Estimation

.github/workflows/ci.yml

@@ -48,6 +48,15 @@ jobs:
           uv pip install -e . --no-deps
           uv pip list
 
+      # Clone the ActivitySim visualizer and build its virtual environment
+      - name: Set up ActivitySim visualizer
+        run: |
+          mkdir -p $GITHUB_WORKSPACE/ext_dependencies
+          cd $GITHUB_WORKSPACE/ext_dependencies
+          git clone --branch main https://github.com/RSGInc/activitysim_visualizer.git
+          cd activitysim_visualizer
+          uv sync
+
       # Run the test
       - name: Run Metro cropped example test
         run: |

README.md

@@ -54,7 +54,7 @@ Before running the model, make sure the following are available:
 
    `sandag_parking` is not required for a typical model run. It is only needed when preparing land use inputs that require expected parking costs. In that workflow, it can be run once to generate the parking cost input file used by the preprocessor.
 
-   If permissions errors arise, try running the setup with Administrator privileges. The Visum Python package install in particular may require elevated permissions.
+   **If permissions errors arise, try running the setup with Administrator privileges. The Visum Python package install in particular may require elevated permissions.**
 
 2. **Place the Visum version file**
 

resident/configs/accessibility.yaml

@@ -1,2 +1,3 @@
 # columns from land_use table to add to df
 land_use_columns: ['EMP_RET', 'EMP_TOTAL', 'TOTHHS', 'walk_dist_local_bus']
+explicit_chunk: 0.2

resident/configs/annotate_households.csv

@@ -26,4 +26,5 @@ number of retired adults in the household,num_retired_adults,"_PERSON_COUNT('pty
 ,num_hh_in_zone,"reindex(households.groupby('home_zone_id').size(), households.home_zone_id)"
 #,multiple_auto_hh_in_zone,"reindex_i(df.groupby('home_zone_id').multiple_auto_hh.sum(), df.index)"
 #,multiple_auto_hh_in_zone_share,multiple_auto_hh_in_zone/num_hh_in_zone
-hh owns an ebike,ebike_owner,"rng.random_for_df(households)[:,0] < ebikeownership"
+hh owns an ebike,ebike_owner,"rng.random_for_df(households)[:,0] < ebikeownership"
+Retired Adults Only Households,retired_adults_only_hh,(households.hhsize > 0) & (households.hhsize == num_retired_adults)

resident/configs/annotate_persons.csv

@@ -11,20 +11,20 @@ employment status type,pemploy,"pd.Series(np.zeros(len(persons)), index=persons.
 ,pemploy,"np.where((persons.age >= 16) & ((persons.ESR == 3) | (persons.ESR == 6)), PEMPLOY_NOT, pemploy)"
 ,pemploy,"np.where((persons.age>=16) & (persons.ESR != 3) & (persons.ESR != 6) & (persons.WKHP >= 35) & (persons.WKW >= 1) & (persons.WKW <= 4), PEMPLOY_FULL, pemploy)"
 student category,pstudent,"pd.Series(np.zeros(len(persons)), index=persons.index)"
-,pstudent,"np.where((pemploy == 1) & (persons.age >= 16), PSTUDENT_NOT, pstudent)"
-,pstudent,"np.where((pemploy == 1) & (persons.age < 16), PSTUDENT_GRADE_OR_HIGH, pstudent)"
+,pstudent,"np.where((pemploy == PEMPLOY_FULL) & (persons.age >= 16), PSTUDENT_NOT, pstudent)"
+,pstudent,"np.where((pemploy == PEMPLOY_FULL) & (persons.age < 16), PSTUDENT_GRADE_OR_HIGH, pstudent)"
 ,pstudent,"np.where((persons.SCHG < 1) & (persons.age >= 16), PSTUDENT_NOT, pstudent)"
 ,pstudent,"np.where((persons.SCHG < 1) & (persons.age < 16), PSTUDENT_GRADE_OR_HIGH, pstudent)"
-,pstudent,"np.where((persons.SCHG >= 15) & (persons.age >= 16) & (pemploy != 1), PSTUDENT_UNIVERSITY, pstudent)"
-,pstudent,"np.where((persons.SCHG >= 15) & (persons.age < 16) & (pemploy != 1), PSTUDENT_GRADE_OR_HIGH, pstudent)"
-,pstudent,"np.where((persons.age <= 19) & (pemploy != 1) & (persons.SCHG >=1) & (persons.SCHG<=14), PSTUDENT_GRADE_OR_HIGH, pstudent)"
-,pstudent,"np.where((persons.age > 19) & (pemploy != 1) & (persons.SCHG >=1) & (persons.SCHG<=14),  PSTUDENT_UNIVERSITY, pstudent)"
+,pstudent,"np.where((persons.SCHG >= 15) & (persons.age >= 16), PSTUDENT_UNIVERSITY, pstudent)"
+,pstudent,"np.where((persons.SCHG >= 15) & (persons.age < 16), PSTUDENT_GRADE_OR_HIGH, pstudent)"
+,pstudent,"np.where((persons.age <= 19) & (persons.SCHG >=1) & (persons.SCHG<=14), PSTUDENT_GRADE_OR_HIGH, pstudent)"
+,pstudent,"np.where((persons.age > 19) & (persons.SCHG >=1) & (persons.SCHG<=14),  PSTUDENT_UNIVERSITY, pstudent)"
 ,pstudent,"np.where(pstudent == 0, 3, pstudent)"
 person type,ptype,"pd.Series(PTYPE_NONWORK, index=persons.index)"
 ,ptype,"np.where(persons.age >= 65, PTYPE_RETIRED, ptype)"
 ,ptype,"np.where(pemploy == 2, PTYPE_PART, ptype)"
 ,ptype,"np.where(pemploy == 1,  PTYPE_FULL, ptype)"
-,ptype,"np.where((persons.age >= 5) & ((persons.age < 18) | ((persons.age == 18) & (pstudent == 1))), PTYPE_SCHOOL, ptype)"
+,ptype,"np.where(((persons.age >= 5) & (persons.age < 18)) | (persons.age.isin([18,19]) & (pstudent == PSTUDENT_GRADE_OR_HIGH)), PTYPE_SCHOOL, ptype)"
 ,ptype,"np.where(persons.age < 5, PTYPE_PRESCHOOL, ptype)"
 ,ptype,"np.where(pstudent == 2, PTYPE_UNIVERSITY, ptype)"
 is_student,is_student,"np.isin(pstudent, [PSTUDENT_GRADE_OR_HIGH, PSTUDENT_UNIVERSITY])"

resident/configs/constants.yaml

@@ -330,4 +330,4 @@ OpVTCr: 0.1442
 # model region constants
 KNR_available: True  # SKATS does not have KNR, so set to False in configs_skats/constants.yaml
 model_region_name: "Metro"
-MSA_POP: 2500000  # used in vehicle type choice model
+MSA_POP: 2500000  # used in vehicle type choice model

resident/configs/disaggregate_accessibility.yaml

@@ -93,7 +93,7 @@ CREATE_TABLES:
                     2: "N"
                 occupation: # occupation codes
                     1: 1   # Management Occupations
-                    2: -1    # Non-worker
+                    2: 999    # Non-worker
 
     PROTO_TOURS:
         index_col: proto_tour_id

resident/configs/external_non_mandatory_identification.yaml

@@ -9,3 +9,5 @@ LOGIT_TYPE: MNL
 EXTERNAL_COL_NAME: is_external_tour
 # set to True if not external but CHOOSER_FILTER_COLUMN_NAME is True
 INTERNAL_COL_NAME: is_internal_tour
+
+EXTERNAL_TOUR_ALT: 0

resident/configs/external_worker_identification.csv

@@ -1,14 +1,14 @@
-Label,Description,Expression,work_external,work_internal
-util_dist_to_nearest_ext_station,Distance to nearest external station,dist_to_external_zone,coef_dist_to_nearest_ext_station,
-util_size_of_nearest_ext_station,Size of nearest external station,"@np.log1p(reindex(land_use.EXTERNAL,df.closest_external_zone))",coef_size_of_nearest_ext_station,
-util_dist_lt_2p5,Distance less than 2.5 miles,"@np.where(df.dist_to_external_zone<2.5,1,0)",coef_dist_lt_2p5,
-util_part_time,Part time worker,"@np.where(df.pemploy == 2,1,0)",coef_part_time,
-util_inc_lt15,Household Income less than $15k,@(df.income<15000),coef_inc_lt15,
-util_inc_15_25,Household income $15k-$25k,@(df.income>=15000) & (df.income<25000) ,coef_inc_15_25,
-util_inc_25_50,Household Income $25k-$50k,@(df.income>=25000) & (df.income<50000) ,coef_inc_25_50,
-util_inc_150_250,Household Income $150k-$50k,@(df.income>=150000) * (df.income<250000) ,coef_inc_150_250,
-util_inc_250plus,Household Income $250k+,@(df.income>=250000),coef_inc_250plus,
-util_asc,Alternative-specific constant for external worker,1,asc_external_worker,
-util_global_switch,Global switch to have everything internal,@NO_EXTERNAL,-999,
-util_2016,Constant for pre-COVID conditions,@PRE_COVID,asc_external_2016,
-util_calib,Constant for calibration,1,0.35,
+Label,Description,Expression,work_internal,work_external
+util_dist_to_nearest_ext_station,Distance to nearest external station,dist_to_external_zone,,coef_dist_to_nearest_ext_station
+util_size_of_nearest_ext_station,Size of nearest external station,"@np.log1p(reindex(land_use.EXTERNAL,df.closest_external_zone))",,coef_size_of_nearest_ext_station
+util_dist_lt_2p5,Distance less than 2.5 miles,"@np.where(df.dist_to_external_zone<2.5,1,0)",,coef_dist_lt_2p5
+util_part_time,Part time worker,"@np.where(df.pemploy == 2,1,0)",,coef_part_time
+util_inc_lt15,Household Income less than $15k,@(df.income<15000),,coef_inc_lt15
+util_inc_15_25,Household income $15k-$25k,@(df.income>=15000) & (df.income<25000) ,,coef_inc_15_25
+util_inc_25_50,Household Income $25k-$50k,@(df.income>=25000) & (df.income<50000) ,,coef_inc_25_50
+util_inc_150_250,Household Income $150k-$50k,@(df.income>=150000) * (df.income<250000) ,,coef_inc_150_250
+util_inc_250plus,Household Income $250k+,@(df.income>=250000),,coef_inc_250plus
+util_asc,Alternative-specific constant for external worker,1,,asc_external_worker
+util_global_switch,Global switch to have everything internal,@NO_EXTERNAL,,-999
+util_2016,Constant for pre-COVID conditions,@PRE_COVID,,asc_external_2016
+util_calib,Constant for calibration,1,,0.35

resident/configs/external_worker_identification.yaml

@@ -4,6 +4,8 @@ COEFFICIENTS: external_worker_identification_coeffs.csv
 
 LOGIT_TYPE: MNL
 
+EXTERNAL_WORKER_ALT: 1
+
 # boolean column to filter choosers (True means keep)
 # will only expose these people to the model
 CHOOSER_FILTER_COLUMN_NAME: is_out_of_home_worker

resident/configs/joint_tour_frequency_composition.csv

@@ -21,7 +21,7 @@ util_constant_for_2_visiting_tour,Constant for 2 visiting tour,@df.social==2,coe
 util_constant_for_1_visiting_tour,Constant for 1 visiting tour,@df.social==2,coef_constant_for_1_visiting_tour
 util_1_visiting_and_1_discretionary_tour,1 Visiting and 1 Discretionary Tour,@((df.social==1) & (df.othdiscr==1)),coef_1_visiting_and_1_discretionary_tour
 util_constant_for_2_discretionary_tour,Constant for 2 discretionary tour,othdiscr==2,coef_constant_for_2_discretionary_tour
-util_constant_for_2_discretionary_tour,Constant for 2 discretionary tour,othdiscr==1,coef_constant_for_1_discretionary_tour
+util_constant_for_1_discretionary_tour,Constant for 1 discretionary tour,othdiscr==1,coef_constant_for_1_discretionary_tour
 util_number_of_active_full_time_workers_shopping,Number of Active Full time workers /Shopping,num_travel_active_full_time_workers * shopping,coef_number_of_active_full_time_workers_shopping
 util_number_of_active_nonworkers_shopping,Number of Active Non-workers /Shopping,num_travel_active_non_workers * shopping,coef_number_of_active_nonworkers_shopping
 util_number_of_active_pre_driving_age_school_children_shopping,Number of Active Pre- Driving Age School Children /Shopping,num_travel_active_pre_driving_age_school_kids * shopping,coef_number_of_active_pre_driving_age_school_children_shopping

resident/configs/logging.yaml

@@ -46,12 +46,12 @@ logging:
       class: logging.Formatter
       # format: '%(levelname)s - %(name)s - %(message)s'
       format: '%(levelname)s - %(message)s'
-      datefmt: '%d/%m/%Y %H:%M:%S'
+      datefmt: '%Y-%m-%dT%H:%M:%S%z'
 
     fileFormatter:
       class: logging.Formatter
       format: '%(asctime)s - %(levelname)s - %(name)s - %(message)s'
-      datefmt: '%d/%m/%Y %H:%M:%S'
+      datefmt: '%Y-%m-%dT%H:%M:%S%z'
 
     elapsedFormatter:
       (): activitysim.core.tracing.ElapsedTimeFormatter

resident/configs/non_mandatory_tour_destination.csv

@@ -71,4 +71,4 @@ util_joint_0_2_ASC,joint tours in 0_2bin calibration constant,"@(_DIST.between(0
 util_joint_2_5_ASC,joint tours in 2_5bin calibration constant,"@(_DIST.between(2,5)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_2_5jointmaint_asc,coef_abm3_dist_2_5jointdisc_asc,coef_abm3_dist_2_5jointmaint_asc,coef_abm3_dist_2_5jointdisc_asc,coef_abm3_dist_2_5jointdisc_asc
 util_joint_5_10_ASC,joint tours in 5_10bin calibration constant,"@(_DIST.between(5,10)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_5_10jointmaint_asc,coef_abm3_dist_5_10jointdisc_asc,coef_abm3_dist_5_10jointmaint_asc,coef_abm3_dist_5_10jointdisc_asc,coef_abm3_dist_5_10jointdisc_asc
 util_joint_10_30_ASC,joint tours in 10_30bin calibration constant,"@(_DIST.between(10,30)) * (df.get('tour_category', default=False) == 'joint')",,coef_abm3_dist_10_30jointmaint_asc,coef_abm3_dist_10_30jointdisc_asc,coef_abm3_dist_10_30jointmaint_asc,coef_abm3_dist_10_30jointdisc_asc,coef_abm3_dist_10_30jointdisc_asc
-util_indiv_dist_ASC,indiv tours in distance calibration constant,"@(_DIST) * (df.get('tour_category', default=False) == 'joint')",,0.10,,0.10,,
+util_joint_dist_ASC,indiv tours in distance calibration constant,"@(_DIST) * (df.get('tour_category', default=False) == 'joint')",,0.10,,0.10,,

resident/configs/non_mandatory_tour_destination.yaml

@@ -3,6 +3,7 @@ SPEC: non_mandatory_tour_destination.csv
 COEFFICIENTS: non_mandatory_tour_destination_coefficients.csv
 
 SAMPLE_SIZE: 30
+ESTIMATION_SAMPLE_SIZE: 30
 
 SIZE_TERM_SELECTOR: non_mandatory
 

resident/configs/non_mandatory_tour_frequency_alternatives.csv

@@ -201,4 +201,5 @@ escort,shopping,othmaint,eatout,social,othdiscr
 2,1,1,0,1,1
 2,1,2,1,0,0
 2,0,2,1,1,0
+2,0,2,0,1,1
 2,1,1,1,1,0

resident/configs/non_mandatory_tour_scheduling.yaml

@@ -30,6 +30,7 @@ SIMULATE_CHOOSER_COLUMNS:
   - num_non_escort_tours
   - num_add_shop_maint_tours
   - num_add_soc_discr_tours
+  - retired_adults_only_hh
 
 LOGSUM_SETTINGS: tour_mode_choice.yaml
 

resident/configs/school_escorting.yaml

@@ -12,6 +12,8 @@ ALTS: school_escorting_alts.csv
 
 LOGIT_TYPE: MNL
 
+ESCORTEE_AGE_CUTOFF: 19
+
 NUM_ESCORTEES: 3
 NUM_CHAPERONES: 2
 

resident/configs/school_location.csv

@@ -2,7 +2,7 @@ Label,Description,Expression,university,highschool,gradeschool,preschool
 local_dist,,"_DIST@skims[('SOV_M_DIST', 'MD')]",1,1,1,1
 dest_college_enroll,total the destination TAZ (summed over MAZs and df index is dest_TAZ),"_dCOLLEGEENROLL@reindex(land_use.groupby('TAZ').COLLEGEENROLL.sum(), df.index)",1,1,1,1
 util_mode_choice_logsum,Mode choice logsum,mode_choice_logsum,coef_mclogsum_univ,coef_mclogsum_hsch,coef_mclogsum_gs,coef_mclogsum_ps
-util_Distance,Distance,@_DIST.clip(upper=50),coef_dist_univ,coef_dist_hsch,coef_dist_gs,coef_dist_ps
+util_Distance,Distance,@_DIST.clip(upper=50).fillna(0),coef_dist_univ,coef_dist_hsch,coef_dist_gs,coef_dist_ps
 util_log_Distance,log_Distance,@np.log(_DIST.clip(upper=50)+1),coef_lndist_univ,coef_zero,coef_zero,coef_lndist_ps
 util_Distance_squareroot,Squareroot of distance,@_DIST.clip(upper=50)**0.5,coef_sqrtdist_univ,coef_sqrtdist_hsch,coef_sqrtdist_gs,coef_sqrtdist_ps
 util_Distance_squared,Distance_squared,@_DIST.clip(upper=50)**2,coef_sqrddist_univ,coef_sqrddist_hsch,coef_sqrddist_gs,coef_sqrddist_ps
@@ -11,10 +11,10 @@ util_Distance_worker_univ,Distance for a worker_university specific,"@np.where(d
 util_Distance_largeuniversity_univ,Distance for large university enrollment,"@np.where(_dCOLLEGEENROLL>5000, _DIST.clip(upper=50), 0)",coef_univenrol_dist_univ,coef_zero,coef_zero,coef_zero
 util_Distance _lowincome_prek,Distance - low income,"@np.where(df.income<60000, _DIST.clip(upper=50), 0)",coef_zero,coef_zero,coef_zero,coef_lowincdist_ps
 util_Distance - age03_prek,Distance - age 0 to 3,"@np.where(df.age<3,_DIST.clip(upper=50),0)",coef_zero,coef_zero,coef_zero,coef_age03dist_ps
-util_LoggedSize,Logged Size variable - University specific,@df['size_term'].apply(np.log1p),coef_lnSize,coef_lnSize,coef_lnSize,coef_lnSize
+util_LoggedSize,Logged Size variable - University specific,@df['size_term'].fillna(0).apply(np.log1p),coef_lnSize,coef_lnSize,coef_lnSize,coef_lnSize
 util_no_attractions,no attractions if logged university size is zero,@df['size_term']==0,-999,-999,-999,-999
 util_sample_of_corrections_factor,Sample of alternatives correction factor,"@np.minimum(np.log(df.pick_count/df.prob), 60)",1,1,1,1
-util_sp_utility_adjustment,shadow price utility adjustment,@df['shadow_price_utility_adjustment'],1,1,1,
+util_sp_utility_adjustment,shadow price utility adjustment,@df['shadow_price_utility_adjustment'].fillna(0),1,1,1,
 #,,,,,,
 util_ABM2calibration_0-1miles,ABM2 calibration_0-1miles,@(_DIST<1),coef_zero,coef_abmcalib_01miles,coef_abmcalib_01miles,coef_abmcalib_01miles
 util_ABM2calibration_1-2miles,ABM2 calibration_1-2miles,@(_DIST<2) * (_DIST>=1),coef_zero,coef_abmcalib_12miles,coef_abmcalib_12miles,coef_abmcalib_12miles

resident/configs/school_location.yaml

@@ -1,5 +1,5 @@
 SAMPLE_SIZE: 30
-ESTIMATION_SAMPLE_SIZE: 10
+ESTIMATION_SAMPLE_SIZE: 30
 
 SIMULATE_CHOOSER_COLUMNS:
   - home_zone_id

resident/configs/tour_mode_choice.yaml

@@ -78,6 +78,7 @@ LOGSUM_CHOOSER_COLUMNS:
   - age
   - SEX
   - auto_ownership
+  - num_drivers
   - num_adults
   - ptype
   - number_of_participants

resident/configs/tour_mode_choice_annotate_choosers_preprocessor.csv

@@ -45,7 +45,7 @@ treat tours as work if tour_type not yet decided,tour_type,"df.get('tour_type',
 popempdenpermi calculated in preprocessor and input via landuse file,_origin_density_measure,"reindex(land_use.popempdenpermi, df[orig_col_name])"
 ,_dest_density_measure,"reindex(land_use.popempdenpermi, df[dest_col_name])"
 ,origin_density,"pd.cut(_origin_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)"
-,dest_density,"pd.cut(_dest_density_measure, bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)"
+,dest_density,"pd.cut(_dest_density_measure.fillna(0), bins=[-np.inf, 500, 2000, 5000, 15000, np.inf], labels=[5, 4, 3, 2, 1]).astype(int)"
 ,origin_zone_taxi_wait_time_mean,"origin_density.map({k: v for k, v in Taxi_waitTime_mean.items()})"
 ,origin_zone_taxi_wait_time_sd,"origin_density.map({k: v for k, v in Taxi_waitTime_sd.items()})"
 ,dest_zone_taxi_wait_time_mean,"dest_density.map({k: v for k, v in Taxi_waitTime_mean.items()})"

resident/configs/transit_pass_subsidy.csv

@@ -20,3 +20,7 @@ util_availability,Availability of transit pass,transit_subsidy_available==False,
 util_ft_ASC,ptype_ft calibration constant,@df.ptype == 1,0,coef_ft_asc
 util_pt_ASC,ptype_pt calibration constant,@df.ptype == 2,0,coef_pt_asc
 util_un_ASC,ptype_un calibration constant,@df.ptype == 3,0,coef_un_asc
+util_transit_ivt_home_to_work_walk_peak,Walk-transit home to work peak availability,@df.transit_ivt_home_to_work_walk_peak.fillna(0),0,coef_transit_ivt_home_to_work_walk_peak
+util_transit_ivt_home_to_work_drive_peak,Drive-transit home to work peak availability,@df.transit_ivt_home_to_work_drive_peak.fillna(0),0,coef_transit_ivt_home_to_work_drive_peak
+util_transit_ivt_home_to_school_walk_peak,Walk-transit home to school peak availability,@df.transit_ivt_home_to_school_walk_peak.fillna(0),0,coef_transit_ivt_home_to_school_walk_peak
+util_transit_ivt_home_to_school_drive_peak,Drive-transit home to school peak availability,@df.transit_ivt_home_to_school_drive_peak.fillna(0),0,coef_transit_ivt_home_to_school_drive_peak

resident/configs/transit_pass_subsidy_coefficients.csv

@@ -21,3 +21,7 @@ coef_unavailable,-999.0,F
 coef_ft_asc,-0.240,F
 coef_pt_asc,-0.250,F
 coef_un_asc,0.30,F
+coef_transit_ivt_home_to_work_walk_peak,0,F
+coef_transit_ivt_home_to_work_drive_peak,0,F
+coef_transit_ivt_home_to_school_walk_peak,0,F
+coef_transit_ivt_home_to_school_drive_peak,0,F

resident/configs/transit_pass_subsidy_preprocessor.csv

@@ -1,5 +1,7 @@
 Description,Target,Expression
-,_transit_ivt_home_to_work_walk_peak,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WTW_TIV', 'AM'))"
-,_transit_ivt_home_to_work_drive_peak,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('KTW_TIV', 'AM')) if KNR_available else 0"
-,transit_subsidy_available,((_transit_ivt_home_to_work_walk_peak > 0) | (_transit_ivt_home_to_work_drive_peak > 0)) & (persons.workplace_zone_id > 0)
+,transit_ivt_home_to_work_walk_peak,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('WTW_TIV', 'AM'))"
+,transit_ivt_home_to_work_drive_peak,"skim_dict.lookup(persons.home_zone_id, persons.workplace_zone_id, ('KTW_TIV', 'AM')) if KNR_available else 0"
+,transit_ivt_home_to_school_walk_peak,"skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, ('WTW_TIV', 'AM'))"
+,transit_ivt_home_to_school_drive_peak,"skim_dict.lookup(persons.home_zone_id, persons.school_zone_id, ('KTW_TIV', 'AM')) if KNR_available else 0"
+,transit_subsidy_available,persons.is_worker
 ,trn_wk_access_hh,"np.where(persons.workplace_zone_id > 0, reindex(accessibility.trPkHH, persons.workplace_zone_id), 0)"