SANDAG · aber-sandag · Sep 11, 2026 · Sep 11, 2026 · Sep 11, 2026
diff --git a/src/asim/configs/common/network_los.yaml b/src/asim/configs/common/network_los.yaml
@@ -32,9 +32,3 @@ skim_time_periods:
     period_minutes: 30
     periods: [0, 6, 12, 25, 32, 48]  # time periods to match documentation
     labels: &skim_time_period_labels ['EA', 'AM', 'MD', 'PM', 'EV']
-
-demographic_segments: &demographic_segments
-  - &low_income_segment_id 0
-  - &high_income_segment_id 1
-
-
diff --git a/src/asim/configs/common/outputs.yaml b/src/asim/configs/common/outputs.yaml
@@ -892,7 +892,6 @@ persons:
     - num_shop_maint_tours
     - num_shop_maint_escort_tours
     - num_soc_discr_tours
-    - demographic_segment
     - time_distrib_mean_work
     - time_distrib_stddev_nonwork
     - time_distrib_mean_nonwork

diff --git a/src/asim/configs/resident/accessibility.csv b/src/asim/configs/resident/accessibility.csv
@@ -19,11 +19,11 @@ auto off-peak total,auOpTotal,df.emp_total * _decay
 #,, transit peak
 #,, assume peak outbound transit occurs in AM
 o-d peak transit time,_inVehicletime_od,"skim_od[('WALK_MIX_TOTALIVTT', 'AM')]"
-o-d out of vehicle transit time,_outOfVehicleTime_od,"skim_od[('WALK_MIX_FIRSTWAIT', 'AM')] + skim_od[('WALK_MIX_XFERWAIT', 'AM')] + reindex(df.walk_dist_local_bus, df.orig) + skim_od[('WALK_MIX_XFERWALK', 'AM')] + reindex(df.walk_dist_local_bus, df.dest)"
+o-d out of vehicle transit time,_outOfVehicleTime_od,"skim_od[('WALK_MIX_FIRSTWAIT', 'AM')] + skim_od[('WALK_MIX_XFERWAIT', 'AM')] + df.landuse_orig_walk_dist_local_bus + skim_od[('WALK_MIX_XFERWALK', 'AM')] + df.walk_dist_local_bus"
 total o-d peak transit time,_trPkTime_od,(_inVehicletime_od +  out_of_vehicle_time_weight * _outOfVehicleTime_od)
 #,, assume peak inbound transit occurs in PM
 o-d peak transit time,_inVehicletime_do,"skim_do[('WALK_MIX_TOTALIVTT', 'PM')]"
-o-d out of vehicle transit time,_outOfVehicleTime_do,"skim_do[('WALK_MIX_FIRSTWAIT', 'PM')] + skim_do[('WALK_MIX_XFERWAIT', 'PM')] + reindex(df.walk_dist_local_bus, df.orig) + skim_do[('WALK_MIX_XFERWALK', 'PM')] + reindex(df.walk_dist_local_bus, df.dest)"
+o-d out of vehicle transit time,_outOfVehicleTime_do,"skim_do[('WALK_MIX_FIRSTWAIT', 'PM')] + skim_do[('WALK_MIX_XFERWAIT', 'PM')] + df.landuse_orig_walk_dist_local_bus + skim_do[('WALK_MIX_XFERWALK', 'PM')] + df.walk_dist_local_bus"
 total o-d peak transit time,_trPkTime_do,(_inVehicletime_do +  out_of_vehicle_time_weight * _outOfVehicleTime_do)
 #,,
 #o-d peak transit time,_trPkTime_do,"skim_do[('WALK_MIX_TOTALIVTT', 'PM')]"

diff --git a/src/asim/configs/resident/accessibility.yaml b/src/asim/configs/resident/accessibility.yaml
@@ -1,7 +1,10 @@
 
-# columns from land_use table to add to df
+# columns from land_use table to add to df for destinations
 land_use_columns: ['emp_ret', 'emp_total', 'hh', 'walk_dist_local_bus']
 
+# columns from land_use table to add to df for origins
+land_use_columns_orig: ['walk_dist_local_bus']
+
 CONSTANTS:
   # dispersion parameters
   dispersion_parameter_automobile: {mode-auto-dispersionParameter:}

diff --git a/src/asim/configs/resident/atwork_subtour_destination.csv b/src/asim/configs/resident/atwork_subtour_destination.csv
@@ -1,8 +1,8 @@
 Label,Description,Expression,atwork
 ,,"_DIST@np.minimum(skims['DIST'], 20)",1
-,,_DIST_SQUARED@_DIST**2,1
-,,_DIST_CUBED@_DIST**3,1
-,,_DIST_LOGGED@np.log(_DIST + 0.001),1
+ATWORK_DIST_SQUARED,,_DIST_SQUARED@_DIST**2,1
+ATWORK_DIST_CUBED,,_DIST_CUBED@_DIST**3,1
+ATWORK_DIST_LOGGED,,_DIST_LOGGED@np.log(_DIST + 0.001),1
 util_Distance,Distance,@_DIST,coef_dist_atwork
 util_Distance_squared,Distance_squared,@_DIST_SQUARED,coef_distsqrd_atwork
 util_Distance_fulltime_worker,Distance_fulltime_worker,@_DIST * (df.ptype==1),coef_dist_ftworker_atwork

diff --git a/src/asim/configs/resident/atwork_subtour_frequency.csv b/src/asim/configs/resident/atwork_subtour_frequency.csv
@@ -4,7 +4,7 @@ util_dummy_for_non_full_time_worker,pemploy!=1,coefficient_dummy_for_non_full_ti
 util_dummy_for_non_workers,"ptype in [4, 5]",coefficient_dummy_for_non_workers_no_subtours,coefficient_dummy_for_non_workers_eat,coefficient_dummy_for_non_workers_business1,coefficient_dummy_for_non_workers_maint,coefficient_dummy_for_non_workers_business2,coefficient_dummy_for_non_workers_eat_business
 util_medium_hh_income_dummy,income_segment == 2,coefficient_medium_hh_income_dummy_no_subtours,coefficient_medium_hh_income_dummy_eat,coefficient_medium_hh_income_dummy_business1,coefficient_medium_hh_income_dummy_maint,coefficient_medium_hh_income_dummy_business2,coefficient_medium_hh_income_dummy_eat_business
 util_high_hh_income_dummy,(income_segment > 2) & (income_segment < 5),coefficient_high_hh_income_dummy_no_subtours,coefficient_high_hh_income_dummy_eat,coefficient_high_hh_income_dummy_business1,coefficient_high_hh_income_dummy_maint,coefficient_high_hh_income_dummy_business2,coefficient_high_hh_income_dummy_eat_business
-util_zero_cars_owned_by_hh_dummy, auto_ownership == 0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business
+util_zero_cars_owned_by_hh_dummy,auto_ownership==0,coefficient_zero_cars_owned_by_hh_dummy_no_subtours,coefficient_zero_cars_owned_by_hh_dummy_eat,coefficient_zero_cars_owned_by_hh_dummy_business1,coefficient_zero_cars_owned_by_hh_dummy_maint,coefficient_zero_cars_owned_by_hh_dummy_business2,coefficient_zero_cars_owned_by_hh_dummy_eat_business
 util_individual_discretionary_tours_made_by_full_time_worker,@(df.pemploy==1)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_full_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat,coefficient_individual_discretionary_tours_made_by_full_time_worker_business1,coefficient_individual_discretionary_tours_made_by_full_time_worker_maint,coefficient_individual_discretionary_tours_made_by_full_time_worker_business2,coefficient_individual_discretionary_tours_made_by_full_time_worker_eat_business
 util_individual_discretionary_tours_made_by_part_time_worker,@(df.pemploy==2)*df.num_discr_tours,coefficient_individual_discretionary_tours_made_by_part_time_worker_no_subtours,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat,coefficient_individual_discretionary_tours_made_by_part_time_worker_business1,coefficient_individual_discretionary_tours_made_by_part_time_worker_maint,coefficient_individual_discretionary_tours_made_by_part_time_worker_business2,coefficient_individual_discretionary_tours_made_by_part_time_worker_eat_business
 util_individual_eating_out_tours_made_by_person,num_eatout_tours,coefficient_individual_eating_out_tours_made_by_person_no_subtours,coefficient_individual_eating_out_tours_made_by_person_eat,coefficient_individual_eating_out_tours_made_by_person_business1,coefficient_individual_eating_out_tours_made_by_person_maint,coefficient_individual_eating_out_tours_made_by_person_business2,coefficient_individual_eating_out_tours_made_by_person_eat_business

diff --git a/src/asim/configs/resident/atwork_subtour_frequency.yaml b/src/asim/configs/resident/atwork_subtour_frequency.yaml
@@ -9,3 +9,6 @@ preprocessor:
     - land_use
     - tours
     - joint_tour_participants
+
+compute_settings:
+  fastmath: false
diff --git a/src/asim/configs/resident/cdap.yaml b/src/asim/configs/resident/cdap.yaml
@@ -45,3 +45,6 @@ annotate_households:
   DF: households
   TABLES:
     - persons
+
+compute_settings:
+  fastmath: false
diff --git a/src/asim/configs/resident/cdap_indiv_and_hhsize1.csv b/src/asim/configs/resident/cdap_indiv_and_hhsize1.csv
@@ -25,7 +25,7 @@ Base works from home 2016,(work_from_home),,coef_base_work_from_home_2016_N,
 Part time worker who works from home,(ptype == 2) & (work_from_home),,coef_part_time_worker_work_from_home_N,
 Part time worker who works from home,@(df.ptype == 2) & (df.work_from_home) & (PRE_COVID),,coef_part_time_worker_work_from_home_2016_N,
 Telecommutes 1 day per week,telecommute_frequency=='1_day_week',,coef_telecommute_1_day_week_N,coef_telecommute_1_day_week_H
-Telecommutes 2-3 days per week,telecommute_frequency=='2_3_days_week',,coef_telecommute_2_3_days_week_N,coef_telecommute_2_3_days_week_H
+Telecommutes 2-3 days per week,@(df.telecommute_frequency=='2_days_week') | (df.telecommute_frequency=='3_days_week'),,coef_telecommute_2_3_days_week_N,coef_telecommute_2_3_days_week_H
 Telecommutes 4 days per week,telecommute_frequency=='4_days_week',,coef_telecommute_4_days_week_N,coef_telecommute_4_days_week_H
 Full-time worker 2016 ASC,@(df.ptype == 1) & (PRE_COVID),coef_full_time_worker_2016_asc_M,coef_full_time_worker_2016_asc_N,
 Part-time worker 2016 ASC,@(df.ptype == 2) & (PRE_COVID),coef_part_time_worker_2016_asc_M,coef_part_time_worker_2016_asc_N,
@@ -36,7 +36,7 @@ Driving-age child who is in school 2016 ASC,@(df.ptype == 6) & (PRE_COVID),coef_
 Pre-driving-age child who is in school 2016 ASC,@(df.ptype == 7) & (PRE_COVID),coef_pre_driving_age_child_2016_asc_M,coef_pre_driving_age_child_2016_asc_N,
 Preschool child 2016 ASC,@(df.ptype == 8) & (PRE_COVID),coef_preschool_child_2016_asc_M,coef_preschool_child_2016_asc_N,
 Telecommutes 1 day per week 2016,@(df.telecommute_frequency=='1_day_week') & (PRE_COVID),,coef_telecommute_1_day_week_2016_N,coef_telecommute_1_day_week_2016_H
-Telecommutes 2-3 days per week 2016,@(df.telecommute_frequency=='2_3_days_week') & (PRE_COVID),,coef_telecommute_2_3_days_week_2016_N,coef_telecommute_2_3_days_week_2016_H
+Telecommutes 2-3 days per week 2016,@((df.telecommute_frequency=='2_days_week') | (df.telecommute_frequency=='3_days_week')) & (PRE_COVID),,coef_telecommute_2_3_days_week_2016_N,coef_telecommute_2_3_days_week_2016_H
 Telecommutes 4 days per week 2016,@(df.telecommute_frequency=='4_days_week') & (PRE_COVID),,coef_telecommute_4_days_week_2016_N,coef_telecommute_4_days_week_2016_H
 Mandatory pattern unavailable if not worker or student,(is_student == False) & (is_worker == False),coef_UNAVAILABLE,,
 Mandatory pattern unavailable if not works from home,(work_from_home == True) & (is_student == False),coef_UNAVAILABLE,,

diff --git a/src/asim/configs/resident/cdap_joint_tour_coefficients.csv b/src/asim/configs/resident/cdap_joint_tour_coefficients.csv
@@ -10,10 +10,10 @@ Label,description,Expression,dependency,coefficient
 ,person x is kid and DAP is N,ptype_px > 6,N_px,1.6898
 ,Accessibility to retail employment/Non-Mandatory Attractions,shopping_accessibility_p1,,0.055031985
 ,Income less than $30k,income_p1 < 30000,,-0.192506367
-,Income between $60k and $100k,(income_p1  >= 60000) & (income_p1 < =100000),,0.104325349
+,Income between $60k and $100k,(income_p1>=60000) & (income_p1<=100000),,0.104325349
 ,Income more than $100k,income_p1 > 100000,,0.104325349
 ,No Car Households,auto_ownership_p1 == 0,,0
 ,Cars Less than Workers,auto_ownership_p1 < num_workers_p1,,0.088402389
 ,Cars More than Workers,auto_ownership_p1 > num_workers_p1,,-0.005896499
-,WorkAccessForMandatoryDap,"@df.workplace_modechoice_logsum_px.fillna(0)",M_px,0.17217579
+,WorkAccessForMandatoryDap,"@np.nan_to_num(df.workplace_modechoice_logsum_px)",M_px,0.17217579
 ,If All Adults stay at Home/ None of the Adults have Dap 1 or 2,(ptype_pxprod < 7) | (ptype_pxprod > 6),H_px,-0.988838929
diff --git a/src/asim/configs/resident/external_non_mandatory_identification.csv b/src/asim/configs/resident/external_non_mandatory_identification.csv
@@ -1,6 +1,6 @@
 Label,Description,Expression,external_tour,internal_tour
 util_dist_to_nearest_ext_station,Distance to nearest external station,dist_to_external_zone,coef_dist_to_nearest_ext_station,
-util_log_size,Log size of nearest station,"@np.log1p(reindex(land_use.external_nonwork, df.closest_external_zone))",coef_log_size_of_nearest_ext_station,
+util_log_size,Log size of nearest station,@np.log1p(df.reindex_land_use_external_nonwork_zone_to_closest_external_zone),coef_log_size_of_nearest_ext_station,
 util_escort,escort tour ASC,"@np.where(df.tour_type == 'escort', 1, 0)",coef_escort,
 util_shopping,shopping tour ASC,"@np.where(df.tour_type == 'shopping', 1, 0)",coef_shopping,
 util_othmaint,othmaint tour ASC,"@np.where(df.tour_type == 'othmaint', 1, 0)",coef_othmaint,

diff --git a/src/asim/configs/resident/external_non_mandatory_identification.yaml b/src/asim/configs/resident/external_non_mandatory_identification.yaml
@@ -9,3 +9,9 @@ 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
+
+preprocessor:
+  SPEC: external_non_mandatory_identification_preprocessor.csv
+  DF: df
+  TABLES:
+    - land_use
diff --git a/src/asim/configs/resident/external_non_mandatory_identification_preprocessor.csv b/src/asim/configs/resident/external_non_mandatory_identification_preprocessor.csv
@@ -0,0 +1,4 @@
+Description,Target,Expression
+reindex_land_use_external_nonwork_zone_to_closest_external_zone,reindex_land_use_external_nonwork_zone_to_closest_external_zone,"reindex(land_use.external_nonwork, df.closest_external_zone)"
+
+
diff --git a/src/asim/configs/resident/external_worker_identification.csv b/src/asim/configs/resident/external_worker_identification.csv
@@ -1,6 +1,6 @@
 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_work,df.closest_external_zone))",coef_size_of_nearest_ext_station,
+util_size_of_nearest_ext_station,Size of nearest external station,"@np.log1p(df.reindex_land_use_external_to_work_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.is_parttime_worker==""TRUE"",1,0)",coef_part_time,
 util_inc_lt15,Household Income less than $15k,@(df.income<15000),coef_inc_lt15,

diff --git a/src/asim/configs/resident/external_worker_identification.yaml b/src/asim/configs/resident/external_worker_identification.yaml
@@ -12,3 +12,9 @@ CHOOSER_FILTER_COLUMN_NAME: is_out_of_home_worker
 EXTERNAL_COL_NAME: is_external_worker
 # set to True if not external but CHOOSER_FILTER_COLUMN_NAME is True
 INTERNAL_COL_NAME: is_internal_worker
+
+preprocessor:
+  SPEC: external_worker_identification_preprocessor.csv
+  DF: df
+  TABLES:
+    - land_use
diff --git a/src/asim/configs/resident/external_worker_identification_preprocessor.csv b/src/asim/configs/resident/external_worker_identification_preprocessor.csv
@@ -0,0 +1,4 @@
+Description,Target,Expression
+reindex_land_use_external_work_external_zone,reindex_land_use_external_to_work_external_zone,"reindex(land_use.external_work,df.closest_external_zone)"
+
+
diff --git a/src/asim/configs/resident/external_workplace_location.csv b/src/asim/configs/resident/external_workplace_location.csv
@@ -3,4 +3,4 @@ local_dist,,_DIST@skims['DIST'],1
 util_dist,,"@np.minimum(_DIST,10)",coef_dist_capped
 util_size_variable,Size variable,@(df['size_term']).apply(np.log1p),coef_size
 util_no_attractions,No attractions,@df['size_term']==0,-999
-mode_choice_logsum,Mode choice logsum,@df.mode_choice_logsum if 'mode_choice_logsum' in df.columns else 0,coef_mode_logsum
+mode_choice_logsum,Mode choice logsum,"@df.get('mode_choice_logsum', 0)",coef_mode_logsum
diff --git a/src/asim/configs/resident/external_workplace_location_archive.csv b/src/asim/configs/resident/external_workplace_location_archive.csv
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,4 @@
		Description,Target,Expression
		reindex_land_use_external_nonwork_zone_to_closest_external_zone,reindex_land_use_external_nonwork_zone_to_closest_external_zone,"reindex(land_use.external_nonwork, df.closest_external_zone)"
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1,4 @@
		Description,Target,Expression
		reindex_land_use_external_work_external_zone,reindex_land_use_external_to_work_external_zone,"reindex(land_use.external_work,df.closest_external_zone)"