Merge branch 'verification-fixes' of https://github.com/ActivitySim/a…

…ctivitysim into verification-fixes

activitysim/abm/models/__init__.py

@@ -6,6 +6,7 @@
 from . import initialize
 from . import accessibility
 from . import auto_ownership
+from . import free_parking
 from . import mandatory_tour_frequency
 from . import mandatory_scheduling
 from . import joint_tour_frequency

activitysim/abm/models/free_parking.py

@@ -0,0 +1,88 @@
+# ActivitySim
+# See full license in LICENSE.txt.
+
+from __future__ import (absolute_import, division, print_function, )
+from future.standard_library import install_aliases
+install_aliases()  # noqa: E402
+
+from future.utils import iteritems
+
+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.mem import force_garbage_collect
+
+from activitysim.core.interaction_sample_simulate import interaction_sample_simulate
+from activitysim.core.interaction_sample import interaction_sample
+
+from .util import expressions
+
+logger = logging.getLogger(__name__)
+
+
+@inject.step()
+def free_parking(
+        persons_merged, persons, households,
+        skim_dict, skim_stack,
+        chunk_size, trace_hh_id, locutor):
+    """
+
+    """
+
+    trace_label = 'free_parking'
+    model_settings = config.read_model_settings('free_parking.yaml')
+
+    choosers = persons_merged.to_frame()
+    choosers = choosers[choosers.workplace_taz > -1]
+
+    logger.info("Running %s with %d persons", trace_label, len(choosers))
+
+    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='free_parking.csv')
+    nest_spec = config.get_logit_model_settings(model_settings)
+
+    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='free_parking_at_work')
+
+    persons = persons.to_frame()
+
+    # no need to reindex as we used all households
+    free_parking_alt = model_settings['FREE_PARKING_ALT']
+    choices = (choices == free_parking_alt)
+    persons['free_parking_at_work'] = choices.reindex(persons.index).fillna(0).astype(bool)
+
+    pipeline.replace_table("persons", persons)
+
+    tracing.print_summary('free_parking', persons.free_parking_at_work, value_counts=True)
+
+    if trace_hh_id:
+        tracing.trace_df(persons,
+                         label=trace_label,
+                         warn_if_empty=True)

activitysim/abm/models/non_mandatory_tour_frequency.py

@@ -54,7 +54,8 @@ def non_mandatory_tour_frequency(persons, persons_merged,
     # 'tot_tours' is used in model_spec expressions
     alternatives['tot_tours'] = alternatives.sum(axis=1)
 
-    no_tours_alt = list((alternatives.sum(axis=1) == 0).values).index(True)
+    # (we expect there to be an alt with no tours - which we can use to backfill non-travelers)
+    no_tours_alt = (alternatives.sum(axis=1) == 0).index[0]
 
     # - preprocessor
     preprocessor_settings = model_settings.get('preprocessor', None)

activitysim/abm/models/util/logsums.py

@@ -90,6 +90,9 @@ def compute_logsums(choosers,
     choosers['in_period'] = expressions.skim_time_period_label(model_settings['IN_PERIOD'])
     choosers['out_period'] = expressions.skim_time_period_label(model_settings['OUT_PERIOD'])
 
+    assert ('duration' not in choosers)
+    choosers['duration'] = model_settings['IN_PERIOD'] - model_settings['OUT_PERIOD']
+
     nest_spec = config.get_logit_model_settings(logsum_settings)
     constants = config.get_model_constants(logsum_settings)
 

activitysim/abm/models/util/vectorize_tour_scheduling.py

@@ -134,17 +134,19 @@ def compute_logsums(alt_tdd, tours_merged, tour_purpose, model_settings, trace_l
     assert 'in_period' not in alt_tdd
     alt_tdd['out_period'] = expressions.skim_time_period_label(alt_tdd['start'])
     alt_tdd['in_period'] = expressions.skim_time_period_label(alt_tdd['end'])
+    alt_tdd['duration'] = alt_tdd['end'] - alt_tdd['start']
 
     USE_BRUTE_FORCE = False
     if USE_BRUTE_FORCE:
         # compute logsums for all the tour alt_tdds (inefficient)
         logsums = _compute_logsums(alt_tdd, tours_merged, tour_purpose, model_settings, trace_label)
         return logsums
 
-    # - get list of unique (tour_id, out_period, in_period) in alt_tdd_periods
+    # - get list of unique (tour_id, out_period, in_period, duration) in alt_tdd_periods
+    # we can cut the number of alts roughly in half (for mtctm1) by conflating duplicates
     index_name = alt_tdd.index.name
-    alt_tdd_periods = \
-        alt_tdd[['out_period', 'in_period']].reset_index().drop_duplicates().set_index(index_name)
+    alt_tdd_periods = alt_tdd[['out_period', 'in_period', 'duration']]\
+        .reset_index().drop_duplicates().set_index(index_name)
 
     # - compute logsums for the alt_tdd_periods
     alt_tdd_periods['logsums'] = \
@@ -154,7 +156,7 @@ def compute_logsums(alt_tdd, tours_merged, tour_purpose, model_settings, trace_l
     logsums = pd.merge(
         alt_tdd.reset_index(),
         alt_tdd_periods.reset_index(),
-        on=[index_name, 'out_period', 'in_period'],
+        on=[index_name, 'out_period', 'in_period', 'duration'],
         how='left'
     ).set_index(index_name).logsums
 

activitysim/abm/test/configs/annotate_persons.csv

@@ -24,9 +24,13 @@ nonstudent_to_school,nonstudent_to_school,"(persons.ptype.isin([constants.PTYPE_
 ,pstudent,"pstudent.where((persons.ptype!=constants.PTYPE_DRIVING) & (persons.ptype!=constants.PTYPE_SCHOOL), constants.PSTUDENT_GRADE_OR_HIGH)"
 #,,
 is_student,is_student,"pstudent.isin([constants.PSTUDENT_GRADE_OR_HIGH, constants.PSTUDENT_UNIVERSITY])"
-is_gradeschool,is_gradeschool,"(pstudent == constants.PSTUDENT_GRADE_OR_HIGH) & (persons.age <= constants.GRADE_SCHOOL_MAX_AGE)"
-is_highschool,is_highschool,"(pstudent == constants.PSTUDENT_GRADE_OR_HIGH) & (persons.age > constants.GRADE_SCHOOL_MAX_AGE)"
-is_university,is_university,"pstudent == constants.PSTUDENT_UNIVERSITY"
+#babies go to school like tm1 bug,is_student,"is_student.where(persons.age > constants.GRADE_SCHOOL_MIN_AGE, True)"
+#babies go to school like tm1 bug,pstudent,"pstudent.where(persons.age > constants.GRADE_SCHOOL_MIN_AGE, constants.PSTUDENT_GRADE_OR_HIGH)"
+babies dont go to school,is_student,"is_student.where(persons.age > constants.GRADE_SCHOOL_MIN_AGE, False)"
+babies dont go to school,pstudent,"pstudent.where(persons.age > constants.GRADE_SCHOOL_MIN_AGE, constants.PSTUDENT_NOT)"
+is_gradeschool,is_gradeschool,(pstudent == constants.PSTUDENT_GRADE_OR_HIGH) & (persons.age <= constants.GRADE_SCHOOL_MAX_AGE)
+is_highschool,is_highschool,(pstudent == constants.PSTUDENT_GRADE_OR_HIGH) & (persons.age > constants.GRADE_SCHOOL_MAX_AGE)
+is_university,is_university,pstudent == constants.PSTUDENT_UNIVERSITY
 school_segment gradeschool,school_segment,"np.where(is_gradeschool, constants.SCHOOL_SEGMENT_GRADE, constants.SCHOOL_SEGMENT_NONE)"
 school_segment highschool,school_segment,"np.where(is_highschool, constants.SCHOOL_SEGMENT_HIGH, school_segment)"
 school_segment university,school_segment,"np.where(is_university, constants.SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)"

activitysim/abm/test/configs/free_parking.csv

@@ -0,0 +1,9 @@
+Description,Expression,free,pay
+,@df.workplace_county_id == ID_SAN_FRANCISCO,-2.6403,
+,@df.workplace_county_id == ID_SANTA_CLARA,0.2118,
+,@df.workplace_county_id == ID_ALAMEDA,-0.1092,
+Very high income household dummy,@df.income>=100000,0.2300,
+High income housheold dummy,@(df.income>=60000) & (df.income<100000),0.2300,
+Household size is greater than 3 dummy,@df.hhsize>3,0.2530,
+More automobiles than workers dummy,@df.auto_ownership>df.num_workers,0.2310,
+Fewer automobiles than workers dummy,@df.auto_ownership<df.num_workers,-1.4790,

activitysim/abm/test/configs/free_parking.yaml

@@ -0,0 +1,21 @@
+#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

activitysim/abm/test/configs/free_parking_annotate_persons_preprocessor.csv

@@ -0,0 +1,2 @@
+Description,Target,Expression
+,workplace_county_id,"reindex(land_use.county_id, persons.workplace_taz)"

activitysim/abm/test/configs/school_location.yaml

@@ -3,12 +3,13 @@ SAMPLE_SIZE: 30
 SIMULATE_CHOOSER_COLUMNS:
   - TAZ
   - school_segment
+  - household_id
 
 # model-specific logsum-related settings
 CHOOSER_ORIG_COL_NAME: TAZ
 ALT_DEST_COL_NAME: alt_dest
-IN_PERIOD: 8
-OUT_PERIOD: 17
+IN_PERIOD: 14
+OUT_PERIOD: 8
 
 DEST_CHOICE_COLUMN_NAME: school_taz
 

activitysim/abm/test/configs/settings.yaml

@@ -38,6 +38,7 @@ models:
   - school_location
   - workplace_location
   - auto_ownership_simulate
+  - free_parking
   - cdap_simulate
   - mandatory_tour_frequency
   - mandatory_tour_scheduling

activitysim/abm/test/configs/tour_mode_choice.yaml

@@ -52,7 +52,7 @@ SPEC: tour_mode_choice.csv
 COEFFS: tour_mode_choice_coeffs.csv
 
 CONSTANTS:
-    valueOfTime: 8.00
+    #valueOfTime: 8.00
     costPerMile: 18.48
     costShareSr2: 1.75
     costShareSr3: 2.50
@@ -97,3 +97,4 @@ LOGSUM_CHOOSER_COLUMNS:
   - tour_category
   - num_workers
   - value_of_time
+  - free_parking_at_work

activitysim/abm/test/configs/tour_mode_choice_annotate_choosers_preprocessor.csv

@@ -1,18 +1,18 @@
 Description,Target,Expression
 #,,
-local,_DF_IS_TOUR,"'tour_type' in df.columns"
+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"
+ 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_bike,_parent_tour_mode=='BIKE'
 ,work_tour_is_SOV,"_parent_tour_mode.isin(['DRIVEALONEFREE','DRIVEALONEPAY'])"
 #,,
 ,is_joint,(df.tour_category=='joint') if 'tour_category' in df.columns else False
 ,is_indiv,~is_joint
-,is_atwork_subtour,(df.tour_category=='joint') if 'tour_category' in df.columns else False
+,is_atwork_subtour,(df.tour_category=='atwork') if 'tour_category' in df.columns else False
 #,,
 ,c_cost,(0.60 * c_ivt) / df.value_of_time
 #,,
@@ -21,26 +21,32 @@ local,_DF_IS_TOUR,"'tour_type' in df.columns"
 ,dest_density_index,"reindex(land_use.density_index, df[dest_col_name])"
 FIXME,origin_walk_time,0
 FIXME,destination_walk_time,0
-FIXME,daily_parking_cost,0
 #,,
-,drive_commuter_available,(odt_skims['DRV_COM_WLK_TOTIVT']>0) & (dot_skims['WLK_COM_DRV_TOTIVT']>0) & ((odt_skims['DRV_COM_WLK_KEYIVT'] + dot_skims['WLK_COM_DRV_KEYIVT'])>0)
-,drive_express_available,(odt_skims['DRV_EXP_WLK_TOTIVT']>0) & (dot_skims['WLK_EXP_DRV_TOTIVT']>0) & ((odt_skims['DRV_EXP_WLK_KEYIVT'] + dot_skims['WLK_EXP_DRV_KEYIVT'])>0)
-,drive_heavyrail_available,(odt_skims['DRV_HVY_WLK_TOTIVT']>0) & (dot_skims['WLK_HVY_DRV_TOTIVT']>0) & ((odt_skims['DRV_HVY_WLK_KEYIVT'] + dot_skims['WLK_HVY_DRV_KEYIVT'])>0)
+,_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])"
+,_hourly_parking_cost,"np.where(_free_parking_available, 0, _dest_hourly_peak_parking_cost)"
+,daily_parking_cost,_hourly_parking_cost * df.duration
+#,,
+,drive_commuter_available,(odt_skims['DRV_COM_WLK_TOTIVT']>0) & (dot_skims['WLK_COM_DRV_TOTIVT']>0) & (odt_skims['DRV_COM_WLK_KEYIVT']>0) & (dot_skims['WLK_COM_DRV_KEYIVT']>0)
+,drive_express_available,(odt_skims['DRV_EXP_WLK_TOTIVT']>0) & (dot_skims['WLK_EXP_DRV_TOTIVT']>0) & (odt_skims['DRV_EXP_WLK_KEYIVT']>0) & (dot_skims['WLK_EXP_DRV_KEYIVT']>0)
+,drive_heavyrail_available,(odt_skims['DRV_HVY_WLK_TOTIVT']>0) & (dot_skims['WLK_HVY_DRV_TOTIVT']>0) & (odt_skims['DRV_HVY_WLK_KEYIVT']>0) & (dot_skims['WLK_HVY_DRV_KEYIVT']>0)
 ,drive_local_available,(odt_skims['DRV_LOC_WLK_TOTIVT']>0) & (dot_skims['WLK_LOC_DRV_TOTIVT']>0)
-,drive_lrf_available,(odt_skims['DRV_LRF_WLK_TOTIVT']>0) & (dot_skims['WLK_LRF_DRV_TOTIVT']>0) & ((odt_skims['DRV_LRF_WLK_KEYIVT'] + dot_skims['WLK_LRF_DRV_KEYIVT'])>0)
+,drive_lrf_available,(odt_skims['DRV_LRF_WLK_TOTIVT']>0) & (dot_skims['WLK_LRF_DRV_TOTIVT']>0) & (odt_skims['DRV_LRF_WLK_KEYIVT']>0) & (dot_skims['WLK_LRF_DRV_KEYIVT']>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
 ,sov_available,(odt_skims['SOV_TIME']>0) & (dot_skims['SOV_TIME']>0)
 ,sovtoll_available,(odt_skims['SOVTOLL_VTOLL']>0) | (dot_skims['SOVTOLL_VTOLL']>0)
-,walk_commuter_available,(odt_skims['WLK_COM_WLK_TOTIVT']>0) & (dot_skims['WLK_COM_WLK_TOTIVT']>0) & ((odt_skims['WLK_COM_WLK_KEYIVT'] + dot_skims['WLK_COM_WLK_KEYIVT'])>0)
-,walk_express_available,(odt_skims['WLK_EXP_WLK_TOTIVT']>0) & (dot_skims['WLK_EXP_WLK_TOTIVT']>0) & ((odt_skims['WLK_EXP_WLK_KEYIVT'] + dot_skims['WLK_EXP_WLK_KEYIVT'])>0)
-,walk_heavyrail_available,(odt_skims['WLK_HVY_WLK_TOTIVT']>0) & (dot_skims['WLK_HVY_WLK_TOTIVT']>0) & ((odt_skims['WLK_HVY_WLK_KEYIVT'] + dot_skims['WLK_HVY_WLK_KEYIVT'])>0)
+,walk_commuter_available,(odt_skims['WLK_COM_WLK_TOTIVT']>0) & (dot_skims['WLK_COM_WLK_TOTIVT']>0) & (odt_skims['WLK_COM_WLK_KEYIVT']>0) & (dot_skims['WLK_COM_WLK_KEYIVT']>0)
+,walk_express_available,(odt_skims['WLK_EXP_WLK_TOTIVT']>0) & (dot_skims['WLK_EXP_WLK_TOTIVT']>0) & (odt_skims['WLK_EXP_WLK_KEYIVT']>0) & (dot_skims['WLK_EXP_WLK_KEYIVT']>0)
+,walk_heavyrail_available,(odt_skims['WLK_HVY_WLK_TOTIVT']>0) & (dot_skims['WLK_HVY_WLK_TOTIVT']>0) & (odt_skims['WLK_HVY_WLK_KEYIVT']>0) & (dot_skims['WLK_HVY_WLK_KEYIVT']>0)
 ,walk_local_available,(odt_skims['WLK_LOC_WLK_TOTIVT']>0) & (dot_skims['WLK_LOC_WLK_TOTIVT']>0)
-,walk_lrf_available,(odt_skims['WLK_LRF_WLK_TOTIVT']>0) & (dot_skims['WLK_LRF_WLK_TOTIVT']>0) & ((odt_skims['WLK_LRF_WLK_KEYIVT'] + dot_skims['WLK_LRF_WLK_KEYIVT'])>0)
+,walk_lrf_available,(odt_skims['WLK_LRF_WLK_TOTIVT']>0) & (dot_skims['WLK_LRF_WLK_TOTIVT']>0) & (odt_skims['WLK_LRF_WLK_KEYIVT']) & (dot_skims['WLK_LRF_WLK_KEYIVT']>0)
 ,distance,od_skims['DIST']
-,walk_ferry_available,walk_lrf_available & ((odt_skims['WLK_LRF_WLK_FERRYIVT'] + dot_skims['WLK_LRF_WLK_FERRYIVT'])>0)
-,drive_ferry_available,drive_lrf_available & ((odt_skims['DRV_LRF_WLK_FERRYIVT'] + dot_skims['WLK_LRF_WLK_FERRYIVT'])>0)
+,walk_ferry_available,walk_lrf_available & (odt_skims['WLK_LRF_WLK_FERRYIVT']>0) & (dot_skims['WLK_LRF_WLK_FERRYIVT']>0)
+,drive_ferry_available,drive_lrf_available & (odt_skims['DRV_LRF_WLK_FERRYIVT']>0) & (dot_skims['WLK_LRF_WLK_FERRYIVT']>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'])

activitysim/abm/test/configs/tour_mode_choice_coeffs.csv

@@ -9,7 +9,8 @@ c_walktimeshort,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt,2.00 * c_ivt
 c_walktimelong,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,-0.188
 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,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,-0.376
-c_cost,(0.60 * c_ivt) / valueOfTime,(0.60 * c_ivt) / valueOfTime,(0.60 * c_ivt) / valueOfTime,(0.60 * c_ivt) / valueOfTime,(0.60 * c_ivt) / valueOfTime,(0.60 * c_ivt) / valueOfTime,(0.60 * c_ivt) / valueOfTime,(0.60 * c_ivt) / valueOfTime,(0.60 * c_ivt) / valueOfTime,(0.6*c_ivt) / valueOfTime
+#value_of_time is a person attribute so we cant compute c_cost as scalar here,,,,,,,,,,
+#c_cost,(0.60 * c_ivt) / valueOfTime,(0.60 * c_ivt) / valueOfTime,(0.60 * c_ivt) / valueOfTime,(0.60 * c_ivt) / valueOfTime,(0.60 * c_ivt) / valueOfTime,(0.60 * c_ivt) / valueOfTime,(0.60 * c_ivt) / valueOfTime,(0.60 * c_ivt) / valueOfTime,(0.60 * c_ivt) / valueOfTime,(0.6*c_ivt) / valueOfTime
 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

activitysim/abm/test/configs/workplace_location.yaml

@@ -14,8 +14,8 @@ LOGSUM_TOUR_PURPOSE: work
 # model-specific logsum-related settings
 CHOOSER_ORIG_COL_NAME: TAZ
 ALT_DEST_COL_NAME: alt_dest
-IN_PERIOD: 8
-OUT_PERIOD: 17
+IN_PERIOD: 17
+OUT_PERIOD: 8
 
 DEST_CHOICE_COLUMN_NAME: workplace_taz
 

activitysim/abm/test/configs_mp/settings.yaml

@@ -11,6 +11,7 @@ models:
   - school_location
   - workplace_location
   - auto_ownership_simulate
+  - free_parking
   - write_data_dictionary
   - write_tables