RSGInc · americalexander · Jan 16, 2026 · Jan 20, 2026 · Jan 26, 2026 · Jan 26, 2026
diff --git a/.vscode/launch.json b/.vscode/launch.json
@@ -99,11 +99,11 @@
                 "-c", "configs_estimation", // check the settings.yaml file for run settings!
                 "-c", "configs",
                 "-d", "model_data/rogue/data_full",
-                "-o", "outputs/rogue",
+                "-o", "outputs/estimation_rogue",
             ]
         },
         {
-            "name": "Run Cropped Example",
+            "name": "Run Cropped Metro Example",
             "type": "debugpy",
             "request": "launch",
             "cwd": "${workspaceFolder}/resident",
@@ -112,7 +112,21 @@
             "args": [
                 "-c", "configs", // check the settings.yaml file for run settings!
                 "-d", "model_data/metro/data_cropped",
-                "-o", "outputs/cropped",
+                "-o", "outputs/metro_cropped",
+            ]
+        },
+        {
+            "name": "Run Cropped SKATS Example",
+            "type": "debugpy",
+            "request": "launch",
+            "cwd": "${workspaceFolder}/resident",
+            "program": "simulation.py",
+            "console": "integratedTerminal",
+            "args": [
+                "-c", "configs_skats",
+                "-c", "configs", // check the settings.yaml file for run settings!
+                "-d", "model_data/skats/data_cropped",
+                "-o", "outputs/skats_cropped",
             ]
         },
         {

diff --git a/resident/configs/accessibility.yaml b/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
diff --git a/resident/configs/annotate_disaggregate_accessibility.csv b/resident/configs/annotate_disaggregate_accessibility.csv
@@ -1,4 +1,4 @@
-# annotating the proto_disaggregate_accessibilty table
+# annotating the proto_disaggregate_accessibilty table,,
 Description,Target,Expression
 workplace location for zero auto only,workplace_location_accessibility_0,"np.where(df.auto_ownership == 0, df.workplace_location_accessibility, np.nan)"
 workplace location for auto deficient,workplace_location_accessibility_1,"np.where(df.auto_ownership == 1, df.workplace_location_accessibility, np.nan)"

diff --git a/resident/configs/annotate_households.csv b/resident/configs/annotate_households.csv
@@ -24,11 +24,9 @@ number of non-working adult in the household,num_nonworker_adults,"_PERSON_COUNT
 number of full time workers,num_fullTime_workers,"_PERSON_COUNT('pemploy == 1', persons, households)"
 number of part time workers,num_partTime_workers,"_PERSON_COUNT('pemploy == 2', persons, households)"
 number of retired adults in the household,num_retired_adults,"_PERSON_COUNT('ptype == 5', persons, households)"
-#,,FIXME is this the best replacement for area_type??
-home_is_urban,home_is_urban,"reindex(land_use.pseudomsa, households.home_zone_id) <= urban_threshold"
-home_is_rural,home_is_rural,"reindex(land_use.pseudomsa, households.home_zone_id) > rural_threshold"
 #,,
 ,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)
diff --git a/resident/configs/annotate_landuse.csv b/resident/configs/annotate_landuse.csv
@@ -1,18 +1,5 @@
 Description,Target,Expression
-#,, annotate landuse table after import
-household_density,household_density,land_use.TOTHHS / land_use.ACRES.clip(lower=1)
-population_density,population_density,land_use['TOTPOP'] / land_use.ACRES.clip(lower=1)
-employment_density,employment_density,(land_use['EMP_TOTAL'] / land_use.ACRES.clip(lower=1)).clip(upper=500)
-density_index,density_index,(household_density * employment_density) / (household_density + employment_density).clip(lower=1)
-# FIXME pseudomsa is used in a few places but there is no equivalent in Metro landuse,,
-,pseudomsa,0
-# FIXME missing in landuse but used in SANDAG -- setting to 0 for now,,
-,ACTIVE_ACRES,0
-,micro_dist_local_bus,0
-,microtransit,0
-,nev,0
-,ESCOOACCTIME,land_use.ESCOOACCTIME.fillna(0)
+density_index,density_index,(land_use['duden'] * land_use['empden']) / (land_use['duden'] + land_use['empden']).clip(lower=1)
 # FIXME need to decide on what the preschool target looks like,,
 preschool target for mandatory constraint - sum of all size terms,preschool_target,land_use['TOTPOP'] + land_use.EMP_TOTAL
-# FIME whats the criteria for parking zone?,,
-Is a zone with parking,is_parking_zone,"np.where(land_use.PRKCST_MNTH > 0, True, False)"
+Is a zone with parking used to apply parking location choice model,is_parking_zone,"np.where(land_use.PARKAREA==1, True, False)"
diff --git a/resident/configs/annotate_persons.csv b/resident/configs/annotate_persons.csv
@@ -11,14 +11,14 @@ 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)"
@@ -62,4 +62,4 @@ travel time sensitivity factor for non-work travel,time_factor_nonwork,"np.minim
 ,naics_code,_naics_code.astype('int') if all(_naics_code!=0) else 0
 #,,
 # FIXME does filling with -1 make sense here? depends on what the missing values mean,,
-occupation categories mapped to workplace segments,occupation,persons.OCCP.fillna(-1).map(occupation_xwalk)
+occupation categories mapped to workplace segments,occupation,persons.OCCP.fillna(999).map(occupation_xwalk)
diff --git a/resident/configs/annotate_persons_workplace.csv b/resident/configs/annotate_persons_workplace.csv
@@ -4,7 +4,6 @@ Description,Target,Expression
 ,workplace_location_logsum,"np.where(persons.get('external_workplace_zone_id',-1) > 0, persons.get('external_workplace_location_logsum',-1), persons.workplace_location_logsum)"
 ,workplace_modechoice_logsum,"np.where(persons.get('external_workplace_zone_id',-1) > 0, persons.get('external_workplace_modechoice_logsum',-1), persons.workplace_modechoice_logsum)"
 ,distance_to_work,"np.where(workplace_zone_id>=0,skim_dict.lookup(persons.home_zone_id, workplace_zone_id, ('SOV_M_DIST', 'MD')),np.nan)"
-work_zone_area_type,work_zone_area_type,"reindex(land_use.pseudomsa, persons.workplace_zone_id)"
 #,, auto time to work - free flow travel time in both directions. MTC TM1 was MD and MD
 #,,roundtrip_auto_time_to_work
 ,auto_time_home_to_work,"skim_dict.lookup(persons.home_zone_id, workplace_zone_id, ('SOV_M_TIME', 'AM'))"

diff --git a/resident/configs/atwork_subtour_frequency.csv b/resident/configs/atwork_subtour_frequency.csv
@@ -15,8 +15,6 @@ util_participation_in_joint_discretionary_tours,num_joint_discr,coefficient_part
 util_log_of_the_work_tour_duration,@np.log(df.duration+0.5),coefficient_log_of_the_work_tour_duration_no_subtours,coefficient_log_of_the_work_tour_duration_eat,coefficient_log_of_the_work_tour_duration_business1,coefficient_log_of_the_work_tour_duration_maint,coefficient_log_of_the_work_tour_duration_business2,coefficient_log_of_the_work_tour_duration_eat_business
 util_dummy_for_drive_alone_mode_for_work_tour,work_tour_is_SOV,coefficient_dummy_for_drive_alone_mode_for_work_tour_no_subtours,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat,coefficient_dummy_for_drive_alone_mode_for_work_tour_business1,coefficient_dummy_for_drive_alone_mode_for_work_tour_maint,coefficient_dummy_for_drive_alone_mode_for_work_tour_business2,coefficient_dummy_for_drive_alone_mode_for_work_tour_eat_business
 util_two_work_tours_by_person,num_work_tours==2,coefficient_two_work_tours_by_person_no_subtours,coefficient_two_work_tours_by_person_eat,coefficient_two_work_tours_by_person_business1,coefficient_two_work_tours_by_person_maint,coefficient_two_work_tours_by_person_business2,coefficient_two_work_tours_by_person_eat_business
-util_workplace_urban_area_dummy,work_zone_area_type<4,coefficient_workplace_urban_area_dummy_no_subtours,coefficient_workplace_urban_area_dummy_eat,coefficient_workplace_urban_area_dummy_business1,coefficient_workplace_urban_area_dummy_maint,coefficient_workplace_urban_area_dummy_business2,coefficient_workplace_urban_area_dummy_eat_business
-util_workplace_suburban_area_dummy,(work_zone_area_type>3) & (work_zone_area_type<6),coefficient_workplace_suburban_area_dummy_no_subtours,coefficient_workplace_suburban_area_dummy_eat,coefficient_workplace_suburban_area_dummy_business1,coefficient_workplace_suburban_area_dummy_maint,coefficient_workplace_suburban_area_dummy_business2,coefficient_workplace_suburban_area_dummy_eat_business
 util_auto_accessibility_to_retail_for_work_taz,auOpRetail,coefficient_auto_accessibility_to_retail_for_work_taz_no_subtours,coefficient_auto_accessibility_to_retail_for_work_taz_eat,coefficient_auto_accessibility_to_retail_for_work_taz_business1,coefficient_auto_accessibility_to_retail_for_work_taz_maint,coefficient_auto_accessibility_to_retail_for_work_taz_business2,coefficient_auto_accessibility_to_retail_for_work_taz_eat_business
 util_walk_accessibility_to_retail_for_work_taz,nmRetail,coefficient_walk_accessibility_to_retail_for_work_taz_no_subtours,coefficient_walk_accessibility_to_retail_for_work_taz_eat,coefficient_walk_accessibility_to_retail_for_work_taz_business1,coefficient_walk_accessibility_to_retail_for_work_taz_maint,coefficient_walk_accessibility_to_retail_for_work_taz_business2,coefficient_walk_accessibility_to_retail_for_work_taz_eat_business
 util_dummy_for_worker_or_student_with_non_mandatory_tour,(is_worker | is_student) * num_non_mand,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_no_subtours,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business1,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_maint,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_business2,coefficient_dummy_for_worker_or_student_with_non_mandatory_tour_eat_business

diff --git a/resident/configs/auto_ownership.csv b/resident/configs/auto_ownership.csv
@@ -14,7 +14,7 @@ util_hh_income_verylow,household income <15k,@(df.income<15000),coef_household_i
 util_hh_income_low,household income 15k-30k,@((df.income>=15000) & (df.income<30000)),coef_household_income_1530k_0_CARS,,coef_household_income_1530k_2_CARS,coef_household_income_1530k_3_CARS,coef_household_income_1530k_4_CARS
 util_hh_income_mid,household income 30-60k,@((df.income>=30000) & (df.income<60000)),coef_household_income_3060k_0_CARS,,coef_household_income_3060k_2_CARS,coef_household_income_3060k_3_CARS,coef_household_income_3060k_4_CARS
 util_hh_income_veryhigh,household income 100k+,@(df.income>=100000),coef_household_income_100k_0_CARS,,coef_household_income_100k_2_CARS,coef_household_income_100k_3_CARS,coef_household_income_100k_4_CARS
-# FIXME no bldgsz equivalent in the Metro data,,,,,,
+# FIXME no bldgsz equivalent in the Metro data,,,,,,,
 # util_attached,attached dwelling,"@np.where(df.bldgsz == 2, 0, 1)",coef_attached_0_CARS,,coef_attached_2_CARS,coef_attached_3_CARS,coef_attached_4_CARS
 # 0.785 sq miles in 1/2 mile radius * 640 acres per square mile = 502,,,,,,,
 util_intersection_density,Intersection count in 1/2 mile radius of household,totint/502,coef_intersection_density_0_CARS,,coef_intersection_density_2_CARS,coef_intersection_density_3_CARS,coef_intersection_density_4_CARS

diff --git a/resident/configs/auto_ownership_preprocessor.csv b/resident/configs/auto_ownership_preprocessor.csv
@@ -1,7 +1,7 @@
 Description,Target,Expression
 ,_PERSON_COUNT,"lambda query, persons, households: persons.query(query).groupby('household_id').size().reindex(households.index).fillna(0).astype(np.int8)"
 ,num_old_retirees,"_PERSON_COUNT('80 <= age', persons, households)"
-# averaging disaggregate accessibilities across all household members
+# averaging disaggregate accessibilities across all household members,,
 ,shopping_accessibility_0,persons_merged.groupby('household_id').shopping_accessibility_0.mean().reindex(df.index)
 ,shopping_accessibility_1,persons_merged.groupby('household_id').shopping_accessibility_1.mean().reindex(df.index)
 ,shopping_accessibility_2,persons_merged.groupby('household_id').shopping_accessibility_2.mean().reindex(df.index)

diff --git a/resident/configs/constants.yaml b/resident/configs/constants.yaml
@@ -99,7 +99,6 @@ occupation_xwalk:
   5: nat_res_cnstr_maint
   6: prod_trans_move
   999: missing # no occupation 
-  -1: missing # no occupation
 
 # RIDEHAIL Settings - all costs in dollars, times in minutes
 Taxi_baseFare: 3.00
@@ -355,4 +354,4 @@ 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"
+model_region_name: "Metro"
diff --git a/resident/configs/destination_choice_size_terms.csv b/resident/configs/destination_choice_size_terms.csv
@@ -1,4 +1,4 @@
-model_selector,segment,TOTHHS,TOTPOP,EMP_NRM,EMP_CON,EMP_MFG,EMP_WT,EMP_TWU,EMP_RET,EMP_IFRPBS,EMP_HCS,EMP_OSV,EMP_GOV,EMP_EDU,EMP_AER,EMP_AFS,EMP_TOTAL,ENROLLGRADEKto8,ENROLLGRADE9to12,COLLEGEENROLL,ACTIVE_ACRES,EXTERNAL
+model_selector,segment,TOTHHS,TOTPOP,EMP_NRM,EMP_CON,EMP_MFG,EMP_WT,EMP_TWU,EMP_RET,EMP_IFRPBS,EMP_HCS,EMP_OSV,EMP_GOV,EMP_EDU,EMP_AER,EMP_AFS,EMP_TOTAL,ENROLLGRADEKto8,ENROLLGRADE9to12,COLLEGEENROLL,PARKATTRACT,EXTERNAL
 workplace,mngt_busi_scic_arts,0,0,0.023150473,0.105478261,0.033921273,0.520588696,0.019155288,0.063194096,1,0.12997306,0.101893393,0.357056089,0.046020203,0.044384337,0.014582422,0,0,0,0,0,0
 workplace,white_collar,0,0,0.011768937,0.051276887,0.174045033,1,0.206618229,0.914135674,0.494698223,0.170534494,0.142953744,0.337702324,0.048183584,0.052183296,0.031177613,0,0,0,0,0,0
 workplace,blue_collar,0,0,0.044941354,0.059838173,0.160289782,0.055761149,0.692090573,0.577996613,0.289719626,0.051119614,0.300100358,0.169102427,0.010851157,0.045537226,0.039515775,0,0,0,0,0,0

diff --git a/resident/configs/disaggregate_accessibility.yaml b/resident/configs/disaggregate_accessibility.yaml
@@ -94,7 +94,7 @@ CREATE_TABLES:
                 # FIXME this should be updated when occupation_xwalk is updated
                 OCCP: # occupation codes
                     1: 1   # Management Occupations
-                    2: -1    # Non-worker
+                    2: 999    # Non-worker
 
     PROTO_TOURS:
         index_col: proto_tour_id

diff --git a/resident/configs/external_non_mandatory_identification.csv b/resident/configs/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,
-# FIXME need actual external counts at external station - fix in destination choice size terms too
+# FIXME need actual external counts at external station - fix in destination choice size terms too,,,,
 # 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(reindex(land_use.EXTERNAL, df.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,

diff --git a/resident/configs/external_non_mandatory_identification.yaml b/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: 1
diff --git a/resident/configs/external_worker_identification.csv b/resident/configs/external_worker_identification.csv
@@ -1,16 +1,16 @@
-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,
+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
 # FIXME just have a single external indicator not counts at the station here and in destination choice size terms,,,,
-#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(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,
+#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(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
diff --git a/resident/configs/external_worker_identification.yaml b/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