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VERPAT Inputs and Parameters

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David Grover edited this page Apr 30, 2018 · 21 revisions

VERPAT contains 34 input files and 5 parameter files, some of which the user must change and others which typically remain unchanged. This page walks the end user through these files and specifies which files must be updated to implement VERPAT in a new region.

Inputs and Parameters for VERPAT

Input Files

Built Environment

Demand

Policy

  • model_commute_options.csv
  • [
  • model_tdm_transit.csv
  • model_tdm_transitlevels.csv
  • model_tdm_vanpooling.csv
  • model_tdm_workschedule.csv
  • model_tdm_workschedulelevels.csv
  • region_accident_rates.csv
  • region_fuel_co2.csv
  • region_fuel_composition_prop.csv
  • region_fuel_prop_by_veh.csv
  • region_gt1_veh_prop.csv
  • region_lt1_veh_prop.csv
  • region_place_type_elasticities.csv
  • region_place_type_relative_values.csv
  • region_transportation_costs.csv
  • region_veh_cumprop_by_vehage.csv
  • region_veh_mpg_by_year.csv
  • region_veh_mpg_dvmt_prop.csv
  • region_veh_prop_by_vehage_vehtype_inc.csv
  • azone_gq_pop_by_age.csv
  • azone_hhsize_targets.csv
  • azone_its_prop.csv
  • azone_relative_employment.csv
  • marea_parking_growth.csv
  • model_light_vehicles.csv
  • model_tdm_ridesharing.csv

Input Parameter Files

  • model_parameters.json
  • run_parameters.json
  • units.csv
  • deflators.csv
  • geo.csv

Input Files to Change

The user should change the input files described here. All files are *.csv.

Built Environment

bzone_pop_emp_prop.csv

Population and Jobs by Place Type: This file contains the distribution of population and employment among the 13 place types for base and future year. See this explanation for more infomation regarding place types. Each column, for each year, must sum to one (1). It is acceptable to have no land use (i.e. a value of 0) in certain categories. The yearly TAZ employment and population totals were summed by the 13 place type and then scaled to total one for both employment and population. Here is a snapshot of the file:

Geo Year Pop Emp
Rur 2005 0.05 0.1
Sub_R 2005 0.3 0
Sub_E 2005 0 0.2
Sub_M 2005 0.1 0.1
Sub_T 2005 0 0
CIC_R 2005 0.15 0
CIC_E 2005 0 0.2
CIC_M 2005 0.1 0.1
CIC_T 2005 0 0
UC_R 2005 0.1 0
UC_E 2005 0 0.1
UC_M 2005 0.1 0.1
UC_T 2005 0.1 0.1
Rur 2035 0.05 0.1
Sub_R 2035 0.3 0
Sub_E 2035 0 0.2
Sub_M 2035 0.1 0.1
Sub_T 2035 0 0
CIC_R 2035 0.15 0
CIC_E 2035 0 0.2
CIC_M 2035 0.1 0.1
CIC_T 2035 0 0
UC_R 2035 0.1 0
UC_E 2035 0 0.1
UC_M 2035 0.1 0.1
UC_T 2035 0.1 0.1

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Demand

region_trips_per_cap.csv

Auto and transit trips per capita: This file contains regional averages for auto and transit trips per capita per day for the base year.

  • Auto is the regional average of auto trips per capita, including drive alone and shared ride travel. This data can be derived from the National Household Travel Survey by region or from a local household travel survey or regional travel demand forecasting model.
  • Transit is the regional average of transit trips per capita, including walk and drive access to transit. This data can be derived from the National Transit Database where the annual database contains a “service” table that has annual transit trip data for each transit operator or from a local household travel survey or regional travel demand forecasting model.

Here is a snapshot of the files:

Mode Trips
Auto 3.2
Transit 0.4

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employment.csv

Employment: This file contains employment data for each of the counties that make up the region. The file is derived from County Business Pattern (CBP) data by county. Industries are categorized by the North American Industrial Classification System (NAICS) 6 digit codes. Firm size categories are:

  • n1_4: 1- 4 employees
  • n5_9: 5-9 employees
  • n10_19: 10-19 employees
  • n20_99: 20-99 employees
  • n100_249: 100-249 employees
  • n250_499: 250-499 employees
  • n500_999: 500-999 employees
  • n1000: 1,000 or More Employee Size Class
  • n1000_1: 1,000-1,499 employees
  • n1000_2: 1,500-2,499 employees
  • n1000_3: 2,500 to 4, 999 Employees
  • n1000_4: Over 5,000 employees

While the county field is required to be present, the business synthesis process does not require a meaningful value and therefore users may simply enter “region”. The consistency in the naming of the "region" should be maintained across all the files that contains the label "county" or "Geo". It is also not necessary to use such detailed NAICS categories if those are not available; the current business synthesis model and subsequent models do not use this level of detail (although future versions of the model may) – at minimum, the number of establishments for all employment types can be provided by size category. Regions with significant employment in industries such as government and public administration that are not covered by the CBP may need to add records to the file that cover this type of employment to more accurately match employment totals in their region. The two additional fields contained in the file are:

  • emp: Total number of employees
  • est: Total number of establishments

Here is the snapshot of the file:

county year naics emp est n1_4 n5_9 n10_19 n20_49 n50_99 n100_249 n250_499 n500_999 n1000 n1000_1 n1000_2 n1000_3 n1000_4
Multnomah 2005 113110 0 5 2 1 0 2 0 0 0 0 0 0 0 0 0
Multnomah 2005 113310 0 3 2 0 0 1 0 0 0 0 0 0 0 0 0
Multnomah 2005 114111 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0
Multnomah 2005 114112 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
Multnomah 2005 115114 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0
Multnomah 2005 115210 0 4 3 1 0 0 0 0 0 0 0 0 0 0 0
Multnomah 2005 115310 0 5 2 0 1 1 1 0 0 0 0 0 0 0 0
Multnomah 2005 212319 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
Multnomah 2005 212321 0 4 1 1 1 1 0 0 0 0 0 0 0 0 0

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azone_hh_pop_by_age.csv

Household population: This file contains population estimates/forecasts by county and age cohort for each of the base and future years. The file format includes six age categories used by the population synthesis model:

  • 0-14
  • 15-19
  • 20-29
  • 30-54
  • 55-64
  • 65 Plus

Future year data must be developed by the user; in many regions population forecasts are available from regional or state agencies and/or local academic sources. As with the employment data inputs the future data need not be county specific. Rather, regional totals by age group can be entered into the file with a value such as “region” entered in the county field.

Here is a snapshot of the file:

Geo Year Age0to14 Age15to19 Age20to29 Age30to54 Age55to64 Age65Plus
Multnomah 2005 129869 41133 99664 277854 71658 72648
Multnomah 2035 169200 48800 144050 327750 116100 162800

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###azone_per_cap_inc.csv

Regional income: This file contains information on regional average per capita household and group quarters income by forecast year in year 2000 dollars. The data can be obtained from the U.S. Department of Commerce Bureau of Economic Analysis for the current year or from regional or state sources for forecast years. In order to use current year dollars just replace 2000 in column labels with current year. For example, if the data is obtained in year 2005 dollars then the column labels in the file shown below will become HHIncomePC.2005 and GQIncomePC.2005. Here is a snapshot of the file:

Geo Year HHIncomePC.2000 GQIncomePC.2000
Multnomah 2005 32515 0
Multnomah 2035 40000 0

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region_truck_bus_vmt.csv

Truck and bus vmt: This file contains the region’s proportion of VMT by truck and bus as well as the distribution of that VMT across functional classes (freeway, arterial, other). The file includes one row for bus VMT data and one row for Truck VMT data. It should be noted that it is not necessary to enter values in the PropVmt column for BusVmt as this is calculated using the values in the transportation_supply.csv #EDIT (marea_rev_miles_pc.csv?) user input file. The truck VMT proportion (PropVMT column, TruckVMT row) can be obtained from Highway Performance Monitoring System data and local sources or the regional travel demand model if one exists. The proportions of VMT by functional class can be derived from the Federal Highway Cost Allocation Study and data from transit operators. The Federal Highway Cost Allocation Study (Table II-6, 1997 Federal Highway Cost Allocation Study Final Report, Chapter II is used to calculate the average proportion of truck VMT by functional class. Data from transit authorities are used to calculate the proportions of bus VMT by urban area functional class. Here is a snapshot of the file:

Type PropVmt Fwy Art Other
BusVmt 0 0.15 0.591854 0.258146
TruckVmt 0.08 0.452028 0.398645 0.149327

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marea_lane_miles.csv

Road lane miles: This file contains the amount of transportation supply by base year in terms of lane miles of freeways and arterial roadways in the region. The base year data is duplicated for future year. Freeway and Arterial are total lane miles for those functional classes in the region. These data can be derived from the Federal Highway Administration’s (FHWA) Highway Statistics data. Here is a snapshot of the file:

Geo Year FwyLaneMi ArtLaneMi
Multnomah 2005 250 900
Multnomah 2035 250 900

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marea_rev_miles_pc.csv

Transit revenue miles: This file contains the amount of transportation supply by base year in terms of the revenue miles operating by the transit system in the region. The base year data is duplicated for future year. Bus and Rail are annual bus and rail revenue miles per capita for the region. These data can be derived from the National Transit Database, where the annual database contains a “service” table that has annual revenue mile data by mode for each transit operator. Here is a snapshot of the file:

Geo Year BusRevMiPC RailRevMiPC
Multnomah 2005 19 4
Multnomah 2035 19 4

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Policy

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