr,DISCRETIONARY - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), np.minimum(df.duration-3,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_greater_than_1_hr -util_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,DISCRETIONARY - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0))*(df.num_add_soc_discr_tours),0)",coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr -util_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<9)), (np.where((df.start<9), np.minimum(9-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48),0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear -util_discretionary_departure_constant_before_7_30_am,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.tour_type == 'othdiscr') & (df.start<10)),coef_discretionary_departure_constant_before_7_30_am -util_discretionary_departure_constant_7_30_am_to_8_am,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.tour_type == 'othdiscr') & (df.start==10)),coef_discretionary_departure_constant_7_30_am_to_8_am -util_discretionary_departure_constant_8_am_to_8_30_am,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.tour_type == 'othdiscr') & (df.start==11)),coef_discretionary_departure_constant_8_am_to_8_30_am -util_discretionary_departure_constant_8_30_am_to_9_am,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.tour_type == 'othdiscr') & (df.start==12)),coef_discretionary_departure_constant_8_30_am_to_9_am -util_discretionary_departure_constant_9_am_to_9_30_am,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.tour_type == 'othdiscr') & (df.start==13)),coef_discretionary_departure_constant_9_am_to_9_30_am -util_discretionary_departure_constant_9_30_am_to_10_am,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.tour_type == 'othdiscr') & (df.start==14)),coef_discretionary_departure_constant_9_30_am_to_10_am -util_discretionary_departure_constant_10_am_to_10_30_am,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.tour_type == 'othdiscr') & (df.start==15)),coef_discretionary_departure_constant_10_am_to_10_30_am -util_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<28)), (np.where((df.start<28), np.minimum(28-df.start,8),0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear -util_discretionary_departure_constant_before_5_pm,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.tour_type == 'othdiscr') & (df.start<29)),coef_discretionary_departure_constant_before_5_pm -util_discretionary_departure_constant_5_pm_to_5_30_pm,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_departure_constant_5_pm_to_5_30_pm -util_discretionary_departure_constant_5_30_pm_to_6_pm,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_departure_constant_5_30_pm_to_6_pm -util_discretionary_departure_constant_6_pm_to_6_30_pm,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_departure_constant_6_pm_to_6_30_pm -util_discretionary_departure_constant_6_30_pm_to_7_pm,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_departure_constant_6_30_pm_to_7_pm -util_discretionary_departure_constant_after_7_pm,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start>32)),coef_discretionary_departure_constant_after_7_pm -util_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start>33)), (np.where((df.start<28), np.minimum(28-df.start,8), 0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear -util_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end<31)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear -util_discretionary_arrival_constant_before_6_30_pm,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.tour_type == 'othdiscr') & (df.end<32)),coef_discretionary_arrival_constant_before_6_30_pm -util_discretionary_arrival_constant_6_30_pm_to_7_pm,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_type == 'othdiscr') & (df.end==32)),coef_discretionary_arrival_constant_6_30_pm_to_7_pm -util_discretionary_arrival_constant_7_pm_to_7_30_pm,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_type == 'othdiscr') & (df.end==33)),coef_discretionary_arrival_constant_7_pm_to_7_30_pm -util_discretionary_arrival_constant_7_30_pm_to_8_pm,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_type == 'othdiscr') & (df.end==34)),coef_discretionary_arrival_constant_7_30_pm_to_8_pm -util_discretionary_arrival_constant_8_pm_to_8_30_pm,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_type == 'othdiscr') & (df.end==35)),coef_discretionary_arrival_constant_8_pm_to_8_30_pm -util_discretionary_arrival_constant_8_30_pm_to_9_pm,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end==36)),coef_discretionary_arrival_constant_8_30_pm_to_9_pm -util_discretionary_arrival_constant_after_9_pm,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end>36)),coef_discretionary_arrival_constant_after_9_pm -util_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48),0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear -util_discretionary_duration_constant_0_hour,DISCRETIONARY - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==0)),coef_discretionary_duration_constant_0_hour -util_discretionary_duration_constant_30_minutes,DISCRETIONARY -Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==1)),coef_discretionary_duration_constant_30_minutes -util_discretionary_duration_constant_1_hour,DISCRETIONARY -Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==2)),coef_discretionary_duration_constant_1_hour -util_discretionary_duration_constant_1_hr_30_minutes,DISCRETIONARY -Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_duration_constant_1_hr_30_minutes -util_discretionary_duration_constant_2_hours,DISCRETIONARY -Duration Constant: 2 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_duration_constant_2_hours -util_discretionary_duration_constant_2_hr_30_minutes,DISCRETIONARY -Duration Constant: 2.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==5)),coef_discretionary_duration_constant_2_hr_30_minutes -util_discretionary_duration_constant_3_hours_or_more,DISCRETIONARY -Duration Constant: 3 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>5)),coef_discretionary_duration_constant_3_hours_or_more -util_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,DISCRETIONARY -Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>6)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>6), np.minimum(df.duration-6,47), 0)), 0)",coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear -util_discretionary_calibration_constant_duration_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_calibration_constant_duration_4 -util_discretionary_calibration_constant_duration_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_calibration_constant_duration_5 -util_discretionary_calibration_constant_departure_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_calibration_constant_departure_29 -util_discretionary_calibration_constant_departure_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_calibration_constant_departure_30 -util_discretionary_calibration_constant_departure_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_calibration_constant_departure_31 -util_discretionary_calibration_constant_departure_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_calibration_constant_departure_32 +Label,Description,Expression,Coefficient +# ESCORT,,, +util_escort_mode_choice_logsum,ESCORT - Mode Choice Logsum,"@np.where(df.tour_type == 'escort', df.mode_choice_logsum, 0) ",coef_escort_mode_choice_logsum +"#Note: In CTRAMP expressions, duration alternative is from 1 to 48 but in ActivitySim, it is from 0 to 47 since the duration alternative ID was calculated as (end - start). Therefore, duration in ActivitySim expression = CTRAMP duration expresssion - 1 ",,,# +util_escort_distance_to_destination_duration_less_than_30_minutes,ESCORT - Distance to destination - Duration less than 0.5 hour (depart and arrive in the same period),"@np.where(((df.tour_type == 'escort') & (df.duration<1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_distance_to_destination_duration_less_than_30_minutes +util_escort_distance_to_destination_duration_greater_than_30_minutes,ESCORT - Distance to destination - Duration greater than 0.5 hour,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), ((df.origin_to_destination_distance) * (np.where((df.duration<=1), np.minimum(1-df.duration, 0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0))), 0)",coef_escort_distance_to_destination_duration_greater_than_30_minutes +util_escort_fulltime_worker_departure_after_8_am_linear,ESCORT - Full-time worker - Departure after 8:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)),0)",coef_escort_fulltime_worker_departure_after_8_am_linear +"#Note: In CTRAMP expression file, the description below says departure is after 3 am but from the expression it seems that it would be 3 pm instead of 3 am",,, +util_escort_fulltime_worker_departure_after_3_am_linear,ESCORT - Full-time worker - Departure after 3:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_fulltime_worker_departure_after_3_am_linear +util_escort_fulltime_worker_duration_less_than_30_minutes,ESCORT - Full-time worker - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_fulltime_worker_duration_less_than_30_minutes +util_escort_fulltime_worker_duration_greater_than_30_minutes,ESCORT - Full-time worker - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_fulltime_worker_duration_greater_than_30_minutes +util_escort_university_student_duration_less_than_30_minutes,ESCORT - University student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 3) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_university_student_duration_less_than_30_minutes +util_escort_non_driving_age_student_duration_greater_than_30_minutes,ESCORT - Non-driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & ((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_non_driving_age_student_duration_greater_than_30_minutes +util_escort_driving_age_student_duration_less_than_30_minutes,ESCORT - Driving age student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_driving_age_student_duration_less_than_30_minutes +util_escort_driving_age_student_duration_greater_than_30_minutes,ESCORT - Driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_driving_age_student_duration_greater_than_30_minutes +"#Note: In CTRAMP expression file, description says the expression below is for duration > 0.5 hr but the expression says duration < 0.5 hr",,, +util_escort_pre_school_kid_duration_greater_than_30_minutes,ESCORT - Pre-school kid - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 8) & (df.duration<1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_pre_school_kid_duration_greater_than_30_minutes +util_escort_med_high_income_duration_greater_than_30_minutes,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.is_income_60K_to_120K) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_med_high_income_duration_greater_than_30_minutes +util_escort_households_with_no_kids_departure_before_7_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_households_with_no_kids_departure_before_7_30_am +util_escort_households_with_no_kids_departure_after_8_00_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_households_with_no_kids_departure_after_8_00_am +util_escort_households_with_no_kids_departure_before_2_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_households_with_no_kids_departure_before_2_30_pm +util_escort_households_with_no_kids_departure_after_3_00_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where ((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_households_with_no_kids_departure_after_3_00_pm +util_escort_households_with_no_kids_arrival_before_8_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_households_with_no_kids_arrival_before_8_am +util_escort_households_with_no_kids_arrival_after_8_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_households_with_no_kids_arrival_after_8_30_am +util_escort_households_with_no_kids_arrival_before_3_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_households_with_no_kids_arrival_before_3_pm +util_escort_households_with_no_kids_arrival_after_3_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_households_with_no_kids_arrival_after_3_30_pm +util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am +util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), (np.where((df.start<=24), np.minimum(24-df.start,3), 0) + np.where((df.start>24), np.minimum(df.start-24,9), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm +util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am +util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), (np.where((df.start<=10), np.minimum(10-df.start,7), 0) + np.where ((df.start>10), np.minimum(df.start-10,35), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), (np.where((df.end<=11), np.minimum(11-df.end,7), 0) + np.where((df.end>11), np.minimum(df.end-11,35), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>25)), (np.where((df.end<=25), np.minimum(25-df.end,3), 0) + np.where((df.end>25), np.minimum(df.end-25,9), 0)), 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm +util_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)), 0)",coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes +util_escort_number_of_individual_tours_duration_greater_than_30_minutes,ESCORT -Number of Individual Tours (excluding escorting) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.num_non_escort_tours > 0) & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) * (df.num_non_escort_tours), 0)",coef_escort_number_of_individual_tours_duration_greater_than_30_minutes +util_escort_number_of_joint_tours_duration_greater_than_30_minutes,ESCORT - Number of joint tours - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), (np.where((df.duration<=1), np.minimum(1-df.duration,0), 0) + np.where((df.duration>1), np.minimum(df.duration-1,47), 0)) *(df.num_joint_tours), 0)",coef_escort_number_of_joint_tours_duration_greater_than_30_minutes +util_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start<8)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + np.where((df.start>13), np.minimum(df.start-13,28), 0)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear +util_escort_departure_constant_before_7_am,ESCORT - Departure Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.start<9)),coef_escort_departure_constant_before_7_am +util_escort_departure_constant_7_am_to_7_30_am,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_departure_constant_7_am_to_7_30_am +util_escort_departure_constant_7_30_am_to_8_am,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_departure_constant_7_30_am_to_8_am +util_escort_departure_constant_8_am_to_8_30_am,ESCORT - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.start==11)),coef_escort_departure_constant_8_am_to_8_30_am +util_escort_departure_constant_8_30_am_to_9_am,ESCORT - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.start==12)),coef_escort_departure_constant_8_30_am_to_9_am +util_escort_departure_constant_after_9_am,ESCORT - Departure Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.start>12)),coef_escort_departure_constant_after_9_am +util_escort_departure_constant_1_30_pm_to_2_pm,ESCORT - Departure Constant: 01:30 PM - 02:00 PM,@((df.tour_type == 'escort') & (df.start==22)),coef_escort_departure_constant_1_30_pm_to_2_pm +util_escort_departure_constant_2_pm_to_2_30_pm,ESCORT - Departure Constant: 02:00 PM - 02:30 PM,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_departure_constant_2_pm_to_2_30_pm +util_escort_departure_constant_2_30_pm_to_3_pm,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_departure_constant_2_30_pm_to_3_pm +util_escort_departure_constant_3_pm_to_3_30_pm,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.start==25)),coef_escort_departure_constant_3_pm_to_3_30_pm +util_escort_departure_constant_after_3_30_pm,ESCORT - Departure Constant: After 03:30 PM,@((df.tour_type == 'escort') & (df.start>25)),coef_escort_departure_constant_after_3_30_pm +util_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>13)), (np.where((df.start<8), np.minimum(8-df.start,4), 0) + (np.where((df.start>13), np.minimum(df.start-13,28), 0))), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear +util_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>26)), (np.where((df.start<0), np.minimum(0-df.start,48), 0) + np.where((df.start>26), np.minimum(df.start-26,15),0)), 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear +util_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end<8)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear +util_escort_arrival_constant_before_7_am,ESCORT - Arrival Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.end<9)),coef_escort_arrival_constant_before_7_am +util_escort_arrival_constant_7_am_to_7_30_am,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.end==9)),coef_escort_arrival_constant_7_am_to_7_30_am +util_escort_arrival_constant_7_30_am_to_8_am,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.end==10)),coef_escort_arrival_constant_7_30_am_to_8_am +util_escort_arrival_constant_8_am_to_8_30_am,ESCORT - Arrival Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.end==11)),coef_escort_arrival_constant_8_am_to_8_30_am +util_escort_arrival_constant_8_30_am_to_9_am,ESCORT - Arrival Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.end==12)),coef_escort_arrival_constant_8_30_am_to_9_am +util_escort_arrival_constant_after_9_am,ESCORT - Arrival Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.end>12)),coef_escort_arrival_constant_after_9_am +util_escort_arrival_constant_2_30_pm_to_3_pm,ESCORT - Arrival Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.end==24)),coef_escort_arrival_constant_2_30_pm_to_3_pm +util_escort_arrival_constant_3_pm_to_3_30_pm,ESCORT - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.end==25)),coef_escort_arrival_constant_3_pm_to_3_30_pm +util_escort_arrival_constant_3_30_pm_to_4_pm,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'escort') & (df.end==26)),coef_escort_arrival_constant_3_30_pm_to_4_pm +util_escort_arrival_constant_4_pm_to_4_30_pm,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'escort') & (df.end==27)),coef_escort_arrival_constant_4_pm_to_4_30_pm +util_escort_arrival_constant_after_4_30_pm,ESCORT - Arrival Constant: After 04:30 PM,@((df.tour_type == 'escort') & (df.end>27)),coef_escort_arrival_constant_after_4_30_pm +util_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>13)), (np.where((df.end<8), np.minimum(8-df.end,2), 0) + np.where((df.end>13), np.minimum(df.end-13,30), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear +util_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>28)), (np.where((df.end<0), np.minimum(0-df.end,48), 0) + np.where((df.start>28), np.minimum(df.end-28,15), 0)), 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear +util_escort_duration_constant_0_hour,ESCORT - Duration Constant: 0 hr,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_duration_constant_0_hour +util_escort_duration_constant_30_minutes,ESCORT - Duration Constant: 0.5 hr,@((df.tour_type == 'escort') & (df.duration==1)),coef_escort_duration_constant_30_minutes +util_escort_duration_constant_1_hour,ESCORT - Duration Constant: 1 hr,@((df.tour_type == 'escort') & (df.duration==2)),coef_escort_duration_constant_1_hour +util_escort_duration_constant_1_hour_30_minutes,ESCORT - Duration Constant: 1.5hrs,@((df.tour_type == 'escort') & (df.duration==3)),coef_escort_duration_constant_1_hour_30_minutes +util_escort_duration_constant_2_hours,ESCORT - Duration Constant: 2 hrs,@((df.tour_type == 'escort') & (df.duration==4)),coef_escort_duration_constant_2_hours +util_escort_duration_constant_longer_than_2_hours,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'escort') & (df.duration>4)),coef_escort_duration_constant_longer_than_2_hours +util_escort_calibration_constant_duration_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_calibration_constant_duration_1 +util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@(((df.tour_type == 'escort') & df.duration==1)),coef_escort_calibration_constant_duration_2 +util_escort_calibration_constant_departure_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_calibration_constant_departure_9 +util_escort_calibration_constant_departure_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_calibration_constant_departure_10 +util_escort_calibration_constant_departure_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_calibration_constant_departure_23 +util_escort_calibration_constant_departure_24,ESCORT - Calibration Constant - Departure = 24,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_calibration_constant_departure_24 +#SHOPPING,,,#SHOPPING +util_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes +util_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Full-time worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes +util_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Non-driving Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes +util_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes,SHOPPING - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes +util_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes +util_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes +util_shopping_retired_duration_less_than_1_hour_30_minutes,SHOPPING - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_less_than_1_hour_30_minutes +util_shopping_retired_duration_greater_than_1_hour_30_minutes,SHOPPING - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_greater_than_1_hour_30_minutes +util_shopping_university_student_duration_greater_than_1_hour_30_minutes,SHOPPING - University Student: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_university_student_duration_greater_than_1_hour_30_minutes +util_shopping_female_duration_less_than_1_hour_30_minutes,SHOPPING - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_less_than_1_hour_30_minutes +util_shopping_female_duration_greater_than_1_hour_30_minutes,SHOPPING - Female: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_greater_than_1_hour_30_minutes +util_shopping_low_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_type == 'shopping') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_low_income_duration_greater_than_1_hour_30_minutes +util_shopping_medium_income_duration_less_than_1_hour_30_minutes,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_income_duration_less_than_1_hour_30_minutes +util_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes +util_shopping_distance_duration_less_than_1_hour_30_minutes,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_less_than_1_hour_30_minutes +util_shopping_distance_duration_greater_than_1_hour_30_minutes,SHOPPING - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_greater_than_1_hour_30_minutes +util_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,SHOPPING - Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes +util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes +util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') &(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes +util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear +util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0))**0.5, 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root +util_shopping_departure_constant_before_9_am,SHOPPING - Departure Constant: Before 09:00 AM,@((df.tour_type == 'shopping') & (df.start<13)),coef_shopping_departure_constant_before_9_am +util_shopping_departure_constant_9_am_to_9_30_am,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'shopping') & (df.start==13)),coef_shopping_departure_constant_9_am_to_9_30_am +util_shopping_departure_constant_9_30_am_to_10_am,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'shopping') & (df.start==14)),coef_shopping_departure_constant_9_30_am_to_10_am +util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'shopping') & (df.start==15)),coef_shopping_departure_constant_10_am_to_10_30_am +util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'shopping') & (df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am +util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_type == 'shopping') & (df.start>16)),coef_shopping_departure_constant_after_11_am +util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear +util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared +util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear +util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_type == 'shopping') & (df.end<20)),coef_shopping_arrival_constant_before_12_30_pm +util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm +util_shopping_arrival_constant_3_pm_to_3_30_pm,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'shopping') & (df.end==25)),coef_shopping_arrival_constant_3_pm_to_3_30_pm +util_shopping_arrival_constant_3_30_pm_to_4_pm,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'shopping') & (df.end==26)),coef_shopping_arrival_constant_3_30_pm_to_4_pm +util_shopping_arrival_constant_4_pm_to_4_30_pm,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'shopping') & (df.end==27)),coef_shopping_arrival_constant_4_pm_to_4_30_pm +util_shopping_arrival_constant_4_30_pm_to_5_pm,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.tour_type == 'shopping') & (df.end==28)),coef_shopping_arrival_constant_4_30_pm_to_5_pm +util_shopping_arrival_constant_5_pm_to_5_30_pm,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'shopping') & (df.end==29)),coef_shopping_arrival_constant_5_pm_to_5_30_pm +util_shopping_arrival_constant_5_30_pm_to_7_pm,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.tour_type == 'shopping') & (df.end>=30) & (df.end<=32)),coef_shopping_arrival_constant_5_30_pm_to_7_pm +util_shopping_arrival_constant_7_pm_to_9_30_pm,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.tour_type == 'shopping') & (df.end>=33) & (df.end<=37)),coef_shopping_arrival_constant_7_pm_to_9_30_pm +util_shopping_arrival_constant_after_9_30_pm,SHOPPING - Arrival Constant: After 09:30 PM,@((df.tour_type == 'shopping') & (df.end>37)),coef_shopping_arrival_constant_after_9_30_pm +util_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end>38)), (np.where((df.end<19), np.minimum(19-df.end,10), 0) + np.where ((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear +util_shopping_duration_constant_0_hr,SHOPPING - Duration Constant: 0 hr,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_duration_constant_0_hr +util_shopping_duration_constant_30_minutes,SHOPPING - Duration Constant: 0.5 hr,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_duration_constant_30_minutes +util_shopping_duration_constant_1_hr,SHOPPING - Duration Constant: 1 hr,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_duration_constant_1_hr +util_shopping_duration_constant_1_hour_30_minutes,SHOPPING - Duration Constant: 1.5hrs,@(df.tour_type == 'shopping') & (df.duration==3),coef_shopping_duration_constant_1_hour_30_minutes +util_shopping_duration_constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.tour_type == 'shopping') & (df.duration==4)),coef_shopping_duration_constant_2_hrs +util_shopping_duration_constant_longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'shopping') & (df.duration>4)),coef_shopping_duration_constant_longer_than_2_hrs +util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear +util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)) ** 0.5), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root +util_shopping_calibration_constant_duration_1,SHOPPING - Calibration Constant - Duration = 1,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_calibration_constant_duration_1 +util_shopping_calibration_constant_duration_2,SHOPPING - Calibration Constant - Duration = 2,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_calibration_constant_duration_2 +util_shopping_calibration_constant_duration_3,SHOPPING - Calibration Constant - Duration = 3,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_calibration_constant_duration_3 +#MAINTENANCE,#MAINTENANCE,,#MAINTENANCE +util_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes +util_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Full-time worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes +util_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Non-driving Student: Duration > 1.5 hrs,"@np.where (((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes +util_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes,MAINTENANCE - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes +util_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes +util_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes +util_maintenance_retired_duration_less_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_less_than_1_hour_30_minutes +util_maintenance_retired_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_greater_than_1_hour_30_minutes +util_maintenance_university_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - University Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_university_student_duration_greater_than_1_hour_30_minutes +util_maintenance_female_duration_less_than_1_hour_30_minutes,MAINTENANCE - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_less_than_1_hour_30_minutes +util_maintenance_female_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Female: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_greater_than_1_hour_30_minutes +util_maintenance_low_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Low Income (<=$25,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_low_income_duration_greater_than_1_hour_30_minutes +util_maintenance_medium_income_duration_less_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_less_than_1_hour_30_minutes +util_maintenance_medium_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_greater_than_1_hour_30_minutes +util_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes +util_maintenance_distance_duration_less_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_less_than_1_hour_30_minutes +util_maintenance_distance_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_greater_than_1_hour_30_minutes +util_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes,Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), np.minimum(df.duration-2,26) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes +util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes +util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes +util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), (np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear +util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + (np.where((df.start>17), np.minimum(df.start-17,24), 0)))** 0.5), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root +util_maintenance_departure_constant_before_8_am,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.tour_type == 'othmaint') & (df.start<11)),coef_maintenance_departure_constant_before_8_am +util_maintenance_departure_constant_8_am_to_8_30_am,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'othmaint') & (df.start==11)),coef_maintenance_departure_constant_8_am_to_8_30_am +util_maintenance_departure_constant_8_30_am_to_9_00_am,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'othmaint') & (df.start==12)),coef_maintenance_departure_constant_8_30_am_to_9_00_am +util_maintenance_departure_constant_9_am_to_9_30_am,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'othmaint') & (df.start==13)),coef_maintenance_departure_constant_9_am_to_9_30_am +util_maintenance_departure_constant_9_30_am_to_10_am,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'othmaint') & (df.start==14)),coef_maintenance_departure_constant_9_30_am_to_10_am +util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'othmaint') & (df.start==15)),coef_maintenance_departure_constant_10_am_to_10_30_am +util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am +util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_type == 'othmaint') & (df.start>16)),coef_maintenance_departure_constant_after_11_am +util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear +util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared +util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear +util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_type == 'othmaint') & (df.end<16)),coef_maintenance_arrival_constant_before_10_30_am +util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am +util_maintenance_arrival_constant_11_am_to_11_30_am,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.tour_type == 'othmaint') & (df.end==17)),coef_maintenance_arrival_constant_11_am_to_11_30_am +util_maintenance_arrival_constant_11_30_am_to_1_30_pm,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.tour_type == 'othmaint') & (df.end>=18) & (df.end<=21)),coef_maintenance_arrival_constant_11_30_am_to_1_30_pm +util_maintenance_arrival_constant_1_30_pm_to_2_30_pm,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.tour_type == 'othmaint') & (df.end>=22) & (df.end<=23)),coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm +util_maintenance_arrival_constant_2_30_pm_to_4_pm,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.tour_type == 'othmaint') & (df.end>=24) & (df.end<=26)),coef_maintenance_arrival_constant_2_30_pm_to_4_pm +util_maintenance_arrival_constant_4_pm_to_4_30_pm,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'othmaint') & (df.end==27)),coef_maintenance_arrival_constant_4_pm_to_4_30_pm +util_maintenance_arrival_constant_after_4_30_pm,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.tour_type == 'othmaint') & (df.end>27)),coef_maintenance_arrival_constant_after_4_30_pm +util_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end>28)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear +util_maintenance_duration_constant_0_hr,MAINTENANCE - Duration Constant: 0 hr,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_duration_constant_0_hr +util_maintenance_duration_constant_30_minutes,MAINTENANCE - Duration Constant: 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_duration_constant_30_minutes +util_maintenance_duration_constant_longer_than_30_minutes,MAINTENANCE - Duration Constant: Longer than 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration>1)),coef_maintenance_duration_constant_longer_than_30_minutes +util_maintenance_duration_constant_duration_greater_than_1_hr_linear,MAINTENANCE - Duration Constant: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_linear +util_maintenance_duration_constant_duration_greater_than_1_hr_square_root,MAINTENANCE - Duration Constant: Duration > 1 hr - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0))** 0.5), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root +util_maintenance_calibration_constant_duration_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_calibration_constant_duration_1 +util_maintenance_calibration_constant_duration_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_calibration_constant_duration_2 +util_maintenance_calibration_constant_duration_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.tour_type == 'othmaint') & (df.duration==2)),coef_maintenance_calibration_constant_duration_3 +util_maintenance_calibration_constant_duration_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.tour_type == 'othmaint') & (df.duration==3)),coef_maintenance_calibration_constant_duration_4 +util_maintenance_calibration_constant_duration_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.tour_type == 'othmaint') & (df.duration==4)),coef_maintenance_calibration_constant_duration_5 +#EAT-OUT,#EAT-OUT,,#EAT-OUT +util_eatout_distance_to_destination_duration_less_than_1_hr,EAT-OUT - Distance to destination - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_less_than_1_hr +util_eatout_distance_to_destination_duration_greater_than_1_hr,EAT-OUT - Distance to destination - Duration > 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) *(df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_greater_than_1_hr +util_eatout_low_income_duration_less_than_1_hr,EAT-OUT - Low income (<25000) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_less25K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_low_income_duration_less_than_1_hr +util_eatout_medium_income_duration_less_than_1_hr,EAT-OUT - Medium (25k to 60k) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_medium_income_duration_less_than_1_hr +util_eatout_zeroauto_HH_duration_greater_than_1_hr,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.tour_type == 'eatout') & (df.auto_ownership == 0) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_zeroauto_HH_duration_greater_than_1_hr +util_eatout_university_student_departure_after_7_pm_linear,EAT-OUT - University student - Departure after 7:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start>32) & (df.ptype == 3)), (np.where((df.start<=32), np.minimum(32-df.start,29), 0) + np.where((df.start>32), np.minimum(df.start-32,8), 0)), 0)",coef_eatout_university_student_departure_after_7_pm_linear +util_eatout_female_duration_less_than_1_hr,EAT-OUT - Female - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_less_than_1_hr +util_eatout_female_duration_greater_than_1_hr,EAT-OUT - Female - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_greater_than_1_hr +util_eatout_time_pressure_departure_before_6_30_pm,EAT-OUT - Time Pressure - Departure before 6:30 pm,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<32)), (np.minimum(32-df.start,29)) * (np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_departure_before_6_30_pm +util_eatout_time_pressure_duration_less_than_1_hr,EAT-OUT - Time Pressure - Duration < 1 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration<2)), np.minimum(2-df.duration,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_duration_less_than_1_hr +util_eatout_departure_constant_7_30_am_to_9_am,EAT-OUT - Departure Constant: 07:30 AM - 09:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>=10) & (df.start<=12)),coef_eatout_departure_constant_7_30_am_to_9_am +util_eatout_departure_constant_10_30_am_to_11_am,EAT-OUT - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==16)),coef_eatout_departure_constant_10_30_am_to_11_am +util_eatout_departure_constant_11_am_to_11_30_am,EAT-OUT - Departure Constant: 11:00 AM - 11:30 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==17)),coef_eatout_departure_constant_11_am_to_11_30_am +util_eatout_departure_constant_11_30_am_to_12_pm,EAT-OUT - Departure Constant: 11:30 AM - 12:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==18)),coef_eatout_departure_constant_11_30_am_to_12_pm +util_eatout_departure_constant_12_pm_to_12_30_pm,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start==19)),coef_eatout_departure_constant_12_pm_to_12_30_pm +util_eatout_departure_constant_12_30_pm_to_1_pm,EAT-OUT - Departure Constant: 12:30 PM - 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==20)),coef_eatout_departure_constant_12_30_pm_to_1_pm +util_eatout_departure_constant_1_pm_to_1_30_pm,EAT-OUT - Departure Constant: 01:00 PM - 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==21)),coef_eatout_departure_constant_1_pm_to_1_30_pm +util_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear +util_eatout_departure_constant_before_6_pm,EAT-OUT - Departure Constant: Before 06:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)),coef_eatout_departure_constant_before_6_pm +util_eatout_departure_constant_6_pm_to_6_30_pm,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==31)),coef_eatout_departure_constant_6_pm_to_6_30_pm +util_eatout_departure_constant_6_30_pm_to_7_pm,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==32)),coef_eatout_departure_constant_6_30_pm_to_7_pm +util_eatout_departure_constant_7_pm_to_7_30_pm,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==33)),coef_eatout_departure_constant_7_pm_to_7_30_pm +util_eatout_departure_constant_after_7_30_pm,EAT-OUT - Departure Constant: After 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>33)),coef_eatout_departure_constant_after_7_30_pm +util_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>34)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear +util_eatout_arrival_constant_9_30_am_to_11_am,EAT-OUT - Arrival Constant: 9:30 AM to 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>=14) & (df.end<=16)),coef_eatout_arrival_constant_9_30_am_to_11_am +util_eatout_arrival_constant_12_30_pm_to_1_pm,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==20)),coef_eatout_arrival_constant_12_30_pm_to_1_pm +util_eatout_arrival_constant_1_pm_to_1_30_pm,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==21)),coef_eatout_arrival_constant_1_pm_to_1_30_pm +util_eatout_arrival_constant_1_30_pm_to_2_pm,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==22)),coef_eatout_arrival_constant_1_30_pm_to_2_pm +util_eatout_arrival_constant_2_pm_to_2_30_pm,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==23)),coef_eatout_arrival_constant_2_pm_to_2_30_pm +util_eatout_arrival_constant_2_30_pm_to_3_pm,EAT-OUT - Arrival Constant: 02:30 PM to 03:00 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==24)),coef_eatout_arrival_constant_2_30_pm_to_3_pm +util_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<31)),coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear +util_eatout_arrival_constant_before_6_30_pm,EAT-OUT - Arrival Constant: Before 6:30 PM,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<32)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_before_6_30_pm +util_eatout_arrival_constant_6_30_pm_to_7_pm,EAT-OUT - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==32)),coef_eatout_arrival_constant_6_30_pm_to_7_pm +util_eatout_arrival_constant_7_pm_to_7_30_pm,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==33)),coef_eatout_arrival_constant_7_pm_to_7_30_pm +util_eatout_arrival_constant_7_30_pm_to_8_pm,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==34)),coef_eatout_arrival_constant_7_30_pm_to_8_pm +util_eatout_arrival_constant_8_pm_to_8_30_pm,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_category != 'joint') &(df.tour_type == 'eatout') & (df.end==35)),coef_eatout_arrival_constant_8_pm_to_8_30_pm +util_eatout_arrival_constant_8_30_pm_to_9_pm,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==36)),coef_eatout_arrival_constant_8_30_pm_to_9_pm +util_eatout_arrival_constant_after_9_pm,EAT-OUT - Arrival Constant: After 9:00 PM,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.end>36)),coef_eatout_arrival_constant_after_9_pm +util_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear +util_eatout_duration_constant_0_hour,EAT-OUT - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration==0)),coef_eatout_duration_constant_0_hour +util_eatout_duration_constant_30_minutes,EAT-OUT - Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_duration_constant_30_minutes +util_eatout_duration_constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==2)),coef_eatout_duration_constant_1_hour +util_eatout_duration_constant_1_hour_30_minutes,EAT-OUT - Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_duration_constant_1_hour_30_minutes +util_eatout_duration_constant_2_hours,EAT-OUT - Duration Constant: 2 hours,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.duration==4)),coef_eatout_duration_constant_2_hours +util_eatout_duration_constant_2_hour_30_minutes_or_more,EAT-OUT - Duration Constant: 2.5 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>4)),coef_eatout_duration_constant_2_hour_30_minutes_or_more +util_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,11), 0)), 0)",coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear +util_eatout_calibration_constant_duration_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_calibration_constant_duration_1 +util_eatout_calibration_constant_duration_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_calibration_constant_duration_2 +util_eatout_calibration_constant_duration_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_calibration_constant_duration_3 +util_eatout_calibration_constant_duration_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_calibration_constant_duration_4 +util_eatout_calibration_constant_departure_1,EAT-OUT - Calibration Constant - Departure = 1,@((df.tour_type == 'eatout') & (df.start == 1)),coef_eatout_calibration_constant_departure_1 +util_eatout_calibration_constant_departure_2,EAT-OUT - Calibration Constant - Departure = 2,@((df.tour_type == 'eatout') & (df.start == 2)),coef_eatout_calibration_constant_departure_2 +util_eatout_calibration_constant_departure_3,EAT-OUT - Calibration Constant - Departure = 3,@((df.tour_type== 'eatout') & (df.start == 3)),coef_eatout_calibration_constant_departure_3 +util_eatout_calibration_constant_departure_17,EAT-OUT - Calibration Constant - Departure = 17,@((df.tour_type == 'eatout') & (df.start ==17)),coef_eatout_calibration_constant_departure_17 +util_eatout_calibration_constant_departure_18,EAT-OUT - Calibration Constant - Departure = 18,@((df.tour_type== 'eatout') & (df.start ==18)),coef_eatout_calibration_constant_departure_18 +util_eatout_calibration_constant_departure_19,EAT-OUT - Calibration Constant - Departure = 19,@((df.tour_type == 'eatout') & (df.start ==19)),coef_eatout_calibration_constant_departure_19 +util_eatout_calibration_constant_departure_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.tour_type == 'eatout') & (df.start ==20)),coef_eatout_calibration_constant_departure_20 +util_eatout_calibration_constant_departure_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.tour_type == 'eatout') & (df.start ==21)),coef_eatout_calibration_constant_departure_21 +#SOCIAL,#SOCIAL,,#SOCIAL +util_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear +util_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear +util_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear +util_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear +util_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear +util_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear +util_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear +util_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear +"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, +util_social_auto_distance_duration_less_than_1_hr_linear,SOCIAL - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0) * (df.origin_to_destination_distance)",coef_social_auto_distance_duration_less_than_1_hr_linear +util_social_auto_distance_duration_greater_than_1_hr_linear,SOCIAL - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0) * (df.origin_to_destination_distance) ",coef_social_auto_distance_duration_greater_than_1_hr_linear +util_social_time_pressure_duration_less_than_1_hr,SOCIAL - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), np.minimum(3-df.duration,47), 0)* (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_less_than_1_hr +util_social_time_pressure_duration_greater_than_1_hr,SOCIAL - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3)), np.minimum(df.duration-3,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_greater_than_1_hr +util_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,SOCIAL - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.num_add_soc_discr_tours), 0)",coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr +util_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@(df.tour_type == 'social') & (df.start<12) * ((np.minimum(12-df.start,48)*(df.start<48)) + (np.minimum(df.start-48,48)*(df.start>48)))",coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear +util_social_departure_constant_before_9_am,SOCIAL - Departure Constant: Before 09:00 AM,@(df.tour_type == 'social') & (df.start<13),coef_social_departure_constant_before_9_am +util_social_departure_constant_9_am_to_9_30_am,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.tour_type == 'social') & (df.start==13),coef_social_departure_constant_9_am_to_9_30_am +util_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.start<29), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear +util_social_departure_constant_before_5_30_pm,SOCIAL - Departure Constant: Before 05:30 PM,@((df.tour_type == 'social') & (df.start<30)),coef_social_departure_constant_before_5_30_pm +util_social_departure_constant_5_30_pm_to_6_pm,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'social') & (df.start==30)),coef_social_departure_constant_5_30_pm_to_6_pm +util_social_departure_constant_6_pm_to_6_30_pm,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'social') & (df.start==31)),coef_social_departure_constant_6_pm_to_6_30_pm +util_social_departure_constant_6_30_pm_to_7_pm,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'social') & (df.start==32)),coef_social_departure_constant_6_30_pm_to_7_pm +util_social_departure_constant_7_pm_to_7_30_pm,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_type == 'social') & (df.start==33)),coef_social_departure_constant_7_pm_to_7_30_pm +util_social_departure_constant_after_7_30_pm,SOCIAL - Departure Constant: After 07:30 PM,@((df.tour_type == 'social') & (df.start>33)),coef_social_departure_constant_after_7_30_pm +util_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.start>34), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear +util_social_arrival_constant_3_pm_to_3_30_pm,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.tour_type == 'social') & (df.end==25)),coef_social_arrival_constant_3_pm_to_3_30_pm +util_social_arrival_constant_3_30_pm_to_4_pm,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.tour_type == 'social') & (df.end==26)),coef_social_arrival_constant_3_30_pm_to_4_pm +util_social_arrival_constant_4_pm_to_4_30_pm,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.tour_type == 'social') & (df.end==27)),coef_social_arrival_constant_4_pm_to_4_30_pm +util_social_arrival_constant_5_pm_to_6_pm,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.tour_type == 'social') & (df.end>=29) & (df.end<=30)),coef_social_arrival_constant_5_pm_to_6_pm +util_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end<35)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) + np.where((df.end>40), np.minimum(df.end-40,48), 0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear +util_social_arrival_constant_before_8_30_pm,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.tour_type == 'social') & (df.end<36)),coef_social_arrival_constant_before_8_30_pm +util_social_arrival_constant_8_30_pm_to_9_pm,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'social') & (df.end==36)),coef_social_arrival_constant_8_30_pm_to_9_pm +util_social_arrival_constant_9_pm_to_9_30_pm,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.tour_type == 'social') & (df.end==37)),coef_social_arrival_constant_9_pm_to_9_30_pm +util_social_arrival_constant_9_30_pm_to_10_pm,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.tour_type == 'social') & (df.end==38)),coef_social_arrival_constant_9_30_pm_to_10_pm +util_social_arrival_constant_10_pm_to_10_30_pm,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.tour_type == 'social') & (df.end==39)),coef_social_arrival_constant_10_pm_to_10_30_pm +util_social_arrival_constant_after_10_30_pm,SOCIAL - Arrival Constant: After 10:30 PM,@((df.tour_type == 'social') & (df.end>39)),coef_social_arrival_constant_after_10_30_pm +util_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end>40)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) +np.where((df.end>40),np.minimum(df.end-40,48),0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear +util_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 2.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<5)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>7), np.minimum(df.duration-7,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear +util_social_duration_constant_less_than_3_hours,SOCIAL - Duration Constant: Less than 3 hrs,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<6)),coef_social_duration_constant_less_than_3_hours +util_social_duration_constant_3_hours,SOCIAL - Duration Constant: 3 hours,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration==6)),coef_social_duration_constant_3_hours +util_social_duration_constant_3_hrs_30_minutes,SOCIAL - Duration Constant: 3.5 hours,@((df.tour_category != 'joint')& (df.tour_type == 'social') & (df.duration==7)),coef_social_duration_constant_3_hrs_30_minutes +util_social_duration_constant_4_hours_or_more,SOCIAL - Duration Constant: 4 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>7)),coef_social_duration_constant_4_hours_or_more +util_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 4.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>8)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>8), np.minimum(df.duration-8,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear +util_social_calibration_constant_duration_1,SOCIAL - Calibration Constant - Duration = 1,@((df.tour_type == 'social') & (df.duration ==0)),coef_social_calibration_constant_duration_1 +util_social_calibration_constant_duration_2,SOCIAL - Calibration Constant - Duration = 2,@((df.tour_type == 'social') & (df.duration == 1)),coef_social_calibration_constant_duration_2 +util_social_calibration_constant_duration_3,SOCIAL - Calibration Constant - Duration = 3,@((df.tour_type == 'social') & (df.duration ==2)),coef_social_calibration_constant_duration_3 +util_social_calibration_constant_duration_4,SOCIAL - Calibration Constant - Duration = 4,@((df.tour_type == 'social') & (df.duration ==3)),coef_social_calibration_constant_duration_4 +util_social_calibration_constant_duration_5,SOCIAL - Calibration Constant - Duration = 5,@((df.tour_type == 'social') & (df.duration ==4)),coef_social_calibration_constant_duration_5 +util_social_calibration_constant_duration_6,SOCIAL - Calibration Constant - Duration = 6,@((df.tour_type == 'social') & (df.duration ==5)),coef_social_calibration_constant_duration_6 +util_social_calibration_constant_duration_7,SOCIAL - Calibration Constant - Duration = 7,@((df.tour_type == 'social') & (df.duration ==6)),coef_social_calibration_constant_duration_7 +util_social_calibration_constant_duration_8,SOCIAL - Calibration Constant - Duration = 8,@((df.tour_type == 'social') & (df.duration ==7)),coef_social_calibration_constant_duration_8 +util_social_calibration_constant_duration_9,SOCIAL - Calibration Constant - Duration = 9,@((df.tour_type == 'social') & (df.duration ==8)),coef_social_calibration_constant_duration_9 +#DISCRETIONARY,#DISCRETIONARY,,#DISCRETIONARY +util_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear +util_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear +util_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear +util_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear +util_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear +util_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear +util_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)),0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear +util_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear +"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, +util_discretionary_auto_distance_duration_less_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_less_than_1_hr_linear +util_discretionary_auto_distance_duration_greater_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_greater_than_1_hr_linear +util_discretionary_time_pressure_duration_less_than_1_hr,DISCRETIONARY - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), np.minimum(3-df.duration,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_discretionary_time_pressure_duration_less_than_1_hr +util_discretionary_time_pressure_duration_greater_than_1_hr,DISCRETIONARY - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), np.minimum(df.duration-3,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_greater_than_1_hr +util_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,DISCRETIONARY - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0))*(df.num_add_soc_discr_tours),0)",coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr +util_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<9)), (np.where((df.start<9), np.minimum(9-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48),0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear +util_discretionary_departure_constant_before_7_30_am,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.tour_type == 'othdiscr') & (df.start<10)),coef_discretionary_departure_constant_before_7_30_am +util_discretionary_departure_constant_7_30_am_to_8_am,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.tour_type == 'othdiscr') & (df.start==10)),coef_discretionary_departure_constant_7_30_am_to_8_am +util_discretionary_departure_constant_8_am_to_8_30_am,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.tour_type == 'othdiscr') & (df.start==11)),coef_discretionary_departure_constant_8_am_to_8_30_am +util_discretionary_departure_constant_8_30_am_to_9_am,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.tour_type == 'othdiscr') & (df.start==12)),coef_discretionary_departure_constant_8_30_am_to_9_am +util_discretionary_departure_constant_9_am_to_9_30_am,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.tour_type == 'othdiscr') & (df.start==13)),coef_discretionary_departure_constant_9_am_to_9_30_am +util_discretionary_departure_constant_9_30_am_to_10_am,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.tour_type == 'othdiscr') & (df.start==14)),coef_discretionary_departure_constant_9_30_am_to_10_am +util_discretionary_departure_constant_10_am_to_10_30_am,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.tour_type == 'othdiscr') & (df.start==15)),coef_discretionary_departure_constant_10_am_to_10_30_am +util_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<28)), (np.where((df.start<28), np.minimum(28-df.start,8),0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear +util_discretionary_departure_constant_before_5_pm,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.tour_type == 'othdiscr') & (df.start<29)),coef_discretionary_departure_constant_before_5_pm +util_discretionary_departure_constant_5_pm_to_5_30_pm,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_departure_constant_5_pm_to_5_30_pm +util_discretionary_departure_constant_5_30_pm_to_6_pm,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_departure_constant_5_30_pm_to_6_pm +util_discretionary_departure_constant_6_pm_to_6_30_pm,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_departure_constant_6_pm_to_6_30_pm +util_discretionary_departure_constant_6_30_pm_to_7_pm,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_departure_constant_6_30_pm_to_7_pm +util_discretionary_departure_constant_after_7_pm,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start>32)),coef_discretionary_departure_constant_after_7_pm +util_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start>33)), (np.where((df.start<28), np.minimum(28-df.start,8), 0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear +util_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end<31)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear +util_discretionary_arrival_constant_before_6_30_pm,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.tour_type == 'othdiscr') & (df.end<32)),coef_discretionary_arrival_constant_before_6_30_pm +util_discretionary_arrival_constant_6_30_pm_to_7_pm,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_type == 'othdiscr') & (df.end==32)),coef_discretionary_arrival_constant_6_30_pm_to_7_pm +util_discretionary_arrival_constant_7_pm_to_7_30_pm,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_type == 'othdiscr') & (df.end==33)),coef_discretionary_arrival_constant_7_pm_to_7_30_pm +util_discretionary_arrival_constant_7_30_pm_to_8_pm,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_type == 'othdiscr') & (df.end==34)),coef_discretionary_arrival_constant_7_30_pm_to_8_pm +util_discretionary_arrival_constant_8_pm_to_8_30_pm,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_type == 'othdiscr') & (df.end==35)),coef_discretionary_arrival_constant_8_pm_to_8_30_pm +util_discretionary_arrival_constant_8_30_pm_to_9_pm,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end==36)),coef_discretionary_arrival_constant_8_30_pm_to_9_pm +util_discretionary_arrival_constant_after_9_pm,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end>36)),coef_discretionary_arrival_constant_after_9_pm +util_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48),0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear +util_discretionary_duration_constant_0_hour,DISCRETIONARY - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==0)),coef_discretionary_duration_constant_0_hour +util_discretionary_duration_constant_30_minutes,DISCRETIONARY -Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==1)),coef_discretionary_duration_constant_30_minutes +util_discretionary_duration_constant_1_hour,DISCRETIONARY -Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==2)),coef_discretionary_duration_constant_1_hour +util_discretionary_duration_constant_1_hr_30_minutes,DISCRETIONARY -Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_duration_constant_1_hr_30_minutes +util_discretionary_duration_constant_2_hours,DISCRETIONARY -Duration Constant: 2 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_duration_constant_2_hours +util_discretionary_duration_constant_2_hr_30_minutes,DISCRETIONARY -Duration Constant: 2.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==5)),coef_discretionary_duration_constant_2_hr_30_minutes +util_discretionary_duration_constant_3_hours_or_more,DISCRETIONARY -Duration Constant: 3 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>5)),coef_discretionary_duration_constant_3_hours_or_more +util_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,DISCRETIONARY -Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>6)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>6), np.minimum(df.duration-6,47), 0)), 0)",coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear +util_discretionary_calibration_constant_duration_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_calibration_constant_duration_4 +util_discretionary_calibration_constant_duration_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_calibration_constant_duration_5 +util_discretionary_calibration_constant_departure_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_calibration_constant_departure_29 +util_discretionary_calibration_constant_departure_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_calibration_constant_departure_30 +util_discretionary_calibration_constant_departure_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_calibration_constant_departure_31 +util_discretionary_calibration_constant_departure_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_calibration_constant_departure_32 diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_shift_out.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_out.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_shift_out.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_out.csv index 5d760c2c38..93b77cbe45 100755 --- a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_shift_out.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shift_out.csv @@ -1,348 +1,348 @@ -Label,Description,Expression,Coefficient -# ESCORT,,, -util_escort_mode_choice_logsum,ESCORT - Mode Choice Logsum,"@np.where(df.tour_type == 'escort', df.mode_choice_logsum, 0) ",coef_escort_mode_choice_logsum -"#Note: In CTRAMP expressions, duration alternative is from 1 to 48 but in ActivitySim, it is from 0 to 47 since the duration alternative ID was calculated as (end - start). Therefore, duration in ActivitySim expression = CTRAMP duration expresssion - 1 ",,,# -util_escort_distance_to_destination_duration_less_than_30_minutes,ESCORT - Distance to destination - Duration less than 0.5 hour (depart and arrive in the same period),"@np.where(((df.tour_type == 'escort') & (df.duration<1)), ((df.origin_to_destination_distance) * df.durationShift_escort), 0)",coef_escort_distance_to_destination_duration_less_than_30_minutes -util_escort_distance_to_destination_duration_greater_than_30_minutes,ESCORT - Distance to destination - Duration greater than 0.5 hour,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), ((df.origin_to_destination_distance) * df.durationShift_escort), 0)",coef_escort_distance_to_destination_duration_greater_than_30_minutes -util_escort_fulltime_worker_departure_after_8_am_linear,ESCORT - Full-time worker - Departure after 8:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>10)), df.departureLinearShift1_escort,0)",coef_escort_fulltime_worker_departure_after_8_am_linear -"#Note: In CTRAMP expression file, the description below says departure is after 3 am but from the expression it seems that it would be 3 pm instead of 3 am",,, -util_escort_fulltime_worker_departure_after_3_am_linear,ESCORT - Full-time worker - Departure after 3:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>24)), df.departureLinearShift2_escort, 0)",coef_escort_fulltime_worker_departure_after_3_am_linear -util_escort_fulltime_worker_duration_less_than_30_minutes,ESCORT - Full-time worker - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration<1)), df.durationShift_escort, 0)",coef_escort_fulltime_worker_duration_less_than_30_minutes -util_escort_fulltime_worker_duration_greater_than_30_minutes,ESCORT - Full-time worker - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration>1)), df.durationShift_escort, 0)",coef_escort_fulltime_worker_duration_greater_than_30_minutes -util_escort_university_student_duration_less_than_30_minutes,ESCORT - University student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 3) & (df.duration<1)), df.durationShift_escort, 0)",coef_escort_university_student_duration_less_than_30_minutes -util_escort_non_driving_age_student_duration_greater_than_30_minutes,ESCORT - Non-driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & ((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), df.durationShift_escort, 0)",coef_escort_non_driving_age_student_duration_greater_than_30_minutes -util_escort_driving_age_student_duration_less_than_30_minutes,ESCORT - Driving age student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration<1)), df.durationShift_escort, 0)",coef_escort_driving_age_student_duration_less_than_30_minutes -util_escort_driving_age_student_duration_greater_than_30_minutes,ESCORT - Driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration>1)), df.durationShift_escort, 0)",coef_escort_driving_age_student_duration_greater_than_30_minutes -"#Note: In CTRAMP expression file, description says the expression below is for duration > 0.5 hr but the expression says duration < 0.5 hr",,, -util_escort_pre_school_kid_duration_greater_than_30_minutes,ESCORT - Pre-school kid - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 8) & (df.duration<1)), df.durationShift_escort, 0)",coef_escort_pre_school_kid_duration_greater_than_30_minutes -util_escort_med_high_income_duration_greater_than_30_minutes,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.is_income_60K_to_120K) & (df.duration>1)), df.durationShift_escort, 0)",coef_escort_med_high_income_duration_greater_than_30_minutes -util_escort_households_with_no_kids_departure_before_7_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<10)), df.departureLinearShift1_escort, 0)",coef_escort_households_with_no_kids_departure_before_7_30_am -util_escort_households_with_no_kids_departure_after_8_00_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>10)), df.departureLinearShift1_escort, 0)",coef_escort_households_with_no_kids_departure_after_8_00_am -util_escort_households_with_no_kids_departure_before_2_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<24)), df.departureLinearShift2_escort, 0)",coef_escort_households_with_no_kids_departure_before_2_30_pm -util_escort_households_with_no_kids_departure_after_3_00_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>24)), df.departureLinearShift2_escort, 0)",coef_escort_households_with_no_kids_departure_after_3_00_pm -util_escort_households_with_no_kids_arrival_before_8_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<11)), df.arrivalLinearShift1_escort, 0)",coef_escort_households_with_no_kids_arrival_before_8_am -util_escort_households_with_no_kids_arrival_after_8_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>11)), df.arrivalLinearShift1_escort, 0)",coef_escort_households_with_no_kids_arrival_after_8_30_am -util_escort_households_with_no_kids_arrival_before_3_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<25)), df.arrivalLinearShift2_escort, 0)",coef_escort_households_with_no_kids_arrival_before_3_pm -util_escort_households_with_no_kids_arrival_after_3_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>25)), df.arrivalLinearShift2_escort, 0)",coef_escort_households_with_no_kids_arrival_after_3_30_pm -util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>10)), df.departureLinearShift1_escort, 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am -util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), df.departureLinearShift2_escort, 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm -util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), df.arrivalLinearShift1_escort, 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am -util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), df.arrivalLinearShift2_escort, 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), df.departureLinearShift1_escort, 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), df.arrivalLinearShift1_escort, 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am -util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>25)), df.arrivalLinearShift2_escort, 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm -util_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), df.durationShift_escort, 0)",coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes -util_escort_number_of_individual_tours_duration_greater_than_30_minutes,ESCORT -Number of Individual Tours (excluding escorting) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.num_non_escort_tours > 0) & (df.duration>1)), df.durationShift_escort*df.num_non_escort_tours, 0)",coef_escort_number_of_individual_tours_duration_greater_than_30_minutes -util_escort_number_of_joint_tours_duration_greater_than_30_minutes,ESCORT - Number of joint tours - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), df.durationShift_escort *(df.num_joint_tours), 0)",coef_escort_number_of_joint_tours_duration_greater_than_30_minutes -util_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start<8)), df.depConstShift1_escort, 0)",coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear -util_escort_departure_constant_before_7_am,ESCORT - Departure Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.start<9)),coef_escort_departure_constant_before_7_am -util_escort_departure_constant_7_am_to_7_30_am,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_departure_constant_7_am_to_7_30_am -util_escort_departure_constant_7_30_am_to_8_am,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_departure_constant_7_30_am_to_8_am -util_escort_departure_constant_8_am_to_8_30_am,ESCORT - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.start==11)),coef_escort_departure_constant_8_am_to_8_30_am -util_escort_departure_constant_8_30_am_to_9_am,ESCORT - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.start==12)),coef_escort_departure_constant_8_30_am_to_9_am -util_escort_departure_constant_after_9_am,ESCORT - Departure Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.start>12)),coef_escort_departure_constant_after_9_am -util_escort_departure_constant_1_30_pm_to_2_pm,ESCORT - Departure Constant: 01:30 PM - 02:00 PM,@((df.tour_type == 'escort') & (df.start==22)),coef_escort_departure_constant_1_30_pm_to_2_pm -util_escort_departure_constant_2_pm_to_2_30_pm,ESCORT - Departure Constant: 02:00 PM - 02:30 PM,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_departure_constant_2_pm_to_2_30_pm -util_escort_departure_constant_2_30_pm_to_3_pm,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_departure_constant_2_30_pm_to_3_pm -util_escort_departure_constant_3_pm_to_3_30_pm,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.start==25)),coef_escort_departure_constant_3_pm_to_3_30_pm -util_escort_departure_constant_after_3_30_pm,ESCORT - Departure Constant: After 03:30 PM,@((df.tour_type == 'escort') & (df.start>25)),coef_escort_departure_constant_after_3_30_pm -util_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>13)), df.depConstShift1_escort, 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear -util_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>26)), df.depConstShift2_escort, 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear -util_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end<8)), df.arrConstShift1_escort, 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear -util_escort_arrival_constant_before_7_am,ESCORT - Arrival Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.end<9)),coef_escort_arrival_constant_before_7_am -util_escort_arrival_constant_7_am_to_7_30_am,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.end==9)),coef_escort_arrival_constant_7_am_to_7_30_am -util_escort_arrival_constant_7_30_am_to_8_am,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.end==10)),coef_escort_arrival_constant_7_30_am_to_8_am -util_escort_arrival_constant_8_am_to_8_30_am,ESCORT - Arrival Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.end==11)),coef_escort_arrival_constant_8_am_to_8_30_am -util_escort_arrival_constant_8_30_am_to_9_am,ESCORT - Arrival Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.end==12)),coef_escort_arrival_constant_8_30_am_to_9_am -util_escort_arrival_constant_after_9_am,ESCORT - Arrival Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.end>12)),coef_escort_arrival_constant_after_9_am -util_escort_arrival_constant_2_30_pm_to_3_pm,ESCORT - Arrival Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.end==24)),coef_escort_arrival_constant_2_30_pm_to_3_pm -util_escort_arrival_constant_3_pm_to_3_30_pm,ESCORT - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.end==25)),coef_escort_arrival_constant_3_pm_to_3_30_pm -util_escort_arrival_constant_3_30_pm_to_4_pm,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'escort') & (df.end==26)),coef_escort_arrival_constant_3_30_pm_to_4_pm -util_escort_arrival_constant_4_pm_to_4_30_pm,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'escort') & (df.end==27)),coef_escort_arrival_constant_4_pm_to_4_30_pm -util_escort_arrival_constant_after_4_30_pm,ESCORT - Arrival Constant: After 04:30 PM,@((df.tour_type == 'escort') & (df.end>27)),coef_escort_arrival_constant_after_4_30_pm -util_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>13)), df.arrConstShift1_escort, 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear -util_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>28)), df.arrConstShift2_escort, 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear -util_escort_duration_constant_0_hour,ESCORT - Duration Constant: 0 hr,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_duration_constant_0_hour -util_escort_duration_constant_30_minutes,ESCORT - Duration Constant: 0.5 hr,@((df.tour_type == 'escort') & (df.duration==1)),coef_escort_duration_constant_30_minutes -util_escort_duration_constant_1_hour,ESCORT - Duration Constant: 1 hr,@((df.tour_type == 'escort') & (df.duration==2)),coef_escort_duration_constant_1_hour -util_escort_duration_constant_1_hour_30_minutes,ESCORT - Duration Constant: 1.5hrs,@((df.tour_type == 'escort') & (df.duration==3)),coef_escort_duration_constant_1_hour_30_minutes -util_escort_duration_constant_2_hours,ESCORT - Duration Constant: 2 hrs,@((df.tour_type == 'escort') & (df.duration==4)),coef_escort_duration_constant_2_hours -util_escort_duration_constant_longer_than_2_hours,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'escort') & (df.duration>4)),coef_escort_duration_constant_longer_than_2_hours -util_escort_calibration_constant_duration_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_calibration_constant_duration_1 -util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@(((df.tour_type == 'escort') & df.duration==1)),coef_escort_calibration_constant_duration_2 -util_escort_calibration_constant_departure_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_calibration_constant_departure_9 -util_escort_calibration_constant_departure_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_calibration_constant_departure_10 -util_escort_calibration_constant_departure_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_calibration_constant_departure_23 -util_escort_calibration_constant_departure_24,,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_calibration_constant_departure_24 -#SHOPPING,ESCORT - Calibration Constant - Departure = 24,,#SHOPPING -util_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes -util_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Full-time worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes -util_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Non-driving Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes -util_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes,SHOPPING - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes -util_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes -util_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes -util_shopping_retired_duration_less_than_1_hour_30_minutes,SHOPPING - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_less_than_1_hour_30_minutes -util_shopping_retired_duration_greater_than_1_hour_30_minutes,SHOPPING - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_greater_than_1_hour_30_minutes -util_shopping_university_student_duration_greater_than_1_hour_30_minutes,SHOPPING - University Student: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_university_student_duration_greater_than_1_hour_30_minutes -util_shopping_female_duration_less_than_1_hour_30_minutes,SHOPPING - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_less_than_1_hour_30_minutes -util_shopping_female_duration_greater_than_1_hour_30_minutes,SHOPPING - Female: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_greater_than_1_hour_30_minutes -util_shopping_low_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_type == 'shopping') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_low_income_duration_greater_than_1_hour_30_minutes -util_shopping_medium_income_duration_less_than_1_hour_30_minutes,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_income_duration_less_than_1_hour_30_minutes -util_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes -util_shopping_distance_duration_less_than_1_hour_30_minutes,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_less_than_1_hour_30_minutes -util_shopping_distance_duration_greater_than_1_hour_30_minutes,SHOPPING - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_greater_than_1_hour_30_minutes -util_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,SHOPPING - Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes -util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes -util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') &(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes -util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear -util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0))**0.5, 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root -util_shopping_departure_constant_before_9_am,SHOPPING - Departure Constant: Before 09:00 AM,@((df.tour_type == 'shopping') & (df.start<13)),coef_shopping_departure_constant_before_9_am -util_shopping_departure_constant_9_am_to_9_30_am,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'shopping') & (df.start==13)),coef_shopping_departure_constant_9_am_to_9_30_am -util_shopping_departure_constant_9_30_am_to_10_am,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'shopping') & (df.start==14)),coef_shopping_departure_constant_9_30_am_to_10_am -util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'shopping') & (df.start==15)),coef_shopping_departure_constant_10_am_to_10_30_am -util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'shopping') & (df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am -util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_type == 'shopping') & (df.start>16)),coef_shopping_departure_constant_after_11_am -util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared -util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear -util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_type == 'shopping') & (df.end<20)),coef_shopping_arrival_constant_before_12_30_pm -util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm -util_shopping_arrival_constant_3_pm_to_3_30_pm,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'shopping') & (df.end==25)),coef_shopping_arrival_constant_3_pm_to_3_30_pm -util_shopping_arrival_constant_3_30_pm_to_4_pm,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'shopping') & (df.end==26)),coef_shopping_arrival_constant_3_30_pm_to_4_pm -util_shopping_arrival_constant_4_pm_to_4_30_pm,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'shopping') & (df.end==27)),coef_shopping_arrival_constant_4_pm_to_4_30_pm -util_shopping_arrival_constant_4_30_pm_to_5_pm,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.tour_type == 'shopping') & (df.end==28)),coef_shopping_arrival_constant_4_30_pm_to_5_pm -util_shopping_arrival_constant_5_pm_to_5_30_pm,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'shopping') & (df.end==29)),coef_shopping_arrival_constant_5_pm_to_5_30_pm -util_shopping_arrival_constant_5_30_pm_to_7_pm,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.tour_type == 'shopping') & (df.end>=30) & (df.end<=32)),coef_shopping_arrival_constant_5_30_pm_to_7_pm -util_shopping_arrival_constant_7_pm_to_9_30_pm,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.tour_type == 'shopping') & (df.end>=33) & (df.end<=37)),coef_shopping_arrival_constant_7_pm_to_9_30_pm -util_shopping_arrival_constant_after_9_30_pm,SHOPPING - Arrival Constant: After 09:30 PM,@((df.tour_type == 'shopping') & (df.end>37)),coef_shopping_arrival_constant_after_9_30_pm -util_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end>38)), (np.where((df.end<19), np.minimum(19-df.end,10), 0) + np.where ((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear -util_shopping_duration_constant_0_hr,SHOPPING - Duration Constant: 0 hr,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_duration_constant_0_hr -util_shopping_duration_constant_30_minutes,SHOPPING - Duration Constant: 0.5 hr,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_duration_constant_30_minutes -util_shopping_duration_constant_1_hr,SHOPPING - Duration Constant: 1 hr,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_duration_constant_1_hr -util_shopping_duration_constant_1_hour_30_minutes,SHOPPING - Duration Constant: 1.5hrs,@(df.tour_type == 'shopping') & (df.duration==3),coef_shopping_duration_constant_1_hour_30_minutes -util_shopping_duration_constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.tour_type == 'shopping') & (df.duration==4)),coef_shopping_duration_constant_2_hrs -util_shopping_duration_constant_longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'shopping') & (df.duration>4)),coef_shopping_duration_constant_longer_than_2_hrs -util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear -util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)) ** 0.5), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root -util_shopping_calibration_constant_duration_1,SHOPPING - Calibration Constant - Duration = 1,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_calibration_constant_duration_1 -util_shopping_calibration_constant_duration_2,SHOPPING - Calibration Constant - Duration = 2,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_calibration_constant_duration_2 -util_shopping_calibration_constant_duration_3,SHOPPING - Calibration Constant - Duration = 3,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_calibration_constant_duration_3 -#MAINTENANCE,#MAINTENANCE,,#MAINTENANCE -util_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes -util_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Full-time worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes -util_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Non-driving Student: Duration > 1.5 hrs,"@np.where (((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes -util_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes,MAINTENANCE - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes -util_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes -util_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes -util_maintenance_retired_duration_less_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_less_than_1_hour_30_minutes -util_maintenance_retired_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_greater_than_1_hour_30_minutes -util_maintenance_university_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - University Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_university_student_duration_greater_than_1_hour_30_minutes -util_maintenance_female_duration_less_than_1_hour_30_minutes,MAINTENANCE - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_less_than_1_hour_30_minutes -util_maintenance_female_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Female: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_greater_than_1_hour_30_minutes -util_maintenance_low_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Low Income (<=$25,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_low_income_duration_greater_than_1_hour_30_minutes -util_maintenance_medium_income_duration_less_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_less_than_1_hour_30_minutes -util_maintenance_medium_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_greater_than_1_hour_30_minutes -util_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes -util_maintenance_distance_duration_less_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_less_than_1_hour_30_minutes -util_maintenance_distance_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_greater_than_1_hour_30_minutes -util_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes,Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), np.minimum(df.duration-2,26) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes -util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes -util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes -util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), (np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear -util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + (np.where((df.start>17), np.minimum(df.start-17,24), 0)))** 0.5), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root -util_maintenance_departure_constant_before_8_am,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.tour_type == 'othmaint') & (df.start<11)),coef_maintenance_departure_constant_before_8_am -util_maintenance_departure_constant_8_am_to_8_30_am,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'othmaint') & (df.start==11)),coef_maintenance_departure_constant_8_am_to_8_30_am -util_maintenance_departure_constant_8_30_am_to_9_00_am,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'othmaint') & (df.start==12)),coef_maintenance_departure_constant_8_30_am_to_9_00_am -util_maintenance_departure_constant_9_am_to_9_30_am,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'othmaint') & (df.start==13)),coef_maintenance_departure_constant_9_am_to_9_30_am -util_maintenance_departure_constant_9_30_am_to_10_am,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'othmaint') & (df.start==14)),coef_maintenance_departure_constant_9_30_am_to_10_am -util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'othmaint') & (df.start==15)),coef_maintenance_departure_constant_10_am_to_10_30_am -util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am -util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_type == 'othmaint') & (df.start>16)),coef_maintenance_departure_constant_after_11_am -util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear -util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared -util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear -util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_type == 'othmaint') & (df.end<16)),coef_maintenance_arrival_constant_before_10_30_am -util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am -util_maintenance_arrival_constant_11_am_to_11_30_am,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.tour_type == 'othmaint') & (df.end==17)),coef_maintenance_arrival_constant_11_am_to_11_30_am -util_maintenance_arrival_constant_11_30_am_to_1_30_pm,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.tour_type == 'othmaint') & (df.end>=18) & (df.end<=21)),coef_maintenance_arrival_constant_11_30_am_to_1_30_pm -util_maintenance_arrival_constant_1_30_pm_to_2_30_pm,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.tour_type == 'othmaint') & (df.end>=22) & (df.end<=23)),coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm -util_maintenance_arrival_constant_2_30_pm_to_4_pm,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.tour_type == 'othmaint') & (df.end>=24) & (df.end<=26)),coef_maintenance_arrival_constant_2_30_pm_to_4_pm -util_maintenance_arrival_constant_4_pm_to_4_30_pm,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'othmaint') & (df.end==27)),coef_maintenance_arrival_constant_4_pm_to_4_30_pm -util_maintenance_arrival_constant_after_4_30_pm,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.tour_type == 'othmaint') & (df.end>27)),coef_maintenance_arrival_constant_after_4_30_pm -util_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end>28)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear -util_maintenance_duration_constant_0_hr,MAINTENANCE - Duration Constant: 0 hr,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_duration_constant_0_hr -util_maintenance_duration_constant_30_minutes,MAINTENANCE - Duration Constant: 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_duration_constant_30_minutes -util_maintenance_duration_constant_longer_than_30_minutes,MAINTENANCE - Duration Constant: Longer than 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration>1)),coef_maintenance_duration_constant_longer_than_30_minutes -util_maintenance_duration_constant_duration_greater_than_1_hr_linear,MAINTENANCE - Duration Constant: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_linear -util_maintenance_duration_constant_duration_greater_than_1_hr_square_root,MAINTENANCE - Duration Constant: Duration > 1 hr - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0))** 0.5), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root -util_maintenance_calibration_constant_duration_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_calibration_constant_duration_1 -util_maintenance_calibration_constant_duration_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_calibration_constant_duration_2 -util_maintenance_calibration_constant_duration_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.tour_type == 'othmaint') & (df.duration==2)),coef_maintenance_calibration_constant_duration_3 -util_maintenance_calibration_constant_duration_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.tour_type == 'othmaint') & (df.duration==3)),coef_maintenance_calibration_constant_duration_4 -util_maintenance_calibration_constant_duration_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.tour_type == 'othmaint') & (df.duration==4)),coef_maintenance_calibration_constant_duration_5 -#EAT-OUT,#EAT-OUT,,#EAT-OUT -util_eatout_distance_to_destination_duration_less_than_1_hr,EAT-OUT - Distance to destination - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_less_than_1_hr -util_eatout_distance_to_destination_duration_greater_than_1_hr,EAT-OUT - Distance to destination - Duration > 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) *(df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_greater_than_1_hr -util_eatout_low_income_duration_less_than_1_hr,EAT-OUT - Low income (<25000) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_less25K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_low_income_duration_less_than_1_hr -util_eatout_medium_income_duration_less_than_1_hr,EAT-OUT - Medium (25k to 60k) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_medium_income_duration_less_than_1_hr -util_eatout_zeroauto_HH_duration_greater_than_1_hr,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.tour_type == 'eatout') & (df.auto_ownership == 0) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_zeroauto_HH_duration_greater_than_1_hr -util_eatout_university_student_departure_after_7_pm_linear,EAT-OUT - University student - Departure after 7:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start>32) & (df.ptype == 3)), (np.where((df.start<=32), np.minimum(32-df.start,29), 0) + np.where((df.start>32), np.minimum(df.start-32,8), 0)), 0)",coef_eatout_university_student_departure_after_7_pm_linear -util_eatout_female_duration_less_than_1_hr,EAT-OUT - Female - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_less_than_1_hr -util_eatout_female_duration_greater_than_1_hr,EAT-OUT - Female - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_greater_than_1_hr -util_eatout_time_pressure_departure_before_6_30_pm,EAT-OUT - Time Pressure - Departure before 6:30 pm,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<32)), (np.minimum(32-df.start,29)) * (np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_departure_before_6_30_pm -util_eatout_time_pressure_duration_less_than_1_hr,EAT-OUT - Time Pressure - Duration < 1 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration<2)), np.minimum(2-df.duration,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_duration_less_than_1_hr -util_eatout_departure_constant_7_30_am_to_9_am,EAT-OUT - Departure Constant: 07:30 AM - 09:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>=10) & (df.start<=12)),coef_eatout_departure_constant_7_30_am_to_9_am -util_eatout_departure_constant_10_30_am_to_11_am,EAT-OUT - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==16)),coef_eatout_departure_constant_10_30_am_to_11_am -util_eatout_departure_constant_11_am_to_11_30_am,EAT-OUT - Departure Constant: 11:00 AM - 11:30 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==17)),coef_eatout_departure_constant_11_am_to_11_30_am -util_eatout_departure_constant_11_30_am_to_12_pm,EAT-OUT - Departure Constant: 11:30 AM - 12:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==18)),coef_eatout_departure_constant_11_30_am_to_12_pm -util_eatout_departure_constant_12_pm_to_12_30_pm,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start==19)),coef_eatout_departure_constant_12_pm_to_12_30_pm -util_eatout_departure_constant_12_30_pm_to_1_pm,EAT-OUT - Departure Constant: 12:30 PM - 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==20)),coef_eatout_departure_constant_12_30_pm_to_1_pm -util_eatout_departure_constant_1_pm_to_1_30_pm,EAT-OUT - Departure Constant: 01:00 PM - 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==21)),coef_eatout_departure_constant_1_pm_to_1_30_pm -util_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear -util_eatout_departure_constant_before_6_pm,EAT-OUT - Departure Constant: Before 06:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)),coef_eatout_departure_constant_before_6_pm -util_eatout_departure_constant_6_pm_to_6_30_pm,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==31)),coef_eatout_departure_constant_6_pm_to_6_30_pm -util_eatout_departure_constant_6_30_pm_to_7_pm,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==32)),coef_eatout_departure_constant_6_30_pm_to_7_pm -util_eatout_departure_constant_7_pm_to_7_30_pm,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==33)),coef_eatout_departure_constant_7_pm_to_7_30_pm -util_eatout_departure_constant_after_7_30_pm,EAT-OUT - Departure Constant: After 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>33)),coef_eatout_departure_constant_after_7_30_pm -util_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>34)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear -util_eatout_arrival_constant_9_30_am_to_11_am,EAT-OUT - Arrival Constant: 9:30 AM to 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>=14) & (df.end<=16)),coef_eatout_arrival_constant_9_30_am_to_11_am -util_eatout_arrival_constant_12_30_pm_to_1_pm,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==20)),coef_eatout_arrival_constant_12_30_pm_to_1_pm -util_eatout_arrival_constant_1_pm_to_1_30_pm,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==21)),coef_eatout_arrival_constant_1_pm_to_1_30_pm -util_eatout_arrival_constant_1_30_pm_to_2_pm,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==22)),coef_eatout_arrival_constant_1_30_pm_to_2_pm -util_eatout_arrival_constant_2_pm_to_2_30_pm,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==23)),coef_eatout_arrival_constant_2_pm_to_2_30_pm -util_eatout_arrival_constant_2_30_pm_to_3_pm,EAT-OUT - Arrival Constant: 02:30 PM to 03:00 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==24)),coef_eatout_arrival_constant_2_30_pm_to_3_pm -util_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<31)),coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear -util_eatout_arrival_constant_before_6_30_pm,EAT-OUT - Arrival Constant: Before 6:30 PM,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<32)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_before_6_30_pm -util_eatout_arrival_constant_6_30_pm_to_7_pm,EAT-OUT - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==32)),coef_eatout_arrival_constant_6_30_pm_to_7_pm -util_eatout_arrival_constant_7_pm_to_7_30_pm,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==33)),coef_eatout_arrival_constant_7_pm_to_7_30_pm -util_eatout_arrival_constant_7_30_pm_to_8_pm,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==34)),coef_eatout_arrival_constant_7_30_pm_to_8_pm -util_eatout_arrival_constant_8_pm_to_8_30_pm,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_category != 'joint') &(df.tour_type == 'eatout') & (df.end==35)),coef_eatout_arrival_constant_8_pm_to_8_30_pm -util_eatout_arrival_constant_8_30_pm_to_9_pm,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==36)),coef_eatout_arrival_constant_8_30_pm_to_9_pm -util_eatout_arrival_constant_after_9_pm,EAT-OUT - Arrival Constant: After 9:00 PM,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.end>36)),coef_eatout_arrival_constant_after_9_pm -util_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear -util_eatout_duration_constant_0_hour,EAT-OUT - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration==0)),coef_eatout_duration_constant_0_hour -util_eatout_duration_constant_30_minutes,EAT-OUT - Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_duration_constant_30_minutes -util_eatout_duration_constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==2)),coef_eatout_duration_constant_1_hour -util_eatout_duration_constant_1_hour_30_minutes,EAT-OUT - Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_duration_constant_1_hour_30_minutes -util_eatout_duration_constant_2_hours,EAT-OUT - Duration Constant: 2 hours,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.duration==4)),coef_eatout_duration_constant_2_hours -util_eatout_duration_constant_2_hour_30_minutes_or_more,EAT-OUT - Duration Constant: 2.5 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>4)),coef_eatout_duration_constant_2_hour_30_minutes_or_more -util_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,11), 0)), 0)",coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear -util_eatout_calibration_constant_duration_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_calibration_constant_duration_1 -util_eatout_calibration_constant_duration_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_calibration_constant_duration_2 -util_eatout_calibration_constant_duration_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_calibration_constant_duration_3 -util_eatout_calibration_constant_duration_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_calibration_constant_duration_4 -util_eatout_calibration_constant_departure_1,EAT-OUT - Calibration Constant - Departure = 1,@((df.tour_type == 'eatout') & (df.start == 1)),coef_eatout_calibration_constant_departure_1 -util_eatout_calibration_constant_departure_2,EAT-OUT - Calibration Constant - Departure = 2,@((df.tour_type == 'eatout') & (df.start == 2)),coef_eatout_calibration_constant_departure_2 -util_eatout_calibration_constant_departure_3,EAT-OUT - Calibration Constant - Departure = 3,@((df.tour_type== 'eatout') & (df.start == 3)),coef_eatout_calibration_constant_departure_3 -util_eatout_calibration_constant_departure_17,EAT-OUT - Calibration Constant - Departure = 17,@((df.tour_type == 'eatout') & (df.start ==17)),coef_eatout_calibration_constant_departure_17 -util_eatout_calibration_constant_departure_18,EAT-OUT - Calibration Constant - Departure = 18,@((df.tour_type== 'eatout') & (df.start ==18)),coef_eatout_calibration_constant_departure_18 -util_eatout_calibration_constant_departure_19,EAT-OUT - Calibration Constant - Departure = 19,@((df.tour_type == 'eatout') & (df.start ==19)),coef_eatout_calibration_constant_departure_19 -util_eatout_calibration_constant_departure_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.tour_type == 'eatout') & (df.start ==20)),coef_eatout_calibration_constant_departure_20 -util_eatout_calibration_constant_departure_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.tour_type == 'eatout') & (df.start ==21)),coef_eatout_calibration_constant_departure_21 -#SOCIAL,#SOCIAL,,#SOCIAL -util_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear -util_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear -util_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear -util_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear -util_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear -util_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear -util_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear -util_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear -"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, -util_social_auto_distance_duration_less_than_1_hr_linear,SOCIAL - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0) * (df.origin_to_destination_distance)",coef_social_auto_distance_duration_less_than_1_hr_linear -util_social_auto_distance_duration_greater_than_1_hr_linear,SOCIAL - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0) * (df.origin_to_destination_distance) ",coef_social_auto_distance_duration_greater_than_1_hr_linear -util_social_time_pressure_duration_less_than_1_hr,SOCIAL - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), np.minimum(3-df.duration,47), 0)* (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_less_than_1_hr -util_social_time_pressure_duration_greater_than_1_hr,SOCIAL - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3)), np.minimum(df.duration-3,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_greater_than_1_hr -util_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,SOCIAL - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.num_add_soc_discr_tours), 0)",coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr -util_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@(df.tour_type == 'social') & (df.start<12) * ((np.minimum(12-df.start,48)*(df.start<48)) + (np.minimum(df.start-48,48)*(df.start>48)))",coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear -util_social_departure_constant_before_9_am,SOCIAL - Departure Constant: Before 09:00 AM,@(df.tour_type == 'social') & (df.start<13),coef_social_departure_constant_before_9_am -util_social_departure_constant_9_am_to_9_30_am,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.tour_type == 'social') & (df.start==13),coef_social_departure_constant_9_am_to_9_30_am -util_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.start<29), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear -util_social_departure_constant_before_5_30_pm,SOCIAL - Departure Constant: Before 05:30 PM,@((df.tour_type == 'social') & (df.start<30)),coef_social_departure_constant_before_5_30_pm -util_social_departure_constant_5_30_pm_to_6_pm,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'social') & (df.start==30)),coef_social_departure_constant_5_30_pm_to_6_pm -util_social_departure_constant_6_pm_to_6_30_pm,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'social') & (df.start==31)),coef_social_departure_constant_6_pm_to_6_30_pm -util_social_departure_constant_6_30_pm_to_7_pm,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'social') & (df.start==32)),coef_social_departure_constant_6_30_pm_to_7_pm -util_social_departure_constant_7_pm_to_7_30_pm,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_type == 'social') & (df.start==33)),coef_social_departure_constant_7_pm_to_7_30_pm -util_social_departure_constant_after_7_30_pm,SOCIAL - Departure Constant: After 07:30 PM,@((df.tour_type == 'social') & (df.start>33)),coef_social_departure_constant_after_7_30_pm -util_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.start>34), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear -util_social_arrival_constant_3_pm_to_3_30_pm,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.tour_type == 'social') & (df.end==25)),coef_social_arrival_constant_3_pm_to_3_30_pm -util_social_arrival_constant_3_30_pm_to_4_pm,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.tour_type == 'social') & (df.end==26)),coef_social_arrival_constant_3_30_pm_to_4_pm -util_social_arrival_constant_4_pm_to_4_30_pm,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.tour_type == 'social') & (df.end==27)),coef_social_arrival_constant_4_pm_to_4_30_pm -util_social_arrival_constant_5_pm_to_6_pm,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.tour_type == 'social') & (df.end>=29) & (df.end<=30)),coef_social_arrival_constant_5_pm_to_6_pm -util_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end<35)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) + np.where((df.end>40), np.minimum(df.end-40,48), 0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear -util_social_arrival_constant_before_8_30_pm,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.tour_type == 'social') & (df.end<36)),coef_social_arrival_constant_before_8_30_pm -util_social_arrival_constant_8_30_pm_to_9_pm,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'social') & (df.end==36)),coef_social_arrival_constant_8_30_pm_to_9_pm -util_social_arrival_constant_9_pm_to_9_30_pm,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.tour_type == 'social') & (df.end==37)),coef_social_arrival_constant_9_pm_to_9_30_pm -util_social_arrival_constant_9_30_pm_to_10_pm,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.tour_type == 'social') & (df.end==38)),coef_social_arrival_constant_9_30_pm_to_10_pm -util_social_arrival_constant_10_pm_to_10_30_pm,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.tour_type == 'social') & (df.end==39)),coef_social_arrival_constant_10_pm_to_10_30_pm -util_social_arrival_constant_after_10_30_pm,SOCIAL - Arrival Constant: After 10:30 PM,@((df.tour_type == 'social') & (df.end>39)),coef_social_arrival_constant_after_10_30_pm -util_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end>40)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) +np.where((df.end>40),np.minimum(df.end-40,48),0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear -util_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 2.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<5)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>7), np.minimum(df.duration-7,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear -util_social_duration_constant_less_than_3_hours,SOCIAL - Duration Constant: Less than 3 hrs,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<6)),coef_social_duration_constant_less_than_3_hours -util_social_duration_constant_3_hours,SOCIAL - Duration Constant: 3 hours,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration==6)),coef_social_duration_constant_3_hours -util_social_duration_constant_3_hrs_30_minutes,SOCIAL - Duration Constant: 3.5 hours,@((df.tour_category != 'joint')& (df.tour_type == 'social') & (df.duration==7)),coef_social_duration_constant_3_hrs_30_minutes -util_social_duration_constant_4_hours_or_more,SOCIAL - Duration Constant: 4 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>7)),coef_social_duration_constant_4_hours_or_more -util_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 4.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>8)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>8), np.minimum(df.duration-8,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear -util_social_calibration_constant_duration_1,SOCIAL - Calibration Constant - Duration = 1,@((df.tour_type == 'social') & (df.duration ==0)),coef_social_calibration_constant_duration_1 -util_social_calibration_constant_duration_2,SOCIAL - Calibration Constant - Duration = 2,@((df.tour_type == 'social') & (df.duration == 1)),coef_social_calibration_constant_duration_2 -util_social_calibration_constant_duration_3,SOCIAL - Calibration Constant - Duration = 3,@((df.tour_type == 'social') & (df.duration ==2)),coef_social_calibration_constant_duration_3 -util_social_calibration_constant_duration_4,SOCIAL - Calibration Constant - Duration = 4,@((df.tour_type == 'social') & (df.duration ==3)),coef_social_calibration_constant_duration_4 -util_social_calibration_constant_duration_5,SOCIAL - Calibration Constant - Duration = 5,@((df.tour_type == 'social') & (df.duration ==4)),coef_social_calibration_constant_duration_5 -util_social_calibration_constant_duration_6,SOCIAL - Calibration Constant - Duration = 6,@((df.tour_type == 'social') & (df.duration ==5)),coef_social_calibration_constant_duration_6 -util_social_calibration_constant_duration_7,SOCIAL - Calibration Constant - Duration = 7,@((df.tour_type == 'social') & (df.duration ==6)),coef_social_calibration_constant_duration_7 -util_social_calibration_constant_duration_8,SOCIAL - Calibration Constant - Duration = 8,@((df.tour_type == 'social') & (df.duration ==7)),coef_social_calibration_constant_duration_8 -util_social_calibration_constant_duration_9,SOCIAL - Calibration Constant - Duration = 9,@((df.tour_type == 'social') & (df.duration ==8)),coef_social_calibration_constant_duration_9 -#DISCRETIONARY,#DISCRETIONARY,,#DISCRETIONARY -util_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear -util_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear -util_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear -util_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear -util_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear -util_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear -util_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)),0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear -util_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear -"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, -util_discretionary_auto_distance_duration_less_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_less_than_1_hr_linear -util_discretionary_auto_distance_duration_greater_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_greater_than_1_hr_linear -util_discretionary_time_pressure_duration_less_than_1_hr,DISCRETIONARY - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), np.minimum(3-df.duration,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_discretionary_time_pressure_duration_less_than_1_hr -util_discretionary_time_pressure_duration_greater_than_1_hr,DISCRETIONARY - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), np.minimum(df.duration-3,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_greater_than_1_hr -util_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,DISCRETIONARY - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0))*(df.num_add_soc_discr_tours),0)",coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr -util_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<9)), (np.where((df.start<9), np.minimum(9-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48),0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear -util_discretionary_departure_constant_before_7_30_am,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.tour_type == 'othdiscr') & (df.start<10)),coef_discretionary_departure_constant_before_7_30_am -util_discretionary_departure_constant_7_30_am_to_8_am,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.tour_type == 'othdiscr') & (df.start==10)),coef_discretionary_departure_constant_7_30_am_to_8_am -util_discretionary_departure_constant_8_am_to_8_30_am,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.tour_type == 'othdiscr') & (df.start==11)),coef_discretionary_departure_constant_8_am_to_8_30_am -util_discretionary_departure_constant_8_30_am_to_9_am,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.tour_type == 'othdiscr') & (df.start==12)),coef_discretionary_departure_constant_8_30_am_to_9_am -util_discretionary_departure_constant_9_am_to_9_30_am,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.tour_type == 'othdiscr') & (df.start==13)),coef_discretionary_departure_constant_9_am_to_9_30_am -util_discretionary_departure_constant_9_30_am_to_10_am,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.tour_type == 'othdiscr') & (df.start==14)),coef_discretionary_departure_constant_9_30_am_to_10_am -util_discretionary_departure_constant_10_am_to_10_30_am,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.tour_type == 'othdiscr') & (df.start==15)),coef_discretionary_departure_constant_10_am_to_10_30_am -util_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<28)), (np.where((df.start<28), np.minimum(28-df.start,8),0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear -util_discretionary_departure_constant_before_5_pm,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.tour_type == 'othdiscr') & (df.start<29)),coef_discretionary_departure_constant_before_5_pm -util_discretionary_departure_constant_5_pm_to_5_30_pm,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_departure_constant_5_pm_to_5_30_pm -util_discretionary_departure_constant_5_30_pm_to_6_pm,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_departure_constant_5_30_pm_to_6_pm -util_discretionary_departure_constant_6_pm_to_6_30_pm,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_departure_constant_6_pm_to_6_30_pm -util_discretionary_departure_constant_6_30_pm_to_7_pm,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_departure_constant_6_30_pm_to_7_pm -util_discretionary_departure_constant_after_7_pm,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start>32)),coef_discretionary_departure_constant_after_7_pm -util_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start>33)), (np.where((df.start<28), np.minimum(28-df.start,8), 0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear -util_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end<31)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear -util_discretionary_arrival_constant_before_6_30_pm,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.tour_type == 'othdiscr') & (df.end<32)),coef_discretionary_arrival_constant_before_6_30_pm -util_discretionary_arrival_constant_6_30_pm_to_7_pm,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_type == 'othdiscr') & (df.end==32)),coef_discretionary_arrival_constant_6_30_pm_to_7_pm -util_discretionary_arrival_constant_7_pm_to_7_30_pm,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_type == 'othdiscr') & (df.end==33)),coef_discretionary_arrival_constant_7_pm_to_7_30_pm -util_discretionary_arrival_constant_7_30_pm_to_8_pm,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_type == 'othdiscr') & (df.end==34)),coef_discretionary_arrival_constant_7_30_pm_to_8_pm -util_discretionary_arrival_constant_8_pm_to_8_30_pm,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_type == 'othdiscr') & (df.end==35)),coef_discretionary_arrival_constant_8_pm_to_8_30_pm -util_discretionary_arrival_constant_8_30_pm_to_9_pm,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end==36)),coef_discretionary_arrival_constant_8_30_pm_to_9_pm -util_discretionary_arrival_constant_after_9_pm,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end>36)),coef_discretionary_arrival_constant_after_9_pm -util_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48),0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear -util_discretionary_duration_constant_0_hour,DISCRETIONARY - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==0)),coef_discretionary_duration_constant_0_hour -util_discretionary_duration_constant_30_minutes,DISCRETIONARY -Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==1)),coef_discretionary_duration_constant_30_minutes -util_discretionary_duration_constant_1_hour,DISCRETIONARY -Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==2)),coef_discretionary_duration_constant_1_hour -util_discretionary_duration_constant_1_hr_30_minutes,DISCRETIONARY -Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_duration_constant_1_hr_30_minutes -util_discretionary_duration_constant_2_hours,DISCRETIONARY -Duration Constant: 2 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_duration_constant_2_hours -util_discretionary_duration_constant_2_hr_30_minutes,DISCRETIONARY -Duration Constant: 2.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==5)),coef_discretionary_duration_constant_2_hr_30_minutes -util_discretionary_duration_constant_3_hours_or_more,DISCRETIONARY -Duration Constant: 3 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>5)),coef_discretionary_duration_constant_3_hours_or_more -util_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,DISCRETIONARY -Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>6)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>6), np.minimum(df.duration-6,47), 0)), 0)",coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear -util_discretionary_calibration_constant_duration_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_calibration_constant_duration_4 -util_discretionary_calibration_constant_duration_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_calibration_constant_duration_5 -util_discretionary_calibration_constant_departure_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_calibration_constant_departure_29 -util_discretionary_calibration_constant_departure_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_calibration_constant_departure_30 -util_discretionary_calibration_constant_departure_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_calibration_constant_departure_31 -util_discretionary_calibration_constant_departure_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_calibration_constant_departure_32 +Label,Description,Expression,Coefficient +# ESCORT,,, +util_escort_mode_choice_logsum,ESCORT - Mode Choice Logsum,"@np.where(df.tour_type == 'escort', df.mode_choice_logsum, 0) ",coef_escort_mode_choice_logsum +"#Note: In CTRAMP expressions, duration alternative is from 1 to 48 but in ActivitySim, it is from 0 to 47 since the duration alternative ID was calculated as (end - start). Therefore, duration in ActivitySim expression = CTRAMP duration expresssion - 1 ",,,# +util_escort_distance_to_destination_duration_less_than_30_minutes,ESCORT - Distance to destination - Duration less than 0.5 hour (depart and arrive in the same period),"@np.where(((df.tour_type == 'escort') & (df.duration<1)), ((df.origin_to_destination_distance) * df.durationShift_escort), 0)",coef_escort_distance_to_destination_duration_less_than_30_minutes +util_escort_distance_to_destination_duration_greater_than_30_minutes,ESCORT - Distance to destination - Duration greater than 0.5 hour,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), ((df.origin_to_destination_distance) * df.durationShift_escort), 0)",coef_escort_distance_to_destination_duration_greater_than_30_minutes +util_escort_fulltime_worker_departure_after_8_am_linear,ESCORT - Full-time worker - Departure after 8:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>10)), df.departureLinearShift1_escort,0)",coef_escort_fulltime_worker_departure_after_8_am_linear +"#Note: In CTRAMP expression file, the description below says departure is after 3 am but from the expression it seems that it would be 3 pm instead of 3 am",,, +util_escort_fulltime_worker_departure_after_3_am_linear,ESCORT - Full-time worker - Departure after 3:00 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.start>24)), df.departureLinearShift2_escort, 0)",coef_escort_fulltime_worker_departure_after_3_am_linear +util_escort_fulltime_worker_duration_less_than_30_minutes,ESCORT - Full-time worker - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration<1)), df.durationShift_escort, 0)",coef_escort_fulltime_worker_duration_less_than_30_minutes +util_escort_fulltime_worker_duration_greater_than_30_minutes,ESCORT - Full-time worker - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 1) & (df.duration>1)), df.durationShift_escort, 0)",coef_escort_fulltime_worker_duration_greater_than_30_minutes +util_escort_university_student_duration_less_than_30_minutes,ESCORT - University student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 3) & (df.duration<1)), df.durationShift_escort, 0)",coef_escort_university_student_duration_less_than_30_minutes +util_escort_non_driving_age_student_duration_greater_than_30_minutes,ESCORT - Non-driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & ((df.ptype == 7)|(df.ptype == 8)) & (df.duration>1)), df.durationShift_escort, 0)",coef_escort_non_driving_age_student_duration_greater_than_30_minutes +util_escort_driving_age_student_duration_less_than_30_minutes,ESCORT - Driving age student - Duration < 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration<1)), df.durationShift_escort, 0)",coef_escort_driving_age_student_duration_less_than_30_minutes +util_escort_driving_age_student_duration_greater_than_30_minutes,ESCORT - Driving age student - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 6) & (df.duration>1)), df.durationShift_escort, 0)",coef_escort_driving_age_student_duration_greater_than_30_minutes +"#Note: In CTRAMP expression file, description says the expression below is for duration > 0.5 hr but the expression says duration < 0.5 hr",,, +util_escort_pre_school_kid_duration_greater_than_30_minutes,ESCORT - Pre-school kid - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.ptype == 8) & (df.duration<1)), df.durationShift_escort, 0)",coef_escort_pre_school_kid_duration_greater_than_30_minutes +util_escort_med_high_income_duration_greater_than_30_minutes,ESCORT - Med-high income (60k to 120k) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.is_income_60K_to_120K) & (df.duration>1)), df.durationShift_escort, 0)",coef_escort_med_high_income_duration_greater_than_30_minutes +util_escort_households_with_no_kids_departure_before_7_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 7:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<10)), df.departureLinearShift1_escort, 0)",coef_escort_households_with_no_kids_departure_before_7_30_am +util_escort_households_with_no_kids_departure_after_8_00_am,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>10)), df.departureLinearShift1_escort, 0)",coef_escort_households_with_no_kids_departure_after_8_00_am +util_escort_households_with_no_kids_departure_before_2_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure before 2:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start<24)), df.departureLinearShift2_escort, 0)",coef_escort_households_with_no_kids_departure_before_2_30_pm +util_escort_households_with_no_kids_departure_after_3_00_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.start>24)), df.departureLinearShift2_escort, 0)",coef_escort_households_with_no_kids_departure_after_3_00_pm +util_escort_households_with_no_kids_arrival_before_8_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<11)), df.arrivalLinearShift1_escort, 0)",coef_escort_households_with_no_kids_arrival_before_8_am +util_escort_households_with_no_kids_arrival_after_8_30_am,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>11)), df.arrivalLinearShift1_escort, 0)",coef_escort_households_with_no_kids_arrival_after_8_30_am +util_escort_households_with_no_kids_arrival_before_3_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end<25)), df.arrivalLinearShift2_escort, 0)",coef_escort_households_with_no_kids_arrival_before_3_pm +util_escort_households_with_no_kids_arrival_after_3_30_pm,"ESCORT - Households with no kids (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.num_children == 0) & (df.end>25)), df.arrivalLinearShift2_escort, 0)",coef_escort_households_with_no_kids_arrival_after_3_30_pm +util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>10)), df.departureLinearShift1_escort, 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_8_am +util_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.start>24)), df.departureLinearShift2_escort, 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_departure_after_3_pm +util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am,"ESCORT -Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<11)), df.arrivalLinearShift1_escort, 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_8_am +util_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm,"ESCORT - Pre-School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival before 3:00 PM","@np.where(((df.tour_type == 'escort') & (df.has_pre_school_child_with_mandatory > 0) & (df.end<25)), df.arrivalLinearShift2_escort, 0)",coef_escort_pre_school_child_in_hh_with_mandatory_tour_arrival_before_3_pm +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Departure after 8:00 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.start>10)), df.departureLinearShift1_escort, 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_departure_after_8_am +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 8:30 AM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>11)), df.arrivalLinearShift1_escort, 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_8_30_am +util_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm,"ESCORT - Driving age School Child in HH with Mandatory tour (Dummy- 1,0) - Arrival after 3:30 PM","@np.where(((df.tour_type == 'escort') & (df.has_driving_age_child_with_mandatory > 0) & (df.end>25)), df.arrivalLinearShift2_escort, 0)",coef_escort_driving_age_school_child_in_hh_with_mandatory_tour_arrival_after_3_30_pm +util_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes,ESCORT - Number of autos greater than number of adults - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>1)), df.durationShift_escort, 0)",coef_escort_number_of_autos_greater_than_number_of_adults_duration_greater_than_30_minutes +util_escort_number_of_individual_tours_duration_greater_than_30_minutes,ESCORT -Number of Individual Tours (excluding escorting) - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.num_non_escort_tours > 0) & (df.duration>1)), df.durationShift_escort*df.num_non_escort_tours, 0)",coef_escort_number_of_individual_tours_duration_greater_than_30_minutes +util_escort_number_of_joint_tours_duration_greater_than_30_minutes,ESCORT - Number of joint tours - Duration > 0.5 hr,"@np.where(((df.tour_type == 'escort') & (df.duration>1)), df.durationShift_escort *(df.num_joint_tours), 0)",coef_escort_number_of_joint_tours_duration_greater_than_30_minutes +util_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes before 06:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start<8)), df.depConstShift1_escort, 0)",coef_escort_departure_constant_shift_for_every_30_minutes_before_6_30_am_linear +util_escort_departure_constant_before_7_am,ESCORT - Departure Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.start<9)),coef_escort_departure_constant_before_7_am +util_escort_departure_constant_7_am_to_7_30_am,ESCORT - Departure Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_departure_constant_7_am_to_7_30_am +util_escort_departure_constant_7_30_am_to_8_am,ESCORT - Departure Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_departure_constant_7_30_am_to_8_am +util_escort_departure_constant_8_am_to_8_30_am,ESCORT - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.start==11)),coef_escort_departure_constant_8_am_to_8_30_am +util_escort_departure_constant_8_30_am_to_9_am,ESCORT - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.start==12)),coef_escort_departure_constant_8_30_am_to_9_am +util_escort_departure_constant_after_9_am,ESCORT - Departure Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.start>12)),coef_escort_departure_constant_after_9_am +util_escort_departure_constant_1_30_pm_to_2_pm,ESCORT - Departure Constant: 01:30 PM - 02:00 PM,@((df.tour_type == 'escort') & (df.start==22)),coef_escort_departure_constant_1_30_pm_to_2_pm +util_escort_departure_constant_2_pm_to_2_30_pm,ESCORT - Departure Constant: 02:00 PM - 02:30 PM,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_departure_constant_2_pm_to_2_30_pm +util_escort_departure_constant_2_30_pm_to_3_pm,ESCORT - Departure Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_departure_constant_2_30_pm_to_3_pm +util_escort_departure_constant_3_pm_to_3_30_pm,ESCORT - Departure Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.start==25)),coef_escort_departure_constant_3_pm_to_3_30_pm +util_escort_departure_constant_after_3_30_pm,ESCORT - Departure Constant: After 03:30 PM,@((df.tour_type == 'escort') & (df.start>25)),coef_escort_departure_constant_after_3_30_pm +util_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>13)), df.depConstShift1_escort, 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_9_30_am_linear +util_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear,ESCORT - Departure Constant: Shift for every 30 minutes after 4:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.start>26)), df.depConstShift2_escort, 0)",coef_escort_departure_constant_shift_for_every_30_minutes_after_4_pm_linear +util_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes before 6:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end<8)), df.arrConstShift1_escort, 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_before_6_30_am_linear +util_escort_arrival_constant_before_7_am,ESCORT - Arrival Constant: Before 07:00 AM,@((df.tour_type == 'escort') & (df.end<9)),coef_escort_arrival_constant_before_7_am +util_escort_arrival_constant_7_am_to_7_30_am,ESCORT - Arrival Constant: 07:00 AM - 07:30 AM,@((df.tour_type == 'escort') & (df.end==9)),coef_escort_arrival_constant_7_am_to_7_30_am +util_escort_arrival_constant_7_30_am_to_8_am,ESCORT - Arrival Constant: 07:30 AM - 08:00 AM,@((df.tour_type == 'escort') & (df.end==10)),coef_escort_arrival_constant_7_30_am_to_8_am +util_escort_arrival_constant_8_am_to_8_30_am,ESCORT - Arrival Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'escort') & (df.end==11)),coef_escort_arrival_constant_8_am_to_8_30_am +util_escort_arrival_constant_8_30_am_to_9_am,ESCORT - Arrival Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'escort') & (df.end==12)),coef_escort_arrival_constant_8_30_am_to_9_am +util_escort_arrival_constant_after_9_am,ESCORT - Arrival Constant: After 09:00 AM,@((df.tour_type == 'escort') & (df.end>12)),coef_escort_arrival_constant_after_9_am +util_escort_arrival_constant_2_30_pm_to_3_pm,ESCORT - Arrival Constant: 02:30 PM - 03:00 PM,@((df.tour_type == 'escort') & (df.end==24)),coef_escort_arrival_constant_2_30_pm_to_3_pm +util_escort_arrival_constant_3_pm_to_3_30_pm,ESCORT - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'escort') & (df.end==25)),coef_escort_arrival_constant_3_pm_to_3_30_pm +util_escort_arrival_constant_3_30_pm_to_4_pm,ESCORT - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'escort') & (df.end==26)),coef_escort_arrival_constant_3_30_pm_to_4_pm +util_escort_arrival_constant_4_pm_to_4_30_pm,ESCORT - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'escort') & (df.end==27)),coef_escort_arrival_constant_4_pm_to_4_30_pm +util_escort_arrival_constant_after_4_30_pm,ESCORT - Arrival Constant: After 04:30 PM,@((df.tour_type == 'escort') & (df.end>27)),coef_escort_arrival_constant_after_4_30_pm +util_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 9:30 am - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>13)), df.arrConstShift1_escort, 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_9_30_am_linear +util_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,ESCORT - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'escort') & (df.end>28)), df.arrConstShift2_escort, 0)",coef_escort_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear +util_escort_duration_constant_0_hour,ESCORT - Duration Constant: 0 hr,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_duration_constant_0_hour +util_escort_duration_constant_30_minutes,ESCORT - Duration Constant: 0.5 hr,@((df.tour_type == 'escort') & (df.duration==1)),coef_escort_duration_constant_30_minutes +util_escort_duration_constant_1_hour,ESCORT - Duration Constant: 1 hr,@((df.tour_type == 'escort') & (df.duration==2)),coef_escort_duration_constant_1_hour +util_escort_duration_constant_1_hour_30_minutes,ESCORT - Duration Constant: 1.5hrs,@((df.tour_type == 'escort') & (df.duration==3)),coef_escort_duration_constant_1_hour_30_minutes +util_escort_duration_constant_2_hours,ESCORT - Duration Constant: 2 hrs,@((df.tour_type == 'escort') & (df.duration==4)),coef_escort_duration_constant_2_hours +util_escort_duration_constant_longer_than_2_hours,ESCORT - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'escort') & (df.duration>4)),coef_escort_duration_constant_longer_than_2_hours +util_escort_calibration_constant_duration_1,ESCORT - Calibration Constant - Duration = 1,@((df.tour_type == 'escort') & (df.duration==0)),coef_escort_calibration_constant_duration_1 +util_escort_calibration_constant_duration_2,ESCORT - Calibration Constant - Duration = 2,@(((df.tour_type == 'escort') & df.duration==1)),coef_escort_calibration_constant_duration_2 +util_escort_calibration_constant_departure_9,ESCORT - Calibration Constant - Departure = 9,@((df.tour_type == 'escort') & (df.start==9)),coef_escort_calibration_constant_departure_9 +util_escort_calibration_constant_departure_10,ESCORT - Calibration Constant - Departure = 10,@((df.tour_type == 'escort') & (df.start==10)),coef_escort_calibration_constant_departure_10 +util_escort_calibration_constant_departure_23,ESCORT - Calibration Constant - Departure = 23,@((df.tour_type == 'escort') & (df.start==23)),coef_escort_calibration_constant_departure_23 +util_escort_calibration_constant_departure_24,,@((df.tour_type == 'escort') & (df.start==24)),coef_escort_calibration_constant_departure_24 +#SHOPPING,ESCORT - Calibration Constant - Departure = 24,,#SHOPPING +util_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_driving_age_student_duration_greater_than_1_hour_30_minutes +util_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Full-time worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_full_time_worker_duration_greater_than_1_hour_30_minutes +util_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes,SHOPPING - Non-driving Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_non_driving_student_duration_greater_than_1_hour_30_minutes +util_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes,SHOPPING - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_pre_school_child_duration_less_than_1_hour_30_minutes +util_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shoppping_part_time_worker_duration_less_than_1_hour_30_minutes +util_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes,SHOPPING - Part Time Worker: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_part_time_worker_duration_greater_than_1_hour_30_minutes +util_shopping_retired_duration_less_than_1_hour_30_minutes,SHOPPING - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_less_than_1_hour_30_minutes +util_shopping_retired_duration_greater_than_1_hour_30_minutes,SHOPPING - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_retired_duration_greater_than_1_hour_30_minutes +util_shopping_university_student_duration_greater_than_1_hour_30_minutes,SHOPPING - University Student: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_university_student_duration_greater_than_1_hour_30_minutes +util_shopping_female_duration_less_than_1_hour_30_minutes,SHOPPING - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_less_than_1_hour_30_minutes +util_shopping_female_duration_greater_than_1_hour_30_minutes,SHOPPING - Female: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_female_duration_greater_than_1_hour_30_minutes +util_shopping_low_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Low Income (<=$25,000): Duration > 1.5 hr","@np.where(((df.tour_type == 'shopping') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_low_income_duration_greater_than_1_hour_30_minutes +util_shopping_medium_income_duration_less_than_1_hour_30_minutes,"SHOPPING - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_income_duration_less_than_1_hour_30_minutes +util_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes,"SHOPPING - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'shopping') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_shopping_medium_high_income_duration_greater_than_1_hour_30_minutes +util_shopping_distance_duration_less_than_1_hour_30_minutes,SHOPPING - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_less_than_1_hour_30_minutes +util_shopping_distance_duration_greater_than_1_hour_30_minutes,SHOPPING - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_shopping_distance_duration_greater_than_1_hour_30_minutes +util_shopping_time_pressure_duration_greater_than_1_hour_30_minutes,SHOPPING - Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'shopping') & (df.duration>2)), np.minimum(df.duration-2,26) *(np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_shopping_time_pressure_duration_greater_than_1_hour_30_minutes +util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes +util_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,SHOPPING - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'shopping') &(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.num_add_shop_maint_tours), 0)",coef_shopping_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes +util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear +util_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root,SHOPPING - Departure Constant: Shift for every 30 minutes before 08:30 am - Square root,"@np.where(((df.tour_type == 'shopping') & (df.start<12)), (np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0))**0.5, 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_before_8_30_am_square_root +util_shopping_departure_constant_before_9_am,SHOPPING - Departure Constant: Before 09:00 AM,@((df.tour_type == 'shopping') & (df.start<13)),coef_shopping_departure_constant_before_9_am +util_shopping_departure_constant_9_am_to_9_30_am,SHOPPING - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'shopping') & (df.start==13)),coef_shopping_departure_constant_9_am_to_9_30_am +util_shopping_departure_constant_9_30_am_to_10_am,SHOPPING - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'shopping') & (df.start==14)),coef_shopping_departure_constant_9_30_am_to_10_am +util_shopping_departure_constant_10_am_to_10_30_am,SHOPPING - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'shopping') & (df.start==15)),coef_shopping_departure_constant_10_am_to_10_30_am +util_shopping_departure_constant_10_30_am_to_11_00_am,SHOPPING - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'shopping') & (df.start==16)),coef_shopping_departure_constant_10_30_am_to_11_00_am +util_shopping_departure_constant_after_11_am,SHOPPING - Departure Constant: After 11:00 AM,@((df.tour_type == 'shopping') & (df.start>16)),coef_shopping_departure_constant_after_11_am +util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), (np.where((df.start<12), np.minimum(12-df.start,7),0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear +util_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,SHOPPING - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'shopping') & (df.start>17)), ((np.where((df.start<12), np.minimum(12-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_shopping_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared +util_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes before 12:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end<19)), (np.where ((df.end<19), np.minimum(19-df.end,10), 0) + np.where((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_before_12_pm_linear +util_shopping_arrival_constant_before_12_30_pm,SHOPPING - Arrival Constant: Before 12:30 PM,@((df.tour_type == 'shopping') & (df.end<20)),coef_shopping_arrival_constant_before_12_30_pm +util_shopping_arrival_constant_12_30_pm_to_3_pm,SHOPPING - Arrival Constant: 12:30 PM - 03:00 PM,@((df.tour_type == 'shopping') & ( df.end>=20) & (df.end<=24)),coef_shopping_arrival_constant_12_30_pm_to_3_pm +util_shopping_arrival_constant_3_pm_to_3_30_pm,SHOPPING - Arrival Constant: 03:00 PM - 03:30 PM,@((df.tour_type == 'shopping') & (df.end==25)),coef_shopping_arrival_constant_3_pm_to_3_30_pm +util_shopping_arrival_constant_3_30_pm_to_4_pm,SHOPPING - Arrival Constant: 03:30 PM - 04:00 PM,@((df.tour_type == 'shopping') & (df.end==26)),coef_shopping_arrival_constant_3_30_pm_to_4_pm +util_shopping_arrival_constant_4_pm_to_4_30_pm,SHOPPING - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'shopping') & (df.end==27)),coef_shopping_arrival_constant_4_pm_to_4_30_pm +util_shopping_arrival_constant_4_30_pm_to_5_pm,SHOPPING - Arrival Constant: 04:30 PM - 05:00 PM,@((df.tour_type == 'shopping') & (df.end==28)),coef_shopping_arrival_constant_4_30_pm_to_5_pm +util_shopping_arrival_constant_5_pm_to_5_30_pm,SHOPPING - Arrival Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'shopping') & (df.end==29)),coef_shopping_arrival_constant_5_pm_to_5_30_pm +util_shopping_arrival_constant_5_30_pm_to_7_pm,SHOPPING - Arrival Constant: 05:30 PM - 07:00 PM,@((df.tour_type == 'shopping') & (df.end>=30) & (df.end<=32)),coef_shopping_arrival_constant_5_30_pm_to_7_pm +util_shopping_arrival_constant_7_pm_to_9_30_pm,SHOPPING - Arrival Constant: 07:00 PM - 09:30 PM,@((df.tour_type == 'shopping') & (df.end>=33) & (df.end<=37)),coef_shopping_arrival_constant_7_pm_to_9_30_pm +util_shopping_arrival_constant_after_9_30_pm,SHOPPING - Arrival Constant: After 09:30 PM,@((df.tour_type == 'shopping') & (df.end>37)),coef_shopping_arrival_constant_after_9_30_pm +util_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear,SHOPPING - Arrival Constant: Shift for every 30 minutes after 10:00 pm - Linear,"@np.where(((df.tour_type == 'shopping') & (df.end>38)), (np.where((df.end<19), np.minimum(19-df.end,10), 0) + np.where ((df.end>38), np.minimum(df.end-38,5), 0)), 0)",coef_shopping_arrival_constant_shift_for_every_30_minutes_after_10_pm_linear +util_shopping_duration_constant_0_hr,SHOPPING - Duration Constant: 0 hr,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_duration_constant_0_hr +util_shopping_duration_constant_30_minutes,SHOPPING - Duration Constant: 0.5 hr,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_duration_constant_30_minutes +util_shopping_duration_constant_1_hr,SHOPPING - Duration Constant: 1 hr,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_duration_constant_1_hr +util_shopping_duration_constant_1_hour_30_minutes,SHOPPING - Duration Constant: 1.5hrs,@(df.tour_type == 'shopping') & (df.duration==3),coef_shopping_duration_constant_1_hour_30_minutes +util_shopping_duration_constant_2_hrs,SHOPPING - Duration Constant: 2 hrs,@((df.tour_type == 'shopping') & (df.duration==4)),coef_shopping_duration_constant_2_hrs +util_shopping_duration_constant_longer_than_2_hrs,SHOPPING - Duration Constant: Longer than 2 hrs,@((df.tour_type == 'shopping') & (df.duration>4)),coef_shopping_duration_constant_longer_than_2_hrs +util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear,SHOPPING - Duration Constant: Duration > 2.5 hrs - Linear,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_linear +util_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root,SHOPPING - Duration Constant: Duration > 2.5 hrs - Square root,"@np.where(((df.tour_type == 'shopping') & (df.duration>5)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,26), 0)) ** 0.5), 0)",coef_shopping_duration_constant_duration_greater_than_2_hr_30_minutes_square_root +util_shopping_calibration_constant_duration_1,SHOPPING - Calibration Constant - Duration = 1,@((df.tour_type == 'shopping') & (df.duration==0)),coef_shopping_calibration_constant_duration_1 +util_shopping_calibration_constant_duration_2,SHOPPING - Calibration Constant - Duration = 2,@((df.tour_type == 'shopping') & (df.duration==1)),coef_shopping_calibration_constant_duration_2 +util_shopping_calibration_constant_duration_3,SHOPPING - Calibration Constant - Duration = 3,@((df.tour_type == 'shopping') & (df.duration==2)),coef_shopping_calibration_constant_duration_3 +#MAINTENANCE,#MAINTENANCE,,#MAINTENANCE +util_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Driving age student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 6)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_driving_age_student_duration_greater_than_1_hour_30_minutes +util_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Full-time worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_full_time_worker_duration_greater_than_1_hour_30_minutes +util_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Non-driving Student: Duration > 1.5 hrs,"@np.where (((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & ((df.ptype == 7)|(df.ptype == 8))), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_non_driving_student_duration_greater_than_1_hour_30_minutes +util_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes,MAINTENANCE - Pre-school Child: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 8)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_pre_school_child_duration_less_than_1_hour_30_minutes +util_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_less_than_1_hour_30_minutes +util_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Part Time Worker: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_part_time_worker_duration_greater_than_1_hour_30_minutes +util_maintenance_retired_duration_less_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.ptype == 1)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_less_than_1_hour_30_minutes +util_maintenance_retired_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Retired: Duration > 1.5 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 5)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_retired_duration_greater_than_1_hour_30_minutes +util_maintenance_university_student_duration_greater_than_1_hour_30_minutes,MAINTENANCE - University Student: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.ptype == 3)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_university_student_duration_greater_than_1_hour_30_minutes +util_maintenance_female_duration_less_than_1_hour_30_minutes,MAINTENANCE - Female: Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_less_than_1_hour_30_minutes +util_maintenance_female_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Female: Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othmaint') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_female_duration_greater_than_1_hour_30_minutes +util_maintenance_low_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Low Income (<=$25,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_less25K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_low_income_duration_greater_than_1_hour_30_minutes +util_maintenance_medium_income_duration_less_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration < 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_less_than_1_hour_30_minutes +util_maintenance_medium_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium Income ($25,001 to $60,000): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_25K_to_60K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_income_duration_greater_than_1_hour_30_minutes +util_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes,"MAINTENANCE - Medium-High Income ($60,001 to $120,00): Duration > 1.5 hrs","@np.where(((df.tour_type == 'othmaint') & (df.is_income_60K_to_120K) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_medium_high_income_duration_greater_than_1_hour_30_minutes +util_maintenance_distance_duration_less_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration < 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) *(df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_less_than_1_hour_30_minutes +util_maintenance_distance_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Distance: Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.origin_to_destination_distance), 0)",coef_maintenance_distance_duration_greater_than_1_hour_30_minutes +util_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes,Time Pressure - Duration > 1.5 hrs,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), np.minimum(df.duration-2,26) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_maintenance_time_pressure_duration_greater_than_1_hour_30_minutes +util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration < 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_less_than_1_hour_30_minutes +util_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes,MAINTENANCE - Number of additional individual shop and maint. tours - Duration > 1.5 hrs,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othmaint')&(df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,1), 0) + np.where((df.duration>2),np.minimum(df.duration-2,26), 0)) * (df.num_add_shop_maint_tours), 0)",coef_maintenance_number_of_additional_individual_shop_and_maint_tours_duration_greater_than_1_hour_30_minutes +util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), (np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_linear +util_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root,MAINTENANCE - Departure Constant: Shift for every 30 minutes before 07:30 am - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.start<10)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + (np.where((df.start>17), np.minimum(df.start-17,24), 0)))** 0.5), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_before_7_30_am_square_root +util_maintenance_departure_constant_before_8_am,MAINTENANCE - Departure Constant: Before 08:00 AM,@((df.tour_type == 'othmaint') & (df.start<11)),coef_maintenance_departure_constant_before_8_am +util_maintenance_departure_constant_8_am_to_8_30_am,MAINTENANCE - Departure Constant: 08:00 AM - 08:30 AM,@((df.tour_type == 'othmaint') & (df.start==11)),coef_maintenance_departure_constant_8_am_to_8_30_am +util_maintenance_departure_constant_8_30_am_to_9_00_am,MAINTENANCE - Departure Constant: 08:30 AM - 09:00 AM,@((df.tour_type == 'othmaint') & (df.start==12)),coef_maintenance_departure_constant_8_30_am_to_9_00_am +util_maintenance_departure_constant_9_am_to_9_30_am,MAINTENANCE - Departure Constant: 09:00 AM - 09:30 AM,@((df.tour_type == 'othmaint') & (df.start==13)),coef_maintenance_departure_constant_9_am_to_9_30_am +util_maintenance_departure_constant_9_30_am_to_10_am,MAINTENANCE - Departure Constant: 09:30 AM - 10:00 AM,@((df.tour_type == 'othmaint') & (df.start==14)),coef_maintenance_departure_constant_9_30_am_to_10_am +util_maintenance_departure_constant_10_am_to_10_30_am,MAINTENANCE - Departure Constant: 10:00 AM - 10:30 AM,@((df.tour_type == 'othmaint') & (df.start==15)),coef_maintenance_departure_constant_10_am_to_10_30_am +util_maintenance_departure_constant_10_30_am_to_11_am,MAINTENANCE - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.start==16)),coef_maintenance_departure_constant_10_30_am_to_11_am +util_maintenance_departure_constant_after_11_am,MAINTENANCE - Departure Constant: After 11:00 AM,@((df.tour_type == 'othmaint') & (df.start>16)),coef_maintenance_departure_constant_after_11_am +util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_linear +util_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared,MAINTENANCE - Departure Constant: Shift for every 30 minutes after 11:30 am - Squared,"@np.where(((df.tour_type == 'othmaint') & (df.start>17)), ((np.where((df.start<10), np.minimum(10-df.start,7), 0) + np.where((df.start>17), np.minimum(df.start-17,24), 0)) ** 2), 0)",coef_maintenance_departure_constant_shift_for_every_30_minutes_after_11_30_am_squared +util_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes before 10:00 am - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end<15)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_before_10_am_linear +util_maintenance_arrival_constant_before_10_30_am,MAINTENANCE - Arrival Constant: Before 10:30 AM,@((df.tour_type == 'othmaint') & (df.end<16)),coef_maintenance_arrival_constant_before_10_30_am +util_maintenance_arrival_constant_10_30_am_to_11_am,MAINTENANCE - Arrival Constant: 10:30 AM - 11:00 AM,@((df.tour_type == 'othmaint') & (df.end==16)),coef_maintenance_arrival_constant_10_30_am_to_11_am +util_maintenance_arrival_constant_11_am_to_11_30_am,MAINTENANCE - Arrival Constant: 11:00 AM - 11:30 AM,@((df.tour_type == 'othmaint') & (df.end==17)),coef_maintenance_arrival_constant_11_am_to_11_30_am +util_maintenance_arrival_constant_11_30_am_to_1_30_pm,MAINTENANCE - Arrival Constant: 11:30 AM - 01:30 PM,@((df.tour_type == 'othmaint') & (df.end>=18) & (df.end<=21)),coef_maintenance_arrival_constant_11_30_am_to_1_30_pm +util_maintenance_arrival_constant_1_30_pm_to_2_30_pm,MAINTENANCE - Arrival Constant: 01:30 PM - 02:30 PM,@((df.tour_type == 'othmaint') & (df.end>=22) & (df.end<=23)),coef_maintenance_arrival_constant_1_30_pm_to_2_30_pm +util_maintenance_arrival_constant_2_30_pm_to_4_pm,MAINTENANCE - Arrival Constant: 02:30 PM - 04:00 PM,@((df.tour_type == 'othmaint') & (df.end>=24) & (df.end<=26)),coef_maintenance_arrival_constant_2_30_pm_to_4_pm +util_maintenance_arrival_constant_4_pm_to_4_30_pm,MAINTENANCE - Arrival Constant: 04:00 PM - 04:30 PM,@((df.tour_type == 'othmaint') & (df.end==27)),coef_maintenance_arrival_constant_4_pm_to_4_30_pm +util_maintenance_arrival_constant_after_4_30_pm,MAINTENANCE - Arrival Constant: After 04:30 PM,@((df.tour_type == 'othmaint') & (df.end>27)),coef_maintenance_arrival_constant_after_4_30_pm +util_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear,MAINTENANCE - Arrival Constant: Shift for every 30 minutes after 5:00 pm - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.end>28)), (np.where((df.end<15), np.minimum(15-df.end,9), 0) + np.where((df.end>28), np.minimum(df.end-28,16), 0)), 0)",coef_maintenance_arrival_constant_shift_for_every_30_minutes_after_5_pm_linear +util_maintenance_duration_constant_0_hr,MAINTENANCE - Duration Constant: 0 hr,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_duration_constant_0_hr +util_maintenance_duration_constant_30_minutes,MAINTENANCE - Duration Constant: 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_duration_constant_30_minutes +util_maintenance_duration_constant_longer_than_30_minutes,MAINTENANCE - Duration Constant: Longer than 0.5 hr,@((df.tour_type == 'othmaint') & (df.duration>1)),coef_maintenance_duration_constant_longer_than_30_minutes +util_maintenance_duration_constant_duration_greater_than_1_hr_linear,MAINTENANCE - Duration Constant: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0)), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_linear +util_maintenance_duration_constant_duration_greater_than_1_hr_square_root,MAINTENANCE - Duration Constant: Duration > 1 hr - Square Root,"@np.where(((df.tour_type == 'othmaint') & (df.duration>2)), ((np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,26), 0))** 0.5), 0)",coef_maintenance_duration_constant_duration_greater_than_1_hr_square_root +util_maintenance_calibration_constant_duration_1,MAINTENANCE - Calibration Constant - Duration = 1,@((df.tour_type == 'othmaint') & (df.duration==0)),coef_maintenance_calibration_constant_duration_1 +util_maintenance_calibration_constant_duration_2,MAINTENANCE - Calibration Constant - Duration = 2,@((df.tour_type == 'othmaint') & (df.duration==1)),coef_maintenance_calibration_constant_duration_2 +util_maintenance_calibration_constant_duration_3,MAINTENANCE - Calibration Constant - Duration = 3,@((df.tour_type == 'othmaint') & (df.duration==2)),coef_maintenance_calibration_constant_duration_3 +util_maintenance_calibration_constant_duration_4,MAINTENANCE - Calibration Constant - Duration = 4,@((df.tour_type == 'othmaint') & (df.duration==3)),coef_maintenance_calibration_constant_duration_4 +util_maintenance_calibration_constant_duration_5,MAINTENANCE - Calibration Constant - Duration = 5,@((df.tour_type == 'othmaint') & (df.duration==4)),coef_maintenance_calibration_constant_duration_5 +#EAT-OUT,#EAT-OUT,,#EAT-OUT +util_eatout_distance_to_destination_duration_less_than_1_hr,EAT-OUT - Distance to destination - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) * (df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_less_than_1_hr +util_eatout_distance_to_destination_duration_greater_than_1_hr,EAT-OUT - Distance to destination - Duration > 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)) *(df.origin_to_destination_distance), 0)",coef_eatout_distance_to_destination_duration_greater_than_1_hr +util_eatout_low_income_duration_less_than_1_hr,EAT-OUT - Low income (<25000) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_less25K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_low_income_duration_less_than_1_hr +util_eatout_medium_income_duration_less_than_1_hr,EAT-OUT - Medium (25k to 60k) - Duration < 1 hr,"@np.where(((df.tour_type == 'eatout') & (df.is_income_25K_to_60K) & (df.duration<2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_medium_income_duration_less_than_1_hr +util_eatout_zeroauto_HH_duration_greater_than_1_hr,EAT-OUT - Zero auto HH - Duration > 1 hrs,"@np.where(((df.tour_type == 'eatout') & (df.auto_ownership == 0) & (df.duration>2)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_zeroauto_HH_duration_greater_than_1_hr +util_eatout_university_student_departure_after_7_pm_linear,EAT-OUT - University student - Departure after 7:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start>32) & (df.ptype == 3)), (np.where((df.start<=32), np.minimum(32-df.start,29), 0) + np.where((df.start>32), np.minimum(df.start-32,8), 0)), 0)",coef_eatout_university_student_departure_after_7_pm_linear +util_eatout_female_duration_less_than_1_hr,EAT-OUT - Female - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration<2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_less_than_1_hr +util_eatout_female_duration_greater_than_1_hr,EAT-OUT - Female - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>2) & (df.female)), (np.where((df.duration<=2), np.minimum(2-df.duration,47), 0) + np.where((df.duration>2), np.minimum(df.duration-2,14), 0)), 0)",coef_eatout_female_duration_greater_than_1_hr +util_eatout_time_pressure_departure_before_6_30_pm,EAT-OUT - Time Pressure - Departure before 6:30 pm,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<32)), (np.minimum(32-df.start,29)) * (np.log10 (30 *(tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_departure_before_6_30_pm +util_eatout_time_pressure_duration_less_than_1_hr,EAT-OUT - Time Pressure - Duration < 1 hrs,"@np.where(((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration<2)), np.minimum(2-df.duration,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_eatout_time_pressure_duration_less_than_1_hr +util_eatout_departure_constant_7_30_am_to_9_am,EAT-OUT - Departure Constant: 07:30 AM - 09:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>=10) & (df.start<=12)),coef_eatout_departure_constant_7_30_am_to_9_am +util_eatout_departure_constant_10_30_am_to_11_am,EAT-OUT - Departure Constant: 10:30 AM - 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==16)),coef_eatout_departure_constant_10_30_am_to_11_am +util_eatout_departure_constant_11_am_to_11_30_am,EAT-OUT - Departure Constant: 11:00 AM - 11:30 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==17)),coef_eatout_departure_constant_11_am_to_11_30_am +util_eatout_departure_constant_11_30_am_to_12_pm,EAT-OUT - Departure Constant: 11:30 AM - 12:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==18)),coef_eatout_departure_constant_11_30_am_to_12_pm +util_eatout_departure_constant_12_pm_to_12_30_pm,EAT-OUT - Departure Constant: 12:00 PM - 12:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.start==19)),coef_eatout_departure_constant_12_pm_to_12_30_pm +util_eatout_departure_constant_12_30_pm_to_1_pm,EAT-OUT - Departure Constant: 12:30 PM - 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==20)),coef_eatout_departure_constant_12_30_pm_to_1_pm +util_eatout_departure_constant_1_pm_to_1_30_pm,EAT-OUT - Departure Constant: 01:00 PM - 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==21)),coef_eatout_departure_constant_1_pm_to_1_30_pm +util_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes before 05:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_before_5_30_pm_linear +util_eatout_departure_constant_before_6_pm,EAT-OUT - Departure Constant: Before 06:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start<31)),coef_eatout_departure_constant_before_6_pm +util_eatout_departure_constant_6_pm_to_6_30_pm,EAT-OUT - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==31)),coef_eatout_departure_constant_6_pm_to_6_30_pm +util_eatout_departure_constant_6_30_pm_to_7_pm,EAT-OUT - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==32)),coef_eatout_departure_constant_6_30_pm_to_7_pm +util_eatout_departure_constant_7_pm_to_7_30_pm,EAT-OUT - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start==33)),coef_eatout_departure_constant_7_pm_to_7_30_pm +util_eatout_departure_constant_after_7_30_pm,EAT-OUT - Departure Constant: After 07:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>33)),coef_eatout_departure_constant_after_7_30_pm +util_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,EAT-OUT - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.start>34)), (np.where((df.start<30), np.minimum(30-df.start,27), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_eatout_departure_constant_shift_for_every_30_minutes_after_8_pm_linear +util_eatout_arrival_constant_9_30_am_to_11_am,EAT-OUT - Arrival Constant: 9:30 AM to 11:00 AM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>=14) & (df.end<=16)),coef_eatout_arrival_constant_9_30_am_to_11_am +util_eatout_arrival_constant_12_30_pm_to_1_pm,EAT-OUT - Arrival Constant: 12:30 PM to 01:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==20)),coef_eatout_arrival_constant_12_30_pm_to_1_pm +util_eatout_arrival_constant_1_pm_to_1_30_pm,EAT-OUT - Arrival Constant: 01:00 PM to 01:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==21)),coef_eatout_arrival_constant_1_pm_to_1_30_pm +util_eatout_arrival_constant_1_30_pm_to_2_pm,EAT-OUT - Arrival Constant: 01:30 PM to 02:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==22)),coef_eatout_arrival_constant_1_30_pm_to_2_pm +util_eatout_arrival_constant_2_pm_to_2_30_pm,EAT-OUT - Arrival Constant: 02:00 PM to 02:30 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==23)),coef_eatout_arrival_constant_2_pm_to_2_30_pm +util_eatout_arrival_constant_2_30_pm_to_3_pm,EAT-OUT - Arrival Constant: 02:30 PM to 03:00 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==24)),coef_eatout_arrival_constant_2_30_pm_to_3_pm +util_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<31)),coef_eatout_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear +util_eatout_arrival_constant_before_6_30_pm,EAT-OUT - Arrival Constant: Before 6:30 PM,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end<32)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_before_6_30_pm +util_eatout_arrival_constant_6_30_pm_to_7_pm,EAT-OUT - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==32)),coef_eatout_arrival_constant_6_30_pm_to_7_pm +util_eatout_arrival_constant_7_pm_to_7_30_pm,EAT-OUT - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.end==33)),coef_eatout_arrival_constant_7_pm_to_7_30_pm +util_eatout_arrival_constant_7_30_pm_to_8_pm,EAT-OUT - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==34)),coef_eatout_arrival_constant_7_30_pm_to_8_pm +util_eatout_arrival_constant_8_pm_to_8_30_pm,EAT-OUT - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_category != 'joint') &(df.tour_type == 'eatout') & (df.end==35)),coef_eatout_arrival_constant_8_pm_to_8_30_pm +util_eatout_arrival_constant_8_30_pm_to_9_pm,EAT-OUT - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end==36)),coef_eatout_arrival_constant_8_30_pm_to_9_pm +util_eatout_arrival_constant_after_9_pm,EAT-OUT - Arrival Constant: After 9:00 PM,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.end>36)),coef_eatout_arrival_constant_after_9_pm +util_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,EAT-OUT - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,24), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_eatout_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear +util_eatout_duration_constant_0_hour,EAT-OUT - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type== 'eatout') & (df.duration==0)),coef_eatout_duration_constant_0_hour +util_eatout_duration_constant_30_minutes,EAT-OUT - Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_duration_constant_30_minutes +util_eatout_duration_constant_1_hour,EAT-OUT - Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==2)),coef_eatout_duration_constant_1_hour +util_eatout_duration_constant_1_hour_30_minutes,EAT-OUT - Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_duration_constant_1_hour_30_minutes +util_eatout_duration_constant_2_hours,EAT-OUT - Duration Constant: 2 hours,@((df.tour_category != 'joint')& (df.tour_type == 'eatout') & (df.duration==4)),coef_eatout_duration_constant_2_hours +util_eatout_duration_constant_2_hour_30_minutes_or_more,EAT-OUT - Duration Constant: 2.5 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>4)),coef_eatout_duration_constant_2_hour_30_minutes_or_more +util_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,EAT-OUT - Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'eatout') & (df.duration>5)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>5), np.minimum(df.duration-5,11), 0)), 0)",coef_eatout_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear +util_eatout_calibration_constant_duration_1,EAT-OUT - Calibration Constant - Duration = 1,@((df.tour_type == 'eatout') & (df.duration==0)),coef_eatout_calibration_constant_duration_1 +util_eatout_calibration_constant_duration_2,EAT-OUT - Calibration Constant - Duration = 2,@((df.tour_type == 'eatout') & (df.duration==1)),coef_eatout_calibration_constant_duration_2 +util_eatout_calibration_constant_duration_3,EAT-OUT - Calibration Constant - Duration = 3,@((df.tour_type== 'eatout') & (df.duration==2)),coef_eatout_calibration_constant_duration_3 +util_eatout_calibration_constant_duration_4,EAT-OUT - Calibration Constant - Duration = 4,@((df.tour_type == 'eatout') & (df.duration==3)),coef_eatout_calibration_constant_duration_4 +util_eatout_calibration_constant_departure_1,EAT-OUT - Calibration Constant - Departure = 1,@((df.tour_type == 'eatout') & (df.start == 1)),coef_eatout_calibration_constant_departure_1 +util_eatout_calibration_constant_departure_2,EAT-OUT - Calibration Constant - Departure = 2,@((df.tour_type == 'eatout') & (df.start == 2)),coef_eatout_calibration_constant_departure_2 +util_eatout_calibration_constant_departure_3,EAT-OUT - Calibration Constant - Departure = 3,@((df.tour_type== 'eatout') & (df.start == 3)),coef_eatout_calibration_constant_departure_3 +util_eatout_calibration_constant_departure_17,EAT-OUT - Calibration Constant - Departure = 17,@((df.tour_type == 'eatout') & (df.start ==17)),coef_eatout_calibration_constant_departure_17 +util_eatout_calibration_constant_departure_18,EAT-OUT - Calibration Constant - Departure = 18,@((df.tour_type== 'eatout') & (df.start ==18)),coef_eatout_calibration_constant_departure_18 +util_eatout_calibration_constant_departure_19,EAT-OUT - Calibration Constant - Departure = 19,@((df.tour_type == 'eatout') & (df.start ==19)),coef_eatout_calibration_constant_departure_19 +util_eatout_calibration_constant_departure_20,EAT-OUT - Calibration Constant - Departure = 20,@((df.tour_type == 'eatout') & (df.start ==20)),coef_eatout_calibration_constant_departure_20 +util_eatout_calibration_constant_departure_21,EAT-OUT - Calibration Constant - Departure = 21,@((df.tour_type == 'eatout') & (df.start ==21)),coef_eatout_calibration_constant_departure_21 +#SOCIAL,#SOCIAL,,#SOCIAL +util_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear +util_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear +util_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_non_working_senior_or_retiree_duration_less_than_1_hr_30_minutes_linear +util_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_retiree_or_non_working_senior_only_HH_duration_less_than_1_hr_30_minutes_linear +util_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_less_than_1_hr_30_minutes_linear +util_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership == 0) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0)",coef_social_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear +util_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) &(df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear +util_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,SOCIAL - Number of auto more than number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_type == 'social') & (df.auto_ownership > 0) & (df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_social_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear +"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, +util_social_auto_distance_duration_less_than_1_hr_linear,SOCIAL - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)), 0) * (df.origin_to_destination_distance)",coef_social_auto_distance_duration_less_than_1_hr_linear +util_social_auto_distance_duration_greater_than_1_hr_linear,SOCIAL - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'social') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0) * (df.origin_to_destination_distance) ",coef_social_auto_distance_duration_greater_than_1_hr_linear +util_social_time_pressure_duration_less_than_1_hr,SOCIAL - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), np.minimum(3-df.duration,47), 0)* (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_less_than_1_hr +util_social_time_pressure_duration_greater_than_1_hr,SOCIAL - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>3)), np.minimum(df.duration-3,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_social_time_pressure_duration_greater_than_1_hr +util_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,SOCIAL - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3),np.minimum(df.duration-3,47), 0)) * (df.num_add_soc_discr_tours), 0)",coef_social_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr +util_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 08:30 am - Linear,"@(df.tour_type == 'social') & (df.start<12) * ((np.minimum(12-df.start,48)*(df.start<48)) + (np.minimum(df.start-48,48)*(df.start>48)))",coef_social_departure_constant_shift_for_every_30_minutes_before_8_30_am_linear +util_social_departure_constant_before_9_am,SOCIAL - Departure Constant: Before 09:00 AM,@(df.tour_type == 'social') & (df.start<13),coef_social_departure_constant_before_9_am +util_social_departure_constant_9_am_to_9_30_am,SOCIAL - Departure Constant: 09:00 AM to 09:30 AM,@(df.tour_type == 'social') & (df.start==13),coef_social_departure_constant_9_am_to_9_30_am +util_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes before 05:00 pm - Linear,"@np.where((df.start<29), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_before_5_pm_linear +util_social_departure_constant_before_5_30_pm,SOCIAL - Departure Constant: Before 05:30 PM,@((df.tour_type == 'social') & (df.start<30)),coef_social_departure_constant_before_5_30_pm +util_social_departure_constant_5_30_pm_to_6_pm,SOCIAL - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'social') & (df.start==30)),coef_social_departure_constant_5_30_pm_to_6_pm +util_social_departure_constant_6_pm_to_6_30_pm,SOCIAL - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'social') & (df.start==31)),coef_social_departure_constant_6_pm_to_6_30_pm +util_social_departure_constant_6_30_pm_to_7_pm,SOCIAL - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'social') & (df.start==32)),coef_social_departure_constant_6_30_pm_to_7_pm +util_social_departure_constant_7_pm_to_7_30_pm,SOCIAL - Departure Constant: 07:00 PM - 07:30 PM,@((df.tour_type == 'social') & (df.start==33)),coef_social_departure_constant_7_pm_to_7_30_pm +util_social_departure_constant_after_7_30_pm,SOCIAL - Departure Constant: After 07:30 PM,@((df.tour_type == 'social') & (df.start>33)),coef_social_departure_constant_after_7_30_pm +util_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear,SOCIAL - Departure Constant: Shift for every 30 minutes after 08:00 pm - Linear,"@np.where((df.start>34), (np.where((df.start<29), np.minimum(29-df.start,8), 0) + np.where((df.start>34), np.minimum(df.start-34,6), 0)), 0)",coef_social_departure_constant_shift_for_every_30_minutes_after_8_pm_linear +util_social_arrival_constant_3_pm_to_3_30_pm,SOCIAL - Arrival Constant: 03:00 PM to 03:30 PM,@((df.tour_type == 'social') & (df.end==25)),coef_social_arrival_constant_3_pm_to_3_30_pm +util_social_arrival_constant_3_30_pm_to_4_pm,SOCIAL - Arrival Constant: 03:30 PM to 04:00 PM,@((df.tour_type == 'social') & (df.end==26)),coef_social_arrival_constant_3_30_pm_to_4_pm +util_social_arrival_constant_4_pm_to_4_30_pm,SOCIAL - Arrival Constant: 04:00 PM to 04:30 PM,@((df.tour_type == 'social') & (df.end==27)),coef_social_arrival_constant_4_pm_to_4_30_pm +util_social_arrival_constant_5_pm_to_6_pm,SOCIAL - Arrival Constant: 05:00 PM to 06:00 PM,@((df.tour_type == 'social') & (df.end>=29) & (df.end<=30)),coef_social_arrival_constant_5_pm_to_6_pm +util_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes before 08:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end<35)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) + np.where((df.end>40), np.minimum(df.end-40,48), 0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_before_8_pm_linear +util_social_arrival_constant_before_8_30_pm,SOCIAL - Arrival Constant: Before 8:30 PM,@((df.tour_type == 'social') & (df.end<36)),coef_social_arrival_constant_before_8_30_pm +util_social_arrival_constant_8_30_pm_to_9_pm,SOCIAL - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'social') & (df.end==36)),coef_social_arrival_constant_8_30_pm_to_9_pm +util_social_arrival_constant_9_pm_to_9_30_pm,SOCIAL - Arrival Constant: 9:00 PM to 9:30 PM,@((df.tour_type == 'social') & (df.end==37)),coef_social_arrival_constant_9_pm_to_9_30_pm +util_social_arrival_constant_9_30_pm_to_10_pm,SOCIAL - Arrival Constant: 9:30 PM to10:00 PM,@((df.tour_type == 'social') & (df.end==38)),coef_social_arrival_constant_9_30_pm_to_10_pm +util_social_arrival_constant_10_pm_to_10_30_pm,SOCIAL - Arrival Constant: 10:00 PM to 10:30 PM,@((df.tour_type == 'social') & (df.end==39)),coef_social_arrival_constant_10_pm_to_10_30_pm +util_social_arrival_constant_after_10_30_pm,SOCIAL - Arrival Constant: After 10:30 PM,@((df.tour_type == 'social') & (df.end>39)),coef_social_arrival_constant_after_10_30_pm +util_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear,SOCIAL - Arrival Constant: Shift for every 30 minutes after 11:00 pm - Linear,"@np.where(((df.tour_type == 'social') & (df.end>40)), (np.where((df.end<35), np.minimum(35-df.end,48), 0) +np.where((df.end>40),np.minimum(df.end-40,48),0)), 0)",coef_social_arrival_constant_shift_for_every_30_minutes_after_11_pm_linear +util_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes less than 2.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<5)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>7), np.minimum(df.duration-7,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_less_than_2_hr_30_minutes_linear +util_social_duration_constant_less_than_3_hours,SOCIAL - Duration Constant: Less than 3 hrs,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration<6)),coef_social_duration_constant_less_than_3_hours +util_social_duration_constant_3_hours,SOCIAL - Duration Constant: 3 hours,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration==6)),coef_social_duration_constant_3_hours +util_social_duration_constant_3_hrs_30_minutes,SOCIAL - Duration Constant: 3.5 hours,@((df.tour_category != 'joint')& (df.tour_type == 'social') & (df.duration==7)),coef_social_duration_constant_3_hrs_30_minutes +util_social_duration_constant_4_hours_or_more,SOCIAL - Duration Constant: 4 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>7)),coef_social_duration_constant_4_hours_or_more +util_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear,SOCIAL - Duration Constant: Shift for every 30 minutes more than 4.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'social') & (df.duration>8)), (np.where((df.duration<5), np.minimum(5-df.duration,47), 0) + np.where((df.duration>8), np.minimum(df.duration-8,47), 0)), 0)",coef_social_duration_constant_shift_for_every_30_minutes_more_than_4_hr_30_minutes_linear +util_social_calibration_constant_duration_1,SOCIAL - Calibration Constant - Duration = 1,@((df.tour_type == 'social') & (df.duration ==0)),coef_social_calibration_constant_duration_1 +util_social_calibration_constant_duration_2,SOCIAL - Calibration Constant - Duration = 2,@((df.tour_type == 'social') & (df.duration == 1)),coef_social_calibration_constant_duration_2 +util_social_calibration_constant_duration_3,SOCIAL - Calibration Constant - Duration = 3,@((df.tour_type == 'social') & (df.duration ==2)),coef_social_calibration_constant_duration_3 +util_social_calibration_constant_duration_4,SOCIAL - Calibration Constant - Duration = 4,@((df.tour_type == 'social') & (df.duration ==3)),coef_social_calibration_constant_duration_4 +util_social_calibration_constant_duration_5,SOCIAL - Calibration Constant - Duration = 5,@((df.tour_type == 'social') & (df.duration ==4)),coef_social_calibration_constant_duration_5 +util_social_calibration_constant_duration_6,SOCIAL - Calibration Constant - Duration = 6,@((df.tour_type == 'social') & (df.duration ==5)),coef_social_calibration_constant_duration_6 +util_social_calibration_constant_duration_7,SOCIAL - Calibration Constant - Duration = 7,@((df.tour_type == 'social') & (df.duration ==6)),coef_social_calibration_constant_duration_7 +util_social_calibration_constant_duration_8,SOCIAL - Calibration Constant - Duration = 8,@((df.tour_type == 'social') & (df.duration ==7)),coef_social_calibration_constant_duration_8 +util_social_calibration_constant_duration_9,SOCIAL - Calibration Constant - Duration = 9,@((df.tour_type == 'social') & (df.duration ==8)),coef_social_calibration_constant_duration_9 +#DISCRETIONARY,#DISCRETIONARY,,#DISCRETIONARY +util_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_less_than_1_hr_30_minutes_linear +util_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Person< 18 years old: Duration > 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3) & (df.age<18)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_person_less_than_18_years_old_duration_greater_than_1_hr_30_minutes_linear +util_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Non-working senior/ retiree: Duration < 1.5 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3) & (df.ptype == 5)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_non_working_senior_retiree_duration_less_than_1_hr_30_minutes_linear +util_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear,DISCRETIONARY - Retiree/ Non-working senior only HH: Duration < 1.5 hrs - Linear,"@np.where(((df.retired_adults_only_hh) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_retiree_non_working_senior_only_HH_duration_1_hr_30_minutes_linear +util_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_less_than_1_hr_30_minutes_linear +util_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Zero auto households: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership == 0) & (df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_zero_auto_households_duration_greater_than_1_hr_30_minutes_linear +util_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration < 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) & (df.tour_type == 'othdiscr') & (df.auto_ownership > df.num_adults) & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)),0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_less_than_1_hr_30_minutes_linear +util_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear,DISCRETIONARY - Number of auto more that number of adults: Duration > 1.5 hrs - Linear,"@np.where(((df.auto_ownership > 0) &(df.tour_type == 'othdiscr')&(df.auto_ownership > df.num_adults) & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)), 0)",coef_discretionary_number_of_auto_more_than_number_of_adults_duration_greater_than_1_hr_30_minutes_linear +"# In CTRAMP, although the description below says duration is less than 1 hr, expression is for less than 1.5 hr",,, +util_discretionary_auto_distance_duration_less_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration < 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_less_than_1_hr_linear +util_discretionary_auto_distance_duration_greater_than_1_hr_linear,DISCRETIONARY - Auto Distance: Duration > 1 hr - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.duration>3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0)) * (df.origin_to_destination_distance), 0)",coef_discretionary_auto_distance_duration_greater_than_1_hr_linear +util_discretionary_time_pressure_duration_less_than_1_hr,DISCRETIONARY - Time Pressure - Duration < 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration<3)), np.minimum(3-df.duration,47), 0) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num))))",coef_discretionary_time_pressure_duration_less_than_1_hr +util_discretionary_time_pressure_duration_greater_than_1_hr,DISCRETIONARY - Time Pressure - Duration > 1 hr,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>3)), np.minimum(df.duration-3,47) * (np.log10 (30 * (tt.remaining_periods_available(df.person_id, df.start, df.end)/(1.0 + df.tour_count - df.tour_num)))), 0)",coef_discretionary_time_pressure_duration_greater_than_1_hr +util_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr,DISCRETIONARY - Number of additional individual social and dicretionary tours - Duration < 1 hr,"@np.where(((df.tour_category != 'joint')&(df.tour_type == 'othdiscr') & (df.duration<3)), (np.where((df.duration<=3), np.minimum(3-df.duration,47), 0) + np.where((df.duration>3), np.minimum(df.duration-3,47), 0))*(df.num_add_soc_discr_tours),0)",coef_discretionary_number_of_additional_individual_social_and_dicretionary_tours_duration_less_than_1_hr +util_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<9)), (np.where((df.start<9), np.minimum(9-df.start,48), 0) + np.where((df.start>48), np.minimum(df.start-48,48),0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_7_30_pm_linear +util_discretionary_departure_constant_before_7_30_am,DISCRETIONARY - Departure Constant: Before 7:30 AM ,@((df.tour_type == 'othdiscr') & (df.start<10)),coef_discretionary_departure_constant_before_7_30_am +util_discretionary_departure_constant_7_30_am_to_8_am,DISCRETIONARY - Departure Constant: 7:30 AM to 8:00 AM,@((df.tour_type == 'othdiscr') & (df.start==10)),coef_discretionary_departure_constant_7_30_am_to_8_am +util_discretionary_departure_constant_8_am_to_8_30_am,DISCRETIONARY - Departure Constant: 8:00 AM to 8:30 AM,@((df.tour_type == 'othdiscr') & (df.start==11)),coef_discretionary_departure_constant_8_am_to_8_30_am +util_discretionary_departure_constant_8_30_am_to_9_am,DISCRETIONARY - Departure Constant: 8:30 AM to 9:00 AM,@((df.tour_type == 'othdiscr') & (df.start==12)),coef_discretionary_departure_constant_8_30_am_to_9_am +util_discretionary_departure_constant_9_am_to_9_30_am,DISCRETIONARY - Departure Constant: 9:00 AM to 9:30 AM,@((df.tour_type == 'othdiscr') & (df.start==13)),coef_discretionary_departure_constant_9_am_to_9_30_am +util_discretionary_departure_constant_9_30_am_to_10_am,DISCRETIONARY - Departure Constant: 9:30 AM to 10:00 AM,@((df.tour_type == 'othdiscr') & (df.start==14)),coef_discretionary_departure_constant_9_30_am_to_10_am +util_discretionary_departure_constant_10_am_to_10_30_am,DISCRETIONARY - Departure Constant: 10:00 AM to 10:30 AM,@((df.tour_type == 'othdiscr') & (df.start==15)),coef_discretionary_departure_constant_10_am_to_10_30_am +util_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes before 04:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start<28)), (np.where((df.start<28), np.minimum(28-df.start,8),0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_before_4_30_pm_linear +util_discretionary_departure_constant_before_5_pm,DISCRETIONARY - Departure Constant: Before 05:00 PM,@((df.tour_type == 'othdiscr') & (df.start<29)),coef_discretionary_departure_constant_before_5_pm +util_discretionary_departure_constant_5_pm_to_5_30_pm,DISCRETIONARY - Departure Constant: 05:00 PM - 05:30 PM,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_departure_constant_5_pm_to_5_30_pm +util_discretionary_departure_constant_5_30_pm_to_6_pm,DISCRETIONARY - Departure Constant: 05:30 PM - 06:00 PM,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_departure_constant_5_30_pm_to_6_pm +util_discretionary_departure_constant_6_pm_to_6_30_pm,DISCRETIONARY - Departure Constant: 06:00 PM - 06:30 PM,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_departure_constant_6_pm_to_6_30_pm +util_discretionary_departure_constant_6_30_pm_to_7_pm,DISCRETIONARY - Departure Constant: 06:30 PM - 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_departure_constant_6_30_pm_to_7_pm +util_discretionary_departure_constant_after_7_pm,DISCRETIONARY - Departure Constant: After 07:00 PM,@((df.tour_type == 'othdiscr') & (df.start>32)),coef_discretionary_departure_constant_after_7_pm +util_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear,DISCRETIONARY - Departure Constant: Shift for every 30 minutes after 07:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.start>33)), (np.where((df.start<28), np.minimum(28-df.start,8), 0) + np.where((df.start>33), np.minimum(df.start-33,6), 0)), 0)",coef_discretionary_departure_constant_shift_for_every_30_minutes_after_7_30_pm_linear +util_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes before 06:00 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end<31)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48), 0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_before_6_pm_linear +util_discretionary_arrival_constant_before_6_30_pm,DISCRETIONARY - Arrival Constant: Before 6:30 PM,@((df.tour_type == 'othdiscr') & (df.end<32)),coef_discretionary_arrival_constant_before_6_30_pm +util_discretionary_arrival_constant_6_30_pm_to_7_pm,DISCRETIONARY - Arrival Constant: 6:30 PM to 7:00 PM,@((df.tour_type == 'othdiscr') & (df.end==32)),coef_discretionary_arrival_constant_6_30_pm_to_7_pm +util_discretionary_arrival_constant_7_pm_to_7_30_pm,DISCRETIONARY - Arrival Constant: 7:00 PM to 7:30 PM,@((df.tour_type == 'othdiscr') & (df.end==33)),coef_discretionary_arrival_constant_7_pm_to_7_30_pm +util_discretionary_arrival_constant_7_30_pm_to_8_pm,DISCRETIONARY - Arrival Constant: 7:30 PM to 8:00 PM,@((df.tour_type == 'othdiscr') & (df.end==34)),coef_discretionary_arrival_constant_7_30_pm_to_8_pm +util_discretionary_arrival_constant_8_pm_to_8_30_pm,DISCRETIONARY - Arrival Constant: 8:00 PM to 8:30 PM,@((df.tour_type == 'othdiscr') & (df.end==35)),coef_discretionary_arrival_constant_8_pm_to_8_30_pm +util_discretionary_arrival_constant_8_30_pm_to_9_pm,DISCRETIONARY - Arrival Constant: 8:30 PM to 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end==36)),coef_discretionary_arrival_constant_8_30_pm_to_9_pm +util_discretionary_arrival_constant_after_9_pm,DISCRETIONARY - Arrival Constant: After 9:00 PM,@((df.tour_type == 'othdiscr') & (df.end>36)),coef_discretionary_arrival_constant_after_9_pm +util_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear,DISCRETIONARY - Arrival Constant: Shift for every 30 minutes after 09:30 pm - Linear,"@np.where(((df.tour_type == 'othdiscr') & (df.end>37)), (np.where((df.end<31), np.minimum(31-df.end,48), 0) + np.where((df.end>37), np.minimum(df.end-37,48),0)), 0)",coef_discretionary_arrival_constant_shift_for_every_30_minutes_after_9_30_pm_linear +util_discretionary_duration_constant_0_hour,DISCRETIONARY - Duration Constant: 0 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==0)),coef_discretionary_duration_constant_0_hour +util_discretionary_duration_constant_30_minutes,DISCRETIONARY -Duration Constant: 0.5 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==1)),coef_discretionary_duration_constant_30_minutes +util_discretionary_duration_constant_1_hour,DISCRETIONARY -Duration Constant: 1 hour,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==2)),coef_discretionary_duration_constant_1_hour +util_discretionary_duration_constant_1_hr_30_minutes,DISCRETIONARY -Duration Constant: 1.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_duration_constant_1_hr_30_minutes +util_discretionary_duration_constant_2_hours,DISCRETIONARY -Duration Constant: 2 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_duration_constant_2_hours +util_discretionary_duration_constant_2_hr_30_minutes,DISCRETIONARY -Duration Constant: 2.5 hours,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration==5)),coef_discretionary_duration_constant_2_hr_30_minutes +util_discretionary_duration_constant_3_hours_or_more,DISCRETIONARY -Duration Constant: 3 hours or more,@((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>5)),coef_discretionary_duration_constant_3_hours_or_more +util_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear,DISCRETIONARY -Duration Constant: Shift for every 30 minutes more than 3 hrs - Linear,"@np.where(((df.tour_category != 'joint') & (df.tour_type == 'othdiscr') & (df.duration>6)), (np.where((df.duration<0), np.minimum(0-df.duration,47), 0) + np.where((df.duration>6), np.minimum(df.duration-6,47), 0)), 0)",coef_discretionary_duration_constant_shift_for_every_30_minutes_more_than_3_hrs_linear +util_discretionary_calibration_constant_duration_4,DISCRETIONARY -Calibration Constant - Duration = 4,@((df.tour_type == 'othdiscr') & (df.duration==3)),coef_discretionary_calibration_constant_duration_4 +util_discretionary_calibration_constant_duration_5,DISCRETIONARY -Calibration Constant - Duration = 5,@((df.tour_type == 'othdiscr') & (df.duration==4)),coef_discretionary_calibration_constant_duration_5 +util_discretionary_calibration_constant_departure_29,DISCRETIONARY -Calibration Constant - Departure = 29,@((df.tour_type == 'othdiscr') & (df.start==29)),coef_discretionary_calibration_constant_departure_29 +util_discretionary_calibration_constant_departure_30,DISCRETIONARY -Calibration Constant - Departure = 30,@((df.tour_type == 'othdiscr') & (df.start==30)),coef_discretionary_calibration_constant_departure_30 +util_discretionary_calibration_constant_departure_31,DISCRETIONARY -Calibration Constant - Departure = 31,@((df.tour_type == 'othdiscr') & (df.start==31)),coef_discretionary_calibration_constant_departure_31 +util_discretionary_calibration_constant_departure_32,DISCRETIONARY -Calibration Constant - Departure = 32,@((df.tour_type == 'othdiscr') & (df.start==32)),coef_discretionary_calibration_constant_departure_32 diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_shopping.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shopping.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_shopping.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shopping.csv diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_shopping_coefficients.csv diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_social.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_social.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_social.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_social.csv diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_social_coefficients.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_social_coefficients.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/tour_scheduling_nonmandatory_social_coefficients.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_nonmandatory_social_coefficients.csv diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_school.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_school.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/tour_scheduling_school.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_school.csv index 9dd9ca8cd7..f84c333d4b 100755 --- a/activitysim/examples/example_semcog/configs/tour_scheduling_school.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_school.csv @@ -1,59 +1,59 @@ -Label,Description,Expression,Coefficient -util_Mode_Choice_Logsum,SCHOOL - Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum -util_Low_income_lt25000_Departure_before_730_am__Linear,SCHOOL - Low income (<25000) - Departure before 7:30 am - Linear,"@((df.is_income_less25K) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Low_income_lt25000_Departure_before_730_am__Linear -util_Low_income_lt25000_Departure_after_800_am_Linear,SCHOOL - Low income (<25000) - Departure after 8:00 am - Linear,"@((df.is_income_less25K) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Low_income_lt25000_Departure_after_800_am_Linear -util_Low_income_lt25000_Duration_lt_8hrs,SCHOOL - Low income (<25000) - Duration < 8hrs,"@((df.is_income_less25K) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Low_income_lt25000_Duration_lt_8hrs -util_Low_income_lt25000_Duration_gt_8hrs,SCHOOL - Low income (<25000) - Duration > 8hrs,"@((df.is_income_less25K) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Low_income_lt25000_Duration_gt_8hrs -util_Med_income_25k_to_60k_Departure_before_730_am__Linear,SCHOOL - Med income (25k to 60k) - Departure before 7:30 am - Linear,"@((df.is_income_25K_to_60K) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Med_income_25k_to_60k_Departure_before_730_am__Linear -util_Age_0_to_5_yrs_Departure_Before_730_am,SCHOOL - Age 0 to 5 yrs - Departure Before 7:30 am,"@(((df.age>=0) & (df.age<=5)) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Age_0_to_5_yrs_Departure_Before_730_am -util_Age_13_to_15_yrs_Departure_Before_730_am,SCHOOL - Age 13 to 15 yrs - Departure Before 7:30 am,"@(((df.age>=13) & (df.age<=15)) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Age_13_to_15_yrs_Departure_Before_730_am -util_Age_13_to_15_yrs_Departure_After_800_am,SCHOOL - Age 13 to 15 yrs - Departure After 8:00 am,"@(((df.age>=13) & (df.age<=15)) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Age_13_to_15_yrs_Departure_After_800_am -util_Age_16_to_17_yrs_Departure_After_800_am,SCHOOL - Age 16 to 17 yrs - Departure After 8:00 am,"@(((df.age>=16) & (df.age<=17)) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Age_16_to_17_yrs_Departure_After_800_am -util_Age_0_to_5_yrs_Duration_lt_8hrs,SCHOOL - Age 0 to 5 yrs - Duration < 8hrs,"@(((df.age>0) & (df.age<=5)) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_0_to_5_yrs_Duration_lt_8hrs -util_Age_0_to_5_yrs_Duration_gt_8hrs,SCHOOL - Age 0 to 5 yrs - Duration > 8hrs,"@(((df.age>0) & (df.age<=5)) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_0_to_5_yrs_Duration_gt_8hrs -util_Age_13_to_15_yrs_Duration_lt_8hrs,SCHOOL - Age 13 to 15 yrs - Duration < 8hrs,"@(((df.age>=13) & (df.age<=15)) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_13_to_15_yrs_Duration_lt_8hrs -util_Age_13_to_15_yrs_Duration_gt_8hrs,SCHOOL - Age 13 to 15 yrs - Duration > 8hrs,"@(((df.age>=13) & (df.age<=15)) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_13_to_15_yrs_Duration_gt_8hrs -util_Age_16_to_17_yrs_Duration_gt_8hrs,SCHOOL - Age 16 to 17 yrs - Duration > 8hrs,"@(((df.age>=16) & (df.age<=17)) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_16_to_17_yrs_Duration_gt_8hrs -util_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear,SCHOOL - Time (SOV freeflow) to destination - Departure before 7:30 am - Linear,"@(df.start<10) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10))) * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear -util_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear,SCHOOL - Time (SOV freeflow) to destination - Departure after 8:00 am - Linear,"@(df.start>10) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10))) * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear -util_Time_SOV_freeflow_to_destination_Duration_lt_8hrs,SCHOOL - Time (SOV freeflow) to destination - Duration < 8hrs,"@(df.end<27) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27))) * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Duration_lt_8hrs -util_Time_SOV_freeflow_to_destination_Duration_gt_8hrs,SCHOOL - Time (SOV freeflow) to destination - Duration > 8hrs,"@(df.end>27) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27))) * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Duration_gt_8hrs -util_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear,SCHOOL - All adults in the household are fulltime workers - Departure before 7:30 am - Linear,"@((df.is_all_adults_full_time_workers) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear -util_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear,SCHOOL - All adults in the household are fulltime workers - Departure after 8:00 am - Linear,"@((df.is_all_adults_full_time_workers) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear -util_All_adults_in_the_household_are_fulltime_workers_Duration_lt_8hrs,SCHOOL - All adults in the household are fulltime workers - Duration < 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end<27)) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27)))",coef_All_adults_in_the_household_are_fulltime_workers_Duration_lt_8hrs -util_All_adults_in_the_household_are_fulltime_workers_Duration_gt_8hrs,SCHOOL - All adults in the household are fulltime workers - Duration > 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end>27)) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27)))",coef_All_adults_in_the_household_are_fulltime_workers_Duration_gt_8hrs -util_Subsequent_tour_is_work_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is work tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.duration<8))) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours -util_Subsequent_tour_is_work_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is work tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.duration>8))) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours -util_Subsequent_tour_is_school_tour_Departure_after_800_am,SCHOOL - Subsequent tour is school tour: Departure after 8:00 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start>10))) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Subsequent_tour_is_school_tour_Departure_after_800_am -util_Subsequent_tour_is_school_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is school tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.duration<8))) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours -util_Subsequent_tour_is_school_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is school tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.duration>8))) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours -util_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<7)) * ((np.minimum(7-df.duration,47)*(df.duration<=7)) + (np.minimum(df.duration-7,47)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours -util_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration>7)) * ((np.minimum(7-df.duration,47)*(df.duration<=7)) + (np.minimum(df.duration-7,47)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours -util_Departure_Constant_Before_0600_AM,SCHOOL - Departure Constant: Before 06:00 AM,@(df.start<7),coef_Departure_Constant_Before_0600_AM -util_Departure_Constant_0600_AM_to_0630_AM_7,SCHOOL - Departure Constant: 06:00 AM to 06:30 AM (7),@(df.start==7),coef_Departure_Constant_0600_AM_to_0630_AM_7 -util_Departure_Constant_0630_AM_to_0700_AM_8,SCHOOL - Departure Constant: 06:30 AM to 07:00 AM (8),@(df.start==8),coef_Departure_Constant_0630_AM_to_0700_AM_8 -util_Departure_Constant_0700_AM_to_0730_AM_9,SCHOOL - Departure Constant: 07:00 AM to 07:30 AM (9),@(df.start==9),coef_Departure_Constant_0700_AM_to_0730_AM_9 -util_Departure_Constant_0730_AM_to_0800_AM_10,SCHOOL - Departure Constant: 07:30 AM to 08:00 AM (10),@(df.start==10),coef_Departure_Constant_0730_AM_to_0800_AM_10 -util_Departure_Constant_After_0800_AM,SCHOOL - Departure Constant: After 08:00 AM,@(df.start>10),coef_Departure_Constant_After_0800_AM -util_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear,SCHOOL - Departure Constant: Shift for every 30 minutes after 8:30 am - Linear,"@((df.start>11)) * ((np.minimum(7-df.start,48)*(df.start<7)) + (np.minimum(df.start-11,23)*(df.start>11)))",coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear -util_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root,SCHOOL - Departure Constant: Shift for every 30 minutes after 8:30 am - Square Root,"@((df.start>11)) * (((np.minimum(7-df.start,48)*(df.start<7)) + (np.minimum(df.start-11,23)*(df.start>11))) ** 0.5)",coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root -util_Arrival_Constant_Before_0230_PM,SCHOOL - Arrival Constant: Before 02:30 PM,@(df.end<24),coef_Arrival_Constant_Before_0230_PM -util_Arrival_Constant_0230_PM_0300_PM_24_,SCHOOL - Arrival Constant: 02:30 PM - 03:00 PM (24) ,@(df.end==24),coef_Arrival_Constant_0230_PM_0300_PM_24_ -util_Arrival_Constant_0300_PM_0330_PM_25_,SCHOOL - Arrival Constant: 03:00 PM - 03:30 PM (25) ,@(df.end==25),coef_Arrival_Constant_0300_PM_0330_PM_25_ -util_Arrival_Constant_0330_PM_0400_PM_26_,SCHOOL - Arrival Constant: 03:30 PM - 04:00 PM (26) ,@(df.end==26),coef_Arrival_Constant_0330_PM_0400_PM_26_ -util_Arrival_Constant_0400_PM_0430_PM_27_,SCHOOL - Arrival Constant: 04:00 PM - 04:30 PM (27) ,@(df.end==27),coef_Arrival_Constant_0400_PM_0430_PM_27_ -util_Arrival_Constant_0430_PM_0500_PM_28_,SCHOOL - Arrival Constant: 04:30 PM - 05:00 PM (28) ,@(df.end==28),coef_Arrival_Constant_0430_PM_0500_PM_28_ -util_Arrival_Constant_0500_PM_0530_PM_29,SCHOOL - Arrival Constant: 05:00 PM - 05:30 PM (29),@(df.end==29),coef_Arrival_Constant_0500_PM_0530_PM_29 -util_Arrival_Constant_0530_PM_0600_PM_30_,SCHOOL - Arrival Constant: 05:30 PM - 06:00 PM (30) ,@(df.end==30),coef_Arrival_Constant_0530_PM_0600_PM_30_ -util_Arrival_Constant_After_0600_PM,SCHOOL - Arrival Constant: After 06:00 PM,@(df.end>30),coef_Arrival_Constant_After_0600_PM -util_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear,SCHOOL - Arrival Constant: Shift for every 30 minutes after 6:30 pm - Linear,"@(df.end>31) * ((np.minimum(24-df.end,6)*(df.end<24)) + (np.minimum(df.end-31,12)*(df.end>31)))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear -util_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear,SCHOOL - Duration Constant: Shift for every 30 minutes less than 6.5 hrs - Linear,"@((df.duration<13)) * ((np.minimum(13-df.duration,48)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear -util_Duration_Constant_Shorter_than_7_hrs,SCHOOL - Duration Constant: Shorter than 7 hrs,@(df.duration<14),coef_Duration_Constant_Shorter_than_7_hrs -util_Duration_Constant_7_hours,SCHOOL - Duration Constant: 7 hours,@(df.duration==14),coef_Duration_Constant_7_hours -util_Duration_Constant_7p5_hours,SCHOOL - Duration Constant: 7.5 hours,@(df.duration==15),coef_Duration_Constant_7p5_hours -util_Duration_Constant_8_hours,SCHOOL - Duration Constant: 8 hours,@(df.duration==16),coef_Duration_Constant_8_hours -util_Duration_Constant_8p5_hours,SCHOOL - Duration Constant: 8.5 hours,@(df.duration==17),coef_Duration_Constant_8p5_hours -util_Duration_Constant_9_hours,SCHOOL - Duration Constant: 9 hours,@(df.duration==18),coef_Duration_Constant_9_hours -util_Duration_Constant_Longer_than_9_hrs,SCHOOL - Duration Constant: Longer than 9 hrs,@(df.duration>18),coef_Duration_Constant_Longer_than_9_hrs -util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Linear,"@(df.duration>19) * ((np.minimum(13-df.duration,47)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19)))",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear -util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Squared,"@(df.duration>19) * (((np.minimum(13-df.duration,47)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19))) ** 2)",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared +Label,Description,Expression,Coefficient +util_Mode_Choice_Logsum,SCHOOL - Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum +util_Low_income_lt25000_Departure_before_730_am__Linear,SCHOOL - Low income (<25000) - Departure before 7:30 am - Linear,"@((df.is_income_less25K) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Low_income_lt25000_Departure_before_730_am__Linear +util_Low_income_lt25000_Departure_after_800_am_Linear,SCHOOL - Low income (<25000) - Departure after 8:00 am - Linear,"@((df.is_income_less25K) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Low_income_lt25000_Departure_after_800_am_Linear +util_Low_income_lt25000_Duration_lt_8hrs,SCHOOL - Low income (<25000) - Duration < 8hrs,"@((df.is_income_less25K) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Low_income_lt25000_Duration_lt_8hrs +util_Low_income_lt25000_Duration_gt_8hrs,SCHOOL - Low income (<25000) - Duration > 8hrs,"@((df.is_income_less25K) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Low_income_lt25000_Duration_gt_8hrs +util_Med_income_25k_to_60k_Departure_before_730_am__Linear,SCHOOL - Med income (25k to 60k) - Departure before 7:30 am - Linear,"@((df.is_income_25K_to_60K) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Med_income_25k_to_60k_Departure_before_730_am__Linear +util_Age_0_to_5_yrs_Departure_Before_730_am,SCHOOL - Age 0 to 5 yrs - Departure Before 7:30 am,"@(((df.age>=0) & (df.age<=5)) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Age_0_to_5_yrs_Departure_Before_730_am +util_Age_13_to_15_yrs_Departure_Before_730_am,SCHOOL - Age 13 to 15 yrs - Departure Before 7:30 am,"@(((df.age>=13) & (df.age<=15)) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Age_13_to_15_yrs_Departure_Before_730_am +util_Age_13_to_15_yrs_Departure_After_800_am,SCHOOL - Age 13 to 15 yrs - Departure After 8:00 am,"@(((df.age>=13) & (df.age<=15)) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Age_13_to_15_yrs_Departure_After_800_am +util_Age_16_to_17_yrs_Departure_After_800_am,SCHOOL - Age 16 to 17 yrs - Departure After 8:00 am,"@(((df.age>=16) & (df.age<=17)) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Age_16_to_17_yrs_Departure_After_800_am +util_Age_0_to_5_yrs_Duration_lt_8hrs,SCHOOL - Age 0 to 5 yrs - Duration < 8hrs,"@(((df.age>0) & (df.age<=5)) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_0_to_5_yrs_Duration_lt_8hrs +util_Age_0_to_5_yrs_Duration_gt_8hrs,SCHOOL - Age 0 to 5 yrs - Duration > 8hrs,"@(((df.age>0) & (df.age<=5)) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_0_to_5_yrs_Duration_gt_8hrs +util_Age_13_to_15_yrs_Duration_lt_8hrs,SCHOOL - Age 13 to 15 yrs - Duration < 8hrs,"@(((df.age>=13) & (df.age<=15)) & (df.duration<8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_13_to_15_yrs_Duration_lt_8hrs +util_Age_13_to_15_yrs_Duration_gt_8hrs,SCHOOL - Age 13 to 15 yrs - Duration > 8hrs,"@(((df.age>=13) & (df.age<=15)) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_13_to_15_yrs_Duration_gt_8hrs +util_Age_16_to_17_yrs_Duration_gt_8hrs,SCHOOL - Age 16 to 17 yrs - Duration > 8hrs,"@(((df.age>=16) & (df.age<=17)) & (df.duration>8)) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Age_16_to_17_yrs_Duration_gt_8hrs +util_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear,SCHOOL - Time (SOV freeflow) to destination - Departure before 7:30 am - Linear,"@(df.start<10) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10))) * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear +util_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear,SCHOOL - Time (SOV freeflow) to destination - Departure after 8:00 am - Linear,"@(df.start>10) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10))) * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear +util_Time_SOV_freeflow_to_destination_Duration_lt_8hrs,SCHOOL - Time (SOV freeflow) to destination - Duration < 8hrs,"@(df.end<27) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27))) * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Duration_lt_8hrs +util_Time_SOV_freeflow_to_destination_Duration_gt_8hrs,SCHOOL - Time (SOV freeflow) to destination - Duration > 8hrs,"@(df.end>27) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27))) * (df.roundtrip_auto_time_to_school)",coef_Time_SOV_freeflow_to_destination_Duration_gt_8hrs +util_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear,SCHOOL - All adults in the household are fulltime workers - Departure before 7:30 am - Linear,"@((df.is_all_adults_full_time_workers) & (df.start<10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear +util_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear,SCHOOL - All adults in the household are fulltime workers - Departure after 8:00 am - Linear,"@((df.is_all_adults_full_time_workers) & (df.start>10)) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear +util_All_adults_in_the_household_are_fulltime_workers_Duration_lt_8hrs,SCHOOL - All adults in the household are fulltime workers - Duration < 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end<27)) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27)))",coef_All_adults_in_the_household_are_fulltime_workers_Duration_lt_8hrs +util_All_adults_in_the_household_are_fulltime_workers_Duration_gt_8hrs,SCHOOL - All adults in the household are fulltime workers - Duration > 8hrs,"@((df.is_all_adults_full_time_workers) & (df.end>27)) * ((np.minimum(27-df.end,48)*(df.end<=27)) + (np.minimum(df.end-27,48)*(df.end>27)))",coef_All_adults_in_the_household_are_fulltime_workers_Duration_gt_8hrs +util_Subsequent_tour_is_work_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is work tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.duration<8))) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours +util_Subsequent_tour_is_work_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is work tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.duration>8))) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours +util_Subsequent_tour_is_school_tour_Departure_after_800_am,SCHOOL - Subsequent tour is school tour: Departure after 8:00 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start>10))) *((np.minimum(10-df.start,48)*(df.start<=10)) + (np.minimum(df.start-10,48)*(df.start>10)))",coef_Subsequent_tour_is_school_tour_Departure_after_800_am +util_Subsequent_tour_is_school_tour_Duration_lt_8_hours,SCHOOL - Subsequent tour is school tour: Duration < 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.duration<8))) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours +util_Subsequent_tour_is_school_tour_Duration_gt_8_hours,SCHOOL - Subsequent tour is school tour: Duration > 8 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.duration>8))) * ((np.minimum(8-df.duration,47)*(df.duration<=8)) + (np.minimum(df.duration-8,47)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours +util_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<7)) * ((np.minimum(7-df.duration,47)*(df.duration<=7)) + (np.minimum(df.duration-7,47)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours +util_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,SCHOOL - Second tour of two mandatory tours: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration>7)) * ((np.minimum(7-df.duration,47)*(df.duration<=7)) + (np.minimum(df.duration-7,47)*(df.duration>7)))",coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours +util_Departure_Constant_Before_0600_AM,SCHOOL - Departure Constant: Before 06:00 AM,@(df.start<7),coef_Departure_Constant_Before_0600_AM +util_Departure_Constant_0600_AM_to_0630_AM_7,SCHOOL - Departure Constant: 06:00 AM to 06:30 AM (7),@(df.start==7),coef_Departure_Constant_0600_AM_to_0630_AM_7 +util_Departure_Constant_0630_AM_to_0700_AM_8,SCHOOL - Departure Constant: 06:30 AM to 07:00 AM (8),@(df.start==8),coef_Departure_Constant_0630_AM_to_0700_AM_8 +util_Departure_Constant_0700_AM_to_0730_AM_9,SCHOOL - Departure Constant: 07:00 AM to 07:30 AM (9),@(df.start==9),coef_Departure_Constant_0700_AM_to_0730_AM_9 +util_Departure_Constant_0730_AM_to_0800_AM_10,SCHOOL - Departure Constant: 07:30 AM to 08:00 AM (10),@(df.start==10),coef_Departure_Constant_0730_AM_to_0800_AM_10 +util_Departure_Constant_After_0800_AM,SCHOOL - Departure Constant: After 08:00 AM,@(df.start>10),coef_Departure_Constant_After_0800_AM +util_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear,SCHOOL - Departure Constant: Shift for every 30 minutes after 8:30 am - Linear,"@((df.start>11)) * ((np.minimum(7-df.start,48)*(df.start<7)) + (np.minimum(df.start-11,23)*(df.start>11)))",coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear +util_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root,SCHOOL - Departure Constant: Shift for every 30 minutes after 8:30 am - Square Root,"@((df.start>11)) * (((np.minimum(7-df.start,48)*(df.start<7)) + (np.minimum(df.start-11,23)*(df.start>11))) ** 0.5)",coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root +util_Arrival_Constant_Before_0230_PM,SCHOOL - Arrival Constant: Before 02:30 PM,@(df.end<24),coef_Arrival_Constant_Before_0230_PM +util_Arrival_Constant_0230_PM_0300_PM_24_,SCHOOL - Arrival Constant: 02:30 PM - 03:00 PM (24) ,@(df.end==24),coef_Arrival_Constant_0230_PM_0300_PM_24_ +util_Arrival_Constant_0300_PM_0330_PM_25_,SCHOOL - Arrival Constant: 03:00 PM - 03:30 PM (25) ,@(df.end==25),coef_Arrival_Constant_0300_PM_0330_PM_25_ +util_Arrival_Constant_0330_PM_0400_PM_26_,SCHOOL - Arrival Constant: 03:30 PM - 04:00 PM (26) ,@(df.end==26),coef_Arrival_Constant_0330_PM_0400_PM_26_ +util_Arrival_Constant_0400_PM_0430_PM_27_,SCHOOL - Arrival Constant: 04:00 PM - 04:30 PM (27) ,@(df.end==27),coef_Arrival_Constant_0400_PM_0430_PM_27_ +util_Arrival_Constant_0430_PM_0500_PM_28_,SCHOOL - Arrival Constant: 04:30 PM - 05:00 PM (28) ,@(df.end==28),coef_Arrival_Constant_0430_PM_0500_PM_28_ +util_Arrival_Constant_0500_PM_0530_PM_29,SCHOOL - Arrival Constant: 05:00 PM - 05:30 PM (29),@(df.end==29),coef_Arrival_Constant_0500_PM_0530_PM_29 +util_Arrival_Constant_0530_PM_0600_PM_30_,SCHOOL - Arrival Constant: 05:30 PM - 06:00 PM (30) ,@(df.end==30),coef_Arrival_Constant_0530_PM_0600_PM_30_ +util_Arrival_Constant_After_0600_PM,SCHOOL - Arrival Constant: After 06:00 PM,@(df.end>30),coef_Arrival_Constant_After_0600_PM +util_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear,SCHOOL - Arrival Constant: Shift for every 30 minutes after 6:30 pm - Linear,"@(df.end>31) * ((np.minimum(24-df.end,6)*(df.end<24)) + (np.minimum(df.end-31,12)*(df.end>31)))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear +util_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear,SCHOOL - Duration Constant: Shift for every 30 minutes less than 6.5 hrs - Linear,"@((df.duration<13)) * ((np.minimum(13-df.duration,48)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear +util_Duration_Constant_Shorter_than_7_hrs,SCHOOL - Duration Constant: Shorter than 7 hrs,@(df.duration<14),coef_Duration_Constant_Shorter_than_7_hrs +util_Duration_Constant_7_hours,SCHOOL - Duration Constant: 7 hours,@(df.duration==14),coef_Duration_Constant_7_hours +util_Duration_Constant_7p5_hours,SCHOOL - Duration Constant: 7.5 hours,@(df.duration==15),coef_Duration_Constant_7p5_hours +util_Duration_Constant_8_hours,SCHOOL - Duration Constant: 8 hours,@(df.duration==16),coef_Duration_Constant_8_hours +util_Duration_Constant_8p5_hours,SCHOOL - Duration Constant: 8.5 hours,@(df.duration==17),coef_Duration_Constant_8p5_hours +util_Duration_Constant_9_hours,SCHOOL - Duration Constant: 9 hours,@(df.duration==18),coef_Duration_Constant_9_hours +util_Duration_Constant_Longer_than_9_hrs,SCHOOL - Duration Constant: Longer than 9 hrs,@(df.duration>18),coef_Duration_Constant_Longer_than_9_hrs +util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Linear,"@(df.duration>19) * ((np.minimum(13-df.duration,47)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19)))",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear +util_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared,SCHOOL - Duration Constant: Shift for every 30 minutes more than 9.5 hrs - Squared,"@(df.duration>19) * (((np.minimum(13-df.duration,47)*(df.duration<13)) + (np.minimum(df.duration-19,9)*(df.duration>19))) ** 2)",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_school_coeffs.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_school_coeffs.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/tour_scheduling_school_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_school_coeffs.csv index af08217498..589b4ce61c 100755 --- a/activitysim/examples/example_semcog/configs/tour_scheduling_school_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_school_coeffs.csv @@ -1,59 +1,59 @@ -coefficient_name,value,constrain -coef_Mode_Choice_Logsum,0.524017431,F -coef_Low_income_lt25000_Departure_before_730_am__Linear,0.134574548,F -coef_Low_income_lt25000_Departure_after_800_am_Linear,-0.075554725,F -coef_Low_income_lt25000_Duration_lt_8hrs,-0.150039779,F -coef_Low_income_lt25000_Duration_gt_8hrs,-0.043562413,F -coef_Med_income_25k_to_60k_Departure_before_730_am__Linear,0.102594589,F -coef_Age_0_to_5_yrs_Departure_Before_730_am,-0.178916721,F -coef_Age_13_to_15_yrs_Departure_Before_730_am,-0.164708594,F -coef_Age_13_to_15_yrs_Departure_After_800_am,0.169641715,F -coef_Age_16_to_17_yrs_Departure_After_800_am,0.077527892,F -coef_Age_0_to_5_yrs_Duration_lt_8hrs,0.254486164,F -coef_Age_0_to_5_yrs_Duration_gt_8hrs,0.14409403,F -coef_Age_13_to_15_yrs_Duration_lt_8hrs,-0.211129273,F -coef_Age_13_to_15_yrs_Duration_gt_8hrs,0.102348303,F -coef_Age_16_to_17_yrs_Duration_gt_8hrs,0.1184616,F -coef_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear,0.011813391,F -coef_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear,-0.0088956,F -coef_Time_SOV_freeflow_to_destination_Duration_lt_8hrs,-0.011793416,F -coef_Time_SOV_freeflow_to_destination_Duration_gt_8hrs,0.001485453,F -coef_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear,0.1625279,F -coef_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear,-0.161840551,F -coef_All_adults_in_the_household_are_fulltime_workers_Duration_lt_8hrs,-0.233061473,F -coef_All_adults_in_the_household_are_fulltime_workers_Duration_gt_8hrs,0.08462748,F -coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours,0.154332088,F -coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours,-0.62871831,F -coef_Subsequent_tour_is_school_tour_Departure_after_800_am,-0.41618671,F -coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours,0.261423274,F -coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours,-0.263857404,F -coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,-0.537535787,F -coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,-0.545479806,F -coef_Departure_Constant_Before_0600_AM,-10.04417122,F -coef_Departure_Constant_0600_AM_to_0630_AM_7,-3.792318538,F -coef_Departure_Constant_0630_AM_to_0700_AM_8,-1.941704371,F -coef_Departure_Constant_0700_AM_to_0730_AM_9,-0.558080224,F -coef_Departure_Constant_0730_AM_to_0800_AM_10,0,T -coef_Departure_Constant_After_0800_AM,-0.280439854,F -coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear,0.293697164,F -coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root,-1.220165702,F -coef_Arrival_Constant_Before_0230_PM,0.720751128,F -coef_Arrival_Constant_0230_PM_0300_PM_24_,1.605012317,F -coef_Arrival_Constant_0300_PM_0330_PM_25_,0.463502951,F -coef_Arrival_Constant_0330_PM_0400_PM_26_,0.196107179,F -coef_Arrival_Constant_0400_PM_0430_PM_27_,0,T -coef_Arrival_Constant_0430_PM_0500_PM_28_,-0.389421484,F -coef_Arrival_Constant_0500_PM_0530_PM_29,-1.412720271,F -coef_Arrival_Constant_0530_PM_0600_PM_30_,-1.938567609,F -coef_Arrival_Constant_After_0600_PM,-2.246103785,F -coef_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear,-0.552223894,F -coef_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear,-0.249724903,F -coef_Duration_Constant_Shorter_than_7_hrs,-2.791243553,F -coef_Duration_Constant_7_hours,-1.679006455,F -coef_Duration_Constant_7p5_hours,-0.555288612,F -coef_Duration_Constant_8_hours,0,T -coef_Duration_Constant_8p5_hours,-0.139412248,F -coef_Duration_Constant_9_hours,-0.509620713,F -coef_Duration_Constant_Longer_than_9_hrs,-0.561449384,F -coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear,0.379484906,F -coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared,-0.028814477,F +coefficient_name,value,constrain +coef_Mode_Choice_Logsum,0.524017431,F +coef_Low_income_lt25000_Departure_before_730_am__Linear,0.134574548,F +coef_Low_income_lt25000_Departure_after_800_am_Linear,-0.075554725,F +coef_Low_income_lt25000_Duration_lt_8hrs,-0.150039779,F +coef_Low_income_lt25000_Duration_gt_8hrs,-0.043562413,F +coef_Med_income_25k_to_60k_Departure_before_730_am__Linear,0.102594589,F +coef_Age_0_to_5_yrs_Departure_Before_730_am,-0.178916721,F +coef_Age_13_to_15_yrs_Departure_Before_730_am,-0.164708594,F +coef_Age_13_to_15_yrs_Departure_After_800_am,0.169641715,F +coef_Age_16_to_17_yrs_Departure_After_800_am,0.077527892,F +coef_Age_0_to_5_yrs_Duration_lt_8hrs,0.254486164,F +coef_Age_0_to_5_yrs_Duration_gt_8hrs,0.14409403,F +coef_Age_13_to_15_yrs_Duration_lt_8hrs,-0.211129273,F +coef_Age_13_to_15_yrs_Duration_gt_8hrs,0.102348303,F +coef_Age_16_to_17_yrs_Duration_gt_8hrs,0.1184616,F +coef_Time_SOV_freeflow_to_destination_Departure_before_730_am__Linear,0.011813391,F +coef_Time_SOV_freeflow_to_destination_Departure_after_800_am_Linear,-0.0088956,F +coef_Time_SOV_freeflow_to_destination_Duration_lt_8hrs,-0.011793416,F +coef_Time_SOV_freeflow_to_destination_Duration_gt_8hrs,0.001485453,F +coef_All_adults_in_the_household_are_fulltime_workers_Departure_before_730_am__Linear,0.1625279,F +coef_All_adults_in_the_household_are_fulltime_workers_Departure_after_800_am_Linear,-0.161840551,F +coef_All_adults_in_the_household_are_fulltime_workers_Duration_lt_8hrs,-0.233061473,F +coef_All_adults_in_the_household_are_fulltime_workers_Duration_gt_8hrs,0.08462748,F +coef_Subsequent_tour_is_work_tour_Duration_lt_8_hours,0.154332088,F +coef_Subsequent_tour_is_work_tour_Duration_gt_8_hours,-0.62871831,F +coef_Subsequent_tour_is_school_tour_Departure_after_800_am,-0.41618671,F +coef_Subsequent_tour_is_school_tour_Duration_lt_8_hours,0.261423274,F +coef_Subsequent_tour_is_school_tour_Duration_gt_8_hours,-0.263857404,F +coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,-0.537535787,F +coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,-0.545479806,F +coef_Departure_Constant_Before_0600_AM,-10.04417122,F +coef_Departure_Constant_0600_AM_to_0630_AM_7,-3.792318538,F +coef_Departure_Constant_0630_AM_to_0700_AM_8,-1.941704371,F +coef_Departure_Constant_0700_AM_to_0730_AM_9,-0.558080224,F +coef_Departure_Constant_0730_AM_to_0800_AM_10,0,T +coef_Departure_Constant_After_0800_AM,-0.280439854,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Linear,0.293697164,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_830_am_Square_Root,-1.220165702,F +coef_Arrival_Constant_Before_0230_PM,0.720751128,F +coef_Arrival_Constant_0230_PM_0300_PM_24_,1.605012317,F +coef_Arrival_Constant_0300_PM_0330_PM_25_,0.463502951,F +coef_Arrival_Constant_0330_PM_0400_PM_26_,0.196107179,F +coef_Arrival_Constant_0400_PM_0430_PM_27_,0,T +coef_Arrival_Constant_0430_PM_0500_PM_28_,-0.389421484,F +coef_Arrival_Constant_0500_PM_0530_PM_29,-1.412720271,F +coef_Arrival_Constant_0530_PM_0600_PM_30_,-1.938567609,F +coef_Arrival_Constant_After_0600_PM,-2.246103785,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_630_pm_Linear,-0.552223894,F +coef_Duration_Constant_Shift_for_every_30_minutes_less_than_6p5_hrs_Linear,-0.249724903,F +coef_Duration_Constant_Shorter_than_7_hrs,-2.791243553,F +coef_Duration_Constant_7_hours,-1.679006455,F +coef_Duration_Constant_7p5_hours,-0.555288612,F +coef_Duration_Constant_8_hours,0,T +coef_Duration_Constant_8p5_hours,-0.139412248,F +coef_Duration_Constant_9_hours,-0.509620713,F +coef_Duration_Constant_Longer_than_9_hrs,-0.561449384,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Linear,0.379484906,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_9p5_hrs_Squared,-0.028814477,F diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_university.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_university.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/tour_scheduling_university.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_university.csv index 7f6e5bdccf..9617383a48 100755 --- a/activitysim/examples/example_semcog/configs/tour_scheduling_university.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_university.csv @@ -1,42 +1,42 @@ -Label,Description,Expression,Coefficient -util_Mode_Choice_Logsum,UNIVERSITY - Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum -util_Low_income_lt25000_Departure_before_800_am_Linear,UNIVERSITY - Low income (<25000) - Departure before 8:00 am - Linear,"@((df.is_income_less25K) & (df.start<11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Low_income_lt25000_Departure_before_800_am_Linear -util_Low_income_lt25000_Duration_lt_4hrs,UNIVERSITY - Low income (<25000) - Duration < 4hrs,"@((df.is_income_less25K) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Low_income_lt25000_Duration_lt_4hrs -util_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear,UNIVERSITY - Medium high income (60k to 120k) - Departure after 8:30 am - Linear,"@((df.is_income_60K_to_120K) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear -util_Medium_high_income_60k_to_120k_Duration_gt_4hrs,UNIVERSITY - Medium high income (60k to 120k) - Duration > 4hrs,"@((df.is_income_60K_to_120K) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Medium_high_income_60k_to_120k_Duration_gt_4hrs -util_High_income_120k_plus_Departure_after_830_am_Linear,UNIVERSITY - High income (120k+) - Departure after 8:30 am - Linear,"@((df.is_income_greater120K) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_High_income_120k_plus_Departure_after_830_am_Linear -util_Age_41_plus_Departure_after_830_am_Linear,UNIVERSITY - Age 41+ - Departure after 8:30 am - Linear,"@((df.age >= 41) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Age_41_plus_Departure_after_830_am_Linear -util_Age_41_plus_Durationlt_4_hrs_Linear,UNIVERSITY - Age 41+ - Duration< 4 hrs -Linear,"@((df.age >= 41) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Age_41_plus_Durationlt_4_hrs_Linear -util_Distance_to_destination_Departure_before_800_am_Linear,UNIVERSITY - Distance to destination - Departure before 8:00 am - Linear,"@((df.start<11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))*(df.distance_to_school)",coef_Distance_to_destination_Departure_before_800_am_Linear -util_Distance_to_destination_Departure_after_830_am_Linear,UNIVERSITY - Distance to destination - Departure after 8:30 am - Linear,"@((df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))*(df.distance_to_school)",coef_Distance_to_destination_Departure_after_830_am_Linear -util_Distance_to_destination_Durationlt_4_hrs_Linear,UNIVERSITY - Distance to destination - Duration< 4 hrs -Linear,"@((df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))*(df.distance_to_school)",coef_Distance_to_destination_Durationlt_4_hrs_Linear -util_Distance_to_destination_Durationgt_4_hrs_Linear,UNIVERSITY - Distance to destination - Duration> 4 hrs- Linear,"@((df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))*(df.distance_to_school)",coef_Distance_to_destination_Durationgt_4_hrs_Linear -util_Distance_to_destination_Durationlt_4_hrs_Square_Root,UNIVERSITY - Distance to destination - Duration< 4 hrs - Square Root,"@((df.duration<8))*(abs(((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))) ** 0.5)*(df.distance_to_school)",coef_Distance_to_destination_Durationlt_4_hrs_Square_Root -util_Subsequent_tour_is_work_tour_Departure_after_830_am,UNIVERSITY - Subsequent tour is work tour: Departure after 8:30 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.start>11)))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Subsequent_tour_is_work_tour_Departure_after_830_am -util_Subsequent_tour_is_work_tour_Duration_lt_4_hours,UNIVERSITY - Subsequent tour is work tour: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.duration<8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_lt_4_hours -util_Subsequent_tour_is_work_tour_Duration_gt_4_hours,UNIVERSITY - Subsequent tour is work tour: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.duration>8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_gt_4_hours -util_Subsequent_tour_is_school_tour_Departure_after_830_am,UNIVERSITY - Subsequent tour is school tour: Departure after 8:30 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start>11)))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Subsequent_tour_is_school_tour_Departure_after_830_am -util_Subsequent_tour_is_school_tour_Duration_lt_4_hours,UNIVERSITY - Subsequent tour is school tour: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.duration<8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_lt_4_hours -util_Subsequent_tour_is_school_tour_Duration_gt_4_hours,UNIVERSITY - Subsequent tour is school tour: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.duration>8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_gt_4_hours -util_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,UNIVERSITY - Second tour of two mandatory tours: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours -util_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,UNIVERSITY - Second tour of two mandatory tours: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours -util_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear,UNIVERSITY - Departure Constant: Shift for every 30 minutes before 07:00 am - Linear,"@((df.start<9))*((np.minimum(9-df.start,6)*(df.start<9)) + (np.minimum(df.start-13,20)*(df.start>13)))",coef_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear -util_Departure_Constant_Before_0730_AM,UNIVERSITY - Departure Constant: Before 07:30 AM,@(df.start<10),coef_Departure_Constant_Before_0730_AM -util_Departure_Constant_0730_AM_0800_AM,UNIVERSITY - Departure Constant: 07:30 AM - 08:00 AM,@(df.start==10),coef_Departure_Constant_0730_AM_0800_AM -util_Departure_Constant_0800_AM_0830_AM,UNIVERSITY - Departure Constant: 08:00 AM - 08:30 AM,@(df.start==11),coef_Departure_Constant_0800_AM_0830_AM -util_Departure_Constant_0830_AM_0900_AM,UNIVERSITY - Departure Constant: 08:30 AM - 09:00 AM,@(df.start==12),coef_Departure_Constant_0830_AM_0900_AM -util_Departure_Constant_After_0900_AM,UNIVERSITY - Departure Constant: After 09:00 AM,@(df.start>12),coef_Departure_Constant_After_0900_AM -util_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root,UNIVERSITY - Departure Constant: Shift for every 30 minutes after 09:30 am - Square Root,"@((df.start>13))*(((np.minimum(9-df.start,6)*(df.start<9)) + (np.minimum(df.start-13,20)*(df.start>13))) ** 0.5)",coef_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root -util_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear,UNIVERSITY - Arrival Constant: Shift for every 30 minutes before 02:30 pm - Linear,"@((df.end<24)) * ((np.minimum(24-df.end,12) * (df.end<24)) + (np.minimum(df.end-28,19) * (df.end>28)))",coef_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear -util_Arrival_Constant_Before_0300_PM,UNIVERSITY - Arrival Constant: Before 03:00 PM,@((df.end<25)),coef_Arrival_Constant_Before_0300_PM -util_Arrival_Constant_0300_PM_0330_PM,UNIVERSITY - Arrival Constant: 03:00 PM - 03:30 PM,@(df.end==25),coef_Arrival_Constant_0300_PM_0330_PM -util_Arrival_Constant_0330_PM_0400_PM,UNIVERSITY - Arrival Constant: 03:30 PM - 04:00 PM,@(df.end==26),coef_Arrival_Constant_0330_PM_0400_PM -util_Arrival_Constant_0400_PM_0430_PM,UNIVERSITY - Arrival Constant: 04:00 PM - 04:30 PM,@(df.end==27),coef_Arrival_Constant_0400_PM_0430_PM -util_Arrival_Constant_After_0430_PM,UNIVERSITY - Arrival Constant: After 04:30 PM,@(df.end>27),coef_Arrival_Constant_After_0430_PM -util_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear,UNIVERSITY - Arrival Constant: Shift for every 30 minutes after 05:00 pm - Linear,"@((df.end>28))*((np.minimum(24-df.end,12)*(df.end<24)) + (np.minimum(df.end-28,19)*(df.end>28)))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear -util_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root,UNIVERSITY - Arrival Constant: Shift for every 30 minutes after 05:00 pm - Square Root,"@((df.end>28)) *(((np.minimum(24-df.end,12)*(df.end<24)) + (np.minimum(df.end-28,19)*(df.end>28))) ** 0.5)",coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root -util_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root,UNIVERSITY - Duration Constant: Shift for every 30 minutes less than 4.5 hrs - Square Root,"@((df.duration<9))*((np.minimum(9-df.duration,7)*(df.duration<9)) + (np.minimum(df.duration-11,25)*(df.duration>11)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root -util_Duration_Constant_4p5_hours_or_less,UNIVERSITY - Duration Constant: 4.5 hours or less,@(df.duration<10),coef_Duration_Constant_4p5_hours_or_less -util_Duration_Constant_5_hours,UNIVERSITY - Duration Constant: 5 hours,@(df.duration==10),coef_Duration_Constant_5_hours -util_Duration_Constant_5p5_hours_or_more,UNIVERSITY - Duration Constant: 5.5 hours or more,@(df.duration>10),coef_Duration_Constant_5p5_hours_or_more -util_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear,UNIVERSITY - Duration Constant: Shift for every 30 minutes more than 5.5 hrs - Linear,"@((df.duration>11))*(((np.minimum(9-df.duration,7)*(df.duration<9)) + (np.minimum(df.duration-11,25)*(df.duration>11))) ** 0.5)",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear +Label,Description,Expression,Coefficient +util_Mode_Choice_Logsum,UNIVERSITY - Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum +util_Low_income_lt25000_Departure_before_800_am_Linear,UNIVERSITY - Low income (<25000) - Departure before 8:00 am - Linear,"@((df.is_income_less25K) & (df.start<11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Low_income_lt25000_Departure_before_800_am_Linear +util_Low_income_lt25000_Duration_lt_4hrs,UNIVERSITY - Low income (<25000) - Duration < 4hrs,"@((df.is_income_less25K) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Low_income_lt25000_Duration_lt_4hrs +util_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear,UNIVERSITY - Medium high income (60k to 120k) - Departure after 8:30 am - Linear,"@((df.is_income_60K_to_120K) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear +util_Medium_high_income_60k_to_120k_Duration_gt_4hrs,UNIVERSITY - Medium high income (60k to 120k) - Duration > 4hrs,"@((df.is_income_60K_to_120K) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Medium_high_income_60k_to_120k_Duration_gt_4hrs +util_High_income_120k_plus_Departure_after_830_am_Linear,UNIVERSITY - High income (120k+) - Departure after 8:30 am - Linear,"@((df.is_income_greater120K) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_High_income_120k_plus_Departure_after_830_am_Linear +util_Age_41_plus_Departure_after_830_am_Linear,UNIVERSITY - Age 41+ - Departure after 8:30 am - Linear,"@((df.age >= 41) & (df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Age_41_plus_Departure_after_830_am_Linear +util_Age_41_plus_Durationlt_4_hrs_Linear,UNIVERSITY - Age 41+ - Duration< 4 hrs -Linear,"@((df.age >= 41) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Age_41_plus_Durationlt_4_hrs_Linear +util_Distance_to_destination_Departure_before_800_am_Linear,UNIVERSITY - Distance to destination - Departure before 8:00 am - Linear,"@((df.start<11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))*(df.distance_to_school)",coef_Distance_to_destination_Departure_before_800_am_Linear +util_Distance_to_destination_Departure_after_830_am_Linear,UNIVERSITY - Distance to destination - Departure after 8:30 am - Linear,"@((df.start>11))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))*(df.distance_to_school)",coef_Distance_to_destination_Departure_after_830_am_Linear +util_Distance_to_destination_Durationlt_4_hrs_Linear,UNIVERSITY - Distance to destination - Duration< 4 hrs -Linear,"@((df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))*(df.distance_to_school)",coef_Distance_to_destination_Durationlt_4_hrs_Linear +util_Distance_to_destination_Durationgt_4_hrs_Linear,UNIVERSITY - Distance to destination - Duration> 4 hrs- Linear,"@((df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))*(df.distance_to_school)",coef_Distance_to_destination_Durationgt_4_hrs_Linear +util_Distance_to_destination_Durationlt_4_hrs_Square_Root,UNIVERSITY - Distance to destination - Duration< 4 hrs - Square Root,"@((df.duration<8))*(abs(((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))) ** 0.5)*(df.distance_to_school)",coef_Distance_to_destination_Durationlt_4_hrs_Square_Root +util_Subsequent_tour_is_work_tour_Departure_after_830_am,UNIVERSITY - Subsequent tour is work tour: Departure after 8:30 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.start>11)))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Subsequent_tour_is_work_tour_Departure_after_830_am +util_Subsequent_tour_is_work_tour_Duration_lt_4_hours,UNIVERSITY - Subsequent tour is work tour: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.duration<8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_lt_4_hours +util_Subsequent_tour_is_work_tour_Duration_gt_4_hours,UNIVERSITY - Subsequent tour is work tour: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'work') & (df.duration>8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_work_tour_Duration_gt_4_hours +util_Subsequent_tour_is_school_tour_Departure_after_830_am,UNIVERSITY - Subsequent tour is school tour: Departure after 8:30 am,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.start>11)))*((np.minimum(11-df.start,8)*(df.start<=11)) + (np.minimum(df.start-11,22)*(df.start>11)))",coef_Subsequent_tour_is_school_tour_Departure_after_830_am +util_Subsequent_tour_is_school_tour_Duration_lt_4_hours,UNIVERSITY - Subsequent tour is school tour: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.duration<8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_lt_4_hours +util_Subsequent_tour_is_school_tour_Duration_gt_4_hours,UNIVERSITY - Subsequent tour is school tour: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num == 1) & (df.tour_type.shift(-1) == 'school') & (df.duration>8)))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Subsequent_tour_is_school_tour_Duration_gt_4_hours +util_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,UNIVERSITY - Second tour of two mandatory tours: Duration < 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours +util_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,UNIVERSITY - Second tour of two mandatory tours: Duration > 4 hours,"@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration>8))*((np.minimum(8-df.duration,6)*(df.duration<=8)) + (np.minimum(df.duration-8,28)*(df.duration>8)))",coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours +util_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear,UNIVERSITY - Departure Constant: Shift for every 30 minutes before 07:00 am - Linear,"@((df.start<9))*((np.minimum(9-df.start,6)*(df.start<9)) + (np.minimum(df.start-13,20)*(df.start>13)))",coef_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear +util_Departure_Constant_Before_0730_AM,UNIVERSITY - Departure Constant: Before 07:30 AM,@(df.start<10),coef_Departure_Constant_Before_0730_AM +util_Departure_Constant_0730_AM_0800_AM,UNIVERSITY - Departure Constant: 07:30 AM - 08:00 AM,@(df.start==10),coef_Departure_Constant_0730_AM_0800_AM +util_Departure_Constant_0800_AM_0830_AM,UNIVERSITY - Departure Constant: 08:00 AM - 08:30 AM,@(df.start==11),coef_Departure_Constant_0800_AM_0830_AM +util_Departure_Constant_0830_AM_0900_AM,UNIVERSITY - Departure Constant: 08:30 AM - 09:00 AM,@(df.start==12),coef_Departure_Constant_0830_AM_0900_AM +util_Departure_Constant_After_0900_AM,UNIVERSITY - Departure Constant: After 09:00 AM,@(df.start>12),coef_Departure_Constant_After_0900_AM +util_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root,UNIVERSITY - Departure Constant: Shift for every 30 minutes after 09:30 am - Square Root,"@((df.start>13))*(((np.minimum(9-df.start,6)*(df.start<9)) + (np.minimum(df.start-13,20)*(df.start>13))) ** 0.5)",coef_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root +util_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear,UNIVERSITY - Arrival Constant: Shift for every 30 minutes before 02:30 pm - Linear,"@((df.end<24)) * ((np.minimum(24-df.end,12) * (df.end<24)) + (np.minimum(df.end-28,19) * (df.end>28)))",coef_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear +util_Arrival_Constant_Before_0300_PM,UNIVERSITY - Arrival Constant: Before 03:00 PM,@((df.end<25)),coef_Arrival_Constant_Before_0300_PM +util_Arrival_Constant_0300_PM_0330_PM,UNIVERSITY - Arrival Constant: 03:00 PM - 03:30 PM,@(df.end==25),coef_Arrival_Constant_0300_PM_0330_PM +util_Arrival_Constant_0330_PM_0400_PM,UNIVERSITY - Arrival Constant: 03:30 PM - 04:00 PM,@(df.end==26),coef_Arrival_Constant_0330_PM_0400_PM +util_Arrival_Constant_0400_PM_0430_PM,UNIVERSITY - Arrival Constant: 04:00 PM - 04:30 PM,@(df.end==27),coef_Arrival_Constant_0400_PM_0430_PM +util_Arrival_Constant_After_0430_PM,UNIVERSITY - Arrival Constant: After 04:30 PM,@(df.end>27),coef_Arrival_Constant_After_0430_PM +util_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear,UNIVERSITY - Arrival Constant: Shift for every 30 minutes after 05:00 pm - Linear,"@((df.end>28))*((np.minimum(24-df.end,12)*(df.end<24)) + (np.minimum(df.end-28,19)*(df.end>28)))",coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear +util_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root,UNIVERSITY - Arrival Constant: Shift for every 30 minutes after 05:00 pm - Square Root,"@((df.end>28)) *(((np.minimum(24-df.end,12)*(df.end<24)) + (np.minimum(df.end-28,19)*(df.end>28))) ** 0.5)",coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root +util_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root,UNIVERSITY - Duration Constant: Shift for every 30 minutes less than 4.5 hrs - Square Root,"@((df.duration<9))*((np.minimum(9-df.duration,7)*(df.duration<9)) + (np.minimum(df.duration-11,25)*(df.duration>11)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root +util_Duration_Constant_4p5_hours_or_less,UNIVERSITY - Duration Constant: 4.5 hours or less,@(df.duration<10),coef_Duration_Constant_4p5_hours_or_less +util_Duration_Constant_5_hours,UNIVERSITY - Duration Constant: 5 hours,@(df.duration==10),coef_Duration_Constant_5_hours +util_Duration_Constant_5p5_hours_or_more,UNIVERSITY - Duration Constant: 5.5 hours or more,@(df.duration>10),coef_Duration_Constant_5p5_hours_or_more +util_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear,UNIVERSITY - Duration Constant: Shift for every 30 minutes more than 5.5 hrs - Linear,"@((df.duration>11))*(((np.minimum(9-df.duration,7)*(df.duration<9)) + (np.minimum(df.duration-11,25)*(df.duration>11))) ** 0.5)",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_university_coeffs.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_university_coeffs.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/tour_scheduling_university_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_university_coeffs.csv index d656a021ee..1472dfb9f8 100755 --- a/activitysim/examples/example_semcog/configs/tour_scheduling_university_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_university_coeffs.csv @@ -1,42 +1,42 @@ -coefficient_name,value,constrain -coef_Mode_Choice_Logsum,0.384091138,F -coef_Low_income_lt25000_Departure_before_800_am_Linear,0.246389489,F -coef_Low_income_lt25000_Duration_lt_4hrs,-0.262288853,F -coef_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear,-0.039079271,F -coef_Medium_high_income_60k_to_120k_Duration_gt_4hrs,-0.041536976,F -coef_High_income_120k_plus_Departure_after_830_am_Linear,-0.039306518,F -coef_Age_41_plus_Departure_after_830_am_Linear,0.055344625,F -coef_Age_41_plus_Durationlt_4_hrs_Linear,-0.152498075,F -coef_Distance_to_destination_Departure_before_800_am_Linear,0.006869786,F -coef_Distance_to_destination_Departure_after_830_am_Linear,0.003686402,F -coef_Distance_to_destination_Durationlt_4_hrs_Linear,-0.04027172,F -coef_Distance_to_destination_Durationgt_4_hrs_Linear,0.003803244,F -coef_Distance_to_destination_Durationlt_4_hrs_Square_Root,0.041070113,F -coef_Subsequent_tour_is_work_tour_Departure_after_830_am,-0.29166292,F -coef_Subsequent_tour_is_work_tour_Duration_lt_4_hours,-0.482292817,F -coef_Subsequent_tour_is_work_tour_Duration_gt_4_hours,-0.364624965,F -coef_Subsequent_tour_is_school_tour_Departure_after_830_am,-0.286206955,F -coef_Subsequent_tour_is_school_tour_Duration_lt_4_hours,0.30341795,F -coef_Subsequent_tour_is_school_tour_Duration_gt_4_hours,-0.247436221,F -coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,-0.211059285,F -coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,-0.35316727,F -coef_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear,-0.947594485,F -coef_Departure_Constant_Before_0730_AM,-0.296228472,F -coef_Departure_Constant_0730_AM_0800_AM,-0.650538708,F -coef_Departure_Constant_0800_AM_0830_AM,0,T -coef_Departure_Constant_0830_AM_0900_AM,-0.525569176,F -coef_Departure_Constant_After_0900_AM,-0.536008149,F -coef_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root,-0.500045988,F -coef_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear,-0.209375282,F -coef_Arrival_Constant_Before_0300_PM,-0.962572172,F -coef_Arrival_Constant_0300_PM_0330_PM,-0.627901132,F -coef_Arrival_Constant_0330_PM_0400_PM,0,T -coef_Arrival_Constant_0400_PM_0430_PM,-0.190818088,F -coef_Arrival_Constant_After_0430_PM,-0.66545038,F -coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear,-0.209562151,F -coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root,0.503497689,F -coef_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root,0.225706446,F -coef_Duration_Constant_4p5_hours_or_less,0.03106769,F -coef_Duration_Constant_5_hours,0,T -coef_Duration_Constant_5p5_hours_or_more,0.343447232,F -coef_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear,-0.115312573,F +coefficient_name,value,constrain +coef_Mode_Choice_Logsum,0.384091138,F +coef_Low_income_lt25000_Departure_before_800_am_Linear,0.246389489,F +coef_Low_income_lt25000_Duration_lt_4hrs,-0.262288853,F +coef_Medium_high_income_60k_to_120k_Departure_after_830_am_Linear,-0.039079271,F +coef_Medium_high_income_60k_to_120k_Duration_gt_4hrs,-0.041536976,F +coef_High_income_120k_plus_Departure_after_830_am_Linear,-0.039306518,F +coef_Age_41_plus_Departure_after_830_am_Linear,0.055344625,F +coef_Age_41_plus_Durationlt_4_hrs_Linear,-0.152498075,F +coef_Distance_to_destination_Departure_before_800_am_Linear,0.006869786,F +coef_Distance_to_destination_Departure_after_830_am_Linear,0.003686402,F +coef_Distance_to_destination_Durationlt_4_hrs_Linear,-0.04027172,F +coef_Distance_to_destination_Durationgt_4_hrs_Linear,0.003803244,F +coef_Distance_to_destination_Durationlt_4_hrs_Square_Root,0.041070113,F +coef_Subsequent_tour_is_work_tour_Departure_after_830_am,-0.29166292,F +coef_Subsequent_tour_is_work_tour_Duration_lt_4_hours,-0.482292817,F +coef_Subsequent_tour_is_work_tour_Duration_gt_4_hours,-0.364624965,F +coef_Subsequent_tour_is_school_tour_Departure_after_830_am,-0.286206955,F +coef_Subsequent_tour_is_school_tour_Duration_lt_4_hours,0.30341795,F +coef_Subsequent_tour_is_school_tour_Duration_gt_4_hours,-0.247436221,F +coef_Second_tour_of_two_mandatory_tours_Duration_lt_4_hours,-0.211059285,F +coef_Second_tour_of_two_mandatory_tours_Duration_gt_4_hours,-0.35316727,F +coef_Departure_Constant_Shift_for_every_30_minutes_before_0700_am_Linear,-0.947594485,F +coef_Departure_Constant_Before_0730_AM,-0.296228472,F +coef_Departure_Constant_0730_AM_0800_AM,-0.650538708,F +coef_Departure_Constant_0800_AM_0830_AM,0,T +coef_Departure_Constant_0830_AM_0900_AM,-0.525569176,F +coef_Departure_Constant_After_0900_AM,-0.536008149,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_0930_am_Square_Root,-0.500045988,F +coef_Arrival_Constant_Shift_for_every_30_minutes_before_0230_pm_Linear,-0.209375282,F +coef_Arrival_Constant_Before_0300_PM,-0.962572172,F +coef_Arrival_Constant_0300_PM_0330_PM,-0.627901132,F +coef_Arrival_Constant_0330_PM_0400_PM,0,T +coef_Arrival_Constant_0400_PM_0430_PM,-0.190818088,F +coef_Arrival_Constant_After_0430_PM,-0.66545038,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Linear,-0.209562151,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_0500_pm_Square_Root,0.503497689,F +coef_Duration_Constant_Shift_for_every_30_minutes_less_than_4p5_hrs_Square_Root,0.225706446,F +coef_Duration_Constant_4p5_hours_or_less,0.03106769,F +coef_Duration_Constant_5_hours,0,T +coef_Duration_Constant_5p5_hours_or_more,0.343447232,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_5p5_hrs_Linear,-0.115312573,F diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_work.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_work.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/tour_scheduling_work.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_work.csv index ebc108bf3a..e180d0c155 100755 --- a/activitysim/examples/example_semcog/configs/tour_scheduling_work.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_work.csv @@ -1,100 +1,100 @@ -Label,Description,Expression,Coefficient -util_Mode_Choice_Logsum,Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum -util_Female_Departure_before_7_am,Female - Departure before 7:00 am - Linear,@((df.female) & (df.start<9)) * df.departureLinearShift1,coef_Female_Departure_before_7_am -util_Female_Arrival_after_6_pm,Female - Arrival after 6:00 pm - Linear,@((df.female) & (df.end>30)) * df.arrivalLinearShift1,coef_Female_Arrival_after_6_pm -util_Female_with_preschool_child_Departure_before_7_am,Female with preschool child - Departure before 7:00 am - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.start<9)) * df.departureLinearShift1,coef_Female_with_preschool_child_Departure_before_7_am -util_Female_with_preschool_child_Departure_after_7_am,Female with preschool child - Departure after 7:30 am - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.start>9)) * df.departureLinearShift1,coef_Female_with_preschool_child_Departure_after_7_am -util_Female_with_preschool_child_Arrival_after_6_pm,Female with preschool child - Arrival after 6:00 pm - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.end>30)) * df.arrivalLinearShift1,coef_Female_with_preschool_child_Arrival_after_6_pm -util_Low_income_lt_25000_Departure_before_7_am,Low income (<25000) - Departure before 7:00 am - Linear,@((df.is_income_less25K) & (df.start<9)) * df.departureLinearShift1,coef_Low_income_lt_25000_Departure_before_7_am -util_Low_income_lt_25000_Departure_after_7_am,Low income (<25000) - Departure after 7:30 am - Linear,@((df.is_income_less25K) & (df.start>9)) * df.departureLinearShift1,coef_Low_income_lt_25000_Departure_after_7_am -util_Low_income_lt_25000_Arrival_after_6_pm,Low income (<25000) - Arrival after 6:00 pm - Linear,@((df.is_income_less25K) & (df.start>30)) * df.arrivalLinearShift1,coef_Low_income_lt_25000_Arrival_after_6_pm -util_Med_income_25k_to_60k_Departure_before_7_am,Med income (25k to 60k) - Departure before 7:00 am - Linear,@((df.is_income_25K_to_60K) & (df.start<9)) * df.departureLinearShift1,coef_Med_income_25k_to_60k_Departure_before_7_am -util_Med_income_25k_to_60k_Arrival_after_6_pm,Med income (25k to 60k) - Arrival after 6:00 pm - Linear,@((df.is_income_25K_to_60K) & (df.end>30)) * df.arrivalLinearShift1,coef_Med_income_25k_to_60k_Arrival_after_6_pm -util_Medhigh_income_60k_to_120k_Departure_before_7_am,Med-high income (60k to 120k) - Departure before 7:00 am - Linear,@((df.is_income_60K_to_120K) & (df.start<9)) * df.departureLinearShift1,coef_Medhigh_income_60k_to_120k_Departure_before_7_am -util_Age_16_to_18_yrs_Departure_Before_7_am,Age 16 to 18 yrs - Departure Before 7:00 am,@(((df.age>=16) & (df.age<=18)) & (df.start<9)) * df.departureLinearShift1,coef_Age_16_to_18_yrs_Departure_Before_7_am -util_Age_16_to_18_yrs_Departure_After_7_am,Age 16 to 18 yrs - Departure After 7:30 am,@(((df.age>=16) & (df.age<=18)) & (df.start>9)) * df.departureLinearShift1,coef_Age_16_to_18_yrs_Departure_After_7_am -util_Age_19_to_24_yrs_Departure_After_7_am,Age 19 to 24 yrs - Departure After 7:30 am,@(((df.age>=19) & (df.age<=24)) & (df.start>9)) * df.departureLinearShift1,coef_Age_19_to_24_yrs_Departure_After_7_am -util_Age_25_to_40_yrs_Departure_Before_7_am,Age 25 to 40 yrs - Departure Before 7:00 am,@(((df.age>=25) & (df.age<=40)) & (df.start<9)) * df.departureLinearShift1,coef_Age_25_to_40_yrs_Departure_Before_7_am -util_Age_65_plus_yrs_Departure_After_7_am,Age 65+ yrs - Departure After 7:30 am,@((df.age>=65) & (df.start>9)) * df.departureLinearShift1,coef_Age_65_plus_yrs_Departure_After_7_am -util_Age_19_to_24_yrs_Arrival_after_6_pm,Age 19 to 24 yrs - Arrival after 6:00 pm ,@(((df.age>=19) & (df.age<=24)) & (df.end>30)) * df.arrivalLinearShift1,coef_Age_19_to_24_yrs_Arrival_after_6_pm -util_Age_25_to_40_yrs_Arrival_before_5_pm,Age 25 to 40 yrs - Arrival before 5:30 pm ,@(((df.age>=25) & (df.age<=40)) & (df.end<30)) * df.arrivalLinearShift1,coef_Age_25_to_40_yrs_Arrival_before_5_pm -util_Age_56_to_64_yrs_Arrival_after_6_pm,Age 56 to 64 yrs - Arrival after 6:00 pm ,@(((df.age>=56) & (df.age<65)) & (df.end>30)) * df.arrivalLinearShift1,coef_Age_56_to_64_yrs_Arrival_after_6_pm -util_Age_65_plus_yrs_Arrival_before_5_pm,Age 65+ yrs - Arrival before 5:30 pm ,@((df.age>=65) & (df.end<30)) * df.arrivalLinearShift1,coef_Age_65_plus_yrs_Arrival_before_5_pm -util_Age_65_plus_yrs_Arrival_after_6_pm,Age 65+ yrs - Arrival after 6:00 pm ,@((df.age>=65) & (df.end>30)) * df.arrivalLinearShift1,coef_Age_65_plus_yrs_Arrival_after_6_pm -util_Zero_auto_HH_Departure_before_7_am,Zero auto HH - Departure before 7:00 am - Linear,@((df.auto_ownership == 0) & (df.start<9)) * df.departureLinearShift1,coef_Zero_auto_HH_Departure_before_7_am -util_Zero_auto_HH_Arrival_after_6_pm,Zero auto HH - Arrival after 6:00 pm - Linear,@((df.auto_ownership == 0) & (df.end>30)) * df.arrivalLinearShift1,coef_Zero_auto_HH_Arrival_after_6_pm -util_Parttime_worker_Departure_before_7_am,Part-time worker - Departure before 7:00 am - Linear,@((df.ptype==2) & (df.start<9)) * df.departureLinearShift1,coef_Parttime_worker_Departure_before_7_am -util_Parttime_worker_Departure_after_7_am,Part-time worker - Departure after 7:30 am - Linear,@((df.ptype==2) & (df.start>9)) * df.departureLinearShift1,coef_Parttime_worker_Departure_after_7_am -util_Parttime_worker_Arrival_before_5_pm,Part-time worker - Arrival before 5:30 pm - Linear,@((df.ptype==2) & (df.end<30)) * df.arrivalLinearShift1,coef_Parttime_worker_Arrival_before_5_pm -util_Parttime_worker_Arrival_after_6_pm,Part-time worker - Arrival after 6:00 pm - Linear,@((df.ptype==2) & (df.end>30)) * df.arrivalLinearShift1,coef_Parttime_worker_Arrival_after_6_pm -util_University_student_Departure_after_7_am,University student - Departure after 7:30 am - Linear,@((df.ptype==3) & (df.start>9)) * df.departureLinearShift1,coef_University_student_Departure_after_7_am -util_University_student_Arrival_before_5_pm,University student - Arrival before 5:30 pm - Linear,@((df.ptype==3) & (df.end<30)) * df.arrivalLinearShift1,coef_University_student_Arrival_before_5_pm -util_University_student_Arrival_after_6_pm,University student - Arrival after 6:00 pm - Linear,@((df.ptype==3) & (df.end>30)) * df.arrivalLinearShift1,coef_University_student_Arrival_after_6_pm -#util_Blue_collar_Departure_before_7_am,#Blue collar - Departure before 7:00 am - Linear,@((df.work_segment==5) & (df.start<9)) * df.departureLinearShift1,coef_Blue_collar_Departure_before_7_am -#util_Blue_collar_Departure_after_7_am,#Blue collar - Departure after 7:30 am - Linear,@((df.work_segment==5)& (df.start>9)) * df.departureLinearShift1,coef_Blue_collar_Departure_after_7_am -#util_Blue_collar_Arrival_before_5_pm,#Blue collar - Arrival before 5:30 pm - Linear,@((df.work_segment==5)& (df.end<30)) * df.arrivalLinearShift1,coef_Blue_collar_Arrival_before_5_pm -#util_Service_Departure_before_7_am,#Service - Departure before 7:00 am - Linear,@((df.work_segment==2) & (df.start<9)) * df.departureLinearShift1,coef_Service_Departure_before_7_am -#util_Service_Departure_after_7_am,#Service - Departure after 7:30 am - Linear,@((df.work_segment==2) & (df.start>9)) * df.departureLinearShift1,coef_Service_Departure_after_7_am -#util_Service_Arrival_before_5_pm,#Service - Arrival before 5:30 pm - Linear,@((df.work_segment==2) & (df.end<30)) * df.arrivalLinearShift1,coef_Service_Arrival_before_5_pm -#util_Health_Departure_before_7_am,#Health - Departure before 7:00 am - Linear,@((df.work_segment==3) & (df.start<9)) * df.departureLinearShift1,coef_Health_Departure_before_7_am -#util_Health_Arrival_after_6_pm,#Health - Arrival after 6:00 pm - Linear,@((df.work_segment==3) & (df.end>30)) * df.arrivalLinearShift1,coef_Health_Arrival_after_6_pm -#util_Retail_and_food_Departure_after_7_am,#Retail and food - Departure after 7:30 am - Linear,@((df.work_segment==4) & (df.start>9)) * df.departureLinearShift1,coef_Retail_and_food_Departure_after_7_am -#util_Retail_and_food_Arrival_before_5_pm,#Retail and food - Arrival before 5:30 pm - Linear,@((df.work_segment==4) & (df.end<30)) * df.arrivalLinearShift1,coef_Retail_and_food_Arrival_before_5_pm -#util_Retail_and_food_Arrival_after_6_pm,#Retail and food - Arrival after 6:00 pm - Linear,@((df.work_segment==4) & (df.end>30)) * df.arrivalLinearShift1,coef_Retail_and_food_Arrival_after_6_pm -util_Time_SOV_freeflowto_destination_Departure_before_7_am,Time (SOV freeflow) to destination - Departure before 7:00 am - Linear,@(df.start<9) * df.departureLinearShift1* (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Departure_before_7_am -util_Time_SOV_freeflowto_destination_Departure_after_7_am,Time (SOV freeflow) to destination - Departure after 7:30 am - Linear,@(df.start>9) * df.departureLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Departure_after_7_am -util_Time_SOV_freeflowto_destination_Arrival_before_5_pm,Time (SOV freeflow) to destination - Arrival before 5:30 pm - Linear,@(df.end<30) * df.arrivalLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Arrival_before_5_pm -util_Time_SOV_freeflowto_destination_Arrival_after_6_pm,Time (SOV freeflow) to destination - Arrival after 6:00 pm - Linear,@(df.end>30) * df.arrivalLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Arrival_after_6_pm -util_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am,Presence of Non-Working Adult in the HH - Departure before 7:00 am - Linear,@((df.is_non_worker_in_HH) & (df.start<9)) * df.departureLinearShift1,coef_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am -util_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm,Presence of Non-Working Adult in the HH - Arrival before 5:30 pm - Linear,@((df.is_non_worker_in_HH) & (df.end<30)) * df.arrivalLinearShift1,coef_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm -util_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am,Presence of Pre-Driving Age Children in the HH - Departure before 7:30 am - Linear,@((df.is_pre_drive_child_in_HH) & (df.start<9)) * df.departureLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am -util_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am,Presence of Pre-Driving Age Children in the HH - Departure after 8 am - Linear,@((df.is_pre_drive_child_in_HH) & (df.start>9)) * df.departureLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am -util_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm,Presence of Pre-Driving Age Children in the HH - Arrival before 5:30 pm - Linear,@((df.is_pre_drive_child_in_HH) & (df.end<30)) * df.arrivalLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm -util_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm,Presence of Pre-Driving Age Children in the HH - Arrival after 6:00 pm - Linear,@((df.is_pre_drive_child_in_HH)& (df.end>30)) * df.arrivalLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm -util_First_of_2_plus_mandatory_tour_Departure_before_7_am,First of 2+ mandatory tour - Departure before 7:00 am,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.start<9)) * df.departureLinearShift1,coef_First_of_2_plus_mandatory_tour_Departure_before_7_am -util_First_of_2_plus_mandatory_tour_Departure_after_7_am,First of 2+ mandatory tour - Departure after 7:30 am,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.start>9)) * df.departureLinearShift1,coef_First_of_2_plus_mandatory_tour_Departure_after_7_am -util_First_of_2_plus_mandatory_tour_Duration_lt_9p5_hours,First of 2+ mandatory tour - Duration < 9.5 hours,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.duration<21)) * df.durationShift,coef_First_of_2_plus_mandatory_tour_Duration_lt_9p5_hours -util_First_of_2_plus_mandatory_tour_Duration_gt_9p5_hours,First of 2+ mandatory tour - Duration > 9.5 hours,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.duration<21)) * df.durationShift,coef_First_of_2_plus_mandatory_tour_Duration_gt_9p5_hours -util_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm,2nd or later of 2+ mandatory tour - Departure before 1:30 pm,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.start<22)) * df.departureLinearShift1,coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm -util_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm,2nd or later of 2+ mandatory tour - Departure after 2:00 pm,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.start>22)) * df.departureLinearShift1,coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm -util_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_9p5_hours,2nd or later of 2+ mandatory tour - Duration < 9.5 hours,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<21)) * df.durationShift,coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_9p5_hours -util_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_9p5_hours,2nd or later of 2+ mandatory tour - Duration > 9.5 hours,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<21)) * df.durationShift,coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_9p5_hours -#,#Departure Constants,,coef_Departure_Constants -util_Departure_Constant_Shift_for_every_30_minutes_before_6_am,Departure Constant: Shift for every 30 minutes before 6:00 am - Linear,"@(df.start<6) * ((np.minimum(6-df.start,48)*(df.start<6)) + (np.minimum(df.start-13,21)*(df.start>13)))",coef_Departure_Constant_Shift_for_every_30_minutes_before_6_am -util_Departure_Constant_Before_06_AM,Departure Constant: Before 06:00 AM,start<7,coef_Departure_Constant_Before_06_AM -util_Departure_Constant_06_AM_06_AM_7,Departure Constant: 06:00 AM - 06:30 AM (7) ,start==7,coef_Departure_Constant_06_AM_06_AM_7 -util_Departure_Constant_06_AM_07_AM_8,Departure Constant: 06:30 AM - 07:00 AM (8) ,start==8,coef_Departure_Constant_06_AM_07_AM_8 -util_Departure_Constant_07_AM_07_AM_9,Departure Constant: 07:00 AM - 07:30 AM (9) ,start==9,coef_Departure_Constant_07_AM_07_AM_9 -util_Departure_Constant_07_AM_08_AM_10,Departure Constant: 07:30 AM - 08:00 AM (10) ,start==10,coef_Departure_Constant_07_AM_08_AM_10 -util_Departure_Constant_08_AM_08_AM_11,Departure Constant: 08:00 AM - 08:30 AM (11) ,start==11,coef_Departure_Constant_08_AM_08_AM_11 -util_Departure_Constant_08_AM_09_AM_12,Departure Constant: 08:30 AM - 09:00 AM (12) ,start==12,coef_Departure_Constant_08_AM_09_AM_12 -util_Departure_Constant_After_09_AM,Departure Constant: After 09:00 AM,start>12,coef_Departure_Constant_After_09_AM -util_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root,Departure Constant: Shift for every 30 minutes after 9:30 am - Square Root,"@(df.start>13) * (((np.minimum(6-df.start,48)*(df.start<6)) + (np.minimum(df.start-13,21)*(df.start>13))) ** 0.5)",coef_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root -#,#Arrival Constants,,coef_Arrival_Constants -util_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm,Arrival Constant: Shift for every 30 minutes before 3:00 pm - Linear,"@(df.end<25) * ((np.minimum(25-df.end, 15)*(df.end<25))+ (np.minimum(df.end-35,11)*(df.end>35)))",coef_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm -util_Arrival_Constant_Before_03_PM,Arrival Constant: Before 03:30 PM,end<26,coef_Arrival_Constant_Before_03_PM -util_Arrival_Constant_03_PM_04_PM_26,Arrival Constant: 03:30 PM - 04:00 PM (26) ,end==26,coef_Arrival_Constant_03_PM_04_PM_26 -util_Arrival_Constant_04_PM_04_PM_27,Arrival Constant: 04:00 PM - 04:30 PM (27) ,end==27,coef_Arrival_Constant_04_PM_04_PM_27 -util_Arrival_Constant_04_PM_05_PM_28,Arrival Constant: 04:30 PM - 05:00 PM (28) ,end==28,coef_Arrival_Constant_04_PM_05_PM_28 -util_Arrival_Constant_05_PM_05_PM_29,Arrival Constant: 05:00 PM - 05:30 PM (29),end==29,coef_Arrival_Constant_05_PM_05_PM_29 -util_Arrival_Constant_05_PM_06_PM_30,Arrival Constant: 05:30 PM - 06:00 PM (30) ,end==30,coef_Arrival_Constant_05_PM_06_PM_30 -util_Arrival_Constant_06_PM_06_PM_31,Arrival Constant: 06:00 PM - 06:30 PM (31) ,end==31,coef_Arrival_Constant_06_PM_06_PM_31 -util_Arrival_Constant_06_PM_7_PM_32,Arrival Constant: 06:30 PM - 7:00 PM (32) ,end==32,coef_Arrival_Constant_06_PM_7_PM_32 -util_Arrival_Constant_7_PM_7_PM_33,Arrival Constant: 7:00 PM - 7:30 PM (33) ,end==33,coef_Arrival_Constant_7_PM_7_PM_33 -util_Arrival_Constant_7_PM_8_PM_34,Arrival Constant: 7:30 PM - 8:00 PM (34) ,end==34,coef_Arrival_Constant_7_PM_8_PM_34 -util_Arrival_Constant_After_08_PM,Arrival Constant: After 08:00 PM,end>34,coef_Arrival_Constant_After_08_PM -util_Arrival_Constant_Shift_for_every_30_minutes_after_6_pm_Square_root,Arrival Constant: Shift for every 30 minutes after 6:30 pm - Square root,"@(df.end>35) * (((np.minimum(25-df.end, 15)*(df.end<25))+ (np.minimum(df.end-35,11)*(df.end>35))) ** 0.5)",coef_Arrival_Constant_Shift_for_every_30_minutes_after_6_pm_Square_root -#,#Duration Constants,,coef_Duration_Constants -util_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs,Duration Constant: Shift for every 30 minutes less than 8.5 hrs - Linear,"@(df.duration<16) * ((np.minimum(16-df.duration,47)*(df.duration<16)) + (np.minimum(df.duration-25,10)*(df.duration>25)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs -util_Duration_Constant_Shorter_than_8p5_hrs,Duration Constant: Shorter than 8.5 hrs,duration<17,coef_Duration_Constant_Shorter_than_8p5_hrs -util_Duration_Constant_8p5_hours,Duration Constant: 8.5 hours,duration==17,coef_Duration_Constant_8p5_hours -util_Duration_Constant_9_hours,Duration Constant: 9 hours,duration==18,coef_Duration_Constant_9_hours -util_Duration_Constant_9p5_hours,Duration Constant: 9.5 hours,duration==19,coef_Duration_Constant_9p5_hours -util_Duration_Constant_10_hours,Duration Constant: 10 hours,duration==20,coef_Duration_Constant_10_hours -util_Duration_Constant_10p5_hours,Duration Constant: 10.5 hours,duration==21,coef_Duration_Constant_10p5_hours -util_Duration_Constant_11_hours,Duration Constant: 11 hours,duration==22,coef_Duration_Constant_11_hours -util_Duration_Constant_11p5_hours,Duration Constant: 11.5 hours,duration==23,coef_Duration_Constant_11p5_hours -util_Duration_Constant_12_hours,Duration Constant: 12 hours,duration==24,coef_Duration_Constant_12_hours -util_Duration_Constant_Longer_than_12_hrs,Duration Constant: Longer than 12 hrs,duration>24,coef_Duration_Constant_Longer_than_12_hrs -util_Duration_Constant_Shift_for_every_30_minutes_more_than_10_hrs,Duration Constant: Shift for every 30 minutes more than 10 hrs - Linear,"@(df.duration>25) * ((np.minimum(16-df.duration,47)*(df.duration<16)) + (np.minimum(df.duration-25,10)*(df.duration>25)))",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_10_hrs -util_Calibration_constant_Duration_0,Calibration constant: Duration = 0,duration == 0,coef_Calibration_constant_Duration_0 +Label,Description,Expression,Coefficient +util_Mode_Choice_Logsum,Mode Choice Logsum,mode_choice_logsum,coef_Mode_Choice_Logsum +util_Female_Departure_before_7_am,Female - Departure before 7:00 am - Linear,@((df.female) & (df.start<9)) * df.departureLinearShift1,coef_Female_Departure_before_7_am +util_Female_Arrival_after_6_pm,Female - Arrival after 6:00 pm - Linear,@((df.female) & (df.end>30)) * df.arrivalLinearShift1,coef_Female_Arrival_after_6_pm +util_Female_with_preschool_child_Departure_before_7_am,Female with preschool child - Departure before 7:00 am - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.start<9)) * df.departureLinearShift1,coef_Female_with_preschool_child_Departure_before_7_am +util_Female_with_preschool_child_Departure_after_7_am,Female with preschool child - Departure after 7:30 am - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.start>9)) * df.departureLinearShift1,coef_Female_with_preschool_child_Departure_after_7_am +util_Female_with_preschool_child_Arrival_after_6_pm,Female with preschool child - Arrival after 6:00 pm - Linear,@((df.female) & (df.is_pre_drive_child_in_HH) & (df.end>30)) * df.arrivalLinearShift1,coef_Female_with_preschool_child_Arrival_after_6_pm +util_Low_income_lt_25000_Departure_before_7_am,Low income (<25000) - Departure before 7:00 am - Linear,@((df.is_income_less25K) & (df.start<9)) * df.departureLinearShift1,coef_Low_income_lt_25000_Departure_before_7_am +util_Low_income_lt_25000_Departure_after_7_am,Low income (<25000) - Departure after 7:30 am - Linear,@((df.is_income_less25K) & (df.start>9)) * df.departureLinearShift1,coef_Low_income_lt_25000_Departure_after_7_am +util_Low_income_lt_25000_Arrival_after_6_pm,Low income (<25000) - Arrival after 6:00 pm - Linear,@((df.is_income_less25K) & (df.start>30)) * df.arrivalLinearShift1,coef_Low_income_lt_25000_Arrival_after_6_pm +util_Med_income_25k_to_60k_Departure_before_7_am,Med income (25k to 60k) - Departure before 7:00 am - Linear,@((df.is_income_25K_to_60K) & (df.start<9)) * df.departureLinearShift1,coef_Med_income_25k_to_60k_Departure_before_7_am +util_Med_income_25k_to_60k_Arrival_after_6_pm,Med income (25k to 60k) - Arrival after 6:00 pm - Linear,@((df.is_income_25K_to_60K) & (df.end>30)) * df.arrivalLinearShift1,coef_Med_income_25k_to_60k_Arrival_after_6_pm +util_Medhigh_income_60k_to_120k_Departure_before_7_am,Med-high income (60k to 120k) - Departure before 7:00 am - Linear,@((df.is_income_60K_to_120K) & (df.start<9)) * df.departureLinearShift1,coef_Medhigh_income_60k_to_120k_Departure_before_7_am +util_Age_16_to_18_yrs_Departure_Before_7_am,Age 16 to 18 yrs - Departure Before 7:00 am,@(((df.age>=16) & (df.age<=18)) & (df.start<9)) * df.departureLinearShift1,coef_Age_16_to_18_yrs_Departure_Before_7_am +util_Age_16_to_18_yrs_Departure_After_7_am,Age 16 to 18 yrs - Departure After 7:30 am,@(((df.age>=16) & (df.age<=18)) & (df.start>9)) * df.departureLinearShift1,coef_Age_16_to_18_yrs_Departure_After_7_am +util_Age_19_to_24_yrs_Departure_After_7_am,Age 19 to 24 yrs - Departure After 7:30 am,@(((df.age>=19) & (df.age<=24)) & (df.start>9)) * df.departureLinearShift1,coef_Age_19_to_24_yrs_Departure_After_7_am +util_Age_25_to_40_yrs_Departure_Before_7_am,Age 25 to 40 yrs - Departure Before 7:00 am,@(((df.age>=25) & (df.age<=40)) & (df.start<9)) * df.departureLinearShift1,coef_Age_25_to_40_yrs_Departure_Before_7_am +util_Age_65_plus_yrs_Departure_After_7_am,Age 65+ yrs - Departure After 7:30 am,@((df.age>=65) & (df.start>9)) * df.departureLinearShift1,coef_Age_65_plus_yrs_Departure_After_7_am +util_Age_19_to_24_yrs_Arrival_after_6_pm,Age 19 to 24 yrs - Arrival after 6:00 pm ,@(((df.age>=19) & (df.age<=24)) & (df.end>30)) * df.arrivalLinearShift1,coef_Age_19_to_24_yrs_Arrival_after_6_pm +util_Age_25_to_40_yrs_Arrival_before_5_pm,Age 25 to 40 yrs - Arrival before 5:30 pm ,@(((df.age>=25) & (df.age<=40)) & (df.end<30)) * df.arrivalLinearShift1,coef_Age_25_to_40_yrs_Arrival_before_5_pm +util_Age_56_to_64_yrs_Arrival_after_6_pm,Age 56 to 64 yrs - Arrival after 6:00 pm ,@(((df.age>=56) & (df.age<65)) & (df.end>30)) * df.arrivalLinearShift1,coef_Age_56_to_64_yrs_Arrival_after_6_pm +util_Age_65_plus_yrs_Arrival_before_5_pm,Age 65+ yrs - Arrival before 5:30 pm ,@((df.age>=65) & (df.end<30)) * df.arrivalLinearShift1,coef_Age_65_plus_yrs_Arrival_before_5_pm +util_Age_65_plus_yrs_Arrival_after_6_pm,Age 65+ yrs - Arrival after 6:00 pm ,@((df.age>=65) & (df.end>30)) * df.arrivalLinearShift1,coef_Age_65_plus_yrs_Arrival_after_6_pm +util_Zero_auto_HH_Departure_before_7_am,Zero auto HH - Departure before 7:00 am - Linear,@((df.auto_ownership == 0) & (df.start<9)) * df.departureLinearShift1,coef_Zero_auto_HH_Departure_before_7_am +util_Zero_auto_HH_Arrival_after_6_pm,Zero auto HH - Arrival after 6:00 pm - Linear,@((df.auto_ownership == 0) & (df.end>30)) * df.arrivalLinearShift1,coef_Zero_auto_HH_Arrival_after_6_pm +util_Parttime_worker_Departure_before_7_am,Part-time worker - Departure before 7:00 am - Linear,@((df.ptype==2) & (df.start<9)) * df.departureLinearShift1,coef_Parttime_worker_Departure_before_7_am +util_Parttime_worker_Departure_after_7_am,Part-time worker - Departure after 7:30 am - Linear,@((df.ptype==2) & (df.start>9)) * df.departureLinearShift1,coef_Parttime_worker_Departure_after_7_am +util_Parttime_worker_Arrival_before_5_pm,Part-time worker - Arrival before 5:30 pm - Linear,@((df.ptype==2) & (df.end<30)) * df.arrivalLinearShift1,coef_Parttime_worker_Arrival_before_5_pm +util_Parttime_worker_Arrival_after_6_pm,Part-time worker - Arrival after 6:00 pm - Linear,@((df.ptype==2) & (df.end>30)) * df.arrivalLinearShift1,coef_Parttime_worker_Arrival_after_6_pm +util_University_student_Departure_after_7_am,University student - Departure after 7:30 am - Linear,@((df.ptype==3) & (df.start>9)) * df.departureLinearShift1,coef_University_student_Departure_after_7_am +util_University_student_Arrival_before_5_pm,University student - Arrival before 5:30 pm - Linear,@((df.ptype==3) & (df.end<30)) * df.arrivalLinearShift1,coef_University_student_Arrival_before_5_pm +util_University_student_Arrival_after_6_pm,University student - Arrival after 6:00 pm - Linear,@((df.ptype==3) & (df.end>30)) * df.arrivalLinearShift1,coef_University_student_Arrival_after_6_pm +#util_Blue_collar_Departure_before_7_am,#Blue collar - Departure before 7:00 am - Linear,@((df.work_segment==5) & (df.start<9)) * df.departureLinearShift1,coef_Blue_collar_Departure_before_7_am +#util_Blue_collar_Departure_after_7_am,#Blue collar - Departure after 7:30 am - Linear,@((df.work_segment==5)& (df.start>9)) * df.departureLinearShift1,coef_Blue_collar_Departure_after_7_am +#util_Blue_collar_Arrival_before_5_pm,#Blue collar - Arrival before 5:30 pm - Linear,@((df.work_segment==5)& (df.end<30)) * df.arrivalLinearShift1,coef_Blue_collar_Arrival_before_5_pm +#util_Service_Departure_before_7_am,#Service - Departure before 7:00 am - Linear,@((df.work_segment==2) & (df.start<9)) * df.departureLinearShift1,coef_Service_Departure_before_7_am +#util_Service_Departure_after_7_am,#Service - Departure after 7:30 am - Linear,@((df.work_segment==2) & (df.start>9)) * df.departureLinearShift1,coef_Service_Departure_after_7_am +#util_Service_Arrival_before_5_pm,#Service - Arrival before 5:30 pm - Linear,@((df.work_segment==2) & (df.end<30)) * df.arrivalLinearShift1,coef_Service_Arrival_before_5_pm +#util_Health_Departure_before_7_am,#Health - Departure before 7:00 am - Linear,@((df.work_segment==3) & (df.start<9)) * df.departureLinearShift1,coef_Health_Departure_before_7_am +#util_Health_Arrival_after_6_pm,#Health - Arrival after 6:00 pm - Linear,@((df.work_segment==3) & (df.end>30)) * df.arrivalLinearShift1,coef_Health_Arrival_after_6_pm +#util_Retail_and_food_Departure_after_7_am,#Retail and food - Departure after 7:30 am - Linear,@((df.work_segment==4) & (df.start>9)) * df.departureLinearShift1,coef_Retail_and_food_Departure_after_7_am +#util_Retail_and_food_Arrival_before_5_pm,#Retail and food - Arrival before 5:30 pm - Linear,@((df.work_segment==4) & (df.end<30)) * df.arrivalLinearShift1,coef_Retail_and_food_Arrival_before_5_pm +#util_Retail_and_food_Arrival_after_6_pm,#Retail and food - Arrival after 6:00 pm - Linear,@((df.work_segment==4) & (df.end>30)) * df.arrivalLinearShift1,coef_Retail_and_food_Arrival_after_6_pm +util_Time_SOV_freeflowto_destination_Departure_before_7_am,Time (SOV freeflow) to destination - Departure before 7:00 am - Linear,@(df.start<9) * df.departureLinearShift1* (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Departure_before_7_am +util_Time_SOV_freeflowto_destination_Departure_after_7_am,Time (SOV freeflow) to destination - Departure after 7:30 am - Linear,@(df.start>9) * df.departureLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Departure_after_7_am +util_Time_SOV_freeflowto_destination_Arrival_before_5_pm,Time (SOV freeflow) to destination - Arrival before 5:30 pm - Linear,@(df.end<30) * df.arrivalLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Arrival_before_5_pm +util_Time_SOV_freeflowto_destination_Arrival_after_6_pm,Time (SOV freeflow) to destination - Arrival after 6:00 pm - Linear,@(df.end>30) * df.arrivalLinearShift1 * (df.roundtrip_auto_time_to_work),coef_Time_SOV_freeflowto_destination_Arrival_after_6_pm +util_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am,Presence of Non-Working Adult in the HH - Departure before 7:00 am - Linear,@((df.is_non_worker_in_HH) & (df.start<9)) * df.departureLinearShift1,coef_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am +util_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm,Presence of Non-Working Adult in the HH - Arrival before 5:30 pm - Linear,@((df.is_non_worker_in_HH) & (df.end<30)) * df.arrivalLinearShift1,coef_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm +util_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am,Presence of Pre-Driving Age Children in the HH - Departure before 7:30 am - Linear,@((df.is_pre_drive_child_in_HH) & (df.start<9)) * df.departureLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am +util_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am,Presence of Pre-Driving Age Children in the HH - Departure after 8 am - Linear,@((df.is_pre_drive_child_in_HH) & (df.start>9)) * df.departureLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am +util_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm,Presence of Pre-Driving Age Children in the HH - Arrival before 5:30 pm - Linear,@((df.is_pre_drive_child_in_HH) & (df.end<30)) * df.arrivalLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm +util_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm,Presence of Pre-Driving Age Children in the HH - Arrival after 6:00 pm - Linear,@((df.is_pre_drive_child_in_HH)& (df.end>30)) * df.arrivalLinearShift1,coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm +util_First_of_2_plus_mandatory_tour_Departure_before_7_am,First of 2+ mandatory tour - Departure before 7:00 am,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.start<9)) * df.departureLinearShift1,coef_First_of_2_plus_mandatory_tour_Departure_before_7_am +util_First_of_2_plus_mandatory_tour_Departure_after_7_am,First of 2+ mandatory tour - Departure after 7:30 am,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.start>9)) * df.departureLinearShift1,coef_First_of_2_plus_mandatory_tour_Departure_after_7_am +util_First_of_2_plus_mandatory_tour_Duration_lt_9p5_hours,First of 2+ mandatory tour - Duration < 9.5 hours,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.duration<21)) * df.durationShift,coef_First_of_2_plus_mandatory_tour_Duration_lt_9p5_hours +util_First_of_2_plus_mandatory_tour_Duration_gt_9p5_hours,First of 2+ mandatory tour - Duration > 9.5 hours,@(((df.tour_count>1) & (df.tour_num == 1)) & (df.duration<21)) * df.durationShift,coef_First_of_2_plus_mandatory_tour_Duration_gt_9p5_hours +util_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm,2nd or later of 2+ mandatory tour - Departure before 1:30 pm,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.start<22)) * df.departureLinearShift1,coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm +util_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm,2nd or later of 2+ mandatory tour - Departure after 2:00 pm,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.start>22)) * df.departureLinearShift1,coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm +util_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_9p5_hours,2nd or later of 2+ mandatory tour - Duration < 9.5 hours,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<21)) * df.durationShift,coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_9p5_hours +util_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_9p5_hours,2nd or later of 2+ mandatory tour - Duration > 9.5 hours,@(((df.tour_count>1) & (df.tour_num > 1)) & (df.duration<21)) * df.durationShift,coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_9p5_hours +#,#Departure Constants,,coef_Departure_Constants +util_Departure_Constant_Shift_for_every_30_minutes_before_6_am,Departure Constant: Shift for every 30 minutes before 6:00 am - Linear,"@(df.start<6) * ((np.minimum(6-df.start,48)*(df.start<6)) + (np.minimum(df.start-13,21)*(df.start>13)))",coef_Departure_Constant_Shift_for_every_30_minutes_before_6_am +util_Departure_Constant_Before_06_AM,Departure Constant: Before 06:00 AM,start<7,coef_Departure_Constant_Before_06_AM +util_Departure_Constant_06_AM_06_AM_7,Departure Constant: 06:00 AM - 06:30 AM (7) ,start==7,coef_Departure_Constant_06_AM_06_AM_7 +util_Departure_Constant_06_AM_07_AM_8,Departure Constant: 06:30 AM - 07:00 AM (8) ,start==8,coef_Departure_Constant_06_AM_07_AM_8 +util_Departure_Constant_07_AM_07_AM_9,Departure Constant: 07:00 AM - 07:30 AM (9) ,start==9,coef_Departure_Constant_07_AM_07_AM_9 +util_Departure_Constant_07_AM_08_AM_10,Departure Constant: 07:30 AM - 08:00 AM (10) ,start==10,coef_Departure_Constant_07_AM_08_AM_10 +util_Departure_Constant_08_AM_08_AM_11,Departure Constant: 08:00 AM - 08:30 AM (11) ,start==11,coef_Departure_Constant_08_AM_08_AM_11 +util_Departure_Constant_08_AM_09_AM_12,Departure Constant: 08:30 AM - 09:00 AM (12) ,start==12,coef_Departure_Constant_08_AM_09_AM_12 +util_Departure_Constant_After_09_AM,Departure Constant: After 09:00 AM,start>12,coef_Departure_Constant_After_09_AM +util_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root,Departure Constant: Shift for every 30 minutes after 9:30 am - Square Root,"@(df.start>13) * (((np.minimum(6-df.start,48)*(df.start<6)) + (np.minimum(df.start-13,21)*(df.start>13))) ** 0.5)",coef_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root +#,#Arrival Constants,,coef_Arrival_Constants +util_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm,Arrival Constant: Shift for every 30 minutes before 3:00 pm - Linear,"@(df.end<25) * ((np.minimum(25-df.end, 15)*(df.end<25))+ (np.minimum(df.end-35,11)*(df.end>35)))",coef_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm +util_Arrival_Constant_Before_03_PM,Arrival Constant: Before 03:30 PM,end<26,coef_Arrival_Constant_Before_03_PM +util_Arrival_Constant_03_PM_04_PM_26,Arrival Constant: 03:30 PM - 04:00 PM (26) ,end==26,coef_Arrival_Constant_03_PM_04_PM_26 +util_Arrival_Constant_04_PM_04_PM_27,Arrival Constant: 04:00 PM - 04:30 PM (27) ,end==27,coef_Arrival_Constant_04_PM_04_PM_27 +util_Arrival_Constant_04_PM_05_PM_28,Arrival Constant: 04:30 PM - 05:00 PM (28) ,end==28,coef_Arrival_Constant_04_PM_05_PM_28 +util_Arrival_Constant_05_PM_05_PM_29,Arrival Constant: 05:00 PM - 05:30 PM (29),end==29,coef_Arrival_Constant_05_PM_05_PM_29 +util_Arrival_Constant_05_PM_06_PM_30,Arrival Constant: 05:30 PM - 06:00 PM (30) ,end==30,coef_Arrival_Constant_05_PM_06_PM_30 +util_Arrival_Constant_06_PM_06_PM_31,Arrival Constant: 06:00 PM - 06:30 PM (31) ,end==31,coef_Arrival_Constant_06_PM_06_PM_31 +util_Arrival_Constant_06_PM_7_PM_32,Arrival Constant: 06:30 PM - 7:00 PM (32) ,end==32,coef_Arrival_Constant_06_PM_7_PM_32 +util_Arrival_Constant_7_PM_7_PM_33,Arrival Constant: 7:00 PM - 7:30 PM (33) ,end==33,coef_Arrival_Constant_7_PM_7_PM_33 +util_Arrival_Constant_7_PM_8_PM_34,Arrival Constant: 7:30 PM - 8:00 PM (34) ,end==34,coef_Arrival_Constant_7_PM_8_PM_34 +util_Arrival_Constant_After_08_PM,Arrival Constant: After 08:00 PM,end>34,coef_Arrival_Constant_After_08_PM +util_Arrival_Constant_Shift_for_every_30_minutes_after_6_pm_Square_root,Arrival Constant: Shift for every 30 minutes after 6:30 pm - Square root,"@(df.end>35) * (((np.minimum(25-df.end, 15)*(df.end<25))+ (np.minimum(df.end-35,11)*(df.end>35))) ** 0.5)",coef_Arrival_Constant_Shift_for_every_30_minutes_after_6_pm_Square_root +#,#Duration Constants,,coef_Duration_Constants +util_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs,Duration Constant: Shift for every 30 minutes less than 8.5 hrs - Linear,"@(df.duration<16) * ((np.minimum(16-df.duration,47)*(df.duration<16)) + (np.minimum(df.duration-25,10)*(df.duration>25)))",coef_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs +util_Duration_Constant_Shorter_than_8p5_hrs,Duration Constant: Shorter than 8.5 hrs,duration<17,coef_Duration_Constant_Shorter_than_8p5_hrs +util_Duration_Constant_8p5_hours,Duration Constant: 8.5 hours,duration==17,coef_Duration_Constant_8p5_hours +util_Duration_Constant_9_hours,Duration Constant: 9 hours,duration==18,coef_Duration_Constant_9_hours +util_Duration_Constant_9p5_hours,Duration Constant: 9.5 hours,duration==19,coef_Duration_Constant_9p5_hours +util_Duration_Constant_10_hours,Duration Constant: 10 hours,duration==20,coef_Duration_Constant_10_hours +util_Duration_Constant_10p5_hours,Duration Constant: 10.5 hours,duration==21,coef_Duration_Constant_10p5_hours +util_Duration_Constant_11_hours,Duration Constant: 11 hours,duration==22,coef_Duration_Constant_11_hours +util_Duration_Constant_11p5_hours,Duration Constant: 11.5 hours,duration==23,coef_Duration_Constant_11p5_hours +util_Duration_Constant_12_hours,Duration Constant: 12 hours,duration==24,coef_Duration_Constant_12_hours +util_Duration_Constant_Longer_than_12_hrs,Duration Constant: Longer than 12 hrs,duration>24,coef_Duration_Constant_Longer_than_12_hrs +util_Duration_Constant_Shift_for_every_30_minutes_more_than_10_hrs,Duration Constant: Shift for every 30 minutes more than 10 hrs - Linear,"@(df.duration>25) * ((np.minimum(16-df.duration,47)*(df.duration<16)) + (np.minimum(df.duration-25,10)*(df.duration>25)))",coef_Duration_Constant_Shift_for_every_30_minutes_more_than_10_hrs +util_Calibration_constant_Duration_0,Calibration constant: Duration = 0,duration == 0,coef_Calibration_constant_Duration_0 diff --git a/activitysim/examples/example_semcog/configs/tour_scheduling_work_coeffs.csv b/activitysim/examples/prototype_semcog/configs/tour_scheduling_work_coeffs.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/tour_scheduling_work_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/tour_scheduling_work_coeffs.csv index d25a423d0e..19a85e8179 100755 --- a/activitysim/examples/example_semcog/configs/tour_scheduling_work_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/tour_scheduling_work_coeffs.csv @@ -1,97 +1,97 @@ -coefficient_name,value,constrain -coef_Mode_Choice_Logsum,0.2279,T -coef_Female_Departure_before_7_am,-0.12935306,F -coef_Female_Arrival_after_6_pm,-0.041312616,F -coef_Female_with_preschool_child_Departure_before_7_am,-0.129130464,F -coef_Female_with_preschool_child_Departure_after_7_am,-0.031947595,F -coef_Female_with_preschool_child_Arrival_after_6_pm,-0.048859218,F -coef_Low_income_lt_25000_Departure_before_7_am,0.232768373,F -coef_Low_income_lt_25000_Departure_after_7_am,0.014908169,F -coef_Low_income_lt_25000_Arrival_after_6_pm,0.039105101,F -coef_Med_income_25k_to_60k_Departure_before_7_am,0.123945957,F -coef_Med_income_25k_to_60k_Arrival_after_6_pm,0.020965221,F -coef_Medhigh_income_60k_to_120k_Departure_before_7_am,0.09890939,F -coef_Age_16_to_18_yrs_Departure_Before_7_am,-0.459593556,F -coef_Age_16_to_18_yrs_Departure_After_7_am,0.060951693,F -coef_Age_19_to_24_yrs_Departure_After_7_am,0.031477187,F -coef_Age_25_to_40_yrs_Departure_Before_7_am,-0.11723451,F -coef_Age_65_plus_yrs_Departure_After_7_am,0.051923956,F -coef_Age_19_to_24_yrs_Arrival_after_6_pm,0.032734453,F -coef_Age_25_to_40_yrs_Arrival_before_5_pm,-0.027623617,F -coef_Age_56_to_64_yrs_Arrival_after_6_pm,-0.049130187,F -coef_Age_65_plus_yrs_Arrival_before_5_pm,0.056774635,F -coef_Age_65_plus_yrs_Arrival_after_6_pm,-0.077532684,F -coef_Zero_auto_HH_Departure_before_7_am,0.396983749,F -coef_Zero_auto_HH_Arrival_after_6_pm,0.050665232,F -coef_Parttime_worker_Departure_before_7_am,-0.264760988,F -coef_Parttime_worker_Departure_after_7_am,0.126626287,F -coef_Parttime_worker_Arrival_before_5_pm,0.175158545,F -coef_Parttime_worker_Arrival_after_6_pm,-0.054124518,F -coef_University_student_Departure_after_7_am,0.024758204,F -coef_University_student_Arrival_before_5_pm,0.035389739,F -coef_University_student_Arrival_after_6_pm,0.06173996,F -coef_Blue_collar_Departure_before_7_am,0.327242475,F -coef_Blue_collar_Departure_after_7_am,0.047214248,F -coef_Blue_collar_Arrival_before_5_pm,0.04197056,F -coef_Service_Departure_before_7_am,0.117783508,F -coef_Service_Departure_after_7_am,0.081611629,F -coef_Service_Arrival_before_5_pm,0,T -coef_Health_Departure_before_7_am,0.135275931,F -coef_Health_Arrival_after_6_pm,0.062010123,F -coef_Retail_and_food_Departure_after_7_am,0.076302969,F -coef_Retail_and_food_Arrival_before_5_pm,0.052905387,F -coef_Retail_and_food_Arrival_after_6_pm,0.027069194,F -coef_Time_SOV_freeflowto_destination_Departure_before_7_am,0.011511462,F -coef_Time_SOV_freeflowto_destination_Departure_after_7_am,-0.003821379,F -coef_Time_SOV_freeflowto_destination_Arrival_before_5_pm,-0.00549578,F -coef_Time_SOV_freeflowto_destination_Arrival_after_6_pm,0.002253695,F -coef_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am,0.069957209,F -coef_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm,-0.019807228,F -coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am,-0.084564489,F -coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am,-0.023894467,F -coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm,0.018983499,F -coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm,-0.032091123,F -coef_First_of_2_plus_mandatory_tour_Departure_before_7_am,0.145890035,F -coef_First_of_2_plus_mandatory_tour_Departure_after_7_am,-0.214531877,F -coef_First_of_2_plus_mandatory_tour_Duration_lt_9p5_hours,0.3069241,F -coef_First_of_2_plus_mandatory_tour_Duration_gt_9p5_hours,-0.526297898,F -coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm,-0.221304523,F -coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm,-0.176348812,F -coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_9p5_hours,0.064893097,F -coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_9p5_hours,-0.656942049,F -coef_Departure_Constant_Shift_for_every_30_minutes_before_6_am,-1.151564775,F -coef_Departure_Constant_Before_06_AM,-2.197677208,F -coef_Departure_Constant_06_AM_06_AM_7,-1.314098638,F -coef_Departure_Constant_06_AM_07_AM_8,-0.558766028,F -coef_Departure_Constant_07_AM_07_AM_9,0,T -coef_Departure_Constant_07_AM_08_AM_10,-0.036957515,F -coef_Departure_Constant_08_AM_08_AM_11,-0.285560423,F -coef_Departure_Constant_08_AM_09_AM_12,-0.555478447,F -coef_Departure_Constant_After_09_AM,-0.865125273,F -coef_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root,-0.435746145,F -coef_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm,-0.191607342,F -coef_Arrival_Constant_Before_03_PM,-0.289333093,F -coef_Arrival_Constant_03_PM_04_PM_26,-0.273555837,F -coef_Arrival_Constant_04_PM_04_PM_27,-0.142653706,F -coef_Arrival_Constant_04_PM_05_PM_28,-0.124814807,F -coef_Arrival_Constant_05_PM_05_PM_29,0.004265544,F -coef_Arrival_Constant_05_PM_06_PM_30,0,T -coef_Arrival_Constant_06_PM_06_PM_31,-0.060515031,F -coef_Arrival_Constant_06_PM_7_PM_32,-0.236621114,F -coef_Arrival_Constant_7_PM_7_PM_33,-0.577646614,F -coef_Arrival_Constant_7_PM_8_PM_34,-0.815994515,F -coef_Arrival_Constant_After_08_PM,-0.854151925,F -coef_Arrival_Constant_Shift_for_every_30_minutes_after_6_pm_Square_root,-0.469720787,F -coef_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs,-0.074266981,F -coef_Duration_Constant_Shorter_than_8p5_hrs,-0.748584335,F -coef_Duration_Constant_8p5_hours,-0.654814097,F -coef_Duration_Constant_9_hours,-0.372064236,F -coef_Duration_Constant_9p5_hours,-0.144226124,F -coef_Duration_Constant_10_hours,0.013153356,F -coef_Duration_Constant_10p5_hours,0,T -coef_Duration_Constant_11_hours,-0.115847245,F -coef_Duration_Constant_11p5_hours,-0.288506368,F -coef_Duration_Constant_12_hours,-0.524241874,F -coef_Duration_Constant_Longer_than_12_hrs,-0.598634071,F -coef_Duration_Constant_Shift_for_every_30_minutes_more_than_10_hrs,-0.293607565,F -coef_Calibration_constant_Duration_0,-10,F +coefficient_name,value,constrain +coef_Mode_Choice_Logsum,0.2279,T +coef_Female_Departure_before_7_am,-0.12935306,F +coef_Female_Arrival_after_6_pm,-0.041312616,F +coef_Female_with_preschool_child_Departure_before_7_am,-0.129130464,F +coef_Female_with_preschool_child_Departure_after_7_am,-0.031947595,F +coef_Female_with_preschool_child_Arrival_after_6_pm,-0.048859218,F +coef_Low_income_lt_25000_Departure_before_7_am,0.232768373,F +coef_Low_income_lt_25000_Departure_after_7_am,0.014908169,F +coef_Low_income_lt_25000_Arrival_after_6_pm,0.039105101,F +coef_Med_income_25k_to_60k_Departure_before_7_am,0.123945957,F +coef_Med_income_25k_to_60k_Arrival_after_6_pm,0.020965221,F +coef_Medhigh_income_60k_to_120k_Departure_before_7_am,0.09890939,F +coef_Age_16_to_18_yrs_Departure_Before_7_am,-0.459593556,F +coef_Age_16_to_18_yrs_Departure_After_7_am,0.060951693,F +coef_Age_19_to_24_yrs_Departure_After_7_am,0.031477187,F +coef_Age_25_to_40_yrs_Departure_Before_7_am,-0.11723451,F +coef_Age_65_plus_yrs_Departure_After_7_am,0.051923956,F +coef_Age_19_to_24_yrs_Arrival_after_6_pm,0.032734453,F +coef_Age_25_to_40_yrs_Arrival_before_5_pm,-0.027623617,F +coef_Age_56_to_64_yrs_Arrival_after_6_pm,-0.049130187,F +coef_Age_65_plus_yrs_Arrival_before_5_pm,0.056774635,F +coef_Age_65_plus_yrs_Arrival_after_6_pm,-0.077532684,F +coef_Zero_auto_HH_Departure_before_7_am,0.396983749,F +coef_Zero_auto_HH_Arrival_after_6_pm,0.050665232,F +coef_Parttime_worker_Departure_before_7_am,-0.264760988,F +coef_Parttime_worker_Departure_after_7_am,0.126626287,F +coef_Parttime_worker_Arrival_before_5_pm,0.175158545,F +coef_Parttime_worker_Arrival_after_6_pm,-0.054124518,F +coef_University_student_Departure_after_7_am,0.024758204,F +coef_University_student_Arrival_before_5_pm,0.035389739,F +coef_University_student_Arrival_after_6_pm,0.06173996,F +coef_Blue_collar_Departure_before_7_am,0.327242475,F +coef_Blue_collar_Departure_after_7_am,0.047214248,F +coef_Blue_collar_Arrival_before_5_pm,0.04197056,F +coef_Service_Departure_before_7_am,0.117783508,F +coef_Service_Departure_after_7_am,0.081611629,F +coef_Service_Arrival_before_5_pm,0,T +coef_Health_Departure_before_7_am,0.135275931,F +coef_Health_Arrival_after_6_pm,0.062010123,F +coef_Retail_and_food_Departure_after_7_am,0.076302969,F +coef_Retail_and_food_Arrival_before_5_pm,0.052905387,F +coef_Retail_and_food_Arrival_after_6_pm,0.027069194,F +coef_Time_SOV_freeflowto_destination_Departure_before_7_am,0.011511462,F +coef_Time_SOV_freeflowto_destination_Departure_after_7_am,-0.003821379,F +coef_Time_SOV_freeflowto_destination_Arrival_before_5_pm,-0.00549578,F +coef_Time_SOV_freeflowto_destination_Arrival_after_6_pm,0.002253695,F +coef_Presence_of_NonWorking_Adult_in_the_HH_Departure_before_7_am,0.069957209,F +coef_Presence_of_NonWorking_Adult_in_the_HH_Arrival_before_5_pm,-0.019807228,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_before_7_am,-0.084564489,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Departure_after_8_am,-0.023894467,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_before_5_pm,0.018983499,F +coef_Presence_of_PreDriving_Age_Children_in_the_HH_Arrival_after_6_pm,-0.032091123,F +coef_First_of_2_plus_mandatory_tour_Departure_before_7_am,0.145890035,F +coef_First_of_2_plus_mandatory_tour_Departure_after_7_am,-0.214531877,F +coef_First_of_2_plus_mandatory_tour_Duration_lt_9p5_hours,0.3069241,F +coef_First_of_2_plus_mandatory_tour_Duration_gt_9p5_hours,-0.526297898,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_before_1_pm,-0.221304523,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Departure_after_2_pm,-0.176348812,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_lt_9p5_hours,0.064893097,F +coef_2nd_or_later_of_2_plus_mandatory_tour_Duration_gt_9p5_hours,-0.656942049,F +coef_Departure_Constant_Shift_for_every_30_minutes_before_6_am,-1.151564775,F +coef_Departure_Constant_Before_06_AM,-2.197677208,F +coef_Departure_Constant_06_AM_06_AM_7,-1.314098638,F +coef_Departure_Constant_06_AM_07_AM_8,-0.558766028,F +coef_Departure_Constant_07_AM_07_AM_9,0,T +coef_Departure_Constant_07_AM_08_AM_10,-0.036957515,F +coef_Departure_Constant_08_AM_08_AM_11,-0.285560423,F +coef_Departure_Constant_08_AM_09_AM_12,-0.555478447,F +coef_Departure_Constant_After_09_AM,-0.865125273,F +coef_Departure_Constant_Shift_for_every_30_minutes_after_9_am_Square_Root,-0.435746145,F +coef_Arrival_Constant_Shift_for_every_30_minutes_before_3_pm,-0.191607342,F +coef_Arrival_Constant_Before_03_PM,-0.289333093,F +coef_Arrival_Constant_03_PM_04_PM_26,-0.273555837,F +coef_Arrival_Constant_04_PM_04_PM_27,-0.142653706,F +coef_Arrival_Constant_04_PM_05_PM_28,-0.124814807,F +coef_Arrival_Constant_05_PM_05_PM_29,0.004265544,F +coef_Arrival_Constant_05_PM_06_PM_30,0,T +coef_Arrival_Constant_06_PM_06_PM_31,-0.060515031,F +coef_Arrival_Constant_06_PM_7_PM_32,-0.236621114,F +coef_Arrival_Constant_7_PM_7_PM_33,-0.577646614,F +coef_Arrival_Constant_7_PM_8_PM_34,-0.815994515,F +coef_Arrival_Constant_After_08_PM,-0.854151925,F +coef_Arrival_Constant_Shift_for_every_30_minutes_after_6_pm_Square_root,-0.469720787,F +coef_Duration_Constant_Shift_for_every_30_minutes_less_than_8p5_hrs,-0.074266981,F +coef_Duration_Constant_Shorter_than_8p5_hrs,-0.748584335,F +coef_Duration_Constant_8p5_hours,-0.654814097,F +coef_Duration_Constant_9_hours,-0.372064236,F +coef_Duration_Constant_9p5_hours,-0.144226124,F +coef_Duration_Constant_10_hours,0.013153356,F +coef_Duration_Constant_10p5_hours,0,T +coef_Duration_Constant_11_hours,-0.115847245,F +coef_Duration_Constant_11p5_hours,-0.288506368,F +coef_Duration_Constant_12_hours,-0.524241874,F +coef_Duration_Constant_Longer_than_12_hrs,-0.598634071,F +coef_Duration_Constant_Shift_for_every_30_minutes_more_than_10_hrs,-0.293607565,F +coef_Calibration_constant_Duration_0,-10,F diff --git a/activitysim/examples/example_semcog/configs/transit_pass_ownership.csv b/activitysim/examples/prototype_semcog/configs/transit_pass_ownership.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/transit_pass_ownership.csv rename to activitysim/examples/prototype_semcog/configs/transit_pass_ownership.csv diff --git a/activitysim/examples/example_semcog/configs/transit_pass_ownership.yaml b/activitysim/examples/prototype_semcog/configs/transit_pass_ownership.yaml similarity index 100% rename from activitysim/examples/example_semcog/configs/transit_pass_ownership.yaml rename to activitysim/examples/prototype_semcog/configs/transit_pass_ownership.yaml diff --git a/activitysim/examples/example_semcog/configs/transit_pass_ownership_coeffs.csv b/activitysim/examples/prototype_semcog/configs/transit_pass_ownership_coeffs.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/transit_pass_ownership_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/transit_pass_ownership_coeffs.csv diff --git a/activitysim/examples/example_semcog/configs/transit_pass_subsidy.csv b/activitysim/examples/prototype_semcog/configs/transit_pass_subsidy.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/transit_pass_subsidy.csv rename to activitysim/examples/prototype_semcog/configs/transit_pass_subsidy.csv diff --git a/activitysim/examples/example_semcog/configs/transit_pass_subsidy.yaml b/activitysim/examples/prototype_semcog/configs/transit_pass_subsidy.yaml similarity index 100% rename from activitysim/examples/example_semcog/configs/transit_pass_subsidy.yaml rename to activitysim/examples/prototype_semcog/configs/transit_pass_subsidy.yaml diff --git a/activitysim/examples/example_semcog/configs/transit_pass_subsidy_coeffs.csv b/activitysim/examples/prototype_semcog/configs/transit_pass_subsidy_coeffs.csv similarity index 100% rename from activitysim/examples/example_semcog/configs/transit_pass_subsidy_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/transit_pass_subsidy_coeffs.csv diff --git a/activitysim/examples/example_semcog/configs/trip_destination.csv b/activitysim/examples/prototype_semcog/configs/trip_destination.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/trip_destination.csv rename to activitysim/examples/prototype_semcog/configs/trip_destination.csv index 827feeabfb..2d9307ab58 100755 --- a/activitysim/examples/example_semcog/configs/trip_destination.csv +++ b/activitysim/examples/prototype_semcog/configs/trip_destination.csv @@ -1,17 +1,17 @@ -Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork -size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",1,1,1,1,1,1,1,1,1,1 -no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -#stop zone CBD area type,"@reindex(land_use.AreaType, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, -distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (od_skims['DIST'] + dp_skims['DIST']),-0.049725916,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 -distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (od_skims['DIST'] + dp_skims['DIST']),0.147813279,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 -distance (calibration adjustment joint),@df.is_joint * (od_skims['DIST'] + dp_skims['DIST']),0,0,0,-0.1238,-0.1238,-0.1238,-0.1238,-0.1238,-0.123801985,0 -stop proximity to home (outbound),@df.outbound * od_skims['DIST'],-0.38,0,0,0,0,0,0,0,0,0 -stop proximity to home (inbound),@~df.outbound * dp_skims['DIST'],-0.15,0,0,0,0,0,0,0,0,0 -stop proximity to main destination (outbound),@df.outbound * dp_skims['DIST'],-0.26,,,,,,,,, -stop proximity to main destination (inbound),@~df.outbound * od_skims['DIST'],0,,,,,,,,, -#,,,,,,,,,,, -Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1,1,1,1,1 -Mode choice logsum from origin to stop,od_logsum,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821 -Can't access stop zone by this tour mode,(od_logsum < -100),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Mode choice logsum from stop to destination,dp_logsum,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821 -Can't access destination zone by this tour mode,(dp_logsum < -100),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork +size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",1,1,1,1,1,1,1,1,1,1 +no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +#stop zone CBD area type,"@reindex(land_use.AreaType, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, +distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (od_skims['DIST'] + dp_skims['DIST']),-0.049725916,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 +distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (od_skims['DIST'] + dp_skims['DIST']),0.147813279,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 +distance (calibration adjustment joint),@df.is_joint * (od_skims['DIST'] + dp_skims['DIST']),0,0,0,-0.1238,-0.1238,-0.1238,-0.1238,-0.1238,-0.123801985,0 +stop proximity to home (outbound),@df.outbound * od_skims['DIST'],-0.38,0,0,0,0,0,0,0,0,0 +stop proximity to home (inbound),@~df.outbound * dp_skims['DIST'],-0.15,0,0,0,0,0,0,0,0,0 +stop proximity to main destination (outbound),@df.outbound * dp_skims['DIST'],-0.26,,,,,,,,, +stop proximity to main destination (inbound),@~df.outbound * od_skims['DIST'],0,,,,,,,,, +#,,,,,,,,,,, +Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1,1,1,1,1,1,1 +Mode choice logsum from origin to stop,od_logsum,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821 +Can't access stop zone by this tour mode,(od_logsum < -100),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Mode choice logsum from stop to destination,dp_logsum,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821,1.821 +Can't access destination zone by this tour mode,(dp_logsum < -100),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 diff --git a/activitysim/examples/example_semcog/configs/trip_destination.yaml b/activitysim/examples/prototype_semcog/configs/trip_destination.yaml similarity index 96% rename from activitysim/examples/example_semcog/configs/trip_destination.yaml rename to activitysim/examples/prototype_semcog/configs/trip_destination.yaml index 1bd5b0fc61..e754e188b7 100755 --- a/activitysim/examples/example_semcog/configs/trip_destination.yaml +++ b/activitysim/examples/prototype_semcog/configs/trip_destination.yaml @@ -1,36 +1,36 @@ -SAMPLE_SIZE: 30 - -DESTINATION_SAMPLE_SPEC: trip_destination_sample.csv -DESTINATION_SPEC: trip_destination.csv -COEFFICIENTS: _dummy_coefficients.csv - -LOGSUM_SETTINGS: trip_mode_choice.yaml - -# optional (comment out if not desired) -DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum - -# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table -DEST_CHOICE_SAMPLE_TABLE_NAME: trip_destination_sample - -# model-specific logsum-related settings -TRIP_ORIGIN: origin -ALT_DEST_COL_NAME: dest_taz -PRIMARY_DEST: tour_leg_dest # must be created in preprocessor - -REDUNDANT_TOURS_MERGED_CHOOSER_COLUMNS: - - tour_mode - -CONSTANTS: - max_walk_distance: 3 - max_bike_distance: 8 - -preprocessor: - SPEC: trip_destination_annotate_trips_preprocessor - DF: trips - TABLES: - - tours - - persons - -# drop failed trips and cleanup failed trip leg_mates for consistency -# (i.e. adjust trip_count, trip_num, first for missing failed trips) -CLEANUP: False +SAMPLE_SIZE: 30 + +DESTINATION_SAMPLE_SPEC: trip_destination_sample.csv +DESTINATION_SPEC: trip_destination.csv +COEFFICIENTS: _dummy_coefficients.csv + +LOGSUM_SETTINGS: trip_mode_choice.yaml + +# optional (comment out if not desired) +DEST_CHOICE_LOGSUM_COLUMN_NAME: destination_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: trip_destination_sample + +# model-specific logsum-related settings +TRIP_ORIGIN: origin +ALT_DEST_COL_NAME: dest_taz +PRIMARY_DEST: tour_leg_dest # must be created in preprocessor + +REDUNDANT_TOURS_MERGED_CHOOSER_COLUMNS: + - tour_mode + +CONSTANTS: + max_walk_distance: 3 + max_bike_distance: 8 + +preprocessor: + SPEC: trip_destination_annotate_trips_preprocessor + DF: trips + TABLES: + - tours + - persons + +# drop failed trips and cleanup failed trip leg_mates for consistency +# (i.e. adjust trip_count, trip_num, first for missing failed trips) +CLEANUP: False diff --git a/activitysim/examples/example_psrc/configs/trip_destination_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/trip_destination_annotate_trips_preprocessor.csv similarity index 98% rename from activitysim/examples/example_psrc/configs/trip_destination_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/trip_destination_annotate_trips_preprocessor.csv index e4317d8e82..1a1afb0748 100755 --- a/activitysim/examples/example_psrc/configs/trip_destination_annotate_trips_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/trip_destination_annotate_trips_preprocessor.csv @@ -1,10 +1,10 @@ -Description,Target,Expression -#,, -,tour_mode,"reindex(tours.tour_mode, df.tour_id)" -,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id),reindex_i(tours.end, df.tour_id))" -,trip_period,network_los.skim_time_period_label(_tod) -,is_joint,"reindex(tours.tour_category, df.tour_id)=='joint'" -#,,not needed as school is not chosen as an intermediate trip destination -#,_grade_school,"(df.primary_purpose == 'school') & reindex(persons.is_gradeschool, df.person_id)" -#,size_segment,"df.primary_purpose.where(df.primary_purpose != 'school', np.where(_grade_school,'gradeschool', 'highschool'))" +Description,Target,Expression +#,, +,tour_mode,"reindex(tours.tour_mode, df.tour_id)" +,_tod,"np.where(df.outbound,reindex_i(tours.start, df.tour_id),reindex_i(tours.end, df.tour_id))" +,trip_period,network_los.skim_time_period_label(_tod) +,is_joint,"reindex(tours.tour_category, df.tour_id)=='joint'" +#,,not needed as school is not chosen as an intermediate trip destination +#,_grade_school,"(df.primary_purpose == 'school') & reindex(persons.is_gradeschool, df.person_id)" +#,size_segment,"df.primary_purpose.where(df.primary_purpose != 'school', np.where(_grade_school,'gradeschool', 'highschool'))" ,tour_leg_dest,"np.where(df.outbound,reindex(tours.destination, df.tour_id), reindex(tours.origin, df.tour_id))" \ No newline at end of file diff --git a/activitysim/examples/example_semcog/configs/trip_destination_sample.csv b/activitysim/examples/prototype_semcog/configs/trip_destination_sample.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/trip_destination_sample.csv rename to activitysim/examples/prototype_semcog/configs/trip_destination_sample.csv index 4eb2d61a6a..6a0a53480e 100755 --- a/activitysim/examples/example_semcog/configs/trip_destination_sample.csv +++ b/activitysim/examples/prototype_semcog/configs/trip_destination_sample.csv @@ -1,18 +1,18 @@ -Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork -,_od_DIST@od_skims['DIST'],1,1,1,1,1,1,1,1,1,1 -,_dp_DIST@dp_skims['DIST'],1,1,1,1,1,1,1,1,1,1 -Not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & (od_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & (dp_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (od_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (dp_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -#If transit tour is not in walk sub-zone it must be walkable,,,,,,,,,,, -size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",1,1,1,1,1,1,1,1,1,1 -no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 -#stop zone CBD area type,"@reindex(land_use.AreaType, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, -distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (_od_DIST + _dp_DIST),-0.049725916,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 -distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (_od_DIST + _dp_DIST),0.147813279,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 -distance (calibration adjustment joint),@df.is_joint * (_od_DIST + _dp_DIST),0,0,0,-0.1238,-0.1238,-0.1238,-0.1238,-0.1238,-0.123801985,0 -stop proximity to home (outbound),@df.outbound * _od_DIST,-0.38,0,0,0,0,0,0,0,0,0 -stop proximity to home (inbound),@~df.outbound * _od_DIST,-0.15,0,0,0,0,0,0,0,0,0 -stop proximity to main destination (outbound),@df.outbound * _dp_DIST,-0.26,,,,,,,,, -stop proximity to main destination (inbound),@~df.outbound * _od_DIST,0,,,,,,,,, +Description,Expression,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork +,_od_DIST@od_skims['DIST'],1,1,1,1,1,1,1,1,1,1 +,_dp_DIST@dp_skims['DIST'],1,1,1,1,1,1,1,1,1,1 +Not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & (od_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if walk tour not within walking distance,@(df.tour_mode=='WALK') & (dp_skims['DISTWALK'] > max_walk_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (od_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +Not available if bike tour not within biking distance,@(df.tour_mode=='BIKE') & (dp_skims['DISTBIKE'] > max_bike_distance),-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +#If transit tour is not in walk sub-zone it must be walkable,,,,,,,,,,, +size term,"@np.log1p(size_terms.get(df.dest_taz, df.purpose))",1,1,1,1,1,1,1,1,1,1 +no attractions,"@size_terms.get(df.dest_taz, df.purpose) == 0",-999,-999,-999,-999,-999,-999,-999,-999,-999,-999 +#stop zone CBD area type,"@reindex(land_use.AreaType, df.dest_taz) < setting('cbd_threshold')",,,,,,,,,, +distance (calibration adjustment individual - inbound),@(~df.is_joint & ~df.outbound) * (_od_DIST + _dp_DIST),-0.049725916,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 +distance (calibration adjustment individual - outbound),@(~df.is_joint & df.outbound) * (_od_DIST + _dp_DIST),0.147813279,-0.0613,-0.1056,-0.1491,-0.1192,-0.1029,-0.0962,-0.1329,-0.126172224,-0.122334597 +distance (calibration adjustment joint),@df.is_joint * (_od_DIST + _dp_DIST),0,0,0,-0.1238,-0.1238,-0.1238,-0.1238,-0.1238,-0.123801985,0 +stop proximity to home (outbound),@df.outbound * _od_DIST,-0.38,0,0,0,0,0,0,0,0,0 +stop proximity to home (inbound),@~df.outbound * _od_DIST,-0.15,0,0,0,0,0,0,0,0,0 +stop proximity to main destination (outbound),@df.outbound * _dp_DIST,-0.26,,,,,,,,, +stop proximity to main destination (inbound),@~df.outbound * _od_DIST,0,,,,,,,,, diff --git a/activitysim/examples/example_semcog/configs/trip_mode_choice.csv b/activitysim/examples/prototype_semcog/configs/trip_mode_choice.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/trip_mode_choice.csv rename to activitysim/examples/prototype_semcog/configs/trip_mode_choice.csv index 0484fec649..9908e69722 100755 --- a/activitysim/examples/example_semcog/configs/trip_mode_choice.csv +++ b/activitysim/examples/prototype_semcog/configs/trip_mode_choice.csv @@ -1,399 +1,399 @@ -Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK,BIKE,WALK_LOC,WALK_PRM,WALK_MIX,PNR_LOC,PNR_PRM,PNR_MIX,KNR_LOC,KNR_PRM,KNR_MIX,SCHOOLBUS,TAXI,TNC_SINGLE,TNC_SHARED -#Drive alone no toll,,,,,,,,,,,,,,,,,,, -DRIVEALONE - Unavailable,sov_available == False,-999,,,,,,,,,,,,,,,,, -DRIVEALONE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,,,,,,, -DRIVEALONE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,,,,,,, -DRIVEALONE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,,,,,,, -DRIVEALONE - In-vehicle time,@c_ivt*odt_skims['SOV_TIME'],1,,,,,,,,,,,,,,,,, -DRIVEALONE - Terminal time,@c_walktimeshort * df.total_terminal_time,1,,,,,,,,,,,,,,,,, -DRIVEALONE - Operating cost ,@df.c_cost * costPerMile * odt_skims['SOV_DIST'],1,,,,,,,,,,,,,,,,, -DRIVEALONE - Parking cost ,c_cost * total_parking_cost,1,,,,,,,,,,,,,,,,, -DRIVEALONE - Person is between 16 and 19 years old,@c_age1619_da * ((df.age >= 16) & (df.age <= 19)),1,,,,,,,,,,,,,,,,, -#Shared ride 2,,,,,,,,,,,,,,,,,,, -SHARED2 - Unavailable,hov2_available == False,,-999,,,,,,,,,,,,,,,, -SHARED2 - Unavailable based on party size,is_joint & (number_of_participants > 2),,-999,,,,,,,,,,,,,,,, -SHARED2 - In-vehicle time,@c_ivt * odt_skims['HOV2_TIME'],,1,,,,,,,,,,,,,,,, -SHARED2 - Terminal time,@c_walktimeshort * df.total_terminal_time,,1,,,,,,,,,,,,,,,, -SHARED2 - Operating cost,@df.c_cost * costPerMile * odt_skims['HOV2_DIST'],,1,,,,,,,,,,,,,,,, -SHARED2 - Parking cost,@df.c_cost * df.total_parking_cost / costShareSr2,,1,,,,,,,,,,,,,,,, -SHARED2 - One person household,@c_hhsize1_sr * (df.hhsize == 1),,1,,,,,,,,,,,,,,,, -SHARED2 - Two person household,@c_hhsize2_sr * (df.hhsize == 2),,1,,,,,,,,,,,,,,,, -SHARED2 - Person is 16 years old or older,@c_age16p_sr * (df.age >= 16),,1,,,,,,,,,,,,,,,, -#Shared ride 3+,,,,,,,,,,,,,,,,,,, -SHARED3 - Unavailable,hov3_available == False,,,-999,,,,,,,,,,,,,,, -SHARED3 - Unavailable based joint tour mode,@df.is_joint & df.i_tour_mode.isin(I_SR2_MODES),,,-999,,,,,,,,,,,,,,, -SHARED3 - Unavailable if tour mode is shared 2,@df.i_tour_mode.isin(I_SR2_MODES),,,-999,,,,,,,,,,,,,,, -SHARED3 - In-vehicle time,@c_ivt * odt_skims['HOV3_TIME'],,,1,,,,,,,,,,,,,,, -SHARED3 - Terminal time,@c_walktimeshort * df.total_terminal_time,,,1,,,,,,,,,,,,,,, -SHARED3 - Operating cost,@df.c_cost * costPerMile * odt_skims['HOV3_DIST'],,,1,,,,,,,,,,,,,,, -SHARED3 - Parking cost,@df.c_cost * df.total_parking_cost / costShareSr3,,,1,,,,,,,,,,,,,,, -SHARED3 - One person household,@c_hhsize1_sr * (df.hhsize == 1),,,1,,,,,,,,,,,,,,, -SHARED3 - Two person household,@c_hhsize2_sr * (df.hhsize == 2),,,1,,,,,,,,,,,,,,, -SHARED3 - Person is 16 years old or older,@c_age16p_sr * (df.age >= 16),,,1,,,,,,,,,,,,,,, -#Walk,,,,,,,,,,,,,,,,,,, -WALK - Time up to 2 miles,@c_walktimeshort * od_skims['DISTWALK'].clip(upper=walkThresh) * 60/walkSpeed,,,,1,,,,,,,,,,,,,, -WALK - Time beyond 2 of a miles,@c_walktimelong * (od_skims['DISTWALK'] - walkThresh).clip(lower=0) * 60/walkSpeed,,,,1,,,,,,,,,,,,,, -WALK - Destination zone densityIndex,@c_density_index * df.density_index,,,,1,,,,,,,,,,,,,, -WALK - Topology,@c_topology_walk * df.trip_topology,,,,1,,,,,,,,,,,,,, -#Bike,,,,,,,,,,,,,,,,,,, -BIKE - Unavailable if tour mode is not bike,~tour_mode_is_bike,,,,,-999,,,,,,,,,,,,, -BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,-999,,,,,,,,,,,,, -BIKE - Time up to 6 miles,@c_biketimeshort * od_skims['DISTBIKE'].clip(upper=bikeThresh)*60/bikeSpeed,,,,,1,,,,,,,,,,,,, -BIKE - Time beyond 6 of a miles,@c_biketimelong * (od_skims['DISTBIKE']-bikeThresh).clip(lower=0)*60/bikeSpeed,,,,,1,,,,,,,,,,,,, -BIKE - Destination zone densityIndex,@c_density_index*df.density_index,,,,,1,,,,,,,,,,,,, -BIKE - Topology,@c_topology_bike * df.trip_topology,,,,,1,,,,,,,,,,,,, -#Walk to Local,,,,,,,,,,,,,,,,,,, -WALK_LOC - Unavailable,walk_local_available == False,,,,,,-999,,,,,,,,,,,, -WALK_LOC - In-vehicle time,@c_ivt * odt_skims['WLK_LOC_IVT'],,,,,,1,,,,,,,,,,,, -WALK_LOC - Short iwait time,@c_short_i_wait * (odt_skims['WLK_LOC_IWAIT']).clip(upper=waitThresh),,,,,,1,,,,,,,,,,,, -WALK_LOC - Long iwait time,@c_long_i_wait * (odt_skims['WLK_LOC_IWAIT']-waitThresh).clip(0),,,,,,1,,,,,,,,,,,, -WALK_LOC - transfer wait time,@c_xwait * odt_skims['WLK_LOC_XWAIT'],,,,,,1,,,,,,,,,,,, -WALK_LOC - number of transfers,@c_xfers_wlk * (odt_skims['WLK_LOC_NT']).clip(0),,,,,,1,,,,,,,,,,,, -WALK_LOC - Walk access time,@c_wacc * df.origin_walk_time,,,,,,1,,,,,,,,,,,, -WALK_LOC - Walk egress time,@c_wegr * df.destination_walk_time,,,,,,1,,,,,,,,,,,, -WALK_LOC - Walk other time,@c_waux * odt_skims['WLK_LOC_WAUX'],,,,,,1,,,,,,,,,,,, -WALK_LOC - Fare,@df.c_cost * odt_skims['WLK_LOC_FARE'],,,,,,1,,,,,,,,,,,, -WALK_LOC - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,1,,,,,,,,,,,, -WALK_LOC - Topology,@c_topology_trn * df.trip_topology,,,,,,1,,,,,,,,,,,, -WALK_LOC - Person is less than 10 years old,@c_age010_trn * (df.age <= 10),,,,,,1,,,,,,,,,,,, -#Walk to Premium,,,,,,,,,,,,,,,,,,, -WALK_PRM - Unavailable,walk_premium_available == False,,,,,,,-999,,,,,,,,,,, -WALK_PRM - In-vehicle time,@c_ivt * odt_skims['WLK_PRM_IVT'],,,,,,,1,,,,,,,,,,, -WALK_PRM - In-vehicle time on PMov,@(c_ivt_pmov - c_ivt) * odt_skims['WLK_PRM_IVTT_PMov'],,,,,,,1,,,,,,,,,,, -WALK_PRM - In-vehicle time on StCar,@(c_ivt_stcar - c_ivt) * odt_skims['WLK_PRM_IVTT_StCar'],,,,,,,1,,,,,,,,,,, -WALK_PRM - In-vehicle time on BRT,@(c_ivt_brt - c_ivt) * odt_skims['WLK_PRM_IVTT_Brt'],,,,,,,1,,,,,,,,,,, -WALK_PRM - In-vehicle time on URB,@(c_ivt_urb - c_ivt) * odt_skims['WLK_PRM_IVTT_UrbRail'],,,,,,,1,,,,,,,,,,, -WALK_PRM - In-vehicle time on COM,@(c_ivt_com - c_ivt) * odt_skims['WLK_PRM_IVTT_ComRail'],,,,,,,1,,,,,,,,,,, -WALK_PRM - Short iwait time,@c_short_i_wait * (odt_skims['WLK_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,1,,,,,,,,,,, -WALK_PRM - Long iwait time,@c_long_i_wait * (odt_skims['WLK_PRM_IWAIT']-waitThresh).clip(0),,,,,,,1,,,,,,,,,,, -WALK_PRM - transfer wait time,@c_xwait * odt_skims['WLK_PRM_XWAIT'],,,,,,,1,,,,,,,,,,, -WALK_PRM - number of transfers,@c_xfers_wlk * (odt_skims['WLK_PRM_NT']).clip(0),,,,,,,1,,,,,,,,,,, -WALK_PRM - Walk access time,@c_wacc * df.origin_walk_time,,,,,,,1,,,,,,,,,,, -WALK_PRM - Walk egress time,@c_wegr * df.destination_walk_time,,,,,,,1,,,,,,,,,,, -WALK_PRM - Walk otherLight rail/Ferry time,@c_waux * odt_skims['WLK_PRM_WAUX'],,,,,,,1,,,,,,,,,,, -WALK_PRM - Fare,@df.c_cost * odt_skims['WLK_PRM_FARE'],,,,,,,1,,,,,,,,,,, -WALK_PRM - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,1,,,,,,,,,,, -WALK_PRM - Topology,@c_topology_trn * df.trip_topology,,,,,,,1,,,,,,,,,,, -WALK_PRM - Person is less than 10 years old,@c_age010_trn * (df.age <= 10),,,,,,,1,,,,,,,,,,, -#Walk to Mix,,,,,,,,,,,,,,,,,,, -WALK_MIX - Unavailable,walk_mix_available == False,,,,,,,,-999,,,,,,,,,, -WALK_MIX - In-vehicle time,@c_ivt * odt_skims['WLK_MIX_IVT'],,,,,,,,1,,,,,,,,,, -WALK_MIX - In-vehicle time on Bus,@c_ivt * odt_skims['WLK_MIX_IVTT_Bus'],,,,,,,,1,,,,,,,,,, -WALK_MIX - In-vehicle time on PMov,@(c_ivt_pmov - c_ivt) * odt_skims['WLK_MIX_IVTT_PMov'],,,,,,,,1,,,,,,,,,, -WALK_MIX - In-vehicle time on StCar,@(c_ivt_stcar - c_ivt) * odt_skims['WLK_MIX_IVTT_StCar'],,,,,,,,1,,,,,,,,,, -WALK_MIX - In-vehicle time on BRT,@(c_ivt_brt - c_ivt) * odt_skims['WLK_MIX_IVTT_Brt'],,,,,,,,1,,,,,,,,,, -WALK_MIX - In-vehicle time on URB,@(c_ivt_urb - c_ivt) * odt_skims['WLK_MIX_IVTT_UrbRail'],,,,,,,,1,,,,,,,,,, -WALK_MIX - In-vehicle time on COM,@(c_ivt_com - c_ivt) * odt_skims['WLK_MIX_IVTT_ComRail'],,,,,,,,1,,,,,,,,,, -WALK_MIX - Short iwait time,@c_short_i_wait * (odt_skims['WLK_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,1,,,,,,,,,, -WALK_MIX - Long iwait time,@c_long_i_wait * (odt_skims['WLK_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,1,,,,,,,,,, -WALK_MIX - transfer wait time,@c_xwait * odt_skims['WLK_MIX_XWAIT'],,,,,,,,1,,,,,,,,,, -WALK_MIX - number of transfers,@c_xfers_wlk * (odt_skims['WLK_MIX_NT']).clip(0),,,,,,,,1,,,,,,,,,, -WALK_MIX - Walk access time,@c_wacc * df.origin_walk_time,,,,,,,,1,,,,,,,,,, -WALK_MIX - Walk egress time,@c_wegr * df.destination_walk_time,,,,,,,,1,,,,,,,,,, -WALK_MIX - Walk other time,@c_waux * odt_skims['WLK_MIX_WAUX'],,,,,,,,1,,,,,,,,,, -WALK_MIX - Fare,@df.c_cost * odt_skims['WLK_MIX_FARE'],,,,,,,,1,,,,,,,,,, -WALK_MIX - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,1,,,,,,,,,, -WALK_MIX - Topology,@c_topology_trn * df.trip_topology,,,,,,,,1,,,,,,,,,, -WALK_MIX - Person is less than 10 years old,@c_age010_trn * (df.age <= 10),,,,,,,,1,,,,,,,,,, -#PNR to Local,,,,,,,,,,,,,,,,,,, -PNR_LOC - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,-999,,,,,,,,, -PNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,-999,,,,,,,,, -PNR_LOC - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,1,,,,,,,,, -PNR_LOC - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,1,,,,,,,,, -PNR_LOC - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - Unavailable,outbound & ~pnr_local_available_outbound,,,,,,,,,-999,,,,,,,,, -PNR_LOC outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['PNR_LOC_IVT'],,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['PNR_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['PNR_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['PNR_LOC_XWAIT'],,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - number of transfers,@df.outbound * c_xfers_wlk * (odt_skims['PNR_LOC_NT']).clip(0),,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - Drive time,@df.outbound * c_dtim * odt_skims['PNR_LOC_DTIME'],,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - Walk egress time,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - Walk other time,@df.outbound * c_waux * odt_skims['PNR_LOC_WAUX'],,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['PNR_LOC_FARE'] + costPerMile*odt_skims['PNR_LOC_DDIST']),,,,,,,,,1,,,,,,,,, -PNR_LOC outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['PNR_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - Unavailable,inbound & ~pnr_local_available_inbound,,,,,,,,,-999,,,,,,,,, -PNR_LOC inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['PNRE_LOC_IVT'],,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['PNRE_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['PNRE_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['PNRE_LOC_XWAIT'],,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - number of transfers,@df.inbound * c_xfers_wlk * (odt_skims['PNRE_LOC_NT']).clip(0),,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - Drive time,@df.inbound * c_dtim * odt_skims['PNRE_LOC_DTIME'],,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - Walk other time,@df.inbound * c_waux * odt_skims['PNRE_LOC_WAUX'],,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['PNRE_LOC_FARE'] + costPerMile*odt_skims['PNRE_LOC_DDIST']),,,,,,,,,1,,,,,,,,, -PNR_LOC inbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['PNRE_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,1,,,,,,,,, -#Drive to Premium,,,,,,,,,,,,,,,,,,, -PNR_PRM - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,-999,,,,,,,, -PNR_PRM - Unavailable for persons less than 16,age < 16,,,,,,,,,,-999,,,,,,,, -PNR_PRM - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,,1,,,,,,,, -PNR_PRM - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,,1,,,,,,,, -PNR_PRM - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - Unavailable,outbound & ~pnr_premium_available_outbound,,,,,,,,,,-999,,,,,,,, -PNR_PRM outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['PNR_PRM_IVT'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on PMov,@df.outbound * (c_ivt_pmov - c_ivt) * odt_skims['PNR_PRM_IVTT_PMov'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on StCar,@df.outbound * (c_ivt_stcar - c_ivt) * odt_skims['PNR_PRM_IVTT_StCar'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on BRT,@df.outbound * (c_ivt_brt - c_ivt) * odt_skims['PNR_PRM_IVTT_Brt'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on URB,@df.outbound * (c_ivt_urb - c_ivt) * odt_skims['PNR_PRM_IVTT_UrbRail'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on COM,@df.outbound * (c_ivt_com - c_ivt) * odt_skims['PNR_PRM_IVTT_ComRail'],,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['PNR_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['PNR_PRM_IWAIT']-waitThresh).clip(0) ,,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['PNR_PRM_XWAIT'],,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - number of transfers,@df.outbound * c_xfers_drv * (odt_skims['PNR_PRM_NT']).clip(0),,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - Drive time,@df.outbound * c_dtim * odt_skims['PNR_PRM_DTIME'],,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - Walk egress time,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - Walk other time,@df.outbound * c_waux * odt_skims['PNR_PRM_WAUX'],,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['PNR_PRM_FARE'] + costPerMile * odt_skims['PNR_PRM_DDIST']),,,,,,,,,,1,,,,,,,, -PNR_PRM outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['PNR_PRM_DDIST']) / od_skims['DIST'],,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - Unavailable,inbound & ~pnr_premium_available_inbound,,,,,,,,,,-999,,,,,,,, -PNR_PRM inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['PNRE_PRM_IVT'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on PMov,@df.inbound * (c_ivt_pmov - c_ivt) * odt_skims['PNRE_PRM_IVTT_PMov'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on StCar,@df.inbound * (c_ivt_stcar - c_ivt) * odt_skims['PNRE_PRM_IVTT_StCar'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on BRT,@df.inbound * (c_ivt_brt - c_ivt) * odt_skims['PNRE_PRM_IVTT_Brt'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on URB,@df.inbound * (c_ivt_urb - c_ivt) * odt_skims['PNRE_PRM_IVTT_UrbRail'],,,,,,,,,,1,,,,,,,, -PNR_PRM - In-vehicle time on COM,@df.inbound * (c_ivt_com - c_ivt) * odt_skims['PNRE_PRM_IVTT_ComRail'],,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['PNRE_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['PNRE_PRM_IWAIT']-waitThresh).clip(0),,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['PNRE_PRM_XWAIT'],,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - number of transfers,@df.inbound * c_xfers_drv * (odt_skims['PNRE_PRM_NT']).clip(0),,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - Drive time,@df.inbound * c_dtim * odt_skims['PNRE_PRM_DTIME'],,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - Walk other time,@df.inbound * c_waux * odt_skims['PNRE_PRM_WAUX'],,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['PNRE_PRM_FARE'] + costPerMile * odt_skims['PNRE_PRM_DDIST']),,,,,,,,,,1,,,,,,,, -PNR_PRM inbound - Ratio of drive access distance to OD distance,@df.inbound * c_dacc_ratio * (odt_skims['PNRE_PRM_DDIST'])/ od_skims['DIST'],,,,,,,,,,1,,,,,,,, -#PNR to Mix,,,,,,,,,,,,,,,,,,, -PNR_MIX - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,-999,,,,,,, -PNR_MIX - Unavailable for persons less than 16,age < 16,,,,,,,,,,,-999,,,,,,, -PNR_MIX - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,,,1,,,,,,, -PNR_MIX - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,,,1,,,,,,, -PNR_MIX - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - Unavailable,outbound & ~pnr_mix_available_outbound,,,,,,,,,,,-999,,,,,,, -PNR_MIX outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['PNR_MIX_IVT'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on Bus,@df.outbound * c_ivt * odt_skims['PNR_MIX_IVTT_Bus'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on PMov,@df.outbound * (c_ivt_pmov - c_ivt) * odt_skims['PNR_MIX_IVTT_PMov'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on StCar,@df.outbound * (c_ivt_stcar - c_ivt) * odt_skims['PNR_MIX_IVTT_StCar'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on BRT,@df.outbound * (c_ivt_brt - c_ivt) * odt_skims['PNR_MIX_IVTT_Brt'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on URB,@df.outbound * (c_ivt_urb - c_ivt) * odt_skims['PNR_MIX_IVTT_UrbRail'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on COM,@df.outbound * (c_ivt_com - c_ivt) * odt_skims['PNR_MIX_IVTT_ComRail'],,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['PNR_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['PNR_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['PNR_MIX_XWAIT'],,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - number of transfers,@df.outbound * c_xfers_drv * (odt_skims['PNR_MIX_NT']).clip(0),,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - Drive time,@df.outbound * c_dtim * odt_skims['PNR_MIX_DTIME'],,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - Walk egress ime,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - Walk other time,@df.outbound * c_waux * odt_skims['PNR_MIX_WAUX'],,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['PNR_MIX_FARE'] + costPerMile * odt_skims['PNR_MIX_DDIST']),,,,,,,,,,,1,,,,,,, -PNR_MIX outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['PNR_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - Unavailable,inbound & ~pnr_mix_available_inbound,,,,,,,,,,,-999,,,,,,, -PNR_MIX inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['PNRE_MIX_IVT'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on Bus,@df.inbound * c_ivt * odt_skims['PNRE_MIX_IVTT_Bus'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on PMov,@df.inbound * (c_ivt_pmov - c_ivt) * odt_skims['PNRE_MIX_IVTT_PMov'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on StCar,@df.inbound * (c_ivt_stcar - c_ivt) * odt_skims['PNRE_MIX_IVTT_StCar'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on BRT,@df.inbound * (c_ivt_brt - c_ivt) * odt_skims['PNRE_MIX_IVTT_Brt'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on URB,@df.inbound * (c_ivt_urb - c_ivt) * odt_skims['PNRE_MIX_IVTT_UrbRail'],,,,,,,,,,,1,,,,,,, -PNR_MIX - In-vehicle time on COM,@df.inbound * (c_ivt_com - c_ivt) * odt_skims['PNRE_MIX_IVTT_ComRail'],,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['PNRE_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['PNRE_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['PNRE_MIX_XWAIT'],,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - number of transfers,@df.inbound * c_xfers_drv * (odt_skims['PNRE_MIX_NT']).clip(0),,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - Drive time,@df.inbound * c_dtim * odt_skims['PNRE_MIX_DTIME'],,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - Walk other time,@df.inbound * c_waux * odt_skims['PNRE_MIX_WAUX'],,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['PNRE_MIX_FARE'] + costPerMile * odt_skims['PNRE_MIX_DDIST']),,,,,,,,,,,1,,,,,,, -PNR_MIX inbound - Ratio of drive access distance to OD distance,@df.inbound * c_dacc_ratio * (odt_skims['PNRE_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,1,,,,,,, -#KNR to Local,,,,,,,,,,,,,,,,,,, -KNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,-999,,,,,, -KNR_LOC - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,,,,1,,,,,, -KNR_LOC - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,,,,1,,,,,, -KNR_LOC - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - Unavailable,outbound & ~knr_local_available_outbound,,,,,,,,,,,,-999,,,,,, -KNR_LOC outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['KNR_LOC_IVT'],,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['KNR_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['KNR_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['KNR_LOC_XWAIT'],,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - number of transfers,@df.outbound * c_xfers_wlk * (odt_skims['KNR_LOC_NT']).clip(0),,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - Drive time,@df.outbound * c_dtim * odt_skims['KNR_LOC_DTIME'],,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - Walk egress time,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - Walk other time,@df.outbound * c_waux * odt_skims['KNR_LOC_WAUX'],,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['KNR_LOC_FARE'] + costPerMile*odt_skims['KNR_LOC_DDIST']),,,,,,,,,,,,1,,,,,, -KNR_LOC outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['KNR_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - Unavailable,inbound & ~knr_local_available_inbound,,,,,,,,,,,,-999,,,,,, -KNR_LOC inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['KNRE_LOC_IVT'],,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['KNRE_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['KNRE_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['KNRE_LOC_XWAIT'],,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - number of transfers,@df.inbound * c_xfers_wlk * (odt_skims['KNRE_LOC_NT']).clip(0),,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - Drive time,@df.inbound * c_dtim * odt_skims['KNRE_LOC_DTIME'],,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - Walk other time,@df.inbound * c_waux * odt_skims['KNRE_LOC_WAUX'],,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['KNRE_LOC_FARE'] + costPerMile*odt_skims['KNRE_LOC_DDIST']),,,,,,,,,,,,1,,,,,, -KNR_LOC inbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['KNRE_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,,,,1,,,,,, -#Drive to Premium,,,,,,,,,,,,,,,,,,, -KNR_PRM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,-999,,,,, -KNR_PRM - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,,,,,1,,,,, -KNR_PRM - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,,,,,1,,,,, -KNR_PRM - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - Unavailable,outbound & ~knr_premium_available_outbound,,,,,,,,,,,,,-999,,,,, -KNR_PRM outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['KNR_PRM_IVT'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on PMov,@df.outbound * (c_ivt_pmov - c_ivt) * odt_skims['KNR_PRM_IVTT_PMov'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on StCar,@df.outbound * (c_ivt_stcar - c_ivt) * odt_skims['KNR_PRM_IVTT_StCar'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on BRT,@df.outbound * (c_ivt_brt - c_ivt) * odt_skims['KNR_PRM_IVTT_Brt'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on URB,@df.outbound * (c_ivt_urb - c_ivt) * odt_skims['KNR_PRM_IVTT_UrbRail'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on COM,@df.outbound * (c_ivt_com - c_ivt) * odt_skims['KNR_PRM_IVTT_ComRail'],,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['KNR_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['KNR_PRM_IWAIT']-waitThresh).clip(0) ,,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['KNR_PRM_XWAIT'],,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - number of transfers,@df.outbound * c_xfers_drv * (odt_skims['KNR_PRM_NT']).clip(0),,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - Drive time,@df.outbound * c_dtim * odt_skims['KNR_PRM_DTIME'],,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - Walk egress time,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - Walk other time,@df.outbound * c_waux * odt_skims['KNR_PRM_WAUX'],,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['KNR_PRM_FARE'] + costPerMile * odt_skims['KNR_PRM_DDIST']),,,,,,,,,,,,,1,,,,, -KNR_PRM outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['KNR_PRM_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - Unavailable,inbound & ~knr_premium_available_inbound,,,,,,,,,,,,,-999,,,,, -KNR_PRM inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['KNRE_PRM_IVT'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on PMov,@df.inbound * (c_ivt_pmov - c_ivt) * odt_skims['KNRE_PRM_IVTT_PMov'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on StCar,@df.inbound * (c_ivt_stcar - c_ivt) * odt_skims['KNRE_PRM_IVTT_StCar'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on BRT,@df.inbound * (c_ivt_brt - c_ivt) * odt_skims['KNRE_PRM_IVTT_Brt'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on URB,@df.inbound * (c_ivt_urb - c_ivt) * odt_skims['KNRE_PRM_IVTT_UrbRail'],,,,,,,,,,,,,1,,,,, -KNR_PRM - In-vehicle time on COM,@df.inbound * (c_ivt_com - c_ivt) * odt_skims['KNRE_PRM_IVTT_ComRail'],,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['KNRE_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['KNRE_PRM_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['KNRE_PRM_XWAIT'],,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - number of transfers,@df.inbound * c_xfers_drv * (odt_skims['KNRE_PRM_NT']).clip(0),,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - Drive time,@df.inbound * c_dtim * odt_skims['KNRE_PRM_DTIME'],,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - Walk other time,@df.inbound * c_waux * odt_skims['KNRE_PRM_WAUX'],,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['KNRE_PRM_FARE'] + costPerMile * odt_skims['KNRE_PRM_DDIST']),,,,,,,,,,,,,1,,,,, -KNR_PRM inbound - Ratio of drive access distance to OD distance,@df.inbound * c_dacc_ratio * (odt_skims['KNRE_PRM_DDIST'])/ od_skims['DIST'],,,,,,,,,,,,,1,,,,, -#KNR to Mix,,,,,,,,,,,,,,,,,,, -KNR_MIX - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,-999,,,, -KNR_MIX - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,,,,,,1,,,, -KNR_MIX - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,,,,,,1,,,, -KNR_MIX - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - Unavailable,outbound & ~knr_mix_available_outbound,,,,,,,,,,,,,,-999,,,, -KNR_MIX outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['KNR_MIX_IVT'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on Bus,@df.outbound * c_ivt * odt_skims['KNR_MIX_IVTT_Bus'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on PMov,@df.outbound * (c_ivt_pmov - c_ivt) * odt_skims['KNR_MIX_IVTT_PMov'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on StCar,@df.outbound * (c_ivt_stcar - c_ivt) * odt_skims['KNR_MIX_IVTT_StCar'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on BRT,@df.outbound * (c_ivt_brt - c_ivt) * odt_skims['KNR_MIX_IVTT_Brt'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on URB,@df.outbound * (c_ivt_urb - c_ivt) * odt_skims['KNR_MIX_IVTT_UrbRail'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on COM,@df.outbound * (c_ivt_com - c_ivt) * odt_skims['KNR_MIX_IVTT_ComRail'],,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['KNR_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['KNR_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['KNR_MIX_XWAIT'],,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - number of transfers,@df.outbound * c_xfers_drv * (odt_skims['KNR_MIX_NT']).clip(0),,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - Drive time,@df.outbound * c_dtim * odt_skims['KNR_MIX_DTIME'],,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - Walk egress ime,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - Walk other time,@df.outbound * c_waux * odt_skims['KNR_MIX_WAUX'],,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['KNR_MIX_FARE'] + costPerMile * odt_skims['KNR_MIX_DDIST']),,,,,,,,,,,,,,1,,,, -KNR_MIX outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['KNR_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - Unavailable,inbound & ~knr_mix_available_inbound,,,,,,,,,,,,,,-999,,,, -KNR_MIX inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['KNRE_MIX_IVT'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on Bus,@df.inbound * c_ivt * odt_skims['KNRE_MIX_IVTT_Bus'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on PMov,@df.inbound * (c_ivt_pmov - c_ivt) * odt_skims['KNRE_MIX_IVTT_PMov'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on StCar,@df.inbound * (c_ivt_stcar - c_ivt) * odt_skims['KNRE_MIX_IVTT_StCar'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on BRT,@df.inbound * (c_ivt_brt - c_ivt) * odt_skims['KNRE_MIX_IVTT_Brt'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on URB,@df.inbound * (c_ivt_urb - c_ivt) * odt_skims['KNRE_MIX_IVTT_UrbRail'],,,,,,,,,,,,,,1,,,, -KNR_MIX - In-vehicle time on COM,@df.inbound * (c_ivt_com - c_ivt) * odt_skims['KNRE_MIX_IVTT_ComRail'],,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['KNRE_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['KNRE_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['KNRE_MIX_XWAIT'],,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - number of transfers,@df.inbound * c_xfers_drv * (odt_skims['KNRE_MIX_NT']).clip(0),,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - Drive time,@df.inbound * c_dtim * odt_skims['KNRE_MIX_DTIME'],,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - Walk other time,@df.inbound * c_waux * odt_skims['KNRE_MIX_WAUX'],,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['KNRE_MIX_FARE'] + costPerMile * odt_skims['KNRE_MIX_DDIST']),,,,,,,,,,,,,,1,,,, -KNR_MIX inbound - Ratio of drive access distance to OD distance,@df.inbound * c_dacc_ratio * (odt_skims['KNRE_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,,1,,,, -#Taxi,,,,,,,,,,,,,,,,,,, -Taxi - In-vehicle time,@c_ivt * odt_skims['HOV2_TIME'],,,,,,,,,,,,,,,,1,, -Taxi - Wait time,@c_ivt * 1.5 * df.origTaxiWaitTime,,,,,,,,,,,,,,,,1,, -#Taxi - Tolls,@df.c_cost * odt_skims['HOV2_VTOLL'],,,,,,,,,,,,,,,,1,, -#Taxi - Bridge toll,@df.c_cost * odt_skims['HOV2_BTOLL'],,,,,,,,,,,,,,,,1,, -Taxi - Fare,@df.c_cost * (Taxi_baseFare + odt_skims['HOV2_DIST'] * Taxi_costPerMile + odt_skims['HOV2_TIME'] * Taxi_costPerMinute)*100,,,,,,,,,,,,,,,,1,, -#TNC Single,,,,,,,,,,,,,,,,,,, -TNC Single - In-vehicle time,@c_ivt * odt_skims['HOV2_TIME'] ,,,,,,,,,,,,,,,,,1, -TNC Single - Wait time,@c_ivt * 1.5 * df.origSingleTNCWaitTime,,,,,,,,,,,,,,,,,1, -#TNC Single - Tolls,@df.c_cost * odt_skims['HOV2_VTOLL'],,,,,,,,,,,,,,,,,1, -#TNC Single - Bridge toll,@df.c_cost * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']),,,,,,,,,,,,,,,,,1, -TNC Single - Cost,"@df.c_cost * np.maximum(TNC_single_baseFare + odt_skims['HOV2_DIST'] * TNC_single_costPerMile + odt_skims['HOV2_TIME'] * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,,,,,,1, -#TNC Shared,,,,,,,,,,,,,,,,,,, -TNC Shared - In-vehicle time,@c_ivt * odt_skims['HOV2_TIME'] * TNC_shared_IVTFactor,,,,,,,,,,,,,,,,,,1 -TNC Shared - Wait time,@c_ivt * 1.5 * df.origSharedTNCWaitTime,,,,,,,,,,,,,,,,,,1 -#TNC Shared - Tolls,@df.c_cost * odt_skims['HOV2_VTOLL'],,,,,,,,,,,,,,,,,,1 -#TNC Shared - Bridge toll,@df.c_cost * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']),,,,,,,,,,,,,,,,,,1 -TNC Shared - Cost,"@df.c_cost * np.maximum(TNC_shared_baseFare + odt_skims['HOV2_DIST'] * TNC_shared_costPerMile + odt_skims['HOV2_TIME']* TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,,,,,,1 -#,,,,,,,,,,,,,,,,,,, -Auto tour mode availability,tour_mode_is_auto,,,,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, -Walk tour mode availability,tour_mode_is_walk,-999,-999,-999,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, -Bike tour mode availability,tour_mode_is_bike,-999,-999,-999,,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, -Walk to Transit tour mode availability,tour_mode_is_walk_transit,-999,,,,-999,,,,-999,-999,-999,-999,-999,-999,-999,,, -Drive to Transit tour mode availability,tour_mode_is_drive_transit,-999,-999,-999,-999,-999,-999,-999,-999,,,,,,,-999,,, -School bus tour mode availability,tour_mode_is_school_bus,-999,,,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,,, -#indiv tour ASCs,,,,,,,,,,,,,,,,,,, -Drive Alone tour mode ASC -- shared ride 2,@sov_ASC_sr2 * (df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,1,,,,,,,,,,,,,,,, -Drive Alone tour mode ASC -- shared ride 3+,@sov_ASC_sr3p * (df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,1,,,,,,,,,,,,,,, -Drive Alone tour mode ASC -- walk,@sov_ASC_walk * (df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,1,,,,,,,,,,,,,, -Drive Alone tour mode ASC -- ride hail,@sov_ASC_rh * (df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,,,,,,,,,,1,1,1 -#Shared Ride 2 tour mode ASC -- shared ride 2 - reference mode,@sr2_ASC_sr2 * (df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,1,,,,,,,,,,,,,,,, -Shared Ride 2 tour mode ASC -- drive alone,@sr2_ASC_sov * (df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),1,,,,,,,,,,,,,,,,, -Shared Ride 2 tour mode ASC -- walk,@sr2_ASC_walk * (df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,1,,,,,,,,,,,,,, -Shared Ride 2 tour mode ASC -- ride hail,@sr2_ASC_rh * (df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,,,,,,,,,,1,1,1 -Shared Ride 3+ tour mode ASC -- drive alone,@sr3p_ASC_sov * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),1,,,,,,,,,,,,,,,,, -Shared Ride 3+ tour mode ASC -- shared ride 2,@sr3p_ASC_sr2 * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,1,,,,,,,,,,,,,,,, -#Shared Ride 3+ tour mode ASC -- shared ride 3+ - reference mode,@sr3p_ASC_sr3p * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,1,,,,,,,,,,,,,,, -Shared Ride 3+ tour mode ASC -- walk,@sr3p_ASC_walk * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,1,,,,,,,,,,,,,, -Shared Ride 3+ tour mode ASC -- ride hail,@sr3p_ASC_rh * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,,,,,,,,,,1,1,1 -Walk tour mode ASC -- ride hail,@walk_ASC_rh * df.is_indiv * (df.i_tour_mode == I_WALK_MODE),,,,,,,,,,,,,,,,1,1,1 -Bike tour mode ASC -- walk,@bike_ASC_walk * df.is_indiv * (df.i_tour_mode == I_BIKE_MODE),,,,1,,,,,,,,,,,,,, -Bike tour mode ASC -- ride hail,@bike_ASC_rh * df.is_indiv * (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,1,1,1 -Walk-transit tour mode ASC -- shared ride 2,@walktransit_ASC_sr2 * (df.is_indiv & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,1,,,,,,,,,,,,,,,, -Walk-transit tour mode ASC -- shared ride 3+,@walktransit_ASC_sr3p * (df.is_indiv & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,1,,,,,,,,,,,,,,, -Walk-transit tour mode ASC -- walk,@walktransit_ASC_walk * (df.is_indiv & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,1,,,,,,,,,,,,,, -Walk-transit tour mode ASC -- ride hail,@walk_transit_ASC_rh * (df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,,,,,1,1,1 -Drive to Transit tour mode ASC -- ride hail,@drive_transit_ASC_rh * (df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,1,1,1 -Ride Hail tour mode ASC -- ride hail,@ride_hail_ASC_taxi * (df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,1,, -Ride Hail tour mode ASC -- ride hail,@ride_hail_ASC_tnc_single * (df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,1, -Ride Hail tour mode ASC -- ride hail,@ride_hail_ASC_tnc_shared * (df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,1 -#Walk-transit tour mode ASC -- walk-transit - reference mode,@walktransit_ASC_walktransit * (df.is_indiv & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,,,1,1,1,,,,,,,,,, -#PNR tour mode ASC -- PNR - reference mode,@pnr_ASC_pnr * (df.is_indiv & df.i_tour_mode.isin(I_PNR_TRANSIT_MODES)),,,,,,,,,1,1,1,,,,,,, -#KNR tour mode ASC -- KNR - reference mode,@knr_ASC_knr * (df.is_indiv & df.i_tour_mode.isin(I_KNR_TRANSIT_MODES)),,,,,,,,,,,,1,1,1,,,, -#joint tour ASCs,,,,,,,,,,,,,,,,,,, -#joint - SR2 tour mode ASC -- shared ride 2 - reference mode,@joint_sr2_ASC_sr2 * (df.is_joint & df.i_tour_mode.isin(I_SR2_MODES)),,1,,,,,,,,,,,,,,,, -joint - SR2 tour mode ASC -- walk,@joint_sr2_ASC_walk * (df.is_joint & df.i_tour_mode.isin(I_SR2_MODES)),,,,1,,,,,,,,,,,,,, -#joint - SR3 tour mode ASC -- shared ride 3+ - reference mode,@joint_sr3p_ASC_sr3p * (df.is_joint & df.i_tour_mode.isin(I_SR3P_MODES)),,,1,,,,,,,,,,,,,,, -joint - SR3 tour mode ASC -- shared ride 2,@joint_sr3p_ASC_sr2 * (df.is_joint & df.i_tour_mode.isin(I_SR3P_MODES)),,1,,,,,,,,,,,,,,,, -joint - SR3 tour mode ASC -- walk,@joint_sr3p_ASC_walk * (df.is_joint & df.i_tour_mode.isin(I_SR3P_MODES)),,,,1,,,,,,,,,,,,,, -joint - auto tour mode ASC -- ride hail,@joint_auto_ASC_rh * (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,1,1,1 -joint - Bike tour mode ASC -- walk,@joint_bike_ASC_walk * df.is_joint * (df.i_tour_mode == I_BIKE_MODE),,,,1,,,,,,,,,,,,,, -joint - Bike tour mode ASC -- ride hail,@joint_bike_ASC_rh * df.is_joint * (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,1,1,1 -joint - Walk-transit tour mode ASC -- shared ride 2,@joint_walktransit_ASC_sr2 * (df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,1,,,,,,,,,,,,,,,, -joint - Walk-transit tour mode ASC -- shared ride 3+,@joint_walktransit_ASC_sr3p * (df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,1,,,,,,,,,,,,,,, -joint - Walk-transit tour mode ASC -- walk,@joint_walktransit_ASC_walk * (df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,1,,,,,,,,,,,,,, -joint - Walk to Transit tour mode ASC -- ride hail,@joint_walk_transit_ASC_rh * (df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,,,,,,,,,,,,,1,1,1 -joint - Drive to Transit tour mode ASC -- ride hail,@joint_drive_transit_ASC_rh * (df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,1,1,1 -joint - Ride Hail tour mode ASC -- ride hail,@joint_ride_hail_ASC_taxi * (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,1,, -joint - Ride Hail tour mode ASC -- ride hail,@joint_ride_hail_ASC_tnc_single * (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,1, -joint - Ride Hail tour mode ASC -- ride hail,@joint_ride_hail_ASC_tnc_shared * (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,1 -#joint - Walk-transit tour mode ASC -- walk-transit - reference mode,@joint_walktransit_ASC_walktransit * (df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,,,1,1,1,,,,,,,,,, -#joint - PNR tour mode ASC -- PNR - reference mode,@joint_pnr_ASC_pnr * (df.is_joint & df.i_tour_mode.isin(I_PNR_TRANSIT_MODES)),,,,,,,,,1,1,1,,,,,,, -#joint - KNR tour mode ASC -- KNR - reference mode,@joint_knr_ASC_knr * (df.is_joint & df.i_tour_mode.isin(I_KNR_TRANSIT_MODES)),,,,,,,,,,,,1,1,1,,,, -#,,,,,,,,,,,,,,,,,,, -Walk not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTWALK'] > 3),,,,-999,,,,,,,,,,,,,, -Bike not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTBIKE'] > 8),,,,,-999,,,,,,,,,,,,, -Origin density index,@(c_origin_density_index*df.origin_density_index).clip(c_origin_density_index_max) if origin_density_applied else 0,,,,1,1,1,1,1,,,,,,,,,, -#Walk-express penalty for intermediate stops,@c_walk_express_penalty * ~(df.first_trip | df.first_trip),,,,,,,,1,,,,,,,,,, -#School Bus,,,,,,,,,,,,,,,,,,, -School Bus Unavailable if primary purpose NOT school,~is_school,,,,,,,,,,,,,,,-999,,, -School Bus Unavailable - Tour Mode = SOV,tour_mode_is_SOV,,,,,,,,,,,,,,,-999,,, -School Bus Unavailable - Tour Mode = Transit,tour_mode_is_drive_transit,,,,,,,,,,,,,,,-999,,, -School Bus - In-vehicle time (20 miles per hour),@c_ivt * odt_skims['HOV3_DIST']*3,,,,,,,,,,,,,,,1,,, -School Bus - Walk Time,@c_wacc*10,,,,,,,,,,,,,,,1,,, -School Bus - Wait Time,@c_short_i_wait*10,,,,,,,,,,,,,,,1,,, -School Bus tour mode ASC -- shared ride 2,@schoolbus_ASC_sr2 * df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,1,,,,,,,,,,,,,,,, -School Bus tour mode ASC -- shared ride 3+,@schoolbus_ASC_sr3p * df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,,1,,,,,,,,,,,,,,, -School Bus tour mode ASC -- walk,@schoolbus_ASC_walk * df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,,,1,,,,,,,,,,,,,, -local_ASC,@local_ASC,,,,,,1,,,1,,,1,,,,,, -premium_ASC,@premium_ASC,,,,,,,1,,,1,,,1,,,,, -mix_ASC,@mix_ASC,,,,,,,,1,,,1,,,1,,,, -Origin density index,@(c_origin_density_index*df.origin_density_index).clip(c_origin_density_index_max) if origin_density_applied else 0,,,,,,,1,1,1,1,1,1,1,,,,1,1 -TNC shared adjustment,@adjust_tnc_shared,,,,,,,,,,,,,,,,,,1 +Description,Expression,DRIVEALONE,SHARED2,SHARED3,WALK,BIKE,WALK_LOC,WALK_PRM,WALK_MIX,PNR_LOC,PNR_PRM,PNR_MIX,KNR_LOC,KNR_PRM,KNR_MIX,SCHOOLBUS,TAXI,TNC_SINGLE,TNC_SHARED +#Drive alone no toll,,,,,,,,,,,,,,,,,,, +DRIVEALONE - Unavailable,sov_available == False,-999,,,,,,,,,,,,,,,,, +DRIVEALONE - Unavailable for persons less than 16,age < 16,-999,,,,,,,,,,,,,,,,, +DRIVEALONE - Unavailable for joint tours,is_joint == True,-999,,,,,,,,,,,,,,,,, +DRIVEALONE - Unavailable if didn't drive to work,is_atwork_subtour & ~work_tour_is_SOV,-999,,,,,,,,,,,,,,,,, +DRIVEALONE - In-vehicle time,@c_ivt*odt_skims['SOV_TIME'],1,,,,,,,,,,,,,,,,, +DRIVEALONE - Terminal time,@c_walktimeshort * df.total_terminal_time,1,,,,,,,,,,,,,,,,, +DRIVEALONE - Operating cost ,@df.c_cost * costPerMile * odt_skims['SOV_DIST'],1,,,,,,,,,,,,,,,,, +DRIVEALONE - Parking cost ,c_cost * total_parking_cost,1,,,,,,,,,,,,,,,,, +DRIVEALONE - Person is between 16 and 19 years old,@c_age1619_da * ((df.age >= 16) & (df.age <= 19)),1,,,,,,,,,,,,,,,,, +#Shared ride 2,,,,,,,,,,,,,,,,,,, +SHARED2 - Unavailable,hov2_available == False,,-999,,,,,,,,,,,,,,,, +SHARED2 - Unavailable based on party size,is_joint & (number_of_participants > 2),,-999,,,,,,,,,,,,,,,, +SHARED2 - In-vehicle time,@c_ivt * odt_skims['HOV2_TIME'],,1,,,,,,,,,,,,,,,, +SHARED2 - Terminal time,@c_walktimeshort * df.total_terminal_time,,1,,,,,,,,,,,,,,,, +SHARED2 - Operating cost,@df.c_cost * costPerMile * odt_skims['HOV2_DIST'],,1,,,,,,,,,,,,,,,, +SHARED2 - Parking cost,@df.c_cost * df.total_parking_cost / costShareSr2,,1,,,,,,,,,,,,,,,, +SHARED2 - One person household,@c_hhsize1_sr * (df.hhsize == 1),,1,,,,,,,,,,,,,,,, +SHARED2 - Two person household,@c_hhsize2_sr * (df.hhsize == 2),,1,,,,,,,,,,,,,,,, +SHARED2 - Person is 16 years old or older,@c_age16p_sr * (df.age >= 16),,1,,,,,,,,,,,,,,,, +#Shared ride 3+,,,,,,,,,,,,,,,,,,, +SHARED3 - Unavailable,hov3_available == False,,,-999,,,,,,,,,,,,,,, +SHARED3 - Unavailable based joint tour mode,@df.is_joint & df.i_tour_mode.isin(I_SR2_MODES),,,-999,,,,,,,,,,,,,,, +SHARED3 - Unavailable if tour mode is shared 2,@df.i_tour_mode.isin(I_SR2_MODES),,,-999,,,,,,,,,,,,,,, +SHARED3 - In-vehicle time,@c_ivt * odt_skims['HOV3_TIME'],,,1,,,,,,,,,,,,,,, +SHARED3 - Terminal time,@c_walktimeshort * df.total_terminal_time,,,1,,,,,,,,,,,,,,, +SHARED3 - Operating cost,@df.c_cost * costPerMile * odt_skims['HOV3_DIST'],,,1,,,,,,,,,,,,,,, +SHARED3 - Parking cost,@df.c_cost * df.total_parking_cost / costShareSr3,,,1,,,,,,,,,,,,,,, +SHARED3 - One person household,@c_hhsize1_sr * (df.hhsize == 1),,,1,,,,,,,,,,,,,,, +SHARED3 - Two person household,@c_hhsize2_sr * (df.hhsize == 2),,,1,,,,,,,,,,,,,,, +SHARED3 - Person is 16 years old or older,@c_age16p_sr * (df.age >= 16),,,1,,,,,,,,,,,,,,, +#Walk,,,,,,,,,,,,,,,,,,, +WALK - Time up to 2 miles,@c_walktimeshort * od_skims['DISTWALK'].clip(upper=walkThresh) * 60/walkSpeed,,,,1,,,,,,,,,,,,,, +WALK - Time beyond 2 of a miles,@c_walktimelong * (od_skims['DISTWALK'] - walkThresh).clip(lower=0) * 60/walkSpeed,,,,1,,,,,,,,,,,,,, +WALK - Destination zone densityIndex,@c_density_index * df.density_index,,,,1,,,,,,,,,,,,,, +WALK - Topology,@c_topology_walk * df.trip_topology,,,,1,,,,,,,,,,,,,, +#Bike,,,,,,,,,,,,,,,,,,, +BIKE - Unavailable if tour mode is not bike,~tour_mode_is_bike,,,,,-999,,,,,,,,,,,,, +BIKE - Unavailable if didn't bike to work,is_atwork_subtour & ~work_tour_is_bike,,,,,-999,,,,,,,,,,,,, +BIKE - Time up to 6 miles,@c_biketimeshort * od_skims['DISTBIKE'].clip(upper=bikeThresh)*60/bikeSpeed,,,,,1,,,,,,,,,,,,, +BIKE - Time beyond 6 of a miles,@c_biketimelong * (od_skims['DISTBIKE']-bikeThresh).clip(lower=0)*60/bikeSpeed,,,,,1,,,,,,,,,,,,, +BIKE - Destination zone densityIndex,@c_density_index*df.density_index,,,,,1,,,,,,,,,,,,, +BIKE - Topology,@c_topology_bike * df.trip_topology,,,,,1,,,,,,,,,,,,, +#Walk to Local,,,,,,,,,,,,,,,,,,, +WALK_LOC - Unavailable,walk_local_available == False,,,,,,-999,,,,,,,,,,,, +WALK_LOC - In-vehicle time,@c_ivt * odt_skims['WLK_LOC_IVT'],,,,,,1,,,,,,,,,,,, +WALK_LOC - Short iwait time,@c_short_i_wait * (odt_skims['WLK_LOC_IWAIT']).clip(upper=waitThresh),,,,,,1,,,,,,,,,,,, +WALK_LOC - Long iwait time,@c_long_i_wait * (odt_skims['WLK_LOC_IWAIT']-waitThresh).clip(0),,,,,,1,,,,,,,,,,,, +WALK_LOC - transfer wait time,@c_xwait * odt_skims['WLK_LOC_XWAIT'],,,,,,1,,,,,,,,,,,, +WALK_LOC - number of transfers,@c_xfers_wlk * (odt_skims['WLK_LOC_NT']).clip(0),,,,,,1,,,,,,,,,,,, +WALK_LOC - Walk access time,@c_wacc * df.origin_walk_time,,,,,,1,,,,,,,,,,,, +WALK_LOC - Walk egress time,@c_wegr * df.destination_walk_time,,,,,,1,,,,,,,,,,,, +WALK_LOC - Walk other time,@c_waux * odt_skims['WLK_LOC_WAUX'],,,,,,1,,,,,,,,,,,, +WALK_LOC - Fare,@df.c_cost * odt_skims['WLK_LOC_FARE'],,,,,,1,,,,,,,,,,,, +WALK_LOC - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,1,,,,,,,,,,,, +WALK_LOC - Topology,@c_topology_trn * df.trip_topology,,,,,,1,,,,,,,,,,,, +WALK_LOC - Person is less than 10 years old,@c_age010_trn * (df.age <= 10),,,,,,1,,,,,,,,,,,, +#Walk to Premium,,,,,,,,,,,,,,,,,,, +WALK_PRM - Unavailable,walk_premium_available == False,,,,,,,-999,,,,,,,,,,, +WALK_PRM - In-vehicle time,@c_ivt * odt_skims['WLK_PRM_IVT'],,,,,,,1,,,,,,,,,,, +WALK_PRM - In-vehicle time on PMov,@(c_ivt_pmov - c_ivt) * odt_skims['WLK_PRM_IVTT_PMov'],,,,,,,1,,,,,,,,,,, +WALK_PRM - In-vehicle time on StCar,@(c_ivt_stcar - c_ivt) * odt_skims['WLK_PRM_IVTT_StCar'],,,,,,,1,,,,,,,,,,, +WALK_PRM - In-vehicle time on BRT,@(c_ivt_brt - c_ivt) * odt_skims['WLK_PRM_IVTT_Brt'],,,,,,,1,,,,,,,,,,, +WALK_PRM - In-vehicle time on URB,@(c_ivt_urb - c_ivt) * odt_skims['WLK_PRM_IVTT_UrbRail'],,,,,,,1,,,,,,,,,,, +WALK_PRM - In-vehicle time on COM,@(c_ivt_com - c_ivt) * odt_skims['WLK_PRM_IVTT_ComRail'],,,,,,,1,,,,,,,,,,, +WALK_PRM - Short iwait time,@c_short_i_wait * (odt_skims['WLK_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,1,,,,,,,,,,, +WALK_PRM - Long iwait time,@c_long_i_wait * (odt_skims['WLK_PRM_IWAIT']-waitThresh).clip(0),,,,,,,1,,,,,,,,,,, +WALK_PRM - transfer wait time,@c_xwait * odt_skims['WLK_PRM_XWAIT'],,,,,,,1,,,,,,,,,,, +WALK_PRM - number of transfers,@c_xfers_wlk * (odt_skims['WLK_PRM_NT']).clip(0),,,,,,,1,,,,,,,,,,, +WALK_PRM - Walk access time,@c_wacc * df.origin_walk_time,,,,,,,1,,,,,,,,,,, +WALK_PRM - Walk egress time,@c_wegr * df.destination_walk_time,,,,,,,1,,,,,,,,,,, +WALK_PRM - Walk otherLight rail/Ferry time,@c_waux * odt_skims['WLK_PRM_WAUX'],,,,,,,1,,,,,,,,,,, +WALK_PRM - Fare,@df.c_cost * odt_skims['WLK_PRM_FARE'],,,,,,,1,,,,,,,,,,, +WALK_PRM - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,1,,,,,,,,,,, +WALK_PRM - Topology,@c_topology_trn * df.trip_topology,,,,,,,1,,,,,,,,,,, +WALK_PRM - Person is less than 10 years old,@c_age010_trn * (df.age <= 10),,,,,,,1,,,,,,,,,,, +#Walk to Mix,,,,,,,,,,,,,,,,,,, +WALK_MIX - Unavailable,walk_mix_available == False,,,,,,,,-999,,,,,,,,,, +WALK_MIX - In-vehicle time,@c_ivt * odt_skims['WLK_MIX_IVT'],,,,,,,,1,,,,,,,,,, +WALK_MIX - In-vehicle time on Bus,@c_ivt * odt_skims['WLK_MIX_IVTT_Bus'],,,,,,,,1,,,,,,,,,, +WALK_MIX - In-vehicle time on PMov,@(c_ivt_pmov - c_ivt) * odt_skims['WLK_MIX_IVTT_PMov'],,,,,,,,1,,,,,,,,,, +WALK_MIX - In-vehicle time on StCar,@(c_ivt_stcar - c_ivt) * odt_skims['WLK_MIX_IVTT_StCar'],,,,,,,,1,,,,,,,,,, +WALK_MIX - In-vehicle time on BRT,@(c_ivt_brt - c_ivt) * odt_skims['WLK_MIX_IVTT_Brt'],,,,,,,,1,,,,,,,,,, +WALK_MIX - In-vehicle time on URB,@(c_ivt_urb - c_ivt) * odt_skims['WLK_MIX_IVTT_UrbRail'],,,,,,,,1,,,,,,,,,, +WALK_MIX - In-vehicle time on COM,@(c_ivt_com - c_ivt) * odt_skims['WLK_MIX_IVTT_ComRail'],,,,,,,,1,,,,,,,,,, +WALK_MIX - Short iwait time,@c_short_i_wait * (odt_skims['WLK_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,1,,,,,,,,,, +WALK_MIX - Long iwait time,@c_long_i_wait * (odt_skims['WLK_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,1,,,,,,,,,, +WALK_MIX - transfer wait time,@c_xwait * odt_skims['WLK_MIX_XWAIT'],,,,,,,,1,,,,,,,,,, +WALK_MIX - number of transfers,@c_xfers_wlk * (odt_skims['WLK_MIX_NT']).clip(0),,,,,,,,1,,,,,,,,,, +WALK_MIX - Walk access time,@c_wacc * df.origin_walk_time,,,,,,,,1,,,,,,,,,, +WALK_MIX - Walk egress time,@c_wegr * df.destination_walk_time,,,,,,,,1,,,,,,,,,, +WALK_MIX - Walk other time,@c_waux * odt_skims['WLK_MIX_WAUX'],,,,,,,,1,,,,,,,,,, +WALK_MIX - Fare,@df.c_cost * odt_skims['WLK_MIX_FARE'],,,,,,,,1,,,,,,,,,, +WALK_MIX - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,1,,,,,,,,,, +WALK_MIX - Topology,@c_topology_trn * df.trip_topology,,,,,,,,1,,,,,,,,,, +WALK_MIX - Person is less than 10 years old,@c_age010_trn * (df.age <= 10),,,,,,,,1,,,,,,,,,, +#PNR to Local,,,,,,,,,,,,,,,,,,, +PNR_LOC - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,-999,,,,,,,,, +PNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,-999,,,,,,,,, +PNR_LOC - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,1,,,,,,,,, +PNR_LOC - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,1,,,,,,,,, +PNR_LOC - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,1,,,,,,,,, +PNR_LOC outbound - Unavailable,outbound & ~pnr_local_available_outbound,,,,,,,,,-999,,,,,,,,, +PNR_LOC outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['PNR_LOC_IVT'],,,,,,,,,1,,,,,,,,, +PNR_LOC outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['PNR_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,1,,,,,,,,, +PNR_LOC outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['PNR_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,1,,,,,,,,, +PNR_LOC outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['PNR_LOC_XWAIT'],,,,,,,,,1,,,,,,,,, +PNR_LOC outbound - number of transfers,@df.outbound * c_xfers_wlk * (odt_skims['PNR_LOC_NT']).clip(0),,,,,,,,,1,,,,,,,,, +PNR_LOC outbound - Drive time,@df.outbound * c_dtim * odt_skims['PNR_LOC_DTIME'],,,,,,,,,1,,,,,,,,, +PNR_LOC outbound - Walk egress time,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,1,,,,,,,,, +PNR_LOC outbound - Walk other time,@df.outbound * c_waux * odt_skims['PNR_LOC_WAUX'],,,,,,,,,1,,,,,,,,, +PNR_LOC outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['PNR_LOC_FARE'] + costPerMile*odt_skims['PNR_LOC_DDIST']),,,,,,,,,1,,,,,,,,, +PNR_LOC outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['PNR_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,1,,,,,,,,, +PNR_LOC inbound - Unavailable,inbound & ~pnr_local_available_inbound,,,,,,,,,-999,,,,,,,,, +PNR_LOC inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['PNRE_LOC_IVT'],,,,,,,,,1,,,,,,,,, +PNR_LOC inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['PNRE_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,1,,,,,,,,, +PNR_LOC inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['PNRE_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,1,,,,,,,,, +PNR_LOC inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['PNRE_LOC_XWAIT'],,,,,,,,,1,,,,,,,,, +PNR_LOC inbound - number of transfers,@df.inbound * c_xfers_wlk * (odt_skims['PNRE_LOC_NT']).clip(0),,,,,,,,,1,,,,,,,,, +PNR_LOC inbound - Drive time,@df.inbound * c_dtim * odt_skims['PNRE_LOC_DTIME'],,,,,,,,,1,,,,,,,,, +PNR_LOC inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,1,,,,,,,,, +PNR_LOC inbound - Walk other time,@df.inbound * c_waux * odt_skims['PNRE_LOC_WAUX'],,,,,,,,,1,,,,,,,,, +PNR_LOC inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['PNRE_LOC_FARE'] + costPerMile*odt_skims['PNRE_LOC_DDIST']),,,,,,,,,1,,,,,,,,, +PNR_LOC inbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['PNRE_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,1,,,,,,,,, +#Drive to Premium,,,,,,,,,,,,,,,,,,, +PNR_PRM - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,-999,,,,,,,, +PNR_PRM - Unavailable for persons less than 16,age < 16,,,,,,,,,,-999,,,,,,,, +PNR_PRM - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,,1,,,,,,,, +PNR_PRM - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,,1,,,,,,,, +PNR_PRM - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,,1,,,,,,,, +PNR_PRM outbound - Unavailable,outbound & ~pnr_premium_available_outbound,,,,,,,,,,-999,,,,,,,, +PNR_PRM outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['PNR_PRM_IVT'],,,,,,,,,,1,,,,,,,, +PNR_PRM - In-vehicle time on PMov,@df.outbound * (c_ivt_pmov - c_ivt) * odt_skims['PNR_PRM_IVTT_PMov'],,,,,,,,,,1,,,,,,,, +PNR_PRM - In-vehicle time on StCar,@df.outbound * (c_ivt_stcar - c_ivt) * odt_skims['PNR_PRM_IVTT_StCar'],,,,,,,,,,1,,,,,,,, +PNR_PRM - In-vehicle time on BRT,@df.outbound * (c_ivt_brt - c_ivt) * odt_skims['PNR_PRM_IVTT_Brt'],,,,,,,,,,1,,,,,,,, +PNR_PRM - In-vehicle time on URB,@df.outbound * (c_ivt_urb - c_ivt) * odt_skims['PNR_PRM_IVTT_UrbRail'],,,,,,,,,,1,,,,,,,, +PNR_PRM - In-vehicle time on COM,@df.outbound * (c_ivt_com - c_ivt) * odt_skims['PNR_PRM_IVTT_ComRail'],,,,,,,,,,1,,,,,,,, +PNR_PRM outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['PNR_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,1,,,,,,,, +PNR_PRM outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['PNR_PRM_IWAIT']-waitThresh).clip(0) ,,,,,,,,,,1,,,,,,,, +PNR_PRM outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['PNR_PRM_XWAIT'],,,,,,,,,,1,,,,,,,, +PNR_PRM outbound - number of transfers,@df.outbound * c_xfers_drv * (odt_skims['PNR_PRM_NT']).clip(0),,,,,,,,,,1,,,,,,,, +PNR_PRM outbound - Drive time,@df.outbound * c_dtim * odt_skims['PNR_PRM_DTIME'],,,,,,,,,,1,,,,,,,, +PNR_PRM outbound - Walk egress time,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,,1,,,,,,,, +PNR_PRM outbound - Walk other time,@df.outbound * c_waux * odt_skims['PNR_PRM_WAUX'],,,,,,,,,,1,,,,,,,, +PNR_PRM outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['PNR_PRM_FARE'] + costPerMile * odt_skims['PNR_PRM_DDIST']),,,,,,,,,,1,,,,,,,, +PNR_PRM outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['PNR_PRM_DDIST']) / od_skims['DIST'],,,,,,,,,,1,,,,,,,, +PNR_PRM inbound - Unavailable,inbound & ~pnr_premium_available_inbound,,,,,,,,,,-999,,,,,,,, +PNR_PRM inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['PNRE_PRM_IVT'],,,,,,,,,,1,,,,,,,, +PNR_PRM - In-vehicle time on PMov,@df.inbound * (c_ivt_pmov - c_ivt) * odt_skims['PNRE_PRM_IVTT_PMov'],,,,,,,,,,1,,,,,,,, +PNR_PRM - In-vehicle time on StCar,@df.inbound * (c_ivt_stcar - c_ivt) * odt_skims['PNRE_PRM_IVTT_StCar'],,,,,,,,,,1,,,,,,,, +PNR_PRM - In-vehicle time on BRT,@df.inbound * (c_ivt_brt - c_ivt) * odt_skims['PNRE_PRM_IVTT_Brt'],,,,,,,,,,1,,,,,,,, +PNR_PRM - In-vehicle time on URB,@df.inbound * (c_ivt_urb - c_ivt) * odt_skims['PNRE_PRM_IVTT_UrbRail'],,,,,,,,,,1,,,,,,,, +PNR_PRM - In-vehicle time on COM,@df.inbound * (c_ivt_com - c_ivt) * odt_skims['PNRE_PRM_IVTT_ComRail'],,,,,,,,,,1,,,,,,,, +PNR_PRM inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['PNRE_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,1,,,,,,,, +PNR_PRM inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['PNRE_PRM_IWAIT']-waitThresh).clip(0),,,,,,,,,,1,,,,,,,, +PNR_PRM inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['PNRE_PRM_XWAIT'],,,,,,,,,,1,,,,,,,, +PNR_PRM inbound - number of transfers,@df.inbound * c_xfers_drv * (odt_skims['PNRE_PRM_NT']).clip(0),,,,,,,,,,1,,,,,,,, +PNR_PRM inbound - Drive time,@df.inbound * c_dtim * odt_skims['PNRE_PRM_DTIME'],,,,,,,,,,1,,,,,,,, +PNR_PRM inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,,1,,,,,,,, +PNR_PRM inbound - Walk other time,@df.inbound * c_waux * odt_skims['PNRE_PRM_WAUX'],,,,,,,,,,1,,,,,,,, +PNR_PRM inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['PNRE_PRM_FARE'] + costPerMile * odt_skims['PNRE_PRM_DDIST']),,,,,,,,,,1,,,,,,,, +PNR_PRM inbound - Ratio of drive access distance to OD distance,@df.inbound * c_dacc_ratio * (odt_skims['PNRE_PRM_DDIST'])/ od_skims['DIST'],,,,,,,,,,1,,,,,,,, +#PNR to Mix,,,,,,,,,,,,,,,,,,, +PNR_MIX - Unavailable for zero auto households,auto_ownership == 0,,,,,,,,,,,-999,,,,,,, +PNR_MIX - Unavailable for persons less than 16,age < 16,,,,,,,,,,,-999,,,,,,, +PNR_MIX - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,,,1,,,,,,, +PNR_MIX - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,,,1,,,,,,, +PNR_MIX - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,,,1,,,,,,, +PNR_MIX outbound - Unavailable,outbound & ~pnr_mix_available_outbound,,,,,,,,,,,-999,,,,,,, +PNR_MIX outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['PNR_MIX_IVT'],,,,,,,,,,,1,,,,,,, +PNR_MIX - In-vehicle time on Bus,@df.outbound * c_ivt * odt_skims['PNR_MIX_IVTT_Bus'],,,,,,,,,,,1,,,,,,, +PNR_MIX - In-vehicle time on PMov,@df.outbound * (c_ivt_pmov - c_ivt) * odt_skims['PNR_MIX_IVTT_PMov'],,,,,,,,,,,1,,,,,,, +PNR_MIX - In-vehicle time on StCar,@df.outbound * (c_ivt_stcar - c_ivt) * odt_skims['PNR_MIX_IVTT_StCar'],,,,,,,,,,,1,,,,,,, +PNR_MIX - In-vehicle time on BRT,@df.outbound * (c_ivt_brt - c_ivt) * odt_skims['PNR_MIX_IVTT_Brt'],,,,,,,,,,,1,,,,,,, +PNR_MIX - In-vehicle time on URB,@df.outbound * (c_ivt_urb - c_ivt) * odt_skims['PNR_MIX_IVTT_UrbRail'],,,,,,,,,,,1,,,,,,, +PNR_MIX - In-vehicle time on COM,@df.outbound * (c_ivt_com - c_ivt) * odt_skims['PNR_MIX_IVTT_ComRail'],,,,,,,,,,,1,,,,,,, +PNR_MIX outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['PNR_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,1,,,,,,, +PNR_MIX outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['PNR_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,1,,,,,,, +PNR_MIX outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['PNR_MIX_XWAIT'],,,,,,,,,,,1,,,,,,, +PNR_MIX outbound - number of transfers,@df.outbound * c_xfers_drv * (odt_skims['PNR_MIX_NT']).clip(0),,,,,,,,,,,1,,,,,,, +PNR_MIX outbound - Drive time,@df.outbound * c_dtim * odt_skims['PNR_MIX_DTIME'],,,,,,,,,,,1,,,,,,, +PNR_MIX outbound - Walk egress ime,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,,,1,,,,,,, +PNR_MIX outbound - Walk other time,@df.outbound * c_waux * odt_skims['PNR_MIX_WAUX'],,,,,,,,,,,1,,,,,,, +PNR_MIX outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['PNR_MIX_FARE'] + costPerMile * odt_skims['PNR_MIX_DDIST']),,,,,,,,,,,1,,,,,,, +PNR_MIX outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['PNR_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,1,,,,,,, +PNR_MIX inbound - Unavailable,inbound & ~pnr_mix_available_inbound,,,,,,,,,,,-999,,,,,,, +PNR_MIX inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['PNRE_MIX_IVT'],,,,,,,,,,,1,,,,,,, +PNR_MIX - In-vehicle time on Bus,@df.inbound * c_ivt * odt_skims['PNRE_MIX_IVTT_Bus'],,,,,,,,,,,1,,,,,,, +PNR_MIX - In-vehicle time on PMov,@df.inbound * (c_ivt_pmov - c_ivt) * odt_skims['PNRE_MIX_IVTT_PMov'],,,,,,,,,,,1,,,,,,, +PNR_MIX - In-vehicle time on StCar,@df.inbound * (c_ivt_stcar - c_ivt) * odt_skims['PNRE_MIX_IVTT_StCar'],,,,,,,,,,,1,,,,,,, +PNR_MIX - In-vehicle time on BRT,@df.inbound * (c_ivt_brt - c_ivt) * odt_skims['PNRE_MIX_IVTT_Brt'],,,,,,,,,,,1,,,,,,, +PNR_MIX - In-vehicle time on URB,@df.inbound * (c_ivt_urb - c_ivt) * odt_skims['PNRE_MIX_IVTT_UrbRail'],,,,,,,,,,,1,,,,,,, +PNR_MIX - In-vehicle time on COM,@df.inbound * (c_ivt_com - c_ivt) * odt_skims['PNRE_MIX_IVTT_ComRail'],,,,,,,,,,,1,,,,,,, +PNR_MIX inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['PNRE_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,1,,,,,,, +PNR_MIX inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['PNRE_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,1,,,,,,, +PNR_MIX inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['PNRE_MIX_XWAIT'],,,,,,,,,,,1,,,,,,, +PNR_MIX inbound - number of transfers,@df.inbound * c_xfers_drv * (odt_skims['PNRE_MIX_NT']).clip(0),,,,,,,,,,,1,,,,,,, +PNR_MIX inbound - Drive time,@df.inbound * c_dtim * odt_skims['PNRE_MIX_DTIME'],,,,,,,,,,,1,,,,,,, +PNR_MIX inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,,,1,,,,,,, +PNR_MIX inbound - Walk other time,@df.inbound * c_waux * odt_skims['PNRE_MIX_WAUX'],,,,,,,,,,,1,,,,,,, +PNR_MIX inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['PNRE_MIX_FARE'] + costPerMile * odt_skims['PNRE_MIX_DDIST']),,,,,,,,,,,1,,,,,,, +PNR_MIX inbound - Ratio of drive access distance to OD distance,@df.inbound * c_dacc_ratio * (odt_skims['PNRE_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,1,,,,,,, +#KNR to Local,,,,,,,,,,,,,,,,,,, +KNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,-999,,,,,, +KNR_LOC - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,,,,1,,,,,, +KNR_LOC - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,,,,1,,,,,, +KNR_LOC - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,,,,1,,,,,, +KNR_LOC outbound - Unavailable,outbound & ~knr_local_available_outbound,,,,,,,,,,,,-999,,,,,, +KNR_LOC outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['KNR_LOC_IVT'],,,,,,,,,,,,1,,,,,, +KNR_LOC outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['KNR_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,1,,,,,, +KNR_LOC outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['KNR_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,1,,,,,, +KNR_LOC outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['KNR_LOC_XWAIT'],,,,,,,,,,,,1,,,,,, +KNR_LOC outbound - number of transfers,@df.outbound * c_xfers_wlk * (odt_skims['KNR_LOC_NT']).clip(0),,,,,,,,,,,,1,,,,,, +KNR_LOC outbound - Drive time,@df.outbound * c_dtim * odt_skims['KNR_LOC_DTIME'],,,,,,,,,,,,1,,,,,, +KNR_LOC outbound - Walk egress time,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,,,,1,,,,,, +KNR_LOC outbound - Walk other time,@df.outbound * c_waux * odt_skims['KNR_LOC_WAUX'],,,,,,,,,,,,1,,,,,, +KNR_LOC outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['KNR_LOC_FARE'] + costPerMile*odt_skims['KNR_LOC_DDIST']),,,,,,,,,,,,1,,,,,, +KNR_LOC outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['KNR_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,,,,1,,,,,, +KNR_LOC inbound - Unavailable,inbound & ~knr_local_available_inbound,,,,,,,,,,,,-999,,,,,, +KNR_LOC inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['KNRE_LOC_IVT'],,,,,,,,,,,,1,,,,,, +KNR_LOC inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['KNRE_LOC_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,1,,,,,, +KNR_LOC inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['KNRE_LOC_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,1,,,,,, +KNR_LOC inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['KNRE_LOC_XWAIT'],,,,,,,,,,,,1,,,,,, +KNR_LOC inbound - number of transfers,@df.inbound * c_xfers_wlk * (odt_skims['KNRE_LOC_NT']).clip(0),,,,,,,,,,,,1,,,,,, +KNR_LOC inbound - Drive time,@df.inbound * c_dtim * odt_skims['KNRE_LOC_DTIME'],,,,,,,,,,,,1,,,,,, +KNR_LOC inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,,,,1,,,,,, +KNR_LOC inbound - Walk other time,@df.inbound * c_waux * odt_skims['KNRE_LOC_WAUX'],,,,,,,,,,,,1,,,,,, +KNR_LOC inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['KNRE_LOC_FARE'] + costPerMile*odt_skims['KNRE_LOC_DDIST']),,,,,,,,,,,,1,,,,,, +KNR_LOC inbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['KNRE_LOC_DDIST'])/ (od_skims['DIST']),,,,,,,,,,,,1,,,,,, +#Drive to Premium,,,,,,,,,,,,,,,,,,, +KNR_PRM - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,-999,,,,, +KNR_PRM - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,,,,,1,,,,, +KNR_PRM - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,,,,,1,,,,, +KNR_PRM - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,,,,,1,,,,, +KNR_PRM outbound - Unavailable,outbound & ~knr_premium_available_outbound,,,,,,,,,,,,,-999,,,,, +KNR_PRM outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['KNR_PRM_IVT'],,,,,,,,,,,,,1,,,,, +KNR_PRM - In-vehicle time on PMov,@df.outbound * (c_ivt_pmov - c_ivt) * odt_skims['KNR_PRM_IVTT_PMov'],,,,,,,,,,,,,1,,,,, +KNR_PRM - In-vehicle time on StCar,@df.outbound * (c_ivt_stcar - c_ivt) * odt_skims['KNR_PRM_IVTT_StCar'],,,,,,,,,,,,,1,,,,, +KNR_PRM - In-vehicle time on BRT,@df.outbound * (c_ivt_brt - c_ivt) * odt_skims['KNR_PRM_IVTT_Brt'],,,,,,,,,,,,,1,,,,, +KNR_PRM - In-vehicle time on URB,@df.outbound * (c_ivt_urb - c_ivt) * odt_skims['KNR_PRM_IVTT_UrbRail'],,,,,,,,,,,,,1,,,,, +KNR_PRM - In-vehicle time on COM,@df.outbound * (c_ivt_com - c_ivt) * odt_skims['KNR_PRM_IVTT_ComRail'],,,,,,,,,,,,,1,,,,, +KNR_PRM outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['KNR_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,1,,,,, +KNR_PRM outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['KNR_PRM_IWAIT']-waitThresh).clip(0) ,,,,,,,,,,,,,1,,,,, +KNR_PRM outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['KNR_PRM_XWAIT'],,,,,,,,,,,,,1,,,,, +KNR_PRM outbound - number of transfers,@df.outbound * c_xfers_drv * (odt_skims['KNR_PRM_NT']).clip(0),,,,,,,,,,,,,1,,,,, +KNR_PRM outbound - Drive time,@df.outbound * c_dtim * odt_skims['KNR_PRM_DTIME'],,,,,,,,,,,,,1,,,,, +KNR_PRM outbound - Walk egress time,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,,,,,1,,,,, +KNR_PRM outbound - Walk other time,@df.outbound * c_waux * odt_skims['KNR_PRM_WAUX'],,,,,,,,,,,,,1,,,,, +KNR_PRM outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['KNR_PRM_FARE'] + costPerMile * odt_skims['KNR_PRM_DDIST']),,,,,,,,,,,,,1,,,,, +KNR_PRM outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['KNR_PRM_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,1,,,,, +KNR_PRM inbound - Unavailable,inbound & ~knr_premium_available_inbound,,,,,,,,,,,,,-999,,,,, +KNR_PRM inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['KNRE_PRM_IVT'],,,,,,,,,,,,,1,,,,, +KNR_PRM - In-vehicle time on PMov,@df.inbound * (c_ivt_pmov - c_ivt) * odt_skims['KNRE_PRM_IVTT_PMov'],,,,,,,,,,,,,1,,,,, +KNR_PRM - In-vehicle time on StCar,@df.inbound * (c_ivt_stcar - c_ivt) * odt_skims['KNRE_PRM_IVTT_StCar'],,,,,,,,,,,,,1,,,,, +KNR_PRM - In-vehicle time on BRT,@df.inbound * (c_ivt_brt - c_ivt) * odt_skims['KNRE_PRM_IVTT_Brt'],,,,,,,,,,,,,1,,,,, +KNR_PRM - In-vehicle time on URB,@df.inbound * (c_ivt_urb - c_ivt) * odt_skims['KNRE_PRM_IVTT_UrbRail'],,,,,,,,,,,,,1,,,,, +KNR_PRM - In-vehicle time on COM,@df.inbound * (c_ivt_com - c_ivt) * odt_skims['KNRE_PRM_IVTT_ComRail'],,,,,,,,,,,,,1,,,,, +KNR_PRM inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['KNRE_PRM_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,1,,,,, +KNR_PRM inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['KNRE_PRM_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,1,,,,, +KNR_PRM inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['KNRE_PRM_XWAIT'],,,,,,,,,,,,,1,,,,, +KNR_PRM inbound - number of transfers,@df.inbound * c_xfers_drv * (odt_skims['KNRE_PRM_NT']).clip(0),,,,,,,,,,,,,1,,,,, +KNR_PRM inbound - Drive time,@df.inbound * c_dtim * odt_skims['KNRE_PRM_DTIME'],,,,,,,,,,,,,1,,,,, +KNR_PRM inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,,,,,1,,,,, +KNR_PRM inbound - Walk other time,@df.inbound * c_waux * odt_skims['KNRE_PRM_WAUX'],,,,,,,,,,,,,1,,,,, +KNR_PRM inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['KNRE_PRM_FARE'] + costPerMile * odt_skims['KNRE_PRM_DDIST']),,,,,,,,,,,,,1,,,,, +KNR_PRM inbound - Ratio of drive access distance to OD distance,@df.inbound * c_dacc_ratio * (odt_skims['KNRE_PRM_DDIST'])/ od_skims['DIST'],,,,,,,,,,,,,1,,,,, +#KNR to Mix,,,,,,,,,,,,,,,,,,, +KNR_MIX - Unavailable for persons less than 16,age < 16,,,,,,,,,,,,,,-999,,,, +KNR_MIX - Destination zone densityIndex,@c_density_index * df.density_index,,,,,,,,,,,,,,1,,,, +KNR_MIX - Topology,@c_topology_trn * df.trip_topology,,,,,,,,,,,,,,1,,,, +KNR_MIX - Person is less than 10 years old,@c_age010_trn * (df.age < 10),,,,,,,,,,,,,,1,,,, +KNR_MIX outbound - Unavailable,outbound & ~knr_mix_available_outbound,,,,,,,,,,,,,,-999,,,, +KNR_MIX outbound - In-vehicle time,@df.outbound * c_ivt * odt_skims['KNR_MIX_IVT'],,,,,,,,,,,,,,1,,,, +KNR_MIX - In-vehicle time on Bus,@df.outbound * c_ivt * odt_skims['KNR_MIX_IVTT_Bus'],,,,,,,,,,,,,,1,,,, +KNR_MIX - In-vehicle time on PMov,@df.outbound * (c_ivt_pmov - c_ivt) * odt_skims['KNR_MIX_IVTT_PMov'],,,,,,,,,,,,,,1,,,, +KNR_MIX - In-vehicle time on StCar,@df.outbound * (c_ivt_stcar - c_ivt) * odt_skims['KNR_MIX_IVTT_StCar'],,,,,,,,,,,,,,1,,,, +KNR_MIX - In-vehicle time on BRT,@df.outbound * (c_ivt_brt - c_ivt) * odt_skims['KNR_MIX_IVTT_Brt'],,,,,,,,,,,,,,1,,,, +KNR_MIX - In-vehicle time on URB,@df.outbound * (c_ivt_urb - c_ivt) * odt_skims['KNR_MIX_IVTT_UrbRail'],,,,,,,,,,,,,,1,,,, +KNR_MIX - In-vehicle time on COM,@df.outbound * (c_ivt_com - c_ivt) * odt_skims['KNR_MIX_IVTT_ComRail'],,,,,,,,,,,,,,1,,,, +KNR_MIX outbound - Short iwait time,@df.outbound * c_short_i_wait * (odt_skims['KNR_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,1,,,, +KNR_MIX outbound - Long iwait time,@df.outbound * c_long_i_wait * (odt_skims['KNR_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,,1,,,, +KNR_MIX outbound - transfer wait time,@df.outbound * c_xwait * odt_skims['KNR_MIX_XWAIT'],,,,,,,,,,,,,,1,,,, +KNR_MIX outbound - number of transfers,@df.outbound * c_xfers_drv * (odt_skims['KNR_MIX_NT']).clip(0),,,,,,,,,,,,,,1,,,, +KNR_MIX outbound - Drive time,@df.outbound * c_dtim * odt_skims['KNR_MIX_DTIME'],,,,,,,,,,,,,,1,,,, +KNR_MIX outbound - Walk egress ime,@df.outbound * c_wegr * df.destination_walk_time,,,,,,,,,,,,,,1,,,, +KNR_MIX outbound - Walk other time,@df.outbound * c_waux * odt_skims['KNR_MIX_WAUX'],,,,,,,,,,,,,,1,,,, +KNR_MIX outbound - Fare and operating cost,@df.outbound * df.c_cost * (odt_skims['KNR_MIX_FARE'] + costPerMile * odt_skims['KNR_MIX_DDIST']),,,,,,,,,,,,,,1,,,, +KNR_MIX outbound - Ratio of drive access distance to OD distance,@df.outbound * c_dacc_ratio * (odt_skims['KNR_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,,1,,,, +KNR_MIX inbound - Unavailable,inbound & ~knr_mix_available_inbound,,,,,,,,,,,,,,-999,,,, +KNR_MIX inbound - In-vehicle time,@df.inbound * c_ivt * odt_skims['KNRE_MIX_IVT'],,,,,,,,,,,,,,1,,,, +KNR_MIX - In-vehicle time on Bus,@df.inbound * c_ivt * odt_skims['KNRE_MIX_IVTT_Bus'],,,,,,,,,,,,,,1,,,, +KNR_MIX - In-vehicle time on PMov,@df.inbound * (c_ivt_pmov - c_ivt) * odt_skims['KNRE_MIX_IVTT_PMov'],,,,,,,,,,,,,,1,,,, +KNR_MIX - In-vehicle time on StCar,@df.inbound * (c_ivt_stcar - c_ivt) * odt_skims['KNRE_MIX_IVTT_StCar'],,,,,,,,,,,,,,1,,,, +KNR_MIX - In-vehicle time on BRT,@df.inbound * (c_ivt_brt - c_ivt) * odt_skims['KNRE_MIX_IVTT_Brt'],,,,,,,,,,,,,,1,,,, +KNR_MIX - In-vehicle time on URB,@df.inbound * (c_ivt_urb - c_ivt) * odt_skims['KNRE_MIX_IVTT_UrbRail'],,,,,,,,,,,,,,1,,,, +KNR_MIX - In-vehicle time on COM,@df.inbound * (c_ivt_com - c_ivt) * odt_skims['KNRE_MIX_IVTT_ComRail'],,,,,,,,,,,,,,1,,,, +KNR_MIX inbound - Short iwait time,@df.inbound * c_short_i_wait * (odt_skims['KNRE_MIX_IWAIT']).clip(upper=waitThresh),,,,,,,,,,,,,,1,,,, +KNR_MIX inbound - Long iwait time,@df.inbound * c_long_i_wait * (odt_skims['KNRE_MIX_IWAIT']-waitThresh).clip(0),,,,,,,,,,,,,,1,,,, +KNR_MIX inbound - transfer wait time,@df.inbound * c_xwait * odt_skims['KNRE_MIX_XWAIT'],,,,,,,,,,,,,,1,,,, +KNR_MIX inbound - number of transfers,@df.inbound * c_xfers_drv * (odt_skims['KNRE_MIX_NT']).clip(0),,,,,,,,,,,,,,1,,,, +KNR_MIX inbound - Drive time,@df.inbound * c_dtim * odt_skims['KNRE_MIX_DTIME'],,,,,,,,,,,,,,1,,,, +KNR_MIX inbound - Walk access time,@df.inbound * c_wacc * df.origin_walk_time,,,,,,,,,,,,,,1,,,, +KNR_MIX inbound - Walk other time,@df.inbound * c_waux * odt_skims['KNRE_MIX_WAUX'],,,,,,,,,,,,,,1,,,, +KNR_MIX inbound - Fare and operating cost,@df.inbound * df.c_cost * (odt_skims['KNRE_MIX_FARE'] + costPerMile * odt_skims['KNRE_MIX_DDIST']),,,,,,,,,,,,,,1,,,, +KNR_MIX inbound - Ratio of drive access distance to OD distance,@df.inbound * c_dacc_ratio * (odt_skims['KNRE_MIX_DDIST']) / od_skims['DIST'],,,,,,,,,,,,,,1,,,, +#Taxi,,,,,,,,,,,,,,,,,,, +Taxi - In-vehicle time,@c_ivt * odt_skims['HOV2_TIME'],,,,,,,,,,,,,,,,1,, +Taxi - Wait time,@c_ivt * 1.5 * df.origTaxiWaitTime,,,,,,,,,,,,,,,,1,, +#Taxi - Tolls,@df.c_cost * odt_skims['HOV2_VTOLL'],,,,,,,,,,,,,,,,1,, +#Taxi - Bridge toll,@df.c_cost * odt_skims['HOV2_BTOLL'],,,,,,,,,,,,,,,,1,, +Taxi - Fare,@df.c_cost * (Taxi_baseFare + odt_skims['HOV2_DIST'] * Taxi_costPerMile + odt_skims['HOV2_TIME'] * Taxi_costPerMinute)*100,,,,,,,,,,,,,,,,1,, +#TNC Single,,,,,,,,,,,,,,,,,,, +TNC Single - In-vehicle time,@c_ivt * odt_skims['HOV2_TIME'] ,,,,,,,,,,,,,,,,,1, +TNC Single - Wait time,@c_ivt * 1.5 * df.origSingleTNCWaitTime,,,,,,,,,,,,,,,,,1, +#TNC Single - Tolls,@df.c_cost * odt_skims['HOV2_VTOLL'],,,,,,,,,,,,,,,,,1, +#TNC Single - Bridge toll,@df.c_cost * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']),,,,,,,,,,,,,,,,,1, +TNC Single - Cost,"@df.c_cost * np.maximum(TNC_single_baseFare + odt_skims['HOV2_DIST'] * TNC_single_costPerMile + odt_skims['HOV2_TIME'] * TNC_single_costPerMinute, TNC_single_costMinimum) * 100",,,,,,,,,,,,,,,,,1, +#TNC Shared,,,,,,,,,,,,,,,,,,, +TNC Shared - In-vehicle time,@c_ivt * odt_skims['HOV2_TIME'] * TNC_shared_IVTFactor,,,,,,,,,,,,,,,,,,1 +TNC Shared - Wait time,@c_ivt * 1.5 * df.origSharedTNCWaitTime,,,,,,,,,,,,,,,,,,1 +#TNC Shared - Tolls,@df.c_cost * odt_skims['HOV2_VTOLL'],,,,,,,,,,,,,,,,,,1 +#TNC Shared - Bridge toll,@df.c_cost * (odt_skims['HOV2_BTOLL'] + dot_skims['HOV2_BTOLL']),,,,,,,,,,,,,,,,,,1 +TNC Shared - Cost,"@df.c_cost * np.maximum(TNC_shared_baseFare + odt_skims['HOV2_DIST'] * TNC_shared_costPerMile + odt_skims['HOV2_TIME']* TNC_shared_costPerMinute, TNC_shared_costMinimum) * 100",,,,,,,,,,,,,,,,,,1 +#,,,,,,,,,,,,,,,,,,, +Auto tour mode availability,tour_mode_is_auto,,,,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, +Walk tour mode availability,tour_mode_is_walk,-999,-999,-999,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, +Bike tour mode availability,tour_mode_is_bike,-999,-999,-999,,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,, +Walk to Transit tour mode availability,tour_mode_is_walk_transit,-999,,,,-999,,,,-999,-999,-999,-999,-999,-999,-999,,, +Drive to Transit tour mode availability,tour_mode_is_drive_transit,-999,-999,-999,-999,-999,-999,-999,-999,,,,,,,-999,,, +School bus tour mode availability,tour_mode_is_school_bus,-999,,,,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,,,, +#indiv tour ASCs,,,,,,,,,,,,,,,,,,, +Drive Alone tour mode ASC -- shared ride 2,@sov_ASC_sr2 * (df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,1,,,,,,,,,,,,,,,, +Drive Alone tour mode ASC -- shared ride 3+,@sov_ASC_sr3p * (df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,1,,,,,,,,,,,,,,, +Drive Alone tour mode ASC -- walk,@sov_ASC_walk * (df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,1,,,,,,,,,,,,,, +Drive Alone tour mode ASC -- ride hail,@sov_ASC_rh * (df.is_indiv & df.i_tour_mode.isin(I_SOV_MODES)),,,,,,,,,,,,,,,,1,1,1 +#Shared Ride 2 tour mode ASC -- shared ride 2 - reference mode,@sr2_ASC_sr2 * (df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,1,,,,,,,,,,,,,,,, +Shared Ride 2 tour mode ASC -- drive alone,@sr2_ASC_sov * (df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),1,,,,,,,,,,,,,,,,, +Shared Ride 2 tour mode ASC -- walk,@sr2_ASC_walk * (df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,1,,,,,,,,,,,,,, +Shared Ride 2 tour mode ASC -- ride hail,@sr2_ASC_rh * (df.is_indiv & df.i_tour_mode.isin(I_SR2_MODES)),,,,,,,,,,,,,,,,1,1,1 +Shared Ride 3+ tour mode ASC -- drive alone,@sr3p_ASC_sov * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),1,,,,,,,,,,,,,,,,, +Shared Ride 3+ tour mode ASC -- shared ride 2,@sr3p_ASC_sr2 * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,1,,,,,,,,,,,,,,,, +#Shared Ride 3+ tour mode ASC -- shared ride 3+ - reference mode,@sr3p_ASC_sr3p * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,1,,,,,,,,,,,,,,, +Shared Ride 3+ tour mode ASC -- walk,@sr3p_ASC_walk * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,1,,,,,,,,,,,,,, +Shared Ride 3+ tour mode ASC -- ride hail,@sr3p_ASC_rh * (df.is_indiv & df.i_tour_mode.isin(I_SR3P_MODES)),,,,,,,,,,,,,,,,1,1,1 +Walk tour mode ASC -- ride hail,@walk_ASC_rh * df.is_indiv * (df.i_tour_mode == I_WALK_MODE),,,,,,,,,,,,,,,,1,1,1 +Bike tour mode ASC -- walk,@bike_ASC_walk * df.is_indiv * (df.i_tour_mode == I_BIKE_MODE),,,,1,,,,,,,,,,,,,, +Bike tour mode ASC -- ride hail,@bike_ASC_rh * df.is_indiv * (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,1,1,1 +Walk-transit tour mode ASC -- shared ride 2,@walktransit_ASC_sr2 * (df.is_indiv & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,1,,,,,,,,,,,,,,,, +Walk-transit tour mode ASC -- shared ride 3+,@walktransit_ASC_sr3p * (df.is_indiv & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,1,,,,,,,,,,,,,,, +Walk-transit tour mode ASC -- walk,@walktransit_ASC_walk * (df.is_indiv & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,1,,,,,,,,,,,,,, +Walk-transit tour mode ASC -- ride hail,@walk_transit_ASC_rh * (df.is_indiv & df.tour_mode_is_walk_transit),,,,,,,,,,,,,,,,1,1,1 +Drive to Transit tour mode ASC -- ride hail,@drive_transit_ASC_rh * (df.is_indiv & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,1,1,1 +Ride Hail tour mode ASC -- ride hail,@ride_hail_ASC_taxi * (df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,1,, +Ride Hail tour mode ASC -- ride hail,@ride_hail_ASC_tnc_single * (df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,1, +Ride Hail tour mode ASC -- ride hail,@ride_hail_ASC_tnc_shared * (df.is_indiv & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,1 +#Walk-transit tour mode ASC -- walk-transit - reference mode,@walktransit_ASC_walktransit * (df.is_indiv & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,,,1,1,1,,,,,,,,,, +#PNR tour mode ASC -- PNR - reference mode,@pnr_ASC_pnr * (df.is_indiv & df.i_tour_mode.isin(I_PNR_TRANSIT_MODES)),,,,,,,,,1,1,1,,,,,,, +#KNR tour mode ASC -- KNR - reference mode,@knr_ASC_knr * (df.is_indiv & df.i_tour_mode.isin(I_KNR_TRANSIT_MODES)),,,,,,,,,,,,1,1,1,,,, +#joint tour ASCs,,,,,,,,,,,,,,,,,,, +#joint - SR2 tour mode ASC -- shared ride 2 - reference mode,@joint_sr2_ASC_sr2 * (df.is_joint & df.i_tour_mode.isin(I_SR2_MODES)),,1,,,,,,,,,,,,,,,, +joint - SR2 tour mode ASC -- walk,@joint_sr2_ASC_walk * (df.is_joint & df.i_tour_mode.isin(I_SR2_MODES)),,,,1,,,,,,,,,,,,,, +#joint - SR3 tour mode ASC -- shared ride 3+ - reference mode,@joint_sr3p_ASC_sr3p * (df.is_joint & df.i_tour_mode.isin(I_SR3P_MODES)),,,1,,,,,,,,,,,,,,, +joint - SR3 tour mode ASC -- shared ride 2,@joint_sr3p_ASC_sr2 * (df.is_joint & df.i_tour_mode.isin(I_SR3P_MODES)),,1,,,,,,,,,,,,,,,, +joint - SR3 tour mode ASC -- walk,@joint_sr3p_ASC_walk * (df.is_joint & df.i_tour_mode.isin(I_SR3P_MODES)),,,,1,,,,,,,,,,,,,, +joint - auto tour mode ASC -- ride hail,@joint_auto_ASC_rh * (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,1,1,1 +joint - Bike tour mode ASC -- walk,@joint_bike_ASC_walk * df.is_joint * (df.i_tour_mode == I_BIKE_MODE),,,,1,,,,,,,,,,,,,, +joint - Bike tour mode ASC -- ride hail,@joint_bike_ASC_rh * df.is_joint * (df.i_tour_mode == I_BIKE_MODE),,,,,,,,,,,,,,,,1,1,1 +joint - Walk-transit tour mode ASC -- shared ride 2,@joint_walktransit_ASC_sr2 * (df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,1,,,,,,,,,,,,,,,, +joint - Walk-transit tour mode ASC -- shared ride 3+,@joint_walktransit_ASC_sr3p * (df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,1,,,,,,,,,,,,,,, +joint - Walk-transit tour mode ASC -- walk,@joint_walktransit_ASC_walk * (df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,1,,,,,,,,,,,,,, +joint - Walk to Transit tour mode ASC -- ride hail,@joint_walk_transit_ASC_rh * (df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,,,,,,,,,,,,,1,1,1 +joint - Drive to Transit tour mode ASC -- ride hail,@joint_drive_transit_ASC_rh * (df.is_joint & df.tour_mode_is_drive_transit),,,,,,,,,,,,,,,,1,1,1 +joint - Ride Hail tour mode ASC -- ride hail,@joint_ride_hail_ASC_taxi * (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,1,, +joint - Ride Hail tour mode ASC -- ride hail,@joint_ride_hail_ASC_tnc_single * (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,1, +joint - Ride Hail tour mode ASC -- ride hail,@joint_ride_hail_ASC_tnc_shared * (df.is_joint & df.i_tour_mode.isin(I_RIDE_HAIL_MODES)),,,,,,,,,,,,,,,,,,1 +#joint - Walk-transit tour mode ASC -- walk-transit - reference mode,@joint_walktransit_ASC_walktransit * (df.is_joint & df.i_tour_mode.isin(I_WALK_TRANSIT_MODES)),,,,,,1,1,1,,,,,,,,,, +#joint - PNR tour mode ASC -- PNR - reference mode,@joint_pnr_ASC_pnr * (df.is_joint & df.i_tour_mode.isin(I_PNR_TRANSIT_MODES)),,,,,,,,,1,1,1,,,,,,, +#joint - KNR tour mode ASC -- KNR - reference mode,@joint_knr_ASC_knr * (df.is_joint & df.i_tour_mode.isin(I_KNR_TRANSIT_MODES)),,,,,,,,,,,,1,1,1,,,, +#,,,,,,,,,,,,,,,,,,, +Walk not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTWALK'] > 3),,,,-999,,,,,,,,,,,,,, +Bike not available for long distances,@df.tour_mode_is_walk & (od_skims['DISTBIKE'] > 8),,,,,-999,,,,,,,,,,,,, +Origin density index,@(c_origin_density_index*df.origin_density_index).clip(c_origin_density_index_max) if origin_density_applied else 0,,,,1,1,1,1,1,,,,,,,,,, +#Walk-express penalty for intermediate stops,@c_walk_express_penalty * ~(df.first_trip | df.first_trip),,,,,,,,1,,,,,,,,,, +#School Bus,,,,,,,,,,,,,,,,,,, +School Bus Unavailable if primary purpose NOT school,~is_school,,,,,,,,,,,,,,,-999,,, +School Bus Unavailable - Tour Mode = SOV,tour_mode_is_SOV,,,,,,,,,,,,,,,-999,,, +School Bus Unavailable - Tour Mode = Transit,tour_mode_is_drive_transit,,,,,,,,,,,,,,,-999,,, +School Bus - In-vehicle time (20 miles per hour),@c_ivt * odt_skims['HOV3_DIST']*3,,,,,,,,,,,,,,,1,,, +School Bus - Walk Time,@c_wacc*10,,,,,,,,,,,,,,,1,,, +School Bus - Wait Time,@c_short_i_wait*10,,,,,,,,,,,,,,,1,,, +School Bus tour mode ASC -- shared ride 2,@schoolbus_ASC_sr2 * df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,1,,,,,,,,,,,,,,,, +School Bus tour mode ASC -- shared ride 3+,@schoolbus_ASC_sr3p * df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,,1,,,,,,,,,,,,,,, +School Bus tour mode ASC -- walk,@schoolbus_ASC_walk * df.i_tour_mode.isin(I_SCHOOLBUS_MODE),,,,1,,,,,,,,,,,,,, +local_ASC,@local_ASC,,,,,,1,,,1,,,1,,,,,, +premium_ASC,@premium_ASC,,,,,,,1,,,1,,,1,,,,, +mix_ASC,@mix_ASC,,,,,,,,1,,,1,,,1,,,, +Origin density index,@(c_origin_density_index*df.origin_density_index).clip(c_origin_density_index_max) if origin_density_applied else 0,,,,,,,1,1,1,1,1,1,1,,,,1,1 +TNC shared adjustment,@adjust_tnc_shared,,,,,,,,,,,,,,,,,,1 diff --git a/activitysim/examples/example_semcog/configs/trip_mode_choice.yaml b/activitysim/examples/prototype_semcog/configs/trip_mode_choice.yaml similarity index 95% rename from activitysim/examples/example_semcog/configs/trip_mode_choice.yaml rename to activitysim/examples/prototype_semcog/configs/trip_mode_choice.yaml index b9f02c0e18..c5f224994f 100755 --- a/activitysim/examples/example_semcog/configs/trip_mode_choice.yaml +++ b/activitysim/examples/prototype_semcog/configs/trip_mode_choice.yaml @@ -1,184 +1,184 @@ -SPEC: trip_mode_choice.csv -#COEFFICIENTS: trip_mode_choice_coeffs.csv -LEGACY_COEFFICIENTS: trip_mode_choice_coeffs.csv - -LOGIT_TYPE: NL - -NESTS: - name: root - coefficient: 1.00 - alternatives: - - name: AUTO - coefficient: 0.72 - alternatives: - - DRIVEALONE - - SHARED2 - - SHARED3 - - name: NONMOTORIZED - coefficient: 0.72 - alternatives: - - WALK - - BIKE - - name: TRANSIT - coefficient: 0.72 - alternatives: - - name: WALKACCESS - coefficient: 0.5 - alternatives: - - WALK_LOC - - WALK_PRM - - WALK_MIX - - name: PNRACCESS - coefficient: 0.5 - alternatives: - - PNR_LOC - - PNR_PRM - - PNR_MIX - - name: KNRACCESS - coefficient: 0.5 - alternatives: - - KNR_LOC - - KNR_PRM - - KNR_MIX - - name: SCHOOL_BUS - coefficient: 0.72 - alternatives: - - SCHOOLBUS - - name: RIDEHAIL - coefficient: 0.36 - alternatives: - - TAXI - - TNC_SINGLE - - TNC_SHARED - -CONSTANTS: - orig_col_name: origin - dest_col_name: destination - costPerMile: 18.29 - costShareSr2: 1.75 - costShareSr3: 2.50 - waitThresh: 10.00 - walkThresh: 1.00 - shortWalk: 0.333 - longWalk: 0.667 - walkSpeed: 3.00 - bikeThresh: 6.00 - bikeSpeed: 12.00 -# maxCbdAreaTypeThresh: 2 -# indivTour: 1.00000 -# upperEA: 5 -# upperAM: 10 -# upperMD: 15 -# upperPM: 19 - I_MODE_MAP: - DRIVEALONE: 1 - SHARED2: 2 - SHARED3: 3 - WALK: 4 - BIKE: 5 - WALK_LOC: 6 - WALK_PRM: 7 - WALK_MIX: 8 - PNR_LOC: 9 - PNR_PRM: 10 - PNR_MIX: 11 - KNR_LOC: 12 - KNR_PRM: 13 - KNR_MIX: 14 - SCHOOLBUS: 15 - TAXI: 16 - TNC_SINGLE: 17 - TNC_SHARED: 18 - I_SOV_MODES: [1] - I_SR2_MODES: [2] - I_SR3P_MODES: [3] - I_AUTO_MODES: [1, 2, 3] - I_WALK_MODE: 4 - I_BIKE_MODE: 5 - I_WALK_TRANSIT_MODES: [6,7,8] - I_DRIVE_TRANSIT_MODES: [9,10,11,12,13,14] - I_PNR_TRANSIT_MODES: [9,10,11] - I_KNR_TRANSIT_MODES: [12,13,14] - I_SCHOOLBUS_MODE: [15] - I_RIDE_HAIL_MODES: [16, 17, 18] - # RIDEHAIL Settings - Taxi_baseFare: 2.20 - Taxi_costPerMile: 2.30 - Taxi_costPerMinute: 0.10 - Taxi_waitTime_mean: - 1: 5.5 - 2: 9.5 - 3: 13.3 - 4: 17.3 - 5: 26.5 - Taxi_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - TNC_single_baseFare: 2.20 - TNC_single_costPerMile: 1.33 - TNC_single_costPerMinute: 0.24 - TNC_single_costMinimum: 7.20 - TNC_single_waitTime_mean: - 1: 3.0 - 2: 6.3 - 3: 8.4 - 4: 8.5 - 5: 10.3 - TNC_single_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - TNC_shared_baseFare: 2.20 - TNC_shared_costPerMile: 0.53 - TNC_shared_costPerMinute: 0.10 - TNC_shared_costMinimum: 3.00 - TNC_shared_IVTFactor: 1.5 - TNC_shared_waitTime_mean: - 1: 5.0 - 2: 8.0 - 3: 11.0 - 4: 15.0 - 5: 15.0 - TNC_shared_waitTime_sd: - 1: 0 - 2: 0 - 3: 0 - 4: 0 - 5: 0 - min_waitTime: 0 - max_waitTime: 50 - -# so far, we can use the same spec as for non-joint tours -preprocessor: - SPEC: trip_mode_choice_annotate_trips_preprocessor - DF: df - TABLES: - - land_use - - tours - -# - SPEC: trip_mode_choice_annotate_trips_preprocessor2 -# DF: df -# TABLES: -# - land_use - -# to reduce memory needs filter chooser table to these fields -TOURS_MERGED_CHOOSER_COLUMNS: - - hhsize - - age - - auto_ownership - - number_of_participants - - tour_category - - parent_tour_id - - tour_mode - - duration - - value_of_time - - tour_type - - free_parking_at_work - - -MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum +SPEC: trip_mode_choice.csv +#COEFFICIENTS: trip_mode_choice_coeffs.csv +LEGACY_COEFFICIENTS: trip_mode_choice_coeffs.csv + +LOGIT_TYPE: NL + +NESTS: + name: root + coefficient: 1.00 + alternatives: + - name: AUTO + coefficient: 0.72 + alternatives: + - DRIVEALONE + - SHARED2 + - SHARED3 + - name: NONMOTORIZED + coefficient: 0.72 + alternatives: + - WALK + - BIKE + - name: TRANSIT + coefficient: 0.72 + alternatives: + - name: WALKACCESS + coefficient: 0.5 + alternatives: + - WALK_LOC + - WALK_PRM + - WALK_MIX + - name: PNRACCESS + coefficient: 0.5 + alternatives: + - PNR_LOC + - PNR_PRM + - PNR_MIX + - name: KNRACCESS + coefficient: 0.5 + alternatives: + - KNR_LOC + - KNR_PRM + - KNR_MIX + - name: SCHOOL_BUS + coefficient: 0.72 + alternatives: + - SCHOOLBUS + - name: RIDEHAIL + coefficient: 0.36 + alternatives: + - TAXI + - TNC_SINGLE + - TNC_SHARED + +CONSTANTS: + orig_col_name: origin + dest_col_name: destination + costPerMile: 18.29 + costShareSr2: 1.75 + costShareSr3: 2.50 + waitThresh: 10.00 + walkThresh: 1.00 + shortWalk: 0.333 + longWalk: 0.667 + walkSpeed: 3.00 + bikeThresh: 6.00 + bikeSpeed: 12.00 +# maxCbdAreaTypeThresh: 2 +# indivTour: 1.00000 +# upperEA: 5 +# upperAM: 10 +# upperMD: 15 +# upperPM: 19 + I_MODE_MAP: + DRIVEALONE: 1 + SHARED2: 2 + SHARED3: 3 + WALK: 4 + BIKE: 5 + WALK_LOC: 6 + WALK_PRM: 7 + WALK_MIX: 8 + PNR_LOC: 9 + PNR_PRM: 10 + PNR_MIX: 11 + KNR_LOC: 12 + KNR_PRM: 13 + KNR_MIX: 14 + SCHOOLBUS: 15 + TAXI: 16 + TNC_SINGLE: 17 + TNC_SHARED: 18 + I_SOV_MODES: [1] + I_SR2_MODES: [2] + I_SR3P_MODES: [3] + I_AUTO_MODES: [1, 2, 3] + I_WALK_MODE: 4 + I_BIKE_MODE: 5 + I_WALK_TRANSIT_MODES: [6,7,8] + I_DRIVE_TRANSIT_MODES: [9,10,11,12,13,14] + I_PNR_TRANSIT_MODES: [9,10,11] + I_KNR_TRANSIT_MODES: [12,13,14] + I_SCHOOLBUS_MODE: [15] + I_RIDE_HAIL_MODES: [16, 17, 18] + # RIDEHAIL Settings + Taxi_baseFare: 2.20 + Taxi_costPerMile: 2.30 + Taxi_costPerMinute: 0.10 + Taxi_waitTime_mean: + 1: 5.5 + 2: 9.5 + 3: 13.3 + 4: 17.3 + 5: 26.5 + Taxi_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_single_baseFare: 2.20 + TNC_single_costPerMile: 1.33 + TNC_single_costPerMinute: 0.24 + TNC_single_costMinimum: 7.20 + TNC_single_waitTime_mean: + 1: 3.0 + 2: 6.3 + 3: 8.4 + 4: 8.5 + 5: 10.3 + TNC_single_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + TNC_shared_baseFare: 2.20 + TNC_shared_costPerMile: 0.53 + TNC_shared_costPerMinute: 0.10 + TNC_shared_costMinimum: 3.00 + TNC_shared_IVTFactor: 1.5 + TNC_shared_waitTime_mean: + 1: 5.0 + 2: 8.0 + 3: 11.0 + 4: 15.0 + 5: 15.0 + TNC_shared_waitTime_sd: + 1: 0 + 2: 0 + 3: 0 + 4: 0 + 5: 0 + min_waitTime: 0 + max_waitTime: 50 + +# so far, we can use the same spec as for non-joint tours +preprocessor: + SPEC: trip_mode_choice_annotate_trips_preprocessor + DF: df + TABLES: + - land_use + - tours + +# - SPEC: trip_mode_choice_annotate_trips_preprocessor2 +# DF: df +# TABLES: +# - land_use + +# to reduce memory needs filter chooser table to these fields +TOURS_MERGED_CHOOSER_COLUMNS: + - hhsize + - age + - auto_ownership + - number_of_participants + - tour_category + - parent_tour_id + - tour_mode + - duration + - value_of_time + - tour_type + - free_parking_at_work + + +MODE_CHOICE_LOGSUM_COLUMN_NAME: mode_choice_logsum diff --git a/activitysim/examples/example_semcog/configs/trip_mode_choice_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/trip_mode_choice_annotate_trips_preprocessor.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/trip_mode_choice_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/trip_mode_choice_annotate_trips_preprocessor.csv index 9d7038014b..1c557d4991 100755 --- a/activitysim/examples/example_semcog/configs/trip_mode_choice_annotate_trips_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/trip_mode_choice_annotate_trips_preprocessor.csv @@ -1,89 +1,89 @@ -Description,Target,Expression, -,is_joint,(df.number_of_participants > 1), -,is_indiv,(df.number_of_participants == 1), -,is_atwork_subtour,~df.parent_tour_id.isnull(), -,is_school,"(df.primary_purpose=='school') & (df.purpose.isin(['school', 'Home']))", -,c_cost,(0.60 * c_ivt) / df.value_of_time, -#,,, -#atwork subtours,,, -#FIXME tripModeChoice uec wrongly conflates these with tour_mode_is_bike?,,, -,parent_tour_mode,"reindex(tours.tour_mode, df.parent_tour_id).fillna('')", -,work_tour_is_SOV,parent_tour_mode.isin(['DRIVEALONE']), -,work_tour_is_bike,parent_tour_mode=='BIKE', -#,,, -,i_tour_mode,df.tour_mode.map(I_MODE_MAP), -,tour_mode_is_SOV,i_tour_mode.isin(I_SOV_MODES), -,tour_mode_is_auto,i_tour_mode.isin(I_AUTO_MODES), -,tour_mode_is_walk,i_tour_mode.isin([I_WALK_MODE]), -,tour_mode_is_bike,i_tour_mode.isin([I_BIKE_MODE]), -,tour_mode_is_walk_transit,i_tour_mode.isin(I_WALK_TRANSIT_MODES), -,tour_mode_is_drive_transit,i_tour_mode.isin(I_DRIVE_TRANSIT_MODES), -,tour_mode_not_drive_transit,~tour_mode_is_drive_transit, -,tour_mode_is_pnr_transit,i_tour_mode.isin(I_PNR_TRANSIT_MODES), -,tour_mode_is_knr_transit,i_tour_mode.isin(I_KNR_TRANSIT_MODES), -,tour_mode_is_school_bus,i_tour_mode.isin(I_SCHOOLBUS_MODE), -,tour_mode_is_ride_hail,i_tour_mode.isin(I_RIDE_HAIL_MODES), -#,,, -,inbound,~df.outbound, -,first_trip,df.trip_num == 1, -,last_trip,df.trip_num == df.trip_count, -origin terminal time not counted at home,_origin_terminal_time,"np.where(df.outbound & first_trip, 0, reindex(land_use.TERMINAL, df[ORIGIN]))", -dest terminal time not counted at home,_dest_terminal_time,"np.where(inbound & last_trip, 0, reindex(land_use.TERMINAL, df[DESTINATION]))", -,total_terminal_time,_origin_terminal_time + _dest_terminal_time, -#,,, -,free_parking_available,(df.tour_type == 'work') & df.free_parking_at_work, -,dest_hourly_peak_parking_cost,"reindex(land_use.PRKCST, df[DESTINATION])", -,origin_hourly_peak_parking_cost,"reindex(land_use.PRKCST, df[ORIGIN])", -,origin_duration,"np.where(first_trip, np.where(inbound,df.duration/2 * ~free_parking_available,0), 1)", -,dest_duration,"np.where(last_trip, np.where(inbound, df.duration/2 * ~free_parking_available, 0), 1)", -,origin_parking_cost,origin_duration*origin_hourly_peak_parking_cost, -,dest_parking_cost,dest_duration*dest_hourly_peak_parking_cost, -,total_parking_cost,(origin_parking_cost + dest_parking_cost) / 2.0, -,trip_topology,"np.where(df.outbound, reindex(land_use.TOPOLOGY, df[DESTINATION]), reindex(land_use.TOPOLOGY, df[ORIGIN]))", -,density_index,"np.where(df.outbound, reindex(land_use.density_index, df[DESTINATION]), reindex(land_use.density_index, df[ORIGIN]))", -,origin_density_index,"np.where(df.outbound, reindex(land_use.density_index, df[ORIGIN]), reindex(land_use.density_index, df[DESTINATION]))", -# FIXME no transit subzones so all zones short walk to transit,,, -,_walk_transit_origin,True, -,_walk_transit_destination,True, -,walk_transit_available,_walk_transit_origin & _walk_transit_destination & (tour_mode_not_drive_transit), -,pnr_transit_available,tour_mode_is_pnr_transit, -,knr_transit_available,tour_mode_is_knr_transit, -,origin_walk_time,shortWalk*60/walkSpeed, -,destination_walk_time,shortWalk*60/walkSpeed, -# RIDEHAIL,,, -,origin_density_measure,"(reindex(land_use.tot_pop, df[orig_col_name]) + reindex(land_use.tot_emp, df[orig_col_name])) / (reindex(land_use.tot_acres, df[orig_col_name]) / 640)", -,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", -,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", -,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", -# ,, Note that the mean and standard deviation are not the values for the distribution itself, but of the underlying normal distribution it is derived from -,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", -,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", -,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", -,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", -,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", -#,,, -,sov_available,(odt_skims['SOV_TIME']>0) & tour_mode_not_drive_transit, -,hov2_available,(odt_skims['HOV2_TIME']>0) & tour_mode_not_drive_transit, -,hov3_available,(odt_skims['HOV3_TIME']>0) & tour_mode_not_drive_transit, -,walk_local_available,walk_transit_available & (odt_skims['WLK_LOC_IVT']>0), -,walk_premium_available,walk_transit_available & (odt_skims['WLK_PRM_IVT']>0), -,walk_mix_available,walk_transit_available & (odt_skims['WLK_MIX_IVT']>0), -,pnr_local_available_outbound,pnr_transit_available & df.outbound & (odt_skims['PNR_LOC_IVT']>0), -,pnr_local_available_inbound,pnr_transit_available & ~df.outbound & (odt_skims['PNRE_LOC_IVT']>0), -,pnr_premium_available_outbound,pnr_transit_available & df.outbound & (odt_skims['PNR_PRM_IVT']>0), -,pnr_premium_available_inbound,pnr_transit_available & ~df.outbound & (odt_skims['PNRE_PRM_IVT']>0), -,pnr_mix_available_outbound,pnr_transit_available & df.outbound & (odt_skims['PNR_MIX_IVT']>0), -,pnr_mix_available_inbound,pnr_transit_available & ~df.outbound & (odt_skims['PNRE_MIX_IVT']>0), -,knr_local_available_outbound,knr_transit_available & df.outbound & (odt_skims['KNR_LOC_IVT']>0), -,knr_local_available_inbound,knr_transit_available & ~df.outbound & (odt_skims['KNRE_LOC_IVT']>0), -,knr_premium_available_outbound,knr_transit_available & df.outbound & (odt_skims['KNR_PRM_IVT']>0), -,knr_premium_available_inbound,knr_transit_available & ~df.outbound & (odt_skims['KNRE_PRM_IVT']>0), -,knr_mix_available_outbound,knr_transit_available & df.outbound & (odt_skims['KNR_MIX_IVT']>0), -,knr_mix_available_inbound,knr_transit_available & ~df.outbound & (odt_skims['KNRE_MIX_IVT']>0), -#,od_dist_walk,od_skims['DISTWALK'], -#,do_dist_walk,od_skims.reverse('DISTWALK'), -#,max_dist_walk,od_skims.max('DISTWALK'), -#,dist_bike,od_skims['DISTBIKE'], -#,dist_only,od_skims['DIST'], +Description,Target,Expression, +,is_joint,(df.number_of_participants > 1), +,is_indiv,(df.number_of_participants == 1), +,is_atwork_subtour,~df.parent_tour_id.isnull(), +,is_school,"(df.primary_purpose=='school') & (df.purpose.isin(['school', 'Home']))", +,c_cost,(0.60 * c_ivt) / df.value_of_time, +#,,, +#atwork subtours,,, +#FIXME tripModeChoice uec wrongly conflates these with tour_mode_is_bike?,,, +,parent_tour_mode,"reindex(tours.tour_mode, df.parent_tour_id).fillna('')", +,work_tour_is_SOV,parent_tour_mode.isin(['DRIVEALONE']), +,work_tour_is_bike,parent_tour_mode=='BIKE', +#,,, +,i_tour_mode,df.tour_mode.map(I_MODE_MAP), +,tour_mode_is_SOV,i_tour_mode.isin(I_SOV_MODES), +,tour_mode_is_auto,i_tour_mode.isin(I_AUTO_MODES), +,tour_mode_is_walk,i_tour_mode.isin([I_WALK_MODE]), +,tour_mode_is_bike,i_tour_mode.isin([I_BIKE_MODE]), +,tour_mode_is_walk_transit,i_tour_mode.isin(I_WALK_TRANSIT_MODES), +,tour_mode_is_drive_transit,i_tour_mode.isin(I_DRIVE_TRANSIT_MODES), +,tour_mode_not_drive_transit,~tour_mode_is_drive_transit, +,tour_mode_is_pnr_transit,i_tour_mode.isin(I_PNR_TRANSIT_MODES), +,tour_mode_is_knr_transit,i_tour_mode.isin(I_KNR_TRANSIT_MODES), +,tour_mode_is_school_bus,i_tour_mode.isin(I_SCHOOLBUS_MODE), +,tour_mode_is_ride_hail,i_tour_mode.isin(I_RIDE_HAIL_MODES), +#,,, +,inbound,~df.outbound, +,first_trip,df.trip_num == 1, +,last_trip,df.trip_num == df.trip_count, +origin terminal time not counted at home,_origin_terminal_time,"np.where(df.outbound & first_trip, 0, reindex(land_use.TERMINAL, df[ORIGIN]))", +dest terminal time not counted at home,_dest_terminal_time,"np.where(inbound & last_trip, 0, reindex(land_use.TERMINAL, df[DESTINATION]))", +,total_terminal_time,_origin_terminal_time + _dest_terminal_time, +#,,, +,free_parking_available,(df.tour_type == 'work') & df.free_parking_at_work, +,dest_hourly_peak_parking_cost,"reindex(land_use.PRKCST, df[DESTINATION])", +,origin_hourly_peak_parking_cost,"reindex(land_use.PRKCST, df[ORIGIN])", +,origin_duration,"np.where(first_trip, np.where(inbound,df.duration/2 * ~free_parking_available,0), 1)", +,dest_duration,"np.where(last_trip, np.where(inbound, df.duration/2 * ~free_parking_available, 0), 1)", +,origin_parking_cost,origin_duration*origin_hourly_peak_parking_cost, +,dest_parking_cost,dest_duration*dest_hourly_peak_parking_cost, +,total_parking_cost,(origin_parking_cost + dest_parking_cost) / 2.0, +,trip_topology,"np.where(df.outbound, reindex(land_use.TOPOLOGY, df[DESTINATION]), reindex(land_use.TOPOLOGY, df[ORIGIN]))", +,density_index,"np.where(df.outbound, reindex(land_use.density_index, df[DESTINATION]), reindex(land_use.density_index, df[ORIGIN]))", +,origin_density_index,"np.where(df.outbound, reindex(land_use.density_index, df[ORIGIN]), reindex(land_use.density_index, df[DESTINATION]))", +# FIXME no transit subzones so all zones short walk to transit,,, +,_walk_transit_origin,True, +,_walk_transit_destination,True, +,walk_transit_available,_walk_transit_origin & _walk_transit_destination & (tour_mode_not_drive_transit), +,pnr_transit_available,tour_mode_is_pnr_transit, +,knr_transit_available,tour_mode_is_knr_transit, +,origin_walk_time,shortWalk*60/walkSpeed, +,destination_walk_time,shortWalk*60/walkSpeed, +# RIDEHAIL,,, +,origin_density_measure,"(reindex(land_use.tot_pop, df[orig_col_name]) + reindex(land_use.tot_emp, df[orig_col_name])) / (reindex(land_use.tot_acres, df[orig_col_name]) / 640)", +,origin_density,"pd.cut(origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)", +,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})", +,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})", +# ,, Note that the mean and standard deviation are not the values for the distribution itself, but of the underlying normal distribution it is derived from +,origTaxiWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_taxi_wait_time_mean, sigma=origin_zone_taxi_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,origin_zone_singleTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_single_waitTime_mean.items()})", +,origin_zone_singleTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_single_waitTime_sd.items()})", +,origSingleTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_singleTNC_wait_time_mean, sigma=origin_zone_singleTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +,origin_zone_sharedTNC_wait_time_mean,"origin_density.map({k: v for k, v in TNC_shared_waitTime_mean.items()})", +,origin_zone_sharedTNC_wait_time_sd,"origin_density.map({k: v for k, v in TNC_shared_waitTime_sd.items()})", +,origSharedTNCWaitTime,"rng.lognormal_for_df(df, mu=origin_zone_sharedTNC_wait_time_mean, sigma=origin_zone_sharedTNC_wait_time_sd, broadcast=True, scale=True).clip(min_waitTime, max_waitTime)", +#,,, +,sov_available,(odt_skims['SOV_TIME']>0) & tour_mode_not_drive_transit, +,hov2_available,(odt_skims['HOV2_TIME']>0) & tour_mode_not_drive_transit, +,hov3_available,(odt_skims['HOV3_TIME']>0) & tour_mode_not_drive_transit, +,walk_local_available,walk_transit_available & (odt_skims['WLK_LOC_IVT']>0), +,walk_premium_available,walk_transit_available & (odt_skims['WLK_PRM_IVT']>0), +,walk_mix_available,walk_transit_available & (odt_skims['WLK_MIX_IVT']>0), +,pnr_local_available_outbound,pnr_transit_available & df.outbound & (odt_skims['PNR_LOC_IVT']>0), +,pnr_local_available_inbound,pnr_transit_available & ~df.outbound & (odt_skims['PNRE_LOC_IVT']>0), +,pnr_premium_available_outbound,pnr_transit_available & df.outbound & (odt_skims['PNR_PRM_IVT']>0), +,pnr_premium_available_inbound,pnr_transit_available & ~df.outbound & (odt_skims['PNRE_PRM_IVT']>0), +,pnr_mix_available_outbound,pnr_transit_available & df.outbound & (odt_skims['PNR_MIX_IVT']>0), +,pnr_mix_available_inbound,pnr_transit_available & ~df.outbound & (odt_skims['PNRE_MIX_IVT']>0), +,knr_local_available_outbound,knr_transit_available & df.outbound & (odt_skims['KNR_LOC_IVT']>0), +,knr_local_available_inbound,knr_transit_available & ~df.outbound & (odt_skims['KNRE_LOC_IVT']>0), +,knr_premium_available_outbound,knr_transit_available & df.outbound & (odt_skims['KNR_PRM_IVT']>0), +,knr_premium_available_inbound,knr_transit_available & ~df.outbound & (odt_skims['KNRE_PRM_IVT']>0), +,knr_mix_available_outbound,knr_transit_available & df.outbound & (odt_skims['KNR_MIX_IVT']>0), +,knr_mix_available_inbound,knr_transit_available & ~df.outbound & (odt_skims['KNRE_MIX_IVT']>0), +#,od_dist_walk,od_skims['DISTWALK'], +#,do_dist_walk,od_skims.reverse('DISTWALK'), +#,max_dist_walk,od_skims.max('DISTWALK'), +#,dist_bike,od_skims['DISTBIKE'], +#,dist_only,od_skims['DIST'], diff --git a/activitysim/examples/example_semcog/configs/trip_mode_choice_coeffs.csv b/activitysim/examples/prototype_semcog/configs/trip_mode_choice_coeffs.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/trip_mode_choice_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/trip_mode_choice_coeffs.csv index a451b62cb0..441bd0e4ff 100755 --- a/activitysim/examples/example_semcog/configs/trip_mode_choice_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/trip_mode_choice_coeffs.csv @@ -1,105 +1,105 @@ -coefficient_name,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork -c_ivt,-0.022,-0.0271,-0.0271,-0.0279,-0.0279,-0.0279,-0.0175,-0.0175,-0.0279,-0.0279 -c_ivt_pmov,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt -c_ivt_stcar,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt -c_ivt_brt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt -c_ivt_urb,0.80 * c_ivt,0.80 * c_ivt,0.80 * c_ivt,0.80 * c_ivt,0.80 * c_ivt,0.80 * c_ivt,0.80 * c_ivt,0.80 * c_ivt,0.80 * c_ivt,0.80 * c_ivt -c_ivt_com,0.70 * c_ivt,0.70 * c_ivt,0.70 * c_ivt,0.70 * c_ivt,0.70 * c_ivt,0.70 * c_ivt,0.70 * c_ivt,0.70 * c_ivt,0.70 * c_ivt,0.70 * c_ivt -c_short_i_wait,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,-0.0376 -c_long_i_wait,1.00 * c_ivt,1.00 * c_ivt,1.00 * c_ivt,1.00 * c_ivt,1.00 * c_ivt,1.00 * c_ivt,1.00 * c_ivt,1.00 * c_ivt,1.00 * c_ivt,-0.0188 -c_wacc,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,-0.0376 -c_wegr,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,-0.0376 -c_waux,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,-0.0376 -c_dtim,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,-0.0376 -c_xfers_wlk,5.00 * c_ivt,5.00 * c_ivt,5.00 * c_ivt,5.00 * c_ivt,5.00 * c_ivt,5.00 * c_ivt,5.00 * c_ivt,5.00 * c_ivt,5.00 * c_ivt,5.00 * c_ivt -c_xfers_drv,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt -c_xwait,c_short_i_wait,c_short_i_wait,c_short_i_wait,c_short_i_wait,c_short_i_wait,c_short_i_wait,c_short_i_wait,c_short_i_wait,c_short_i_wait,c_short_i_wait -c_walktimeshort,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,-0.0376 -c_walktimelong,5.00 * c_walktimeshort,5.00 * c_walktimeshort,5.00 * c_walktimeshort,5.00 * c_walktimeshort,5.00 * c_walktimeshort,5.00 * c_walktimeshort,5.00 * c_walktimeshort,5.00 * c_walktimeshort,5.00 * c_walktimeshort,5.00 * c_walktimeshort -c_biketimeshort,4.00 * c_ivt,4.00 * c_ivt,4.00 * c_ivt,4.00 * c_ivt,4.00 * c_ivt,4.00 * c_ivt,4.00 * c_ivt,4.00 * c_ivt,4.00 * c_ivt,-0.0752 -c_biketimelong,5.00 * c_biketimeshort,5.00 * c_biketimeshort,5.00 * c_biketimeshort,5.00 * c_biketimeshort,5.00 * c_biketimeshort,5.00 * c_biketimeshort,5.00 * c_biketimeshort,5.00 * c_biketimeshort,5.00 * c_biketimeshort,5.00 * c_biketimeshort -#value_of_time is a person attribute so we cant compute c_cost as scalar here,,,,,,,,,, -#c_cost,(0.60 * ivt) / valueOfTime,(0.60 * ivt) / valueOfTime,(0.60 * ivt) / valueOfTime,(0.60 * ivt) / valueOfTime,(0.60 * ivt) / valueOfTime,(0.60 * ivt) / valueOfTime,(0.60 * ivt) / valueOfTime,(0.60 * ivt) / valueOfTime,(0.60 * ivt) / valueOfTime,(0.6*ivt) / valueOfTime -c_dacc_ratio,0,0,0,0,0,0,0,0,0,0 -c_topology_walk,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt -c_topology_bike,20.00 * c_ivt,20.00 * c_ivt,20.00 * c_ivt,20.00 * c_ivt,20.00 * c_ivt,20.00 * c_ivt,20.00 * c_ivt,20.00 * c_ivt,20.00 * c_ivt,20.00 * c_ivt -c_topology_trn,2.20 * c_ivt,2.20 * c_ivt,2.20 * c_ivt,2.20 * c_ivt,2.20 * c_ivt,2.20 * c_ivt,2.20 * c_ivt,2.20 * c_ivt,2.20 * c_ivt,2.20 * c_ivt -c_density_index,c_ivt / -5.0,c_ivt / -5.0,c_ivt / -5.0,c_ivt / -5.0,c_ivt / -5.0,c_ivt / -5.0,c_ivt / -5.0,c_ivt / -5.0,c_ivt / -5.0,c_ivt / -5.0 -c_origin_density_index,c_density_index * 3,c_density_index * 3,c_density_index * 3,c_density_index * 3,c_density_index * 3,c_density_index * 3,c_density_index * 3,c_density_index * 3,c_density_index * 3,c_density_index * 3 -c_origin_density_index_max,c_ivt * -15,c_ivt * -15,c_ivt * -15,c_ivt * -15,c_ivt * -15,c_ivt * -15,c_ivt * -15,c_ivt * -15,c_ivt * -15,c_ivt * -15 -origin_density_applied,False,False,False,True,True,True,True,True,True,True -c_age1619_da,0,0,0,0,0,0,0,0,0,0 -c_age010_trn,0,0,0,0,0,0,0,0,0,0 -c_age16p_sr,0,0,0,0,0,0,0,0,0,0 -c_hhsize1_sr,-0.7346,-0.7346,-0.7346,-0.7346,-0.7346,-0.7346,-0.7346,-0.7346,-0.7346,-0.7346 -c_hhsize2_sr,0,0,0,0,0,0,0,0,0,0 -#free_parking_allowed,1,0,0,0,0,0,0,0,0,0 -#indiv tour ASCs,,,,,,,,,, -sov_ASC_sr2,-999,-999,-999,0,-999,-999,-999,-999,-999,-999 -sov_ASC_sr3p,-999,-999,-999,0,-999,-999,-999,-999,-999,-999 -sov_ASC_walk,-1.579278297,-0.329078195,-3,-0.407754765,-1.884354765,-1.129357279,-1.707054765,-1.592757279,-1.335757279,-3.034080783 -sov_ASC_rh,-7,-6.649,-5.6549,0,0,0,0,0,0,-7 -#sr2_ASC_sr2 - reference mode,0,0,0,0,0,0,0,0,0,0 -sr2_ASC_sov,-0.51299105,-1.058942553,-0.293588173,-0.536498222,-0.536498222,-0.849808648,-0.536498222,-0.849808648,-0.849808648,-0.601604001 -sr2_ASC_walk,-0.407385039,0.166984338,-0.173411375,-3.466936859,-1.525836859,-0.739819085,-2.013036859,-1.460119085,-0.693719085,-4.398412986 -sr2_ASC_rh,-7,-6.6897,-7,-7,-7,-7,-7,-7,-7,-7 -sr3p_ASC_sov,-0.229516694,-1.142035901,-1.860536359,-1.111545636,-1.111545636,-1.592880167,-1.111545636,-1.592880167,-1.592880167,-0.915383849 -sr3p_ASC_sr2,-0.333410245,-0.795945688,-1.095643656,-0.656228794,-0.466128794,-0.748226556,-0.683828794,-0.586026556,-1.044626556,-0.75108718 -#sr3p_ASC_sr3p - reference mode,0,0,0,0,0,0,0,0,0,0 -sr3p_ASC_walk,-0.72844504,0.021931966,-0.70641187,-2.362025557,-1.862425557,-0.146994878,-1.312325557,-0.416994878,-0.630794878,-3.892581674 -sr3p_ASC_rh,-71.9177,-7,-6.6714,-7,-7,-7,-7,-7,-7,-6.026 -walk_ASC_rh,-7,-7,-7,-7,-7,-7,-7,-7,-7,-7 -bike_ASC_walk,-2.8333,-2.49405068,-3.238666604,-15.5203,-3.0245,-3.699,-2.5706,-14.2384,-2.3577,-5.0532 -bike_ASC_rh,-7,-7,-7,-7,-7,-7,-7,-7,-7,-7 -#walktransit_ASC_walktransit - reference mode,0,0,0,0,0,0,0,0,0,0 -walktransit_ASC_sr2,-3.705971533,-4.132166342,-3.186743446,-3.139673944,-3.139673944,-3.696770924,-3.139673944,-3.696770924,-3.696770924,-4 -walktransit_ASC_sr3p,-3.578423306,-5.496253927,-3.595736858,-6.198039835,-6.198039835,-3.455537128,-6.198039835,-3.455537128,-3.455537128,-1.156825048 -walktransit_ASC_walk,2.289791885,0.742780398,-0.458321333,1.976505237,1.976505237,2.544557044,1.976505237,2.544557044,2.544557044,1.038327796 -walk_transit_ASC_rh,-3.7408,-4.2691,-7,-3.1136,-3.1136,-4.7508,-3.1136,-4.7508,-4.7508,-3.8506 -drive_transit_ASC_rh,0.6674,-0.6328,-0.1272,0.687,0.687,3.5701,0.687,3.5701,3.5701,-6.952 -ride_hail_ASC_sr2,-3.9085,-2.5785,-3.0613,-3.3353,-3.3353,-3.301,-3.3353,-3.301,-3.301,-4.2636 -ride_hail_ASC_sr3p,-10.8661,-3.1888,-4.0886,-7,-7,-4.0649,-7,-4.0649,-4.0649,-7 -ride_hail_ASC_walk,1.497,-10.8471,-1.2386,-7,-7,-1.3318,-7,-1.3318,-1.3318,-1.6755 -ride_hail_ASC_walk_transit,-10.8661,-7,-7,-7,-7,-7,-7,-7,-7,-7 -drive_transit_ASC_rh,-4.25,-4.25,-4.25,-4.25,-4.25,-4.25,-4.25,-4.25,-4.25,-4.25 -ride_hail_ASC_sr2,-6.108,-4.3372,-1.5869,-5.6483,-5.6483,-5.9692,-5.6483,-5.9692,-5.9692,-5.2763 -ride_hail_ASC_sr3p,-7,-4.922,-2.5362,-7,-7,-6.7199,-7,-6.7199,-6.7199,-7 -ride_hail_ASC_walk,0.2858,-7,0.245,-7,-7,-3.3603,-7,-3.3603,-3.3603,-2.6103 -ride_hail_ASC_walk_transit,0,0,0,0,0,0,0,0,0,0 -ride_hail_ASC_taxi,-2.385,-1.5994,-0.5869,-3.6629,-3.6629,-2.5543,-3.6629,-2.5543,-2.5543,-1.7764 -ride_hail_ASC_tnc_single,0.735,0.1081,0.3201,0.7879,0.7879,0.8274,0.7879,0.8274,0.8274,1.2143 -ride_hail_ASC_tnc_shared,0,0,0,0,0,0,0,0,0,0 -#pnr_ASC_pnr - reference mode,0,0,0,0,0,0,0,0,0,0 -#knr_ASC_knr - reference mode,0,0,0,0,0,0,0,0,0,0 -#joint tour ASCs,,,,,,,,,, -joint_auto_ASC_rh,0,0,0,0,-7,-7,-7,-7,-7,0 -joint_auto_ASC_rh,0,0,0,0,-7,-7,-7,-7,-7,0 -joint_walk_ASC_rh,0,0,0,0,-3.0362,-3.0362,-3.0362,-3.0362,-3.0362,0 -#joint_sr2_ASC_sr2 - reference mode,0,0,0,0,0,0,0,0,0,0 -joint_sr2_ASC_walk,0,0,0,-2.620466384,-2.620466384,-2.620466384,-2.620466384,-2.620466384,-2.620466384,0 -#joint_sr3p_ASC_sr3p - reference mode,0,0,0,0,0,0,0,0,0,0 -joint_sr3p_ASC_sr2,0,0,0,1.576317444,1.576317444,1.576317444,1.576317444,1.576317444,1.576317444,0 -joint_sr3p_ASC_walk,0,0,0,-1.238904557,-1.238904557,-1.238904557,-1.238904557,-1.238904557,-1.238904557,0 -joint_bike_ASC_walk,0,0,0,-3,-3,-3,-3,-3,-3,0 -joint_bike_ASC_rh,0,0,0,0,-12.3057,-12.3057,-12.3057,-12.3057,-12.3057,0 -#joint_walktransit_ASC_walktransit - reference mode,0,0,0,0,0,0,0,0,0,0 -joint_walktransit_ASC_sr2,0,0,0,-2.312902942,-2.312902942,-2.312902942,-2.312902942,-2.312902942,-2.312902942,0 -joint_walktransit_ASC_sr3p,0,0,0,-3.877768519,-3.877768519,-3.877768519,-3.877768519,-3.877768519,-3.877768519,0 -joint_walktransit_ASC_walk,0,0,0,1.576823424,1.576823424,1.576823424,1.576823424,1.576823424,1.576823424,0 -joint_walk_transit_ASC_rh,0,0,0,0,1.2266,1.2266,1.2266,1.2266,1.2266,0 -joint_drive_transit_ASC_rh,0,0,0,0,4.6138,4.6138,4.6138,4.6138,4.6138,0 -joint_ride_hail_ASC_taxi,0,0,0,0,-7,-7,-7,-7,-7,0 -joint_ride_hail_ASC_tnc_single,0,0,0,0,-4.7339,-4.7339,-4.7339,-4.7339,-4.7339,0 -joint_ride_hail_ASC_tnc_shared,0,0,0,0,0,0,0,0,0,0 -#joint_pnr_ASC_pnr - reference mode,0,0,0,0,0,0,0,0,0,0 -#joint_knr_ASC_knr - reference mode,0,0,0,0,0,0,0,0,0,0 -#,,,,,,,,,, -schoolbus_ASC_sr2,0,0,-3.289069276,0,0,0,0,0,0,0 -schoolbus_ASC_sr3p,0,0,-3.217740129,0,0,0,0,0,0,0 -schoolbus_ASC_walk,0,0,-2.277998613,0,0,0,0,0,0,0 -#c_walk_express_penalty,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt -local_ASC,0,0,0,0,0,0,0,0,0,0 -premium_ASC,0,0,0,0,0,0,0,0,0,0 -mix_ASC,0,0,0,0,0,0,0,0,0,0 -c_walk_express_penalty,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt -adjust_tnc_shared,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt +coefficient_name,work,univ,school,escort,shopping,eatout,othmaint,social,othdiscr,atwork +c_ivt,-0.022,-0.0271,-0.0271,-0.0279,-0.0279,-0.0279,-0.0175,-0.0175,-0.0279,-0.0279 +c_ivt_pmov,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt,1.0 * c_ivt +c_ivt_stcar,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt +c_ivt_brt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt,0.90 * c_ivt +c_ivt_urb,0.80 * c_ivt,0.80 * c_ivt,0.80 * c_ivt,0.80 * c_ivt,0.80 * c_ivt,0.80 * c_ivt,0.80 * c_ivt,0.80 * c_ivt,0.80 * c_ivt,0.80 * c_ivt +c_ivt_com,0.70 * c_ivt,0.70 * c_ivt,0.70 * c_ivt,0.70 * c_ivt,0.70 * c_ivt,0.70 * c_ivt,0.70 * c_ivt,0.70 * c_ivt,0.70 * c_ivt,0.70 * c_ivt +c_short_i_wait,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,-0.0376 +c_long_i_wait,1.00 * c_ivt,1.00 * c_ivt,1.00 * c_ivt,1.00 * c_ivt,1.00 * c_ivt,1.00 * c_ivt,1.00 * c_ivt,1.00 * c_ivt,1.00 * c_ivt,-0.0188 +c_wacc,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,-0.0376 +c_wegr,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,-0.0376 +c_waux,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,-0.0376 +c_dtim,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,-0.0376 +c_xfers_wlk,5.00 * c_ivt,5.00 * c_ivt,5.00 * c_ivt,5.00 * c_ivt,5.00 * c_ivt,5.00 * c_ivt,5.00 * c_ivt,5.00 * c_ivt,5.00 * c_ivt,5.00 * c_ivt +c_xfers_drv,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt +c_xwait,c_short_i_wait,c_short_i_wait,c_short_i_wait,c_short_i_wait,c_short_i_wait,c_short_i_wait,c_short_i_wait,c_short_i_wait,c_short_i_wait,c_short_i_wait +c_walktimeshort,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,-0.0376 +c_walktimelong,5.00 * c_walktimeshort,5.00 * c_walktimeshort,5.00 * c_walktimeshort,5.00 * c_walktimeshort,5.00 * c_walktimeshort,5.00 * c_walktimeshort,5.00 * c_walktimeshort,5.00 * c_walktimeshort,5.00 * c_walktimeshort,5.00 * c_walktimeshort +c_biketimeshort,4.00 * c_ivt,4.00 * c_ivt,4.00 * c_ivt,4.00 * c_ivt,4.00 * c_ivt,4.00 * c_ivt,4.00 * c_ivt,4.00 * c_ivt,4.00 * c_ivt,-0.0752 +c_biketimelong,5.00 * c_biketimeshort,5.00 * c_biketimeshort,5.00 * c_biketimeshort,5.00 * c_biketimeshort,5.00 * c_biketimeshort,5.00 * c_biketimeshort,5.00 * c_biketimeshort,5.00 * c_biketimeshort,5.00 * c_biketimeshort,5.00 * c_biketimeshort +#value_of_time is a person attribute so we cant compute c_cost as scalar here,,,,,,,,,, +#c_cost,(0.60 * ivt) / valueOfTime,(0.60 * ivt) / valueOfTime,(0.60 * ivt) / valueOfTime,(0.60 * ivt) / valueOfTime,(0.60 * ivt) / valueOfTime,(0.60 * ivt) / valueOfTime,(0.60 * ivt) / valueOfTime,(0.60 * ivt) / valueOfTime,(0.60 * ivt) / valueOfTime,(0.6*ivt) / valueOfTime +c_dacc_ratio,0,0,0,0,0,0,0,0,0,0 +c_topology_walk,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt,15.00 * c_ivt +c_topology_bike,20.00 * c_ivt,20.00 * c_ivt,20.00 * c_ivt,20.00 * c_ivt,20.00 * c_ivt,20.00 * c_ivt,20.00 * c_ivt,20.00 * c_ivt,20.00 * c_ivt,20.00 * c_ivt +c_topology_trn,2.20 * c_ivt,2.20 * c_ivt,2.20 * c_ivt,2.20 * c_ivt,2.20 * c_ivt,2.20 * c_ivt,2.20 * c_ivt,2.20 * c_ivt,2.20 * c_ivt,2.20 * c_ivt +c_density_index,c_ivt / -5.0,c_ivt / -5.0,c_ivt / -5.0,c_ivt / -5.0,c_ivt / -5.0,c_ivt / -5.0,c_ivt / -5.0,c_ivt / -5.0,c_ivt / -5.0,c_ivt / -5.0 +c_origin_density_index,c_density_index * 3,c_density_index * 3,c_density_index * 3,c_density_index * 3,c_density_index * 3,c_density_index * 3,c_density_index * 3,c_density_index * 3,c_density_index * 3,c_density_index * 3 +c_origin_density_index_max,c_ivt * -15,c_ivt * -15,c_ivt * -15,c_ivt * -15,c_ivt * -15,c_ivt * -15,c_ivt * -15,c_ivt * -15,c_ivt * -15,c_ivt * -15 +origin_density_applied,False,False,False,True,True,True,True,True,True,True +c_age1619_da,0,0,0,0,0,0,0,0,0,0 +c_age010_trn,0,0,0,0,0,0,0,0,0,0 +c_age16p_sr,0,0,0,0,0,0,0,0,0,0 +c_hhsize1_sr,-0.7346,-0.7346,-0.7346,-0.7346,-0.7346,-0.7346,-0.7346,-0.7346,-0.7346,-0.7346 +c_hhsize2_sr,0,0,0,0,0,0,0,0,0,0 +#free_parking_allowed,1,0,0,0,0,0,0,0,0,0 +#indiv tour ASCs,,,,,,,,,, +sov_ASC_sr2,-999,-999,-999,0,-999,-999,-999,-999,-999,-999 +sov_ASC_sr3p,-999,-999,-999,0,-999,-999,-999,-999,-999,-999 +sov_ASC_walk,-1.579278297,-0.329078195,-3,-0.407754765,-1.884354765,-1.129357279,-1.707054765,-1.592757279,-1.335757279,-3.034080783 +sov_ASC_rh,-7,-6.649,-5.6549,0,0,0,0,0,0,-7 +#sr2_ASC_sr2 - reference mode,0,0,0,0,0,0,0,0,0,0 +sr2_ASC_sov,-0.51299105,-1.058942553,-0.293588173,-0.536498222,-0.536498222,-0.849808648,-0.536498222,-0.849808648,-0.849808648,-0.601604001 +sr2_ASC_walk,-0.407385039,0.166984338,-0.173411375,-3.466936859,-1.525836859,-0.739819085,-2.013036859,-1.460119085,-0.693719085,-4.398412986 +sr2_ASC_rh,-7,-6.6897,-7,-7,-7,-7,-7,-7,-7,-7 +sr3p_ASC_sov,-0.229516694,-1.142035901,-1.860536359,-1.111545636,-1.111545636,-1.592880167,-1.111545636,-1.592880167,-1.592880167,-0.915383849 +sr3p_ASC_sr2,-0.333410245,-0.795945688,-1.095643656,-0.656228794,-0.466128794,-0.748226556,-0.683828794,-0.586026556,-1.044626556,-0.75108718 +#sr3p_ASC_sr3p - reference mode,0,0,0,0,0,0,0,0,0,0 +sr3p_ASC_walk,-0.72844504,0.021931966,-0.70641187,-2.362025557,-1.862425557,-0.146994878,-1.312325557,-0.416994878,-0.630794878,-3.892581674 +sr3p_ASC_rh,-71.9177,-7,-6.6714,-7,-7,-7,-7,-7,-7,-6.026 +walk_ASC_rh,-7,-7,-7,-7,-7,-7,-7,-7,-7,-7 +bike_ASC_walk,-2.8333,-2.49405068,-3.238666604,-15.5203,-3.0245,-3.699,-2.5706,-14.2384,-2.3577,-5.0532 +bike_ASC_rh,-7,-7,-7,-7,-7,-7,-7,-7,-7,-7 +#walktransit_ASC_walktransit - reference mode,0,0,0,0,0,0,0,0,0,0 +walktransit_ASC_sr2,-3.705971533,-4.132166342,-3.186743446,-3.139673944,-3.139673944,-3.696770924,-3.139673944,-3.696770924,-3.696770924,-4 +walktransit_ASC_sr3p,-3.578423306,-5.496253927,-3.595736858,-6.198039835,-6.198039835,-3.455537128,-6.198039835,-3.455537128,-3.455537128,-1.156825048 +walktransit_ASC_walk,2.289791885,0.742780398,-0.458321333,1.976505237,1.976505237,2.544557044,1.976505237,2.544557044,2.544557044,1.038327796 +walk_transit_ASC_rh,-3.7408,-4.2691,-7,-3.1136,-3.1136,-4.7508,-3.1136,-4.7508,-4.7508,-3.8506 +drive_transit_ASC_rh,0.6674,-0.6328,-0.1272,0.687,0.687,3.5701,0.687,3.5701,3.5701,-6.952 +ride_hail_ASC_sr2,-3.9085,-2.5785,-3.0613,-3.3353,-3.3353,-3.301,-3.3353,-3.301,-3.301,-4.2636 +ride_hail_ASC_sr3p,-10.8661,-3.1888,-4.0886,-7,-7,-4.0649,-7,-4.0649,-4.0649,-7 +ride_hail_ASC_walk,1.497,-10.8471,-1.2386,-7,-7,-1.3318,-7,-1.3318,-1.3318,-1.6755 +ride_hail_ASC_walk_transit,-10.8661,-7,-7,-7,-7,-7,-7,-7,-7,-7 +drive_transit_ASC_rh,-4.25,-4.25,-4.25,-4.25,-4.25,-4.25,-4.25,-4.25,-4.25,-4.25 +ride_hail_ASC_sr2,-6.108,-4.3372,-1.5869,-5.6483,-5.6483,-5.9692,-5.6483,-5.9692,-5.9692,-5.2763 +ride_hail_ASC_sr3p,-7,-4.922,-2.5362,-7,-7,-6.7199,-7,-6.7199,-6.7199,-7 +ride_hail_ASC_walk,0.2858,-7,0.245,-7,-7,-3.3603,-7,-3.3603,-3.3603,-2.6103 +ride_hail_ASC_walk_transit,0,0,0,0,0,0,0,0,0,0 +ride_hail_ASC_taxi,-2.385,-1.5994,-0.5869,-3.6629,-3.6629,-2.5543,-3.6629,-2.5543,-2.5543,-1.7764 +ride_hail_ASC_tnc_single,0.735,0.1081,0.3201,0.7879,0.7879,0.8274,0.7879,0.8274,0.8274,1.2143 +ride_hail_ASC_tnc_shared,0,0,0,0,0,0,0,0,0,0 +#pnr_ASC_pnr - reference mode,0,0,0,0,0,0,0,0,0,0 +#knr_ASC_knr - reference mode,0,0,0,0,0,0,0,0,0,0 +#joint tour ASCs,,,,,,,,,, +joint_auto_ASC_rh,0,0,0,0,-7,-7,-7,-7,-7,0 +joint_auto_ASC_rh,0,0,0,0,-7,-7,-7,-7,-7,0 +joint_walk_ASC_rh,0,0,0,0,-3.0362,-3.0362,-3.0362,-3.0362,-3.0362,0 +#joint_sr2_ASC_sr2 - reference mode,0,0,0,0,0,0,0,0,0,0 +joint_sr2_ASC_walk,0,0,0,-2.620466384,-2.620466384,-2.620466384,-2.620466384,-2.620466384,-2.620466384,0 +#joint_sr3p_ASC_sr3p - reference mode,0,0,0,0,0,0,0,0,0,0 +joint_sr3p_ASC_sr2,0,0,0,1.576317444,1.576317444,1.576317444,1.576317444,1.576317444,1.576317444,0 +joint_sr3p_ASC_walk,0,0,0,-1.238904557,-1.238904557,-1.238904557,-1.238904557,-1.238904557,-1.238904557,0 +joint_bike_ASC_walk,0,0,0,-3,-3,-3,-3,-3,-3,0 +joint_bike_ASC_rh,0,0,0,0,-12.3057,-12.3057,-12.3057,-12.3057,-12.3057,0 +#joint_walktransit_ASC_walktransit - reference mode,0,0,0,0,0,0,0,0,0,0 +joint_walktransit_ASC_sr2,0,0,0,-2.312902942,-2.312902942,-2.312902942,-2.312902942,-2.312902942,-2.312902942,0 +joint_walktransit_ASC_sr3p,0,0,0,-3.877768519,-3.877768519,-3.877768519,-3.877768519,-3.877768519,-3.877768519,0 +joint_walktransit_ASC_walk,0,0,0,1.576823424,1.576823424,1.576823424,1.576823424,1.576823424,1.576823424,0 +joint_walk_transit_ASC_rh,0,0,0,0,1.2266,1.2266,1.2266,1.2266,1.2266,0 +joint_drive_transit_ASC_rh,0,0,0,0,4.6138,4.6138,4.6138,4.6138,4.6138,0 +joint_ride_hail_ASC_taxi,0,0,0,0,-7,-7,-7,-7,-7,0 +joint_ride_hail_ASC_tnc_single,0,0,0,0,-4.7339,-4.7339,-4.7339,-4.7339,-4.7339,0 +joint_ride_hail_ASC_tnc_shared,0,0,0,0,0,0,0,0,0,0 +#joint_pnr_ASC_pnr - reference mode,0,0,0,0,0,0,0,0,0,0 +#joint_knr_ASC_knr - reference mode,0,0,0,0,0,0,0,0,0,0 +#,,,,,,,,,, +schoolbus_ASC_sr2,0,0,-3.289069276,0,0,0,0,0,0,0 +schoolbus_ASC_sr3p,0,0,-3.217740129,0,0,0,0,0,0,0 +schoolbus_ASC_walk,0,0,-2.277998613,0,0,0,0,0,0,0 +#c_walk_express_penalty,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt +local_ASC,0,0,0,0,0,0,0,0,0,0 +premium_ASC,0,0,0,0,0,0,0,0,0,0 +mix_ASC,0,0,0,0,0,0,0,0,0,0 +c_walk_express_penalty,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt,10.00 * c_ivt +adjust_tnc_shared,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt,30 * c_ivt diff --git a/activitysim/examples/example_semcog/configs/trip_purpose.yaml b/activitysim/examples/prototype_semcog/configs/trip_purpose.yaml similarity index 94% rename from activitysim/examples/example_semcog/configs/trip_purpose.yaml rename to activitysim/examples/prototype_semcog/configs/trip_purpose.yaml index 8f9306ae4c..10668a6180 100755 --- a/activitysim/examples/example_semcog/configs/trip_purpose.yaml +++ b/activitysim/examples/prototype_semcog/configs/trip_purpose.yaml @@ -1,7 +1,7 @@ - -preprocessor: - SPEC: trip_purpose_annotate_trips_preprocessor - DF: trips - TABLES: - - persons - - tours + +preprocessor: + SPEC: trip_purpose_annotate_trips_preprocessor + DF: trips + TABLES: + - persons + - tours diff --git a/activitysim/examples/example_semcog/configs/trip_purpose_and_destination.yaml b/activitysim/examples/prototype_semcog/configs/trip_purpose_and_destination.yaml similarity index 96% rename from activitysim/examples/example_semcog/configs/trip_purpose_and_destination.yaml rename to activitysim/examples/prototype_semcog/configs/trip_purpose_and_destination.yaml index 4895aa5268..76f5923489 100755 --- a/activitysim/examples/example_semcog/configs/trip_purpose_and_destination.yaml +++ b/activitysim/examples/prototype_semcog/configs/trip_purpose_and_destination.yaml @@ -1,6 +1,6 @@ - -MAX_ITERATIONS: 5 - -# drop failed trips and cleanup failed trip leg_mates for consistency -# (i.e. adjust trip_count, trip_num, first for missing failed trips) -CLEANUP: True + +MAX_ITERATIONS: 5 + +# drop failed trips and cleanup failed trip leg_mates for consistency +# (i.e. adjust trip_count, trip_num, first for missing failed trips) +CLEANUP: True diff --git a/activitysim/examples/example_psrc/configs/trip_purpose_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/trip_purpose_annotate_trips_preprocessor.csv similarity index 96% rename from activitysim/examples/example_psrc/configs/trip_purpose_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/trip_purpose_annotate_trips_preprocessor.csv index 0e20453303..782116aa99 100755 --- a/activitysim/examples/example_psrc/configs/trip_purpose_annotate_trips_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/trip_purpose_annotate_trips_preprocessor.csv @@ -1,5 +1,5 @@ -Description,Target,Expression -#,, -,ptype,"reindex(persons.ptype, df.person_id)" -,person_type,ptype.map(PTYPE_NAME) -,start,"reindex_i(tours.start, df.tour_id)" +Description,Target,Expression +#,, +,ptype,"reindex(persons.ptype, df.person_id)" +,person_type,ptype.map(PTYPE_NAME) +,start,"reindex_i(tours.start, df.tour_id)" diff --git a/activitysim/examples/example_semcog/configs/trip_purpose_probs.csv b/activitysim/examples/prototype_semcog/configs/trip_purpose_probs.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/trip_purpose_probs.csv rename to activitysim/examples/prototype_semcog/configs/trip_purpose_probs.csv index 737f4fd507..3cd1aaa27f 100755 --- a/activitysim/examples/example_semcog/configs/trip_purpose_probs.csv +++ b/activitysim/examples/prototype_semcog/configs/trip_purpose_probs.csv @@ -1,132 +1,132 @@ -primary_purpose,outbound,depart_range_start,depart_range_end,person_type,work,univ,school,escort,shopping,othmaint,eatout,social,othdiscr -work,TRUE,1,18,PTYPE_FULL,0.198,0.004,0,0.466,0.083,0.086,0.093,0.004,0.066 -work,TRUE,1,18,PTYPE_PART,0.094,0,0,0.657,0.076,0.07,0.067,0.009,0.027 -work,TRUE,1,18,PTYPE_UNIVERSITY,0.067,0.081,0,0.433,0.005,0.038,0.153,0.108,0.115 -work,TRUE,19,48,PTYPE_FULL,0.278,0.008,0,0.172,0.18,0.193,0.107,0.016,0.046 -work,TRUE,19,48,PTYPE_PART,0.442,0,0,0.089,0.105,0.175,0.102,0.03,0.057 -work,TRUE,19,48,PTYPE_UNIVERSITY,0.049,0.086,0,0.392,0.159,0.157,0.069,0.073,0.015 -work,TRUE,1,48,PTYPE_DRIVING,0,0,0,0,0.2,0.2,0.2,0.2,0.2 -univ,TRUE,1,48,PTYPE_FULL,0.526,0.178,0,0.016,0.16,0.035,0.028,0.057,0 -univ,TRUE,1,48,PTYPE_PART,0.059,0.941,0,0,0,0,0,0,0 -univ,TRUE,1,48,PTYPE_UNIVERSITY,0.109,0.034,0,0.382,0.136,0.147,0.094,0.048,0.05 -school,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.548,0.015,0.1,0.206,0.073,0.058 -school,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.53,0.025,0.084,0.112,0.048,0.201 -school,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.772,0.007,0.086,0.023,0.071,0.041 -escort,TRUE,1,48,PTYPE_FULL,0,0,0,0.55,0.153,0.084,0.104,0.049,0.06 -escort,TRUE,1,48,PTYPE_PART,0,0,0,0.449,0.194,0.07,0.167,0.059,0.061 -escort,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0.509,0.193,0.158,0.048,0.058,0.034 -escort,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.444,0.216,0.084,0.108,0.118,0.03 -escort,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.37,0.204,0.192,0.03,0.068,0.136 -escort,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.586,0.227,0,0.072,0.115,0 -escort,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.37,0.183,0.29,0.064,0.013,0.08 -escort,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.531,0.064,0,0.131,0.196,0.078 -shopping,TRUE,1,48,PTYPE_FULL,0,0,0,0.102,0.456,0.226,0.11,0.06,0.046 -shopping,TRUE,1,48,PTYPE_PART,0,0,0,0.182,0.291,0.311,0.108,0.031,0.077 -shopping,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0.13,0.262,0.36,0.124,0.06,0.064 -shopping,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.144,0.336,0.274,0.122,0.068,0.056 -shopping,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.058,0.357,0.418,0.05,0.047,0.07 -shopping,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.076,0.193,0.298,0.047,0.13,0.256 -shopping,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.121,0.142,0.232,0.291,0.03,0.184 -shopping,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.138,0.292,0.301,0.187,0.064,0.018 -othmaint,TRUE,1,48,PTYPE_FULL,0,0,0,0.201,0.252,0.366,0.117,0.032,0.032 -othmaint,TRUE,1,48,PTYPE_PART,0,0,0,0.27,0.259,0.325,0.109,0,0.037 -othmaint,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0.489,0.13,0.167,0.025,0.15,0.039 -othmaint,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.279,0.229,0.344,0.078,0.039,0.031 -othmaint,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.224,0.139,0.321,0.098,0.064,0.154 -othmaint,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.135,0,0.259,0.083,0.523,0 -othmaint,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.191,0.408,0.344,0.041,0.008,0.008 -othmaint,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.143,0.301,0.464,0.017,0.029,0.046 -eatout,TRUE,1,48,PTYPE_FULL,0,0,0,0.144,0.283,0.202,0.036,0.129,0.206 -eatout,TRUE,1,48,PTYPE_PART,0,0,0,0.169,0.374,0.179,0.013,0.135,0.13 -eatout,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0.32,0.085,0.111,0,0.153,0.331 -eatout,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.201,0.224,0.269,0.063,0.082,0.161 -eatout,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.142,0.237,0.237,0.034,0.123,0.227 -eatout,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.175,0.289,0.346,0,0.105,0.085 -eatout,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.124,0.135,0.135,0.04,0.048,0.518 -eatout,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.055,0.329,0.165,0.061,0,0.39 -social,TRUE,1,48,PTYPE_FULL,0,0,0,0.186,0.382,0.144,0.122,0.126,0.04 -social,TRUE,1,48,PTYPE_PART,0,0,0,0.175,0.153,0.167,0.147,0.183,0.175 -social,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0,0.212,0.091,0.432,0.234,0.031 -social,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.311,0.392,0.149,0.071,0.058,0.019 -social,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.12,0.407,0.203,0.151,0.102,0.017 -social,TRUE,1,48,PTYPE_DRIVING,0,0,0,0,0,0,0,0.415,0.585 -social,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.322,0.11,0.05,0,0.378,0.14 -social,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.294,0,0.159,0,0.547,0 -othdiscr,TRUE,1,48,PTYPE_FULL,0,0,0,0.236,0.169,0.143,0.19,0.093,0.169 -othdiscr,TRUE,1,48,PTYPE_PART,0,0,0,0.223,0.208,0.181,0.193,0.129,0.066 -othdiscr,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0.135,0.123,0.061,0.342,0.123,0.216 -othdiscr,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.263,0.295,0.148,0.088,0.082,0.124 -othdiscr,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.225,0.056,0.389,0.16,0.091,0.079 -othdiscr,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.311,0.126,0.051,0.018,0.142,0.352 -othdiscr,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.222,0.112,0.172,0.173,0.141,0.18 -othdiscr,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.271,0.108,0.393,0.146,0.043,0.039 -atwork,TRUE,1,48,PTYPE_FULL,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014 -atwork,TRUE,1,48,PTYPE_PART,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014 -atwork,TRUE,1,48,PTYPE_UNIVERSITY,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014 -atwork,TRUE,1,48,PTYPE_DRIVING,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014 -#,,,,,,,,,,,,, -work,FALSE,1,30,PTYPE_FULL,0.175,0,0,0.14,0.27,0.162,0.134,0.05,0.069 -work,FALSE,1,30,PTYPE_PART,0.097,0,0,0.252,0.211,0.192,0.159,0.089,0 -work,FALSE,1,30,PTYPE_UNIVERSITY,0.134,0,0,0.329,0.114,0.212,0.169,0.042,0 -work,FALSE,31,48,PTYPE_FULL,0.151,0.011,0,0.201,0.28,0.127,0.103,0.035,0.092 -work,FALSE,31,48,PTYPE_PART,0.11,0,0,0.243,0.281,0.13,0.119,0.036,0.081 -work,FALSE,31,48,PTYPE_UNIVERSITY,0.058,0.127,0,0.224,0.269,0.079,0.072,0.108,0.063 -work,FALSE,1,48,PTYPE_DRIVING,0,0,0,0,0.2,0.2,0.2,0.2,0.2 -univ,FALSE,1,48,PTYPE_FULL,0.352,0.032,0,0.032,0.146,0.114,0.177,0.028,0.119 -univ,FALSE,1,48,PTYPE_PART,0,0,0,0.822,0.178,0,0,0,0 -univ,FALSE,1,48,PTYPE_UNIVERSITY,0.054,0.025,0,0.194,0.209,0.179,0.159,0.067,0.113 -school,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.301,0.117,0.098,0.169,0.186,0.129 -school,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.166,0.158,0.147,0.122,0.133,0.274 -school,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.38,0.148,0.089,0.146,0.102,0.135 -escort,FALSE,1,48,PTYPE_FULL,0,0,0,0.343,0.235,0.114,0.222,0.039,0.047 -escort,FALSE,1,48,PTYPE_PART,0,0,0,0.24,0.298,0.128,0.157,0.045,0.132 -escort,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.195,0.319,0.287,0.02,0.027,0.152 -escort,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.28,0.325,0.169,0.103,0.05,0.073 -escort,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.31,0.317,0.073,0.111,0.112,0.077 -escort,FALSE,1,48,PTYPE_DRIVING,0,0,0,0,0.489,0,0.148,0.363,0 -escort,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.188,0.259,0.129,0.202,0.06,0.162 -escort,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.413,0.215,0.118,0.211,0.019,0.024 -shopping,FALSE,1,48,PTYPE_FULL,0,0,0,0.091,0.526,0.159,0.152,0.047,0.025 -shopping,FALSE,1,48,PTYPE_PART,0,0,0,0.104,0.553,0.156,0.105,0.037,0.045 -shopping,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.1,0.43,0.064,0.344,0.003,0.059 -shopping,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.11,0.528,0.158,0.122,0.059,0.023 -shopping,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.052,0.549,0.159,0.123,0.06,0.057 -shopping,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.118,0.707,0,0.041,0.134,0 -shopping,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.015,0.19,0.256,0.157,0.179,0.203 -shopping,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.206,0.172,0.22,0.202,0.158,0.042 -othmaint,FALSE,1,48,PTYPE_FULL,0,0,0,0.171,0.364,0.215,0.159,0.029,0.062 -othmaint,FALSE,1,48,PTYPE_PART,0,0,0,0.228,0.365,0.17,0.13,0.041,0.066 -othmaint,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.046,0.345,0.192,0.298,0.06,0.059 -othmaint,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.17,0.423,0.158,0.171,0.064,0.014 -othmaint,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.099,0.391,0.213,0.241,0.036,0.02 -othmaint,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.031,0.356,0.075,0.458,0.031,0.049 -othmaint,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.181,0.255,0.142,0.313,0,0.109 -othmaint,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.164,0.249,0.338,0.053,0.006,0.19 -eatout,FALSE,1,48,PTYPE_FULL,0,0,0,0.106,0.44,0.112,0.041,0.128,0.173 -eatout,FALSE,1,48,PTYPE_PART,0,0,0,0.168,0.331,0.225,0.023,0.063,0.19 -eatout,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.165,0.334,0.104,0.088,0.135,0.174 -eatout,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.148,0.547,0.092,0.056,0.055,0.102 -eatout,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.166,0.414,0.169,0.02,0.166,0.065 -eatout,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.195,0.332,0.114,0.114,0,0.245 -eatout,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.072,0.356,0.053,0.019,0.169,0.331 -eatout,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.01,0.286,0.045,0.117,0.064,0.478 -social,FALSE,1,48,PTYPE_FULL,0,0,0,0.12,0.286,0.123,0.19,0.255,0.026 -social,FALSE,1,48,PTYPE_PART,0,0,0,0.106,0.122,0.039,0.553,0.047,0.133 -social,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.105,0.274,0.176,0,0.206,0.239 -social,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.313,0.326,0.13,0.062,0.075,0.094 -social,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.097,0.338,0.067,0.156,0.328,0.014 -social,FALSE,1,48,PTYPE_DRIVING,0,0,0,0,0,0.368,0.15,0.482,0 -social,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.058,0.162,0.085,0.281,0.125,0.289 -social,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.23,0.028,0.072,0.23,0.44,0 -othdiscr,FALSE,1,48,PTYPE_FULL,0,0,0,0.108,0.319,0.132,0.27,0.112,0.059 -othdiscr,FALSE,1,48,PTYPE_PART,0,0,0,0.102,0.346,0.154,0.181,0.087,0.13 -othdiscr,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.116,0.374,0.124,0.162,0.033,0.191 -othdiscr,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.11,0.389,0.19,0.19,0.067,0.054 -othdiscr,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.111,0.284,0.186,0.197,0.111,0.111 -othdiscr,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.277,0.304,0.057,0.205,0.157,0 -othdiscr,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.114,0.204,0.148,0.291,0.089,0.154 -othdiscr,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.335,0.133,0.111,0.282,0.052,0.087 -atwork,FALSE,1,48,PTYPE_FULL,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018 -atwork,FALSE,1,48,PTYPE_PART,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018 -atwork,FALSE,1,48,PTYPE_UNIVERSITY,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018 -atwork,FALSE,1,48,PTYPE_DRIVING,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018 +primary_purpose,outbound,depart_range_start,depart_range_end,person_type,work,univ,school,escort,shopping,othmaint,eatout,social,othdiscr +work,TRUE,1,18,PTYPE_FULL,0.198,0.004,0,0.466,0.083,0.086,0.093,0.004,0.066 +work,TRUE,1,18,PTYPE_PART,0.094,0,0,0.657,0.076,0.07,0.067,0.009,0.027 +work,TRUE,1,18,PTYPE_UNIVERSITY,0.067,0.081,0,0.433,0.005,0.038,0.153,0.108,0.115 +work,TRUE,19,48,PTYPE_FULL,0.278,0.008,0,0.172,0.18,0.193,0.107,0.016,0.046 +work,TRUE,19,48,PTYPE_PART,0.442,0,0,0.089,0.105,0.175,0.102,0.03,0.057 +work,TRUE,19,48,PTYPE_UNIVERSITY,0.049,0.086,0,0.392,0.159,0.157,0.069,0.073,0.015 +work,TRUE,1,48,PTYPE_DRIVING,0,0,0,0,0.2,0.2,0.2,0.2,0.2 +univ,TRUE,1,48,PTYPE_FULL,0.526,0.178,0,0.016,0.16,0.035,0.028,0.057,0 +univ,TRUE,1,48,PTYPE_PART,0.059,0.941,0,0,0,0,0,0,0 +univ,TRUE,1,48,PTYPE_UNIVERSITY,0.109,0.034,0,0.382,0.136,0.147,0.094,0.048,0.05 +school,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.548,0.015,0.1,0.206,0.073,0.058 +school,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.53,0.025,0.084,0.112,0.048,0.201 +school,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.772,0.007,0.086,0.023,0.071,0.041 +escort,TRUE,1,48,PTYPE_FULL,0,0,0,0.55,0.153,0.084,0.104,0.049,0.06 +escort,TRUE,1,48,PTYPE_PART,0,0,0,0.449,0.194,0.07,0.167,0.059,0.061 +escort,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0.509,0.193,0.158,0.048,0.058,0.034 +escort,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.444,0.216,0.084,0.108,0.118,0.03 +escort,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.37,0.204,0.192,0.03,0.068,0.136 +escort,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.586,0.227,0,0.072,0.115,0 +escort,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.37,0.183,0.29,0.064,0.013,0.08 +escort,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.531,0.064,0,0.131,0.196,0.078 +shopping,TRUE,1,48,PTYPE_FULL,0,0,0,0.102,0.456,0.226,0.11,0.06,0.046 +shopping,TRUE,1,48,PTYPE_PART,0,0,0,0.182,0.291,0.311,0.108,0.031,0.077 +shopping,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0.13,0.262,0.36,0.124,0.06,0.064 +shopping,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.144,0.336,0.274,0.122,0.068,0.056 +shopping,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.058,0.357,0.418,0.05,0.047,0.07 +shopping,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.076,0.193,0.298,0.047,0.13,0.256 +shopping,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.121,0.142,0.232,0.291,0.03,0.184 +shopping,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.138,0.292,0.301,0.187,0.064,0.018 +othmaint,TRUE,1,48,PTYPE_FULL,0,0,0,0.201,0.252,0.366,0.117,0.032,0.032 +othmaint,TRUE,1,48,PTYPE_PART,0,0,0,0.27,0.259,0.325,0.109,0,0.037 +othmaint,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0.489,0.13,0.167,0.025,0.15,0.039 +othmaint,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.279,0.229,0.344,0.078,0.039,0.031 +othmaint,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.224,0.139,0.321,0.098,0.064,0.154 +othmaint,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.135,0,0.259,0.083,0.523,0 +othmaint,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.191,0.408,0.344,0.041,0.008,0.008 +othmaint,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.143,0.301,0.464,0.017,0.029,0.046 +eatout,TRUE,1,48,PTYPE_FULL,0,0,0,0.144,0.283,0.202,0.036,0.129,0.206 +eatout,TRUE,1,48,PTYPE_PART,0,0,0,0.169,0.374,0.179,0.013,0.135,0.13 +eatout,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0.32,0.085,0.111,0,0.153,0.331 +eatout,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.201,0.224,0.269,0.063,0.082,0.161 +eatout,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.142,0.237,0.237,0.034,0.123,0.227 +eatout,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.175,0.289,0.346,0,0.105,0.085 +eatout,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.124,0.135,0.135,0.04,0.048,0.518 +eatout,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.055,0.329,0.165,0.061,0,0.39 +social,TRUE,1,48,PTYPE_FULL,0,0,0,0.186,0.382,0.144,0.122,0.126,0.04 +social,TRUE,1,48,PTYPE_PART,0,0,0,0.175,0.153,0.167,0.147,0.183,0.175 +social,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0,0.212,0.091,0.432,0.234,0.031 +social,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.311,0.392,0.149,0.071,0.058,0.019 +social,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.12,0.407,0.203,0.151,0.102,0.017 +social,TRUE,1,48,PTYPE_DRIVING,0,0,0,0,0,0,0,0.415,0.585 +social,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.322,0.11,0.05,0,0.378,0.14 +social,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.294,0,0.159,0,0.547,0 +othdiscr,TRUE,1,48,PTYPE_FULL,0,0,0,0.236,0.169,0.143,0.19,0.093,0.169 +othdiscr,TRUE,1,48,PTYPE_PART,0,0,0,0.223,0.208,0.181,0.193,0.129,0.066 +othdiscr,TRUE,1,48,PTYPE_UNIVERSITY,0,0,0,0.135,0.123,0.061,0.342,0.123,0.216 +othdiscr,TRUE,1,48,PTYPE_NONWORK,0,0,0,0.263,0.295,0.148,0.088,0.082,0.124 +othdiscr,TRUE,1,48,PTYPE_RETIRED,0,0,0,0.225,0.056,0.389,0.16,0.091,0.079 +othdiscr,TRUE,1,48,PTYPE_DRIVING,0,0,0,0.311,0.126,0.051,0.018,0.142,0.352 +othdiscr,TRUE,1,48,PTYPE_SCHOOL,0,0,0,0.222,0.112,0.172,0.173,0.141,0.18 +othdiscr,TRUE,1,48,PTYPE_PRESCHOOL,0,0,0,0.271,0.108,0.393,0.146,0.043,0.039 +atwork,TRUE,1,48,PTYPE_FULL,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014 +atwork,TRUE,1,48,PTYPE_PART,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014 +atwork,TRUE,1,48,PTYPE_UNIVERSITY,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014 +atwork,TRUE,1,48,PTYPE_DRIVING,0.206,0,0,0.122,0.166,0.247,0.241,0.004,0.014 +#,,,,,,,,,,,,, +work,FALSE,1,30,PTYPE_FULL,0.175,0,0,0.14,0.27,0.162,0.134,0.05,0.069 +work,FALSE,1,30,PTYPE_PART,0.097,0,0,0.252,0.211,0.192,0.159,0.089,0 +work,FALSE,1,30,PTYPE_UNIVERSITY,0.134,0,0,0.329,0.114,0.212,0.169,0.042,0 +work,FALSE,31,48,PTYPE_FULL,0.151,0.011,0,0.201,0.28,0.127,0.103,0.035,0.092 +work,FALSE,31,48,PTYPE_PART,0.11,0,0,0.243,0.281,0.13,0.119,0.036,0.081 +work,FALSE,31,48,PTYPE_UNIVERSITY,0.058,0.127,0,0.224,0.269,0.079,0.072,0.108,0.063 +work,FALSE,1,48,PTYPE_DRIVING,0,0,0,0,0.2,0.2,0.2,0.2,0.2 +univ,FALSE,1,48,PTYPE_FULL,0.352,0.032,0,0.032,0.146,0.114,0.177,0.028,0.119 +univ,FALSE,1,48,PTYPE_PART,0,0,0,0.822,0.178,0,0,0,0 +univ,FALSE,1,48,PTYPE_UNIVERSITY,0.054,0.025,0,0.194,0.209,0.179,0.159,0.067,0.113 +school,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.301,0.117,0.098,0.169,0.186,0.129 +school,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.166,0.158,0.147,0.122,0.133,0.274 +school,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.38,0.148,0.089,0.146,0.102,0.135 +escort,FALSE,1,48,PTYPE_FULL,0,0,0,0.343,0.235,0.114,0.222,0.039,0.047 +escort,FALSE,1,48,PTYPE_PART,0,0,0,0.24,0.298,0.128,0.157,0.045,0.132 +escort,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.195,0.319,0.287,0.02,0.027,0.152 +escort,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.28,0.325,0.169,0.103,0.05,0.073 +escort,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.31,0.317,0.073,0.111,0.112,0.077 +escort,FALSE,1,48,PTYPE_DRIVING,0,0,0,0,0.489,0,0.148,0.363,0 +escort,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.188,0.259,0.129,0.202,0.06,0.162 +escort,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.413,0.215,0.118,0.211,0.019,0.024 +shopping,FALSE,1,48,PTYPE_FULL,0,0,0,0.091,0.526,0.159,0.152,0.047,0.025 +shopping,FALSE,1,48,PTYPE_PART,0,0,0,0.104,0.553,0.156,0.105,0.037,0.045 +shopping,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.1,0.43,0.064,0.344,0.003,0.059 +shopping,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.11,0.528,0.158,0.122,0.059,0.023 +shopping,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.052,0.549,0.159,0.123,0.06,0.057 +shopping,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.118,0.707,0,0.041,0.134,0 +shopping,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.015,0.19,0.256,0.157,0.179,0.203 +shopping,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.206,0.172,0.22,0.202,0.158,0.042 +othmaint,FALSE,1,48,PTYPE_FULL,0,0,0,0.171,0.364,0.215,0.159,0.029,0.062 +othmaint,FALSE,1,48,PTYPE_PART,0,0,0,0.228,0.365,0.17,0.13,0.041,0.066 +othmaint,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.046,0.345,0.192,0.298,0.06,0.059 +othmaint,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.17,0.423,0.158,0.171,0.064,0.014 +othmaint,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.099,0.391,0.213,0.241,0.036,0.02 +othmaint,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.031,0.356,0.075,0.458,0.031,0.049 +othmaint,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.181,0.255,0.142,0.313,0,0.109 +othmaint,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.164,0.249,0.338,0.053,0.006,0.19 +eatout,FALSE,1,48,PTYPE_FULL,0,0,0,0.106,0.44,0.112,0.041,0.128,0.173 +eatout,FALSE,1,48,PTYPE_PART,0,0,0,0.168,0.331,0.225,0.023,0.063,0.19 +eatout,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.165,0.334,0.104,0.088,0.135,0.174 +eatout,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.148,0.547,0.092,0.056,0.055,0.102 +eatout,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.166,0.414,0.169,0.02,0.166,0.065 +eatout,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.195,0.332,0.114,0.114,0,0.245 +eatout,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.072,0.356,0.053,0.019,0.169,0.331 +eatout,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.01,0.286,0.045,0.117,0.064,0.478 +social,FALSE,1,48,PTYPE_FULL,0,0,0,0.12,0.286,0.123,0.19,0.255,0.026 +social,FALSE,1,48,PTYPE_PART,0,0,0,0.106,0.122,0.039,0.553,0.047,0.133 +social,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.105,0.274,0.176,0,0.206,0.239 +social,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.313,0.326,0.13,0.062,0.075,0.094 +social,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.097,0.338,0.067,0.156,0.328,0.014 +social,FALSE,1,48,PTYPE_DRIVING,0,0,0,0,0,0.368,0.15,0.482,0 +social,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.058,0.162,0.085,0.281,0.125,0.289 +social,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.23,0.028,0.072,0.23,0.44,0 +othdiscr,FALSE,1,48,PTYPE_FULL,0,0,0,0.108,0.319,0.132,0.27,0.112,0.059 +othdiscr,FALSE,1,48,PTYPE_PART,0,0,0,0.102,0.346,0.154,0.181,0.087,0.13 +othdiscr,FALSE,1,48,PTYPE_UNIVERSITY,0,0,0,0.116,0.374,0.124,0.162,0.033,0.191 +othdiscr,FALSE,1,48,PTYPE_NONWORK,0,0,0,0.11,0.389,0.19,0.19,0.067,0.054 +othdiscr,FALSE,1,48,PTYPE_RETIRED,0,0,0,0.111,0.284,0.186,0.197,0.111,0.111 +othdiscr,FALSE,1,48,PTYPE_DRIVING,0,0,0,0.277,0.304,0.057,0.205,0.157,0 +othdiscr,FALSE,1,48,PTYPE_SCHOOL,0,0,0,0.114,0.204,0.148,0.291,0.089,0.154 +othdiscr,FALSE,1,48,PTYPE_PRESCHOOL,0,0,0,0.335,0.133,0.111,0.282,0.052,0.087 +atwork,FALSE,1,48,PTYPE_FULL,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018 +atwork,FALSE,1,48,PTYPE_PART,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018 +atwork,FALSE,1,48,PTYPE_UNIVERSITY,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018 +atwork,FALSE,1,48,PTYPE_DRIVING,0.189,0,0,0.067,0.163,0.149,0.395,0.019,0.018 diff --git a/activitysim/examples/example_semcog/configs/trip_scheduling.yaml b/activitysim/examples/prototype_semcog/configs/trip_scheduling.yaml similarity index 96% rename from activitysim/examples/example_semcog/configs/trip_scheduling.yaml rename to activitysim/examples/prototype_semcog/configs/trip_scheduling.yaml index 1d08b77ba7..a006e7436b 100755 --- a/activitysim/examples/example_semcog/configs/trip_scheduling.yaml +++ b/activitysim/examples/prototype_semcog/configs/trip_scheduling.yaml @@ -1,10 +1,10 @@ - -# int to add to probs column index to get time period it represents. -# e.g. depart_alt_base = 5 means first column (column 0) represents period 5 -DEPART_ALT_BASE: 1 - -MAX_ITERATIONS: 100 - -#FAILFIX: drop_and_cleanup -FAILFIX: choose_most_initial - + +# int to add to probs column index to get time period it represents. +# e.g. depart_alt_base = 5 means first column (column 0) represents period 5 +DEPART_ALT_BASE: 1 + +MAX_ITERATIONS: 100 + +#FAILFIX: drop_and_cleanup +FAILFIX: choose_most_initial + diff --git a/activitysim/examples/example_semcog/configs/trip_scheduling_probs.csv b/activitysim/examples/prototype_semcog/configs/trip_scheduling_probs.csv similarity index 99% rename from activitysim/examples/example_semcog/configs/trip_scheduling_probs.csv rename to activitysim/examples/prototype_semcog/configs/trip_scheduling_probs.csv index f164aa8c4f..5566771bbc 100755 --- a/activitysim/examples/example_semcog/configs/trip_scheduling_probs.csv +++ b/activitysim/examples/prototype_semcog/configs/trip_scheduling_probs.csv @@ -1,3457 +1,3457 @@ -primary_purpose,outbound,tour_hour,trip_num,HR1,HR2,HR3,HR4,HR5,HR6,HR7,HR8,HR9,HR10,HR11,HR12,HR13,HR14,HR15,HR16,HR17,HR18,HR19,HR20,HR21,HR22,HR23,HR24,HR25,HR26,HR27,HR28,HR29,HR30,HR31,HR32,HR33,HR34,HR35,HR36,HR37,HR38,HR39,HR40,HR41,HR42,HR43,HR44,HR45,HR46,HR47,HR48 -work,TRUE,1,1,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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-othmaint,FALSE,21,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01296464330035067,0.019431511915681883,0.12796516251327822,0.12038114156421421,0.32403248652446454,0.21674864025422566,0.1784764139277847,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,21,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01792011576845662,0.06208672015339636,0.1062533245383361,0.15041992892327583,0.03809564431008291,0.30789565418224935,0.09302162593642888,0.1874045193083271,0.036902466879446885,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,22,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.003235564656425117,0.004059647865136972,0.004883731073848827,0.005707814282560683,0.009376716397792549,0.05769416148958721,0.3998346149627327,0.5152077492719161,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,22,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0071726385759043705,0.006622610393140245,0.11288778176588511,0.04664972554363386,0.13764948978101244,0.36923180450724463,0.2772828582344299,0.04250309119874959,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,22,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.016048896218461176,0.03013883289662701,0.04422876957479284,0.05831870625295868,0.25801809433483547,0.37468065947759205,0.08704532084439774,0.13152072040033494,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,22,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.027512933811721808,0.03363867443387706,0.11011937594350217,0.5562285735635365,0.034064125978050516,0.18266553472105468,0.05577078154825726,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,23,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0018711775879847318,0.008523750748782012,0.010751717326866457,0.02122959968228724,0.11121343910404836,0.39961212291552406,0.4467981926345071,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,23,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0039809191906446315,0.014099470355290855,0.02421802151993708,0.0683948802551518,0.1344713597255731,0.1426732278980772,0.5812363003995857,0.030925820655739712,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,23,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.03331514945719014,0.005329026415862597,0.1227610101634701,0.08799501808596004,0.11977914009061238,0.5599269658664612,0.07089368992044365,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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-othmaint,FALSE,24,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.013304807789220639,0.03483393803383822,0.035909799971495314,0.11869562635252902,0.08277072235477378,0.24798580935842027,0.462030370539622,0.004468925600100787,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,24,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02376908405429902,0.04148133021901569,0.05919357638373238,0.13435610447864868,0.1301319681220956,0.1838195905506085,0.10931583186363945,0.21840164033885306,0.09953087398910763,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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-othmaint,FALSE,25,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.008996395106685718,0.022382357584461898,0.012339507421560958,0.009079526231841549,0.0654251394630445,0.15731473205772087,0.18856619111517983,0.43788173311094597,0.09801441790855868,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,25,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.025178665775238963,0.04729960344379564,0.002356272354193297,0.13534608716995253,0.10258795702320934,0.11604589090388041,0.21080310440473107,0.36038241892499884,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,25,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0068411562930526075,0.10800977315445119,0.22220392296105196,0.005071684131950922,0.08143894381236849,0.16261693001006536,0.08038752486773086,0.3334300647693287,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,26,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.03344378219719876,0.026866065331058783,0.07040583259081826,0.48194475919000535,0.3873395606909189,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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-othmaint,FALSE,27,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.13391789707153173,0.06985033461845441,0.005782772165377069,0.053993876364141546,0.054367033328449665,0.053292092685866946,0.11725007206220496,0.1503866603184519,0.2697309441487835,0.07989092661457484,0.011537390622163345,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,28,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.014697870992410348,0.005784583113275807,0.01889597354449021,0.06014764709413824,0.48769672163529637,0.41277720362038905,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,28,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.001117061876608237,0.005553742736376234,0.009990423596144233,0.01442710445591223,0.00416689684216162,0.026081562754801644,0.047996228667441666,0.06819403102061676,0.0015822980921750658,0.05149862431134258,0.02357798951197528,0.10851105297124357,0.2801522688420516,0.23203367073028075,0.12511704359086864,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,28,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.016221208133358277,0.029170171892430683,0.003377407719398701,0.022477407098078276,0.08301847925366398,0.09395007480185141,0.05583007008505986,0.036996944151528585,0.1207305437148142,0.21104780787433902,0.10985059253190989,0.21732929274356705,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,28,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.019279993107393367,0.10710515873535675,0.07180865257712939,0.03787655979974105,0.003944467022352705,0.05771572968018896,0.44589928175194055,0.1741872663479892,0.08218289097790815,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,29,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0013045506656333293,0.022999955438425288,0.06988008707374721,0.49703946202490973,0.40877594479728435,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,29,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.049641886937836645,0.06485394960214345,0.01054206590041185,0.036159177640203335,0.017761670773606582,0.09962245903996725,0.08919510978226165,0.11249794162709666,0.2578788294699805,0.21723216886150792,0.044614740364984314,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,29,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.14666399442576072,0.1402561938207772,0.010751874512273309,0.01482902625216663,0.1984320936067995,0.01957827948346152,0.11311038160744949,0.11427849746710017,0.1477905533750072,0.06236861879411428,0.03194048665509008,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,29,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.006362869336904934,0.010136022095299334,0.013909174853693732,0.01768232761208813,0.021455480370482533,0.07129524209158307,0.04994994483459023,0.17098321485750503,0.16068258751758796,0.15038196017767091,0.061974759370397474,0.15626205693308,0.039595502072518336,0.05920163789685102,0.005579347307107501,0.004547872672639853,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,30,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.003156893410151791,0.01539168726158279,0.027626481113013787,0.043752865385778106,0.13060216459324184,0.496255313062491,0.2832145951737405,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,30,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.009362068994872288,0.009910361492654507,0.025818547238421346,0.018542256915277493,0.006192456677157952,0.06936491452103992,0.09078753567477497,0.1736471712756763,0.32341830844485986,0.2490449117453256,0.023911467019939802,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,30,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.015253920563101039,0.01734829095140272,0.0194426613397044,0.028208512702923263,0.007732651137986171,0.05312809580471804,0.11563584746658268,0.04387532725200658,0.04618950581949503,0.100915627695279,0.15564174957106294,0.06991750140350364,0.3081526955992089,0.018557612693025517,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,30,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.019200174799962152,0.04579024098480264,0.20204626534531056,0.12465229586447808,0.04725832638364561,0.04125743612273031,0.035256545861815,0.0292556556008997,0.08402593149907742,0.17073248276161623,0.20052464477566231,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,31,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.004100543198560938,0.011576373608166025,0.057556167084768,0.04392898341172154,0.16297002964866683,0.3389579626165197,0.3809099404315969,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,31,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.006895373125020818,0.01184834669751427,0.02189955013756038,0.031950753577606496,0.024344741736817523,0.13707203448520286,0.0332776307149174,0.03480799299800823,0.16505811950879215,0.22829288269631942,0.25505272315002625,0.049499851172214136,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,31,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.01331449750441225,0.15426115820681183,0.10084727562244192,0.04743339303807202,0.023822402385287843,0.011712569006320441,0.0325994160135087,0.021377933644737477,0.022937486281917746,0.06978910906438544,0.19175303621656664,0.09318005064798796,0.21697167236754966,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,31,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02982464615399378,0.07678704126850915,0.09482206161283911,0.1128570819571691,0.08563231393612793,0.05840754591508675,0.08477919232701116,0.11115083873893557,0.281788056065831,0.03478709497992229,0.029164127044574086,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,32,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.010279413725647892,0.03271759607102003,0.07386224918245629,0.5395624119749622,0.34357832904591357,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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-othmaint,FALSE,33,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.008515856188299909,0.10132304262805399,0.09393984784872157,0.08655665306938916,0.038281662772794454,0.03618539761861302,0.034089132464431585,0.6011084074096963,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,34,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0059105150143989255,0.005211681919841845,0.004512848825284765,0.0038140157307276854,0.0031151826361706055,0.0024163495416135252,0.0017175164470564453,0.001018683352499365,0.008408360743176616,0.009528699113689238,0.02342975425960364,0.04169506196223676,0.3966823092394524,0.4925390212142482,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,34,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.018110305692000582,0.02075011517088262,0.023389924649764655,0.026029734128646693,0.028669543607528727,0.031309353086410766,0.033949162565292804,0.03658897204417484,0.03384296470232274,0.031096957360470634,0.028350950018618534,0.0628357410044171,0.05574759488054363,0.02555920754610205,0.19656577780922838,0.27859243684535,0.06861125888824532,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,34,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.03122930767687022,0.033604535127655155,0.03597976257844008,0.03835499002922502,0.04073021748000995,0.03323420846564394,0.025738199451277942,0.01824219043691194,0.010746181422545934,0.0032501724081799286,0.004788046773441346,0.060325816177862805,0.03654188179605468,0.05558148684528954,0.15905273919339807,0.3738958244844022,0.03870443965279116,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,34,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.048848207807632466,0.04945660271365498,0.0500649976196775,0.05067339252570001,0.05128178743172252,0.05189018233774503,0.052498577243767545,0.05310697214979006,0.05371536705581258,0.052839241470542045,0.05321817884158052,0.03839151634541094,0.02356485384924137,0.009981650711084268,0.24879033588266883,0.013911443063807075,0.037226045337989774,0.060540647612172475,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,35,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0034998920984917153,0.003531745208663175,0.0035635983188346348,0.0035954514290060945,0.003627304539177554,0.003659157649349014,0.0036910107595204736,0.0037228638696919334,0.003754716979863393,0.003786570090034853,0.0038184232002063125,0.008590061786535264,0.05318429979531184,0.051618815933729176,0.3627713785258771,0.48358470981570767,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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-othmaint,FALSE,35,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.008343521773058847,0.010485187459810398,0.01262685314656195,0.014909675927860454,0.017192498709158954,0.009727276049726116,0.0131812403057675,0.016635204561808885,0.050996802614955226,0.08535840066810158,0.6188442602003162,0.0642050699670376,0.014013865905601969,0.053832403175892975,0.009647739534341228,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,36,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.007373770821427729,0.023908887659757808,0.01668432475894998,0.13952362702703705,0.38392166115640247,0.4285877285764251,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,36,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02215220814036823,0.023433505458302212,0.1124378659395947,0.019451944725702244,0.13761635684281193,0.05675617975817877,0.1956179021841774,0.30142703227963263,0.10993356953178486,0.02117343513944714,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -othmaint,FALSE,36,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02744719910988627,0.02824098053297181,0.02903476195605735,0.02722988203630843,0.15257056479029496,0.06084629568319994,0.21075024624391514,0.17269331877257915,0.1346363913012432,0.09657946382990718,0.035898480414789026,0.02407241532884767,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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-escort,TRUE,21,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,21,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.25003132023583635,0.5362903946357147,0.1172175969489665,0.07122609504281636,0.02523459313666624,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,21,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02446208671855894,0.3333333333333333,0.6422045799481078,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,21,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,22,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,22,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.21438391240532625,0.7549633831268727,0.01946892967386199,0.011183774793939042,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,22,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.34537929121534533,0.6306705203403534,0.02395018844430129,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,22,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9275756687493233,0.07242433125067678,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,23,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,23,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.24626498820715811,0.5875075907073781,0.07796380190012697,0.055409140361821284,0.03285447882351561,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,23,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.49488868532476943,0.17458362683218098,0.12744343270080508,0.0803032385694292,0.03316304443805331,0.08961797213476193,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,23,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.36496155111662887,0.2824807755583144,0.19999999999999996,0.1175192244416855,0.03503844888337105,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,24,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,24,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7566234191098833,0.21291012065547055,0.03046646023464622,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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-escort,TRUE,25,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1831099788864871,0.6474888950395127,0.1694011260740003,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,25,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1078813556421454,0.7350209243987343,0.1570977199591203,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,25,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,26,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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-escort,TRUE,27,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.13023089022400866,0.8053988566875924,0.016127893204456404,0.016083707754592842,0.013401629232188027,0.010719550709783211,0.008037472187378398,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,27,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.004256896296630929,0.009246887493529287,0.21591695644910233,0.18934039000932226,0.16276382356954225,0.13618725712976223,0.1096106906899822,0.08303412425020218,0.05645755781042213,0.029880991370642086,0.003304424930862062,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,27,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.016228927127358597,0.3333333333333333,0.6504377395393081,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,28,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,28,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.533792440291268,0.46620755970873196,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,28,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07397406559413638,0.7370597675968947,0.1889661668089689,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,28,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.472492983314563,0.3333333333333333,0.19417368335210364,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,29,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,29,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.48969361016894036,0.5047835007226725,0.00552288910838719,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,29,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02996762765509789,0.8643501983316954,0.10568217401320677,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,29,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0736396890781448,0.11085048011355354,0.14806127114896228,0.18527206218437106,0.22248285321977979,0.25969364425518854,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,30,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -escort,TRUE,30,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.34034639266622857,0.5076849639231412,0.14544698296631678,0.006521660444313618,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 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+eatout,FALSE,45,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 +eatout,FALSE,45,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 +eatout,FALSE,46,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6563319176641682,0.34366808233583185,0.0,0.0 +eatout,FALSE,46,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +eatout,FALSE,46,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +eatout,FALSE,46,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +eatout,FALSE,47,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0 +eatout,FALSE,47,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +eatout,FALSE,47,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +eatout,FALSE,47,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 +eatout,FALSE,48,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +eatout,FALSE,48,2,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +eatout,FALSE,48,3,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 +eatout,FALSE,48,4,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 diff --git a/activitysim/examples/example_semcog/configs/work_from_home.csv b/activitysim/examples/prototype_semcog/configs/work_from_home.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/work_from_home.csv rename to activitysim/examples/prototype_semcog/configs/work_from_home.csv index 6ee474f292..11cfe76b4c 100755 --- a/activitysim/examples/example_semcog/configs/work_from_home.csv +++ b/activitysim/examples/prototype_semcog/configs/work_from_home.csv @@ -1,15 +1,15 @@ -Label,Description,Expression,work_at_home,work_away_from_home -util_work_from_home_constant,Constant for Working from home,1,coef_work_from_home_constant,0 -util_full_time_worker,Full time worker (1 if true),@df.pemploy==PEMPLOY_FULL,coef_full_time_worker,0 -util_female_worker,Female Worker,@df.sex==2,coef_female_worker,0 -util_female_worker_preschool_child,Female worker with a Preschool Child in Household,"@df.sex==2 & other_than(df.household_id, df.ptype == PTYPE_SCHOOL)",coef_female_worker_preschool_child,0 -util_access_to_workplaces,Accessibility to workplaces of the home mgra,@df.auPkTotal,coef_access_to_workplaces,0 -util_non_working_adult_in_hh,Presence of Non Working Adult in the Household,"@other_than(df.household_id, df.ptype == PTYPE_NONWORK)",coef_non_working_adult_in_hh,0 -util_education_ba_plus,Education Level Bachelors or higher degree,0,coef_education_ba_plus,0 -util_low_income,Household income Less than 30K,@df.income < 30000,coef_low_income,0 -util_age_lt_35,Age Group - Less than 35 years,@df.age < 35,coef_age_lt_35,0 -util_age_35_to_45,Age Group - 35 yrs to 45 yrs,"@df.age.between(35, 45)",coef_age_35_to_45,0 -util_age_45_to_55,Age Group - 45 yrs to 55 yrs,"@df.age.between(45, 55)",coef_age_45_to_55,0 -util_age_55_to_65,Age Group - 55 yrs to 65 yrs,"@df.age.between(55, 65)",coef_age_55_to_65,0 -util_age_65_plus,Age Group - Older than 65yrs,@df.age > 65,coef_age_65_plus,0 -util_calibration,ABM2 calibration - work from home,1,coef_calibration,0 +Label,Description,Expression,work_at_home,work_away_from_home +util_work_from_home_constant,Constant for Working from home,1,coef_work_from_home_constant,0 +util_full_time_worker,Full time worker (1 if true),@df.pemploy==PEMPLOY_FULL,coef_full_time_worker,0 +util_female_worker,Female Worker,@df.sex==2,coef_female_worker,0 +util_female_worker_preschool_child,Female worker with a Preschool Child in Household,"@df.sex==2 & other_than(df.household_id, df.ptype == PTYPE_SCHOOL)",coef_female_worker_preschool_child,0 +util_access_to_workplaces,Accessibility to workplaces of the home mgra,@df.auPkTotal,coef_access_to_workplaces,0 +util_non_working_adult_in_hh,Presence of Non Working Adult in the Household,"@other_than(df.household_id, df.ptype == PTYPE_NONWORK)",coef_non_working_adult_in_hh,0 +util_education_ba_plus,Education Level Bachelors or higher degree,0,coef_education_ba_plus,0 +util_low_income,Household income Less than 30K,@df.income < 30000,coef_low_income,0 +util_age_lt_35,Age Group - Less than 35 years,@df.age < 35,coef_age_lt_35,0 +util_age_35_to_45,Age Group - 35 yrs to 45 yrs,"@df.age.between(35, 45)",coef_age_35_to_45,0 +util_age_45_to_55,Age Group - 45 yrs to 55 yrs,"@df.age.between(45, 55)",coef_age_45_to_55,0 +util_age_55_to_65,Age Group - 55 yrs to 65 yrs,"@df.age.between(55, 65)",coef_age_55_to_65,0 +util_age_65_plus,Age Group - Older than 65yrs,@df.age > 65,coef_age_65_plus,0 +util_calibration,ABM2 calibration - work from home,1,coef_calibration,0 diff --git a/activitysim/examples/example_semcog/configs/work_from_home.yaml b/activitysim/examples/prototype_semcog/configs/work_from_home.yaml similarity index 96% rename from activitysim/examples/example_semcog/configs/work_from_home.yaml rename to activitysim/examples/prototype_semcog/configs/work_from_home.yaml index 4fa41b5ef2..b94c138836 100755 --- a/activitysim/examples/example_semcog/configs/work_from_home.yaml +++ b/activitysim/examples/prototype_semcog/configs/work_from_home.yaml @@ -1,19 +1,19 @@ - -SPEC: work_from_home.csv -COEFFICIENTS: work_from_home_coeffs.csv - -#LOGIT_TYPE: NL -LOGIT_TYPE: MNL - -WORK_FROM_HOME_ALT: 0 - -# boolean column to filter choosers (True means keep) -CHOOSER_FILTER_COLUMN_NAME: is_worker - -# iterative what-if analysis example -# omit these settings to not iterate -# WORK_FROM_HOME_ITERATIONS: 3 -# WORK_FROM_HOME_CHOOSER_FILTER: is_worker -# WORK_FROM_HOME_TARGET_PERCENT: 0.1 -# WORK_FROM_HOME_TARGET_PERCENT_TOLERANCE: 0.01 + +SPEC: work_from_home.csv +COEFFICIENTS: work_from_home_coeffs.csv + +#LOGIT_TYPE: NL +LOGIT_TYPE: MNL + +WORK_FROM_HOME_ALT: 0 + +# boolean column to filter choosers (True means keep) +CHOOSER_FILTER_COLUMN_NAME: is_worker + +# iterative what-if analysis example +# omit these settings to not iterate +# WORK_FROM_HOME_ITERATIONS: 3 +# WORK_FROM_HOME_CHOOSER_FILTER: is_worker +# WORK_FROM_HOME_TARGET_PERCENT: 0.1 +# WORK_FROM_HOME_TARGET_PERCENT_TOLERANCE: 0.01 # WORK_FROM_HOME_COEFFICIENT_CONSTANT: coef_work_from_home_constant \ No newline at end of file diff --git a/activitysim/examples/example_semcog/configs/work_from_home_coeffs.csv b/activitysim/examples/prototype_semcog/configs/work_from_home_coeffs.csv similarity index 97% rename from activitysim/examples/example_semcog/configs/work_from_home_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/work_from_home_coeffs.csv index eb9843335e..a3868e981e 100755 --- a/activitysim/examples/example_semcog/configs/work_from_home_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/work_from_home_coeffs.csv @@ -1,15 +1,15 @@ -coefficient_name,value,constrain -coef_work_from_home_constant,0.438361,T -coef_full_time_worker,-0.811935,F -coef_female_worker,-0.347009,F -coef_female_worker_preschool_child,0.572657,F -coef_access_to_workplaces,-0.140426,F -coef_non_working_adult_in_hh,-0.372481,F -coef_education_ba_plus,0.284678,F -coef_low_income,-0.393068,F -coef_age_lt_35,-0.573545,F -coef_age_35_to_45,0,F -coef_age_45_to_55,0.21438,F -coef_age_55_to_65,0.451657,F -coef_age_65_plus,0.583518,F -coef_calibration,-0.0162,T +coefficient_name,value,constrain +coef_work_from_home_constant,0.438361,T +coef_full_time_worker,-0.811935,F +coef_female_worker,-0.347009,F +coef_female_worker_preschool_child,0.572657,F +coef_access_to_workplaces,-0.140426,F +coef_non_working_adult_in_hh,-0.372481,F +coef_education_ba_plus,0.284678,F +coef_low_income,-0.393068,F +coef_age_lt_35,-0.573545,F +coef_age_35_to_45,0,F +coef_age_45_to_55,0.21438,F +coef_age_55_to_65,0.451657,F +coef_age_65_plus,0.583518,F +coef_calibration,-0.0162,T diff --git a/activitysim/examples/example_semcog/configs/workplace_location.csv b/activitysim/examples/prototype_semcog/configs/workplace_location.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/workplace_location.csv rename to activitysim/examples/prototype_semcog/configs/workplace_location.csv index 2cc4838383..d065c3e01d 100755 --- a/activitysim/examples/example_semcog/configs/workplace_location.csv +++ b/activitysim/examples/prototype_semcog/configs/workplace_location.csv @@ -1,16 +1,16 @@ -Label,Description,Expression,coefficient -local_dist,,_DIST@skims['DIST'],1 -util_dist,Distance,@_DIST,coef_dist -util_dist_squared,"Distance squared, capped at 20 miles","@(_DIST).clip(0,20)**2",coef_dist_squared -util_dist_cubed,"Distance cubed, capped at 20 miles","@(_DIST).clip(0,20)**3",coef_dist_cubed -util_dist_logged,Distance logged,@(_DIST).apply(np.log1p),coef_dist_logged -util_dist_low,"Distance,low income",@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,coef_dist_low_inc -util_dist_med,"Distance,med income",@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,coef_dist_med_inc -util_dist_high,"Distance,high income",@(df['income_segment']==WORK_HIGH_SEGMENT_ID) * _DIST,coef_dist_high_inc -util_dist_part_time,"Distance,part_time",@(df['pemploy']==2) * _DIST,coef_dist_part_time -util_dist_student,"Distance,student",@(df['is_student']==True) * _DIST,coef_dist_student -util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1 -util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1 -util_no_attractions,No attractions,@df['size_term']==0,-999 -util_mode_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum -util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1 +Label,Description,Expression,coefficient +local_dist,,_DIST@skims['DIST'],1 +util_dist,Distance,@_DIST,coef_dist +util_dist_squared,"Distance squared, capped at 20 miles","@(_DIST).clip(0,20)**2",coef_dist_squared +util_dist_cubed,"Distance cubed, capped at 20 miles","@(_DIST).clip(0,20)**3",coef_dist_cubed +util_dist_logged,Distance logged,@(_DIST).apply(np.log1p),coef_dist_logged +util_dist_low,"Distance,low income",@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,coef_dist_low_inc +util_dist_med,"Distance,med income",@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,coef_dist_med_inc +util_dist_high,"Distance,high income",@(df['income_segment']==WORK_HIGH_SEGMENT_ID) * _DIST,coef_dist_high_inc +util_dist_part_time,"Distance,part_time",@(df['pemploy']==2) * _DIST,coef_dist_part_time +util_dist_student,"Distance,student",@(df['is_student']==True) * _DIST,coef_dist_student +util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1 +util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1 +util_no_attractions,No attractions,@df['size_term']==0,-999 +util_mode_logsum,Mode choice logsum,mode_choice_logsum,coef_mode_logsum +util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1 diff --git a/activitysim/examples/example_semcog/configs/workplace_location.yaml b/activitysim/examples/prototype_semcog/configs/workplace_location.yaml similarity index 96% rename from activitysim/examples/example_semcog/configs/workplace_location.yaml rename to activitysim/examples/prototype_semcog/configs/workplace_location.yaml index 54cbe3129f..80a350a6b2 100755 --- a/activitysim/examples/example_semcog/configs/workplace_location.yaml +++ b/activitysim/examples/prototype_semcog/configs/workplace_location.yaml @@ -1,77 +1,77 @@ -SAMPLE_SIZE: 30 - -SIMULATE_CHOOSER_COLUMNS: - - income_segment - - home_zone_id - - is_student - - pemploy - -SAMPLE_SPEC: workplace_location_sample.csv -SPEC: workplace_location.csv -COEFFICIENTS: workplace_location_coeffs.csv - -LOGSUM_SETTINGS: tour_mode_choice.yaml -LOGSUM_PREPROCESSOR: nontour_preprocessor -LOGSUM_TOUR_PURPOSE: work - -# model-specific logsum-related settings -CHOOSER_ORIG_COL_NAME: home_zone_id -ALT_DEST_COL_NAME: alt_dest -IN_PERIOD: 17 -OUT_PERIOD: 8 - -DEST_CHOICE_COLUMN_NAME: workplace_zone_id -# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table -DEST_CHOICE_LOGSUM_COLUMN_NAME: workplace_location_logsum - -# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table -DEST_CHOICE_SAMPLE_TABLE_NAME: workplace_location_sample - -annotate_persons: - SPEC: annotate_persons_workplace - DF: persons - TABLES: - - land_use - -annotate_households: - SPEC: annotate_households_workplace - DF: households - TABLES: - - persons - -# - shadow pricing - - -# income_segment is in households, but we want to count persons -CHOOSER_TABLE_NAME: persons_merged - -# size_terms model_selector -MODEL_SELECTOR: workplace - -# we can't use use household income_segment as this will also be set for non-workers -CHOOSER_SEGMENT_COLUMN_NAME: income_segment - -# boolean column to filter choosers (True means keep) -CHOOSER_FILTER_COLUMN_NAME: is_out_of_home_worker - -# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this -# FIXME - these are not needed for this model and should be re/factored out -SEGMENT_IDS: - work_low: 1 - work_med: 2 - work_high: 3 - work_veryhigh: 4 - -CONSTANTS: - WORK_LOW_SEGMENT_ID: 1 - WORK_MED_SEGMENT_ID: 2 - WORK_HIGH_SEGMENT_ID: 3 - WORK_VERYHIGH_SEGMENT_ID: 4 - - -# model adds these tables (informational - not added if commented out) -SHADOW_PRICE_TABLE: workplace_shadow_prices -MODELED_SIZE_TABLE: workplace_modeled_size - -# not loaded if commented out -SAVED_SHADOW_PRICE_TABLE_NAME: final_workplace_shadow_prices.csv +SAMPLE_SIZE: 30 + +SIMULATE_CHOOSER_COLUMNS: + - income_segment + - home_zone_id + - is_student + - pemploy + +SAMPLE_SPEC: workplace_location_sample.csv +SPEC: workplace_location.csv +COEFFICIENTS: workplace_location_coeffs.csv + +LOGSUM_SETTINGS: tour_mode_choice.yaml +LOGSUM_PREPROCESSOR: nontour_preprocessor +LOGSUM_TOUR_PURPOSE: work + +# model-specific logsum-related settings +CHOOSER_ORIG_COL_NAME: home_zone_id +ALT_DEST_COL_NAME: alt_dest +IN_PERIOD: 17 +OUT_PERIOD: 8 + +DEST_CHOICE_COLUMN_NAME: workplace_zone_id +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if not desired in persons table +DEST_CHOICE_LOGSUM_COLUMN_NAME: workplace_location_logsum + +# comment out DEST_CHOICE_LOGSUM_COLUMN_NAME if saved alt logsum table +DEST_CHOICE_SAMPLE_TABLE_NAME: workplace_location_sample + +annotate_persons: + SPEC: annotate_persons_workplace + DF: persons + TABLES: + - land_use + +annotate_households: + SPEC: annotate_households_workplace + DF: households + TABLES: + - persons + +# - shadow pricing + + +# income_segment is in households, but we want to count persons +CHOOSER_TABLE_NAME: persons_merged + +# size_terms model_selector +MODEL_SELECTOR: workplace + +# we can't use use household income_segment as this will also be set for non-workers +CHOOSER_SEGMENT_COLUMN_NAME: income_segment + +# boolean column to filter choosers (True means keep) +CHOOSER_FILTER_COLUMN_NAME: is_out_of_home_worker + +# FIXME - these are assigned to persons in annotate_persons. we need a better way to manage this +# FIXME - these are not needed for this model and should be re/factored out +SEGMENT_IDS: + work_low: 1 + work_med: 2 + work_high: 3 + work_veryhigh: 4 + +CONSTANTS: + WORK_LOW_SEGMENT_ID: 1 + WORK_MED_SEGMENT_ID: 2 + WORK_HIGH_SEGMENT_ID: 3 + WORK_VERYHIGH_SEGMENT_ID: 4 + + +# model adds these tables (informational - not added if commented out) +SHADOW_PRICE_TABLE: workplace_shadow_prices +MODELED_SIZE_TABLE: workplace_modeled_size + +# not loaded if commented out +SAVED_SHADOW_PRICE_TABLE_NAME: final_workplace_shadow_prices.csv diff --git a/activitysim/examples/example_semcog/configs/workplace_location_coeffs.csv b/activitysim/examples/prototype_semcog/configs/workplace_location_coeffs.csv similarity index 96% rename from activitysim/examples/example_semcog/configs/workplace_location_coeffs.csv rename to activitysim/examples/prototype_semcog/configs/workplace_location_coeffs.csv index aa75a35b91..f3e7c9df58 100755 --- a/activitysim/examples/example_semcog/configs/workplace_location_coeffs.csv +++ b/activitysim/examples/prototype_semcog/configs/workplace_location_coeffs.csv @@ -1,11 +1,11 @@ -coefficient_name,value,constrain -coef_dist,-0.063338148,F -coef_dist_squared,0.001294031,F -coef_dist_cubed,-6.70E-05,F -coef_dist_logged,-0.479603662,F -coef_dist_low_inc,-0.029396589,F -coef_dist_med_inc,-0.024858401,F -coef_dist_high_inc,-0.013891636,F -coef_dist_part_time,-0.047762376,F -coef_dist_student,-0.024044614,F -coef_mode_logsum,0.25,F +coefficient_name,value,constrain +coef_dist,-0.063338148,F +coef_dist_squared,0.001294031,F +coef_dist_cubed,-6.70E-05,F +coef_dist_logged,-0.479603662,F +coef_dist_low_inc,-0.029396589,F +coef_dist_med_inc,-0.024858401,F +coef_dist_high_inc,-0.013891636,F +coef_dist_part_time,-0.047762376,F +coef_dist_student,-0.024044614,F +coef_mode_logsum,0.25,F diff --git a/activitysim/examples/example_semcog/configs/workplace_location_sample.csv b/activitysim/examples/prototype_semcog/configs/workplace_location_sample.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/workplace_location_sample.csv rename to activitysim/examples/prototype_semcog/configs/workplace_location_sample.csv index e6caa34418..22e3251d6a 100755 --- a/activitysim/examples/example_semcog/configs/workplace_location_sample.csv +++ b/activitysim/examples/prototype_semcog/configs/workplace_location_sample.csv @@ -1,14 +1,14 @@ -Label,Description,Expression,coefficient -local_dist,,_DIST@skims['DIST'],1 -util_dist,Distance,@_DIST,coef_dist -util_dist_squared,"Distance squared, capped at 20 miles","@(_DIST).clip(0,20)**2",coef_dist_squared -util_dist_cubed,"Distance cubed, capped at 20 miles","@(_DIST).clip(0,20)**3",coef_dist_cubed -util_dist_logged,Distance logged,@(_DIST).apply(np.log1p),coef_dist_logged -util_dist_low,"Distance,low income",@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,coef_dist_low_inc -util_dist_med,"Distance,med income",@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,coef_dist_med_inc -util_dist_high,"Distance,high income",@(df['income_segment']==WORK_HIGH_SEGMENT_ID) * _DIST,coef_dist_high_inc -util_dist_part_time,"Distance,part_time",@(df['pemploy']==2) * _DIST,coef_dist_part_time -util_dist_student,"Distance,student",@(df['is_student']==True) * _DIST,coef_dist_student -util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1 -util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1 -util_no_attractions,No attractions,@df['size_term']==0,-999 +Label,Description,Expression,coefficient +local_dist,,_DIST@skims['DIST'],1 +util_dist,Distance,@_DIST,coef_dist +util_dist_squared,"Distance squared, capped at 20 miles","@(_DIST).clip(0,20)**2",coef_dist_squared +util_dist_cubed,"Distance cubed, capped at 20 miles","@(_DIST).clip(0,20)**3",coef_dist_cubed +util_dist_logged,Distance logged,@(_DIST).apply(np.log1p),coef_dist_logged +util_dist_low,"Distance,low income",@(df['income_segment']==WORK_LOW_SEGMENT_ID) * _DIST,coef_dist_low_inc +util_dist_med,"Distance,med income",@(df['income_segment']==WORK_MED_SEGMENT_ID) * _DIST,coef_dist_med_inc +util_dist_high,"Distance,high income",@(df['income_segment']==WORK_HIGH_SEGMENT_ID) * _DIST,coef_dist_high_inc +util_dist_part_time,"Distance,part_time",@(df['pemploy']==2) * _DIST,coef_dist_part_time +util_dist_student,"Distance,student",@(df['is_student']==True) * _DIST,coef_dist_student +util_size_variable,Size variable,@(df['size_term'] * df['shadow_price_size_term_adjustment']).apply(np.log1p),1 +util_utility_adjustment,utility adjustment,@df['shadow_price_utility_adjustment'],1 +util_no_attractions,No attractions,@df['size_term']==0,-999 diff --git a/activitysim/examples/example_semcog/configs/write_trip_matrices.yaml b/activitysim/examples/prototype_semcog/configs/write_trip_matrices.yaml similarity index 96% rename from activitysim/examples/example_semcog/configs/write_trip_matrices.yaml rename to activitysim/examples/prototype_semcog/configs/write_trip_matrices.yaml index 3acdef9106..1ee28c0f8b 100755 --- a/activitysim/examples/example_semcog/configs/write_trip_matrices.yaml +++ b/activitysim/examples/prototype_semcog/configs/write_trip_matrices.yaml @@ -1,258 +1,258 @@ -# read trips table post preprocessor and run expressions to code -# additional data fields, with one data fields for each matrix specified below - -preprocessor: - SPEC: write_trip_matrices_annotate_trips_preprocessor - DF: trips - TABLES: - - tours - -# divide trip counts by household expansion factor -HH_EXPANSION_WEIGHT_COL: sample_rate # added when households read in - -# save preprocessed trips table to pipeline if desired -SAVE_TRIPS_TABLE: False - -MATRICES: - - file_name: trips_ea.omx - tables: - - name: DRIVEALONE - data_field: DRIVEALONE_EA - - name: SHARED2 - data_field: SHARED2_EA - - name: SHARED3 - data_field: SHARED3_EA - - name: WALK - data_field: WALK_EA - - name: BIKE - data_field: BIKE_EA - - name: WALK_LOC - data_field: WALK_LOC_EA - - name: WALK_PRM - data_field: WALK_PRM_EA - - name: WALK_MIX - data_field: WALK_MIX_EA - - name: PNR_LOC - data_field: PNR_LOC_EA - - name: PNR_PRM - data_field: PNR_PRM_EA - - name: PNR_MIX - data_field: PNR_MIX_EA - - name: KNR_LOC - data_field: KNR_LOC_EA - - name: KNR_PRM - data_field: KNR_PRM_EA - - name: KNR_MIX - data_field: KNR_MIX_EA - - name: PNRE_LOC - data_field: PNRE_LOC_EA - - name: PNRE_PRM - data_field: PNRE_PRM_EA - - name: PNRE_MIX - data_field: PNRE_MIX_EA - - name: KNRE_LOC - data_field: KNRE_LOC_EA - - name: KNRE_PRM - data_field: KNRE_PRM_EA - - name: KNRE_MIX - data_field: KNRE_MIX_EA - - name: SCHOOLBUS - data_field: SCHOOLBUS_EA - - file_name: trips_am.omx - tables: - - name: DRIVEALONE - data_field: DRIVEALONE_AM - - name: SHARED2 - data_field: SHARED2_AM - - name: SHARED3 - data_field: SHARED3_AM - - name: WALK - data_field: WALK_AM - - name: BIKE - data_field: BIKE_AM - - name: WALK_LOC - data_field: WALK_LOC_AM - - name: WALK_PRM - data_field: WALK_PRM_AM - - name: WALK_MIX - data_field: WALK_MIX_AM - - name: PNR_LOC - data_field: PNR_LOC_AM - - name: PNR_PRM - data_field: PNR_PRM_AM - - name: PNR_MIX - data_field: PNR_MIX_AM - - name: KNR_LOC - data_field: KNR_LOC_AM - - name: KNR_PRM - data_field: KNR_PRM_AM - - name: KNR_MIX - data_field: KNR_MIX_AM - - name: PNRE_LOC - data_field: PNRE_LOC_AM - - name: PNRE_PRM - data_field: PNRE_PRM_AM - - name: PNRE_MIX - data_field: PNRE_MIX_AM - - name: KNRE_LOC - data_field: KNRE_LOC_AM - - name: KNRE_PRM - data_field: KNRE_PRM_AM - - name: KNRE_MIX - data_field: KNRE_MIX_AM - - name: SCHOOLBUS - data_field: SCHOOLBUS_AM - - file_name: trips_md.omx - tables: - - name: DRIVEALONE - data_field: DRIVEALONE_MD - - name: SHARED2 - data_field: SHARED2_MD - - name: SHARED3 - data_field: SHARED3_MD - - name: WALK - data_field: WALK_MD - - name: BIKE - data_field: BIKE_MD - - name: WALK_LOC - data_field: WALK_LOC_MD - - name: WALK_PRM - data_field: WALK_PRM_MD - - name: WALK_MIX - data_field: WALK_MIX_MD - - name: PNR_LOC - data_field: PNR_LOC_MD - - name: PNR_PRM - data_field: PNR_PRM_MD - - name: PNR_MIX - data_field: PNR_MIX_MD - - name: KNR_LOC - data_field: KNR_LOC_MD - - name: KNR_PRM - data_field: KNR_PRM_MD - - name: KNR_MIX - data_field: KNR_MIX_MD - - name: PNRE_LOC - data_field: PNRE_LOC_MD - - name: PNRE_PRM - data_field: PNRE_PRM_MD - - name: PNRE_MIX - data_field: PNRE_MIX_MD - - name: KNRE_LOC - data_field: KNRE_LOC_MD - - name: KNRE_PRM - data_field: KNRE_PRM_MD - - name: KNRE_MIX - data_field: KNRE_MIX_MD - - name: SCHOOLBUS - data_field: SCHOOLBUS_MD - - file_name: trips_pm.omx - tables: - - name: DRIVEALONE - data_field: DRIVEALONE_PM - - name: SHARED2 - data_field: SHARED2_PM - - name: SHARED3 - data_field: SHARED3_PM - - name: WALK - data_field: WALK_PM - - name: BIKE - data_field: BIKE_PM - - name: WALK_LOC - data_field: WALK_LOC_PM - - name: WALK_PRM - data_field: WALK_PRM_PM - - name: WALK_MIX - data_field: WALK_MIX_PM - - name: PNR_LOC - data_field: PNR_LOC_PM - - name: PNR_PRM - data_field: PNR_PRM_PM - - name: PNR_MIX - data_field: PNR_MIX_PM - - name: KNR_LOC - data_field: KNR_LOC_PM - - name: KNR_PRM - data_field: KNR_PRM_PM - - name: KNR_MIX - data_field: KNR_MIX_PM - - name: PNRE_LOC - data_field: PNRE_LOC_PM - - name: PNRE_PRM - data_field: PNRE_PRM_PM - - name: PNRE_MIX - data_field: PNRE_MIX_PM - - name: KNRE_LOC - data_field: KNRE_LOC_PM - - name: KNRE_PRM - data_field: KNRE_PRM_PM - - name: KNRE_MIX - data_field: KNRE_MIX_PM - - name: SCHOOLBUS - data_field: SCHOOLBUS_PM - - file_name: trips_ev.omx - tables: - - name: DRIVEALONE - data_field: DRIVEALONE_EV - - name: SHARED2 - data_field: SHARED2_EV - - name: SHARED3 - data_field: SHARED3_EV - - name: WALK - data_field: WALK_EV - - name: BIKE - data_field: BIKE_EV - - name: WALK_LOC - data_field: WALK_LOC_EV - - name: WALK_PRM - data_field: WALK_PRM_EV - - name: WALK_MIX - data_field: WALK_MIX_EV - - name: PNR_LOC - data_field: PNR_LOC_EV - - name: PNR_PRM - data_field: PNR_PRM_EV - - name: PNR_MIX - data_field: PNR_MIX_EV - - name: KNR_LOC - data_field: KNR_LOC_EV - - name: KNR_PRM - data_field: KNR_PRM_EV - - name: KNR_MIX - data_field: KNR_MIX_EV - - name: PNRE_LOC - data_field: PNRE_LOC_EV - - name: PNRE_PRM - data_field: PNRE_PRM_EV - - name: PNRE_MIX - data_field: PNRE_MIX_EV - - name: KNRE_LOC - data_field: KNRE_LOC_EV - - name: KNRE_PRM - data_field: KNRE_PRM_EV - - name: KNRE_MIX - data_field: KNRE_MIX_EV - - name: SCHOOLBUS - data_field: SCHOOLBUS_EV - -CONSTANTS: - time_periods: - EA: - first_hour: 1 - last_hour: 6 - AM: - first_hour: 7 - last_hour: 12 - MD: - first_hour: 13 - last_hour: 24 - PM: - first_hour: 25 - last_hour: 32 - EV: - first_hour: 33 - last_hour: 48 - - # SHARED2 and SHARED2 Occupancies - OCC_SHARED2: 2.0 - OCC_SHARED3: 3.33 +# read trips table post preprocessor and run expressions to code +# additional data fields, with one data fields for each matrix specified below + +preprocessor: + SPEC: write_trip_matrices_annotate_trips_preprocessor + DF: trips + TABLES: + - tours + +# divide trip counts by household expansion factor +HH_EXPANSION_WEIGHT_COL: sample_rate # added when households read in + +# save preprocessed trips table to pipeline if desired +SAVE_TRIPS_TABLE: False + +MATRICES: + - file_name: trips_ea.omx + tables: + - name: DRIVEALONE + data_field: DRIVEALONE_EA + - name: SHARED2 + data_field: SHARED2_EA + - name: SHARED3 + data_field: SHARED3_EA + - name: WALK + data_field: WALK_EA + - name: BIKE + data_field: BIKE_EA + - name: WALK_LOC + data_field: WALK_LOC_EA + - name: WALK_PRM + data_field: WALK_PRM_EA + - name: WALK_MIX + data_field: WALK_MIX_EA + - name: PNR_LOC + data_field: PNR_LOC_EA + - name: PNR_PRM + data_field: PNR_PRM_EA + - name: PNR_MIX + data_field: PNR_MIX_EA + - name: KNR_LOC + data_field: KNR_LOC_EA + - name: KNR_PRM + data_field: KNR_PRM_EA + - name: KNR_MIX + data_field: KNR_MIX_EA + - name: PNRE_LOC + data_field: PNRE_LOC_EA + - name: PNRE_PRM + data_field: PNRE_PRM_EA + - name: PNRE_MIX + data_field: PNRE_MIX_EA + - name: KNRE_LOC + data_field: KNRE_LOC_EA + - name: KNRE_PRM + data_field: KNRE_PRM_EA + - name: KNRE_MIX + data_field: KNRE_MIX_EA + - name: SCHOOLBUS + data_field: SCHOOLBUS_EA + - file_name: trips_am.omx + tables: + - name: DRIVEALONE + data_field: DRIVEALONE_AM + - name: SHARED2 + data_field: SHARED2_AM + - name: SHARED3 + data_field: SHARED3_AM + - name: WALK + data_field: WALK_AM + - name: BIKE + data_field: BIKE_AM + - name: WALK_LOC + data_field: WALK_LOC_AM + - name: WALK_PRM + data_field: WALK_PRM_AM + - name: WALK_MIX + data_field: WALK_MIX_AM + - name: PNR_LOC + data_field: PNR_LOC_AM + - name: PNR_PRM + data_field: PNR_PRM_AM + - name: PNR_MIX + data_field: PNR_MIX_AM + - name: KNR_LOC + data_field: KNR_LOC_AM + - name: KNR_PRM + data_field: KNR_PRM_AM + - name: KNR_MIX + data_field: KNR_MIX_AM + - name: PNRE_LOC + data_field: PNRE_LOC_AM + - name: PNRE_PRM + data_field: PNRE_PRM_AM + - name: PNRE_MIX + data_field: PNRE_MIX_AM + - name: KNRE_LOC + data_field: KNRE_LOC_AM + - name: KNRE_PRM + data_field: KNRE_PRM_AM + - name: KNRE_MIX + data_field: KNRE_MIX_AM + - name: SCHOOLBUS + data_field: SCHOOLBUS_AM + - file_name: trips_md.omx + tables: + - name: DRIVEALONE + data_field: DRIVEALONE_MD + - name: SHARED2 + data_field: SHARED2_MD + - name: SHARED3 + data_field: SHARED3_MD + - name: WALK + data_field: WALK_MD + - name: BIKE + data_field: BIKE_MD + - name: WALK_LOC + data_field: WALK_LOC_MD + - name: WALK_PRM + data_field: WALK_PRM_MD + - name: WALK_MIX + data_field: WALK_MIX_MD + - name: PNR_LOC + data_field: PNR_LOC_MD + - name: PNR_PRM + data_field: PNR_PRM_MD + - name: PNR_MIX + data_field: PNR_MIX_MD + - name: KNR_LOC + data_field: KNR_LOC_MD + - name: KNR_PRM + data_field: KNR_PRM_MD + - name: KNR_MIX + data_field: KNR_MIX_MD + - name: PNRE_LOC + data_field: PNRE_LOC_MD + - name: PNRE_PRM + data_field: PNRE_PRM_MD + - name: PNRE_MIX + data_field: PNRE_MIX_MD + - name: KNRE_LOC + data_field: KNRE_LOC_MD + - name: KNRE_PRM + data_field: KNRE_PRM_MD + - name: KNRE_MIX + data_field: KNRE_MIX_MD + - name: SCHOOLBUS + data_field: SCHOOLBUS_MD + - file_name: trips_pm.omx + tables: + - name: DRIVEALONE + data_field: DRIVEALONE_PM + - name: SHARED2 + data_field: SHARED2_PM + - name: SHARED3 + data_field: SHARED3_PM + - name: WALK + data_field: WALK_PM + - name: BIKE + data_field: BIKE_PM + - name: WALK_LOC + data_field: WALK_LOC_PM + - name: WALK_PRM + data_field: WALK_PRM_PM + - name: WALK_MIX + data_field: WALK_MIX_PM + - name: PNR_LOC + data_field: PNR_LOC_PM + - name: PNR_PRM + data_field: PNR_PRM_PM + - name: PNR_MIX + data_field: PNR_MIX_PM + - name: KNR_LOC + data_field: KNR_LOC_PM + - name: KNR_PRM + data_field: KNR_PRM_PM + - name: KNR_MIX + data_field: KNR_MIX_PM + - name: PNRE_LOC + data_field: PNRE_LOC_PM + - name: PNRE_PRM + data_field: PNRE_PRM_PM + - name: PNRE_MIX + data_field: PNRE_MIX_PM + - name: KNRE_LOC + data_field: KNRE_LOC_PM + - name: KNRE_PRM + data_field: KNRE_PRM_PM + - name: KNRE_MIX + data_field: KNRE_MIX_PM + - name: SCHOOLBUS + data_field: SCHOOLBUS_PM + - file_name: trips_ev.omx + tables: + - name: DRIVEALONE + data_field: DRIVEALONE_EV + - name: SHARED2 + data_field: SHARED2_EV + - name: SHARED3 + data_field: SHARED3_EV + - name: WALK + data_field: WALK_EV + - name: BIKE + data_field: BIKE_EV + - name: WALK_LOC + data_field: WALK_LOC_EV + - name: WALK_PRM + data_field: WALK_PRM_EV + - name: WALK_MIX + data_field: WALK_MIX_EV + - name: PNR_LOC + data_field: PNR_LOC_EV + - name: PNR_PRM + data_field: PNR_PRM_EV + - name: PNR_MIX + data_field: PNR_MIX_EV + - name: KNR_LOC + data_field: KNR_LOC_EV + - name: KNR_PRM + data_field: KNR_PRM_EV + - name: KNR_MIX + data_field: KNR_MIX_EV + - name: PNRE_LOC + data_field: PNRE_LOC_EV + - name: PNRE_PRM + data_field: PNRE_PRM_EV + - name: PNRE_MIX + data_field: PNRE_MIX_EV + - name: KNRE_LOC + data_field: KNRE_LOC_EV + - name: KNRE_PRM + data_field: KNRE_PRM_EV + - name: KNRE_MIX + data_field: KNRE_MIX_EV + - name: SCHOOLBUS + data_field: SCHOOLBUS_EV + +CONSTANTS: + time_periods: + EA: + first_hour: 1 + last_hour: 6 + AM: + first_hour: 7 + last_hour: 12 + MD: + first_hour: 13 + last_hour: 24 + PM: + first_hour: 25 + last_hour: 32 + EV: + first_hour: 33 + last_hour: 48 + + # SHARED2 and SHARED2 Occupancies + OCC_SHARED2: 2.0 + OCC_SHARED3: 3.33 diff --git a/activitysim/examples/example_semcog/configs/write_trip_matrices_annotate_trips_preprocessor.csv b/activitysim/examples/prototype_semcog/configs/write_trip_matrices_annotate_trips_preprocessor.csv similarity index 98% rename from activitysim/examples/example_semcog/configs/write_trip_matrices_annotate_trips_preprocessor.csv rename to activitysim/examples/prototype_semcog/configs/write_trip_matrices_annotate_trips_preprocessor.csv index 163aaf21a7..d7b80f3a16 100755 --- a/activitysim/examples/example_semcog/configs/write_trip_matrices_annotate_trips_preprocessor.csv +++ b/activitysim/examples/prototype_semcog/configs/write_trip_matrices_annotate_trips_preprocessor.csv @@ -1,120 +1,120 @@ -Description,Target,Expression -# add additional fields,, -,tour_participants,trips.tour_id.map(tours.number_of_participants) -,distance,od_skims['DIST'] -# code time periods,, -,is_ea,"trips.depart.between(time_periods['EA']['first_hour'], time_periods['EA']['last_hour'])" -,is_am,"trips.depart.between(time_periods['AM']['first_hour'], time_periods['AM']['last_hour'])" -,is_md,"trips.depart.between(time_periods['MD']['first_hour'], time_periods['MD']['last_hour'])" -,is_pm,"trips.depart.between(time_periods['PM']['first_hour'], time_periods['PM']['last_hour'])" -,is_ev,"trips.depart.between(time_periods['EV']['first_hour'], time_periods['EV']['last_hour'])" -# ea trips,, -,DRIVEALONE_EA,((trips.trip_mode == 'DRIVEALONE') & is_ea) * tour_participants -,SHARED2_EA,((trips.trip_mode == 'SHARED2') & is_ea) * tour_participants / OCC_SHARED2 -,SHARED3_EA,((trips.trip_mode == 'SHARED3') & is_ea) * tour_participants / OCC_SHARED3 -,WALK_EA,((trips.trip_mode == 'WALK') & is_ea) * tour_participants -,BIKE_EA,((trips.trip_mode == 'BIKE') & is_ea) * tour_participants -,WALK_LOC_EA,((trips.trip_mode == 'WALK_LOC') & is_ea) * tour_participants -,WALK_PRM_EA,((trips.trip_mode == 'WALK_PRM') & is_ea) * tour_participants -,WALK_MIX_EA,((trips.trip_mode == 'WALK_MIX') & is_ea) * tour_participants -,PNR_LOC_EA,((trips.trip_mode == 'PNR_LOC') & is_ea & trips.outbound) * tour_participants -,PNR_PRM_EA,((trips.trip_mode == 'PNR_PRM') & is_ea & trips.outbound) * tour_participants -,PNR_MIX_EA,((trips.trip_mode == 'PNR_MIX') & is_ea & trips.outbound) * tour_participants -,KNR_LOC_EA,((trips.trip_mode == 'KNR_LOC') & is_ea & trips.outbound) * tour_participants -,KNR_PRM_EA,((trips.trip_mode == 'KNR_PRM') & is_ea & trips.outbound) * tour_participants -,KNR_MIX_EA,((trips.trip_mode == 'KNR_MIX') & is_ea & trips.outbound) * tour_participants -,PNRE_LOC_EA,((trips.trip_mode == 'PNR_LOC') & is_ea & ~trips.outbound) * tour_participants -,PNRE_PRM_EA,((trips.trip_mode == 'PNR_PRM') & is_ea & ~trips.outbound) * tour_participants -,PNRE_MIX_EA,((trips.trip_mode == 'PNR_MIX') & is_ea & ~trips.outbound) * tour_participants -,KNRE_LOC_EA,((trips.trip_mode == 'KNR_LOC') & is_ea & ~trips.outbound) * tour_participants -,KNRE_PRM_EA,((trips.trip_mode == 'KNR_PRM') & is_ea & ~trips.outbound) * tour_participants -,KNRE_MIX_EA,((trips.trip_mode == 'KNR_MIX') & is_ea & ~trips.outbound) * tour_participants -,SCHOOLBUS_EA,((trips.trip_mode == 'SCHOOLBUS') & is_ea) * tour_participants -# am trips,, -,DRIVEALONE_AM,((trips.trip_mode == 'DRIVEALONE') & is_am) * tour_participants -,SHARED2_AM,((trips.trip_mode == 'SHARED2') & is_am) * tour_participants / OCC_SHARED2 -,SHARED3_AM,((trips.trip_mode == 'SHARED3') & is_am) * tour_participants / OCC_SHARED3 -,WALK_AM,((trips.trip_mode == 'WALK') & is_am) * tour_participants -,BIKE_AM,((trips.trip_mode == 'BIKE') & is_am) * tour_participants -,WALK_LOC_AM,((trips.trip_mode == 'WALK_LOC') & is_am) * tour_participants -,WALK_PRM_AM,((trips.trip_mode == 'WALK_PRM') & is_am) * tour_participants -,WALK_MIX_AM,((trips.trip_mode == 'WALK_MIX') & is_am) * tour_participants -,PNR_LOC_AM,((trips.trip_mode == 'PNR_LOC') & is_am & trips.outbound) * tour_participants -,PNR_PRM_AM,((trips.trip_mode == 'PNR_PRM') & is_am & trips.outbound) * tour_participants -,PNR_MIX_AM,((trips.trip_mode == 'PNR_MIX') & is_am & trips.outbound) * tour_participants -,KNR_LOC_AM,((trips.trip_mode == 'KNR_LOC') & is_am & trips.outbound) * tour_participants -,KNR_PRM_AM,((trips.trip_mode == 'KNR_PRM') & is_am & trips.outbound) * tour_participants -,KNR_MIX_AM,((trips.trip_mode == 'KNR_MIX') & is_am & trips.outbound) * tour_participants -,PNRE_LOC_AM,((trips.trip_mode == 'PNR_LOC') & is_am & ~trips.outbound) * tour_participants -,PNRE_PRM_AM,((trips.trip_mode == 'PNR_PRM') & is_am & ~trips.outbound) * tour_participants -,PNRE_MIX_AM,((trips.trip_mode == 'PNR_MIX') & is_am & ~trips.outbound) * tour_participants -,KNRE_LOC_AM,((trips.trip_mode == 'KNR_LOC') & is_am & ~trips.outbound) * tour_participants -,KNRE_PRM_AM,((trips.trip_mode == 'KNR_PRM') & is_am & ~trips.outbound) * tour_participants -,KNRE_MIX_AM,((trips.trip_mode == 'KNR_MIX') & is_am & ~trips.outbound) * tour_participants -,SCHOOLBUS_AM,((trips.trip_mode == 'SCHOOLBUS') & is_am) * tour_participants -# md trips,, -,DRIVEALONE_MD,((trips.trip_mode == 'DRIVEALONE') & is_md) * tour_participants -,SHARED2_MD,((trips.trip_mode == 'SHARED2') & is_md) * tour_participants / OCC_SHARED2 -,SHARED3_MD,((trips.trip_mode == 'SHARED3') & is_md) * tour_participants / OCC_SHARED3 -,WALK_MD,((trips.trip_mode == 'WALK') & is_md) * tour_participants -,BIKE_MD,((trips.trip_mode == 'BIKE') & is_md) * tour_participants -,WALK_LOC_MD,((trips.trip_mode == 'WALK_LOC') & is_md) * tour_participants -,WALK_PRM_MD,((trips.trip_mode == 'WALK_PRM') & is_md) * tour_participants -,WALK_MIX_MD,((trips.trip_mode == 'WALK_MIX') & is_md) * tour_participants -,PNR_LOC_MD,((trips.trip_mode == 'PNR_LOC') & is_md & trips.outbound) * tour_participants -,PNR_PRM_MD,((trips.trip_mode == 'PNR_PRM') & is_md & trips.outbound) * tour_participants -,PNR_MIX_MD,((trips.trip_mode == 'PNR_MIX') & is_md & trips.outbound) * tour_participants -,KNR_LOC_MD,((trips.trip_mode == 'KNR_LOC') & is_md & trips.outbound) * tour_participants -,KNR_PRM_MD,((trips.trip_mode == 'KNR_PRM') & is_md & trips.outbound) * tour_participants -,KNR_MIX_MD,((trips.trip_mode == 'KNR_MIX') & is_md & trips.outbound) * tour_participants -,PNRE_LOC_MD,((trips.trip_mode == 'PNR_LOC') & is_md & ~trips.outbound) * tour_participants -,PNRE_PRM_MD,((trips.trip_mode == 'PNR_PRM') & is_md & ~trips.outbound) * tour_participants -,PNRE_MIX_MD,((trips.trip_mode == 'PNR_MIX') & is_md & ~trips.outbound) * tour_participants -,KNRE_LOC_MD,((trips.trip_mode == 'KNR_LOC') & is_md & ~trips.outbound) * tour_participants -,KNRE_PRM_MD,((trips.trip_mode == 'KNR_PRM') & is_md & ~trips.outbound) * tour_participants -,KNRE_MIX_MD,((trips.trip_mode == 'KNR_MIX') & is_md & ~trips.outbound) * tour_participants -,SCHOOLBUS_MD,((trips.trip_mode == 'SCHOOLBUS') & is_md) * tour_participants -# pm trips,, -,DRIVEALONE_PM,((trips.trip_mode == 'DRIVEALONE') & is_pm) * tour_participants -,SHARED2_PM,((trips.trip_mode == 'SHARED2') & is_pm) * tour_participants / OCC_SHARED2 -,SHARED3_PM,((trips.trip_mode == 'SHARED3') & is_pm) * tour_participants / OCC_SHARED3 -,WALK_PM,((trips.trip_mode == 'WALK') & is_pm) * tour_participants -,BIKE_PM,((trips.trip_mode == 'BIKE') & is_pm) * tour_participants -,WALK_LOC_PM,((trips.trip_mode == 'WALK_LOC') & is_pm) * tour_participants -,WALK_PRM_PM,((trips.trip_mode == 'WALK_PRM') & is_pm) * tour_participants -,WALK_MIX_PM,((trips.trip_mode == 'WALK_MIX') & is_pm) * tour_participants -,PNR_LOC_PM,((trips.trip_mode == 'PNR_LOC') & is_pm & trips.outbound) * tour_participants -,PNR_PRM_PM,((trips.trip_mode == 'PNR_PRM') & is_pm & trips.outbound) * tour_participants -,PNR_MIX_PM,((trips.trip_mode == 'PNR_MIX') & is_pm & trips.outbound) * tour_participants -,KNR_LOC_PM,((trips.trip_mode == 'KNR_LOC') & is_pm & trips.outbound) * tour_participants -,KNR_PRM_PM,((trips.trip_mode == 'KNR_PRM') & is_pm & trips.outbound) * tour_participants -,KNR_MIX_PM,((trips.trip_mode == 'KNR_MIX') & is_pm & trips.outbound) * tour_participants -,PNRE_LOC_PM,((trips.trip_mode == 'PNR_LOC') & is_pm & ~trips.outbound) * tour_participants -,PNRE_PRM_PM,((trips.trip_mode == 'PNR_PRM') & is_pm & ~trips.outbound) * tour_participants -,PNRE_MIX_PM,((trips.trip_mode == 'PNR_MIX') & is_pm & ~trips.outbound) * tour_participants -,KNRE_LOC_PM,((trips.trip_mode == 'KNR_LOC') & is_pm & ~trips.outbound) * tour_participants -,KNRE_PRM_PM,((trips.trip_mode == 'KNR_PRM') & is_pm & ~trips.outbound) * tour_participants -,KNRE_MIX_PM,((trips.trip_mode == 'KNR_MIX') & is_pm & ~trips.outbound) * tour_participants -,SCHOOLBUS_PM,((trips.trip_mode == 'SCHOOLBUS') & is_pm) * tour_participants -# ev trips,, -,DRIVEALONE_EV,((trips.trip_mode == 'DRIVEALONE') & is_ev) * tour_participants -,SHARED2_EV,((trips.trip_mode == 'SHARED2') & is_ev) * tour_participants / OCC_SHARED2 -,SHARED3_EV,((trips.trip_mode == 'SHARED3') & is_ev) * tour_participants / OCC_SHARED3 -,WALK_EV,((trips.trip_mode == 'WALK') & is_ev) * tour_participants -,BIKE_EV,((trips.trip_mode == 'BIKE') & is_ev) * tour_participants -,WALK_LOC_EV,((trips.trip_mode == 'WALK_LOC') & is_ev) * tour_participants -,WALK_PRM_EV,((trips.trip_mode == 'WALK_PRM') & is_ev) * tour_participants -,WALK_MIX_EV,((trips.trip_mode == 'WALK_MIX') & is_ev) * tour_participants -,PNR_LOC_EV,((trips.trip_mode == 'PNR_LOC') & is_ev & trips.outbound) * tour_participants -,PNR_PRM_EV,((trips.trip_mode == 'PNR_PRM') & is_ev & trips.outbound) * tour_participants -,PNR_MIX_EV,((trips.trip_mode == 'PNR_MIX') & is_ev & trips.outbound) * tour_participants -,KNR_LOC_EV,((trips.trip_mode == 'KNR_LOC') & is_ev & trips.outbound) * tour_participants -,KNR_PRM_EV,((trips.trip_mode == 'KNR_PRM') & is_ev & trips.outbound) * tour_participants -,KNR_MIX_EV,((trips.trip_mode == 'KNR_MIX') & is_ev & trips.outbound) * tour_participants -,PNRE_LOC_EV,((trips.trip_mode == 'PNR_LOC') & is_ev & ~trips.outbound) * tour_participants -,PNRE_PRM_EV,((trips.trip_mode == 'PNR_PRM') & is_ev & ~trips.outbound) * tour_participants -,PNRE_MIX_EV,((trips.trip_mode == 'PNR_MIX') & is_ev & ~trips.outbound) * tour_participants -,KNRE_LOC_EV,((trips.trip_mode == 'KNR_LOC') & is_ev & ~trips.outbound) * tour_participants -,KNRE_PRM_EV,((trips.trip_mode == 'KNR_PRM') & is_ev & ~trips.outbound) * tour_participants -,KNRE_MIX_EV,((trips.trip_mode == 'KNR_MIX') & is_ev & ~trips.outbound) * tour_participants -,SCHOOLBUS_EV,((trips.trip_mode == 'SCHOOLBUS') & is_ev) * tour_participants +Description,Target,Expression +# add additional fields,, +,tour_participants,trips.tour_id.map(tours.number_of_participants) +,distance,od_skims['DIST'] +# code time periods,, +,is_ea,"trips.depart.between(time_periods['EA']['first_hour'], time_periods['EA']['last_hour'])" +,is_am,"trips.depart.between(time_periods['AM']['first_hour'], time_periods['AM']['last_hour'])" +,is_md,"trips.depart.between(time_periods['MD']['first_hour'], time_periods['MD']['last_hour'])" +,is_pm,"trips.depart.between(time_periods['PM']['first_hour'], time_periods['PM']['last_hour'])" +,is_ev,"trips.depart.between(time_periods['EV']['first_hour'], time_periods['EV']['last_hour'])" +# ea trips,, +,DRIVEALONE_EA,((trips.trip_mode == 'DRIVEALONE') & is_ea) * tour_participants +,SHARED2_EA,((trips.trip_mode == 'SHARED2') & is_ea) * tour_participants / OCC_SHARED2 +,SHARED3_EA,((trips.trip_mode == 'SHARED3') & is_ea) * tour_participants / OCC_SHARED3 +,WALK_EA,((trips.trip_mode == 'WALK') & is_ea) * tour_participants +,BIKE_EA,((trips.trip_mode == 'BIKE') & is_ea) * tour_participants +,WALK_LOC_EA,((trips.trip_mode == 'WALK_LOC') & is_ea) * tour_participants +,WALK_PRM_EA,((trips.trip_mode == 'WALK_PRM') & is_ea) * tour_participants +,WALK_MIX_EA,((trips.trip_mode == 'WALK_MIX') & is_ea) * tour_participants +,PNR_LOC_EA,((trips.trip_mode == 'PNR_LOC') & is_ea & trips.outbound) * tour_participants +,PNR_PRM_EA,((trips.trip_mode == 'PNR_PRM') & is_ea & trips.outbound) * tour_participants +,PNR_MIX_EA,((trips.trip_mode == 'PNR_MIX') & is_ea & trips.outbound) * tour_participants +,KNR_LOC_EA,((trips.trip_mode == 'KNR_LOC') & is_ea & trips.outbound) * tour_participants +,KNR_PRM_EA,((trips.trip_mode == 'KNR_PRM') & is_ea & trips.outbound) * tour_participants +,KNR_MIX_EA,((trips.trip_mode == 'KNR_MIX') & is_ea & trips.outbound) * tour_participants +,PNRE_LOC_EA,((trips.trip_mode == 'PNR_LOC') & is_ea & ~trips.outbound) * tour_participants +,PNRE_PRM_EA,((trips.trip_mode == 'PNR_PRM') & is_ea & ~trips.outbound) * tour_participants +,PNRE_MIX_EA,((trips.trip_mode == 'PNR_MIX') & is_ea & ~trips.outbound) * tour_participants +,KNRE_LOC_EA,((trips.trip_mode == 'KNR_LOC') & is_ea & ~trips.outbound) * tour_participants +,KNRE_PRM_EA,((trips.trip_mode == 'KNR_PRM') & is_ea & ~trips.outbound) * tour_participants +,KNRE_MIX_EA,((trips.trip_mode == 'KNR_MIX') & is_ea & ~trips.outbound) * tour_participants +,SCHOOLBUS_EA,((trips.trip_mode == 'SCHOOLBUS') & is_ea) * tour_participants +# am trips,, +,DRIVEALONE_AM,((trips.trip_mode == 'DRIVEALONE') & is_am) * tour_participants +,SHARED2_AM,((trips.trip_mode == 'SHARED2') & is_am) * tour_participants / OCC_SHARED2 +,SHARED3_AM,((trips.trip_mode == 'SHARED3') & is_am) * tour_participants / OCC_SHARED3 +,WALK_AM,((trips.trip_mode == 'WALK') & is_am) * tour_participants +,BIKE_AM,((trips.trip_mode == 'BIKE') & is_am) * tour_participants +,WALK_LOC_AM,((trips.trip_mode == 'WALK_LOC') & is_am) * tour_participants +,WALK_PRM_AM,((trips.trip_mode == 'WALK_PRM') & is_am) * tour_participants +,WALK_MIX_AM,((trips.trip_mode == 'WALK_MIX') & is_am) * tour_participants +,PNR_LOC_AM,((trips.trip_mode == 'PNR_LOC') & is_am & trips.outbound) * tour_participants +,PNR_PRM_AM,((trips.trip_mode == 'PNR_PRM') & is_am & trips.outbound) * tour_participants +,PNR_MIX_AM,((trips.trip_mode == 'PNR_MIX') & is_am & trips.outbound) * tour_participants +,KNR_LOC_AM,((trips.trip_mode == 'KNR_LOC') & is_am & trips.outbound) * tour_participants +,KNR_PRM_AM,((trips.trip_mode == 'KNR_PRM') & is_am & trips.outbound) * tour_participants +,KNR_MIX_AM,((trips.trip_mode == 'KNR_MIX') & is_am & trips.outbound) * tour_participants +,PNRE_LOC_AM,((trips.trip_mode == 'PNR_LOC') & is_am & ~trips.outbound) * tour_participants +,PNRE_PRM_AM,((trips.trip_mode == 'PNR_PRM') & is_am & ~trips.outbound) * tour_participants +,PNRE_MIX_AM,((trips.trip_mode == 'PNR_MIX') & is_am & ~trips.outbound) * tour_participants +,KNRE_LOC_AM,((trips.trip_mode == 'KNR_LOC') & is_am & ~trips.outbound) * tour_participants +,KNRE_PRM_AM,((trips.trip_mode == 'KNR_PRM') & is_am & ~trips.outbound) * tour_participants +,KNRE_MIX_AM,((trips.trip_mode == 'KNR_MIX') & is_am & ~trips.outbound) * tour_participants +,SCHOOLBUS_AM,((trips.trip_mode == 'SCHOOLBUS') & is_am) * tour_participants +# md trips,, +,DRIVEALONE_MD,((trips.trip_mode == 'DRIVEALONE') & is_md) * tour_participants +,SHARED2_MD,((trips.trip_mode == 'SHARED2') & is_md) * tour_participants / OCC_SHARED2 +,SHARED3_MD,((trips.trip_mode == 'SHARED3') & is_md) * tour_participants / OCC_SHARED3 +,WALK_MD,((trips.trip_mode == 'WALK') & is_md) * tour_participants +,BIKE_MD,((trips.trip_mode == 'BIKE') & is_md) * tour_participants +,WALK_LOC_MD,((trips.trip_mode == 'WALK_LOC') & is_md) * tour_participants +,WALK_PRM_MD,((trips.trip_mode == 'WALK_PRM') & is_md) * tour_participants +,WALK_MIX_MD,((trips.trip_mode == 'WALK_MIX') & is_md) * tour_participants +,PNR_LOC_MD,((trips.trip_mode == 'PNR_LOC') & is_md & trips.outbound) * tour_participants +,PNR_PRM_MD,((trips.trip_mode == 'PNR_PRM') & is_md & trips.outbound) * tour_participants +,PNR_MIX_MD,((trips.trip_mode == 'PNR_MIX') & is_md & trips.outbound) * tour_participants +,KNR_LOC_MD,((trips.trip_mode == 'KNR_LOC') & is_md & trips.outbound) * tour_participants +,KNR_PRM_MD,((trips.trip_mode == 'KNR_PRM') & is_md & trips.outbound) * tour_participants +,KNR_MIX_MD,((trips.trip_mode == 'KNR_MIX') & is_md & trips.outbound) * tour_participants +,PNRE_LOC_MD,((trips.trip_mode == 'PNR_LOC') & is_md & ~trips.outbound) * tour_participants +,PNRE_PRM_MD,((trips.trip_mode == 'PNR_PRM') & is_md & ~trips.outbound) * tour_participants +,PNRE_MIX_MD,((trips.trip_mode == 'PNR_MIX') & is_md & ~trips.outbound) * tour_participants +,KNRE_LOC_MD,((trips.trip_mode == 'KNR_LOC') & is_md & ~trips.outbound) * tour_participants +,KNRE_PRM_MD,((trips.trip_mode == 'KNR_PRM') & is_md & ~trips.outbound) * tour_participants +,KNRE_MIX_MD,((trips.trip_mode == 'KNR_MIX') & is_md & ~trips.outbound) * tour_participants +,SCHOOLBUS_MD,((trips.trip_mode == 'SCHOOLBUS') & is_md) * tour_participants +# pm trips,, +,DRIVEALONE_PM,((trips.trip_mode == 'DRIVEALONE') & is_pm) * tour_participants +,SHARED2_PM,((trips.trip_mode == 'SHARED2') & is_pm) * tour_participants / OCC_SHARED2 +,SHARED3_PM,((trips.trip_mode == 'SHARED3') & is_pm) * tour_participants / OCC_SHARED3 +,WALK_PM,((trips.trip_mode == 'WALK') & is_pm) * tour_participants +,BIKE_PM,((trips.trip_mode == 'BIKE') & is_pm) * tour_participants +,WALK_LOC_PM,((trips.trip_mode == 'WALK_LOC') & is_pm) * tour_participants +,WALK_PRM_PM,((trips.trip_mode == 'WALK_PRM') & is_pm) * tour_participants +,WALK_MIX_PM,((trips.trip_mode == 'WALK_MIX') & is_pm) * tour_participants +,PNR_LOC_PM,((trips.trip_mode == 'PNR_LOC') & is_pm & trips.outbound) * tour_participants +,PNR_PRM_PM,((trips.trip_mode == 'PNR_PRM') & is_pm & trips.outbound) * tour_participants +,PNR_MIX_PM,((trips.trip_mode == 'PNR_MIX') & is_pm & trips.outbound) * tour_participants +,KNR_LOC_PM,((trips.trip_mode == 'KNR_LOC') & is_pm & trips.outbound) * tour_participants +,KNR_PRM_PM,((trips.trip_mode == 'KNR_PRM') & is_pm & trips.outbound) * tour_participants +,KNR_MIX_PM,((trips.trip_mode == 'KNR_MIX') & is_pm & trips.outbound) * tour_participants +,PNRE_LOC_PM,((trips.trip_mode == 'PNR_LOC') & is_pm & ~trips.outbound) * tour_participants +,PNRE_PRM_PM,((trips.trip_mode == 'PNR_PRM') & is_pm & ~trips.outbound) * tour_participants +,PNRE_MIX_PM,((trips.trip_mode == 'PNR_MIX') & is_pm & ~trips.outbound) * tour_participants +,KNRE_LOC_PM,((trips.trip_mode == 'KNR_LOC') & is_pm & ~trips.outbound) * tour_participants +,KNRE_PRM_PM,((trips.trip_mode == 'KNR_PRM') & is_pm & ~trips.outbound) * tour_participants +,KNRE_MIX_PM,((trips.trip_mode == 'KNR_MIX') & is_pm & ~trips.outbound) * tour_participants +,SCHOOLBUS_PM,((trips.trip_mode == 'SCHOOLBUS') & is_pm) * tour_participants +# ev trips,, +,DRIVEALONE_EV,((trips.trip_mode == 'DRIVEALONE') & is_ev) * tour_participants +,SHARED2_EV,((trips.trip_mode == 'SHARED2') & is_ev) * tour_participants / OCC_SHARED2 +,SHARED3_EV,((trips.trip_mode == 'SHARED3') & is_ev) * tour_participants / OCC_SHARED3 +,WALK_EV,((trips.trip_mode == 'WALK') & is_ev) * tour_participants +,BIKE_EV,((trips.trip_mode == 'BIKE') & is_ev) * tour_participants +,WALK_LOC_EV,((trips.trip_mode == 'WALK_LOC') & is_ev) * tour_participants +,WALK_PRM_EV,((trips.trip_mode == 'WALK_PRM') & is_ev) * tour_participants +,WALK_MIX_EV,((trips.trip_mode == 'WALK_MIX') & is_ev) * tour_participants +,PNR_LOC_EV,((trips.trip_mode == 'PNR_LOC') & is_ev & trips.outbound) * tour_participants +,PNR_PRM_EV,((trips.trip_mode == 'PNR_PRM') & is_ev & trips.outbound) * tour_participants +,PNR_MIX_EV,((trips.trip_mode == 'PNR_MIX') & is_ev & trips.outbound) * tour_participants +,KNR_LOC_EV,((trips.trip_mode == 'KNR_LOC') & is_ev & trips.outbound) * tour_participants +,KNR_PRM_EV,((trips.trip_mode == 'KNR_PRM') & is_ev & trips.outbound) * tour_participants +,KNR_MIX_EV,((trips.trip_mode == 'KNR_MIX') & is_ev & trips.outbound) * tour_participants +,PNRE_LOC_EV,((trips.trip_mode == 'PNR_LOC') & is_ev & ~trips.outbound) * tour_participants +,PNRE_PRM_EV,((trips.trip_mode == 'PNR_PRM') & is_ev & ~trips.outbound) * tour_participants +,PNRE_MIX_EV,((trips.trip_mode == 'PNR_MIX') & is_ev & ~trips.outbound) * tour_participants +,KNRE_LOC_EV,((trips.trip_mode == 'KNR_LOC') & is_ev & ~trips.outbound) * tour_participants +,KNRE_PRM_EV,((trips.trip_mode == 'KNR_PRM') & is_ev & ~trips.outbound) * tour_participants +,KNRE_MIX_EV,((trips.trip_mode == 'KNR_MIX') & is_ev & ~trips.outbound) * tour_participants +,SCHOOLBUS_EV,((trips.trip_mode == 'SCHOOLBUS') & is_ev) * tour_participants diff --git a/activitysim/examples/example_semcog/configs_mp/logging.yaml b/activitysim/examples/prototype_semcog/configs_mp/logging.yaml similarity index 96% rename from activitysim/examples/example_semcog/configs_mp/logging.yaml rename to activitysim/examples/prototype_semcog/configs_mp/logging.yaml index 779b63632d..e932009c5d 100755 --- a/activitysim/examples/example_semcog/configs_mp/logging.yaml +++ b/activitysim/examples/prototype_semcog/configs_mp/logging.yaml @@ -1,57 +1,57 @@ -# Config for logging -# ------------------ -# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema - -logging: - version: 1 - disable_existing_loggers: true - - - # Configuring the default (root) logger is highly recommended - root: - level: DEBUG - handlers: [console, logfile] - - loggers: - - activitysim: - level: DEBUG - handlers: [console, logfile] - propagate: false - - orca: - level: WARNING - handlers: [console, logfile] - propagate: false - - handlers: - - logfile: - class: logging.FileHandler - filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] - mode: w - formatter: fileFormatter - level: NOTSET - - console: - class: logging.StreamHandler - stream: ext://sys.stdout - formatter: simpleFormatter - #level: NOTSET - level: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [WARNING, NOTSET] - - formatters: - - simpleFormatter: - class: logging.Formatter - #format: '%(processName)-10s %(levelname)s - %(name)s - %(message)s' - format: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [ - '%(processName)-10s %(levelname)s - %(name)s - %(message)s', - '%(levelname)s - %(name)s - %(message)s'] - datefmt: '%d/%m/%Y %H:%M:%S' - - fileFormatter: - class: logging.Formatter - format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' - datefmt: '%d/%m/%Y %H:%M:%S' - +# Config for logging +# ------------------ +# See http://docs.python.org/2.7/library/logging.config.html#configuration-dictionary-schema + +logging: + version: 1 + disable_existing_loggers: true + + + # Configuring the default (root) logger is highly recommended + root: + level: DEBUG + handlers: [console, logfile] + + loggers: + + activitysim: + level: DEBUG + handlers: [console, logfile] + propagate: false + + orca: + level: WARNING + handlers: [console, logfile] + propagate: false + + handlers: + + logfile: + class: logging.FileHandler + filename: !!python/object/apply:activitysim.core.config.log_file_path ['activitysim.log'] + mode: w + formatter: fileFormatter + level: NOTSET + + console: + class: logging.StreamHandler + stream: ext://sys.stdout + formatter: simpleFormatter + #level: NOTSET + level: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [WARNING, NOTSET] + + formatters: + + simpleFormatter: + class: logging.Formatter + #format: '%(processName)-10s %(levelname)s - %(name)s - %(message)s' + format: !!python/object/apply:activitysim.core.mp_tasks.if_sub_task [ + '%(processName)-10s %(levelname)s - %(name)s - %(message)s', + '%(levelname)s - %(name)s - %(message)s'] + datefmt: '%d/%m/%Y %H:%M:%S' + + fileFormatter: + class: logging.Formatter + format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s' + datefmt: '%d/%m/%Y %H:%M:%S' + diff --git a/activitysim/examples/example_semcog/configs_mp/settings.yaml b/activitysim/examples/prototype_semcog/configs_mp/settings.yaml similarity index 95% rename from activitysim/examples/example_semcog/configs_mp/settings.yaml rename to activitysim/examples/prototype_semcog/configs_mp/settings.yaml index 07e443c48b..f25ab67983 100755 --- a/activitysim/examples/example_semcog/configs_mp/settings.yaml +++ b/activitysim/examples/prototype_semcog/configs_mp/settings.yaml @@ -1,97 +1,97 @@ -inherit_settings: True - -# raise error if any sub-process fails without waiting for others to complete -fail_fast: True - -# - ------------------------- dev config -multiprocess: True -strict: False -use_shadow_pricing: True - - -## - example sample -households_sample_size: 0 -chunk_size: 0 -num_processes: 24 - -# - tracing -trace_hh_id: -trace_od: -#trace_hh_id: 1482966 -#trace_od: [5, 11] - -# to resume after last successful checkpoint, specify resume_after: _ -resume_after: - -models: - ### mp_initialize step - - initialize_landuse - - initialize_households - - compute_accessibility - - work_from_home - - add_size_tables - ### mp_households step - - school_location - - workplace_location - - auto_ownership_simulate - - free_parking - - telecommute_frequency - - cdap_simulate - - mandatory_tour_frequency - - mandatory_tour_scheduling - - joint_tour_frequency - - joint_tour_composition - - joint_tour_participation - - joint_tour_destination - - joint_tour_scheduling - - non_mandatory_tour_frequency - - non_mandatory_tour_destination - - non_mandatory_tour_scheduling - - tour_mode_choice_simulate - - atwork_subtour_frequency - - atwork_subtour_destination - - atwork_subtour_scheduling - - atwork_subtour_mode_choice - - stop_frequency - - trip_purpose - - trip_destination - - trip_purpose_and_destination - - trip_scheduling - - trip_mode_choice - ### mp_summarize step - - write_data_dictionary - - write_tables - - write_trip_matrices - -multiprocess_steps: - - name: mp_initialize - begin: initialize_landuse - - name: mp_households - begin: school_location - slice: - tables: - - households - - persons - - name: mp_summarize - begin: write_data_dictionary - - -output_tables: - action: include - prefix: final_ - tables: - - checkpoints - - accessibility - - land_use - - households - - persons - - tours - - trips - - school_shadow_prices - - workplace_shadow_prices - - joint_tour_participants - -# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) -#read_skim_cache: True -# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs -#write_skim_cache: True +inherit_settings: True + +# raise error if any sub-process fails without waiting for others to complete +fail_fast: True + +# - ------------------------- dev config +multiprocess: True +strict: False +use_shadow_pricing: True + + +## - example sample +households_sample_size: 0 +chunk_size: 0 +num_processes: 24 + +# - tracing +trace_hh_id: +trace_od: +#trace_hh_id: 1482966 +#trace_od: [5, 11] + +# to resume after last successful checkpoint, specify resume_after: _ +resume_after: + +models: + ### mp_initialize step + - initialize_landuse + - initialize_households + - compute_accessibility + - work_from_home + - add_size_tables + ### mp_households step + - school_location + - workplace_location + - auto_ownership_simulate + - free_parking + - telecommute_frequency + - cdap_simulate + - mandatory_tour_frequency + - mandatory_tour_scheduling + - joint_tour_frequency + - joint_tour_composition + - joint_tour_participation + - joint_tour_destination + - joint_tour_scheduling + - non_mandatory_tour_frequency + - non_mandatory_tour_destination + - non_mandatory_tour_scheduling + - tour_mode_choice_simulate + - atwork_subtour_frequency + - atwork_subtour_destination + - atwork_subtour_scheduling + - atwork_subtour_mode_choice + - stop_frequency + - trip_purpose + - trip_destination + - trip_purpose_and_destination + - trip_scheduling + - trip_mode_choice + ### mp_summarize step + - write_data_dictionary + - write_tables + - write_trip_matrices + +multiprocess_steps: + - name: mp_initialize + begin: initialize_landuse + - name: mp_households + begin: school_location + slice: + tables: + - households + - persons + - name: mp_summarize + begin: write_data_dictionary + + +output_tables: + action: include + prefix: final_ + tables: + - checkpoints + - accessibility + - land_use + - households + - persons + - tours + - trips + - school_shadow_prices + - workplace_shadow_prices + - joint_tour_participants + +# read cached skims (using numpy memmap) from output directory (memmap is faster than omx ) +#read_skim_cache: True +# write memmapped cached skims to output directory after reading from omx, for use in subsequent runs +#write_skim_cache: True diff --git a/activitysim/examples/example_semcog/configs_mp/shadow_pricing.yaml b/activitysim/examples/prototype_semcog/configs_mp/shadow_pricing.yaml similarity index 96% rename from activitysim/examples/example_semcog/configs_mp/shadow_pricing.yaml rename to activitysim/examples/prototype_semcog/configs_mp/shadow_pricing.yaml index 57b462b160..fda98f4d4b 100755 --- a/activitysim/examples/example_semcog/configs_mp/shadow_pricing.yaml +++ b/activitysim/examples/prototype_semcog/configs_mp/shadow_pricing.yaml @@ -1,37 +1,37 @@ - -inherit_settings: True - -shadow_pricing_models: - school: school_location - workplace: workplace_location - -# global switch to enable/disable loading of saved shadow prices -# (ignored if global use_shadow_pricing switch is False) -LOAD_SAVED_SHADOW_PRICES: False - -# number of shadow price iterations for cold start -MAX_ITERATIONS: 10 - -# number of shadow price iterations for warm start (after loading saved shadow_prices) -MAX_ITERATIONS_SAVED: 1 - -# ignore criteria for zones smaller than size_threshold -SIZE_THRESHOLD: 10 - -# zone passes if modeled is within percent_tolerance of predicted_size -PERCENT_TOLERANCE: 5 - -# max percentage of zones allowed to fail -FAIL_THRESHOLD: 1 - -# CTRAMP or daysim -SHADOW_PRICE_METHOD: ctramp -#SHADOW_PRICE_METHOD: daysim - -# ctramp-style shadow_pricing_method parameters -DAMPING_FACTOR: 1 - -# daysim-style shadow_pricing_method parameters -# FIXME should these be the same as PERCENT_TOLERANCE and FAIL_THRESHOLD above? -DAYSIM_ABSOLUTE_TOLERANCE: 50 -DAYSIM_PERCENT_TOLERANCE: 10 + +inherit_settings: True + +shadow_pricing_models: + school: school_location + workplace: workplace_location + +# global switch to enable/disable loading of saved shadow prices +# (ignored if global use_shadow_pricing switch is False) +LOAD_SAVED_SHADOW_PRICES: False + +# number of shadow price iterations for cold start +MAX_ITERATIONS: 10 + +# number of shadow price iterations for warm start (after loading saved shadow_prices) +MAX_ITERATIONS_SAVED: 1 + +# ignore criteria for zones smaller than size_threshold +SIZE_THRESHOLD: 10 + +# zone passes if modeled is within percent_tolerance of predicted_size +PERCENT_TOLERANCE: 5 + +# max percentage of zones allowed to fail +FAIL_THRESHOLD: 1 + +# CTRAMP or daysim +SHADOW_PRICE_METHOD: ctramp +#SHADOW_PRICE_METHOD: daysim + +# ctramp-style shadow_pricing_method parameters +DAMPING_FACTOR: 1 + +# daysim-style shadow_pricing_method parameters +# FIXME should these be the same as PERCENT_TOLERANCE and FAIL_THRESHOLD above? +DAYSIM_ABSOLUTE_TOLERANCE: 50 +DAYSIM_PERCENT_TOLERANCE: 10 diff --git a/activitysim/examples/example_semcog/data/households.csv b/activitysim/examples/prototype_semcog/data/households.csv similarity index 100% rename from activitysim/examples/example_semcog/data/households.csv rename to activitysim/examples/prototype_semcog/data/households.csv diff --git a/activitysim/examples/example_semcog/data/land_use.csv b/activitysim/examples/prototype_semcog/data/land_use.csv similarity index 100% rename from activitysim/examples/example_semcog/data/land_use.csv rename to activitysim/examples/prototype_semcog/data/land_use.csv diff --git a/activitysim/examples/example_semcog/data/persons.csv b/activitysim/examples/prototype_semcog/data/persons.csv similarity index 100% rename from activitysim/examples/example_semcog/data/persons.csv rename to activitysim/examples/prototype_semcog/data/persons.csv diff --git a/activitysim/examples/example_semcog/data/skims.omx b/activitysim/examples/prototype_semcog/data/skims.omx similarity index 100% rename from activitysim/examples/example_semcog/data/skims.omx rename to activitysim/examples/prototype_semcog/data/skims.omx diff --git a/activitysim/examples/example_semcog/extensions/__init__.py b/activitysim/examples/prototype_semcog/extensions/__init__.py similarity index 100% rename from activitysim/examples/example_semcog/extensions/__init__.py rename to activitysim/examples/prototype_semcog/extensions/__init__.py diff --git a/activitysim/examples/example_semcog/extensions/telecommute_frequency.py b/activitysim/examples/prototype_semcog/extensions/telecommute_frequency.py similarity index 97% rename from activitysim/examples/example_semcog/extensions/telecommute_frequency.py rename to activitysim/examples/prototype_semcog/extensions/telecommute_frequency.py index bbe72e11f1..92b4647d0e 100755 --- a/activitysim/examples/example_semcog/extensions/telecommute_frequency.py +++ b/activitysim/examples/prototype_semcog/extensions/telecommute_frequency.py @@ -1,98 +1,98 @@ -# ActivitySim -# See full license in LICENSE.txt. -import logging - -import pandas as pd - -from activitysim.core import tracing -from activitysim.core import config -from activitysim.core import pipeline -from activitysim.core import simulate -from activitysim.core import inject -from activitysim.core import expressions - -from activitysim.abm.models.util import estimation - -logger = logging.getLogger("activitysim") - - -@inject.step() -def telecommute_frequency( - persons_merged, persons, - chunk_size, trace_hh_id): - """ - This model predicts the frequency of telecommute for a person (worker) who - does not works from home. The alternatives of this model are 'No Telecommute', - '1 day per week', '2 to 3 days per week' and '4 days per week'. This model - reflects the choices of people who prefer a combination of working from home and - office during a week. - """ - - trace_label = 'telecommute_frequency' - model_settings_file_name = 'telecommute_frequency.yaml' - - choosers = persons_merged.to_frame() - choosers = choosers[choosers.workplace_zone_id > -1] - - logger.info("Running %s with %d persons", trace_label, len(choosers)) - - model_settings = config.read_model_settings(model_settings_file_name) - estimator = estimation.manager.begin_estimation('telecommute_frequency') - - constants = config.get_model_constants(model_settings) - - # - preprocessor - preprocessor_settings = model_settings.get('preprocessor', None) - if preprocessor_settings: - - locals_d = {} - if constants is not None: - locals_d.update(constants) - - expressions.assign_columns( - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_d, - trace_label=trace_label) - - model_spec = simulate.read_model_spec(file_name=model_settings['SPEC']) - coefficients_df = simulate.read_model_coefficients(model_settings) - model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator) - - nest_spec = config.get_logit_model_settings(model_settings) - - if estimator: - estimator.write_model_settings(model_settings, model_settings_file_name) - estimator.write_spec(model_settings) - estimator.write_coefficients(coefficients_df) - estimator.write_choosers(choosers) - - choices = simulate.simple_simulate( - choosers=choosers, - spec=model_spec, - nest_spec=nest_spec, - locals_d=constants, - chunk_size=chunk_size, - trace_label=trace_label, - trace_choice_name='telecommute_frequency', - estimator=estimator) - - choices = pd.Series(model_spec.columns[choices.values], index=choices.index) - - if estimator: - estimator.write_choices(choices) - choices = estimator.get_survey_values(choices, 'persons', 'telecommute_frequency') - estimator.write_override_choices(choices) - estimator.end_estimation() - - persons = persons.to_frame() - persons['telecommute_frequency'] = choices.reindex(persons.index).fillna('').astype(str) - - pipeline.replace_table("persons", persons) - - tracing.print_summary('telecommute_frequency', persons.telecommute_frequency, value_counts=True) - - if trace_hh_id: - tracing.trace_df(persons, - label=trace_label, - warn_if_empty=True) +# ActivitySim +# See full license in LICENSE.txt. +import logging + +import pandas as pd + +from activitysim.core import tracing +from activitysim.core import config +from activitysim.core import pipeline +from activitysim.core import simulate +from activitysim.core import inject +from activitysim.core import expressions + +from activitysim.abm.models.util import estimation + +logger = logging.getLogger("activitysim") + + +@inject.step() +def telecommute_frequency( + persons_merged, persons, + chunk_size, trace_hh_id): + """ + This model predicts the frequency of telecommute for a person (worker) who + does not works from home. The alternatives of this model are 'No Telecommute', + '1 day per week', '2 to 3 days per week' and '4 days per week'. This model + reflects the choices of people who prefer a combination of working from home and + office during a week. + """ + + trace_label = 'telecommute_frequency' + model_settings_file_name = 'telecommute_frequency.yaml' + + choosers = persons_merged.to_frame() + choosers = choosers[choosers.workplace_zone_id > -1] + + logger.info("Running %s with %d persons", trace_label, len(choosers)) + + model_settings = config.read_model_settings(model_settings_file_name) + estimator = estimation.manager.begin_estimation('telecommute_frequency') + + constants = config.get_model_constants(model_settings) + + # - preprocessor + preprocessor_settings = model_settings.get('preprocessor', None) + if preprocessor_settings: + + locals_d = {} + if constants is not None: + locals_d.update(constants) + + expressions.assign_columns( + df=choosers, + model_settings=preprocessor_settings, + locals_dict=locals_d, + trace_label=trace_label) + + model_spec = simulate.read_model_spec(file_name=model_settings['SPEC']) + coefficients_df = simulate.read_model_coefficients(model_settings) + model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator) + + nest_spec = config.get_logit_model_settings(model_settings) + + if estimator: + estimator.write_model_settings(model_settings, model_settings_file_name) + estimator.write_spec(model_settings) + estimator.write_coefficients(coefficients_df) + estimator.write_choosers(choosers) + + choices = simulate.simple_simulate( + choosers=choosers, + spec=model_spec, + nest_spec=nest_spec, + locals_d=constants, + chunk_size=chunk_size, + trace_label=trace_label, + trace_choice_name='telecommute_frequency', + estimator=estimator) + + choices = pd.Series(model_spec.columns[choices.values], index=choices.index) + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, 'persons', 'telecommute_frequency') + estimator.write_override_choices(choices) + estimator.end_estimation() + + persons = persons.to_frame() + persons['telecommute_frequency'] = choices.reindex(persons.index).fillna('').astype(str) + + pipeline.replace_table("persons", persons) + + tracing.print_summary('telecommute_frequency', persons.telecommute_frequency, value_counts=True) + + if trace_hh_id: + tracing.trace_df(persons, + label=trace_label, + warn_if_empty=True) diff --git a/activitysim/examples/example_semcog/extensions/transit_pass_ownership.py b/activitysim/examples/prototype_semcog/extensions/transit_pass_ownership.py similarity index 100% rename from activitysim/examples/example_semcog/extensions/transit_pass_ownership.py rename to activitysim/examples/prototype_semcog/extensions/transit_pass_ownership.py diff --git a/activitysim/examples/example_semcog/extensions/transit_pass_subsidy.py b/activitysim/examples/prototype_semcog/extensions/transit_pass_subsidy.py similarity index 100% rename from activitysim/examples/example_semcog/extensions/transit_pass_subsidy.py rename to activitysim/examples/prototype_semcog/extensions/transit_pass_subsidy.py diff --git a/activitysim/examples/example_semcog/extensions/work_from_home.py b/activitysim/examples/prototype_semcog/extensions/work_from_home.py similarity index 97% rename from activitysim/examples/example_semcog/extensions/work_from_home.py rename to activitysim/examples/prototype_semcog/extensions/work_from_home.py index f6e2728b23..e4081f321b 100755 --- a/activitysim/examples/example_semcog/extensions/work_from_home.py +++ b/activitysim/examples/prototype_semcog/extensions/work_from_home.py @@ -1,135 +1,135 @@ -# ActivitySim -# See full license in LICENSE.txt. -import logging - -import numpy as np - -from activitysim.core import tracing -from activitysim.core import config -from activitysim.core import pipeline -from activitysim.core import simulate -from activitysim.core import inject -from activitysim.core import expressions - -from activitysim.abm.models.util import estimation - -logger = logging.getLogger("activitysim") - - -@inject.step() -def work_from_home( - persons_merged, persons, - chunk_size, trace_hh_id): - """ - This model predicts whether a person (worker) works from home. The output - from this model is TRUE (if works from home) or FALSE (works away from home). - """ - - trace_label = 'work_from_home' - model_settings_file_name = 'work_from_home.yaml' - - choosers = persons_merged.to_frame() - model_settings = config.read_model_settings(model_settings_file_name) - chooser_filter_column_name = model_settings.get('CHOOSER_FILTER_COLUMN_NAME') - choosers = choosers[choosers[chooser_filter_column_name]] - logger.info("Running %s with %d persons", trace_label, len(choosers)) - - estimator = estimation.manager.begin_estimation('work_from_home') - - constants = config.get_model_constants(model_settings) - work_from_home_alt = model_settings['WORK_FROM_HOME_ALT'] - - # - preprocessor - preprocessor_settings = model_settings.get('preprocessor', None) - if preprocessor_settings: - - locals_d = {} - if constants is not None: - locals_d.update(constants) - - expressions.assign_columns( - df=choosers, - model_settings=preprocessor_settings, - locals_dict=locals_d, - trace_label=trace_label) - - model_spec = simulate.read_model_spec(file_name=model_settings['SPEC']) - coefficients_df = simulate.read_model_coefficients(model_settings) - - nest_spec = config.get_logit_model_settings(model_settings) - - if estimator: - estimator.write_model_settings(model_settings, model_settings_file_name) - estimator.write_spec(model_settings) - estimator.write_coefficients(coefficients_df) - estimator.write_choosers(choosers) - - # - iterative single process what-if adjustment if specified - iterations = model_settings.get('WORK_FROM_HOME_ITERATIONS', 1) - iterations_chooser_filter = model_settings.get('WORK_FROM_HOME_CHOOSER_FILTER', None) - iterations_coefficient_constant = model_settings.get('WORK_FROM_HOME_COEFFICIENT_CONSTANT', None) - iterations_target_percent = model_settings.get('WORK_FROM_HOME_TARGET_PERCENT', None) - iterations_target_percent_tolerance = model_settings.get('WORK_FROM_HOME_TARGET_PERCENT_TOLERANCE', None) - - for iteration in range(iterations): - - logger.info("Running %s with %d persons iteration %d", trace_label, len(choosers), iteration) - - # re-read spec to reset substitution - model_spec = simulate.read_model_spec(file_name=model_settings['SPEC']) - model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator) - - choices = simulate.simple_simulate( - choosers=choosers, - spec=model_spec, - nest_spec=nest_spec, - locals_d=constants, - chunk_size=chunk_size, - trace_label=trace_label, - trace_choice_name='work_from_home', - estimator=estimator) - - if iterations_target_percent is not None: - choices_for_filter = choices[choosers[iterations_chooser_filter]] - - current_percent = ((choices_for_filter == work_from_home_alt).sum() / len(choices_for_filter)) - logger.info("Running %s iteration %i choosers %i current percent %f target percent %f", - trace_label, iteration, len(choices_for_filter), current_percent, iterations_target_percent) - - if current_percent <= (iterations_target_percent + - iterations_target_percent_tolerance - ) and current_percent >= (iterations_target_percent - - iterations_target_percent_tolerance): - logger.info("Running %s iteration %i converged with coefficient %f", trace_label, iteration, - coefficients_df.value[iterations_coefficient_constant]) - break - - else: - new_value = np.log(iterations_target_percent / - np.maximum(current_percent, 0.0001) - ) + coefficients_df.value[iterations_coefficient_constant] - coefficients_df.value[iterations_coefficient_constant] = new_value - logger.info("Running %s iteration %i new coefficient for next iteration %f", - trace_label, iteration, new_value) - iteration = iteration + 1 - - choices = (choices == work_from_home_alt) - - if estimator: - estimator.write_choices(choices) - choices = estimator.get_survey_values(choices, 'persons', 'work_from_home') - estimator.write_override_choices(choices) - estimator.end_estimation() - - persons = persons.to_frame() - persons['work_from_home'] = choices.reindex(persons.index).fillna(0).astype(bool) - persons['is_out_of_home_worker'] = persons[chooser_filter_column_name] & ~persons['work_from_home'] - - pipeline.replace_table("persons", persons) - - tracing.print_summary('work_from_home', persons.work_from_home, value_counts=True) - - if trace_hh_id: - tracing.trace_df(persons, - label=trace_label, - warn_if_empty=True) +# ActivitySim +# See full license in LICENSE.txt. +import logging + +import numpy as np + +from activitysim.core import tracing +from activitysim.core import config +from activitysim.core import pipeline +from activitysim.core import simulate +from activitysim.core import inject +from activitysim.core import expressions + +from activitysim.abm.models.util import estimation + +logger = logging.getLogger("activitysim") + + +@inject.step() +def work_from_home( + persons_merged, persons, + chunk_size, trace_hh_id): + """ + This model predicts whether a person (worker) works from home. The output + from this model is TRUE (if works from home) or FALSE (works away from home). + """ + + trace_label = 'work_from_home' + model_settings_file_name = 'work_from_home.yaml' + + choosers = persons_merged.to_frame() + model_settings = config.read_model_settings(model_settings_file_name) + chooser_filter_column_name = model_settings.get('CHOOSER_FILTER_COLUMN_NAME') + choosers = choosers[choosers[chooser_filter_column_name]] + logger.info("Running %s with %d persons", trace_label, len(choosers)) + + estimator = estimation.manager.begin_estimation('work_from_home') + + constants = config.get_model_constants(model_settings) + work_from_home_alt = model_settings['WORK_FROM_HOME_ALT'] + + # - preprocessor + preprocessor_settings = model_settings.get('preprocessor', None) + if preprocessor_settings: + + locals_d = {} + if constants is not None: + locals_d.update(constants) + + expressions.assign_columns( + df=choosers, + model_settings=preprocessor_settings, + locals_dict=locals_d, + trace_label=trace_label) + + model_spec = simulate.read_model_spec(file_name=model_settings['SPEC']) + coefficients_df = simulate.read_model_coefficients(model_settings) + + nest_spec = config.get_logit_model_settings(model_settings) + + if estimator: + estimator.write_model_settings(model_settings, model_settings_file_name) + estimator.write_spec(model_settings) + estimator.write_coefficients(coefficients_df) + estimator.write_choosers(choosers) + + # - iterative single process what-if adjustment if specified + iterations = model_settings.get('WORK_FROM_HOME_ITERATIONS', 1) + iterations_chooser_filter = model_settings.get('WORK_FROM_HOME_CHOOSER_FILTER', None) + iterations_coefficient_constant = model_settings.get('WORK_FROM_HOME_COEFFICIENT_CONSTANT', None) + iterations_target_percent = model_settings.get('WORK_FROM_HOME_TARGET_PERCENT', None) + iterations_target_percent_tolerance = model_settings.get('WORK_FROM_HOME_TARGET_PERCENT_TOLERANCE', None) + + for iteration in range(iterations): + + logger.info("Running %s with %d persons iteration %d", trace_label, len(choosers), iteration) + + # re-read spec to reset substitution + model_spec = simulate.read_model_spec(file_name=model_settings['SPEC']) + model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator) + + choices = simulate.simple_simulate( + choosers=choosers, + spec=model_spec, + nest_spec=nest_spec, + locals_d=constants, + chunk_size=chunk_size, + trace_label=trace_label, + trace_choice_name='work_from_home', + estimator=estimator) + + if iterations_target_percent is not None: + choices_for_filter = choices[choosers[iterations_chooser_filter]] + + current_percent = ((choices_for_filter == work_from_home_alt).sum() / len(choices_for_filter)) + logger.info("Running %s iteration %i choosers %i current percent %f target percent %f", + trace_label, iteration, len(choices_for_filter), current_percent, iterations_target_percent) + + if current_percent <= (iterations_target_percent + + iterations_target_percent_tolerance + ) and current_percent >= (iterations_target_percent - + iterations_target_percent_tolerance): + logger.info("Running %s iteration %i converged with coefficient %f", trace_label, iteration, + coefficients_df.value[iterations_coefficient_constant]) + break + + else: + new_value = np.log(iterations_target_percent / + np.maximum(current_percent, 0.0001) + ) + coefficients_df.value[iterations_coefficient_constant] + coefficients_df.value[iterations_coefficient_constant] = new_value + logger.info("Running %s iteration %i new coefficient for next iteration %f", + trace_label, iteration, new_value) + iteration = iteration + 1 + + choices = (choices == work_from_home_alt) + + if estimator: + estimator.write_choices(choices) + choices = estimator.get_survey_values(choices, 'persons', 'work_from_home') + estimator.write_override_choices(choices) + estimator.end_estimation() + + persons = persons.to_frame() + persons['work_from_home'] = choices.reindex(persons.index).fillna(0).astype(bool) + persons['is_out_of_home_worker'] = persons[chooser_filter_column_name] & ~persons['work_from_home'] + + pipeline.replace_table("persons", persons) + + tracing.print_summary('work_from_home', persons.work_from_home, value_counts=True) + + if trace_hh_id: + tracing.trace_df(persons, + label=trace_label, + warn_if_empty=True) diff --git a/activitysim/examples/example_semcog/output/.gitignore b/activitysim/examples/prototype_semcog/output/.gitignore similarity index 100% rename from activitysim/examples/example_semcog/output/.gitignore rename to activitysim/examples/prototype_semcog/output/.gitignore diff --git a/activitysim/examples/example_semcog/output/cache/.gitignore b/activitysim/examples/prototype_semcog/output/cache/.gitignore similarity index 100% rename from activitysim/examples/example_semcog/output/cache/.gitignore rename to activitysim/examples/prototype_semcog/output/cache/.gitignore diff --git a/activitysim/examples/example_semcog/output/log/.gitignore b/activitysim/examples/prototype_semcog/output/log/.gitignore similarity index 100% rename from activitysim/examples/example_semcog/output/log/.gitignore rename to activitysim/examples/prototype_semcog/output/log/.gitignore diff --git a/activitysim/examples/example_semcog/output/trace/.gitignore b/activitysim/examples/prototype_semcog/output/trace/.gitignore similarity index 100% rename from activitysim/examples/example_semcog/output/trace/.gitignore rename to activitysim/examples/prototype_semcog/output/trace/.gitignore diff --git a/activitysim/examples/example_semcog/scripts/reindex_household_ids.py b/activitysim/examples/prototype_semcog/scripts/reindex_household_ids.py similarity index 100% rename from activitysim/examples/example_semcog/scripts/reindex_household_ids.py rename to activitysim/examples/prototype_semcog/scripts/reindex_household_ids.py diff --git a/activitysim/examples/example_semcog/scripts/semcog_crop.py b/activitysim/examples/prototype_semcog/scripts/semcog_crop.py similarity index 100% rename from activitysim/examples/example_semcog/scripts/semcog_crop.py rename to activitysim/examples/prototype_semcog/scripts/semcog_crop.py diff --git a/activitysim/examples/example_semcog/simulation.py b/activitysim/examples/prototype_semcog/simulation.py similarity index 94% rename from activitysim/examples/example_semcog/simulation.py rename to activitysim/examples/prototype_semcog/simulation.py index 480b298cec..dc077fe3d9 100755 --- a/activitysim/examples/example_semcog/simulation.py +++ b/activitysim/examples/prototype_semcog/simulation.py @@ -1,17 +1,17 @@ -# ActivitySim -# See full license in LICENSE.txt. - -import sys -import argparse - -from activitysim.cli.run import add_run_args, run - -import extensions - -if __name__ == '__main__': - - parser = argparse.ArgumentParser() - add_run_args(parser) - args = parser.parse_args() - - sys.exit(run(args)) +# ActivitySim +# See full license in LICENSE.txt. + +import sys +import argparse + +from activitysim.cli.run import add_run_args, run + +import extensions + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + add_run_args(parser) + args = parser.parse_args() + + sys.exit(run(args)) diff --git a/activitysim/examples/example_semcog/test/configs/settings.yaml b/activitysim/examples/prototype_semcog/test/configs/settings.yaml similarity index 100% rename from activitysim/examples/example_semcog/test/configs/settings.yaml rename to activitysim/examples/prototype_semcog/test/configs/settings.yaml diff --git a/activitysim/examples/example_semcog/test/output/.gitignore b/activitysim/examples/prototype_semcog/test/output/.gitignore similarity index 100% rename from activitysim/examples/example_semcog/test/output/.gitignore rename to activitysim/examples/prototype_semcog/test/output/.gitignore diff --git a/activitysim/examples/example_semcog/test/output/cache/.gitignore b/activitysim/examples/prototype_semcog/test/output/cache/.gitignore similarity index 100% rename from activitysim/examples/example_semcog/test/output/cache/.gitignore rename to activitysim/examples/prototype_semcog/test/output/cache/.gitignore diff --git a/activitysim/examples/example_semcog/test/output/trace/.gitignore b/activitysim/examples/prototype_semcog/test/output/trace/.gitignore similarity index 100% rename from activitysim/examples/example_semcog/test/output/trace/.gitignore rename to activitysim/examples/prototype_semcog/test/output/trace/.gitignore diff --git a/activitysim/examples/example_semcog/test/regress/final_trips.csv b/activitysim/examples/prototype_semcog/test/regress/final_trips.csv similarity index 100% rename from activitysim/examples/example_semcog/test/regress/final_trips.csv rename to activitysim/examples/prototype_semcog/test/regress/final_trips.csv diff --git a/activitysim/examples/example_semcog/test/test_semcog.py b/activitysim/examples/prototype_semcog/test/test_semcog.py similarity index 94% rename from activitysim/examples/example_semcog/test/test_semcog.py rename to activitysim/examples/prototype_semcog/test/test_semcog.py index df63c872be..0decf6ba34 100644 --- a/activitysim/examples/example_semcog/test/test_semcog.py +++ b/activitysim/examples/prototype_semcog/test/test_semcog.py @@ -18,7 +18,7 @@ def teardown_function(func): def test_semcog(): def example_path(dirname): - resource = os.path.join('examples', 'example_semcog', dirname) + resource = os.path.join('examples', 'prototype_semcog', dirname) return pkg_resources.resource_filename('activitysim', resource) def test_path(dirname): diff --git a/docs/cli.rst b/docs/cli.rst index 191a85a770..c309971b29 100644 --- a/docs/cli.rst +++ b/docs/cli.rst @@ -15,7 +15,7 @@ Create ------ Create an ActivitySim example setup. See ``activitysim create -h`` for more information. -More complete examples, including the full scale MTC +More complete examples, including the full scale prototype MTC regional demand model are available for creation by typing ``activitysim create -l``. To create these examples, ActivitySim downloads the large input files from the `ActivitySim resources `__ repository. diff --git a/docs/core.rst b/docs/core.rst index f9625ef72a..bd4a3a30f1 100644 --- a/docs/core.rst +++ b/docs/core.rst @@ -430,7 +430,7 @@ In the output directory, add a new summarize directory, which must contain: * ``topsheet.yaml``: configuration for calculated statistics in the ‘At-a-Glance’ table at the top of the dashboard * The ``/output/summarize`` directory may also contain one or more .geojson files to support map-based visualizations in the dashboard. -At present, example versions of all of the items above are located in the MTC example model: ``/activitysim/examples/example_mtc``. Complete documentation for configuring dashboards is available in the `SimWrapper Docs `_. +At present, example versions of all of the items above are located in the prototype MTC example model: ``/activitysim/examples/prototype_mtc``. Complete documentation for configuring dashboards is available in the `SimWrapper Docs `_. Configure the Summarize Model @@ -438,7 +438,7 @@ Configure the Summarize Model Summary Expressions ^^^^^^^^^^^^^^^^^^^ -Example configuration files for the summarize model step (as listed above) are included in MTC example. These files will need to be adjusted to produce customized SimWrapper dashboards. These files are structured as standard ActivitySim expression (CSV) and configuration (YAML) files. More detailed information about configuration of the summarize model step is available in the Models documentation. +Example configuration files for the summarize model step (as listed above) are included in prototype MTC example. These files will need to be adjusted to produce customized SimWrapper dashboards. These files are structured as standard ActivitySim expression (CSV) and configuration (YAML) files. More detailed information about configuration of the summarize model step is available in the Models documentation. You may wish to manipulate the default expression files to suit your particular needs. Expression files are formatted as CSVs and structured according to ActivitySim conventions with three columns: @@ -471,7 +471,7 @@ The ``label_format`` parameter uses f-string formatting to specify how bins shou By default, bins are labeled with their extents using the following f-string: ``'{left:,.2f} - {right:,.2f}'``. The ``'{rank}'`` option demonstrated above would label each bin with its ordinal rank. Numeric labels are converted to numeric data types, if possible. -Examples of each summarize function are included in the ``summarize.csv`` expression file for the MTC example. Consult the docstrings for each function in the ``/activitysim/abm/models/summarize.py`` module for complete specification of parameters. +Examples of each summarize function are included in the ``summarize.csv`` expression file for the prototype MTC example. Consult the docstrings for each function in the ``/activitysim/abm/models/summarize.py`` module for complete specification of parameters. Preprocessing ^^^^^^^^^^^^^ @@ -503,7 +503,7 @@ Binning during the preprocessing stage uses the same convenience functions avail - High Income ($75k-$100k) - Very High Income (>$100k) -Example uses of each binning function are included in the ``summarize.yaml`` configuration file in the MTC example. +Example uses of each binning function are included in the ``summarize.yaml`` configuration file in the prototype MTC example. Table columns can also be aggregated, or "remapped," during the preprocessing stage. Aggregations are specified in the configuration YAML using a key-value structure: @@ -538,7 +538,7 @@ Table columns can also be aggregated, or "remapped," during the preprocessing st TNC_SHARED: Ride Hail -Trip-level skim data are also made available in the preprocessing stage by attaching columns to the ``trips_merged`` table based on expressions in ``summarize_preprocessor.csv``. This process uses skim wrappers indexed by origin, destination, and time of day to gather distance, time, and cost data and each trip, enabling calculation of variables such as vehicle miles traveled (VMT). Preprocessing expressions are interpreted with standard ActivitySim annotation methods, including definition of scalar and vector temporary variables based on underscores and capitalization. The preprocessor expressions included in the MTC example demonstrate calculation of a number of skim-based variables involving distance, time, and cost. The system for joining skim data to trips is currently configured for the one-zone MTC example model and will need to be generalized for multi-zone systems in future work. +Trip-level skim data are also made available in the preprocessing stage by attaching columns to the ``trips_merged`` table based on expressions in ``summarize_preprocessor.csv``. This process uses skim wrappers indexed by origin, destination, and time of day to gather distance, time, and cost data and each trip, enabling calculation of variables such as vehicle miles traveled (VMT). Preprocessing expressions are interpreted with standard ActivitySim annotation methods, including definition of scalar and vector temporary variables based on underscores and capitalization. The preprocessor expressions included in the prototype MTC example demonstrate calculation of a number of skim-based variables involving distance, time, and cost. The system for joining skim data to trips is currently configured for the one-zone MTC example model and will need to be generalized for multi-zone systems in future work. Install and Run Simwrapper diff --git a/docs/development.rst b/docs/development.rst index fda4ab0bc4..98edd7baec 100644 --- a/docs/development.rst +++ b/docs/development.rst @@ -330,10 +330,10 @@ Examples Generally speaking, there are two types of ActivitySim examples: test examples and agency examples. * Test examples - these are the core ActivitySim maintained and tested examples developed to date. The current test - examples are :ref:`example_mtc`, :ref:`example_estimation`, :ref:`example_multiple_zones`, and :ref:`example_marin`. + examples are :ref:`prototype_mtc`, :ref:`example_estimation`, :ref:`placeholder_multiple_zone`, and :ref:`prototype_marin`. These examples are owned and maintained by the project. * Agency examples - these are agency partner model implementations currently being setup. The current agency examples - are :ref:`example_arc`, :ref:`example_semcog`, :ref:`example_psrc`, :ref:`example_sandag`, and :ref:`example_sandag_xborder`. These examples can be + are :ref:`prototype_arc`, :ref:`prototype_semcog`, :ref:`placeholder_psrc`, :ref:`placeholder_sandag`, and :ref:`prototype_sandag_xborder`. These examples can be configured in ways different from the test examples, include new inputs and expressions, and may include new planned software components for contribution to ActivitySim. These examples are owned by the agency. @@ -350,8 +350,8 @@ Furthermore, multiple versions of these examples can exist, and be used for vari questions related to runtime, such as improvements to methods indifferent to the size of the population and number of zones. * Other - a specific route/path through the code for testing. For example, the estimation example tests the estimation - mode functionality. The estimation example is a version of the example mtc example - it inherits most settings from - example mtc and includes additional settings for reading in survey files and producing estimation data bundles. + mode functionality. The estimation example is a version of the example prototype MTC example - it inherits most settings from + prototype_mtc and includes additional settings for reading in survey files and producing estimation data bundles. Regardless of the type or version, all functioning examples are described in a common list stored in `example_manifest.yaml `_. @@ -406,9 +406,9 @@ Running the Test System The automatic TravisCI test system runs the test examples and the cropped agency examples. Examples of the testing resources for each agency example that need to be up-to-date are: -* `scripts folder (including crop script) `_ -* `test folder (including test script) `_ -* `regress folder (including expected outputs) `_ +* `scripts folder (including crop script) `_ +* `test folder (including test script) `_ +* `regress folder (including expected outputs) `_ For the time being, running the full scale examples is done manually since it involves getting and running several large examples that take many hours to run. The entire system could be fully automated, and either run in the cloud or on a local server. @@ -425,7 +425,7 @@ When a new version of the code is pushed to develop: * If the agency example previous ran without error or future warnings (i.e. deprecation warnings and is therefore up-to-date), then the developer will be responsible for updating the agency example so it passes the tests * If the agency example previously threw errors or future warnings (i.e. is not up-to-date), then the developer will not update the example and the responsibility will fall to the agency to update it when they have time. This will not preclude development from advancing since the agency specific test can fail while the other tests continue to pass. If the agency example is not updated within an agreed upon time frame, then the example is removed from the test system. -To help understand this case, the addition of support for representative logsums to :ref:`example_mtc` is discussed. Example_mtc was selected as the test case for development of this feature because this feature could be implemented and tested against this example, which is the primary example to date. With the new feature configured for this example, the automatic test system was run to ensure all the existing test examples pass their tests. The automatic test system was also run to ensure all the cropped agency examples passed their tests, but since not of them include this new feature in their configuration, the test results were the same and therefore the tests passed. +To help understand this case, the addition of support for representative logsums to :ref:`prototype_mtc` is discussed. prototype_mtc was selected as the test case for development of this feature because this feature could be implemented and tested against this example, which is the primary example to date. With the new feature configured for this example, the automatic test system was run to ensure all the existing test examples pass their tests. The automatic test system was also run to ensure all the cropped agency examples passed their tests, but since not of them include this new feature in their configuration, the test results were the same and therefore the tests passed. When an agency wants to update their example: @@ -433,7 +433,7 @@ When an agency wants to update their example: * When making an agency model update, agencies update their example through a pull request. This pull request changes nothing outside their example folder. The updated resources may include updated configs, inputs, revisions to the cropped data/cropping script, and expected test results. The automatic cropped example test must run without warnings. The results of the full scale version is shared with the development team in the PR comments. -To help understand this case, the inclusion of :ref:`example_psrc` as an agency example is discussed. Example_psrc is PSRC's experimentation of a two zone model and is useful for testing the two zone features, including runtime. A snapshot of PSRC's efforts to setup an ActivitySim model with PSRC inputs was added to the test system as a new agency example, called example_psrc. After some back and forth between the development team and PSRC, a full scale version of example_psrc was successfully run. The revisions required to create a cropped version and full scale version were saved in a change log included with the example. When PSRC wants to update example_psrc, PSRC will pull the latest develop code branch and then update example_psrc so the cropped and full scale example both run without errors. PSRC also needs to update the expected test results. Once everything is in good working order, then PSRC issues a pull request to develop to pull their updated example. Once pulled, the automatic test system will run the cropped version of example_psrc. +To help understand this case, the inclusion of :ref:`placeholder_psrc` as an agency example is discussed. This model is PSRC's experimentation of a two zone model and is useful for testing the two zone features, including runtime. A snapshot of PSRC's efforts to setup an ActivitySim model with PSRC inputs was added to the test system as a new agency example, called placeholder_psrc. After some back and forth between the development team and PSRC, a full scale version of placeholder_psrc was successfully run. The revisions required to create a cropped version and full scale version were saved in a change log included with the example. When PSRC wants to update placeholder_psrc, PSRC will pull the latest develop code branch and then update placeholder_psrc so the cropped and full scale example both run without errors. PSRC also needs to update the expected test results. Once everything is in good working order, then PSRC issues a pull request to develop to pull their updated example. Once pulled, the automatic test system will run the cropped version of placeholder_psrc. When an agency example includes new submodels and/or contributions to the core that need to be reviewed and then pulled/accepted: @@ -441,4 +441,4 @@ When an agency example includes new submodels and/or contributions to the core t * Second, the agency example must be up-to-date with the latest develop version of the code so the revisions to the code are only the exact revisions for the new submodels and/or contributions to the core. * The new submodels and/or contributions to the core will then be reviewed by the repository manager and it's likely some revisions will be required for acceptance. Key items in the review include python code, user documentation, and testable examples for all new components. If the contribution is just new submodels, then the agency example that exercises the new submodel is sufficient for test coverage since TravisCI will automatically test the cropped version of the new submodel. If the contribution includes revisions to the core that impact other test examples, then the developer is responsible for ensuring all the other tests that are up-to-date are updated/passing as well. This includes other agency examples that are up-to-date. This is required to ensure the contribution to the core is adequately complete. -To help understand this case, the addition of the parking location choice model for :ref:`example_arc` is discussed. First, ARC gets their example in good working order - i.e. updates to develop, makes any required revisions to their model to get it working, creates a cropped and full scaled example, and creates the expected test results. In addition, this use case includes additional submodel and/or core code so ARC also authors the new feature, including documentation and any other relevant requirements such as logging, tracing, support for estimation, etc. With the new example and feature working offline, then ARC issues a pull request to add example_arc and the new submodel/core code and makes sure the automatic tests are passing. Once accepted, the automatic test system will run the test example tests and the cropped agency examples. Since the new feature - parking location choice model - is included in example_arc, then new feature is now tested. Any testing of downstream impacts from the parking location choice model would also need to be implemented in the example. +To help understand this case, the addition of the parking location choice model for :ref:`prototype_arc` is discussed. First, ARC gets their example in good working order - i.e. updates to develop, makes any required revisions to their model to get it working, creates a cropped and full scaled example, and creates the expected test results. In addition, this use case includes additional submodel and/or core code so ARC also authors the new feature, including documentation and any other relevant requirements such as logging, tracing, support for estimation, etc. With the new example and feature working offline, then ARC issues a pull request to add prototype_arc and the new submodel/core code and makes sure the automatic tests are passing. Once accepted, the automatic test system will run the test example tests and the cropped agency examples. Since the new feature - parking location choice model - is included in prototype_arc, then new feature is now tested. Any testing of downstream impacts from the parking location choice model would also need to be implemented in the example. diff --git a/docs/examples.rst b/docs/examples.rst index a2768ea44f..1b2c56adee 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -7,30 +7,54 @@ Examples ======== -This page describes the example models included with ActivitySim. The current examples are: +This page describes the example models included with ActivitySim. There are three +basic types of example model: + +* "production" examples are calibrated and validated by the relevant agency, and + are intended to be replicas of an "official" travel model used by that agency, + although generally we expect agencies to maintain independent repositories and + not rely on the Consortium maintained version as a single source for the model. + At this time there are no production examples in the consortium's collection, + but we expect that to change soon. +* "prototype" examples are not representative of any "official" travel model + used by the relevant agency, but they are viewed as "ok" models by the + consortium: they are usually at least loosely calibrated and/or validated, and + typically contain at least some components or parameters specialized for the + relevant region. They may be in-development models that are not quite finished, + or consortium maintained models that are derived from but now different from + the official model of some region. They should not be used in place of + "official" models for policy analysis in any given region, but could serve as + a donor model for new users who want to implement ActivitySim somewhere new + (subject to all the caveats that go along with transferring models). +* "placeholder" examples are computational testbeds that technically run but + have not been calibrated nor validated in any meaningful way. These examples + are early stage development models used for testing purposes, and users are + strongly cautioned not to use them for any policy or planning purpose. + +The current examples are: +---------------------------------+-----------------------------------------------------------+--------------+----------------------+ | Example | Purpose | Zone Systems | Status | +=================================+===========================================================+==============+======================+ -| :ref:`example_mtc` | Primary MTC travel model one example | 1 | Mature | +| :ref:`prototype_mtc` | Original ActivitySim Example, derived from MTC TM1 | 1 | Mature | +---------------------------------+-----------------------------------------------------------+--------------+----------------------+ -| :ref:`example_mtc_extended` | example_mtc with additional optional models | 1 | In development | +| :ref:`prototype_mtc_extended` | Prototype MTC example with additional optional models | 1 | In development | +---------------------------------+-----------------------------------------------------------+--------------+----------------------+ -| :ref:`example_estimation` | Estimation example with example_mtc | 1 | Mature | +| :ref:`example_estimation` | Estimation example with prototype_mtc | 1 | Mature | +---------------------------------+-----------------------------------------------------------+--------------+----------------------+ -| :ref:`example_multiple_zones` | 2 or 3 zone system example using example_mtc data | 2 or 3 | Simple test example | +| :ref:`placeholder_multiple_zone`| 2 or 3 zone system example using MTC data | 2 or 3 | Simple test example | +---------------------------------+-----------------------------------------------------------+--------------+----------------------+ -| :ref:`example_marin` | 3 zone system example using Marin tour mode choice model | 3 | Mature | +| :ref:`prototype_marin` | 3 zone system example using Marin tour mode choice model | 3 | Mature | +---------------------------------+-----------------------------------------------------------+--------------+----------------------+ -| :ref:`example_arc` | ARC agency example | 1 | In development | +| :ref:`prototype_arc` | ARC agency example | 1 | In development | +---------------------------------+-----------------------------------------------------------+--------------+----------------------+ -| :ref:`example_semcog` | SEMCOG agency example | 1 | In development | +| :ref:`prototype_semcog` | SEMCOG agency example | 1 | In development | +---------------------------------+-----------------------------------------------------------+--------------+----------------------+ -| :ref:`example_psrc` | PSRC agency example | 2 | In development | +| :ref:`placeholder_psrc` | PSRC agency example | 2 | Future development | +---------------------------------+-----------------------------------------------------------+--------------+----------------------+ -| :ref:`example_sandag` | SANDAG agency example | 3 | In development | +| :ref:`placeholder_sandag` | SANDAG agency example | 3 | Future development | +---------------------------------+-----------------------------------------------------------+--------------+----------------------+ -| :ref:`example_sandag_xborder` | SANDAG agency example | 3 | In development | +| :ref:`prototype_sandag_xborder` | SANDAG agency example | 3 | In development | +---------------------------------+-----------------------------------------------------------+--------------+----------------------+ .. note:: @@ -38,12 +62,12 @@ This page describes the example models included with ActivitySim. The current e contains example commands to create and run several versions of the examples. See also :ref:`adding_agency_examples` for more information on agency example models. -.. _example_mtc : +.. _prototype_mtc : -example_mtc ------------ +prototype_mtc +------------- -The initial example implemented in ActivitySim was example_mtc. This section described the example_mtc +The initial example implemented in ActivitySim was prototype_mtc. This section described the prototype_mtc model design, how to setup and run the example, and how to review outputs. The default configuration of the example is limited to a small sample of households and zones so that it can be run quickly and require less than 1 GB of RAM. The full scale example can be configured and run as well. @@ -51,7 +75,7 @@ less than 1 GB of RAM. The full scale example can be configured and run as well Model Design ~~~~~~~~~~~~ -The example_mtc example is based on the +The prototype_mtc example is based on (but has evolved away from) the `Bay Area Metro Travel Model One `__ (TM1). TM1 has its roots in a wide array of analytical approaches, including discrete choice forms (multinomial and nested logit models), activity duration models, time-use models, @@ -65,7 +89,7 @@ individual decision-makers. Space ^^^^^ -TM1 uses the 1454 TAZ zone system developed for the MTC trip-based model. The zones are fairly large for the region, +The prototype MTC model uses the 1454 TAZ zone system developed for the MTC trip-based model. The zones are fairly large for the region, which may somewhat distort the representation of transit access in mode choice. To ameliorate this problem, the original model zones were further sub-divided into three categories of transit access: short walk, long walk, and not walkable. However, support for transit subzones is not included in the activitysim implementation since the latest generation @@ -244,7 +268,7 @@ transit line-haul modes, and ride hail with taxi, single TNC (Transportation Net Sub-models ^^^^^^^^^^ -The general design of the example_mtc model is presented below. Long-term choices that relate to +The general design of the prototype_mtc model is presented below. Long-term choices that relate to the usual workplace/university/school for each worker and student, household car ownership, and the availability of free parking at workplaces are first. @@ -281,12 +305,12 @@ can be aggregated into travel demand matrices for network loading. Setup ~~~~~ -The following describes the example_mtc model setup. +The following describes the prototype_mtc model setup. Folder and File Setup -The example_mtc has the following root folder/file setup: +The prototype_mtc has the following root folder/file setup: * configs - settings, expressions files, etc. * configs_mp - override settings for the multiprocess configuration @@ -295,9 +319,9 @@ The example_mtc has the following root folder/file setup: Inputs ^^^^^^ -In order to run example_mtc, you first need the input files in the ``data`` folder as identified in the ``configs\settings.yaml`` file and the ``configs\network_los.yaml`` file: +In order to run prototype_mtc, you first need the input files in the ``data`` folder as identified in the ``configs\settings.yaml`` file and the ``configs\network_los.yaml`` file: -* input_table_list: the input CSV tables from MTC travel model one (see below for column definitions): +* input_table_list: the input CSV tables for MTC (see below for column definitions): * households - Synthetic population household records for a subset of zones. * persons - Synthetic population person records for a subset of zones. @@ -306,7 +330,7 @@ In order to run example_mtc, you first need the input files in the ``data`` fold * taz_skims: skims.omx - an OMX matrix file containing the MTC TM1 skim matrices for a subset of zones. The time period for the matrix must be represented at the end of the matrix name and be seperated by a double_underscore (e.g. BUS_IVT__AM indicates base skim BUS_IVT with a time period of AM). These files are used in the tests as well. The full set -of MTC TM1 households, persons, and OMX skims are on the ActivitySim `resources repository `__. +of MTC households, persons, and OMX skims are on the ActivitySim `resources repository `__. Additional details on these files is available in the original `Travel Model 1 repository `_, although many of the files described there are not used in ActivitySim. @@ -475,10 +499,10 @@ Area types OMX and HDF5 files can be viewed with the `OMX Viewer `__ or `HDFView `__. - The ``other_resources\scripts\build_omx.py`` script will build one OMX file containing all the skims. The original MTC TM1 skims were converted from + The ``other_resources\scripts\build_omx.py`` script will build one OMX file containing all the skims. The original MTC TM1 skims were converted for the prototype from Cube to OMX using the ``other_resources\scripts\mtc_tm1_omx_export.s`` script. - The example_mtc inputs were created by the ``other_resources\scripts\create_sf_example.py`` script, which creates the land use, synthetic population, and + The prototype_mtc_sf inputs were created by the ``other_resources\scripts\create_sf_example.py`` script, which creates the land use, synthetic population, and skim inputs for a subset of user-defined zones. .. index:: configuration @@ -546,8 +570,8 @@ Included in the ``configs`` folder are the model specification files that store Python/pandas/numpy expressions, alternatives, and other settings used by each model. Some models includes an alternatives file since the alternatives are not easily described as columns in the expressions file. An example of this is the ``non_mandatory_tour_frequency_alternatives.csv`` file, which lists each alternative as a row and each -columns indicates the number of non-mandatory tours by purpose. The set of files for the example_mtc are below. The -:ref:`example_arc`, :ref:`example_semcog`, and :ref:`example_mtc_extended` examples added additional submodels. +columns indicates the number of non-mandatory tours by purpose. The set of files for the prototype_mtc are below. The +:ref:`prototype_arc`, :ref:`prototype_semcog`, and :ref:`prototype_mtc_extended` examples added additional submodels. +------------------------------------------------+--------------------------------------------------------------------+ | Model | Specification Files | @@ -809,7 +833,7 @@ To run the example, do the following: :: - activitysim create --example example_mtc --destination my_test_example + activitysim create --example prototype_mtc --destination my_test_example * Run the example @@ -850,7 +874,7 @@ The multiprocessing example also writes outputs to the output folder. The default multiprocessed example is configured to run with two processors and chunking training: ``num_processes: 2``, ``chunk_size: 0``, and ``chunk_training_mode: training``. Additional more performant configurations are included and commented out in the example settings file. For example, the 100 percent sample full scale multiprocessing example -- ``example_mtc_full`` - was run on a Windows Server machine with 28 cores and 256GB RAM with the configuration below. +- ``prototype_mtc_full`` - was run on a Windows Server machine with 28 cores and 256GB RAM with the configuration below. The default setup runs with ``chunk_training_mode: training`` since no chunk cache file is present. To run the example significantly faster, try ``chunk_training_mode: disabled`` if the machine has sufficient RAM, or try ``chunk_training_mode: production``. To configure ``chunk_training_mode: production``, first configure chunking as @@ -872,9 +896,16 @@ Configuring chunking To configure chunking, ActivitySim must first be trained to determine reasonable chunking settings given the model setup and machine. The steps to configure chunking are: -* Run the full scale model with ``chunk_training_mode: training``. Set ``num_processors`` to about 80% of the available physical processors and ``chunk_size`` to about 80% of the available RAM. This will run the model and create the ``chunk_cache.csv`` file in the output\cache directory for reuse. -* The ``households_sample_size`` for training chunking should be at least 1 / num_processors to provide sufficient data for training and the ``chunk_method: hybrid_uss`` typically performs best. -* Run the full scale model with ``chunk_training_mode: production``. Experiment with different ``num_processors`` and ``chunk_size`` settings depending on desired runtimes and machine resources. +* Run the full scale model with ``chunk_training_mode: training``. + Set ``num_processors`` to about 80% of the available physical processors + and ``chunk_size`` to about 80% of the available RAM. This will run the model + and create the ``chunk_cache.csv`` file in the output\cache directory for reuse. +* The ``households_sample_size`` for training chunking should be at least 1 / num_processors + to provide sufficient data for training and the ``chunk_method: hybrid_uss`` + typically performs best. +* Run the full scale model with ``chunk_training_mode: production``. Experiment + with different ``num_processors`` and ``chunk_size`` settings depending on desired + runtimes and machine resources. See :ref:`chunk_size` for more information. Users can run ``chunk_training_mode: disabled`` if the machine has an abundance of RAM for the model setup. @@ -1057,14 +1088,14 @@ only be activated when writing logsums for a small set of households for model e | mode_choice_logsum | mode choice logsum | +-----------------------------------+---------------------------------------+ -.. _example_mtc_extended : +.. _prototype_mtc_extended : -example_mtc_extended --------------------- +prototype_mtc_extended +---------------------- -example_mtc_extended contains additional models that were developed to enhance ActivitySim's modeling -capabilities, but are not part of travel model one that example_mtc was developed from. This example inherets -the data and configuration files from example_mtc. The current list of additional models included +prototype_mtc_extended contains additional models that were developed to enhance ActivitySim's modeling +capabilities. This example inherets +the data and configuration files from prototype_mtc. The current list of additional models included in this example are: * :ref:`vehicle_type_choice`: Selects a vehicle type for each household vehicle. Runs after auto_ownership. @@ -1084,10 +1115,10 @@ for examples that illustrate running ActivitySim in estimation mode and using la .. index:: multiple_zone_systems .. _multiple_zone_systems : -.. _example_multiple_zones : +.. _placeholder_multiple_zone : -example_multiple_zones ----------------------- +placeholder_multiple_zone +------------------------- In a multiple zone system approach, households, land use, and trips are modeled at the microzone (MAZ) level. MAZs are smaller than traditional TAZs and therefore make for a more precise system. However, when considering network level-of-service (LOS) @@ -1097,12 +1128,46 @@ system for non-motorized travel, and optionally a transit access points (TAPs) z ActivitySim supports models with multiple zone systems. The three versions of multiple zone systems are one-zone, two-zone, and three-zone. - * **One-zone**: This version is based on TM1 and supports only TAZs. All origins and destinations are represented at the TAZ level, and all skims including auto, transit, and non-motorized times and costs are also represented at the TAZ level. - * **Two-zone**: This version is similar to many DaySim models. It uses microzones (MAZs) for origins and destinations, and TAZs for specification of auto and transit times and costs. Impedance for walk or bike all-the-way from the origin to the destination can be specified at the MAZ level for close together origins and destinations, and at the TAZ level for further origins and destinations. Users can also override transit walk access and egress times with times specified in the MAZ file by transit mode. Careful pre-calculation of the assumed transit walk access and egress time by MAZ and transit mode is required depending on the network scenario. - * **Three-zone**: This version is based on the SANDAG generation of CT-RAMP models. Origins and destinations are represented at the MAZ level. Impedance for walk or bike all-the-way from the origin to the destination can be specified at the MAZ level for close together origins and destinations, and at the TAZ level for further origins and destinations, just like the two-zone system. TAZs are used for auto times and costs. The difference between this system and the two-zone system is that transit times and costs are represented between Transit Access Points (TAPs), which are essentially dummy zones that represent transit stops or clusters of stops. Transit skims are built between TAPs, since there are typically too many MAZs to build skims between them. Often multiple sets of TAP to TAP skims (local bus only, all modes, etc.) are created and input to the demand model for consideration. Walk access and egress times are also calculated between the MAZ and the TAP, and total transit path utilities are assembled from their respective components - from MAZ to first boarding TAP, from first boarding to final alighting TAP, and from alighting TAP to destination MAZ. This assembling is done via the :ref:`transit_virtual_path_builder` (TVPB), which considers all possible combinations of nearby boarding and alighting TAPs for each origin destination MAZ pair. - -Regions that have an interest in more precise transit forecasts may wish to adopt the three-zone approach, while other regions may adopt the one or two-zone approach. The microzone version requires coding households and land use at the microzone level. Typically an all-streets network is used for representation of non-motorized impedances. This requires a routable all-streets network, with centroids and connectors for microzones. If the three-zone system is adopted, procedures need to be developed to code TAPs from transit stops and populate the all-street network with TAP centroids and centroid connectors. A model with transit virtual path building takes longer to run than a traditional -TAZ only model, but it provides a much richer framework for transit modeling. + * **One-zone**: This version is based on TM1 and supports only TAZs. All origins and + destinations are represented at the TAZ level, and all skims including auto, transit, + and non-motorized times and costs are also represented at the TAZ level. + * **Two-zone**: This version is similar to many DaySim models. It uses microzones (MAZs) + for origins and destinations, and TAZs for specification of auto and transit times and + costs. Impedance for walk or bike all-the-way from the origin to the destination can + be specified at the MAZ level for close together origins and destinations, and at + the TAZ level for further origins and destinations. Users can also override transit + walk access and egress times with times specified in the MAZ file by transit mode. + Careful pre-calculation of the assumed transit walk access and egress time by MAZ + and transit mode is required depending on the network scenario. + * **Three-zone**: This version is based on the SANDAG generation of CT-RAMP models. + Origins and destinations are represented at the MAZ level. Impedance for walk or + bike all-the-way from the origin to the destination can be specified at the MAZ + level for close together origins and destinations, and at the TAZ level for further + origins and destinations, just like the two-zone system. TAZs are used for auto + times and costs. The difference between this system and the two-zone system is that + transit times and costs are represented between Transit Access Points (TAPs), which + are essentially dummy zones that represent transit stops or clusters of stops. + Transit skims are built between TAPs, since there are typically too many MAZs to + build skims between them. Often multiple sets of TAP to TAP skims (local bus only, + all modes, etc.) are created and input to the demand model for consideration. Walk + access and egress times are also calculated between the MAZ and the TAP, and total + transit path utilities are assembled from their respective components - from MAZ to + first boarding TAP, from first boarding to final alighting TAP, and from alighting + TAP to destination MAZ. This assembling is done via the + :ref:`transit_virtual_path_builder` (TVPB), which considers all possible + combinations of nearby boarding and alighting TAPs for each origin destination MAZ + pair. + +Regions that have an interest in more precise transit forecasts may wish to adopt the +three-zone approach, while other regions may adopt the one or two-zone approach. The +microzone version requires coding households and land use at the microzone level. +Typically an all-streets network is used for representation of non-motorized impedances. +This requires a routable all-streets network, with centroids and connectors for +microzones. If the three-zone system is adopted, procedures need to be developed to +code TAPs from transit stops and populate the all-street network with TAP centroids +and centroid connectors. A model with transit virtual path building takes longer to +run than a traditional TAZ only model, but it provides a much richer framework for +transit modeling. .. note:: The two and three zone system test examples are simple test examples developed from the TM1 example. To develop the two zone system @@ -1110,7 +1175,7 @@ TAZ only model, but it provides a much richer framework for transit modeling. TAZ to TAZ impedances. To develop the three zone example system example, the TM1 TAZ model was further transformed so select TAZs also became TAPs and TAP to TAP skims and MAZ to TAP impedances files were created. While sufficient for initial development, these examples were insufficient for validation and performance testing of the new software. As a result, - the :ref:`example_marin` example was created. + the :ref:`prototype_marin` example was created. Example simple test configurations and inputs for two and three-zone system models are described below. @@ -1125,10 +1190,10 @@ To run the two zone and three zone system examples, do the following: :: # simple two zone example - activitysim create -e example_2_zone -d test_example_2_zone + activitysim create -e placeholder_2_zone -d test_placeholder_2_zone # simple three zone example - activitysim create -e example_3_zone -d test_example_3_zone + activitysim create -e placeholder_3_zone -d test_placeholder_3_zone * Change to the example directory @@ -1154,16 +1219,28 @@ Two Zone In ``settings.yaml``: -* ``want_dest_choice_presampling`` - enable presampling for multizone systems, which means first select a TAZ using the sampling model and then select a microzone within the TAZ based on the microzone share of TAZ size term. +* ``want_dest_choice_presampling`` - enable presampling for multizone systems, which + means first select a TAZ using the sampling model and then select a microzone within + the TAZ based on the microzone share of TAZ size term. In ``network_los.yaml``: -The additional two zone system settings and inputs are described and illustrated below. No additional utility expression files or expression revisions are required beyond the one zone approach. The MAZ data is available as zone data and the MAZ to MAZ data is available using the existing skim expressions. Users can specify mode utilities using MAZ data, MAZ to MAZ impedances, and TAZ to TAZ impedances. +The additional two zone system settings and inputs are described and illustrated below. +No additional utility expression files or expression revisions are required beyond the +one zone approach. The MAZ data is available as zone data and the MAZ to MAZ data is +available using the existing skim expressions. Users can specify mode utilities using +MAZ data, MAZ to MAZ impedances, and TAZ to TAZ impedances. * ``zone_system`` - set to 2 for two zone system * ``maz`` - MAZ data file, with MAZ ID, TAZ, and land use and other MAZ attributes -* ``maz_to_maz:tables`` - list of MAZ to MAZ impedance tables. These tables are read as pandas DataFrames and the columns are exposed to expressions. -* ``maz_to_maz:max_blend_distance`` - in order to avoid cliff effects, the lookup of MAZ to MAZ impedance can be a blend of origin MAZ to destination MAZ impedance and origin TAZ to destination TAZ impedance up to a max distance. The blending formula is below. This requires specifying a distance TAZ skim and distance columns from the MAZ to MAZ files. The TAZ skim name and MAZ to MAZ column name need to be the same so the blending can happen on-the-fly or else a value of 0 is returned. +* ``maz_to_maz:tables`` - list of MAZ to MAZ impedance tables. These tables are read + as pandas DataFrames and the columns are exposed to expressions. +* ``maz_to_maz:max_blend_distance`` - in order to avoid cliff effects, the lookup of + MAZ to MAZ impedance can be a blend of origin MAZ to destination MAZ impedance and + origin TAZ to destination TAZ impedance up to a max distance. The blending formula + is below. This requires specifying a distance TAZ skim and distance columns from + the MAZ to MAZ files. The TAZ skim name and MAZ to MAZ column name need to be the + same so the blending can happen on-the-fly or else a value of 0 is returned. :: @@ -1353,21 +1430,21 @@ number of TAZs based on impedance and size, the model selects a microzone for ea on the microzone share of TAZ size. Presampling significantly reduces runtime while producing similar results. -.. _example_marin : +.. _prototype_marin : -example_marin -------------- +prototype_marin +--------------- To finalize development and verification of the multiple zone system and transit virtual path building components, the `Transportation Authority of Marin County `__ version of MTC travel model two (TM2) work tour mode choice model was implemented. This example was also developed to test multiprocessed runtime performance. -The complete runnable setup is available from the ActivitySim command line interface as `example_3_marin_full`. This example +The complete runnable setup is available from the ActivitySim command line interface as `prototype_3_marin_full`. This example has essentially the same configuration as the simpler three zone example above. Example ~~~~~~~ -To run example_marin, do the following: +To run prototype_marin, do the following: * Activate the correct conda environment if needed * Create a local copy of the example @@ -1375,7 +1452,7 @@ To run example_marin, do the following: :: # Marin TM2 work tour mode choice for the MTC region - activitysim create -e example_3_marin_full -d test_example_3_marin_full + activitysim create -e prototype_3_marin_full -d test_prototype_3_marin_full * Change to the example directory * Run the example @@ -1402,18 +1479,18 @@ file to include as it can considerably reduce runtimes. tap_lines: tap_lines.csv -.. _example_arc : +.. _prototype_arc : -example_arc ------------ +prototype_arc +------------- .. note:: This example is in development -The example_arc added a :ref:`trip_scheduling_choice`, :ref:`trip_departure_choice`, and :ref:`parking_location_choice` -submodel. These submodel specification files are below, and are in addition to the :ref:`example_mtc` +The prototype_arc added a :ref:`trip_scheduling_choice`, :ref:`trip_departure_choice`, and :ref:`parking_location_choice` +submodel. These submodel specification files are below, and are in addition to the :ref:`prototype_mtc` submodel :ref:`sub-model-spec-files`. .. _arc-sub-model-spec-files: @@ -1442,21 +1519,21 @@ Example ~~~~~~~ See example commands in `example_manifest.yaml `_ -for running example_arc. For optimal performance, configure multiprocessing and chunk_size based on machine hardware. +for running prototype_arc. For optimal performance, configure multiprocessing and chunk_size based on machine hardware. -.. _example_semcog : +.. _prototype_semcog : -example_semcog --------------- +prototype_semcog +---------------- .. note:: This example is in development -The example_semcog added a :ref:`work_from_home`, :ref:`telecommute_frequency`, :ref:`transit_pass_subsidy` -and :ref:`transit_pass_ownership` submodel. These submodel specification files are below, and are in addition to the :ref:`example_mtc` -submodel :ref:`sub-model-spec-files`. These submodels were added to example_semcog as extensions, which is a way for users to add +The prototype_semcog added a :ref:`work_from_home`, :ref:`telecommute_frequency`, :ref:`transit_pass_subsidy` +and :ref:`transit_pass_ownership` submodel. These submodel specification files are below, and are in addition to the :ref:`prototype_mtc` +submodel :ref:`sub-model-spec-files`. These submodels were added to prototype_semcog as extensions, which is a way for users to add submodels within their model setup as opposed to formally adding them to the activitysim package. Extension submodels are run through the `models` settings. However, the model must be run with the `simulation.py` script instead of the command line interface in order to load the extensions folder. @@ -1490,61 +1567,68 @@ Example ~~~~~~~ See example commands in `example_manifest.yaml `_ -for running example_semcog. For optimal performance, configure multiprocessing and chunk_size based on machine hardware. +for running prototype_semcog. For optimal performance, configure multiprocessing and chunk_size based on machine hardware. -.. _example_psrc : +.. _placeholder_psrc : -example_psrc ------------- +placeholder_psrc +---------------- .. note:: - This example is in development + This example is a placeholder model used only for code development and debugging, and is not suitable for policy analysis -The example_psrc is a two zone system (MAZs and TAZs) implementation of the -example_mtc model design. It uses PSRC zones, land use, synthetic population, and network LOS (skims). +The placeholder_psrc is a two zone system (MAZs and TAZs) implementation of the +prototype_mtc model design. It uses PSRC zones, land use, synthetic population, and network LOS (skims). Example ~~~~~~~ See example commands in `example_manifest.yaml `_ -for running example_psrc. For optimal performance, configure multiprocessing and chunk_size based on machine hardware. +for running placeholder_psrc. For optimal performance, configure multiprocessing and chunk_size based on machine hardware. -.. _example_sandag : +.. _placeholder_sandag : -example_sandag --------------- +placeholder_sandag +------------------ .. note:: This example is in development -The example_sandag is a three zone system (MAZs, TAZs, and TAPs) implementation of the -example_mtc model design. It uses SANDAG zones, land use, synthetic population, and network LOS (skims). +The placeholder_sandag is a multi-part model, containing one-, two-, and three- zone system (MAZs, TAZs, and TAPs) implementation of the +prototype_mtc model design. It uses SANDAG zones, land use, synthetic population, and network LOS (skims). Example ~~~~~~~ See example commands in `example_manifest.yaml `_ -for running example_sandag. For optimal performance, configure multiprocessing and chunk_size based on machine hardware. +for running placeholder_sandag. For optimal performance, configure multiprocessing and chunk_size based on machine hardware. -.. _example_sandag_xborder : +.. _prototype_sandag_xborder : -example_sandag_xborder ----------------------- +prototype_sandag_xborder +------------------------ .. note:: This example is in development -The example_sandag_xborder is a three zone system (MAZs, TAZs, and TAPs) that generates cross-border activities for a tour-based population of Mexican residents. In addition to the normal SANDAG zones, there are external MAZs and TAZs defined for each border crossing station (Port of Entry). Because the model is tour-based, there are no household or person-level attributes in the synthetic population. The principal difference between this and the standard 3-zone implementation is that since household do not have a default tour origin (home zones), a tour OD choice model is required to assign tour origins and destinations simultaneously. +The prototype_sandag_xborder is a three zone system (MAZs, TAZs, and TAPs) that +generates cross-border activities for a tour-based population of Mexican residents. +In addition to the normal SANDAG zones, there are external MAZs and TAZs defined for +each border crossing station (Port of Entry). Because the model is tour-based, there +are no household or person-level attributes in the synthetic population. The +principal difference between this and the standard 3-zone implementation is that +since household do not have a default tour origin (home zones), a tour OD choice +model is required to assign tour origins and destinations simultaneously. Example ~~~~~~~ See example commands in `example_manifest.yaml `_ -for running example_sandag_xborder. For optimal performance, configure multiprocessing and chunk_size based on machine hardware. +for running prototype_sandag_xborder. For optimal performance, configure multiprocessing and chunk_size based on machine hardware. diff --git a/docs/gettingstarted.rst b/docs/gettingstarted.rst index 41595ae3d1..e0201b1d94 100644 --- a/docs/gettingstarted.rst +++ b/docs/gettingstarted.rst @@ -156,8 +156,8 @@ To setup and run the primary example (see :ref:`examples`), do the following: * Open a command prompt * Activate the conda environment with ActivitySim installed (i.e. ``conda activate asim``) -* Type ``activitysim create -e example_mtc -d test_example_mtc`` to copy the very small MTC example to a new test_example_mtc directory -* Change to the test_example_mtc directory +* Type ``activitysim create -e prototype_mtc -d test_prototype_mtc`` to copy the very small prototype_mtc example to a new test_prototype_mtc directory +* Change to the test_prototype_mtc directory * Type ``activitysim run -c configs -o output -d data`` to run the example * Review the outputs in the output directory @@ -165,7 +165,7 @@ To setup and run the primary example (see :ref:`examples`), do the following: Common configuration settings can be overridden at runtime. See ``activitysim -h``, ``activitysim create -h`` and ``activitysim run -h``. ActivitySim model runs can be configured with settings file inheritance to avoid duplicating settings across model configurations. See :ref:`cli` for more information. -Additional examples, including the full scale MTC regional demand model, estimation integration examples, multiple zone system examples, +Additional examples, including the full scale prototype MTC regional demand model, estimation integration examples, multiple zone system examples, and examples for agency partners are available for creation by typing ``activitysim create -l``. To create these examples, ActivitySim downloads the (large) input files from the `ActivitySim resources `__ repository. See :ref:`examples` for more information. @@ -177,13 +177,13 @@ ActivitySim includes a `Jupyter Notebook `__ recipe book wi * Open a conda prompt and activate the conda environment with ActivitySim installed * If needed, ``conda install jupyterlab`` so you can run jupyter notebooks * Type ``jupyter notebook`` to launch the web-based notebook manager -* Navigate to the ``examples/example_mtc/notebooks`` folder and select a notebook to learn more: +* Navigate to the ``examples/prototype_mtc/notebooks`` folder and select a notebook to learn more: - * `Getting started `__ - * `Summarizing results `__ - * `Testing a change in auto ownership `__ - * `Adding TNCs `__ - * `Memory usage `__ + * `Getting started `__ + * `Summarizing results `__ + * `Testing a change in auto ownership `__ + * `Adding TNCs `__ + * `Memory usage `__ Hardware -------- @@ -197,7 +197,7 @@ The computing hardware required to run a model implemented in the ActivitySim fr * The desired runtimes ActivitySim framework models use a significant amount of RAM since they store data in-memory to reduce -data access time in order to minimize runtime. For example, the example MTC Travel Model One model has 2.7 million +data access time in order to minimize runtime. For example, the prototype MTC example model has 2.7 million households, 7.5 million people, 1475 zones, 826 network skims and has been run between one hour and one day depending on the amount of RAM and number of processors allocated. See :ref:`multiprocessing` and :ref:`chunk_size` for more information. diff --git a/docs/models.rst b/docs/models.rst index f8290fb0ac..00dd28aa4a 100644 --- a/docs/models.rst +++ b/docs/models.rst @@ -118,12 +118,12 @@ the coefficient adjustment at each iteration is: ``new_coefficient = log( target_percent / current_percent ) + current_coefficient``. The main interface to the work from home model is the -:py:func:`~activitysim.examples.example_semcog.extensions.work_from_home` function. This +:py:func:`~activitysim.examples.prototype_semcog.extensions.work_from_home` function. This function is registered as an Inject step in the example Pipeline. Core Table: ``persons`` | Result Field: ``work_from_home`` | Skims Keys: NA -.. automodule:: activitysim.examples.example_semcog.extensions.work_from_home +.. automodule:: activitysim.examples.prototype_semcog.extensions.work_from_home :members: .. _school_location: @@ -248,12 +248,12 @@ person :ref:`transit_pass_ownership` model and the tour and trip mode choice mod via fare discount adjustments. The main interface to the transit pass subsidy model is the -:py:func:`~activitysim.examples.example_semcog.extensions.transit_pass_subsidy` function. This +:py:func:`~activitysim.examples.prototype_semcog.extensions.transit_pass_subsidy` function. This function is registered as an Inject step in the example Pipeline. Core Table: ``persons`` | Result Field: ``transit_pass_subsidy`` | Skims Keys: NA -.. automodule:: activitysim.examples.example_semcog.extensions.transit_pass_subsidy +.. automodule:: activitysim.examples.prototype_semcog.extensions.transit_pass_subsidy :members: .. _transit_pass_ownership: @@ -268,12 +268,12 @@ result of this model can be used to condition downstream models such as the tour mode choice models via fare discount adjustments. The main interface to the transit pass ownership model is the -:py:func:`~activitysim.examples.example_semcog.extensions.transit_pass_ownership` function. This +:py:func:`~activitysim.examples.prototype_semcog.extensions.transit_pass_ownership` function. This function is registered as an Inject step in the example Pipeline. Core Table: ``persons`` | Result Field: ``transit_pass_ownership`` | Skims Keys: NA -.. automodule:: activitysim.examples.example_semcog.extensions.transit_pass_ownership +.. automodule:: activitysim.examples.prototype_semcog.extensions.transit_pass_ownership :members: .. _auto_ownership: @@ -335,7 +335,7 @@ The *vehicle_type_choice.yaml* file contains the following model specific option different scenario years. * Optional additional settings that work the same in other models are constants, expression preprocessor, and annotate tables. -Input vehicle type data included in :ref:`example_mtc_extended` came from a variety of sources. The number of vehicle makes, models, MPG, and +Input vehicle type data included in :ref:`prototype_mtc_extended` came from a variety of sources. The number of vehicle makes, models, MPG, and electric vehicle range was sourced from the Enivornmental Protection Agency (EPA). Additional data on vehicle costs were derived from the National Household Travel Survey. Auto operating costs in the vehicle type data file were a sum of fuel costs and maintenance costs. Fuel costs were calculated from MPG assuming a $3.00 cost for a gallon of gas. When MPG was not available to calculate fuel costs, @@ -347,12 +347,12 @@ Maintenance costs were not varied by vehicle year. (According to `data from the U.S. Bureau of Labor Statistics `_, there was no consistent relationship between vehicle age and maintenance costs.) -Using the above methodology, the average auto operating costs of vehicles output from :ref:`example_mtc_extended` was 18.4 cents. -This value is very close to the auto operating cost of 18.3 cents used in :ref:`example_mtc`. -Non-household vehicles in example_mtc_extended use the auto operating cost of 18.3 cents used in example_mtc. +Using the above methodology, the average auto operating costs of vehicles output from :ref:`prototype_mtc_extended` was 18.4 cents. +This value is very close to the auto operating cost of 18.3 cents used in :ref:`prototype_mtc`. +Non-household vehicles in prototype_mtc_extended use the auto operating cost of 18.3 cents used in prototype_mtc. Users are encouraged to make their own assumptions and calculate auto operating costs as they see fit. -The distribution of fuel type probabilities included in :ref:`example_mtc_extended` are computed directly from the National Household Travel Survey data +The distribution of fuel type probabilities included in :ref:`prototype_mtc_extended` are computed directly from the National Household Travel Survey data and include the entire US. Therefore, there is "lumpiness" in probabilities due to poor statistics in the data for some vehicle types. The user is encouraged to adjust the probabilities to their modeling region and "smooth" them for more consistent results. @@ -375,12 +375,12 @@ level of telecommuting. The model alternatives are the frequency of telecommutin days per week (0 days, 1 day, 2 to 3 days, 4+ days). The main interface to the work from home model is the -:py:func:`~activitysim.examples.example_semcog.extensions.telecommute_frequency` function. This +:py:func:`~activitysim.examples.prototype_semcog.extensions.telecommute_frequency` function. This function is registered as an Inject step in the example Pipeline. Core Table: ``persons`` | Result Field: ``telecommute_frequency`` | Skims Keys: NA -.. automodule:: activitysim.examples.example_semcog.extensions.telecommute_frequency +.. automodule:: activitysim.examples.prototype_semcog.extensions.telecommute_frequency :members: .. _freeparking: @@ -471,7 +471,7 @@ from previously scheduled tours. This model uses person :ref:`time_windows`. .. note:: - For ``example_mtc``, the modeled time periods for all submodels are hourly from 3 am to 3 am the next day, and any times before 5 am are shifted to time period 5, and any times after 11 pm are shifted to time period 23. + For ``prototype_mtc``, the modeled time periods for all submodels are hourly from 3 am to 3 am the next day, and any times before 5 am are shifted to time period 5, and any times after 11 pm are shifted to time period 23. If ``tour_departure_and_duration_segments.csv`` is included in the configs, then the model @@ -701,11 +701,11 @@ A vehicle is selected for each occupancy level set by the user such that differe characteristics. The output of the vehicle allocation model is appended to the tour table with column names ``vehicle_occup_{occupancy}`` and the values are the vehicle type selected. -In :ref:`example_mtc_extended`, three occupancy levels are used: 1, 2, and 3.5. The auto operating cost +In :ref:`prototype_mtc_extended`, three occupancy levels are used: 1, 2, and 3.5. The auto operating cost for occupancy level 1 is used in the drive alone mode and drive to transit modes. Occupancy levels 2 and 3.5 are used for shared ride 2 and shared ride 3+ auto operating costs, respectively. Auto operating costs are selected in the mode choice pre-processors by selecting the allocated vehicle type data from the vehicles table. If the allocated vehicle type was the non-household vehicle, the auto operating costs uses -the previous default value from :ref:`example_mtc`. All trips and atwork subtours use the auto operating cost of the parent tour. Functionality +the previous default value from :ref:`prototype_mtc`. All trips and atwork subtours use the auto operating cost of the parent tour. Functionality was added in tour and atwork subtour mode choice to annotate the tour table and create a ``selected_vehicle`` which denotes the actual vehicle used. If the tour mode does not include a vehicle, then the ``selected_vehicle`` entry is left blank.