diff --git a/activitysim/activitysim.py b/activitysim/activitysim.py
index ef2e60b95c..a9484e5f91 100644
--- a/activitysim/activitysim.py
+++ b/activitysim/activitysim.py
@@ -45,6 +45,9 @@ def read_model_spec(fname,
         expression values are set as the table index.
     """
     cfg = pd.read_csv(fname, comment='#')
+
+    cfg = cfg.dropna(subset=[expression_name])
+
     # don't need description and set the expression to the index
     cfg = cfg.drop(description_name, axis=1).set_index(expression_name)
     return cfg
diff --git a/activitysim/cdap/__init__.py b/activitysim/cdap/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/activitysim/cdap/cdap.py b/activitysim/cdap/cdap.py
new file mode 100644
index 0000000000..813980fd81
--- /dev/null
+++ b/activitysim/cdap/cdap.py
@@ -0,0 +1,385 @@
+import itertools
+
+import numpy as np
+import pandas as pd
+from zbox import toolz as tz, gen
+
+from ..activitysim import eval_variables
+from .. import mnl
+
+
+def make_interactions(people, hh_id_col, p_type_col):
+    """
+    Make two Pandas DataFrames associating people IDs with two
+    and three person interactions they have within their households.
+
+    Interactions are strings of numbers representing the makeup
+    of the interaction, e.g. '12' or '341'.
+
+    Note that for two-person interactions the interaction string is ordered
+    with the person from the index in the first position of the string
+    and some other person in the second position. In contrast,
+    the interaction strings for three-person interactions are not ordered.
+    The person from the index may be in any position of the string.
+
+    Parameters
+    ----------
+    people : pandas.DataFrame
+        Table of people data. Must contain at least a household ID
+        column and a categorization of person type.
+    hh_id_col : str
+        Name of the column in `people` that has their household ID.
+    p_type_col : str
+        Name of the column in `people` that contains the person type number.
+
+    Returns
+    -------
+    two_interaction : pandas.DataFrame
+        Interactions between two people. Index will be person IDs taken
+        from the index of `people`.
+        The table will have one column called `interaction`.
+    three_interaction : pandas.DataFrame
+        Interactions between three people. Index will be person IDs taken
+        from the index of `people`.
+        The table will have one column called `interaction`.
+
+    """
+    two_fmt = '{}{}'.format
+    three_fmt = '{}{}{}'.format
+    two = []
+    three = []
+
+    for hh, df in people.groupby(hh_id_col, sort=False):
+        # skip households with only one person
+        if len(df) == 1:
+            continue
+
+        ptypes = df[p_type_col]
+
+        for pA, pB in itertools.permutations(df.index, 2):
+            two.append((pA, two_fmt(*ptypes[[pA, pB]])))
+
+        # now skip households with two people
+        if len(df) == 2:
+            continue
+
+        for idx in itertools.combinations(df.index, 3):
+            combo = three_fmt(*ptypes[list(idx)])
+            three.extend((p, combo) for p in idx)
+
+    if two:
+        two_idx, two_val = zip(*two)
+    else:
+        two_idx, two_val = [], []
+
+    if three:
+        three_idx, three_val = zip(*three)
+    else:
+        three_idx, three_val = [], []
+
+    return (
+        pd.DataFrame({'interaction': two_val}, index=two_idx),
+        pd.DataFrame({'interaction': three_val}, index=three_idx))
+
+
+def individual_utilities(
+        people, hh_id_col, p_type_col, one_spec, two_spec, three_spec):
+    """
+    Calculate CDAP utilities for all individuals.
+
+    Parameters
+    ----------
+    people : pandas.DataFrame
+        DataFrame of individual people data.
+    hh_id_col : str
+        Name of the column in `people` that has their household ID.
+    p_type_col : str
+        Name of the column in `people` that contains the person type number.
+    one_spec : pandas.DataFrame
+        CDAP spec applied to individuals.
+    two_spec : pandas.DataFrame
+        CDAP spec applied to interactions between two people.
+    three_spec : pandas.DataFrame
+        CDAP spec applied to interactions between three people.
+
+    Returns
+    -------
+    utilities : pandas.DataFrame
+        Will have index of `people` and columns for each of the alternatives.
+
+    """
+    # calculate single person utilities
+    #     evaluate variables from one_spec expressions
+    #     multiply by one_spec alternative values
+    one_vars = eval_variables(one_spec.index, people)
+    one_utils = one_vars.dot(one_spec)
+
+    # make two- and three-person interactions
+    two_int, three_int = make_interactions(people, hh_id_col, p_type_col)
+
+    # calculate two-interaction utilities
+    #     evaluate variables from two_spec expressions
+    #     multiply by two_spec alternative values
+    #     groupby person and sum
+    two_vars = eval_variables(two_spec.index, two_int)
+    two_utils = two_vars.dot(two_spec).groupby(level=0).sum()
+
+    # calculate three-interaction utilities
+    #     evaluate variables from three_spec expressions
+    #     multiply by three_spec alternative values
+    #     groupby person and sum
+    three_vars = eval_variables(three_spec.index, three_int)
+    three_utils = three_vars.dot(three_spec).groupby(level=0).sum()
+
+    # add one-, two-, and three-person utilities
+    utils = one_utils.add(
+        two_utils, fill_value=0).add(three_utils, fill_value=0)
+
+    return utils
+
+
+def initial_household_utilities(utilities, people, hh_id_col):
+    """
+    Create initial household utilities by grouping and summing utilities
+    from individual household members.
+
+    Parameters
+    ----------
+    utilities : pandas.DataFrame
+        Should have the index of `people` and columns for each alternative.
+    people : pandas.DataFrame
+        DataFrame of individual people data.
+    hh_id_col : str
+        Name of the column in `people` that has their household ID.
+
+    Returns
+    -------
+    hh_util : dict of pandas.Series
+        Keys will be household IDs and values will be Series
+        mapping alternative choices to their utility.
+
+    """
+    hh_util = {}
+
+    alts = utilities.columns
+
+    for hh_id, df in people.groupby(hh_id_col, sort=False):
+        utils = utilities.loc[df.index]
+        hh = []
+
+        for combo in itertools.product(alts, repeat=len(df)):
+            hh.append(
+                (combo, utils.lookup(df.index, combo).sum()))
+
+        idx, u = zip(*hh)
+        hh_util[hh_id] = pd.Series(u, index=idx)
+
+    return hh_util
+
+
+def apply_final_rules(hh_util, people, hh_id_col, final_rules):
+    """
+    Final rules can be used to set the utility values for certain
+    household alternatives. Often they are set to zero to reflect
+    the unavailability of certain alternatives to certain types of people.
+
+    This modifies the `hh_util` data inplace.
+
+    Parameters
+    ----------
+    hh_util : dict of pandas.Series
+        Keys will be household IDs and values will be Series
+        mapping alternative choices to their utility.
+    people : pandas.DataFrame
+        DataFrame of individual people data.
+    hh_id_col : str
+        Name of the column in `people` that has their household ID.
+    final_rules : pandas.DataFrame
+        This table must have an index of expressions that can be used
+        to filter the `people` table. It must have two columns:
+        the first must have the name of the alternative to which the rule
+        applies, and the second must have the value of the utility for that
+        alternative. The names of the columns is not important, but
+        the order is.
+
+    """
+    rule_mask = eval_variables(final_rules.index, people)
+
+    for hh_id, df in people.groupby(hh_id_col, sort=False):
+        mask = rule_mask.loc[df.index]
+        utils = hh_util[hh_id]
+
+        for exp, row in final_rules.iterrows():
+            m = mask[exp].as_matrix()
+
+            # this crazy business combines three things to figure out
+            # which household alternatives need to be modified by this rule.
+            # the three things are:
+            # - the mask of people for whom the rule expression is true (m)
+            # - the individual alternative to which the rule applies
+            #   (row.iloc[0])
+            # - the alternative combinations for the household (combo)
+            app = [
+                ((np.array([row.iloc[0]] * len(utils.index[0])) == combo) & m
+                 ).any()
+                for combo in utils.index]
+
+            utils[app] = row.iloc[1]
+
+
+def apply_all_people(hh_util, all_people):
+    """
+    Apply utility adjustments to household alternatives.
+
+    This modifies the `hh_util` data inplace.
+
+    Parameters
+    ----------
+    hh_util : dict of pandas.Series
+        Keys will be household IDs and values will be Series
+        mapping alternative choices to their utility.
+    all_people : pandas.DataFrame
+        Adjustments to household alternatives, with alternatives in the
+        index and the adjustment values in the first column.
+        Index should be household alternatives in the form of tuples
+        containing individual alternatives, e.g.
+        ('Mandatory', 'Mandatory', 'Mandatory'), where 'Mandatory' is
+        one of the alternatives available to individual household members.
+        Note that these may also be expressed as Python code to save space,
+        so the previous could also be written as ('Mandatory',) * 3.
+
+    """
+    # evaluate all the expressions in the all_people index
+    all_people.index = [eval(x) for x in all_people.index]
+    all_people = all_people.icol(0)
+
+    matching_idx = {}
+
+    for hh in hh_util.values():
+        l = len(hh)
+        if l in matching_idx:
+            matching = matching_idx[l]
+        else:
+            matching = hh.index.intersection(all_people.index)
+            matching_idx[l] = matching
+
+        hh.loc[matching] += all_people.loc[matching]
+
+
+def make_household_choices(hh_util):
+    """
+    Decide on the activity pattern for each household.
+
+    Parameters
+    ----------
+    hh_util : dict of pandas.Series
+        Keys will be household IDs and values will be Series
+        mapping alternative choices to their utility.
+
+    Returns
+    -------
+    choices : pandas.Series
+        Maps household ID to chosen alternative, where the alternative
+        is a tuple of individual utilities.
+
+    """
+    # convert hh_util dict to a few DFs with alternatives in the columns
+    # and household IDs in the index
+    df_func = tz.compose(
+        pd.DataFrame.transpose,
+        pd.DataFrame.from_dict)
+    grouped_by_size = (
+        tz.valfilter(lambda x: len(x) == l, hh_util)
+        for l in tz.unique(tz.map(len, hh_util.values())))
+    dfs = tz.map(df_func, grouped_by_size)
+
+    # go over all the DFs and do utils_to_probs and make_choices
+    choices = (
+        pd.Series(
+            df.columns[mnl.make_choices(mnl.utils_to_probs(df))].values,
+            index=df.index)
+        for df in dfs)
+
+    # concat all the resulting Series
+    return pd.concat(choices)
+
+
+def household_choices_to_people(hh_choices, people):
+    """
+    Map household choices to people so that we know the activity pattern
+    for individuals.
+
+    Parameters
+    ----------
+    hh_choices : pandas.Series
+        Maps household ID to chosen alternative, where the alternative
+        is a tuple of individual utilities.
+    people : pandas.DataFrame
+        DataFrame of individual people data.
+
+    Returns
+    -------
+    choices : pandas.Series
+        Maps index of `people` to their activity pattern choice.
+
+    """
+    return pd.Series(
+        gen(tz.concat(hh_choices.values)), index=people.index)
+
+
+def run_cdap(
+        people, hh_id_col, p_type_col, one_spec, two_spec, three_spec,
+        final_rules, all_people):
+    """
+    Choose individual activity patterns for people.
+
+    Parameters
+    ----------
+    people : pandas.DataFrame
+        Table of people data. Must contain at least a household ID
+        column and a categorization of person type.
+    hh_id_col : str
+        Name of the column in `people` that has their household ID.
+    p_type_col : str
+        Name of the column in `people` that contains the person type number.
+    one_spec : pandas.DataFrame
+        CDAP spec applied to individuals.
+    two_spec : pandas.DataFrame
+        CDAP spec applied to interactions between two people.
+    three_spec : pandas.DataFrame
+        CDAP spec applied to interactions between three people.
+    final_rules : pandas.DataFrame
+        This table must have an index of expressions that can be used
+        to filter the `people` table. It must have two columns:
+        the first must have the name of the alternative to which the rule
+        applies, and the second must have the value of the utility for that
+        alternative. The names of the columns is not important, but
+        the order is.
+    all_people : pandas.DataFrame
+        Adjustments to household alternatives, with alternatives in the
+        index and the adjustment values in the first column.
+        Index should be household alternatives in the form of tuples
+        containing individual alternatives, e.g.
+        ('Mandatory', 'Mandatory', 'Mandatory'), where 'Mandatory' is
+        one of the alternatives available to individual household members.
+        Note that these may also be expressed as Python code to save space,
+        so the previous could also be written as ('Mandatory',) * 3.
+
+    Returns
+    -------
+    choices : pandas.Series
+        Maps index of `people` to their activity pattern choice,
+        where that choice is taken from the columns of specs
+        (so it's important that the specs all refer to alternatives
+        in the same way).
+
+    """
+    ind_utils = individual_utilities(
+        people, hh_id_col, p_type_col, one_spec, two_spec, three_spec)
+    hh_utils = initial_household_utilities(ind_utils, people, hh_id_col)
+    if final_rules is not None:
+        apply_final_rules(hh_utils, people, hh_id_col, final_rules)
+    if all_people is not None:
+        apply_all_people(hh_utils, all_people)
+    hh_choices = make_household_choices(hh_utils)
+    return household_choices_to_people(hh_choices, people)
diff --git a/activitysim/cdap/tests/__init__.py b/activitysim/cdap/tests/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/activitysim/cdap/tests/data/cdap_1_person.csv b/activitysim/cdap/tests/data/cdap_1_person.csv
new file mode 100644
index 0000000000..befb8ffa4a
--- /dev/null
+++ b/activitysim/cdap/tests/data/cdap_1_person.csv
@@ -0,0 +1,24 @@
+Description,Expression,Mandatory,NonMandatory,Home
+Age filter 1,age < 30,1,0,0
+Age filter 2,age >= 30,0,1,0
+Type 1 and low age,(ptype == 1) & (age < 25),1,0,0
+Type 2 and high age,(ptype == 2) & (age >= 35),0,0,1
+# The rest of these are corrections so results come out the way I want
+Person 2 -> H,age == 21,-1,0,1
+Person 5 -> N,age == 24,-1,0,0
+Person 6 -> M,age == 25,0,-1,0
+Person 7 -> M,age == 26,0,-2,-1
+Person 8 -> N,age == 27,-1,0,-1
+Person 9 -> H,age == 28,-1,-2,0
+Person 10 -> M,age == 29,0,0,-2
+Person 11 -> H,age == 30,0,-1,0
+Person 12 -> H,age == 31,-1,-1,0
+Person 13 -> M,age == 32,0,-1,-3
+Person 14 -> M,age == 33,1,-3,-4
+Person 15 -> N,age == 34,-1,0,-2
+Person 16 -> N,age == 35,-1,0,-3
+Person 17 -> H,age == 36,-2,-1,0
+Person 18 -> H,age == 37,0,-2,0
+# This 50 will get zeroed out later by the forbidden alternatives rules
+Person 19 -> H,age == 38,50,-4,0
+Person 20 -> M,age == 39,0,-1,-4
diff --git a/activitysim/cdap/tests/data/cdap_2_person.csv b/activitysim/cdap/tests/data/cdap_2_person.csv
new file mode 100644
index 0000000000..9fdda047f9
--- /dev/null
+++ b/activitysim/cdap/tests/data/cdap_2_person.csv
@@ -0,0 +1,8 @@
+Description,Expression,Mandatory,NonMandatory,Home
+Two type 1,interaction == '11',1,0,0
+Two type 2,interaction == '22',0,1,0
+Two type 3,interaction == '33',0,0,1
+One and two,interaction == '12',1,0,0
+Two and one,interaction == '21',0,1,0
+One + three,"interaction in ['13', '31']",0,0,1
+Two + three,"interaction in ['23', '32']",0,1,0
diff --git a/activitysim/cdap/tests/data/cdap_3_person.csv b/activitysim/cdap/tests/data/cdap_3_person.csv
new file mode 100644
index 0000000000..eb7e1794d4
--- /dev/null
+++ b/activitysim/cdap/tests/data/cdap_3_person.csv
@@ -0,0 +1,11 @@
+Description,Expression,Mandatory,NonMandatory,Home
+All one,interaction == '111',1,0,0
+All two,interaction == '222',0,1,0
+All three,interaction == '333',0,0,1
+One + one + two,"interaction in ['112', '121', '211']",0,0,1
+One + one + three,"interaction in ['113', '131', '311']",0,0,1
+One + two + two,"interaction in ['122', '212', '221']",1,0,0
+One + two + three,"interaction in ['123', '132', '213', '231', '312', '321']",0,0,1
+One + three + three,"interaction in ['133', '313', '331']",0,1,0
+Two + two + three,"interaction in ['223', '232', '322']",0,0,1
+Two + three + three,"interaction in ['233', '323', '332']",1,0,0
diff --git a/activitysim/cdap/tests/data/cdap_all_people.csv b/activitysim/cdap/tests/data/cdap_all_people.csv
new file mode 100644
index 0000000000..7d838aea5e
--- /dev/null
+++ b/activitysim/cdap/tests/data/cdap_all_people.csv
@@ -0,0 +1,7 @@
+Description,Alternative,Value
+Three Person All M,"('Mandatory',) * 3",0
+Three Person All N,"('NonMandatory',) * 3",0
+Three Person All H,"('Home',) * 3",0
+Four Person All M,"('Mandatory',) * 4",0
+Four Person All N,"('NonMandatory',) * 4",0
+Four Person All H,"('Home',) * 4",0
diff --git a/activitysim/cdap/tests/data/cdap_final_rules.csv b/activitysim/cdap/tests/data/cdap_final_rules.csv
new file mode 100644
index 0000000000..f33694a182
--- /dev/null
+++ b/activitysim/cdap/tests/data/cdap_final_rules.csv
@@ -0,0 +1,2 @@
+Description,Expression,Alternative,Value
+M not allowed for Age 38 and Income 1900,(age == 38) & (income == 1900),Mandatory,0
diff --git a/activitysim/cdap/tests/data/people.csv b/activitysim/cdap/tests/data/people.csv
new file mode 100644
index 0000000000..1bb990eb4a
--- /dev/null
+++ b/activitysim/cdap/tests/data/people.csv
@@ -0,0 +1,21 @@
+id,household,ptype,age,income
+1,1,1,20,100
+2,2,3,21,200
+3,3,1,22,300
+4,3,1,23,400
+5,4,3,24,500
+6,4,2,25,600
+7,5,3,26,700
+8,5,2,27,800
+9,5,2,28,900
+10,6,1,29,1000
+11,6,3,30,1100
+12,6,1,31,1200
+13,7,1,32,1300
+14,7,3,33,1400
+15,7,2,34,1500
+16,7,2,35,1600
+17,8,1,36,1700
+18,8,3,37,1800
+19,8,2,38,1900
+20,8,1,39,2000
diff --git a/activitysim/cdap/tests/test_cdap.py b/activitysim/cdap/tests/test_cdap.py
new file mode 100644
index 0000000000..ffc8cf470d
--- /dev/null
+++ b/activitysim/cdap/tests/test_cdap.py
@@ -0,0 +1,353 @@
+import os.path
+from itertools import product
+
+import pandas as pd
+import pandas.util.testing as pdt
+import pytest
+
+from .. import cdap
+from ...activitysim import read_model_spec
+
+
+@pytest.fixture(scope='module')
+def people():
+    return pd.read_csv(
+        os.path.join(os.path.dirname(__file__), 'data', 'people.csv'),
+        index_col='id')
+
+
+@pytest.fixture(scope='module')
+def one_spec():
+    return read_model_spec(
+        os.path.join(
+            os.path.dirname(__file__), 'data', 'cdap_1_person.csv'))
+
+
+@pytest.fixture(scope='module')
+def two_spec():
+    return read_model_spec(
+        os.path.join(
+            os.path.dirname(__file__), 'data', 'cdap_2_person.csv'))
+
+
+@pytest.fixture(scope='module')
+def three_spec():
+    return read_model_spec(
+        os.path.join(
+            os.path.dirname(__file__), 'data', 'cdap_3_person.csv'))
+
+
+@pytest.fixture(scope='module')
+def final_rules():
+    return read_model_spec(
+        os.path.join(
+            os.path.dirname(__file__), 'data', 'cdap_final_rules.csv'))
+
+
+@pytest.fixture
+def all_people():
+    return read_model_spec(
+        os.path.join(
+            os.path.dirname(__file__), 'data', 'cdap_all_people.csv'),
+        expression_name='Alternative')
+
+
+@pytest.fixture(scope='module')
+def hh_id_col():
+    return 'household'
+
+
+@pytest.fixture(scope='module')
+def p_type_col():
+    return 'ptype'
+
+
+@pytest.fixture(scope='module')
+def individual_utils(
+        people, hh_id_col, p_type_col, one_spec, two_spec, three_spec):
+    return cdap.individual_utilities(
+        people, hh_id_col, p_type_col, one_spec, two_spec, three_spec)
+
+
+@pytest.fixture
+def hh_utils(individual_utils, people, hh_id_col):
+    hh_utils = cdap.initial_household_utilities(
+        individual_utils, people, hh_id_col)
+    return hh_utils
+
+
+@pytest.fixture
+def hh_choices(random_seed, hh_utils):
+    return cdap.make_household_choices(hh_utils)
+
+
+def test_make_interactions(people, hh_id_col, p_type_col):
+    expected_two = pd.DataFrame(
+        {'interaction': [
+            '11',  # household 3; person 3
+            '11',  # household 3; person 4
+            '32',  # household 4; person 5
+            '23',  # household 4; person 6
+            '32', '32',  # household 5; person 7
+            '23', '22',  # household 5; person 8
+            '23', '22',  # household 5; person 9
+            '13', '11',  # household 6; person 10
+            '31', '31',  # household 6; person 11
+            '11', '13',  # household 6; person 12
+            '13', '12', '12',  # household 7; person 13
+            '31', '32', '32',  # household 7; person 14
+            '21', '23', '22',  # household 7; person 15
+            '21', '23', '22',  # household 7; person 16
+            '13', '12', '11',  # household 8; person 17
+            '31', '32', '31',  # household 8; person 18
+            '21', '23', '21',  # household 8; person 19
+            '11', '13', '12'   # household 8; person 20
+        ]},
+        index=[
+            3, 4, 5, 6, 7, 7, 8, 8, 9, 9, 10, 10, 11, 11, 12, 12,
+            13, 13, 13, 14, 14, 14, 15, 15, 15, 16, 16, 16,
+            17, 17, 17, 18, 18, 18, 19, 19, 19, 20, 20, 20
+        ])
+
+    expected_three = pd.DataFrame(
+        {'interaction': [
+            '322', '322', '322',  # household 5; people 7, 8, 9
+            '131', '131', '131',  # household 6; people 10, 11, 12
+            '132', '132', '132',  # household 7; people 13, 14, 15
+            '132', '132', '132',  # household 7; people 13, 14, 16
+            '122', '122', '122',  # household 7; people 13, 15, 16
+            '322', '322', '322',  # household 7; people 14, 15, 16
+            '132', '132', '132',  # household 8; people 17, 18, 19
+            '131', '131', '131',  # household 8; people 17, 18, 20
+            '121', '121', '121',  # household 8; people 17, 19, 20
+            '321', '321', '321'   # household 8; people 18, 19, 20
+        ]},
+        index=[
+            7, 8, 9, 10, 11, 12,
+            13, 14, 15, 13, 14, 16, 13, 15, 16, 14, 15, 16,
+            17, 18, 19, 17, 18, 20, 17, 19, 20, 18, 19, 20
+        ])
+
+    two, three = cdap.make_interactions(people, hh_id_col, p_type_col)
+
+    pdt.assert_frame_equal(two, expected_two)
+    pdt.assert_frame_equal(three, expected_three)
+
+
+def test_make_interactions_no_interactions(people, hh_id_col, p_type_col):
+    people = people.loc[[1, 2, 3]]
+
+    two, three = cdap.make_interactions(people, hh_id_col, p_type_col)
+
+    pdt.assert_frame_equal(two, pd.DataFrame(columns=['interaction']))
+    pdt.assert_frame_equal(three, pd.DataFrame(columns=['interaction']))
+
+
+def test_make_interactions_only_twos(people, hh_id_col, p_type_col):
+    people = people.loc[[1, 2, 3, 4, 5, 6]]
+
+    expected_two = pd.DataFrame(
+        {'interaction': [
+            '11',  # household 3; person 3
+            '11',  # household 3; person 4
+            '32',  # household 4; person 5
+            '23',  # household 4; person 6
+        ]},
+        index=[3, 4, 5, 6]
+    )
+
+    two, three = cdap.make_interactions(people, hh_id_col, p_type_col)
+
+    pdt.assert_frame_equal(two, expected_two)
+    pdt.assert_frame_equal(three, pd.DataFrame(columns=['interaction']))
+
+
+def test_individual_utilities(people, one_spec, individual_utils):
+    expected = pd.DataFrame([
+        [2, 0, 0],  # person 1
+        [0, 0, 1],  # person 2
+        [3, 0, 0],  # person 3
+        [3, 0, 0],  # person 4
+        [0, 1, 0],  # person 5
+        [1, 0, 0],  # person 6
+        [1, 0, 0],  # person 7
+        [0, 2, 0],  # person 8
+        [0, 0, 1],  # person 9
+        [2, 0, 0],  # person 10
+        [0, 0, 3],  # person 11
+        [0, 0, 2],  # person 12
+        [3, 0, 0],  # person 13
+        [1, 0, 0],  # person 14
+        [0, 4, 0],  # person 15
+        [0, 4, 0],  # person 16
+        [0, 0, 4],  # person 17
+        [0, 0, 5],  # person 18
+        [50, 0, 4],  # person 19
+        [2, 0, 0]  # person 20
+        ], index=people.index, columns=one_spec.columns)
+
+    pdt.assert_frame_equal(
+        individual_utils, expected, check_dtype=False, check_names=False)
+
+
+def test_initial_household_utilities(hh_utils):
+    alts = ['Mandatory', 'NonMandatory', 'Home']
+    one_alts = list(product(alts, repeat=1))
+    two_alts = list(product(alts, repeat=2))
+    three_alts = list(product(alts, repeat=3))
+    four_alts = list(product(alts, repeat=4))
+
+    expected = {
+        1: pd.Series([2, 0, 0], index=one_alts),
+        2: pd.Series([0, 0, 1], index=one_alts),
+        3: pd.Series([6, 3, 3, 3, 0, 0, 3, 0, 0], index=two_alts),
+        4: pd.Series([1, 0, 0, 2, 1, 1, 1, 0, 0], index=two_alts),
+        5: pd.Series([
+            1, 1, 2, 3, 3, 4, 1, 1, 2,
+            0, 0, 1, 2, 2, 3, 0, 0, 1,
+            0, 0, 1, 2, 2, 3, 0, 0, 1,
+            ], index=three_alts),
+        6: pd.Series([
+            2, 2, 4, 2, 2, 4, 5, 5, 7,
+            0, 0, 2, 0, 0, 2, 3, 3, 5,
+            0, 0, 2, 0, 0, 2, 3, 3, 5,
+            ], index=three_alts),
+        7: pd.Series([
+            4, 8, 4, 8, 12, 8, 4, 8, 4,
+            3, 7, 3, 7, 11, 7, 3, 7, 3,
+            3, 7, 3, 7, 11, 7, 3, 7, 3,
+            1, 5, 1, 5, 9, 5, 1, 5, 1,
+            0, 4, 0, 4, 8, 4, 0, 4, 0,
+            0, 4, 0, 4, 8, 4, 0, 4, 0,
+            1, 5, 1, 5, 9, 5, 1, 5, 1,
+            0, 4, 0, 4, 8, 4, 0, 4, 0,
+            0, 4, 0, 4, 8, 4, 0, 4, 0
+            ], index=four_alts),
+        8: pd.Series([
+            52, 50, 50, 2, 0, 0, 6, 4, 4,
+            52, 50, 50, 2, 0, 0, 6, 4, 4,
+            57, 55, 55, 7, 5, 5, 11, 9, 9,
+            52, 50, 50, 2, 0, 0, 6, 4, 4,
+            52, 50, 50, 2, 0, 0, 6, 4, 4,
+            57, 55, 55, 7, 5, 5, 11, 9, 9,
+            56, 54, 54, 6, 4, 4, 10, 8, 8,
+            56, 54, 54, 6, 4, 4, 10, 8, 8,
+            61, 59, 59, 11, 9, 9, 15, 13, 13
+            ], index=four_alts)
+    }
+
+    assert list(hh_utils.keys()) == list(expected.keys())
+    for k in expected:
+        pdt.assert_series_equal(hh_utils[k], expected[k], check_dtype=False)
+
+
+def test_apply_final_rules(hh_utils, final_rules, people, hh_id_col):
+    expected = hh_utils.copy()
+    expected[8] = pd.Series([
+        0, 0, 0, 2, 0, 0, 6, 4, 4,
+        0, 0, 0, 2, 0, 0, 6, 4, 4,
+        0, 0, 0, 7, 5, 5, 11, 9, 9,
+        0, 0, 0, 2, 0, 0, 6, 4, 4,
+        0, 0, 0, 2, 0, 0, 6, 4, 4,
+        0, 0, 0, 7, 5, 5, 11, 9, 9,
+        0, 0, 0, 6, 4, 4, 10, 8, 8,
+        0, 0, 0, 6, 4, 4, 10, 8, 8,
+        0, 0, 0, 11, 9, 9, 15, 13, 13
+        ], index=expected[7].index)
+
+    cdap.apply_final_rules(hh_utils, people, hh_id_col, final_rules)
+
+    for k in expected:
+        pdt.assert_series_equal(hh_utils[k], expected[k], check_dtype=False)
+
+
+def test_apply_all_people(hh_utils, all_people):
+    all_people.at["('Mandatory',) * 3", 'Value'] = 300
+    all_people.at["('Home',) * 4", 'Value'] = 500
+
+    expected = hh_utils.copy()
+    expected[5] = pd.Series([
+        301, 1, 2, 3, 3, 4, 1, 1, 2,
+        0, 0, 1, 2, 2, 3, 0, 0, 1,
+        0, 0, 1, 2, 2, 3, 0, 0, 1,
+        ], index=hh_utils[5].index)
+    expected[6] = pd.Series([
+        302, 2, 4, 2, 2, 4, 5, 5, 7,
+        0, 0, 2, 0, 0, 2, 3, 3, 5,
+        0, 0, 2, 0, 0, 2, 3, 3, 5,
+        ], index=hh_utils[6].index)
+    expected[7] = pd.Series([
+        4, 8, 4, 8, 12, 8, 4, 8, 4,
+        3, 7, 3, 7, 11, 7, 3, 7, 3,
+        3, 7, 3, 7, 11, 7, 3, 7, 3,
+        1, 5, 1, 5, 9, 5, 1, 5, 1,
+        0, 4, 0, 4, 8, 4, 0, 4, 0,
+        0, 4, 0, 4, 8, 4, 0, 4, 0,
+        1, 5, 1, 5, 9, 5, 1, 5, 1,
+        0, 4, 0, 4, 8, 4, 0, 4, 0,
+        0, 4, 0, 4, 8, 4, 0, 4, 500
+        ], index=hh_utils[7].index)
+    expected[8] = pd.Series([
+        52, 50, 50, 2, 0, 0, 6, 4, 4,
+        52, 50, 50, 2, 0, 0, 6, 4, 4,
+        57, 55, 55, 7, 5, 5, 11, 9, 9,
+        52, 50, 50, 2, 0, 0, 6, 4, 4,
+        52, 50, 50, 2, 0, 0, 6, 4, 4,
+        57, 55, 55, 7, 5, 5, 11, 9, 9,
+        56, 54, 54, 6, 4, 4, 10, 8, 8,
+        56, 54, 54, 6, 4, 4, 10, 8, 8,
+        61, 59, 59, 11, 9, 9, 15, 13, 513
+        ], index=hh_utils[8].index)
+
+    cdap.apply_all_people(hh_utils, all_people)
+
+    for k in expected:
+        pdt.assert_series_equal(hh_utils[k], expected[k], check_dtype=False)
+
+
+def test_make_household_choices(hh_choices):
+    expected = pd.Series([
+        ('Mandatory',),
+        ('Home',),
+        ('Mandatory', 'Mandatory'),
+        ('NonMandatory', 'NonMandatory'),
+        ('Mandatory', 'NonMandatory', 'Home'),
+        ('Mandatory', 'Home', 'Home'),
+        ('Mandatory', 'Mandatory', 'NonMandatory', 'NonMandatory'),
+        ('Home', 'Home', 'Mandatory', 'NonMandatory')],
+        index=range(1, 9))
+    pdt.assert_series_equal(hh_choices, expected)
+
+
+def test_household_choices_to_people(hh_choices, people):
+    people_choices = cdap.household_choices_to_people(hh_choices, people)
+    expected = pd.Series([
+        'Mandatory',
+        'Home',
+        'Mandatory', 'Mandatory',
+        'NonMandatory', 'NonMandatory',
+        'Mandatory', 'NonMandatory', 'Home',
+        'Mandatory', 'Home', 'Home',
+        'Mandatory', 'Mandatory', 'NonMandatory', 'NonMandatory',
+        'Home', 'Home', 'Mandatory', 'NonMandatory'],
+        index=people.index)
+    pdt.assert_series_equal(people_choices, expected)
+
+
+def test_run_cdap(
+        people, hh_id_col, p_type_col, one_spec, two_spec, three_spec,
+        final_rules, all_people, random_seed):
+    people_choices = cdap.run_cdap(
+        people, hh_id_col, p_type_col, one_spec, two_spec, three_spec,
+        final_rules, all_people)
+    expected = pd.Series([
+        'Mandatory',
+        'Home',
+        'Mandatory', 'Mandatory',
+        'NonMandatory', 'NonMandatory',
+        'Mandatory', 'NonMandatory', 'Home',
+        'Mandatory', 'Home', 'Home',
+        'Mandatory', 'Mandatory', 'NonMandatory', 'NonMandatory',
+        'Home', 'Home', 'Home', 'NonMandatory'],
+        index=people.index)
+    pdt.assert_series_equal(people_choices, expected)
diff --git a/activitysim/defaults/models/__init__.py b/activitysim/defaults/models/__init__.py
index 1339f6c1a9..9fc4e04846 100644
--- a/activitysim/defaults/models/__init__.py
+++ b/activitysim/defaults/models/__init__.py
@@ -10,3 +10,4 @@
 import non_mandatory_scheduling
 import workplace_location
 import mode
+import cdap
diff --git a/activitysim/defaults/models/auto_ownership.py b/activitysim/defaults/models/auto_ownership.py
index e0bd2abf68..d35b2666a6 100644
--- a/activitysim/defaults/models/auto_ownership.py
+++ b/activitysim/defaults/models/auto_ownership.py
@@ -20,6 +20,7 @@ def auto_ownership_spec(configs_dir):
 @sim.model()
 def auto_ownership_simulate(set_random_seed, households_merged,
                             auto_ownership_spec):
+
     choices, _ = asim.simple_simulate(
         households_merged.to_frame(), auto_ownership_spec)
 
diff --git a/activitysim/defaults/models/cdap.py b/activitysim/defaults/models/cdap.py
new file mode 100644
index 0000000000..d50f4ae544
--- /dev/null
+++ b/activitysim/defaults/models/cdap.py
@@ -0,0 +1,63 @@
+import os
+import pandas as pd
+import urbansim.sim.simulation as sim
+from activitysim import activitysim as asim
+from activitysim.cdap import cdap
+
+"""
+CDAP stands for Coordinated Daily Activity Pattern, which is a choice of
+high-level activity pattern for each person, in a coordinated way with other
+members of a person's household.
+
+Because Python requires vectorization of computation, there are some specialized
+routines in the cdap directory of activitysim for this purpose.  This module
+simply applies those utilities using the simulation framework.
+"""
+
+
+@sim.injectable()
+def cdap_1_person_spec(configs_dir):
+    f = os.path.join(configs_dir, 'configs', "cdap_1_person.csv")
+    return asim.read_model_spec(f).fillna(0)
+
+
+@sim.injectable()
+def cdap_2_person_spec(configs_dir):
+    f = os.path.join(configs_dir, 'configs', "cdap_2_person.csv")
+    return asim.read_model_spec(f).fillna(0)
+
+
+@sim.injectable()
+def cdap_3_person_spec(configs_dir):
+    f = os.path.join(configs_dir, 'configs', "cdap_3_person.csv")
+    return asim.read_model_spec(f).fillna(0)
+
+
+@sim.injectable()
+def cdap_final_rules(configs_dir):
+    f = os.path.join(configs_dir, 'configs', "cdap_final_rules.csv")
+    return asim.read_model_spec(f).fillna(0)
+
+
+@sim.injectable()
+def cdap_all_people(configs_dir):
+    f = os.path.join(configs_dir, 'configs', "cdap_all_people.csv")
+    return asim.read_model_spec(f).fillna(0)
+
+
+@sim.model()
+def cdap_simulate(set_random_seed, persons_merged,
+                  cdap_1_person_spec, cdap_2_person_spec, cdap_3_person_spec,
+                  cdap_final_rules, cdap_all_people):
+
+    choices = cdap.run_cdap(persons_merged.to_frame(),
+                            "household_id",
+                            "ptype",
+                            cdap_1_person_spec,
+                            cdap_2_person_spec,
+                            cdap_3_person_spec,
+                            cdap_final_rules,
+                            cdap_all_people)
+
+    print "Choices:\n", choices.value_counts()
+    sim.add_column("persons", "cdap_activity", choices)
diff --git a/activitysim/defaults/tables/persons.py b/activitysim/defaults/tables/persons.py
index 8dbdaaeb73..a0191166ca 100644
--- a/activitysim/defaults/tables/persons.py
+++ b/activitysim/defaults/tables/persons.py
@@ -34,11 +34,10 @@ def age_16_p(persons):
     return persons.to_frame(["age"]).eval("16 <= age")
 
 
-# FIXME - this is my "placeholder" for the CDAP model ;)
 @sim.column("persons")
-def cdap_activity(set_random_seed, persons):
-    return pd.Series(np.random.randint(3, size=len(persons)),
-                     index=persons.index).map({0: 'M', 1: 'N', 2: 'H'})
+def cdap_activity(persons):
+    # return a default until it gets filled in by the model
+    return pd.Series('M', persons.index)
 
 
 # FIXME - these are my "placeholder" for joint trip generation
diff --git a/activitysim/skim.py b/activitysim/skim.py
index ccb04799f6..a4cd046bf6 100644
--- a/activitysim/skim.py
+++ b/activitysim/skim.py
@@ -72,6 +72,7 @@ class Skims(object):
     It is assumed that left_key and right_key identify columns in df.  The
     parameter df is usually set by the simulation itself as it's a result of
     interacting choosers and alternatives.
+
     When the user calls skims[key], key is an identifier for which skim
     to use, and the object automatically looks up impedances of that skim
     using the specified left_key column in df as the origin and
@@ -80,6 +81,7 @@ class Skims(object):
     for this lookup.  This is the only purpose of this object: to
     abstract away the O-D lookup and use skims by specifiying which skim
     to use in the expressions.
+
     Note that keys are any hashable object, not just strings.  So calling
     skim[('AM', 'SOV')] is valid and useful.
     """
@@ -228,6 +230,7 @@ class Skims3D(object):
     ('SOV', 'AM"), ('SOV', 'PM') etc.  The time of day is then taken to
     be different for every row in the tours table, and the 'SOV' portion
     of the key can be used in __getitem__.
+
     To be more explicit, the input is a dictionary of Skims objects, each of
     which contains a 2D matrix.  These are stacked into a 3D matrix with a
     mapping of keys to indexes which is applied using pandas .map to a third
@@ -238,7 +241,7 @@ class Skims3D(object):
     __getitem__ below (i.e. the one used in the specs).  By convention,
     every key in the Skims object that is passed in MUST be a tuple with 2
     items.  The second item in the tuple maps to the items in the dataframe
-    referred to be the skim_key column and the first item in the tuple is
+    referred to by the skim_key column and the first item in the tuple is
     then available to pass directly to __getitem__.  This is now made
     explicit by adding the set_3d and get_3d methods in the Skims object which
     take the two keys independently and convert to the tuple internally.
diff --git a/example/configs/cdap_1_person.csv b/example/configs/cdap_1_person.csv
new file mode 100644
index 0000000000..408be97c44
--- /dev/null
+++ b/example/configs/cdap_1_person.csv
@@ -0,0 +1,55 @@
+Description,Expression,Mandatory,NonMandatory,Home
+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 too young for school alternative-specific constants,ptype == 8,1.052531189,-0.837567776,
+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,,
+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,
+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 > workers),0.6515,0.8168,
+Retired interaction with more cars than workers,(ptype == 5) & (auto_ownership > 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 > workers),,0.2991,
+Full-time worker interaction with fewer cars than workers,(ptype == 1) & (auto_ownership < workers),,,0.5039
+Non-working adult interaction with fewer cars than workers,(ptype == 4) & (auto_ownership < workers),,,0.8965
+Retired interaction with fewer cars than workers,(ptype == 5) & (auto_ownership < workers),,,0.5496
+Driving-age child who is in school interaction with fewer cars than workers,(ptype == 6) & (auto_ownership < workers),,,0.6475
+Pre-driving-age child who is in school interaction with fewer cars than workers,(ptype == 7) & (auto_ownership < workers),,,0.5862
+Pre-driving-age child who is too young for school interaction with fewer cars than workers,(ptype == 8) & (auto_ownership < 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) * AUTOPEAKTOTAL,0.1212,,
+Part-time worker interaction with peak accessibility to all employment,(ptype == 2) * AUTOPEAKTOTAL,0.2004,,
+Non-working adult interaction with peak accessibility to all employment,(ptype == 4) * AUTOPEAKTOTAL,0.2314,,
+Retired interaction with peak accessibility to all employment,(ptype == 5) * AUTOPEAKTOTAL,0.2792,,
+Non-working adult interaction with off-peak accessibility to retail,(ptype == 4) * AUTOOFFPEAKRETAIL,,0.07207,
+Retired interaction with off-peak accessibility to retail,(ptype == 5) * AUTOOFFPEAKRETAIL,,0.07207,
+University student interaction with off-peak accessibility to retail,(ptype == 3) * AUTOOFFPEAKRETAIL,,0.07207,
+Driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 6) * AUTOOFFPEAKRETAIL,,0.08233,
+Pre-driving-age child who is in school interaction with off-peak accessibility to retail,(ptype == 7) * AUTOOFFPEAKRETAIL,,0.08233,
+Pre-driving-age child who is too young for school interaction with off-peak accessibility to retail,(ptype == 8) * AUTOOFFPEAKRETAIL,,0.08233,
+# 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,,
diff --git a/example/configs/cdap_2_person.csv b/example/configs/cdap_2_person.csv
new file mode 100644
index 0000000000..0fe6e4bc4b
--- /dev/null
+++ b/example/configs/cdap_2_person.csv
@@ -0,0 +1,37 @@
+Description,Expression,Mandatory,NonMandatory,Home
+Full-time worker interaction with full-time worker,interaction == '11',0.141,1.123,1.626
+Full-time worker interaction with part-time worker,"interaction in ['12',  '21']",0.08845,0.4947,0.7407
+Full-time worker interaction with university student,"interaction in ['13',  '31']",0.4273,0.5523,1.183
+Full-time worker interaction with non-working adult,"interaction in ['14',  '41']",,0.02186,0.9436
+Full-time worker interaction with retired,"interaction in ['15',  '51']",,0.3115,1.298
+Full-time worker interaction with driving-age child who is in school,"interaction in ['16',  '61']",0.3842,0.4095,2.064
+Full-time worker interaction with pre-driving-age child who is in school,"interaction in ['17',  '71']",0.2623,0.6008,1.501
+Full-time worker interaction with pre-driving-age child who is too young for school,"interaction in ['18',  '81']",0.5118,0.751,0.9912
+Part-time worker interaction with part-time worker,interaction == '22',1.135,1.032,0.8911
+Part-time worker interaction with university student,"interaction in ['23',  '32']",0.173,0.3355,1.642
+Part-time worker interaction with non-working adult,"interaction in ['24',  '42']",,0.7477,0.7057
+Part-time worker interaction with retired,"interaction in ['25',  '52']",,0.09831,0.463
+Part-time worker interaction with driving-age child who is in school,"interaction in ['26',  '62']",1.103,0.495,3.057
+Part-time worker interaction with pre-driving-age child who is in school,"interaction in ['27',  '72']",0.3079,0.8984,0.7685
+Part-time worker interaction with pre-driving-age child who is too young for school,"interaction in ['82',  '82']",0.5074,1.452,1.07
+University student interaction with university student,interaction == '33',0.8726,1.054,1.018
+University student interaction with non-working adult,"interaction in ['34',  '43']",,0.193,1.781
+University student interaction with retired,"interaction in ['35',  '53']",,0.4065,0.4835
+University student interaction with driving-age child who is in school,"interaction in ['36',  '63']",-0.0021,1.62,1.546
+University student interaction with pre-driving-age child who is in school,"interaction in ['37',  '73']",0.2975,0.5165,1.552
+University student interaction with pre-driving-age child who is too young for school,"interaction in ['38',  '83']",0.2254,0.8973,1.34
+Non-working adult interaction with non-working adult,interaction == '44',,0.6984,1.352
+Non-working adult interaction with retired,"interaction in ['45',  '54']",,0.1864,1.209
+Non-working adult interaction with driving-age child who is in school,"interaction in ['46',  '64']",,0.6801,0.5243
+Non-working adult interaction with pre-driving-age child who is in school,"interaction in ['47',  '74']",,0.5646,0.8112
+Non-working adult interaction with pre-driving-age child who is too young for school,"interaction in ['48',  '84']",,1.164,1.167
+Retired interaction with retired,interaction == '55',,0.7291,1.407
+Retired interaction with driving-age child who is in school,"interaction in ['56',  '65']",,0.2919,0.8632
+Retired interaction with pre-driving-age child who is in school,"interaction in ['57',  '75']",,0.2919,0.8632
+Retired interaction with pre-driving-age child who is too young for school,"interaction in ['58',  '85']",,0.2919,0.8632
+Driving-age child who is in school interaction with driving-age child who is in school,interaction == '66',0.4794,1.512,2.198
+Driving-age child who is in school interaction with pre-driving-age child who is in school,"interaction in ['67',  '76']",0.5151,1.422,0.977
+Driving-age child who is in school interaction with pre-driving-age child who is too young for school,"interaction in ['68',  '86']",0.5516,1.273,1.467
+Pre-driving-age child who is in school interaction with pre-Pre-driving-age child who is in school,interaction == '77',0.9731,1.553,2.8
+Pre-driving-age child who is in school interaction with pre-driving-age child who is too young for school,"interaction in ['78',  '87']",0.5961,0.6184,1.434
+Pre-driving-age child who is too young for school interaction with pre-driving-age child who is too young for school,interaction == '88',1.651,0.8771,1.378
diff --git a/example/configs/cdap_3_person.csv b/example/configs/cdap_3_person.csv
new file mode 100644
index 0000000000..7e5840d9a3
--- /dev/null
+++ b/example/configs/cdap_3_person.csv
@@ -0,0 +1,10 @@
+Description,Expression,Mandatory,NonMandatory,Home
+Full-time worker / full-time worker / full-time worker,interaction == '111',0.3133,,
+Full-time worker / full-time worker / part-time worker or non-working adult,"interaction in ['112', '121', '211', '114', '141', '411']",0.3495,0.4637,
+Full-time worker / part-time worker or non-working adult / part-time worker or non-working adult,"interaction in ['124', '142', '214', '412', '241', '421', '122', '144', '212', '414', '221', '441']",,0.3491,0.9573
+Full-time worker / part-time worker or non-working adult / pre-driving-age child,"interaction in ['162', '126', '216', '612', '261', '621']",,,0.2939
+Full-time worker / pre-driving-age child / pre-driving-age child,"interaction in ['166', '616', '661']",,0.3553,
+Part-time worker or non-working adult / part-time worker or non-working adult / part-time worker or non-working adult,"interaction in ['222', '224', '242', '422', '424', '442', '244', '444']",,-1.386,0.9881
+Part-time worker or non-working adult / part-time worker or non-working adult / pre-driving-age child,"interaction in ['624', '642', '264', '462', '246', '426', '622', '644', '262', '464', '226', '446']",,-0.8571,0.4374
+Part-time worker or non-working adult / pre-driving-age child / pre-driving-age child,"interaction in ['662', '626', '266', '664', '646', '466']",,,0.4747
+Pre-driving-age child / pre-driving-age child / pre-driving-age child,interaction == '666',-0.3906,,
diff --git a/example/configs/cdap_all_people.csv b/example/configs/cdap_all_people.csv
new file mode 100644
index 0000000000..13b22cac72
--- /dev/null
+++ b/example/configs/cdap_all_people.csv
@@ -0,0 +1,10 @@
+Description,Expression,Mandatory,NonMandatory,Home
+Three Person All M,"('Mandatory',) * 3",-0.0671,-0.3653,-1.1810
+Three Person All N,"('NonMandatory',) * 3",-0.0671,-0.3653,-1.1810
+Three Person All H,"('Home',) * 3",-0.0671,-0.3653,-1.1810
+Four Person All M,"('Mandatory',) * 4",-0.6104,-1.3460,-3.7330
+Four Person All N,"('NonMandatory',) * 4",-0.6104,-1.3460,-3.7330
+Four Person All H,"('Home',) * 4",-0.6104,-1.3460,-3.7330
+Five Person All M,"('Mandatory',) * 5",-1.5280,-3.4530,-8.6210
+Five Person All N,"('NonMandatory',) * 5",-1.5280,-3.4530,-8.6210
+Five Person All H,"('Home',) * 5",-1.5280,-3.4530,-8.6210
diff --git a/example/configs/cdap_final_rules.csv b/example/configs/cdap_final_rules.csv
new file mode 100644
index 0000000000..98aced7c12
--- /dev/null
+++ b/example/configs/cdap_final_rules.csv
@@ -0,0 +1,3 @@
+Description,Expression,Alternative,Value
+M not allowed for retired,ptype == 5,Mandatory,-999
+M not allowed for non-working,ptype == 4,Mandatory,-999
diff --git a/example/simulation.ipynb b/example/simulation.ipynb
index ce8a4ba4e0..3e8ddc568b 100644
--- a/example/simulation.ipynb
+++ b/example/simulation.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:939241517691379f8bdce8274305d3f99517bc098d843aa83af88483299fc4b6"
+  "signature": "sha256:5843fef02535f277e14c945b692abec5411cdf0980fe2342d146c176ccd4db60"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -22,6 +22,99 @@
      "outputs": [],
      "prompt_number": 1
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sim.run([\"cdap_simulate\"])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Running model 'cdap_simulate'\n",
+        "Choices:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Home            1225\n",
+        "Mandatory        704\n",
+        "NonMandatory     660\n",
+        "dtype: int64\n",
+        "Time to execute model 'cdap_simulate': 110.06s\n",
+        "Total time to execute: 110.06s\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sim.run([\"school_location_simulate\"])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Running model 'school_location_simulate'\n",
+        "WARNING: Some columns have no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "['mode_choice_logsums']\n",
+        "WARNING: Some columns have no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "['mode_choice_logsums']\n",
+        "WARNING: Some columns have no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "['mode_choice_logsums']\n",
+        "Describe of choices:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "count    2538.000000\n",
+        "mean      190.435776\n",
+        "std       389.915126\n",
+        "min        -1.000000\n",
+        "25%        -1.000000\n",
+        "50%        -1.000000\n",
+        "75%        69.000000\n",
+        "max      1450.000000\n",
+        "Name: TAZ, dtype: float64\n",
+        "Time to execute model 'school_location_simulate': 18.74s\n",
+        "Total time to execute: 18.74s\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -51,21 +144,21 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "count    2598.000000\n",
-        "mean      700.162433\n",
-        "std       449.674185\n",
-        "min         1.000000\n",
-        "25%       340.000000\n",
-        "50%       661.000000\n",
-        "75%      1123.000000\n",
-        "max      1448.000000\n",
+        "count    2538.000000\n",
+        "mean      332.508668\n",
+        "std       473.007494\n",
+        "min        -1.000000\n",
+        "25%        -1.000000\n",
+        "50%        -1.000000\n",
+        "75%       651.000000\n",
+        "max      1452.000000\n",
         "Name: TAZ, dtype: float64\n",
-        "Time to execute model 'workplace_location_simulate': 18.29s\n",
-        "Total time to execute: 18.29s\n"
+        "Time to execute model 'workplace_location_simulate': 10.24s\n",
+        "Total time to execute: 10.24s\n"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 3
     },
     {
      "cell_type": "code",
@@ -80,14 +173,14 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "count    2598.000000\n",
-        "mean       38.641813\n",
-        "std        23.368224\n",
-        "min         0.420000\n",
-        "25%        21.250000\n",
-        "50%        35.270000\n",
-        "75%        51.875000\n",
-        "max       134.970000\n",
+        "count    2662.000000\n",
+        "mean        5.954801\n",
+        "std         5.547754\n",
+        "min         0.160000\n",
+        "25%         2.510000\n",
+        "50%         4.135000\n",
+        "75%         7.170000\n",
+        "max        45.990000\n",
         "dtype: float64\n"
        ]
       }
@@ -116,21 +209,22 @@
        "stream": "stdout",
        "text": [
         "['@1' 'work_tour_auto_time_savings / (workers+1)']\n",
-        "Choices:\n"
+        "Choices:\n",
+        "2    402\n",
+        "1    319\n",
+        "3    151\n",
+        "0     88\n",
+        "4     40\n",
+        "dtype: int64"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "2    387\n",
-        "1    301\n",
-        "3    162\n",
-        "0     87\n",
-        "4     63\n",
-        "dtype: int64\n",
-        "Time to execute model 'auto_ownership_simulate': 0.39s\n",
-        "Total time to execute: 0.39s\n"
+        "\n",
+        "Time to execute model 'auto_ownership_simulate': 0.25s\n",
+        "Total time to execute: 0.25s\n"
        ]
       }
      ],
@@ -150,7 +244,7 @@
        "stream": "stdout",
        "text": [
         "Running model 'mandatory_tour_frequency'\n",
-        "856 persons run for mandatory tour model"
+        "884 persons run for mandatory tour model"
        ]
       },
       {
@@ -165,11 +259,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "['(ptype == 3) & (distance_to_work < 3)'\n",
-        " '(ptype == 3) & (distance_to_school < 3)'\n",
-        " '(ptype == 7) & (distance_to_school < 3)'\n",
-        " '(ptype == 3) & (distance_to_work < 3 | distance_to_school < 3)'\n",
-        " '(ptype == 2) & nonstudent_to_school'\n",
+        "['(ptype == 2) & nonstudent_to_school'\n",
         " '(ptype == 4) & nonstudent_to_school'\n",
         " '(ptype == 5) & nonstudent_to_school' '(ptype == 6) & non_family'\n",
         " '~(workplace_taz > -1)' '~(school_taz > -1)']\n",
@@ -180,14 +270,14 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "school1            378\n",
-        "work1              284\n",
-        "work2              109\n",
-        "work_and_school     70\n",
-        "school2             15\n",
+        "school1            394\n",
+        "work1              252\n",
+        "work2              146\n",
+        "work_and_school     73\n",
+        "school2             19\n",
         "dtype: int64\n",
-        "Time to execute model 'mandatory_tour_frequency': 3.55s\n",
-        "Total time to execute: 3.55s\n"
+        "Time to execute model 'mandatory_tour_frequency': 2.60s\n",
+        "Total time to execute: 2.60s\n"
        ]
       }
      ],
@@ -207,8 +297,8 @@
        "output_type": "pyout",
        "prompt_number": 6,
        "text": [
-        "work      572\n",
-        "school    478\n",
+        "work      617\n",
+        "school    505\n",
         "dtype: int64"
        ]
       }
@@ -229,7 +319,7 @@
        "stream": "stdout",
        "text": [
         "Running model 'non_mandatory_tour_frequency'\n",
-        "1744 persons run for non-mandatory tour model"
+        "1753 persons run for non-mandatory tour model"
        ]
       },
       {
@@ -237,7 +327,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "Running segment 'driving' of size 46\n",
+        "Running segment 'driving' of size 52\n",
         "WARNING: Some columns have no variability:\n"
        ]
       },
@@ -252,12 +342,12 @@
         " 'max_window*(tot_tours == 0)' 'max_window*(tot_tours == 1)'\n",
         " 'max_window*(tot_tours == 2)' 'max_window*(tot_tours == 3)'\n",
         " 'max_window*(tot_tours == 4)' 'max_window*(tot_tours >  4)'\n",
-        " '(car_sufficiency > 0) & (tot_tours == 1)'\n",
-        " '(car_sufficiency > 0) & (tot_tours == 2)'\n",
-        " '(car_sufficiency > 0) & (tot_tours == 3)'\n",
-        " '(car_sufficiency > 0) & (tot_tours == 4)'\n",
-        " '(car_sufficiency > 0) & (tot_tours > 4)']\n",
-        "Running segment 'full' of size 606"
+        " 'has_preschool_kid_at_home * escort'\n",
+        " 'has_preschool_kid_at_home * shopping'\n",
+        " 'has_preschool_kid_at_home * othmaint'\n",
+        " 'has_preschool_kid_at_home * eatout'\n",
+        " 'has_preschool_kid_at_home * othdiscr']\n",
+        "Running segment 'full' of size 600"
        ]
       },
       {
@@ -279,7 +369,7 @@
         " 'max_window*(tot_tours == 0)' 'max_window*(tot_tours == 1)'\n",
         " 'max_window*(tot_tours == 2)' 'max_window*(tot_tours == 3)'\n",
         " 'max_window*(tot_tours == 4)' 'max_window*(tot_tours >  4)']\n",
-        "Running segment 'nonwork' of size 322"
+        "Running segment 'nonwork' of size 305"
        ]
       },
       {
@@ -301,7 +391,7 @@
         " 'max_window*(tot_tours == 0)' 'max_window*(tot_tours == 1)'\n",
         " 'max_window*(tot_tours == 2)' 'max_window*(tot_tours == 3)'\n",
         " 'max_window*(tot_tours == 4)' 'max_window*(tot_tours >  4)']\n",
-        "Running segment 'part' of size 136"
+        "Running segment 'part' of size 109"
        ]
       },
       {
@@ -323,7 +413,7 @@
         " 'max_window*(tot_tours == 0)' 'max_window*(tot_tours == 1)'\n",
         " 'max_window*(tot_tours == 2)' 'max_window*(tot_tours == 3)'\n",
         " 'max_window*(tot_tours == 4)' 'max_window*(tot_tours >  4)']\n",
-        "Running segment 'preschool' of size 139"
+        "Running segment 'preschool' of size 150"
        ]
       },
       {
@@ -345,7 +435,7 @@
         " 'max_window*(tot_tours == 0)' 'max_window*(tot_tours == 1)'\n",
         " 'max_window*(tot_tours == 2)' 'max_window*(tot_tours == 3)'\n",
         " 'max_window*(tot_tours == 4)' 'max_window*(tot_tours >  4)']\n",
-        "Running segment 'retired' of size 203"
+        "Running segment 'retired' of size 192"
        ]
       },
       {
@@ -367,7 +457,7 @@
         " 'max_window*(tot_tours == 0)' 'max_window*(tot_tours == 1)'\n",
         " 'max_window*(tot_tours == 2)' 'max_window*(tot_tours == 3)'\n",
         " 'max_window*(tot_tours == 4)' 'max_window*(tot_tours >  4)']\n",
-        "Running segment 'school' of size 195"
+        "Running segment 'school' of size 228"
        ]
       },
       {
@@ -388,8 +478,13 @@
         " 'num_shop_j' 'num_main_j' 'num_eat_j' 'num_visi_j' 'num_disc_j'\n",
         " 'max_window*(tot_tours == 0)' 'max_window*(tot_tours == 1)'\n",
         " 'max_window*(tot_tours == 2)' 'max_window*(tot_tours == 3)'\n",
-        " 'max_window*(tot_tours == 4)' 'max_window*(tot_tours >  4)']\n",
-        "Running segment 'university' of size 97"
+        " 'max_window*(tot_tours == 4)' 'max_window*(tot_tours >  4)'\n",
+        " '(car_sufficiency > 0) & (tot_tours == 1)'\n",
+        " '(car_sufficiency > 0) & (tot_tours == 2)'\n",
+        " '(car_sufficiency > 0) & (tot_tours == 3)'\n",
+        " '(car_sufficiency > 0) & (tot_tours == 4)'\n",
+        " '(car_sufficiency > 0) & (tot_tours > 4)']\n",
+        "Running segment 'university' of size 117"
        ]
       },
       {
@@ -418,55 +513,56 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "0     845\n",
-        "16    232\n",
-        "1     144\n",
-        "8     108\n",
-        "32     92\n",
-        "4      70\n",
-        "2      55\n",
-        "64     26\n",
-        "17     20\n",
-        "20     18\n",
-        "5      15\n",
-        "24     14\n",
-        "9      11\n",
-        "48     11\n",
-        "18     10\n",
-        "33      9\n",
-        "49      7\n",
-        "12      6\n",
-        "40      5\n",
-        "56      4\n",
-        "34      4\n",
-        "80      4\n",
-        "37      3\n",
-        "81      3\n",
+        "0     565\n",
+        "16    306\n",
+        "1     208\n",
+        "8     154\n",
+        "32    104\n",
+        "2      89\n",
+        "4      86\n",
+        "64     37\n",
+        "17     23\n",
+        "24     20\n",
+        "48     15\n",
+        "9      15\n",
+        "5      11\n",
+        "36      9\n",
+        "20      9\n",
+        "80      9\n",
+        "40      9\n",
+        "33      8\n",
+        "65      7\n",
+        "25      5\n",
+        "18      5\n",
+        "34      5\n",
+        "10      4\n",
+        "68      4\n",
+        "6       4\n",
+        "21      4\n",
+        "72      4\n",
+        "12      3\n",
+        "52      3\n",
         "19      3\n",
-        "21      2\n",
-        "25      2\n",
-        "36      2\n",
-        "41      2\n",
-        "76      1\n",
+        "11      2\n",
+        "3       2\n",
+        "88      2\n",
+        "28      2\n",
+        "49      2\n",
+        "50      2\n",
+        "53      2\n",
+        "56      2\n",
+        "22      1\n",
+        "26      1\n",
+        "81      1\n",
+        "37      1\n",
+        "38      1\n",
         "7       1\n",
-        "6       1\n",
-        "3       1\n",
-        "10      1\n",
-        "68      1\n",
-        "13      1\n",
-        "72      1\n",
-        "66      1\n",
-        "28      1\n",
-        "35      1\n",
-        "44      1\n",
-        "50      1\n",
-        "51      1\n",
-        "52      1\n",
-        "65      1\n",
-        "84      1\n",
+        "42      1\n",
+        "76      1\n",
+        "27      1\n",
         "dtype: int64\n",
-        "Time to execute model 'non_mandatory_tour_frequency': 21.97s\n",
-        "Total time to execute: 21.97s\n"
+        "Time to execute model 'non_mandatory_tour_frequency': 17.48s\n",
+        "Total time to execute: 17.48s\n"
        ]
       }
      ],
@@ -486,12 +582,12 @@
        "output_type": "pyout",
        "prompt_number": 8,
        "text": [
-        "shopping    335\n",
-        "othdiscr    227\n",
-        "escort      222\n",
-        "othmaint    158\n",
-        "eatout      125\n",
-        "social       80\n",
+        "shopping    418\n",
+        "othdiscr    296\n",
+        "escort      294\n",
+        "othmaint    226\n",
+        "eatout      142\n",
+        "social      122\n",
         "dtype: int64"
        ]
       }
@@ -512,7 +608,7 @@
        "stream": "stdout",
        "text": [
         "Running model 'destination_choice'\n",
-        "Running segment 'eatout' of size 125"
+        "Running segment 'eatout' of size 142"
        ]
       },
       {
@@ -520,7 +616,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "Running segment 'othdiscr' of size 227"
+        "Running segment 'othdiscr' of size 296"
        ]
       },
       {
@@ -528,7 +624,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "Running segment 'othmaint' of size 158"
+        "Running segment 'othmaint' of size 226"
        ]
       },
       {
@@ -536,7 +632,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "Running segment 'shopping' of size 335"
+        "Running segment 'shopping' of size 418"
        ]
       },
       {
@@ -544,7 +640,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "Running segment 'social' of size 80"
+        "Running segment 'social' of size 122"
        ]
       },
       {
@@ -559,21 +655,21 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "count     925.000000\n",
-        "mean      747.828108\n",
-        "std       413.552200\n",
+        "count    1204.000000\n",
+        "mean      749.697674\n",
+        "std       424.261384\n",
         "min         1.000000\n",
-        "25%       433.000000\n",
-        "50%       796.000000\n",
-        "75%      1094.000000\n",
-        "max      1446.000000\n",
+        "25%       412.250000\n",
+        "50%       764.500000\n",
+        "75%      1137.000000\n",
+        "max      1452.000000\n",
         "Name: TAZ, dtype: float64\n",
-        "Time to execute model 'destination_choice': 2.45s\n",
-        "Total time to execute: 2.45s\n"
+        "Time to execute model 'destination_choice': 7.85s\n",
+        "Total time to execute: 7.85s\n"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 20
     },
     {
      "cell_type": "code",