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‎.gitignore‎

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/test/prototype_mtc_reference_pipeline.zip
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/test/prototype_mtc_reference_pipeline_sharrow.zip
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/test/.coverage
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.idea/**

‎README.md‎

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# activitysim-prototype-mtc
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The canonical prototype MTC model.
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The primary ActivitySim example model.
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The `prototype_mtc` example is based on (but has evolved away from) the
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[Bay Area Metro Travel Model One](https://github.com/BayAreaMetro/travel-model-one),
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also known as "TM1". TM1 has its roots in a wide array of analytical approaches,
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including discrete choice forms (multinomial and nested logit models), activity
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duration models, time-use models, models of individual micro-simulation with
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constraints, entropy-maximization models, etc. These tools are combined in the
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model design to realistically represent travel behavior, adequately replicate
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observed activity-travel patterns, and ensure model sensitivity to infrastructure
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and policies. The model is implemented in a micro-simulation framework. Microsimulation
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methods capture aggregate outcomes through the representation of the behavior of
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individual decision-makers.
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See https://activitysim.github.io for more information.

‎configs/accessibility.csv‎

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Description,Target,Expression
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#,,
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#,, auto peak
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#,,
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#,, assume peak occurs in AM for outbound and PM for inbound
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peak round trip distance,_auPkTime,"skim_od[('SOVTOLL_TIME', 'AM')] + skim_do[('SOVTOLL_TIME', 'PM')]"
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decay function,_decay, exp(_auPkTime * dispersion_parameter_automobile)
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auto peak retail,auPkRetail,df.RETEMPN * _decay
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auto peak total,auPkTotal,df.TOTEMP * _decay
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#,,
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#,, auto off-peak
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#,,
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#,, assume midday occurs entirely in the midday period
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off-peak round trip distance,_auOpTime,"skim_od[('SOVTOLL_TIME', 'MD')] + skim_do[('SOVTOLL_TIME', 'MD')]"
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decay function,_decay, exp(_auOpTime * dispersion_parameter_automobile)
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auto off-peak retail,auOpRetail,df.RETEMPN * _decay
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auto off-peak total,auOpTotal,df.TOTEMP * _decay
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#,,
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#,, transit peak
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#,,
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#,, assume peak outbound transit occurs in AM
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o-d peak transit ivt,_inVehicleTime,"skim_od[('WLK_TRN_WLK_IVT', 'AM')]"
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o-d peak transit ovt,_outOfVehicleTime,"skim_od[('WLK_TRN_WLK_IWAIT', 'AM')] + skim_od[('WLK_TRN_WLK_XWAIT', 'AM')] + skim_od[('WLK_TRN_WLK_WACC', 'AM')] + skim_od[('WLK_TRN_WLK_WAUX', 'AM')] + skim_od[('WLK_TRN_WLK_WEGR', 'AM')]"
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o-d peak transit time,_trPkTime_od,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) / TRANSIT_SCALE_FACTOR
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#,, assume peak inbound transit occurs in PM
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d-o peak transit ivt,_inVehicleTime,"skim_do[('WLK_TRN_WLK_IVT', 'PM')]"
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d-o peak transit ovt,_outOfVehicleTime,"skim_do[('WLK_TRN_WLK_IWAIT', 'PM')] + skim_do[('WLK_TRN_WLK_XWAIT', 'PM')] + skim_do[('WLK_TRN_WLK_WACC', 'PM')] + skim_do[('WLK_TRN_WLK_WAUX', 'PM')] + skim_do[('WLK_TRN_WLK_WEGR', 'PM')]"
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d-o peak transit time,_trPkTime_do,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) / TRANSIT_SCALE_FACTOR
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peak transit time,_trPkTime,(_trPkTime_od + _trPkTime_do).clip(0)
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round trip path is available,_rt_available,(_trPkTime_od > 0) & (_trPkTime_do > 0)
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decay function,_decay,_rt_available * exp(_trPkTime * dispersion_parameter_transit)
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transit peak retail,trPkRetail,df.RETEMPN * _decay
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transit peak total,trPkTotal,df.TOTEMP * _decay
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#,,
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#,, transit off-peak
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#,,
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#,, assume off-peak outbound transit occurs in the MD time period
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o-d off-peak transit ivt,_inVehicleTime,"skim_od[('WLK_TRN_WLK_IVT', 'MD')]"
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o-d off-peak transit ovt,_outOfVehicleTime,"skim_od[('WLK_TRN_WLK_IWAIT', 'MD')] + skim_od[('WLK_TRN_WLK_XWAIT', 'MD')] + skim_od[('WLK_TRN_WLK_WACC', 'MD')] + skim_od[('WLK_TRN_WLK_WAUX', 'MD')] + skim_od[('WLK_TRN_WLK_WEGR', 'MD')]"
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o-d off-peak transit time,_trOpTime_od,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) / TRANSIT_SCALE_FACTOR
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#,, assume off-peak inbound transit occurs in the MD time period
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d-o off-peak transit ivt,_inVehicleTime,"skim_do[('WLK_TRN_WLK_IVT', 'MD')]"
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d-o off-peak transit ovt,_outOfVehicleTime,"skim_do[('WLK_TRN_WLK_IWAIT', 'MD')] + skim_do[('WLK_TRN_WLK_XWAIT', 'MD')] + skim_do[('WLK_TRN_WLK_WACC', 'MD')] + skim_do[('WLK_TRN_WLK_WAUX', 'MD')] + skim_do[('WLK_TRN_WLK_WEGR', 'MD')]"
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d-o off-peak transit time,_trOpTime_do,(_inVehicleTime + out_of_vehicle_time_weight * _outOfVehicleTime) / TRANSIT_SCALE_FACTOR
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peak transit time,_trOpTime,(_trOpTime_od + _trOpTime_do).clip(0)
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#,,FIXME - _rt_available calculation appears to be wrong in mtctm1 accessibility.job
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#round trip path is available,_rt_available,(_trOpTime > 0)
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round trip path is available,_rt_available,(_trOpTime_od > 0) & (_trOpTime_do > 0)
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decay function,_decay,_rt_available * exp(_trOpTime * dispersion_parameter_transit)
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transit off-peak retail,trOpRetail,df.RETEMPN * _decay
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transit off-peak total,trOpTotal,df.TOTEMP * _decay
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#,,
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#,, non motorized
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#,,
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non-motorized round trip distance,_nmDist,skim_od['DISTWALK'] + skim_do['DISTWALK']
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round trip path is available,_rt_available,_nmDist <= maximum_walk_distance
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decay function,_decay,_rt_available * exp(_nmDist * dispersion_parameter_walk)
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retail accessibility,nmRetail,df.RETEMPN * _decay
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total accessibility,nmTotal,df.TOTEMP * _decay

‎configs/accessibility.yaml‎

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# columns from land_use table to add to df
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land_use_columns: ['RETEMPN', 'TOTEMP']
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CONSTANTS:
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# dispersion parameters
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dispersion_parameter_automobile: -0.05
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dispersion_parameter_transit: -0.05
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dispersion_parameter_walk: -1.00
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# maximum walk distance in miles
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maximum_walk_distance: 3.0
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# perceived minute of in-vehicle time for every minute of out-of-vehicle time
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out_of_vehicle_time_weight: 2.0

‎configs/annotate_households.csv‎

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Description,Target,Expression
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#,,annotate households table after import
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,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)"
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#,,FIXME households.income can be negative - so we clip?
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income_in_thousands,income_in_thousands,(households.income / 1000).clip(lower=0)
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income_segment,income_segment,"pd.cut(income_in_thousands, bins=[-np.inf, 30, 60, 100, np.inf], labels=[INCOME_SEGMENT_LOW, INCOME_SEGMENT_MED, INCOME_SEGMENT_HIGH, INCOME_SEGMENT_VERYHIGH]).astype(int)"
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#,,
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,_MIN_VOT,setting('min_value_of_time')
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,_MAX_VOT,setting('max_value_of_time')
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,_MU,setting('distributed_vot_mu')
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,_SIGMA,setting('distributed_vot_sigma')
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median_value_of_time,median_value_of_time,"income_segment.map({k: v for k, v in setting('household_median_value_of_time').items()})"
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hh_value_of_time,hh_value_of_time,"rng.lognormal_for_df(df, mu=np.log(median_value_of_time * _MU), sigma=_SIGMA).clip(_MIN_VOT, _MAX_VOT)"
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#,,
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#num_workers was renamed in import,,
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#,num_workers,households.workers
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number of non_workers,num_non_workers,households.hhsize - households.num_workers
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#,,
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#,,we assume that everyone 16 and older is a potential driver
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number of drivers,num_drivers,"_PERSON_COUNT('16 <= age', persons, households)"
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num_adults,num_adults,"_PERSON_COUNT('adult', persons, households)"
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num_children,num_children,"_PERSON_COUNT('~adult', persons, households)"
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num_young_children,num_young_children,"_PERSON_COUNT('age <= 5', persons, households)"
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num_children_5_to_15,num_children_5_to_15,"_PERSON_COUNT('5 <= age <= 15', persons, households)"
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num_children_16_to_17,num_children_16_to_17,"_PERSON_COUNT('16 <= age <= 17', persons, households)"
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num_college_age,num_college_age,"_PERSON_COUNT('18 <= age <= 24', persons, households)"
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num_young_adults,num_young_adults,"_PERSON_COUNT('25 <= age <= 34', persons, households)"
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non_family,non_family,households.HHT.isin(HHT_NONFAMILY)
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family,family,households.HHT.isin(HHT_FAMILY)
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home_is_urban,home_is_urban,"reindex(land_use.area_type, households.home_zone_id) < setting('urban_threshold')"
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home_is_rural,home_is_rural,"reindex(land_use.area_type, households.home_zone_id) > setting('rural_threshold')"

‎configs/annotate_households_cdap.csv‎

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Description,Target,Expression
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#,, annotate households table after cdap model has run
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num_under16_not_at_school,num_under16_not_at_school,persons.under16_not_at_school.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
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num_travel_active,num_travel_active,persons.travel_active.astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
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num_travel_active_adults,num_travel_active_adults,(persons.adult & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
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num_travel_active_preschoolers,num_travel_active_preschoolers,((persons.ptype == PTYPE_PRESCHOOL) & persons.travel_active).astype(int).groupby(persons.household_id).sum().reindex(households.index).fillna(0).astype(np.int8)
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num_travel_active_children,num_travel_active_children,num_travel_active - num_travel_active_adults
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num_travel_active_non_preschoolers,num_travel_active_non_preschoolers,num_travel_active - num_travel_active_preschoolers
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#participates_in_jtf_model,participates_in_jtf_model,(num_travel_active > 1) & (num_travel_active_non_preschoolers > 0)
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participates_in_jtf_model,participates_in_jtf_model,(num_travel_active > 1)

‎configs/annotate_households_workplace.csv‎

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Description,Target,Expression
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#,, annotate households table after workplace_location model has run
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#,, hh_work_auto_savings_ratio is sum of persons work_auto_savings_ratio
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,hh_work_auto_savings_ratio,persons.work_auto_savings_ratio.groupby(persons.household_id).sum().reindex(households.index).fillna(0.0)
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#,,handle persons with no location

‎configs/annotate_landuse.csv‎

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Description,Target,Expression
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#,, annotate landuse table after import
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household_density,household_density,land_use.TOTHH / (land_use.RESACRE + land_use.CIACRE)
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employment_density,employment_density,land_use.TOTEMP / (land_use.RESACRE + land_use.CIACRE)
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density_index,density_index,(household_density *employment_density) / (household_density + employment_density).clip(lower=1)
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,is_cbd,land_use.area_type == 1
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‎configs/annotate_persons.csv‎

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Description,Target,Expression
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#,, annotate persons table after import
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age_16_to_19,age_16_to_19,"persons.age.between(16, 19)"
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age_16_p,age_16_p,persons.age >= 16
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adult,adult,persons.age >= 18
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male,male,persons.sex == 1
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female,female,persons.sex == 2
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presence of non_worker other than self in household,has_non_worker,"other_than(persons.household_id, persons.ptype == PTYPE_NONWORK)"
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presence of retiree other than self in household,has_retiree,"other_than(persons.household_id, persons.ptype == PTYPE_RETIRED)"
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presence of preschooler other than self in household,has_preschool_kid,"other_than(persons.household_id, persons.ptype == PTYPE_PRESCHOOL)"
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presence of driving_kid other than self in household,has_driving_kid,"other_than(persons.household_id, persons.ptype == PTYPE_DRIVING)"
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presence of school_kid other than self in household,has_school_kid,"other_than(persons.household_id, persons.ptype == PTYPE_SCHOOL)"
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presence of full_time worker other than self in household (independent of person type),has_full_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_FULL)"
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presence of part_time worker other than self in household (independent of person type),has_part_time,"other_than(persons.household_id, persons.pemploy==PEMPLOY_PART)"
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presence of university student other than self in household,has_university,"other_than(persons.household_id, persons.ptype == PTYPE_UNIVERSITY)"
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student_is_employed,student_is_employed,"(persons.ptype.isin([PTYPE_UNIVERSITY, PTYPE_DRIVING]) & persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART]))"
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nonstudent_to_school,nonstudent_to_school,"(persons.ptype.isin([PTYPE_FULL, PTYPE_PART, PTYPE_NONWORK, PTYPE_RETIRED]) & persons.pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY]))"
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#,,
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#,, FIXME - if person is a university student but has school age student category value then reset student category value
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,pstudent,"persons.pstudent.where(persons.ptype!=PTYPE_UNIVERSITY, PSTUDENT_UNIVERSITY)"
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#,, FIXME if person is a student of any kind but has full-time employment status then reset student category value to non-student
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,pstudent,"pstudent.where(persons.ptype!=PTYPE_FULL, PSTUDENT_NOT)"
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#,, FIXME if student category is non-student and employment is student then reset student category value to student
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,pstudent,"pstudent.where((persons.ptype!=PTYPE_DRIVING) & (persons.ptype!=PTYPE_SCHOOL), PSTUDENT_GRADE_OR_HIGH)"
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#,,
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is_student,is_student,"pstudent.isin([PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY])"
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preschool age can go to preschool,is_student,"is_student.where(persons.age > GRADE_SCHOOL_MIN_AGE, True)"
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preschool age can go to preschool,pstudent,"pstudent.where(persons.age > GRADE_SCHOOL_MIN_AGE, PSTUDENT_GRADE_OR_HIGH)"
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is_gradeschool,is_gradeschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age <= GRADE_SCHOOL_MAX_AGE)
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is_highschool,is_highschool,(pstudent == PSTUDENT_GRADE_OR_HIGH) & (persons.age > GRADE_SCHOOL_MAX_AGE)
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is_university,is_university,pstudent == PSTUDENT_UNIVERSITY
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school_segment gradeschool,school_segment,"np.where(is_gradeschool, SCHOOL_SEGMENT_GRADE, SCHOOL_SEGMENT_NONE)"
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school_segment highschool,school_segment,"np.where(is_highschool, SCHOOL_SEGMENT_HIGH, school_segment)"
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school_segment university,school_segment,"np.where(is_university, SCHOOL_SEGMENT_UNIV, school_segment).astype(np.int8)"
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#,,
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is_worker,is_worker,"persons.pemploy.isin([PEMPLOY_FULL, PEMPLOY_PART])"
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#,,
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home_zone_id,home_zone_id,"reindex(households.home_zone_id, persons.household_id)"

‎configs/annotate_persons_after_hh.csv‎

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Description,Target,Expression
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#,, annotate persons table after annotate_households
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#,, adults get full hh_value_of_time and children get 60%
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,_hh_vot,"reindex(households.hh_value_of_time, persons.household_id)"
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,value_of_time,"_hh_vot.where(persons.age>=18, _hh_vot * 0.667)"

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