Park-and-ride modeling

configs/park_and_ride_lot_choice.csv

@@ -0,0 +1,16 @@
+Label,Description,Expression,coefficient
+# Driving utilities,,,
+util_drive_time,drive time from origin to lot,@(olt_skims['SOV_NT_M_TIME'] + lot_skims['SOV_NT_M_TIME']),coef_ivt_work
+util_drive_cost,auto operating cost,@df.sov_auto_op_cost * (olt_skims['SOV_NT_M_TIME'] + lot_skims['SOV_NT_M_DIST']) / df.cost_sensitivity,coef_income_work
+util_drive_rel_out,reliability outbound,"@olt_skims['SOV_NT_M_REL'] / np.minimum(olt_skims['SOV_NT_M_DIST'], 0.1)",coef_rel_out_work
+util_drive_rel_inb,reliability inbound,"@lot_skims['SOV_NT_M_REL'] / np.minimum(olt_skims['SOV_NT_M_DIST'], 0.1)",coef_rel_inb_work
+# Transit utilties,,,
+util_first_wait,first wait time,@(ldt_skims['WALK_LOC_FIRSTWAIT'] + dlt_skims['WALK_LOC_FIRSTWAIT']),coef_wait_work
+util_xfer_wait,transfer wait time,@(ldt_skims['WALK_LOC_XFERWAIT'] + dlt_skims['WALK_LOC_XFERWAIT']),coef_xwait_work
+util_walk_time,walk time,@(ldt_skims['WALK_LOC_XFERWALK'] + dlt_skims['WALK_LOC_XFERWALK']),coef_xwalk_work
+util_ivtt,in vehcile transit time,@(ldt_skims['WALK_LOC_TOTALIVTT'] + dlt_skims['WALK_LOC_TOTALIVTT']),coef_ivt_work
+util_xfer_penalty,transfer penalty,"@(-23+23*np.exp(0.414*np.clip(ldt_skims['WALK_LOC_XFERS'], a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dlt_skims['WALK_LOC_XFERS'], a_min=None,a_max=4)))",coef_xfer_work
+util_fare,transit fare,"@df.transitSubsidyPassDiscount * (odt_skims['WALK_LOC_FARE'] + dot_skims['WALK_LOC_FARE']) * 100*df.num_participants/df.cost_sensitivity",coef_income_work
+# Size Term,,,
+util_size_variable,Size variable,@df['park_and_ride_spaces'].apply(np.log1p),1
+#util_sp_utility_adjustment,shadow price utility adjustment,@df['shadow_price_utility_adjustment'],1

configs/park_and_ride_lot_choice.yaml

@@ -0,0 +1,25 @@
+SPEC: park_and_ride_lot_choice.csv
+
+COEFFICIENTS: park_and_ride_lot_choice_coeffs.csv
+
+preprocessor:
+  SPEC: park_and_ride_lot_choice_preprocessor.csv
+  DF: df
+  TABLES:
+    - land_use
+    - vehicles
+    - tours
+
+
+LANDUSE_PNR_SPACES_COLUMN: park_and_ride_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:
+  - WALK_LOC_TOTALIVTT__PM
+
+compute_settings:
+  protect_columns:
+  - out_period
+  - in_period

configs/park_and_ride_lot_choice_coeffs.csv

@@ -0,0 +1,11 @@
+coefficient_name,value,constrain
+coef_zero,0.0,T
+# took work coefficients from tour mode choice model,,
+coef_ivt_work,-0.016,F
+coef_rel_out_work,-0.224,F
+coef_rel_inb_work,-0.192,F
+coef_income_work,-0.625,F
+coef_xwalk_work,-0.04,F
+coef_wait_work,-0.024,F
+coef_xfer_work,-0.024,F
+coef_xwait_work,-0.032,F

configs/park_and_ride_lot_choice_preprocessor.csv

@@ -0,0 +1,16 @@
+Description,Target,Expression,
+local,_DF_IS_TOUR,'tour_type' in df.columns,
+,is_atwork_subtour,(df.tour_category == 'atwork') if _DF_IS_TOUR else False,
+,_parent_tour_veh,"reindex(tours['selected_vehicle'], df['parent_tour_id']) if 'selected_vehicle' in df.columns else np.nan",
+,_parent_tour_mode,"reindex(tours['tour_mode'], df['parent_tour_id']) if 'tour_mode' in df.columns else np.nan",
+,_sov_veh_option,"np.where(is_atwork_subtour, _parent_tour_veh, df.get('vehicle_occup_1', np.nan))",
+,num_participants,df.number_of_participants if _DF_IS_TOUR else 1,
+# Auto operating costs,,,
+,sov_auto_op_cost,"reindex(vehicles.groupby('vehicle_type')['auto_operating_cost'].mean(), pd.Series(_sov_veh_option, df.index)) if 'vehicle_occup_1' in df.columns else np.nan",
+,sov_auto_op_cost,"np.where(sov_auto_op_cost.isna() | (pd.Series(_sov_veh_option, df.index) == 'non_hh_veh'), costPerMile, sov_auto_op_cost)",
+# cost coef,,,
+,_income_exponent,"np.where(df.get('tour_type') == 'work', 0.6, 0.5) if _DF_IS_TOUR else 0.5",
+,cost_sensitivity,"np.maximum(df.income,1000).pow(_income_exponent)",
+transit pass subsidy,_transit_pass_subsidy,"df.get('transit_pass_subsidy', False)",
+transit pass ownership,_transit_pass_ownership,"df.get('transit_pass_ownership', False)",
+transit subsidy pass discount, transitSubsidyPassDiscount,"np.where(_transit_pass_subsidy | _transit_pass_ownership,0,1)",

configs/settings.yaml

@@ -118,6 +118,7 @@ models:
   - external_non_mandatory_destination
   - non_mandatory_tour_scheduling
   - vehicle_allocation
+  - park_and_ride_lot_choice
   - tour_mode_choice_simulate
   - atwork_subtour_frequency
   - atwork_subtour_destination

configs/tour_mode_choice.csv

@@ -84,19 +84,19 @@ util_WALK_LOC - Origin Mix,WALK_LOC - Origin Mix,oMGRAMix,,,,,,coef_oMix_wTran,,
 util_WALK_LOC - Origin Intersection Density,WALK_LOC - Origin Intersection Density,oMGRATotInt,,,,,,coef_oIntDen_wTran,,,,,,,,,
 util_WALK_LOC - Destination Employment Density,WALK_LOC - Destination Employment Density,dMGRAEmpDen,,,,,,coef_dEmpDen_wTran,,,,,,,,,
 #,PNR_LOC,,,,,,,,,,,,,,,,
-util_PNR_LOC_Unavailable,PNR_LOC - Unavailable,"@(df.pnr_local_available == False)|(df.get('num_escortees', 0)>0)",,,,,,,-999,,,,,,,,
+util_PNR_LOC_Unavailable,PNR_LOC - Unavailable,"@(df.is_valid_pnr == False)|(df.get('num_escortees', 0)>0)",,,,,,,-999,,,,,,,,
 util_PNRTransit_0Auto,PNRTransit_0Auto,@(df.auto_ownership==0),,,,,,,-999,,,,,,,,
 util_PNR_LOC_Unavailable_for_persons_less_than_16,PNR_LOC - Unavailable for persons less than 16,age < 16,,,,,,,-999,,,,,,,,
-util_PNR_LOC_In_vehicle_time,PNR_LOC - In-vehicle time,@(odt_skims['PNROUT_LOC_TOTALIVTT'] + dot_skims['PNRIN_LOC_TOTALIVTT'])*df.time_factor,,,,,,,coef_ivt,,,,,,,,
-util_PNR_LOC_iwait_time,PNR_LOC - First iwait time,@(odt_skims['PNROUT_LOC_FIRSTWAIT']) + (dot_skims['PNRIN_LOC_FIRSTWAIT'])*df.time_factor,,,,,,,coef_wait,,,,,,,,
-util_PNR_LOC_transfer_wait_time,PNR_LOC - transfer wait time,@(odt_skims['PNROUT_LOC_XFERWAIT'] + dot_skims['PNRIN_LOC_XFERWAIT'])*df.time_factor,,,,,,,coef_xwait,,,,,,,,
-util_PNR_LOC_number_of_transfers,PNR_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(odt_skims['PNROUT_LOC_XFERS'] + df.mtnev_egr_xfer, a_min=None,a_max=4))) + (-23+23*np.exp(0.414*np.clip(dot_skims['PNRIN_LOC_XFERS'] + df.mtnev_egr_xfer, a_min=None,a_max=4))) *df.time_factor",,,,,,,coef_xferdrive,,,,,,,,
-util_PNR_LOC_PNR_time,PNR_LOC - PNR time,@(odt_skims['PNROUT_LOC_ACC'] + dot_skims['PNRIN_LOC_EGR']) *df.time_factor,,,,,,,coef_acctime,,,,,,,,
-util_PNR_LOC_PNR_cost,PNR_LOC - PNR cost,@(df.sov_auto_op_cost * ((odt_skims['PNROUT_LOC_ACC'] + dot_skims['PNRIN_LOC_EGR'])/60) *driveSpeed )/df.cost_sensitivity,,,,,,,coef_income,,,,,,,,
-util_PNR_LOC_Walk_egress_time_(at_attraction_end),PNR_LOC - Walk egress time (at attraction end),"@np.where(df.nev_local_egress_available, df.nev_local_egress_time, np.where(df.microtransit_local_egress_available, df.microtransit_local_egress_time, 2*df.dest_local_time)) * df.time_factor",,,,,,,coef_acctime,,,,,,,,
+util_PNR_LOC_In_vehicle_time,PNR_LOC - In-vehicle time,@df.transit_time_pnr * df.time_factor,,,,,,,coef_ivt,,,,,,,,
+util_PNR_LOC_iwait_time,PNR_LOC - First iwait time,@df.iwait_pnr * df.time_factor,,,,,,,coef_wait,,,,,,,,
+util_PNR_LOC_transfer_wait_time,PNR_LOC - transfer wait time,@df.xfer_wait_pnr * df.time_factor,,,,,,,coef_xwait,,,,,,,,
+util_PNR_LOC_number_of_transfers,PNR_LOC - number of transfers,"@(-23+23*np.exp(0.414*np.clip(df.xfer_walk_pnr + df.mtnev_egr_xfer, a_min=None,a_max=4))) * df.time_factor",,,,,,,coef_xferdrive,,,,,,,,
+util_PNR_LOC_PNR_time,PNR_LOC - PNR time,@(df.auto_time_pnr_out + df.auto_time_pnr_inb) * df.time_factor,,,,,,,coef_acctime,,,,,,,,
+util_PNR_LOC_PNR_cost,PNR_LOC - PNR cost,@df.sov_auto_op_cost * (df.auto_dist_pnr_out + df.auto_dist_pnr_inb) / df.cost_sensitivity,,,,,,,coef_income,,,,,,,,
+util_PNR_LOC_Walk_egress_time_(at_attraction_end),PNR_LOC - Walk egress time (at attraction end),"@df.walk_at_dest_end_pnr * df.time_factor",,,,,,,coef_acctime,,,,,,,,
 util_PNR_LOC_wait_egress_time_(at_attraction_end),PNR_LOC - Egress mt/nev wait time (at attraction end),"@np.where(df.nev_local_egress_available, 2*nevWaitTime, np.where(df.microtransit_local_egress_available, 2*microtransitWaitTime, 0)) * df.time_factor",,,,,,,coef_wait,,,,,,,,
-util_PNR_LOC_Walk_other_time,PNR_LOC - Walk other time,@(odt_skims['PNROUT_LOC_XFERWALK'] + dot_skims['PNRIN_LOC_XFERWALK'])*df.time_factor,,,,,,,coef_xwalk,,,,,,,,
-util_PNR_LOC_Fare_and_operating_cost,PNR_LOC - Fare ,@df.transitSubsidyPassDiscount*(odt_skims['PNROUT_LOC_FARE'] + dot_skims['PNRIN_LOC_FARE'])*100*df.num_participants/df.cost_sensitivity,,,,,,,coef_income,,,,,,,,
+util_PNR_LOC_Walk_other_time,PNR_LOC - Walk other time,@df.xfer_walk_pnr * df.time_factor,,,,,,,coef_xwalk,,,,,,,,
+util_PNR_LOC_Fare_and_operating_cost,PNR_LOC - Fare ,@df.transitSubsidyPassDiscount * df.fare_pnr * 100 * df.num_participants / df.cost_sensitivity,,,,,,,coef_income,,,,,,,,
 util_PNR_LOC - Age 16 to 24,PNR_LOC - Age 16 to 24,@(df.age > 15) & (df.age < 25),,,,,,,coef_age1624_tran,,,,,,,,
 util_PNR_LOC - Age 41 to 55,PNR_LOC - Age 41 to 55,@(df.age > 40) & (df.age < 56),,,,,,,coef_age4155_tran,,,,,,,,
 util_PNR_LOC - Age 56 to 64,PNR_LOC - Age 56 to 64,@(df.age > 55) & (df.age < 65),,,,,,,coef_age5664_tran,,,,,,,,

configs/tour_mode_choice.yaml

@@ -43,6 +43,9 @@ SPEC: tour_mode_choice.csv
 COEFFICIENTS: tour_mode_choice_coefficients.csv
 COEFFICIENT_TEMPLATE: tour_mode_choice_coefficients_template.csv
 
+include_explicit_pnr: True
+include_pnr_for_logsums: False
+
 # so far, we can use the same spec as for non-joint tours
 preprocessor:
   SPEC: tour_mode_choice_annotate_choosers_preprocessor

configs/tour_mode_choice_annotate_choosers_preprocessor.csv

@@ -183,7 +183,6 @@ Determining Tour Destination,destination,df.destination if 'destination' in df.c
 ,dest_local_time,dest_local_dist * 60/walkSpeed,
 #,,,
 ,walk_local_available,(odt_skims['WALK_LOC_TOTALIVTT']>0) & (dot_skims['WALK_LOC_TOTALIVTT']>0),
-,pnr_local_available,(odt_skims['PNROUT_LOC_TOTALIVTT']>0) & (dot_skims['PNRIN_LOC_TOTALIVTT']>0),
 ,knr_local_available,(odt_skims['KNROUT_LOC_TOTALIVTT']>0) & (dot_skims['KNRIN_LOC_TOTALIVTT']>0),
 ,tnc_local_available,(odt_skims['KNROUT_LOC_TOTALIVTT']>0) & (dot_skims['KNRIN_LOC_TOTALIVTT']>0),
 # Micromobility times,,,
@@ -228,4 +227,18 @@ microtransit/nev egress transfer,mtnev_egr_xfer,microtransit_local_egress_availa
 # transit subsidi pass discount,,,
 transit pas subsidy,transit_pass_subsidy,"df.get('transit_pass_subsidy', False)",
 transit pass ownership,transit_pass_ownership,"df.get('transit_pass_ownership', False)",
-transit subsidy pass discount, transitSubsidyPassDiscount,"np.where(transit_pass_subsidy | transit_pass_ownership,0,1)",
+transit subsidy pass discount, transitSubsidyPassDiscount,"np.where(transit_pass_subsidy | transit_pass_ownership,0,1)",
+# PNR with lot choice known -- needs to preface expressions on available lot since -1 index on skims returns error,,,
+park and ride zone id,_CALC_PNR,"'pnr_zone_id' in df.columns",
+has valid pnr lot choice,is_valid_pnr,((df.pnr_zone_id > 0) & (ldt_skims['WALK_LOC_TOTALIVTT']>0) & (dlt_skims['WALK_LOC_TOTALIVTT']>0)) if _CALC_PNR else False,
+outbound auto time for pnr,auto_time_pnr_out,"0 if not _CALC_PNR else np.where(is_valid_pnr, (olt_skims['SOV_NT_L_TIME'] * (_vot_bin_da == 1)) + (olt_skims['SOV_NT_M_TIME'] * (_vot_bin_da == 2)) + (olt_skims['SOV_NT_H_TIME'] * (_vot_bin_da == 3)), 0)",
+inbound auto time for pnr,auto_time_pnr_inb,"0 if not _CALC_PNR else np.where(is_valid_pnr, (lot_skims['SOV_NT_L_TIME'] * (_vot_bin_da == 1)) + (lot_skims['SOV_NT_M_TIME'] * (_vot_bin_da == 2)) + (lot_skims['SOV_NT_H_TIME'] * (_vot_bin_da == 3)), 0)",
+outbound auto dist for pnr,auto_dist_pnr_out,"0 if not _CALC_PNR else np.where(is_valid_pnr, (olt_skims['SOV_NT_L_DIST'] * (_vot_bin_da == 1)) + (olt_skims['SOV_NT_M_DIST'] * (_vot_bin_da == 2)) + (olt_skims['SOV_NT_H_DIST'] * (_vot_bin_da == 3)), 0)",
+inbound auto dist for pnr,auto_dist_pnr_inb,"0 if not _CALC_PNR else np.where(is_valid_pnr, (lot_skims['SOV_NT_L_DIST'] * (_vot_bin_da == 1)) + (lot_skims['SOV_NT_M_DIST'] * (_vot_bin_da == 2)) + (lot_skims['SOV_NT_H_DIST'] * (_vot_bin_da == 3)), 0)",
+in vehicle transit time pnr,transit_time_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WALK_LOC_TOTALIVTT'] + dlt_skims['WALK_LOC_TOTALIVTT']), 0)",
+transfers outbound pnr,transfers_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WALK_LOC_XFERS'] + dlt_skims['WALK_LOC_XFERS']), 0)",
+transfer walk times pnr,xfer_walk_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WALK_LOC_XFERWALK'] + dlt_skims['WALK_LOC_XFERWALK']), 0)",
+transfer wait times pnr,xfer_wait_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WALK_LOC_XFERWAIT'] + dlt_skims['WALK_LOC_XFERWAIT']), 0)",
+first wait times pnr,iwait_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WALK_LOC_FIRSTWAIT'] + dlt_skims['WALK_LOC_FIRSTWAIT']), 0)",
+walking access and egress times at dest end pnr,walk_at_dest_end_pnr,"2 * dest_local_time",
+fare pnr,fare_pnr,"0 if not _CALC_PNR else np.where(is_valid_pnr, (ldt_skims['WALK_LOC_FARE'] + dlt_skims['WALK_LOC_FARE']), 0)",

configs/tour_mode_choice_with_pnr.yaml

@@ -0,0 +1 @@
+inherit_settings: tour_mode_choice.yaml

extensions/__init__.py

@@ -1,4 +1,4 @@
 from . import av_ownership
 from . import external_identification
 from . import external_location_choice
-from . import adjust_auto_operating_cost
+from . import adjust_auto_operating_cost

extensions/adjust_auto_operating_cost.py

@@ -31,4 +31,4 @@ def adjust_auto_operating_cost(state: workflow.State, vehicles: pd.DataFrame):
     )
     vehicles["auto_operating_cost"] *= adjustment_factor
 
-    state.add_table("vehicles", vehicles)
+    state.add_table("vehicles", vehicles)

extensions/av_ownership.py

@@ -204,4 +204,4 @@ def av_ownership(
     tracing.print_summary("av_ownership", households.av_ownership, value_counts=True)
 
     if trace_hh_id:
-        state.tracing.trace_df(households, label=trace_label, warn_if_empty=True)
+        state.tracing.trace_df(households, label=trace_label, warn_if_empty=True)

extensions/external_identification.py

@@ -417,4 +417,4 @@ def external_joint_tour_identification(
     )
 
     if trace_hh_id:
-        state.tracing.trace_df(tours, label=trace_label, warn_if_empty=True)
+        state.tracing.trace_df(tours, label=trace_label, warn_if_empty=True)

extensions/external_location_choice.py

@@ -322,4 +322,4 @@ def external_joint_tour_destination(
             index_label="tour",
             columns=None,
             warn_if_empty=True,
-        )
+        )

model_data/crop_metro_data.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": null,
    "id": "93a5ee20",
    "metadata": {},
    "outputs": [],
@@ -24,21 +24,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": null,
    "id": "b9c8494b",
    "metadata": {},
    "outputs": [],
    "source": [
     "# read full input data\n",
-    "input_folder = r\"C:\\Users\\david.hensle\\OneDrive - Resource Systems Group, Inc\\Documents\\projects\\ODOT\\ctramp_to_asim_data_conversion\\output\"\n",
+    "input_folder = r\".\\metro_data\"\n",
     "full_households = pd.read_csv(os.path.join(input_folder, 'households.csv'))\n",
     "full_persons = pd.read_csv(os.path.join(input_folder, 'persons.csv'))\n",
     "full_landuse = pd.read_csv(os.path.join(input_folder, 'land_use.csv'))\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": null,
    "id": "c5745470",
    "metadata": {},
    "outputs": [],
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": null,
    "id": "c6f3e2ea",
    "metadata": {},
    "outputs": [],
@@ -65,33 +65,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": null,
    "id": "cc20387b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Missing 14 zones in cropped land use data:\n",
-      "       MAZ   TAZ\n",
-      "860  24282  1448\n",
-      "861  24283  1470\n",
-      "862  24322     1\n",
-      "863  24323     2\n",
-      "864  24324     4\n",
-      "865  24325     6\n",
-      "866  24326     7\n",
-      "867  24327     8\n",
-      "868  24328     9\n",
-      "869  24329    10\n",
-      "870  24330    12\n",
-      "871  24331     3\n",
-      "872  24332     5\n",
-      "873  24333    11\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "missed_zones = sandag_landuse[~sandag_landuse['MAZ'].isin(cropped_landuse['MAZ'])]\n",
     "print(f\"Missing {len(missed_zones)} zones in cropped land use data:\")\n",
@@ -100,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": null,
    "id": "2f2a12dd",
    "metadata": {},
    "outputs": [],
@@ -114,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": null,
    "id": "4131663e",
    "metadata": {},
    "outputs": [],
@@ -127,18 +104,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": null,
    "id": "234b6584",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saving 22484 households and 51830 persons to cropped data.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cropped_households = full_households[full_households['MAZ'].isin(cropped_landuse['MAZ'])].copy()\n",
     "cropped_persons = full_persons[full_persons['household_id'].isin(cropped_households['household_id'])].copy()\n",
@@ -151,31 +120,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": null,
    "id": "9599ec8d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Adding ENROLL_COLL data to land use.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# need to add some school enrollment data to land use or else size terms are zero\n",
     "for col in ['ENROLL_K8','ENROLL_912','ENROLL_COLL']:\n",
     "    if cropped_landuse[col].sum() == 0:\n",
     "        print(f\"Adding {col} data to land use.\")\n",
     "        # sample 10% of the zones to have enrollment\n",
     "        enrollment_zones = cropped_landuse.sample(frac=.1).index\n",
-    "        cropped_landuse.loc[enrollment_zones, col] = np.random.randint(100, 1000, size=len(enrollment_zones))"
+    "        cropped_landuse.loc[enrollment_zones, col] = np.random.randint(100, 1000, size=len(enrollment_zones))\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": null,
    "id": "0d190500",
    "metadata": {},
    "outputs": [],

model_data/ctramp_to_asim_conversion/SyntheticPop_Processing.ipynb

@@ -19,8 +19,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "input_data_folder = r'./PopulationSim_to_RSG/HH_GQ_combined'\n",
-    "output_data_folder = r'./output'\n",
+    "input_data_folder = r'C:\\Users\\david.hensle\\OneDrive - Resource Systems Group, Inc\\Documents\\projects\\ODOT\\ctramp_to_asim_data_conversion\\PopulationSim_to_RSG\\HH_GQ_combined'\n",
+    "output_data_folder = r'.\\..\\metro_data'\n",
     "\n",
     "assert os.path.exists(input_data_folder), f\"Input data folder {input_data_folder} does not exist.\"\n",
     "assert os.path.exists(output_data_folder), f\"Output data folder {output_data_folder} does not exist.\""
@@ -290,7 +290,10 @@
     "landuse['external_work'] = np.where(landuse['TAZ'] <= 10, 10, 0)  # adding non-zero size for external models so model won't crash\n",
     "landuse['external_nonwork'] = np.where(landuse['TAZ'] <= 10, 10, 0)  # adding non-zero size for external models so model won't crash\n",
     "landuse['external_MAZ'] = np.where(landuse['TAZ'] <= 10, 1, 0)\n",
-    "landuse['totint'] = 10  # total intersections"
+    "landuse['totint'] = 10  # total intersections\n",
+    "# number of park-and-ride spaces in the zone\n",
+    "# just sample a random 10 percent of zones to have park-and-ride spaces\n",
+    "landuse['park_and_ride_spaces'] = np.where(np.random.rand(len(landuse)) < 0.1, np.random.randint(50, 200), 0)"
    ]
   },
   {