Estimation spec changes

.github/workflows/ci.yml

@@ -16,7 +16,7 @@ jobs:
           - "3.10"
       fail-fast: false
 
-    name: cropped-example-py${{ matrix.python-version }}
+    name: metro-cropped-example-py${{ matrix.python-version }}
     runs-on: ubuntu-latest
 
     steps:
@@ -49,7 +49,51 @@ jobs:
           uv pip list
 
       # Run the test
-      - name: Run cropped example test
+      - name: Run Metro cropped example test
         run: |
           cd ../activitysim
-          uv run python $GITHUB_WORKSPACE/resident/test/test_cropped_dataset.py
+          uv run python $GITHUB_WORKSPACE/resident/test/test_cropped_dataset_metro.py
+  skats_cropped_example:
+    strategy:
+      matrix:
+        python-version:
+          - "3.10"
+      fail-fast: false
+
+    name: skats-cropped-example-py${{ matrix.python-version }}
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+
+      # Clone ActivitySim from SimOR_pnr branch
+      - name: Clone ActivitySim
+        run: |
+          cd ..
+          git clone --branch SimOR_pnr https://github.com/RSGInc/activitysim.git
+          pwd
+          ls -la
+
+      # Install UV
+      - name: Install UV
+        uses: astral-sh/setup-uv@v4
+        with:
+          version: "latest"
+
+      # Set up Python with UV
+      - name: Set up Python
+        run: uv python install ${{ matrix.python-version }}
+
+      # Install dependencies using UV and the lock file from ActivitySim
+      - name: Install dependencies with UV
+        run: |
+          cd ../activitysim
+          uv sync --frozen
+          uv pip install -e . --no-deps
+          uv pip list
+
+      # Run the test
+      - name: Run SKATS cropped example test
+        run: |
+          cd ../activitysim
+          uv run python $GITHUB_WORKSPACE/resident/test/test_cropped_dataset_skats.py

.vscode/launch.json

@@ -17,6 +17,20 @@
                 "-o", "outputs/full",
             ]
         },
+        {
+            "name": "Run Estimation Mode",
+            "type": "debugpy",
+            "request": "launch",
+            "cwd": "${workspaceFolder}/resident",
+            "program": "simulation.py",
+            "console": "integratedTerminal",
+            "args": [
+                "-c", "configs_estimation", // check the settings.yaml file for run settings!
+                "-c", "configs",
+                "-d", "model_data/metro/data_full",
+                "-o", "outputs/estimation",
+            ]
+        },
         {
             "name": "Run Cropped Example",
             "type": "debugpy",

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_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)"

resident/configs/annotate_landuse.csv

@@ -4,10 +4,10 @@ household_density,household_density,land_use.TOTHHS / land_use.ACRES.clip(lower=
 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)
+effective active acres,ACTIVE_ACRES,land_use.ACRES * land_use.PARKATTRACT
 # 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

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 == 18) & (pstudent == 1))), PTYPE_SCHOOL, ptype)"
 ,ptype,"np.where(persons.age < 5, PTYPE_PRESCHOOL, ptype)"
 ,ptype,"np.where(pstudent == 2, PTYPE_UNIVERSITY, ptype)"
 # FIXME are these rules for preschoolers still wanted?,,
@@ -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)

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

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)

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
@@ -351,4 +350,8 @@ PkVTCr: 0.1858
 OpVTCa: 0.0432
 OpVTCe: 0.0984
 OpVTCl: 0.1442
-OpVTCr: 0.1442
+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"

resident/configs/destination_choice_size_terms.csv

@@ -10,21 +10,22 @@ school,preschool,0,0.1888,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0
 school,gradeschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0
 school,highschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0
 school,university,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0
-non_mandatory,escort,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0
-non_mandatory,shopping,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0
-non_mandatory,othmaint,0,0,0,0,0,0,0,1.60296,0.42255,0.42255,0,0.24001,0,0,0,0,0,0,0,0,0
-non_mandatory,eatout,0.5512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-non_mandatory,social,0.3006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+non_mandatory,escort,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.1,0
+non_mandatory,shopping,0.01,0,0.1,0.1,0.1,0.1,0.1,1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0,0,0,0,0.1,0
+non_mandatory,othmaint,0.01,0,0.1,0.1,0.1,0.1,0.1,1.60296,0.42255,0.42255,0.1,0.24001,0.1,0.1,0.1,0,0,0,0,0.1,0
+non_mandatory,eatout,0.5512,0,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,1,0,0,0,0,0.1,0
+non_mandatory,social,0.3006,0,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0,0,0,0,0.1,0
 non_mandatory,othdiscr,0.04333,0,0,0,0,0.004465,0,0.042025,0.004465,0,1,0.005953,0,0.20337,0.03453,0,0.03167,0.05136,0.02258,3.71685,0
-atwork,atwork,0,0,0,0,0,0,0,0.104,0.0145,0,0,0,0,0,0,0,0,0,0,0,0
+atwork,atwork,0,0,0.1,0.1,0.1,0.1,0.1,0.104,0.0145,0.1,0.1,0.1,0.1,0.1,0.1,0,0,0,0,0,0
 trip,work,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0
-trip,escort,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0
-trip,shopping,0.000001,0,0,0,0,0,0,0.375,0,0,0,0,0,0,0,0.0001,0,0,0,0,0
-trip,othmaint,0.000001,0,0,0,0,0,0,1.2379255,0.025,1.464014,0,0.661904,0,0,0,0.0001,0,0,0,0,0
-trip,eatout,0.010162,0,0,0,0,0,0,0.0689145,0,0,0,0,0,0,0,0,0,0,0,0,0
-trip,social,0.495249,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+trip,escort,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.1,0
+trip,shopping,0.000001,0,0,0,0,0,0,0.375,0,0,0,0,0,0,0,0.0001,0,0,0,0.1,0
+trip,othmaint,0.000001,0,0,0,0,0,0,1.2379255,0.025,1.464014,0,0.661904,0,0,0,0.0001,0,0,0,0.1,0
+trip,eatout,0.010162,0,0,0,0,0,0,0.0689145,0,0,0,0,0,0,0,0,0,0,0,0.1,0
+trip,social,0.495249,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0
 trip,othdiscr,0.027342,0,0,0,0,0,0,0.0194215,0,0,1,0,0,0.473744,0.092343,0,0,0,0,3.71685,0.05
 trip,univ,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0
+trip,school,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0
 # not needed as school is not chosen as an intermediate trip destination,,,,,,,,,,,,,,,,,,,,,,
 #trip,gradeschool,0,,,,,,,,,,,,,,,,,,,,
 #trip,highschool,0,,,,,,,,,,,,,,,,,,,,

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

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,

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

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

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/license_holding_status.csv

@@ -1,5 +1,5 @@
 Label,Description,Expression,has_license,does_not_have_license
-# dummy specification for license holding status model,,,
+# dummy specification for license holding status model,,,,
 util_constant,alternative specific constant,1,coef_has_license_ASC,0
 util_unavailable_for_under_16,unavailable for persons under 16 years old,@df.age < 16,coef_unavailable,0
 util_age,Age of person in years,@df.age,coef_age,0

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/mandatory_tour_scheduling_annotate_alts_preprocessor.csv

@@ -6,7 +6,7 @@ duration_reference_bin,durationRefBin,20
 departure_shift,departureLinearShift1,"(9-df.start)*(df.start<=9) + (df.start-9)*(df.start>9)"
 arrival_shift,arrivalLinearShift1,"(29-df.end)*(df.end<=29) + (df.end-29)*(df.end>29)"
 duration_shift,durationShift,"(20-df.duration)*(df.duration<=20) + (df.duration-20)*(df.duration>20)"
-# school specific bins
+# school specific bins,,
 departure_reference_bin_school,departureRefBin_school,9
 arrival_reference_bin_school,arrivalRefBin_school,29
 duration_reference_bin_school,durationRefBin_school,16