PnR Capacity and New Landuse files from Metro

.github/workflows/ci.yml

@@ -9,22 +9,14 @@ on:
       - '**'
 
 jobs:
-  oregon_metro_mini_example:
-    env:
-      conda-env-prefix: /usr/share/miniconda3/envs/conda-abm_dev
-      label: linux-64
+  oregon_metro_cropped_example:
     strategy:
       matrix:
-        region:
-          - oregon_metro
         python-version:
           - "3.10"
       fail-fast: false
-    defaults:
-      run:
-        shell: bash -l {0}
 
-    name: ${{ matrix.region }}-py${{ matrix.python-version }}
+    name: cropped-example-py${{ matrix.python-version }}
     runs-on: ubuntu-latest
 
     steps:
@@ -36,32 +28,28 @@ jobs:
           cd ..
           git clone --branch SimOR_pnr https://github.com/RSGInc/activitysim.git
           pwd
-          tree -L 2
+          ls -la
 
-      - name: Setup Miniforge
-        uses: conda-incubator/setup-miniconda@v3
+      # Install UV
+      - name: Install UV
+        uses: astral-sh/setup-uv@v4
         with:
-          auto-update-conda: true
-          miniforge-version: latest
-          mamba-version: "2.0.5"
-          conda-solver: classic
-          conda-remove-defaults: true
-          environment-file: ../activitysim/conda-environments/github-actions-tests.yml
-          activate-environment: asim-test
-          python-version: ${{ matrix.python-version }}
+          version: "latest"
 
-      # Install cloned fork of ActivitySim
-      - name: Install ActivitySim in editable mode
+      # 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: |
-          pwd
           cd ../activitysim
-          python -m pip install -e . --no-deps
-          cd ..
-          cd SimOR
-          ls
-          conda info -a
-          conda list
+          uv sync --frozen
+          uv pip install -e . --no-deps
+          uv pip list
 
       # Run the test
       - name: Run cropped example test
-        run: python resident/test/test_cropped_dataset.py
+        run: |
+          cd ../activitysim
+          uv run python $GITHUB_WORKSPACE/resident/test/test_cropped_dataset.py

.vscode/launch.json

@@ -27,8 +27,17 @@
             "args": [
                 "-c", "configs", // check the settings.yaml file for run settings!
                 "-d", "model_data/metro/data_cropped",
-                "-o", "outputs/test",
+                "-o", "outputs/cropped",
             ]
+        },
+        {
+            "name": "Run Preprocessor",
+            "type": "debugpy",
+            "request": "launch",
+            "cwd": "${workspaceFolder}/resident",
+            "program": "preprocessor.py",
+            "console": "integratedTerminal",
+            "args": "preprocessor_settings.yaml"
         }
     ]
 }

resident/configs/accessibility.csv

@@ -6,15 +6,15 @@ Description,Target,Expression
 peak round trip distance,_auPkTime,"skim_od[('SOV_M_TIME', 'AM')] + skim_do[('SOV_M_TIME', 'PM')]"
 decay function,_decay, exp(_auPkTime * dispersion_parameter_automobile)
 auto peak retail,auPkRetail,df.EMP_RET * _decay
-auto peak total,auPkTotal,df.EMP_TOT * _decay
+auto peak total,auPkTotal,df.EMP_TOTAL * _decay
 #,,
 #,, auto off-peak
 #,,
 #,, assume midday occurs entirely in the midday period
 off-peak round trip distance,_auOpTime,"skim_od[('SOV_M_TIME', 'MD')] + skim_do[('SOV_M_TIME', 'MD')]"
 decay function,_decay, exp(_auOpTime * dispersion_parameter_automobile)
 auto off-peak retail,auOpRetail,df.EMP_RET * _decay
-auto off-peak total,auOpTotal,df.EMP_TOT * _decay
+auto off-peak total,auOpTotal,df.EMP_TOTAL * _decay
 #,,
 #,, transit peak
 #,, assume peak outbound transit occurs in AM
@@ -30,7 +30,7 @@ peak transit time,_trPkTime,(_trPkTime_od + _trPkTime_do).clip(0)
 round trip path is available,_rt_available,(_trPkTime_od > 0) & (_trPkTime_do > 0)
 decay function,_decay,_rt_available * exp(_trPkTime * dispersion_parameter_transit)
 transit peak retail,trPkRetail,df.EMP_RET * _decay
-transit peak total,trPkTotal,df.EMP_TOT * _decay
+transit peak total,trPkTotal,df.EMP_TOTAL * _decay
 transit peak total,trPkHH,df.TOTHHS * _decay
 ####,,
 ####,, transit off-peak
@@ -42,12 +42,12 @@ off-peak transit time,_trOpTime,(_trOpTime_od + _trPkTime_do).clip(0)
 round trip path is available,_rt_available,(_trOpTime_od > 0) & (_trOpTime_do > 0)
 decay function,_decay,_rt_available * exp(_trOpTime * dispersion_parameter_transit)
 transit off-peak retail,trOpRetail,df.EMP_RET * _decay
-transit off-peak total,trOpTotal,df.EMP_TOT * _decay
+transit off-peak total,trOpTotal,df.EMP_TOTAL * _decay
 #,,
 #,, non motorized
 #,,
 non-motorized round trip distance,_nmDist,"skim_od[('SOV_M_DIST', 'MD')] + skim_do[('SOV_M_DIST', 'MD')]"
 round trip path is available,_rt_available,_nmDist <= max_walk_distance
 decay function,_decay,_rt_available * exp(_nmDist * dispersion_parameter_walk)
 retail accessibility,nmRetail,df.EMP_RET * _decay
-total accessibility,nmTotal,df.EMP_TOT * _decay
+total accessibility,nmTotal,df.EMP_TOTAL * _decay

resident/configs/accessibility.yaml

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

resident/configs/annotate_landuse.csv

@@ -4,16 +4,21 @@ 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)
 # 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
+,totint,0
+,micro_dist_local_bus,0
+,microtransit,0
+,nev,0
+,ESCOOACCTIME,land_use.ESCOOACCTIME.fillna(0)
 # 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_TOT
-Is a zone with parking,is_parking_zone,"np.where(land_use.PRKSPACES > 0, True, False)"
-employment_density,employment_density,(land_use['EMP_TOT'] / land_use.ACRES.clip(lower=1)).clip(upper=500)
+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)"
+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 not sure what the mapping between parking_type in the sandag model is and our current parkingConstrainedArea
-temporary parking type,parking_type,land_use['PARKAREA']
 # FIXME temporary density calculation -- again I don't think this calculation matches what's in sandag,,
 dwelling unit density,duden,(land_use['TOTHHS'] / land_use.ACRES.clip(lower=1)).clip(upper=500)
 population density,popden,(land_use['TOTPOP'] / land_use.ACRES.clip(lower=1)).clip(upper=500)
-employment unit density,empden,(land_use['EMP_TOT'] / land_use.ACRES.clip(lower=1)).clip(upper=500)
-retail employment density,retempden,(land_use['EMP_RET'] / land_use.ACRES.clip(lower=1)).clip(upper=500)
-total park and ride capacity,PNR_CAP_TOTAL,(land_use['PNR_CAP_FORMAL'] + land_use['PNR_CAP_INFORMAL'])
+employment unit density,empden,(land_use['EMP_TOTAL'] / land_use.ACRES.clip(lower=1)).clip(upper=500)
+retail employment density,retempden,(land_use['EMP_RET'] / land_use.ACRES.clip(lower=1)).clip(upper=500)

resident/configs/destination_choice_size_terms.csv

@@ -1,38 +1,38 @@
-model_selector,segment,TOTHHS,TOTPOP,EMP_NRM,EMP_CON,EMP_NHTMFG,EMP_HTMFG,EMP_WT,EMP_TWU,EMP_RET,EMP_IFRPBS,EMP_HCS,EMP_OSV,EMP_GOV,EMP_EDU,EMP_AER,EMP_ACC,EMP_FSD,EMP_TOT,ENROLL_K8,ENROLL_912,ENROLL_COLL,ACTIVE_ACRES,external_work,external_nonwork
-workplace,constr_maint,0,0,0.114467809,1,0.019444044,0.078208267,0.053921216,0.042038744,0.057720006,0.149701138,0.007849633,0.160935475,0.137710644,0.014673052,0.021406453,0.007723607,0.001836382,0,0,0,0,0,0,0
-workplace,health,0,0,0,0.000229486,0.000272515,0.003012005,0.028269818,0,0.056869523,0.064585993,1,0.078627673,0.181566529,0.019735804,0.006167439,0.001161773,8.61E-05,0,0,0,0,0,0,0
-workplace,military,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0
-workplace,mngt_busi_scic_arts,0,0,0.023150473,0.105478261,0.033921273,0.345664415,0.520588696,0.019155288,0.063194096,1,0.12997306,0.101893393,0.357056089,0.046020203,0.044384337,0.014582422,0.071675764,0,0,0,0,0,0,0
-workplace,prod_trans_move,0,0,0.044941354,0.059838173,0.160289782,1,0.055761149,0.692090573,0.577996613,0.289719626,0.051119614,0.300100358,0.169102427,0.010851157,0.045537226,0.039515775,0.116759706,0,0,0,0,0,0,0
-workplace,sales_office,0,0,0.011768937,0.051276887,0.174045033,0.201999856,1,0.206618229,0.914135674,0.494698223,0.170534494,0.142953744,0.337702324,0.048183584,0.052183296,0.031177613,0.216603122,0,0,0,0,0,0,0
-workplace,services,0,0,0.03214445,0.013782902,0.002031041,0.02819977,0.064277161,0.007689779,0.04669046,0.477412008,0.145096152,0.403566649,0.565075489,0.264904083,0.154746531,0.079950222,1,0,0,0,0,0,0,0
-school,preschool,0,0.1888,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0
-school,gradeschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0
-school,highschool,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0
-school,university,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0
-non_mandatory,escort,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0
-non_mandatory,shopping,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
-non_mandatory,othmaint,0,0,0,0,0,0,0,0,1.60296,0.42255,0.42255,0,0.24001,0,0,0,0,0,0,0,0,0,0,0
-non_mandatory,eatout,0.5512,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
-non_mandatory,social,0.3006,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0,0,0,0,0,0,0
-non_mandatory,othdiscr,0.04333,0,0,0,0,0,0.004465,0,0.042025,0.004465,0,1,0.005953,0,0.20337,0.03453,0.15911,0,0.03167,0.05136,0.02258,3.71685,0,0
-atwork,atwork,0,0,0,0,0,0,0,0,0.104,0.0145,0,0,0,0,0,0,0.367,0,0,0,0,0,0,0
-trip,work,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0
-trip,escort,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0
-trip,shopping,0.000001,0,0,0,0,0,0,0,0.375,0,0,0,0,0,0,0,0.214689,0.0001,0,0,0,0,0,0
-trip,othmaint,0.000001,0,0,0,0,0,0,0,1.2379255,0.025,1.464014,0,0.661904,0,0,0,0,0.0001,0,0,0,0,0,0
-trip,eatout,0.010162,0,0,0,0,0,0,0,0.0689145,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
-trip,social,0.495249,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0
-trip,othdiscr,0.027342,0,0,0,0,0,0,0,0.0194215,0,0,1,0,0,0.473744,0.092343,0.122872,0,0,0,0,3.71685,0.05,0
-trip,univ,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0
-# not needed as school is not chosen as an intermediate trip destination,,,,,,,,,,,,,,,,,,,,,,,,,
-#trip,gradeschool,0,,,,,,,,,,,,,,,,,,,,,,,
-#trip,highschool,0,,,,,,,,,,,,,,,,,,,,,,,
-external_workplace,external_workplace,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0
-external_nonwork,external_nonwork,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
-external_non_mandatory,escort,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
-external_non_mandatory,shopping,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
-external_non_mandatory,othmaint,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
-external_non_mandatory,eatout,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
-external_non_mandatory,social,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
-external_non_mandatory,othdiscr,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
+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
+workplace,constr_maint,0,0,0.114467809,1,0.019444044,0.053921216,0.042038744,0.057720006,0.149701138,0.007849633,0.160935475,0.137710644,0.014673052,0.021406453,0.007723607,0,0,0,0,0,0
+workplace,health,0,0,0,0.000229486,0.000272515,0.028269818,0,0.056869523,0.064585993,1,0.078627673,0.181566529,0.019735804,0.006167439,0.001161773,0,0,0,0,0,0
+workplace,military,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0
+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,prod_trans_move,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
+workplace,sales_office,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,services,0,0,0.03214445,0.013782902,0.002031041,0.064277161,0.007689779,0.04669046,0.477412008,0.145096152,0.403566649,0.565075489,0.264904083,0.154746531,0.079950222,0,0,0,0,0,0
+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,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
+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,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
+# not needed as school is not chosen as an intermediate trip destination,,,,,,,,,,,,,,,,,,,,,,
+#trip,gradeschool,0,,,,,,,,,,,,,,,,,,,,
+#trip,highschool,0,,,,,,,,,,,,,,,,,,,,
+external_workplace,external_workplace,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
+external_nonwork,external_nonwork,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
+external_non_mandatory,escort,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
+external_non_mandatory,shopping,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
+external_non_mandatory,othmaint,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
+external_non_mandatory,eatout,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
+external_non_mandatory,social,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
+external_non_mandatory,othdiscr,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1

resident/configs/external_non_mandatory_identification.csv

@@ -1,6 +1,8 @@
 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,
+# 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,
 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,

resident/configs/external_worker_identification.csv

@@ -1,6 +1,8 @@
 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,
+# 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,

resident/configs/park_and_ride_lot_choice.csv

@@ -8,9 +8,11 @@ util_xfer_wait,transfer wait time,@(ldt_skims['WTW_XWT'] + dlt_skims['WTW_XWT'])
 util_walk_time,walk time,@(ldt_skims['WTW_AUX'] + dlt_skims['WTW_AUX']),coef_xwalk_work
 util_ivtt,in vehcile transit time,@(ldt_skims['WTW_TIV'] + dlt_skims['WTW_TIV']),coef_ivt_work
 util_xfer_penalty,transfer penalty,"@(-23+23*np.exp(0.414*np.clip(ldt_skims['WTW_XFR'], a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dlt_skims['WTW_XFR'], a_min=None,a_max=4)))",coef_xfer_work
+util_stop_type_constant,stop type constant,@(ldt_skims['WTW_RST'] + dlt_skims['WTW_RST']),coef_ivt_work
+util_vehicle_type_constant,vehicle type constant,@(ldt_skims['WTW_VTC'] + dlt_skims['WTW_VTC']),coef_ivt_work
 # FIXME need fares,,,
 #util_fare,transit fare,"@df.transitSubsidyPassDiscount * (odt_skims['WTW_FARE'] + dot_skims['WTW_FARE']) * 100*df.num_participants/df.cost_sensitivity",coef_income_work
-util_parking_cost,parking cost,@df['PNR_PRKCST'],coef_income_work
-# Size Term,,,
-util_size_variable,Size variable,@df['PNR_CAP_TOTAL'].apply(np.log1p),1
+util_small_lot,Small lot penalty -- 10 min penalty,"@np.where(df['PNR_SPACES'] < 100,10,0)",coef_ivt_work
+#util_med_lot,med lot penalty -- no penalty,"@np.where((df['PNR_SPACES'] >= 100) & (df['PNR_SPACES'] < 500),1,0) * 0",coef_ivt_work
+util_large_lot,large lot penalty -- 10 min back,"@np.where(df['PNR_SPACES'] >= 500,-10,0)",coef_ivt_work
 util_capacity,lot capacity availability,@df['pnr_lot_full'],coef_unavailable

resident/configs/park_and_ride_lot_choice.yaml

@@ -10,14 +10,14 @@ preprocessor:
     - vehicles
     - tours
 
-
-LANDUSE_PNR_SPACES_COLUMN: PNR_CAP_TOTAL
+LANDUSE_PNR_SPACES_COLUMN: PNR_SPACES
 
 # will check the tour destination to make sure transit access is available
 # if no transit access, then no PNR lot will be available and could get 
 # zero probabilities error
 TRANSIT_SKIMS_FOR_ELIGIBILITY:
   - WTW_TIV__PM
+  - WTW_TIV__MD
 
 # Precompute destination eligibility for tours
 # LANDUSE_COL_FOR_PNR_ELIGIBLE_DEST: is_pnr_eligible_destination