VERPAT Inputs and Outputs
This page describes the VERPAT inputs (including definitions) and outputs by module.
The following five files need to be configured in the "defs" directory:
The "run_parameters.json" file contains parameters that define key attributes of the model run and relationships to other model runs. A more detailed description of the file can be found here. The format of the VERPAT run_parameters.json file is as follows:
{
"Model": ["RPAT"],
"Scenario": ["RPAT Pilot"],
"Description": ["Pilot RPAT module in VisionEval"],
"Region": ["Multnomah County Oregon"],
"BaseYear": ["2005"],
"Years": ["2005", "2035"],
"DatastoreName": ["Datastore"],
"DatastoreType": ["RD"],
"Seed": [1],
"RunTypes": ["E", "ELESNP"]
}The "model_parameters.json" can contain global parameters for a particular model configuration that may be used by multiple modules. A more detailed description of the file and its structure can be found here. The description about the variables, required for VERPAT, listed in the file are documented by the modules that uses them in the inputs and outputs section. The format of the VERPAT model_parameters.json file is as follows:
[
{"NAME": "EmploymentGrowth",
"VALUE": "1.5",
"TYPE": "double",
"UNITS": "multiplier",
"PROHIBIT": "",
"ISELEMENTOF": ""},
{
"NAME": "FwyLaneMiGrowth",
"VALUE": "1",
"TYPE" : "double",
"UNITS" : "multiplier",
"PROHIBIT" : "c('NA', '< 0')",
"ISELEMENTOF" : ""
},
{
"NAME" : "ArtLaneMiGrowth",
"VALUE": "1",
"TYPE" : "double",
"UNITS" : "multiplier",
"PROHIBIT" : "c('NA', '< 0')",
"ISELEMENTOF" : ""
},
.
.
.
{
"NAME" : "AutoCostGrowth",
"VALUE": "1.5",
"TYPE" : "double",
"UNITS" : "multiplier",
"PROHIBIT" : "c('NA', '< 0')",
"ISELEMENTOF" : ""
}
]The deflators.csv file defines the annual deflator values, such as the consumer price index, that are used to convert currency values between different years for currency denomination. The format of the file is as follows:
| Year | Value |
|---|---|
| 1999 | 172.6 |
| 2000 | 178 |
| 2001 | 182.4 |
 |
|
The "geography.csv" file describes all of the geographic relationships for the model and the names of geographic entities in a CSV-formatted text file. The format of the file is as follows:
| Azone | Bzone | Czone | Marea |
|---|---|---|---|
| Multnomah | Rur | NA | Multnomah |
| Multnomah | Sub_R | NA | Multnomah |
| Multnomah | Sub_E | NA | Multnomah |
| Multnomah | Sub_M | NA | Multnomah |
| Multnomah | Sub_T | NA | Multnomah |
| Multnomah | CIC_R | NA | Multnomah |
| Multnomah | CIC_E | NA | Multnomah |
| Multnomah | CIC_M | NA | Multnomah |
| Multnomah | CIC_T | NA | Multnomah |
| Multnomah | UC_R | NA | Multnomah |
| Multnomah | UC_E | NA | Multnomah |
| Multnomah | UC_M | NA | Multnomah |
| Multnomah | UC_T | NA | Multnomah |
The geography is described by 13 place types as shown below. One emerging school of thought in land use planning is to consider land uses in terms of place types instead of simply residential or commercial or high density compared to low density. A place type refers to all of the characteristics of a developed area including the types of uses included, the mix of uses, the density and intensity of uses.
| URBAN CORE |
CLOSE-IN COMMUNITY |
SUBURBAN | RURAL | |
|---|---|---|---|---|
| Residential |  |
|
|
|
| Commercial | |
|
|
|
| Mixed-Use | |
|
|
|
| Transit-Oriented Development | |
|
|
|
| Rural/Greenfield | |
An initial typology or system to organize place types can be traced to the Smart Growth Transect, which contained six zones in its original configuration including:
- Rural Preserve
- Rural Reserve
- Edge
- General
- Center
- Core
This approach to classifying place types was further refined in the Caltrans Smart Mobility which defined the following seven place types including:
- Urban Centers
- Close-In Compact Communities
- Compact Communities
- Suburban Communities
- Rural and Agricultural Lands
- Protected Lands
- Special Use Areas
Several of these place type categories provided additional options such as the Close-In Compact Communities which had three sub-definitions including Close-In-Centers, Close-In Corridors, and Close-In Neighborhoods.
An alternative view of place types was provided by Reconnecting America, which developed a performance based place type approach for describing areas proximate to transit stations. Station areas would vary in terms of their relative focus between residential units, employees or a mix of the two. Station areas are also characterized on their relative intensity as well as shown below.
The approach employed for the place types in RPAT is therefore an amalgam of these approaches, in that the terminology is borrowed from the Smart Growth Transect and Caltrans Smart Mobility Study, while the relative performance of each place type is taken from the Reconnecting America approach but applied to a region instead of transit station sites.
Four general area types are defined in RPAT including:
- The Urban Core is the high-density mixed-use places with high jobs-housing ratios, well connected streets and high levels of pedestrian activities. It is anticipated that for many regions, the Urban Core will be the traditional downtown area of which there likely would be only one.
- The Close-in Community would be those areas located near to the Urban Cores and would consist primarily of housing with scattered mixed-use centers and arterial corridors. Housing would be varied in terms of density and type. Transit would be available with a primary focus on commute trips. These areas may be classified by their residents as suburban would be considered to be close-in communities given their adjacency to the Downtown and therefore the higher levels of regional accessibility.
- The Suburban place type is anticipated to represent the majority of development within regions. These communities are characterized by low level of integration of housing with jobs, retail, and services, poorly connected street networks, low levels of transit service, large amounts of surface parking, and limited walk ability.
- The Rural place type is defined as settlements of widely spaced towns separated by firms, vineyards, orchards, or grazing lands. These areas would be characterized by widely dispersed residential uses, little or no transit service, and very limited pedestrian facilities.
Further definition of the place types is allowed through the use of development types within the Urban Core, Close-in Community, and Suburban area types including:
- Residential includes all place types that are predominantly residential in character with limited employment and retail opportunities. Examples of this development type might include typical Suburban Residential or areas of the Downtown which are primarily residential as well. It is anticipated that this development type may be found in all of the area types except for rural.
- Employment includes those areas which are focused on employment with limited retail and residential. An example of this might include a Suburban Office Complex or a large cluster of office buildings within a Close-in Community or Urban Core. As with the residential development type, it is anticipated that this type of use would be found in all place types except for rural.
- Mixed-Use are those areas within a region which have a mix of residential, employment, and retail uses. While this development type can be found in the Suburban place type, it is most commonly found in the close-in community and urban core place type. Downtown areas that have retained their residential population to complement the employment are examples of this development type.
- Transit-Oriented Development (TOD) which is similar to the other development types in that it is applied to all area types except for Rural areas since it is thought to be highly unlikely that a rural TOD would be developed. The TOD development type is characterized by greater access to transit in all area types. Examples of this development type might include a Suburban TOD focused on a commuter rail station.
The process of allocating existing land use to the 13 place types is somewhat dependent on the types of data available in a region that describe existing land use, and the process can be either very detailed or somewhat simplified. The following description relays the process developed by Atlanta Regional Commission (ARC) as part of the pilot testing of RPAT and provides an example of how, mechanically, an agency can approach this allocation. It should be noted that this is merely one approach and not a specific recommendation for a method that should be followed.
In general, ARC followed a somewhat detailed process to derive input data from land use data as presented in their “Unified Growth Policy Map”, and from their regional travel demand model. They developed heuristics to align their land use with the 13 place types that RPAT uses.
The conversion of land use data to the place type scheme used in RPAT involved taking ARC’s Unified Growth Policy Map (UGPM) Areas and converting them to the 13 RPAT place types.
- The first step was to allocate the UGPM areas to the four area types used in RPAT. The Urban Core area type includes Region Core, Region Employment Centers and Aerotropolis UGPM areas; Close-in Community includes Maturing Neighborhoods; Suburban includes Developing Suburbs and Established Suburbs; and Rural includes Rural Areas and Developing Rural.
- The ARC traffic analysis zone (TAZ) system was overlaid with the area types and the centroid of the TAZ was used to determine its area type.
- The RPAT development type, the other dimension of the place type matrix, which included residential, mixed-use, employment, and TOD development types was determined for each TAZ not in the rural area type using the base year percentage of the TAZ’s employment in relation to the total of the population and employment in the TAZ. The mix between the employment and employment was used to determine the TAZs development type using the following cut points:
- Residential: < 33.33%
- Mixed Use: 33.33% to 66.67%
- Employment: > 66.67%
- Identify any TAZs that are TOD based on transit service and specific development types: only one TAZ was determined to be TOD as a development type, Lindbergh Center, in the Urban Core area type.
- The combination of the area type and the development type was then used to allocate all TAZs to one of the 13 place types.
The following is an enumeration of each place type abbreviation as it appears in the input file as well as a brief description of that value:
| Abbreviation | Description |
|---|---|
| Rur | Rural |
| Sub_R | Suburban Residential |
| Sub_E | Suburban Employment (i.e. Commercial) |
| Sub_M | Suburban Mixed Use |
| Sub_T | Suburban Transit Oriented Development |
| CIC_R | Close-in Community Residential |
| CIC_E | Close-in Community Employment (i.e. Commercial) |
| CIC_M | Close-in Community Mixed Use |
| CIC_T | Close-in Community Transit Oriented Development |
| UC_R | Urban Core Residential |
| UC_E | Urban Core Employment (i.e. Commercial) |
| UC_M | Urban Core Mixed Use |
| UC_T | Urban Core Transit Oriented Development |
The "units.csv" file describes the default units to be used for storing complex data types in the model. The format of the file is as follows:
| Type | Units |
|---|---|
| currency | USD |
| distance | MI |
| area | SQMI |
|
|
The VisionEval model system keeps track of the types and units of measure of all data that is processed. More details about the file and structure can be found here.
The VERPAT model is a compilation of several packages, listed below, the inputs of which are described respectively. The inputs are classified into five categories:
- User input files: These are input files (model or scenario specific) that a user is recommended to change.
- User input model parameters: These are input parameters (model or scenario specific), defined in model_parameters.json, that a user is recommended to change.
- Fixed input files: These are input parameters specific to the model that are fixed.
- Fixed input model parameters: These are input parameters specific to the model, defined in model_parameters.json, that are fixed.
- Internal module inputs: These are inputs produced as output by other modules.
| MODULE | PACKAGE | RPAT |
|---|---|---|
| CreateHouseholds | VESimHouseholds | household |
| PredictWorkers | VESimHouseholds | household |
| PredictIncome | VESimHouseholds | household |
| CreateBaseSyntheticFirms | VESyntheticFirms | household |
| CreateFutureSyntheticFirms | VESyntheticFirms | household |
| CreateBasePlaceTypes | VELandUse | urban |
| CreateFuturePlaceTypes | VELandUse | urban |
| CreateBaseAccessibility | VETransportSupply | accessibility |
| CreateFutureAccessibility | VETransportSupply | accessibility |
| AssignVehicleFeatures | VEHouseholdVehicles | vehicle |
| AssignVehicleFeaturesFuture | VEHouseholdVehicles | vehicle |
| CalculateTravelDemand | VEHouseholdTravel | demand |
| CalculateTravelDemandFuture | VEHouseholdTravel | demand |
| CalculateCongestionBase | VETransportSupplyUse | congestion |
| CalculateCongestionFuture | VETransportSupplyUse | congestion |
| CalculateInducedDemand | VEHouseholdTravel | induced |
| CalculatePolicyVmt | VEHouseholdTravel | policyvmt |
| CalculateCongestionPolicy | VETransportSupplyUse | policycongestion |
| ReportRPATMetrics | VEReports | metrics |
This module creates simulated households for a model using inputs of population by age group for each Azone and year.
-
Household population (azone_hh_pop_by_age.csv): 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 -
Household size (azone_hhsize_targets.csv): This file contains the household specific targets. This contain two household specific attributes:
- AveHhSize: Average household size of households (non-group quarters)
- Prop1PerHh: Proportion of households (non-group quarters) having only one person
Here is a snapshot of the file:
Geo Year AveHhSize Prop1PerHh Multnomah 2005 NA NA Multnomah 2035 NA NA -
Group quarter population (azone_gq_pop_by_age.csv): This file contains group quarters 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
Here is a snapshot of the file:
Geo Year GrpAge0to14 GrpAge15to19 GrpAge20to29 GrpAge30to54 GrpAge55to64 GrpAge65Plus Multnomah 2005 0 0 0 1 0 0 Multnomah 2035 0 0 0 1 0 0
- NumHh: Number of households (non-group quarters)
- HhId: Unique household ID
- HhSize: Number of persons
- Age0to14: Persons in 0 to 14 year old age group
- Age15to19: Persons in 15 to 19 year old age group
- Age20to29: Persons in 20 to 29 year old age group
- Age30to54: Persons in 30 to 54 year old age group
- Age55to64: Persons in 55 to 64 year old age group
- Age65Plus: Persons in 65 or older age group
- HhType: Coded household age composition (e.g. 2-1-0-2-0-0) or Grp for group quarters
This module assigns workers by age to households and to non-institutional group quarters population. It is a simple model which predicts workers as a function of the household type and age composition. There is no responsiveness to jobs or how changes in the job market and demographics might change the worker age composition, but the user can exogenously adjust the relative employment by age group, Azone, and year. The values are the proportions of persons in the age group who are workers relative to the proportions in the estimation year.
-
Relative employment (azone_relative_employment.csv): This file contains ratio of workers to persons by age cohort in model year vs. estimation data year. This file contains five age cohorts:
- RelEmp15to19: Ratio of workers to persons age 15 to 19 in model year vs. in estimation data year
- RelEmp20to29: Ratio of workers to persons age 20 to 29 in model year vs. in estimation data year
- RelEmp30to54: Ratio of workers to persons age 30 to 54 in model year vs. in estimation data year
- RelEmp55to64: Ratio of workers to persons age 55 to 64 in model year vs. in estimation data year
- RelEmp65Plus: Ratio of workers to persons age 65 or older in model year vs. in estimation data year
Here is a snapshot of the file:
Geo Year RelEmp15to19 RelEmp20to29 RelEmp30to54 RelEmp55to64 RelEmp65Plus Multnomah 2005 1 1 1 1 1 Multnomah 2035 1 1 1 1 1
| Package | Module | Outputs | Description |
|---|---|---|---|
| VESimHouseholds | CreateHouseholds | Age0to14 | Persons in 0 to 14 year old age group |
| VESimHouseholds | CreateHouseholds | Age15to19 | Persons in 15 to 19 year old age group |
| VESimHouseholds | CreateHouseholds | Age20to29 | Persons in 20 to 29 year old age group |
| VESimHouseholds | CreateHouseholds | Age30to54 | Persons in 30 to 54 year old age group |
| VESimHouseholds | CreateHouseholds | Age55to64 | Persons in 55 to 64 year old age group |
| VESimHouseholds | CreateHouseholds | Age65Plus | Persons in 65 or older age group |
| VESimHouseholds | CreateHouseholds | HhType | Coded household age composition (e.g. 2-1-0-2-0-0) or Grp for group quarters |
- Wkr15to19: Workers in 15 to 19 year old age group
- Wkr20to29: Workers in 20 to 29 year old age group
- Wkr30to54: Workers in 30 to 54 year old age group
- Wkr55to64: Workers in 55 to 64 year old age group
- Wkr65Plus: Workers in 65 or older age group
- Workers: Total number of workers
- NumWkr: Number of workers residing in the zone
This module predicts the income for each simulated household given the number of workers in each age group and the average per capita income for the Azone where the household resides.
-
Regional income (azone_per_cap_inc.csv): 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
| Package | Module | Outputs | Description |
|---|---|---|---|
| VESimHouseholds | CreateHouseholds | HhSize | Number of persons |
| VESimHouseholds | CreateHouseholds | HhType | Coded household age composition (e.g. 2-1-0-2-0-0) or Grp for group quarters |
| VESimHouseholds | PredictWorkers | Wkr15to19 | Workers in 15 to 19 year old age group |
| VESimHouseholds | PredictWorkers | Wkr20to29 | Workers in 20 to 29 year old age group |
| VESimHouseholds | PredictWorkers | Wkr30to54 | Workers in 30 to 54 year old age group |
| VESimHouseholds | PredictWorkers | Wkr55to64 | Workers in 55 to 64 year old age group |
| VESimHouseholds | PredictWorkers | Wkr65Plus | Workers in 65 or older age group |
- Income: Total annual household (non-qroup & group quarters) income in year 1999 dollars
This module creates a set of firms for base year that represents the likely firm composition for the region, given the County Business Pattern data of firms by size and industry. Each firm is described in terms of the number of employees and its industry.
-
Employment (azone_employment_by_naics.csv): 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
- naics: The six digit naics code
- esizecat: The employment size category
- numbus: The number of businesses
- emp: The number of employees in a business
This module creates a set of firms for future year that represents the likely firm composition for the region, given the County Business Pattern data of firms by size and industry. Each firm is described in terms of the number of employees and its industry.
-
Employment Growth (EmploymentGrowth): This variable represents a growth rate for employment in the region between the base year and the future year. A rate of 1 indicates no changes in overall employment, a value of more than 1 indicates some growth (e.g., 1.5 = 50% growth) and a value of less than 1 indicates decline in employment. It should be defined in model_parameters.json as follows:
{ "NAME": "EmploymentGrowth", "VALUE": "1.5", "TYPE": "double", "UNITS": "multiplier", "PROHIBIT": "", "ISELEMENTOF": "" }
| Package | Module | Outputs | Description |
|---|---|---|---|
| VESyntheticFirms | CreateBaseSyntheticFirms | naics | The six digit naics code |
| VESyntheticFirms | CreateBaseSyntheticFirms | esizecat | The employment size category |
| VESyntheticFirms | CreateBaseSyntheticFirms | numbus | The number of businesses |
| VESyntheticFirms | CreateBaseSyntheticFirms | emp | The number of employees in a business |
- naics: The six digit naics code
- esizecat: The employment size category
- numbus: The number of businesses
- emp: The number of employees in a business
Population and employment location characteristics are important variables in the vehicle ownership, travel demand, and accessibility models. There are four place types (urban core, Close-in Community, suburban, and rural and five location categories (residential, commercial, mixed-use, transit-oriented development, and Greenfield). This module utilizes models for households that were developed to estimate location characteristics using National Household Travel Survey data for the base year. Firms are currently allocated randomly to fit the employment allocation inputs since there are no national datasets from which to draw these relationships.
-
Population and Jobs by Place Type (bzone_pop_emp_prop.csv): This file contains the distribution of population and employment among the 13 place types for base and future year. 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
| Package | Module | Outputs | Description |
|---|---|---|---|
| VESimHouseholds | CreateHouseholds | HhId | Unique household ID |
| VESimHouseholds | CreateHouseholds | Age0to14 | Persons in 0 to 14 year old age group |
| VESimHouseholds | CreateHouseholds | Age15to19 | Persons in 15 to 19 year old age group |
| VESimHouseholds | CreateHouseholds | Age20to29 | Persons in 20 to 29 year old age group |
| VESimHouseholds | CreateHouseholds | Age30to54 | Persons in 30 to 54 year old age group |
| VESimHouseholds | CreateHouseholds | Age55to64 | Persons in 55 to 64 year old age group |
| VESimHouseholds | CreateHouseholds | Age65Plus | Persons in 65 or older age group |
| VESimHouseholds | CreateHouseholds | HhSize | Number of persons |
| VESimHouseholds | PredictIncome | Income | Total annual household (non-qroup & group quarters) income in year 1999 dollars |
| VESyntheticFirms | CreateBaseSyntheticFirms | naics | The six digit naics code |
| VESyntheticFirms | CreateBaseSyntheticFirms | esizecat | The employment size category |
| VESyntheticFirms | CreateBaseSyntheticFirms | numbus | The number of businesses |
| VESyntheticFirms | CreateBaseSyntheticFirms | emp | The number of employees in a business |
The outputs produced by this module is for base year.
- DrvLevels: The number of people in a household who can drive classified in three categories ("Drv1", "Drv2", "Drv3Plus")
- HhPlaceTypes: A place type as assigned to the households
- EmpPlaceTypes: A place types as assigned to the businesses
- UrbanPop: Total population by place types
- UrbanEmp: Total employees by place types
- UrbanIncome: Total income by place types
This module is similar to CalculateBasePlaceTypes module but utilizes future year data to assign population and employment location characteristics.
- Population and Jobs by Place Type (bzone_pop_emp_prop.csv): This is the same file used as input in CalculateBasePlaceTypes module.
| Package | Module | Outputs | Description |
|---|---|---|---|
| VESimHouseholds | CreateHouseholds | HhId | Unique household ID |
| VESimHouseholds | CreateHouseholds | Age0to14 | Persons in 0 to 14 year old age group |
| VESimHouseholds | CreateHouseholds | Age15to19 | Persons in 15 to 19 year old age group |
| VESimHouseholds | CreateHouseholds | Age20to29 | Persons in 20 to 29 year old age group |
| VESimHouseholds | CreateHouseholds | Age30to54 | Persons in 30 to 54 year old age group |
| VESimHouseholds | CreateHouseholds | Age55to64 | Persons in 55 to 64 year old age group |
| VESimHouseholds | CreateHouseholds | Age65Plus | Persons in 65 or older age group |
| VESimHouseholds | CreateHouseholds | HhSize | Number of persons |
| VESimHouseholds | PredictIncome | Income | Total annual household (non-qroup & group quarters) income in year 1999 dollars |
| VESyntheticFirms | CreateFutureSyntheticFirms | naics | The six digit naics code |
| VESyntheticFirms | CreateFutureSyntheticFirms | esizecat | The employment size category |
| VESyntheticFirms | CreateFutureSyntheticFirms | numbus | The number of businesses |
| VESyntheticFirms | CreateFutureSyntheticFirms | emp | The number of employees in a business |
| VELandUse | CalculateBasePlaceTypes | UrbanPop | Total population by place types |
| VELandUse | CalculateBasePlaceTypes | UrbanEmp | Total employees by place types |
The outputs produced by this module is for future year.
- DrvLevels: The number of people in a household who can drive classified in three categories ("Drv1", "Drv2", "Drv3Plus")
- HhPlaceTypes: A place type as assigned to the households
- EmpPlaceTypes: A place types as assigned to the businesses
- UrbanPop: Total population by place types
- UrbanEmp: Total employees by place types
- UrbanIncome: Total income by place types
This module calculates freeway, arterial, and public transit supply levels for all years using existing (base) data. The number of lane miles of freeways and arterials is computed for each region based on the change in inventories for a particular scenario. For public transit, the inputs specify the change in transit revenue miles relative to the base. Inputs for each area also specify the revenue mile split between electrified rail and buses.
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Road lane miles (marea_lane_miles.csv): 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 -
Transit revenue miles (marea_rev_miles_pc.csv): 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
| Package | Module | Outputs | Description |
|---|---|---|---|
| VELandUse | CalculateBasePlaceTypes | UrbanPop | Total population by place types |
| VELandUse | CalculateFuturePlaceTypes | UrbanPop | Total population by place types |
- FwyLaneMiPC: Ratio of urbanized area freeway and expressway lane-miles to urbanized area population
- ArtLaneMiPC: Ratio of urbanized area arterial lane-miles to urbanized area population
- TranRevMiPC: Transit revenue miles per capita for the region
- BusRevMi: Bus revenue miles for the region
- RailRevMi: Rail revenue miles for the region
This module calculates freeway, arterial, and public transit supply levels for all years using future (estimated) data.
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Road lane miles (marea_lane_miles.csv): 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 -
Transit revenue miles (marea_rev_miles_pc.csv): 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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FwyLaneMiGrowth: The variable indicates the percent increase in supply of freeways lane miles in the future year compared to base year. By default, the transportation supply is assumed to grow in line with population increase; therefore a value of 1 indicates growth in proportion with population growth. A value less than 1 indicates that there will be less freeway lane mile supply, per person, in the future. A value of 1 indicates faster freeway expansion than population growth. It should be defined in model_parameters.json as follows:
{ "NAME": "FwyLaneMiGrowth", "VALUE": "1", "TYPE" : "double", "UNITS" : "multiplier", "PROHIBIT" : "c('NA', '< 0')", "ISELEMENTOF" : "" } -
ArtLaneMiGrowth: The variable indicates the percent increase in supply of arterial lane miles in the future year compared to base year. It is a similar value to freeway except that it measures arterial lane mile growth. It is also proportional to population growth. It should be defined in model_parameters.json as follows:
{ "NAME" : "ArtLaneMiGrowth", "VALUE": "1", "TYPE" : "double", "UNITS" : "multiplier", "PROHIBIT" : "c('NA', '< 0')", "ISELEMENTOF" : "" } -
BusRevMiPCGrowth: It is the percent increase in transit revenue miles per capita for bus. It behaves in a similar way to the freeway and rail values in that a value of 1 indicates per capita revenue miles stays constant. It should be defined in model_parameters.json as follows:
{ "NAME" : "BusRevMiPCGrowth", "VALUE": "1", "TYPE" : "double", "UNITS" : "multiplier", "PROHIBIT" : "c('NA', '< 0')", "ISELEMENTOF" : "" } -
RailRevMiPCGrowth: It is the percent increase in transit revenue miles per capita for rail. This encompasses all rail modes, from light rail through to commuter rail. It should be defined in model_parameters.json as follows:
{ "NAME" : "RailRevMiPCGrowth", "VALUE": "1", "TYPE" : "double", "UNITS" : "multiplier", "PROHIBIT" : "c('NA', '< 0')", "ISELEMENTOF" : "" }
| Package | Module | Outputs | Description |
|---|---|---|---|
| VELandUse | CalculateBasePlaceTypes | UrbanPop | Total population by place types |
| VELandUse | CalculateFuturePlaceTypes | UrbanPop | Total population by place types |
- FwyLaneMiPCFuture: Ratio of urbanized area freeway and expressway lane-miles to urbanized area population calculated using future (estimated) data
- ArtLaneMiPCFuture: Ratio of urbanized area arterial lane-miles to urbanized area population calculated using future (estimated) data
- TranRevMiPCFuture: Transit revenue miles per capita for the region calculated using future (estimated) data
- BusRevMiFuture: Bus revenue miles for the region calculated using future (estimated) data
- RailRevMiFuture: Rail revenue miles for the region calculated using future (estimated) data
This module assigns each household a number of vehicles it is likely to own based on the number of persons of driving age in the household, whether only elderly persons live in the household, the income of the household, the population density where the household lives, the freeway supply, the transit supply, and whether the household is located in an urban mixed-use area.
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Vehicle fuel economy (model_veh_mpg_by_year.csv): This file contains the estimates and forecasts of average fuel economy and power economy in miles per gallon for autos, light trucks, heavy trucks (trucks) and miles per kilowatt for trains by vehicle model year. Note that this is not the fleet average for that year. It is the average for new vehicles sold in that year. The fuel economy is the same for all fuel types and is measured in gasoline equivalent gallons (i.e., energy content of a gallon of gasoline). This file is used in the calculations of fuel consumption. This file can be used to test alternative vehicle development scenarios, such as improved technology and/or fuel economy standards that lead to higher vehicle fuel economies. Here is a snapshot of the file:
ModelYear AutoMpg LtTruckMpg TruckMpg BusMpg TrainMpg 1975 15.1 12.7 5.1 4.2 0.098266 1976 16.6 13.2 5.1 4.1 0.098266 1977 17.4 14.1 5.1 4.1 0.098266 1978 19.2 13.7 5.1 4 0.098266 2046 63.7 41.1 5.6 4.8 0.121191 2047 63.7 41.1 5.6 4.8 0.121191 2048 63.7 41.1 5.6 4.8 0.121191 2049 63.7 41.1 5.6 4.8 0.121191 2050 63.7 41.1 5.6 4.8 0.121191
- Less than one vehicle per driving age person (model_lt1_veh_prop.csv): This file contains distribution of number of vehicles for a household with less than one vehicle per driving age person. The sample file is not displayed as this data should not be altered.
- Greater than one vehicle per driving age person (model_gt1_veh_prop.csv): This file contains distribution of number of vehicles for a household with greater than one vehicle per driving age person. The sample file is not displayed as this data should not be altered.
- Vehicle distribution by age (model_veh_cumprop_by_vehage.csv): This file contains the cumulative distribution of vehicles, of type automobiles and light truck, by vehicle age.
- Vehicle distribution by age and income (model_veh_prop_by_vehage_vehtype_inc.csv): This file contains the distribution of vehicles, of type automobiles and light truck, by vehicle age and household income group.
- Distribution of DVMT split (model_veh_mpg_dvmt_prop.csv): This file contains the probability distribution of DVMT split between vehicles for households with one, two, three, four, and five plus vehicles.
| Package | Module | Outputs | Description |
|---|---|---|---|
| VESimHouseholds | CreateHouseholds | HhId | Unique household ID |
| VESimHouseholds | CreateHouseholds | Age0to14 | Persons in 0 to 14 year old age group |
| VESimHouseholds | CreateHouseholds | Age65Plus | Persons in 65 or older age group |
| VESimHouseholds | CreateHouseholds | HhSize | Number of persons |
| VESimHouseholds | CreateHouseholds | HhType | Coded household age composition (e.g. 2-1-0-2-0-0) or Grp for group quarters |
| VESimHouseholds | PredictIncome | Income | Total annual household (non-qroup & group quarters) income in year 1999 dollars |
| VELandUse | CalculateFuturePlaceTypes | DrvLevels | The number of people in a household who can drive classified in three categories ("Drv1", "Drv2", "Drv3Plus") |
| VELandUse | CalculateFuturePlaceTypes | HhPlaceTypes | A place type as assigned to the households |
| VETransportSupply | CreateBaseAccessibility | FwyLaneMiPC | Ratio of urbanized area freeway and expressway lane-miles to urbanized area population |
| VETransportSupply | CreateBaseAccessibility | TranRevMiPC | Transit revenue miles per capita for the region |
- VehId: Unique vehicle ID
- Type: Vehicle body type: Auto = automobile, LtTrk = light trucks (i.e. pickup, SUV, Van)
- Age: Vehicle age in years
- Mileage: Mileage of vehicles (automobiles and light truck)
- DvmtProp: Proportion of average vehicle DVMT
- Vehicles: Number of automobiles and light trucks owned or leased by the household
- NumLtTrk: Number of light trucks (pickup, sport-utility vehicle, and van) owned or leased by household
- NumAuto: Number of automobiles (i.e. 4-tire passenger vehicles that are not light trucks) owned or leased by household
Calculate Travel Demand - The average daily vehicle miles traveled, auto and transit trips for each household is modeled based on household information determined in previous steps for the base conditions. The model is sensitive to household income, population density of the neighborhood where the household resides, number of household vehicles, whether the household owns no vehicles, the levels of public transportation and freeway supplies in the region, the driving age population in the household, the presence of persons over age 65, and whether the neighborhood is characterized by mixed-use development. Calculate Truck and Bus Vehicle Miles Traveled (VMT) - Regional truck VMT is calculated based on changes in the regional household income. As a default, a one-to-one relationship between regional income growth and truck VMT growth is assumed. In other words, a doubling of total regional income would result in a doubling of truck VMT. Bus VMT is calculated from bus revenue miles that are factored up to total vehicle miles to account for miles driven in non-revenue service.
Calculate the amount of congestion – Auto, and light truck VMT, truck VMT and bus VMT in are allocated to freeways, arterials, and other roadways. Truck and bus VMT are allocated based on mode-specific data, and auto and light truck VMT are allocated based on a combination of factors and a model that is sensitive to the relative supplies of freeway and arterial lane miles. System-wide ratios of VMT to lane miles for freeways and arterials are used to allocate VMT to congestion levels using congestion levels defined by the Texas Transportation Institute for the Urban Mobility Report. Each freeway and arterial congestion level is associated with an average trip speed for conditions that do and do not include ITS treatment for incident management on the roadway. Overall average speeds by congestion level are calculated based on input assumptions about the degree of incident management. Speed vs. fuel efficiency relationships for light vehicles, trucks, and buses are used to adjust the fleet fuel efficiency averages computed for the region.
Calculate Induced Travel Demand – Induced demand is calculated for changes in roadway supply in the near term as a function of speed, based on potential mode and route shifts to produce changes in VMT and in the longer term to include changes in vehicle ownership, still as a function of speed. This model does not include induced demand as a result of changes in growth that may occur as part of a smart growth scenario because the evidence is limited empirical evidence.
Calculate Scenario Travel Demand – The average daily VMT for each household can be adjusted based on changes in growth patterns by place type, changes in auto operating cost, changes in road lane miles or transit revenue miles for any scenario. There are also a series of policy assumptions that can contribute to changes in VMT: pricing such as VMT charges or parking pricing, ITS strategies for freeways and arterials, and vanpool, telecommuting, ridesharing, and transit pass programs. All of these will contribute to shifts in travel demand for a given scenario.