BayDAG parking location choice configs and test

activitysim/abm/models/parking_location_choice.py

@@ -152,7 +152,6 @@ def choose_parking_location(
     destination_sample = logit.interaction_dataset(trips, alternatives, alt_index_id=alt_dest_col_name)
     destination_sample.index = np.repeat(trips.index.values, len(alternatives))
     destination_sample.index.name = trips.index.name
-    destination_sample = destination_sample[[alt_dest_col_name]].copy()
 
     # # - trip_destination_simulate
     destinations = parking_destination_simulate(
@@ -199,9 +198,6 @@ def run_parking_destination(
 
     alt_column_filter_name = model_settings.get('ALTERNATIVE_FILTER_COLUMN_NAME')
     alternatives = land_use[land_use[alt_column_filter_name]]
-
-    # don't need size terms in alternatives, just TAZ index
-    alternatives = alternatives.drop(alternatives.columns, axis=1)
     alternatives.index.name = parking_location_column_name
 
     choices_list = []

test/parking_location/compare-parking-location-results.R

@@ -0,0 +1,62 @@
+library(tidyverse)
+library(ggplot2)
+
+trips_df <- read_csv("./output/trips_after_parking_choice.csv")
+
+walk_dist_df <- read_csv("./data/ped_distance_maz_maz.csv")
+
+landuse_df <- read_csv("./data/land_use.csv")
+
+landuse_df <- landuse_df %>%
+  select(maz, maz_county_based, TAZ_ORIGINAL)
+
+work_df <- trips_df %>%
+  rename(parking_maz_target = parking_mgra) %>%
+  rename(parking_maz_simulated = parking_zone) %>%
+  select(trip_id, household_id, person_id, person_num, stop_id, tour_purpose, orig_purpose, dest_purpose, orig_maz = origin, dest_maz = destination, 
+         activity_duration_in_hours, parking_maz_target, parking_maz_simulated) %>%
+  left_join(landuse_df, by = c("orig_maz" = "maz")) %>%
+  rename(orig_maz_county_based = maz_county_based, orig_taz = TAZ_ORIGINAL) %>%
+  left_join(landuse_df, by = c("dest_maz" = "maz")) %>%
+  rename(dest_maz_county_based = maz_county_based, dest_taz = TAZ_ORIGINAL) %>%
+  left_join(landuse_df, by = c("parking_maz_target" = "maz")) %>%
+  rename(parking_maz_county_based_target = maz_county_based, parking_taz_target = TAZ_ORIGINAL) %>%
+  left_join(landuse_df, by = c("parking_maz_simulated" = "maz")) %>%
+  rename(parking_maz_county_based_simulated = maz_county_based, parking_taz_simulated = TAZ_ORIGINAL) %>%
+  left_join(walk_dist_df, by = c("parking_maz_simulated" = "OMAZ", "dest_maz" = "DMAZ")) %>%
+  rename(parking_distance_simulated = DISTWALK) %>%
+  left_join(walk_dist_df, by = c("parking_maz_target" = "OMAZ", "dest_maz" = "DMAZ")) %>%
+  rename(parking_distance_target = DISTWALK) %>%
+  left_join(walk_dist_df, by = c("parking_maz_target" = "OMAZ", "parking_maz_simulated" = "DMAZ")) %>%
+  rename(distance_target_simulation = DISTWALK) %>%
+  select(trip_id, household_id, person_id, stop_id, tour_purpose, orig_purpose, dest_purpose,
+         orig_maz, dest_maz, orig_maz_county_based, dest_maz_county_based, orig_taz, dest_taz,
+         parking_maz_target, parking_maz_simulated, parking_taz_target, parking_taz_simulated,
+         parking_distance_target, parking_distance_simulated, distance_target_simulation)
+
+
+write.csv(work_df, "./output/parking-location-choice-results.csv", row.names = F)
+
+
+summary_df <- work_df %>%
+  group_by(dest_purpose) %>%
+  summarise(
+    trips_count = n(),
+    min_distance_target = round(min(parking_distance_target, na.rm = T), 3),
+    min_distance_simulated = round(min(parking_distance_simulated, na.rm = T), 3),
+    max_distance_target = round(max(parking_distance_target, na.rm = T), 3),
+    max_distance_simulated = round(max(parking_distance_simulated, na.rm = T), 3),
+    mean_distance_target = round(mean(parking_distance_target, na.rm = T), 3),
+    mean_distance_simulated = round(mean(parking_distance_simulated, na.rm = T), 3),
+    median_distance_target = round(median(parking_distance_target, na.rm = T), 3),
+    median_distance_simulated = round(median(parking_distance_simulated, na.rm = T), 3)
+  )
+
+#ggplot(work_df, aes(x=distance_target_simulation)) + geom_histogram(binwidth=0.025, color="black", fill="white")
+
+#ggplot(work_df, aes(x=parking_distance_target)) + geom_histogram(binwidth=0.05, color="black", fill="white")
+
+#ggplot(work_df, aes(x=parking_distance_simulated)) + geom_histogram(binwidth=0.05, color="black", fill="white")
+
+
+

test/parking_location/configs/annotate_households.csv

@@ -0,0 +1,13 @@
+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)"
+number of fulltime workers,num_full_time_workers,"_PERSON_COUNT('ptype == 1', persons, households)"
+number of parttime workers,num_part_time_workers,"_PERSON_COUNT('ptype == 2', persons, households)"
+number of university student,num_university_students,"_PERSON_COUNT('ptype == 3', persons, households)"
+number of non-workers,num_non_workers,"_PERSON_COUNT('ptype == 4', persons, households)"
+number of retirees,num_retirees,"_PERSON_COUNT('ptype == 5', persons, households)"
+number of driving age students,num_driving_age_students,"_PERSON_COUNT('ptype == 6', persons, households)"
+number of pre-driving age school kids,num_pre_driving_age_school_kids,"_PERSON_COUNT('ptype == 7', persons, households)"
+number of pre-school kids,num_pre_school_kids,"_PERSON_COUNT('ptype == 8', persons, households)"
+number of pre-driving age school kids who go out,num_pre_driving_age_school_kids_go_out,"_PERSON_COUNT('(ptype == 7) & (cdap_activity != \'H\')', persons, households)"
+number of pre-school kids who go out,num_pre_school_kids_go_out,"_PERSON_COUNT('(ptype == 8) & (cdap_activity != \'H\')', persons, households)"

test/parking_location/configs/annotate_landuse.csv

@@ -0,0 +1,7 @@
+Description,Target,Expression
+#,, annotate landuse table after import
+household_density,household_density,land_use.TOTHH / land_use.TOTACRE
+employment_density,employment_density,land_use.TOTEMP / land_use.TOTACRE
+population_density,population_density,land_use.TOTPOP / land_use.TOTACRE
+density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1)
+is_parking_zone,is_parking_zone,"np.where(land_use.parkarea==1,True,False)"

test/parking_location/configs/annotate_persons.csv

@@ -0,0 +1,16 @@
+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
+,ptype,0
+,ptype,"np.where(persons.type == 'Full-time worker', 1, ptype)"
+,ptype,"np.where(persons.type == 'Part-time worker', 2, ptype)"
+,ptype,"np.where(persons.type == 'University student', 3, ptype)"
+,ptype,"np.where(persons.type == 'Non-worker', 4, ptype)"
+,ptype,"np.where(persons.type == 'Retired', 5, ptype)"
+,ptype,"np.where(persons.type == 'Student of driving age', 6, ptype)"
+,ptype,"np.where(persons.type == 'Student of non-driving age', 7, ptype)"
+,ptype,"np.where(persons.type == 'Child too young for school', 8, ptype)"

test/parking_location/configs/annotate_persons_after_hh.csv

@@ -0,0 +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)"

test/parking_location/configs/initialize_households.yaml

@@ -0,0 +1,37 @@
+annotate_tables:
+  - tablename: persons
+    column_map:
+      HHID: household_id
+      PERID: person_id
+      AGEP: age
+      SEX: sex
+      SCHL: education_attainment
+      OCCP: occupation
+      WKHP: hours_worked
+      WKW: weeks_worked
+      EMPLOYED: employment_status
+      ESR: esr
+      SCHG: grade_attending
+      activity_pattern: cdap_activity
+    annotate:
+      SPEC: annotate_persons
+      DF: persons
+      TABLES:
+        - households
+  - tablename: households
+    column_map:
+      HHID: household_id
+      HHINCADJ: income
+      NWRKRS_ESR: num_workers
+      VEH: auto_ownership
+      NP: hhsize
+      HHT: hh_type
+      BLD: building_size
+      TYPE: hh_unit_type
+      MTCCountyID: county_id
+    annotate:
+      SPEC: annotate_households
+      DF: households
+      TABLES:
+        - persons
+        - land_use

test/parking_location/configs/initialize_landuse.yaml

@@ -0,0 +1,52 @@
+annotate_tables:
+  - tablename: land_use
+    column_map:
+        CountyID: county_id
+        DistID: DISTRICT
+        HH: TOTHH
+        POP: TOTPOP
+        ACRES: TOTACRE
+        emp_total: TOTEMP
+    annotate:
+      SPEC: annotate_landuse
+      DF: land_use
+  - tablename: accessibility
+    column_map:
+        column_1: nonmandatory_auto_accessibility
+        column_2: nonmandatory_transit_accessibility
+        column_3: nonmandatory_nm_accessibility
+        column_4: nonmandatory_sov0_accessibility
+        column_5: nonmandatory_sov1_accessibility
+        column_6: nonmandatory_sov2_accessibility
+        column_7: nonmandatory_hov0_accessibility
+        column_8: nonmandatory_hov1_accessibility
+        column_9: nonmandatory_hov2_accessibility
+        column_10: shop_hov_insufficient_accessibility
+        column_11: shop_hov_sufficient_accessibility
+        column_12: shop_hov_oversufficient_accessibility
+        column_13: maint_hov_insufficient_accessibility
+        column_14: maint_hov_sufficient_accessibility
+        column_15: maint_hov_oversufficient_accessibility
+        column_16: eat_hov_insufficient_accessibility
+        column_17: eat_hov_sufficient_accessibility
+        column_18: eat_hov_oversufficient_accessibility
+        column_19: visit_hov_insufficient_accessibility
+        column_20: visit_hov_sufficient_accessibility
+        column_21: visit_hov_oversufficient_accessibility
+        column_22: discr_hov_insufficient_accessibility
+        column_23: discr_hov_sufficient_accessibility
+        column_24: discr_hov_oversufficient_accessibility
+        column_25: escort_hov_insufficient_accessibility
+        column_26: escort_hov_sufficient_accessibility
+        column_27: escort_hov_oversufficient_accessibility
+        column_28: shop_sov_insufficient_accessibility
+        column_29: shop_sov_sufficient_accessibility
+        column_30: shop_sov_oversufficient_accessibility
+        column_31: maint_sov_insufficient_accessibility
+        column_32: maint_sov_sufficient_accessibility
+        column_33: maint_sov_oversufficient_accessibility
+        column_40: discr_sov_insufficient_accessibility
+        column_41: discr_sov_sufficient_accessibility
+        column_42: discr_sov_oversufficient_accessibility
+        column_45: total_emp_accessibility
+        column_47: hh_walktransit_accessibility

test/parking_location/configs/network_los.yaml

@@ -0,0 +1,24 @@
+# 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: False
+
+zone_system: 2
+
+taz_skims: skims.omx
+
+maz: maz.csv
+
+maz_to_maz:
+  tables:
+    - ped_distance_maz_maz.csv
+
+  # maz_to_maz blending distance (missing or 0 means no blending)
+  max_blend_distance:
+    DISTWALK: 0
+
+skim_time_periods:
+    time_window: 1440
+    period_minutes: 60
+    periods: [0, 3, 5, 9, 14, 18, 24] # 3=3:00-3:59, 5=5:00-5:59, 9=9:00-9:59, 14=2:00-2:59, 18=6:00-6:59
+    labels: ['EA', 'EA', 'AM', 'MD', 'PM', 'EV']

test/parking_location/configs/parking_location_choice.csv

@@ -0,0 +1,34 @@
+Description,Expression,no_segmentation
+Person is worker,_person_is_worker@((df.ptype == 1) | (df.ptype == 2)),1
+Trip is work purpose,_work_trip@(df.purpose == 'work'),1
+Number of hourly stalls,[email protected],1
+Number of daily stalls,[email protected],1
+Number of monthly stalls,[email protected],1
+Hourly stalls available,_hourly_stalls_available@_hourly_stalls > 0,1
+Daily stalls available,_daily_stalls_available@_daily_stalls > 0,1
+Monthly stalls available,_monthly_stalls_available@_monthly_stalls > 0,1
+Hourly parking cost cents,[email protected] * 100,1
+Number of free parking hours,[email protected],1
+Daily parking cost cents,[email protected] * 100,1
+Monthly parking cost cents,[email protected] * 100,1
+Monthly parking is allowed,_monthly_allowed@_monthly_stalls_available & _work_trip & _person_is_worker,1
+Total hourly parking cost,_total_hourly_parking_cost@(_hourly_parking_cost * (df.activity_duration - _num_free_hours)),1
+Hourly is cheaper than daily,"[email protected](_daily_stalls_available, _total_hourly_parking_cost < _daily_parking_cost,True)",1
+Hourly is cheaper than monthly,"[email protected](_monthly_allowed, _total_hourly_parking_cost < _monthly_parking_cost,True)",1
+Hourly parking is best,_hourly_best@(_hourly_stalls_available & _hourly_cheaper_than_daily & _hourly_cheaper_than_monthly),1
+Daily is cheaper than hourly,"[email protected](_hourly_stalls_available, _daily_parking_cost < _total_hourly_parking_cost,True)",1
+Daily is cheaper than monthly,"[email protected](_monthly_allowed, _daily_parking_cost < _monthly_parking_cost,True)",1
+Daily parking is best,_daily_best@(_daily_stalls_available & _daily_cheaper_than_hourly & _daily_cheaper_than_monthly),1
+Monthly is cheaper than hourly,"[email protected](_hourly_stalls_available, _monthly_parking_cost < _total_hourly_parking_cost,True)",1
+Monthly is cheaper than daily,"[email protected](_daily_stalls_available, _monthly_parking_cost < _daily_stalls_available,True)",1
+Monthly parking is best,_monthly_best@(_monthly_allowed & _monthly_cheaper_than_hourly & _monthly_cheaper_than_daily),1
+Parking size,_parking_size@(_hourly_best * _hourly_stalls) + (_daily_best * _daily_stalls) + (_monthly_best * _monthly_stalls),1
+Parking cost,_parking_cost@(_hourly_best * _total_hourly_parking_cost) + (_daily_best * _daily_parking_cost) + (_monthly_best * _monthly_parking_cost),1
+Parking to destination distance skim,_walk_distance@pd_skims['DISTWALK'],1
+# actual utility terms starts from here
+Log of parking size,"@np.where(_parking_size>0,np.log(_parking_size),-9999)",1
+Cost work trip,"@np.where(df.purpose == 'work', _parking_cost, 0)",coef_cost_work_trip
+Cost other trip,"@np.where(df.purpose != 'work', _parking_cost, 0)",coef_cost_other_trip
+Walk distance,@_walk_distance,coef_walk_distance
+Walk Unavailability,"@np.where(_walk_distance == 0, 1, 0)",coef_unavailable
+Require parking zone to be less than 0.75 mile from dest,"@np.where(_walk_distance > 0.75, 1, 0) * (df.destination != df.parking_zone)",coef_unavailable

test/parking_location/configs/parking_location_choice.yaml

@@ -0,0 +1,22 @@
+SPECIFICATION: parking_location_choice.csv
+
+COEFFICIENTS: parking_location_choice_coeffs.csv
+
+PREPROCESSOR:
+  SPEC: parking_location_choice_annotate_trips_preprocessor
+  DF: trips_merged
+  TABLES:
+    - land_use
+    - persons
+
+# boolean column to filter choosers (True means keep)
+CHOOSER_FILTER_COLUMN_NAME: is_park_eligible
+CHOOSER_SEGMENT_COLUMN_NAME: parking_segment
+
+ALTERNATIVE_FILTER_COLUMN_NAME: is_parking_zone
+ALT_DEST_COL_NAME: parking_zone
+
+TRIP_DEPARTURE_PERIOD: 'stop_period'
+
+TRIP_ORIGIN: origin
+TRIP_DESTINATION: destination

test/parking_location/configs/parking_location_choice_annotate_trips_preprocessor.csv

@@ -0,0 +1,6 @@
+Description,Target,Expression
+Person Type,ptype,"reindex(persons.ptype, df.person_id)"
+Trip mode is drive,drive_trip,df.trip_mode <= 8
+Parking segment,parking_segment,setting('segment')
+Parking eligible trip,is_park_eligible,(drive_trip & (df.purpose != "home"))
+Activity duration,activity_duration,df.activity_duration_in_hours

test/parking_location/configs/parking_location_choice_coeffs.csv

@@ -0,0 +1,5 @@
+coefficient_name,value,constrain
+coef_unavailable,-9999,T
+coef_walk_distance,-11.8,F
+coef_cost_work_trip,-0.0072,F
+coef_cost_other_trip,-0.0041,F

test/parking_location/configs/settings.yaml

@@ -0,0 +1,62 @@
+# input tables
+input_table_list:
+  - tablename: households
+    filename: households.csv
+    index_col: household_id
+    rename_columns:
+      unique_hh_id: household_id
+      NP: hhsize
+      hh_workers_from_esr: num_workers
+      VEH: auto_ownership
+      MAZ: home_zone_id
+      HINCP: income
+    keep_columns:
+      - home_zone_id
+      - income
+      - hhsize
+      - HHT
+      - auto_ownership
+      - num_workers
+  - tablename: persons
+    filename: persons.csv
+    index_col: person_id
+    rename_columns:
+      unique_hh_id: household_id
+      AGEP: age
+      SPORDER: PNUM
+      SEX: sex
+      employed: pemploy
+      student_status: pstudent
+      person_type: ptype
+    keep_columns:
+      - household_id
+      - age
+      - PNUM
+      - sex
+      - pemploy
+      - pstudent
+      - ptype
+  - tablename: land_use
+    filename: land_use.csv
+    index_col: zone_id
+    rename_columns:
+      MAZ_ORIGINAL: zone_id
+      CountyID: county_id
+      TAZ_ORIGINAL: TAZ
+      DistID: DISTRICT
+      HH: TOTHH
+      POP: TOTPOP
+      ACRES: TOTACRE
+      emp_total: TOTEMP
+    keep_columns:
+      - TAZ
+      - DISTRICT
+      - SD
+      - county_id
+      - TOTHH
+      - TOTPOP
+      - TOTACRE
+
+trace_hh_id: 5924
+
+segment: "no_segmentation"