VERPAT Modules and Outputs

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
CalculateBasePlaceTypes	VELandUse	urban
CalculateFuturePlaceTypes	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

Top Definitions Inputs/Outputs

CreateHouseholds

This module creates simulated households for a model using inputs of population by age group for each Azone and year.

User Input Files

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

Module Outputs

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

Top Definitions Inputs/Outputs

PredictWorkers

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.

User Input Files

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

Internal Module Inputs

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

Module Outputs

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

Top Definitions Inputs/Outputs

PredictIncome

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.

User Input Files

Regional income (azone_per_cap_inc.csv): This file contains information on regional average per capita household (HHIncomePC) and group quarters (GQIncomePC) 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

Internal Module Inputs

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

Module Outputs

Income: Total annual household (non-group & group quarters) income in year 1999 dollars

Top Definitions Inputs/Outputs

CreateBaseSyntheticFirms

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.

User Input Files

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	est	n1_4	n5_9	n10_19	n20_49	n50_99	n250_499
Multnomah	2005	113110	5	2	1	0	2	0	0
Multnomah	2005	113310	3	2	0	0	1	0	0
Multnomah	2005	114111	1	0	1	0	0	0	0
Multnomah	2005	114112	1	1	0	0	0	0	0
Multnomah	2005	115114	1	0	0	0	0	0	1
Multnomah	2005	115210	4	3	1	0	0	0	0
Multnomah	2005	115310	5	2	0	1	1	1	0
Multnomah	2005	212319	1	1	0	0	0	0	0
Multnomah	2005	212321	4	1	1	1	1	0	0

Module Outputs

naics: The six digit naics code
esizecat: The employment size category
numbus: The number of businesses
emp: The number of employees in a business

Top Definitions Inputs/Outputs

CreateFutureSyntheticFirms

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.

User Input Parameters

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": ""
}
```

Internal Module Inputs

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

Module Outputs

naics: The six digit naics code
esizecat: The employment size category
numbus: The number of businesses
emp: The number of employees in a business

Top Definitions Inputs/Outputs

CalculateBasePlaceTypes

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.

User Input Files

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

Internal Module Inputs

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

Module Outputs

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

Top Definitions Inputs/Outputs

CalculateFuturePlaceTypes

This module is similar to CalculateBasePlaceTypes module but utilizes the future year data to assign population and employment location characteristics.

User Input Files

Population and Jobs by Place Type (bzone_pop_emp_prop.csv): This is the same file used as input in CalculateBasePlaceTypes module.

Internal Module Inputs:

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

Module Outputs

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

Top Definitions Inputs/Outputs

CreateBaseAccessibility

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.

User Input Files

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

Internal Module Inputs

Package	Module	Outputs	Description
VELandUse	CalculateBasePlaceTypes	UrbanPop	Total population by place types
VELandUse	CalculateFuturePlaceTypes	UrbanPop	Total population by place types

Module Outputs

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

Top Definitions Inputs/Outputs

CreateFutureAccessibility

This module calculates freeway, arterial, and public transit supply levels for all years using model estimation of future data.

User Input Files

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

User Input Parameters

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" : ""
}
```

Internal Module Inputs

Package	Module	Outputs	Description
VELandUse	CalculateBasePlaceTypes	UrbanPop	Total population by place types
VELandUse	CalculateFuturePlaceTypes	UrbanPop	Total population by place types

Module Outputs

FwyLaneMiPCFuture: Ratio of urbanized area freeway and expressway lane-miles to urbanized area population calculated using model estimation of future data
ArtLaneMiPCFuture: Ratio of urbanized area arterial lane-miles to urbanized area population calculated using model estimation of future data
TranRevMiPCFuture: Transit revenue miles per capita for the region calculated using model estimation of future data
BusRevMiFuture: Bus revenue miles for the region calculated using model estimation of future data
RailRevMiFuture: Rail revenue miles for the region calculated using model estimation of future data

Top Definitions Inputs/Outputs

AssignVehicleFeatures

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.

User Input Files

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

User Input Parameters

Light Truck Proportions (LtTruckProp): The proportion of household vehicles that are light trucks (SUVs, pickup trucks) as opposed to autos. It should be defined in model_parameters.json as follows:

{
    "NAME" : "LtTruckProp",
    "VALUE": "0.45",
    "TYPE" : "double",
    "UNITS" : "multiplier",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}

Internal Module Inputs

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

Module Outputs

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

Top Definitions Inputs/Outputs

AssignVehicleFeaturesFuture

This module is similar to AssignVehicleFeatures, but uses model estimation of future year data on population and transportation supply as a basis of the calculations.

Internal Module Inputs

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	CreateFutureAccessibility	FwyLaneMiPCFuture	Ratio of urbanized area freeway and expressway lane-miles to urbanized area population calculated using future (estimated) data
VETransportSupply	CreateFutureAccessibility	TranRevMiPCFuture	Transit revenue miles per capita for the region calculated using future (estimated) data

Module Outputs

VehIdFuture: Unique vehicle ID
TypeFuture: Vehicle body type: Auto = automobile, LtTrk = light trucks (i.e. pickup, SUV, Van)
AgeFuture: Vehicle age in years
MileageFuture: Mileage of vehicles (automobiles and light truck)
DvmtPropFuture: Proportion of average vehicle DVMT
VehiclesFuture: Number of automobiles and light trucks owned or leased by the household
NumLtTrkFuture: Number of light trucks (pickup, sport-utility vehicle, and van) owned or leased by household
NumAutoFuture: Number of automobiles (i.e. 4-tire passenger vehicles that are not light trucks) owned or leased by household

Top](#contents) Definitions Inputs/Outputs

CalculateTravelDemand

This module calculates the average daily vehicle miles traveled, auto and transit trips for each household is modeled based on household information determined by previous modules 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. It also calculates 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.

User Input Files

Emission Rate (model_fuel_co2.csv): The emissions rate file contains information on “pump-to-wheels” CO2 equivalent emissions by fuel type in grams per mega Joule of fuel energy content. There is one row for each fuel type: ULSD, biodiesel, RFG (reformulated gasoline), CARBOB (gasoline formulated to be blended with ethanol), ethanol, and CNG. Electricity is an optional fuel type not displayed in the table. If the intensity for power generation is known then that can be entered at the end of the row with the Fuel column containing the word Electricity and the respective value next to it. This file is used to convert fuel use to CO2 equivalent emissions. Here is a snapshot of the file:

Fuel Intensity

ULSD 77.19

Biodiesel 76.81

RFG 75.65

CARBOB 75.65

Ethanol 74.88

Cng 62.14
Vehicle VMT proportion by fuel (model_fuel_prop_by_veh.csv): The file contains allocation of VMT for each of the four road vehicle types that VERPAT represents (auto, light truck, bus, and heavy truck) to different fuel types (Diesel, CNG, Gasoline). This file is used in the calculations of fuel consumption. This file can be used to test alternative fuel scenarios by varying the shares of non-gasoline fuels.
- PropDiesel: The proportion of the fleet that uses diesel
- PropCng: The proportion of the fleet that uses CNG
- PropGas: The proportion of the fleet that uses gasoline
Here is a snapshot of the file:

VehType PropDiesel PropCng PropGas

Auto 0.007 0 0.993

LtTruck 0.04 0 0.96

Bus 0.995 0.005 0

Truck 0.945 0.005 0.05
Fuel composition (model_fuel_composition_prop.csv): This file contains the composition of fuel used for each of the four road vehicle types that VERPAT represents (auto, light truck, bus, and heavy truck). This file is also used in the calculations of fuel consumption along with the aforementioned file. The column labels in the file are:
- GasPropEth: The average ethanol proportion in gasoline sold
- DieselPropBio: The average biodiesel proportion in diesel sold
Here is a snapshot of the file:

VehType GasPropEth DieselPropBio

Auto 0.1 0.05

LtTruck 0.1 0.05

Bus 0.1 0.05

Truck 0.1 0.01
Truck and bus vmt (region_truck_bus_vmt.csv): 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 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

Plugin hybrid electric vehicles characteristics (model_phev_range_prop_mpg_mpkwh.csv): This file contains the estimates and forecasts of range, fuel efficiency (mpg), power efficiency (mpkwh), and proportions of automobiles and light trucks that are plugin hybrid electric vehicles by vehicle model year. Note that this is not the fleet average for that year. It is the average of new vehicles sold in that year. This file is used in identification of plugin hybrid electric and electric vehicles. Here is a snapshot of the file:

ModelYear	AutoPhevRange	AutoPropPhev	AutoMpkwh	AutoMpg	LtTruckPhevRange	LtTruckPropPhev	LtTruckMpkwh	LtTruckMpg
1975	0	0	0	0	0	0	0	0
1976	0	0	0	0	0	0	0	0
1977	0	0	0	0	0	0	0	0
1978	0	0	0	0	0	0	0	0
1979	0	0	0	0	0	0	0	0

2045	40	0.66	4.375	74.5	40	0.586666667	2.875	58.2
2046	40	0.708	4.4	74.9	40	0.629333333	2.9	58.5
2047	40	0.756	4.425	75.3	40	0.672	2.925	58.8
2048	40	0.816	4.45	75.6	40	0.725333333	2.95	59.1
2049	40	0.864	4.475	76	40	0.768	2.975	59.3
2050	40	0.9	4.5	76.4	40

Hybrid electric vehicles characteristics (model_hev_prop_mpg.csv): This file contains the estimates and forecasts of fuel efficiency (mpg), and proportions of automobiles and light trucks that are hybrid electric vehicles (that are neither plugin hybrid nor electric vehicle) by vehicle model year. Note that this is not the fleet average for that year. It is the average of new vehicles sold in that year. This file is used in identification of hybrid electric that are not plugin hybrid or electric vehicles. Here is a snapshot of the file:

ModelYear	AutoPropHev	AutoHevMpg	LtTruckPropHev	LtTruckHevMpg
1975	0	0	0	0
1976	0	0	0	0
1977	0	0	0	0
1978	0	0	0	0
1979	0	0	0	0
1980	0	0	0	0

2046	0.981481481	74.2	0.736111111	55.4
2047	0.981481481	74.5	0.736111111	55.6
2048	1	74.9	0.75	55.9
2049	1	75.2	0.75	56.1
2050	1	75.5	0.75	56.3

Electric vehicles characteristics (model_ev_range_prop_mpkwh.csv): This file contains the estimates and forecasts of range, power efficiency (mpkwh), and proportions of automobiles and light trucks that are electric vehicles by vehicle model year. Note that this is not the fleet average for that year. It is the average of new vehicles sold in that year. This file is used in identification of electric vehicles. Here is a snapshot of the file:

ModelYear	AutoRange	AutoPropEv	AutoMpkwh	LtTruckRange	LtTruckPropEv	LtTruckMpkwh
1975	0	0	0	0	0	0
1976	0	0	0	0	0	0
1977	0	0	0	0	0	0
1978	0	0	0	0	0	0
1979	0	0	0	0	0	0

2046	277.5	0.72	4.4	185	0.56	2.9
2047	283.125	0.75	4.425	188.75	0.583333333	2.925
2048	288.75	0.795	4.45	192.5	0.618333333	2.95
2049	294.375	0.855	4.475	196.25	0.665	2.975
2050	300	0.9	4.5	200	0.7	3

User Input Parameters

Base cost per mile (BaseCostPerMile): Base cost per mile in dollars for transportation costs used in the travel demand model to initially represent travel as close to unconstrained by transportation costs. The transportation costs are then fully constrained later in the model as travel reductions caused by budget constraints are estimated. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "BaseCostPerMile",
    "VALUE": "0.04",
    "TYPE" : "compound",
    "UNITS" : "USD/MI",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}
```
Dvmt budget proportion (DvmtBudgetProp): The proportion of household budget that can be allocated to transportation spending. The travel demand models includes a constraint that reduces household travel if too high a proportion of household spending is spent on transportation. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "DvmtBudgetProp",
    "VALUE": "0.1",
    "TYPE" : "double",
    "UNITS" : "multiplier",
    "PROHIBIT" : "c('NA', '< 0', '> 1')",
    "ISELEMENTOF" : ""
}
```

Annual vmt inflator (AnnVmtInflator): Factor to convert VMT between annual and average daily amounts. It should be defined in model_parameters.json as follows:

{
    "NAME" : "AnnVmtInflator",
    "VALUE": "365",
    "TYPE" : "integer",
    "UNITS" : "DAYS",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}

Fuel cost (FuelCost): Average cost per gallon of fuel (before taxes are added). It should be defined in model_parameters.json as follows:

{
    "NAME" : "FuelCost",
    "VALUE": "1.82",
    "TYPE" : "compound",
    "UNITS" : "USD/GAL",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}

Power cost (KwhCost): Average cost per kwh of power consumption (before taxes are added). It should be defined in model_parameters.json as follows:

{
    "NAME" : "KwhCost",
    "VALUE": "0.257",
    "TYPE" : "compound",
    "UNITS" : "USD/KWH",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}

Gas tax (GasTax): Average cost per gallon of fuel of gas taxes. It should be defined in model_parameters.json as follows:

{
    "NAME" : "GasTax",
    "VALUE": "0.424",
    "TYPE" : "compound",
    "UNITS" : "USD/GAL",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}

Light vehicle dvmt (BaseLtVehDvmt): Total light vehicle daily VMT for the base year in thousands of miles. This data can be derived from a combination of Highway Performance Monitoring System data, Federal Highway Cost Allocation Study data, and regional data. Light vehicle daily VMT can be estimated by subtracting truck and bus VMT from total VMT provided in the Highway Performance Monitoring System (HPMS). Regional travel demand model outputs can also be used to derive these data. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "BaseLtVehDvmt",
    "VALUE": "27244",
    "TYPE" : "compound",
    "UNITS" : "MI/DAY",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}
```
Dvmt proportion by functional class (BaseFwyArtProp): The proportions of daily VMT for light vehicles that takes place on freeways and arterials (i.e., the remainder of VMT takes place on lower functional class roads for the base year. This data can be derived from a combination of Highway Performance Monitoring System data, Federal Highway Cost Allocation Study data, and regional data. The proportions of light vehicle daily VMT on freeways and arterials can be derived from the HPMS data. Regional travel demand model outputs can also be used to derive these data. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "BaseFwyArtProp",
    "VALUE": "0.77",
    "TYPE" : "double",
    "UNITS" : "proportion",
    "PROHIBIT" : "c('NA', '< 0', '> 1')",
    "ISELEMENTOF" : ""
}
```
Truck vmt growth multiplier (TruckVmtGrowthMultiplier): Rate at which heavy truck VMT growth in relation to total regional household income growth in the region. A value of 1 indicates that heavy truck VMT grows at the same rate as total regional household income. A value less than 1 means slower growth in truck VMT, and greater than 1 means faster growth in truck VMT than total regional household income. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "TruckVmtGrowthMultiplier",
    "VALUE": "1",
    "TYPE" : "double",
    "UNITS" : "multiplier",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}
```

Fixed Input Parameters

Carbon cost (CarbonCost): Average cost of green house gas emissions. It should be defined in model_parameters.json as follows:

{
    "NAME" : "CarbonCost",
    "VALUE": "0",
    "TYPE" : "compound",
    "UNITS" : "USD/GAL",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}

Vmt cost (VmtCost): Average cost of vehicle miles traveled. It should be defined in model_parameters.json as follows:

{
    "NAME" : "VmtCost",
    "VALUE": "0",
    "TYPE" : "compound",
    "UNITS" : "USD/GAL",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}

Internal Module Inputs

Package	Module	Outputs	Description
VESimHouseholds	CreateHouseholds	HhId	Unique household ID
VESimHouseholds	CreateHouseholds	HhSize	Number of persons
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	PredictIncome	Income	Total annual household (non-qroup & group quarters) income in year 1999 dollars
VELandUse	CalculateBasePlaceTypes	UrbanIncome	Total income by place types
VELandUse	CalculateFuturePlaceTypes	HhPlaceTypes	A place type as assigned to the households
VELandUse	CalculateFuturePlaceTypes	UrbanIncome	Total income by place types
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
VEHouseholdVehicles	AssignVehicleFeatures	VehId	Unique vehicle ID
VEHouseholdVehicles	AssignVehicleFeatures	Type	Vehicle body type: Auto = automobile, LtTrk = light trucks (i.e. pickup, SUV, Van)
VEHouseholdVehicles	AssignVehicleFeatures	Mileage	Mileage of vehicles (automobiles and light truck)
VEHouseholdVehicles	AssignVehicleFeatures	DvmtProp	Proportion of average vehicle DVMT
VEHouseholdVehicles	AssignVehicleFeatures	Vehicles	Number of automobiles and light trucks owned or leased by the household
VEHouseholdVehicles	AssignVehicleFeatures	Age	Age of the vehicles

Module Outputs

TruckDvmt: Average daily vehicle miles traveled by trucks
Dvmt (bzones): Average daily vehicle miles traveled by place types
Dvmt (households): Average daily vehicle miles traveled by household
Dvmt (vehicles): Average daily vehicle miles traveled by each vehicle
EvDvmt (bzones): Average daily electric vehicle miles traveled by place types
EvDvmt (vehicles): Average daily electric vehicle miles traveled by each vehicle
HcDvmt (bzones): Average daily vehicle (ICE) miles traveled by place types
HcDvmt (vehicles): Average daily vehicle (ICE) miles traveled by each vehicle
FuelGallons: Average daily fuel consumption in gallons by each household
FuelCo2e: Average daily Co2 equivalent greenhouse gas emissions by each household due to traditional fuel consumption
ElecKwh: Average daily power consumption in kilowatt-hours by each household
ElecCo2e: Average daily Co2 equivalent greenhouse gas emissions by each household due to power consumption
MpKwh: Power efficiency of electric vehicles by each vehicle
Powertrain: Powertrain of each vehicle.
DailyParkingCost: Average daily parking cost by each household
FutureCostPerMile: Total cost per mile for future year for each household

Top Definitions Inputs/Outputs

CalculateTravelDemandFuture

This module is similar to CalculateTravelDemand module but uses future year (estimated) data for transportation supply and for vehicles like number of vehicles, mileage, type, etc.

User Input Files

Emission Rate (model_fuel_co2.csv): The emissions rate file contains information on “pump-to-wheels” CO2 equivalent emissions by fuel type in grams per mega Joule of fuel energy content. There is one row for each fuel type: ULSD, biodiesel, RFG (reformulated gasoline), CARBOB (gasoline formulated to be blended with ethanol), ethanol, and CNG. This file is used to convert fuel use to CO2 equivalent emissions. Here is a snapshot of the file:

Fuel Intensity

ULSD 77.19

Biodiesel 76.81

RFG 75.65

CARBOB 75.65

Ethanol 74.88

Cng 62.14
Vehicle VMT proportion by fuel (model_fuel_prop_by_veh.csv): The file contains allocation of VMT for each of the four road vehicle types that VERPAT represents (auto, light truck, bus, and heavy truck) to different fuel types (Diesel, CNG, Gasoline). This file is used in the calculations of fuel consumption. This file can be used to test alternative fuel scenarios by varying the shares of non-gasoline fuels.
- PropDiesel: The proportion of the fleet that uses diesel
- PropCng: The proportion of the fleet that uses CNG
- PropGas: The proportion of the fleet that uses gasoline
Here is a snapshot of the file:

VehType PropDiesel PropCng PropGas

Auto 0.007 0 0.993

LtTruck 0.04 0 0.96

Bus 0.995 0.005 0

Truck 0.945 0.005 0.05
Fuel composition (model_fuel_composition_prop.csv): This file contains the composition of fuel used for each of the four road vehicle types that VERPAT represents (auto, light truck, bus, and heavy truck). This file is also used in the calculations of fuel consumption along with the aforementioned file. The column labels in the file are:
- GasPropEth: The average ethanol proportion in gasoline sold
- DieselPropBio: The average biodiesel proportion in diesel sold
Here is a snapshot of the file:

VehType GasPropEth DieselPropBio

Auto 0.1 0.05

LtTruck 0.1 0.05

Bus 0.1 0.05

Truck 0.1 0.01
Truck and bus vmt (region_truck_bus_vmt.csv): 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 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

Plugin hybrid electric vehicles characteristics (model_phev_range_prop_mpg_mpkwh.csv): This file contains the estimates and forecasts of range, fuel efficiency (mpg), power efficiency (mpkwh), and proportions of automobiles and light trucks that are plugin hybrid electric vehicles by vehicle model year. Note that this is not the fleet average for that year. It is the average of new vehicles sold in that year. This file is used in identification of plugin hybrid electric and electric vehicles. Here is a snapshot of the file:

ModelYear	AutoPhevRange	AutoPropPhev	AutoMpkwh	AutoMpg	LtTruckPhevRange	LtTruckPropPhev	LtTruckMpkwh	LtTruckMpg
1975	0	0	0	0	0	0	0	0
1976	0	0	0	0	0	0	0	0
1977	0	0	0	0	0	0	0	0
1978	0	0	0	0	0	0	0	0
1979	0	0	0	0	0	0	0	0

2045	40	0.66	4.375	74.5	40	0.586666667	2.875	58.2
2046	40	0.708	4.4	74.9	40	0.629333333	2.9	58.5
2047	40	0.756	4.425	75.3	40	0.672	2.925	58.8
2048	40	0.816	4.45	75.6	40	0.725333333	2.95	59.1
2049	40	0.864	4.475	76	40	0.768	2.975	59.3
2050	40	0.9	4.5	76.4	40

Hybrid electric vehicles characteristics (model_hev_prop_mpg.csv): This file contains the estimates and forecasts of fuel efficiency (mpg), and proportions of automobiles and light trucks that are hybrid electric vehicles (that are neither plugin hybrid nor electric vehicle) by vehicle model year. Note that this is not the fleet average for that year. It is the average of new vehicles sold in that year. This file is used in identification of hybrid electric that are not plugin hybrid or electric vehicles. Here is a snapshot of the file:

ModelYear	AutoPropHev	AutoHevMpg	LtTruckPropHev	LtTruckHevMpg
1975	0	0	0	0
1976	0	0	0	0
1977	0	0	0	0
1978	0	0	0	0
1979	0	0	0	0
1980	0	0	0	0

2046	0.981481481	74.2	0.736111111	55.4
2047	0.981481481	74.5	0.736111111	55.6
2048	1	74.9	0.75	55.9
2049	1	75.2	0.75	56.1
2050	1	75.5	0.75	56.3

Electric vehicles characteristics (model_ev_range_prop_mpkwh.csv): This file contains the estimates and forecasts of range, power efficiency (mpkwh), and proportions of automobiles and light trucks that are electric vehicles by vehicle model year. Note that this is not the fleet average for that year. It is the average of new vehicles sold in that year. This file is used in identification of electric vehicles. Here is a snapshot of the file:

ModelYear	AutoRange	AutoPropEv	AutoMpkwh	LtTruckRange	LtTruckPropEv	LtTruckMpkwh
1975	0	0	0	0	0	0
1976	0	0	0	0	0	0
1977	0	0	0	0	0	0
1978	0	0	0	0	0	0
1979	0	0	0	0	0	0

2046	277.5	0.72	4.4	185	0.56	2.9
2047	283.125	0.75	4.425	188.75	0.583333333	2.925
2048	288.75	0.795	4.45	192.5	0.618333333	2.95
2049	294.375	0.855	4.475	196.25	0.665	2.975
2050	300	0.9	4.5	200	0.7	3

User Input Parameters

Base cost per mile (BaseCostPerMile): Base cost per mile in dollars for transportation costs used in the travel demand model to initially represent travel as close to unconstrained by transportation costs. The transportation costs are then fully constrained later in the model as travel reductions caused by budget constraints are estimated. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "BaseCostPerMile",
    "VALUE": "0.04",
    "TYPE" : "compound",
    "UNITS" : "USD/MI",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}
```
Dvmt budget proportion (DvmtBudgetProp): The proportion of household budget that can be allocated to transportation spending. The travel demand models includes a constraint that reduces household travel if too high a proportion of household spending is spent on transportation. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "DvmtBudgetProp",
    "VALUE": "0.1",
    "TYPE" : "double",
    "UNITS" : "multiplier",
    "PROHIBIT" : "c('NA', '< 0', '> 1')",
    "ISELEMENTOF" : ""
}
```

Annual vmt inflator (AnnVmtInflator): Factor to convert VMT between annual and average daily amounts. It should be defined in model_parameters.json as follows:

{
    "NAME" : "AnnVmtInflator",
    "VALUE": "365",
    "TYPE" : "integer",
    "UNITS" : "DAYS",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}

Fuel cost (FuelCost): Average cost per gallon of fuel (before taxes are added). It should be defined in model_parameters.json as follows:

{
    "NAME" : "FuelCost",
    "VALUE": "1.82",
    "TYPE" : "compound",
    "UNITS" : "USD/GAL",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}

Power cost (KwhCost): Average cost per kwh of power consumption (before taxes are added). It should be defined in model_parameters.json as follows:

{
    "NAME" : "KwhCost",
    "VALUE": "0.257",
    "TYPE" : "compound",
    "UNITS" : "USD/KWH",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}

Gas tax (GasTax): Average cost per gallon of fuel of gas taxes. It should be defined in model_parameters.json as follows:

{
    "NAME" : "GasTax",
    "VALUE": "0.424",
    "TYPE" : "compound",
    "UNITS" : "USD/GAL",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}

Light vehicle dvmt (BaseLtVehDvmt): Total light vehicle daily VMT for the base year in thousands of miles. This data can be derived from a combination of Highway Performance Monitoring System data, Federal Highway Cost Allocation Study data, and regional data. Light vehicle daily VMT can be estimated by subtracting truck and bus VMT from total VMT provided in the Highway Performance Monitoring System (HPMS). Regional travel demand model outputs can also be used to derive these data. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "BaseLtVehDvmt",
    "VALUE": "27244",
    "TYPE" : "compound",
    "UNITS" : "MI/DAY",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}
```
Dvmt proportion by functional class (BaseFwyArtProp): The proportions of daily VMT for light vehicles that takes place on freeways and arterials (i.e., the remainder of VMT takes place on lower functional class roads for the base year. This data can be derived from a combination of Highway Performance Monitoring System data, Federal Highway Cost Allocation Study data, and regional data. The proportions of light vehicle daily VMT on freeways and arterials can be derived from the HPMS data. Regional travel demand model outputs can also be used to derive these data. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "BaseFwyArtProp",
    "VALUE": "0.77",
    "TYPE" : "double",
    "UNITS" : "proportion",
    "PROHIBIT" : "c('NA', '< 0', '> 1')",
    "ISELEMENTOF" : ""
}
```
Truck vmt growth multiplier (TruckVmtGrowthMultiplier): Rate at which heavy truck VMT growth in relation to total regional household income growth in the region. A value of 1 indicates that heavy truck VMT grows at the same rate as total regional household income. A value less than 1 means slower growth in truck VMT, and greater than 1 means faster growth in truck VMT than total regional household income. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "TruckVmtGrowthMultiplier",
    "VALUE": "1",
    "TYPE" : "double",
    "UNITS" : "multiplier",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}
```

Fixed Input Parameters

Carbon cost (CarbonCost): Average cost of green house gas emissions. It should be defined in model_parameters.json as follows:

{
    "NAME" : "CarbonCost",
    "VALUE": "0",
    "TYPE" : "compound",
    "UNITS" : "USD/GAL",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}

Vmt cost (VmtCost): Average cost of vehicle miles traveled. It should be defined in model_parameters.json as follows:

{
    "NAME" : "VmtCost",
    "VALUE": "0",
    "TYPE" : "compound",
    "UNITS" : "USD/GAL",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}

Internal Module Inputs

Package	Module	Outputs	Description
VESimHouseholds	CreateHouseholds	HhId	Unique household ID
VESimHouseholds	CreateHouseholds	HhSize	Number of persons
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	PredictIncome	Income	Total annual household (non-qroup & group quarters) income in year 1999 dollars
VELandUse	CalculateBasePlaceTypes	UrbanIncome	Total income by place types
VELandUse	CalculateFuturePlaceTypes	HhPlaceTypes	A place type as assigned to the households
VELandUse	CalculateFuturePlaceTypes	UrbanIncome	Total income by place types
VETransportSupply	CreateFutureAccessibility	FwyLaneMiPCFuture	Ratio of urbanized area freeway and expressway lane-miles to urbanized area population
VETransportSupply	CreateFutureAccessibility	TranRevMiPCFuture	Transit revenue miles per capita for the region
VEHouseholdVehicles	AssignVehicleFeaturesFuture	VehIdFuture	Unique vehicle ID
VEHouseholdVehicles	AssignVehicleFeaturesFuture	TypeFuture	Vehicle body type: Auto = automobile, LtTrk = light trucks (i.e. pickup, SUV, Van)
VEHouseholdVehicles	AssignVehicleFeaturesFuture	MileageFuture	Mileage of vehicles (automobiles and light truck)
VEHouseholdVehicles	AssignVehicleFeaturesFuture	DvmtPropFuture	Proportion of average vehicle DVMT
VEHouseholdVehicles	AssignVehicleFeaturesFuture	VehiclesFuture	Number of automobiles and light trucks owned or leased by the household
VEHouseholdVehicles	AssignVehicleFeaturesFuture	AgeFuture	Age of the vehicles

Module Outputs

TruckDvmtFuture: Average daily vehicle miles traveled by trucks estimated using model estimations of future year data
DvmtFuture (bzones): Average daily vehicle miles traveled by place types estimated using model estimations of future year data
DvmtFuture (households): Average daily vehicle miles traveled by household estimated using model estimations of future year data
DvmtFuture (vehicles): Average daily vehicle miles traveled by each vehicle estimated using model estimations of future year data
EvDvmtFuture (bzones): Average daily electric vehicle miles traveled by place types estimated using model estimations of future year data
EvDvmtFuture (vehicles): Average daily electric vehicle miles traveled by each vehicle estimated using model estimations of future year data
HcDvmtFuture (bzones): Average daily vehicle (ICE) miles traveled by place types estimated using model estimations of future year data
HcDvmtFuture (vehicles): Average daily vehicle (ICE) miles traveled by each vehicle estimated using model estimations of future year data
FuelGallonsFuture: Average daily fuel consumption in gallons by each household estimated using model estimations of future year data
FuelCo2eFuture: Average daily Co2 equivalent greenhouse gas emissions by each household due to traditional fuel consumption estimated using model estimations of future year data
ElecKwhFuture: Average daily power consumption in kilowatt-hours by each household estimated using model estimations of future year data
ElecCo2eFuture: Average daily Co2 equivalent greenhouse gas emissions by each household due to power consumption estimated using model estimations of future year data
MpKwhFuture: Power efficiency of electric vehicles by each vehicle estimated using model estimations of future year data
PowertrainFuture: Powertrain of each vehicle estimated using model estimations of future year data
DailyParkingCostFuture: Average daily parking cost by each household estimated using model estimations of future year data
FutureCostPerMileFuture: Total cost per mile for future year for each household estimated using model estimations of future year data

Top](#contents) Definitions Inputs/Outputs

CalculateCongestionBase

This module calculates the amount of congestion using estimated data. Auto, and light truck VMT, truck VMT and bus VMT 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.

User Input Files

Percent road miles with ITS treatment (azone_its_prop.csv): This file is an estimate of the proportion of road miles that have improvements which reduce incidents through ITS treatments in both the base and future years. Values entered should be between 0 and 1, with 1 indicating that 100% of road miles are treated. The ITS policy measures the effects of incident management supported by ITS. The ITS table is used to inform the congestion model and the travel demand model. The model uses the mean speeds with and without incidents to compute an overall average speed by road type and congestion level providing a simple level of sensitivity to the potential effects of incident management programs on delay and emissions. The ITS treatments are evaluated only on freeways and arterials. The ITS treatments that can be evaluated are those that the analyst considers will reduce non-recurring congestion due to incidents. This policy does not deal with other operational improvements such as signal coordination, or temporary capacity increases such as allowing shoulder use in the peak. Here is a snapshot of the file:

Geo Year ITS

Multnomah 2005 0

Multnomah 2035 0
Truck and bus vmt (region_truck_bus_vmt.csv): 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 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

User Input Parameters

Light vehicle dvmt (BaseLtVehDvmt): Total light vehicle daily VMT for the base year in thousands of miles. This data can be derived from a combination of Highway Performance Monitoring System data, Federal Highway Cost Allocation Study data, and regional data. Light vehicle daily VMT can be estimated by subtracting truck and bus VMT from total VMT provided in the Highway Performance Monitoring System (HPMS). Regional travel demand model outputs can also be used to derive these data. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "BaseLtVehDvmt",
    "VALUE": "27244",
    "TYPE" : "compound",
    "UNITS" : "MI/DAY",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}
```
Dvmt proportion by functional class (BaseFwyArtProp): The proportions of daily VMT for light vehicles that takes place on freeways and arterials (i.e., the remainder of VMT takes place on lower functional class roads for the base year. This data can be derived from a combination of Highway Performance Monitoring System data, Federal Highway Cost Allocation Study data, and regional data. The proportions of light vehicle daily VMT on freeways and arterials can be derived from the HPMS data. Regional travel demand model outputs can also be used to derive these data. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "BaseFwyArtProp",
    "VALUE": "0.77",
    "TYPE" : "double",
    "UNITS" : "proportion",
    "PROHIBIT" : "c('NA', '< 0', '> 1')",
    "ISELEMENTOF" : ""
}
```
Transit revenue adjustment factor (TranRevMiAdjFactor): Factor to convert transit revenue miles (i.e. miles run in service on routes) to total transit vehicle miles operated to account for vehicle miles run while deadheading from depots to route endpoints. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "TranRevMiAdjFactor",
    "VALUE": "1.12",
    "TYPE" : "double",
    "UNITS" : "multiplier",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}
```

Internal Module Inputs

Package	Module	Outputs	Description
VELandUse	CalculateBasePlaceTypes	UrbanPop	Total population by place types
VELandUse	CalculateFuturePlaceTypes	UrbanPop	Total population by place types
VETransportSupply	CreateBaseAccessibility	FwyLaneMiPC	Ratio of urbanized area freeway and expressway lane-miles to urbanized area population
VETransportSupply	CreateBaseAccessibility	ArtLaneMiPC	Ratio of urbanized area arterial lane-miles to urbanized area population
VETransportSupply	CreateBaseAccessibility	TranRevMiPC	Transit revenue miles per capita for the region
VETransportSupply	CreateBaseAccessibility	BusRevMi	Bus revenue miles for the region
VETransportSupply	CreateBaseAccessibility	RailRevMi	Rail revenue miles for the region
VEHouseholdTravel	CalculateTravelDemand	TruckDvmt	Average daily vehicle miles traveled by trucks
VEHouseholdTravel	CalculateTravelDemand	Dvmt (bzones)	Average daily vehicle miles traveled by place types

Module Outputs

LtVehDvmt: Daily vehicle miles travelled by light vehicles in the region
BusDvmt: Daily vehicle miles travelled by bus in the region
MpgAdjLtVeh: Average fuel efficiency adjustment for light vehicles in the region
MpgAdjBus: Average fuel efficiency adjustment for buses in the region
MpgAdjTruck: Average fuel efficiency adjustment for heavy trucks in the region
MpKwhAdjLtVehEv: Average power efficiency adjustment for electric light vehicles in the region
MpKwhAdjLtVehHev: Average power efficiency adjustment for hybrid electric light vehicles in the region
MpKwhAdjBus: Average power efficiency adjustment for buses in the region
MpKwhAdjTruck: Average power efficiency adjustment for heavy trucks in the region
VehHrLtVeh: Total vehicle travel time for light vehicles in the region
VehHrBus: Total vehicle travel time for buses in the region
VehHrTruck: Total vehicle travel time for heavy trucks in the region
AveSpeedLtVeh: Average speed for light vehicles in the region
AveSpeedBus: Average speed for buses in the region
AveSpeedTruck: Average speed for heavy trucks in the region
FfVehHrLtVeh: Average free-flow travel time for light vehicles in the region
FfVehHrBus: Average free-flow travel time for buses in the region
FfVehHrTruck: Average free-flow travel time for heavy trucks in the region
DelayVehHrLtVeh: Total vehicle delay time for light vehicles in the region
DelayVehHrBus: Total vehicle delay time for buses in the region
DelayVehHrTruck: Total vehicle delay time for heavy trucks in the region
MpgAdjHh: Average fuel efficiency adjustment for households in the region
MpKwhAdjEvHh: Average power efficiency adjustment for electric vehicles for households in the region
MpKwhAdjHevHh: Average power efficiency adjustment for hybrid electric vehicles for households in the region
LtVehDvmtFactor: Light vehicle Dvmt factor adjustment factor

Top](#contents) Definitions Inputs/Outputs

CalculateCongestionFuture

This module is similar to CalculateCongestionBase module but uses model estimation of future transportation supply as the basis of calculations.

User Input Files

Percent road miles with ITS treatment (azone_its_prop.csv): This file is an estimate of the proportion of road miles that have improvements which reduce incidents through ITS treatments in both the base and future years. Values entered should be between 0 and 1, with 1 indicating that 100% of road miles are treated. The ITS policy measures the effects of incident management supported by ITS. The ITS table is used to inform the congestion model and the travel demand model. The model uses the mean speeds with and without incidents to compute an overall average speed by road type and congestion level providing a simple level of sensitivity to the potential effects of incident management programs on delay and emissions. The ITS treatments are evaluated only on freeways and arterials. The ITS treatments that can be evaluated are those that the analyst considers will reduce non-recurring congestion due to incidents. This policy does not deal with other operational improvements such as signal coordination, or temporary capacity increases such as allowing shoulder use in the peak. Here is a snapshot of the file:

Geo Year ITS

Multnomah 2005 0

Multnomah 2035 0
Truck and bus vmt (region_truck_bus_vmt.csv): 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 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

User Input Parameters

Light vehicle dvmt (BaseLtVehDvmt): Total light vehicle daily VMT for the base year in thousands of miles. This data can be derived from a combination of Highway Performance Monitoring System data, Federal Highway Cost Allocation Study data, and regional data. Light vehicle daily VMT can be estimated by subtracting truck and bus VMT from total VMT provided in the Highway Performance Monitoring System (HPMS). Regional travel demand model outputs can also be used to derive these data. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "BaseLtVehDvmt",
    "VALUE": "27244",
    "TYPE" : "compound",
    "UNITS" : "MI/DAY",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}
```
Dvmt proportion by functional class (BaseFwyArtProp): The proportions of daily VMT for light vehicles that takes place on freeways and arterials (i.e., the remainder of VMT takes place on lower functional class roads for the base year. This data can be derived from a combination of Highway Performance Monitoring System data, Federal Highway Cost Allocation Study data, and regional data. The proportions of light vehicle daily VMT on freeways and arterials can be derived from the HPMS data. Regional travel demand model outputs can also be used to derive these data. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "BaseFwyArtProp",
    "VALUE": "0.77",
    "TYPE" : "double",
    "UNITS" : "proportion",
    "PROHIBIT" : "c('NA', '< 0', '> 1')",
    "ISELEMENTOF" : ""
}
```
Transit revenue adjustment factor (TranRevMiAdjFactor): Factor to convert transit revenue miles (i.e. miles run in service on routes) to total transit vehicle miles operated to account for vehicle miles run while deadheading from depots to route endpoints. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "TranRevMiAdjFactor",
    "VALUE": "1.12",
    "TYPE" : "double",
    "UNITS" : "multiplier",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}
```

Internal Module Inputs

Package	Module	Outputs	Description
VELandUse	CalculateBasePlaceTypes	UrbanPop	Total population by place types
VELandUse	CalculateFuturePlaceTypes	UrbanPop	Total population by place types
VETransportSupply	CreateFutureAccessibility	FwyLaneMiPCFuture	Ratio of urbanized area freeway and expressway lane-miles to urbanized area population calculated using future (estimated) data
VETransportSupply	CreateFutureAccessibility	ArtLaneMiPCFuture	Ratio of urbanized area arterial lane-miles to urbanized area population calculated using future (estimated) data
VETransportSupply	CreateFutureAccessibility	TranRevMiPCFuture	Transit revenue miles per capita for the region calculated using future (estimated) data
VETransportSupply	CreateFutureAccessibility	BusRevMiFuture	Bus revenue miles for the region calculated using future (estimated) data
VETransportSupply	CreateFutureAccessibility	RailRevMiFuture	Rail revenue miles for the region calculated using future (estimated) data
VEHouseholdTravel	CalculateTravelDemandFuture	TruckDvmtFuture	Average daily vehicle miles traveled by trucks estimated using model estimations of future year data
VEHouseholdTravel	CalculateTravelDemandFuture	DvmtFuture (bzones)	Average daily vehicle miles traveled by place types estimated using model estimations of future year data
VETransportSupplyUse	CalculateCongestionBase	LtVehDvmtFactor	Light vehicle Dvmt factor adjustment factor

Module Outputs

LtVehDvmtFuture: Daily vehicle miles travelled by light vehicles in the region calculated using model estimation of future data
BusDvmtFuture: Daily vehicle miles travelled by bus in the region calculated using model estimation of future data
MpgAdjLtVehFuture: Average fuel efficiency adjustment for light vehicles in the region calculated using model estimation of future data
MpgAdjBusFuture: Average fuel efficiency adjustment for buses in the region calculated using model estimation of future data
MpgAdjTruckFuture: Average fuel efficiency adjustment for heavy trucks in the region calculated using model estimation of future data
VehHrLtVehFuture: Total vehicle travel time for light vehicles in the region calculated using model estimation of future data
VehHrBusFuture: Total vehicle travel time for buses in the region calculated using model estimation of future data
VehHrTruckFuture: Total vehicle travel time for heavy trucks in the region calculated using model estimation of future data
AveSpeedLtVehFuture: Average speed for light vehicles in the region calculated using model estimation of future data
AveSpeedBusFuture: Average speed for buses in the region calculated using model estimation of future data
AveSpeedTruckFuture: Average speed for heavy trucks in the region calculated using model estimation of future data
FfVehHrLtVehFuture: Average free-flow travel time for light vehicles in the region calculated using model estimation of future data
FfVehHrBusFuture: Average free-flow travel time for buses in the region calculated using model estimation of future data
FfVehHrTruckFuture: Average free-flow travel time for heavy trucks in the region calculated using model estimation of future data
DelayVehHrLtVehFuture: Total vehicle delay time for light vehicles in the region calculated using model estimation of future data
DelayVehHrBusFuture: Total vehicle delay time for buses in the region calculated using model estimation of future data
DelayVehHrTruckFuture: Total vehicle delay time for heavy trucks in the region calculated using model estimation of future data
MpgAdjHhFuture: Average fuel efficiency adjustment for households in the region calculated using model estimation of future data
LtVehDvmtFactorFuture: Light vehicle Dvmt factor adjustment factor calculated using model estimation of future data

Top Definitions Inputs/Outputs

CalculateInducedDemand

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.

User Input Files

Auto and transit trips per capita (region_trips_per_cap.csv): This file contains regional averages for auto and transit trips 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

Place type adjustments to travel demand (model_place_type_elasticities.csv): This file contains elasticities for four performance metrics:

VMT – Following the estimate of travel demand that incorporates induced demand, an adjustment is made to travel demand that accounts for changes in growth by the place types that are used in the model to describe urban form. These changes are interpreted as changes in design (intersection street density), accessibility (job accessibility by auto), distance to transit (nearest transit stop), density (population density) and diversity (land use mix). The effect on travel demand is determined as changes in VMT by these urban form categories, as shown in the table below. The elasticities that are shown in the table are multiplied by the D values for each place type. The D values are proportion values for each place type that are relative to the regional average, which is set to 1.0.
VehicleTrips – The change in the number of vehicle trips is calculated using a set of elasticities from Index 4D Values (2001) that pivots from the current number of vehicle trips per capita based on the scenario’s allocation of growth by place type. The elasticities shown in the table are applied to D values, which are proportional values for each place type that are relative to a regional average for that D value that is set to 1.0. The model reports the additional number of trips caused by the growth assumed in the scenario and not the regional total.
TransitTrips – The change in the number of transit trips is calculated using a set of elasticities from Index 4D Values (2001) that pivots from the current number of transit trips per capita based on the scenario’s allocation of growth by place type. The elasticities shown in the table are applied to D values, which are proportional values for each place type that are relative to a regional average for that D value that is set to 1.0. The model reports the additional number of trips caused by the growth assumed in the scenario and not the regional total.
Walking – The elasticities shown in the table are applied to D values, which are proportional values for each place type that are relative to a regional average for that D value that is set to 1.0. The product of the elasticity and D value is applied to the place type growth quantities for the scenario to calculated the percentage increase or decrease in walking for new residents in the region relative to a the current place type distribution.

Here is a snapshot of the file:

Parameters	VMT	VehicleTrips	TransitTrips	Walking
Density	-0.04	-0.043	0.07	0.07
Diversity	-0.09	-0.051	0.12	0.15
Design	-0.12	-0.031	0.23	0.39
Regional_Accessibility	-0.2	-0.036	0	0
Distance_to_Transit	-0.05	0	0.29	0.15

Place type adjustments to travel demand (model_place_type_relative_values.csv): This file contains the D values, which are proportional values for each of the 13 place types (Bzones) that are relative to a regional average, for each of the five Ds used in VERPAT - design (intersection street density), accessibility (job accessibility by auto), distance to transit (nearest transit stop), density (population density) and diversity (land use mix). Here is a snapshot of the file:

Geo	Density	Diversity	Design	Regional_Accessibility	Distance_to_Transit
Rur	0.5	0.5	0.5	0.5	0.5
Sub_R	0.75	0.75	0.75	0.75	0.75
Sub_E	0.75	0.75	0.75	0.75	0.75
Sub_M	1	1	1	0.75	0.75
Sub_T	1	1	1	1	1
CIC_R	1.2	1.2	1.2	1.2	1
CIC_E	1.2	1.2	1.2	1.2	1
CIC_M	1.2	1.2	1.2	1.2	1
CIC_T	1.2	1.2	1.2	1.2	1.2
UC_R	1.5	1.2	1.5	1.5	1.2
UC_E	1.5	1.2	1.5	1.5	1.2
UC_M	1.5	1.5	1.5	1.5	1.2
UC_T	1.5	1.5	1.5	1.5	1.5

Internal Module Inputs

Package	Module	Outputs	Description
VESimHouseholds	PredictIncome	Income	Total annual household (non-qroup & group quarters) income in year 1999 dollars
VELandUse	CalculateBasePlaceTypes	HhPlaceTypes	A place type as assigned to the households
VELandUse	CalculateBasePlaceTypes	UrbanPop	Total population by place types
VELandUse	CalculateBasePlaceTypes	UrbanEmp	Total employees by place types
VELandUse	CalculateFuturePlaceTypes	HhPlaceTypes	A place type as assigned to the households
VELandUse	CalculateFuturePlaceTypes	UrbanPop	Total population by place types
VELandUse	CalculateFuturePlaceTypes	UrbanEmp	Total employees by place types
VEHouseholdTravel	CalculateTravelDemandFuture	DvmtFuture (households)	Average daily vehicle miles traveled by household estimated using model estimations of future year data

Module Outputs

DvmtFuture (households): Average daily vehicle miles traveled by household estimated using model estimations of future year data
DvmtFuture (bzones): Average daily vehicle miles traveled by place types estimated using model estimations of future year data
DvmtPtAdj: Dvmt adjustment by place types
Access: Growth in job access
Equity: Growth in equity by income group
VehicleTrips: Policy adjusted vehicle trips
TransitTrips: Policy adjusted transit trips
Walking: Growth in walking

Top Definitions Inputs/Outputs

CalculatePolicyVmt

This module calculates 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.

User Input Files

Percentage of employees offered commute options (region_commute_options.csv): This file contains assumptions about the availability and participation in work based travel demand management programs. The policies are ridesharing programs, transit pass programs, telecommuting or alternative work schedule programs, and vanpool programs. For each, the user enters the proportion of workers who participate (the data items with the “Participation” suffix). For one program, the transit subsidy, the user must also enter the subsidy level in dollars for the TransitSubsidyLevel data item. Here is a snapshot of the file:

TDMProgram	DataItem	DataValue
Ridesharing	RidesharingParticipation	0.05
TransitSubsidy	TransitSubsidyParticipation	0.1
TransitSubsidy	TransitSubsidyLevel	1.25
WorkSchedule	Schedule980Participation	0.01
WorkSchedule	Schedule440Participation	0.01
WorkSchedule	Telecommute1.5DaysParticipation	0.01
Vanpooling	LowLevelParticipation	0.04
Vanpooling	MediumLevelParticipation	0.01
Vanpooling	HighLevelParticipation	0.01

Bicycling/light vehicles targets (region_light_vehicles.csv): This file contains input data for the non-motorized vehicle model. In VERPAT, non-motorized vehicles are bicycles, and also electric bicycles, segways, and similar vehicles that are small, light-weight and can travel at bicycle speeds or slightly higher. The parameters are as follows:
- TargetProp: non-motorized vehicle ownership rate (average ratio of non-motorized vehicles to driver age population)
- Threshold: single-occupant vehicle (SOV) tour mileage threshold used in the SOV travel proportion model. This is the upper limit for tour lengths that are suitable for reallocation to non-motorized modes.
- PropSuitable: proportion of SOV travel suitable for non-motorized vehicle travel. This variable describes the proportion of SOV tours within the mileage threshold for which non-motorized vehicles might be substituted. This variable takes into account such factors as weather and trip purpose.
The non-motorized vehicle model predicts the ownership and use of non-motorized vehicles (where non-motorized vehicles are bicycles, and also electric bicycles, segways and similar vehicles that are small, light-weight and can travel at bicycle speeds or slightly higher than bicycle speeds). The core concept of the model is that non-motorized vehicle usage will primarily be a substitute for short-distance SOV travel. Therefore, the model estimates the proportion of the household vehicle travel that occurs in short-distance SOV tours. The model determines the maximum potential for household VMT to be diverted to non-motorized vehicles, which is also dependent on the availability of non-motorized vehicles. Note that bike share programs (BSP) serve to increase the availability of non-motorized vehicles and can be taken into account by increasing the TargetProp variable. Use national estimates of non-motorized ownership if regional estimates of non-motorized ownership are not available (unless the region has notably atypical levels of bicycle usage). See Bicycle Ownership in the United States for an analysis of regional differences. Here is a snapshot of the file:

DataItem DataValue

TargetProp 0.2

Threshold 2

PropSuitable 0.1
Increase in parking cost and supply (marea_parking_growth.csv): This file contains information that allows the effects of policies such as workplace parking charges and "cash-out buy-back" programs to be tested. The input parameters are as follows and should be entered for both the base and future year:
- PropWrkPkg: proportion of employees that park at work
- PropWrkChrgd: proportion of employers that charge for parking
- PropCashOut: proportion of employment parking that is converted from being free to pay under a "cash-out buy-back" type of program
- PrkOthChrgd: proportion of other parking that is not free
- PkgCost: average daily parking cost. This variable is the average daily parking cost for those who incur a fee to park. If the paid parking varies across the region, then the "PkgCost" value should reflect the average of those parking fees, but weighted by the supply – so if most in the Center City, then the average will be heavily weighted toward the price in the Center City.
Here is a snapshot of the file:

Geo Year PropWorkParking PropWorkCharged PropCashOut PropOtherCharged ParkingCost.2000

Multnomah 2005 1 0.1 0 0.05 5

Multnomah 2035 1 0.1 0 0.05 5
Travel Demand Management: Ridesharing (model_tdm_ridesharing.csv): The ridesharing Travel Demand Management file contains parameters describing the effectiveness of ridersharing programs by place type. The proportion of employees participating in the ridesharing program is a policy input. This is converted into a proportion of working-age persons by using an assumed labor force participation rate (0.65) to sample working-age persons in households. The ridesharing submodel then computes the anticipated level of VMT reduction resulting from the implementation of ridesharing, based on the place type the household lives in, using the effectiveness values shown in this parameter file. Previous studies have determined that the level of ridesharing participation will be less in the rural and suburban areas, as compared to the more-urban areas. Typically, more people will carpool in the more urbanized areas due to the presence of parking charges, potential difficulties in finding parking, and other disincentives that are typically present in more urbanized areas. Here is a snapshot of the file:

ModelGeo Effectiveness

Rur 0

Sub 0.05

CIC 0.1

UC 0.15
Travel Demand Management: Transit Fares (model_tdm_transit.csv): The transit fare Travel Demand Management files are parameters for the effectiveness (level of VMT reduction) and fare subsidy values for employer. The subsidized/discounted transit model begins by evaluating the level of participation within the region. Monte Carlo processes are used to identify which households participate in transit pass programs. The proportion of employees participating in this program is a policy input. This is converted into a proportion of working-age persons by using an assumed labor force participation rate (0.65) to sample working-age persons in households. The model then allows the selection of one of four potential subsidy levels (also a policy inputs), which influence the level of VMT reduction based on the level of subsidy applied to the place type. The anticipated level of VMT reduction is then further reduced to account for the proportion of work travel in overall daily travel. Here is a snapshot of the file:

ModelGeo Subsidy0 Subsidy1 Subsidy2 Subsidy3 Subsidy4

Rur 0 0 0 0 0

Sub 0 0.02 0.033 0.079 0.2

CIC 0 0.034 0.073 0.164 0.2

UC 0 0.062 0.129 0.2 0.2
Travel Demand Management: Transit Subsidy Levels (model_tdm_transitlevels.csv): This file contains the dollar value match to the subsidy levels used in model_tdm_transit.csv file. Here is a snapshot of the file:

SubsidyLevel SubsidyValue.2000

Subsidy0 0

Subsidy1 0.75

Subsidy2 1.49

Subsidy3 2.98

Subsidy4 5.96
Travel Demand Management: Vanpooling (model_tdm_vanpooling.csv): This file contains parameters describing the effectiveness in terms of VMT reductions for vanpooling programs across three levels of employee involvement. The vanpool program submodel operates by evaluating the likely level of participation. Monte Carlo processes are used to identify which households participate in vanpool programs. The proportion of employees participating in this program is a policy input. This is converted into a proportion of working-age persons by using an assumed labor force participation rate (0.65) to sample working-age persons in households. Those employers that would participate in the program are then categorized into three levels of involvement from low to medium to high. The level of involvement reflects the extent to which an employer would actively facilitate and promote vanpooling. For example, a low level of involvement might represent an employer who organizes only a minimal number of vanpools. The high level of involvement could represent an employer who has an extensive vanpooling program to cover a large number of employees. Based on the level of involvement, the reduction in VMT is estimated on the basis of the values in this file. Here is a snapshot of the file:

VanpoolingParticipation VMTReduction

Low 0.003

Medium 0.0685

High 0.134

Travel Demand Management: Work Schedule (model_tdm_workschedule.csv): This file contains parameters that describe the effectiveness for different participation levels for three different telecommuting or alternative work schedules. The telecommuting or alternative work schedule model first evaluates the likely level of participation throughout the region in terms of telecommuting or alternatively-works schedules. Monte Carlo processes are used to identify which households participate in telecommuting programs. The proportion of employees participating in this program is a policy input. This is converted into a proportion of working-age persons by using an assumed labor force participation rate (0.65) to sample working-age persons in households. The model then determines the type of programs that might be implemented. Three potential alternatives are offered including:

4/40 Schedule: 4 days per week with 40 hours per week
9/80 Schedule: working 4 days every other week with an average of 80 hours over 2 weeks
Telecommuting: Workers may work 1 to 2 days a week remotely

Once the option has been identified and the level of participation, the estimated VMT is determined on the basis of the parameters in this file. Here is a snapshot of the file:

WorkSchedulePolicy	Participation1	Participation2	Participation3	Participation4	Participation5
Schedule980	0.0007	0.0021	0.0035	0.007	0.0175
Schedule440	0.0015	0.0045	0.007	0.015	0.0375
TelecommuteoneandhalfDays	0.0022	0.0066	0.011	0.022	0.055

Travel Demand Management: Work Schedule Participation Levels (model_tdm_workschedulelevels.csv): This file describes the proportion of employees participating in the program corresponding to the participation levels used in model_tdm_workschedule.csv file. Here is a snapshot of the file:

ParticipationLevel ParticipationValue

Participation0 0

Participation1 0.01

Participation2 0.03

Participation3 0.05

Participation4 0.1

Participation5 0.25

User Input Parameters:

% Increase in Auto Operating Cost (AutoCostGrowth): This parameter reflects the proportional increase in auto operating cost. This can be used to test different assumptions for future gas prices or the effects of increased gas taxes. A value of 1.5 multiplies base year operating costs by 1.5 and thus reflects a 50% increase. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "AutoCostGrowth",
    "VALUE": "1.5",
    "TYPE" : "double",
    "UNITS" : "multiplier",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}
```
Auto Operating Surcharge Per VMT (VmtCharge): It is a cost in cents per mile that would be levied on auto users through the form of a VMT charge. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "VmtCharge",
    "VALUE": "0.05",
    "TYPE" : "compound",
    "UNITS" : "USD/MI",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}
```
Work VMT Proportion (WorkVmtProp): The proportion of household VMT that takes place for commute to work purposes; used to differentiate between travel purposes for use in estimating overall VMT reductions that are targeted at the commute to work. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "WorkVmtProp",
    "VALUE": "0.25",
    "TYPE" : "double",
    "UNITS" : "proportion",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}
```

Internal Module Inputs

Package	Module	Outputs	Description
VESimHouseholds	CreateHouseholds	HhId	Unique household ID
VESimHouseholds	CreateHouseholds	HhSize	Number of persons
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	PredictIncome	Income	Total annual household (non-qroup & group quarters) income in year 1999 dollars
VELandUse	CalculateFuturePlaceTypes	HhPlaceTypes	A place type as assigned to the households
VELandUse	CalculateFuturePlaceTypes	UrbanPop	Total population by place types
VELandUse	CalculateFuturePlaceTypes	UrbanEmp	Total employees by place types
VELandUse	CalculateFuturePlaceTypes	UrbanIncome	Total income by place types
VETransportSupply	CreateFutureAccessibility	FwyLaneMiPCFuture	Ratio of urbanized area freeway and expressway lane-miles to urbanized area population calculated using future (estimated) data
VETransportSupply	CreateFutureAccessibility	ArtLaneMiPCFuture	Ratio of urbanized area arterial lane-miles to urbanized area population calculated using future (estimated) data
VETransportSupply	CreateFutureAccessibility	TranRevMiPCFuture	Transit revenue miles per capita for the region calculated using future (estimated) data
VETransportSupply	CreateFutureAccessibility	BusRevMiFuture	Bus revenue miles for the region calculated using future (estimated) data
VETransportSupply	CreateFutureAccessibility	RailRevMiFuture	Rail revenue miles for the region calculated using future (estimated) data
VEHouseholdVehicles	AssignVehicleFeaturesFuture	VehIdFuture	Unique vehicle ID
VEHouseholdVehicles	AssignVehicleFeaturesFuture	VehiclesFuture	Number of automobiles and light trucks owned or leased by the household
VEHouseholdVehicles	AssignVehicleFeaturesFuture	NumLtTrkFuture	Number of light trucks (pickup, sport-utility vehicle, and van) owned or leased by household
VEHouseholdVehicles	AssignVehicleFeaturesFuture	NumAutoFuture	Number of automobiles (i.e. 4-tire passenger vehicles that are not light trucks) owned or leased by household
VEHouseholdTravel	CalculateTravelDemandFuture	DvmtFuture (vehicles)	Average daily vehicle miles traveled by each vehicle estimated using model estimations of future year data
VEHouseholdTravel	CalculateTravelDemandFuture	FuelGallonsFuture	Average daily fuel consumption in gallons by each household estimated using model estimations of future year data
VEHouseholdTravel	CalculateTravelDemandFuture	FuelCo2eFuture	Average daily Co2 equivalent greenhouse gas emissions by each household estimated using model estimations of future year data
VEHouseholdTravel	CalculateTravelDemandFuture	FutureCostPerMileFuture	Total cost per mile for future year for each household estimated using model estimations of future year data
VETransportSupplyUse	CalculateCongestionFuture	MpgAdjLtVehFuture	Average fuel efficiency adjustment for light vehicles in the region calculated using model estimation of future data
VETransportSupplyUse	CalculateCongestionFuture	MpgAdjBusFuture	Average fuel efficiency adjustment for buses in the region calculated using model estimation of future data
VETransportSupplyUse	CalculateCongestionFuture	MpgAdjTruckFuture	Average fuel efficiency adjustment for heavy trucks in the region calculated using model estimation of future data
VETransportSupplyUse	CalculateCongestionFuture	VehHrLtVehFuture	Total vehicle travel time for light vehicles in the region calculated using model estimation of future data
VETransportSupplyUse	CalculateCongestionFuture	VehHrBusFuture	Total vehicle travel time for buses in the region calculated using model estimation of future data
VETransportSupplyUse	CalculateCongestionFuture	VehHrTruckFuture	Total vehicle travel time for heavy trucks in the region calculated using model estimation of future data
VETransportSupplyUse	CalculateCongestionFuture	AveSpeedLtVehFuture	Average speed for light vehicles in the region calculated using model estimation of future data
VETransportSupplyUse	CalculateCongestionFuture	AveSpeedBusFuture	Average speed for buses in the region calculated using model estimation of future data
VETransportSupplyUse	CalculateCongestionFuture	AveSpeedTruckFuture	Average speed for heavy trucks in the region calculated using model estimation of future data
VETransportSupplyUse	CalculateCongestionFuture	FfVehHrLtVehFuture	Average free-flow travel time for light vehicles in the region calculated using model estimation of future data
VETransportSupplyUse	CalculateCongestionFuture	FfVehHrBusFuture	Average free-flow travel time for buses in the region calculated using model estimation of future data
VETransportSupplyUse	CalculateCongestionFuture	FfVehHrTruckFuture	Average free-flow travel time for heavy trucks in the region calculated using model estimation of future data
VETransportSupplyUse	CalculateCongestionFuture	DelayVehHrLtVehFuture	Total vehicle delay time for light vehicles in the region calculated using model estimation of future data
VETransportSupplyUse	CalculateCongestionFuture	DelayVehHrBusFuture	Total vehicle delay time for buses in the region calculated using model estimation of future data
VETransportSupplyUse	CalculateCongestionFuture	DelayVehHrTruckFuture	Total vehicle delay time for heavy trucks in the region calculated using model estimation of future data
VETransportSupplyUse	CalculateCongestionFuture	MpgAdjHhFuture	Average fuel efficiency adjustment for households in the region calculated using model estimation of future data
VEHouseholdTravel	CalculateInducedDemand	DvmtFuture (households)	Average daily vehicle miles traveled by household estimated using model estimations of future year data
VEHouseholdTravel	CalculateInducedDemand	DvmtFuture (bzones)	Average daily vehicle miles traveled by place types estimated using model estimations of future year data
VEHouseholdTravel	CalculateInducedDemand	Access	Growth in job access
VEHouseholdTravel	CalculateInducedDemand	VehicleTrips	Policy adjusted vehicle trips
VEHouseholdTravel	CalculateInducedDemand	TransitTrips	Policy adjusted transit trips
VEHouseholdTravel	CalculateInducedDemand	Walking	Growth in walking

Module Outputs

DvmtPolicy (bzones): Average daily vehicle miles traveled by place types after policy adjustments
DvmtPolicy (households): Average daily vehicle miles traveled by households after policy adjustments
DvmtPolicy (vehicles): Average daily vehicle miles traveled by vehicles after policy adjustments
LtVehiclesPolicy: Number of light vehicles by households after policy adjustments
LtVehAdjFactorPolicy: Light vehicles adjustment factor for households
TdmLtVehAdjFactorPolicy: TDM Light vehicles adjustment factor for households
TdmAdjFactorPolicy: TDM adjustment factor for households
LtVehDvmtPolicy: Average daily vehicle miles traveled by light vehicles by households after policy adjustments
FutureCostPerMilePolicy: Total cost per mile for future year for each household estimated after making policy adjustments
DailyParkingCostPolicy: Daily parking cost calculated after making policy adjustments
CashOutIncAdjPolicy: Adjustment to income after cash out
IncomePolicy: Income of the households after applying policy
CostsPolicy: Various policy related energy costs

Top Definitions Inputs/Outputs

CalculateCongestionPolicy

This module is similar to CalculateCongestionBase module but uses model estimation of future transportation supply adjusted to policies as the basis of calculations.

User Input Files

Percent road miles with ITS treatment (azone_its_prop.csv): This file is an estimate of the proportion of road miles that have improvements which reduce incidents through ITS treatments in both the base and future years. Values entered should be between 0 and 1, with 1 indicating that 100% of road miles are treated. The ITS policy measures the effects of incident management supported by ITS. The ITS table is used to inform the congestion model and the travel demand model. The model uses the mean speeds with and without incidents to compute an overall average speed by road type and congestion level providing a simple level of sensitivity to the potential effects of incident management programs on delay and emissions. The ITS treatments are evaluated only on freeways and arterials. The ITS treatments that can be evaluated are those that the analyst considers will reduce non-recurring congestion due to incidents. This policy does not deal with other operational improvements such as signal coordination, or temporary capacity increases such as allowing shoulder use in the peak. Here is a snapshot of the file:

Geo Year ITS

Multnomah 2005 0

Multnomah 2035 0
Truck and bus vmt (region_truck_bus_vmt.csv): 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 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

User Input Parameters

Light vehicle dvmt (BaseLtVehDvmt): Total light vehicle daily VMT for the base year in thousands of miles. This data can be derived from a combination of Highway Performance Monitoring System data, Federal Highway Cost Allocation Study data, and regional data. Light vehicle daily VMT can be estimated by subtracting truck and bus VMT from total VMT provided in the Highway Performance Monitoring System (HPMS). Regional travel demand model outputs can also be used to derive these data. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "BaseLtVehDvmt",
    "VALUE": "27244",
    "TYPE" : "compound",
    "UNITS" : "MI/DAY",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}
```
Dvmt proportion by functional class (BaseFwyArtProp): The proportions of daily VMT for light vehicles that takes place on freeways and arterials (i.e., the remainder of VMT takes place on lower functional class roads for the base year. This data can be derived from a combination of Highway Performance Monitoring System data, Federal Highway Cost Allocation Study data, and regional data. The proportions of light vehicle daily VMT on freeways and arterials can be derived from the HPMS data. Regional travel demand model outputs can also be used to derive these data. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "BaseFwyArtProp",
    "VALUE": "0.77",
    "TYPE" : "double",
    "UNITS" : "proportion",
    "PROHIBIT" : "c('NA', '< 0', '> 1')",
    "ISELEMENTOF" : ""
}
```
Transit revenue adjustment factor (TranRevMiAdjFactor): Factor to convert transit revenue miles (i.e. miles run in service on routes) to total transit vehicle miles operated to account for vehicle miles run while deadheading from depots to route endpoints. It should be defined in model_parameters.json as follows:
```
{
    "NAME" : "TranRevMiAdjFactor",
    "VALUE": "1.12",
    "TYPE" : "double",
    "UNITS" : "multiplier",
    "PROHIBIT" : "c('NA', '< 0')",
    "ISELEMENTOF" : ""
}
```

Internal Module Inputs

Package	Module	Outputs	Description
VELandUse	CalculateBasePlaceTypes	UrbanPop	Total population by place types
VELandUse	CalculateFuturePlaceTypes	UrbanPop	Total population by place types
VETransportSupply	CreateFutureAccessibility	FwyLaneMiPCFuture	Ratio of urbanized area freeway and expressway lane-miles to urbanized area population calculated using future (estimated) data
VETransportSupply	CreateFutureAccessibility	ArtLaneMiPCFuture	Ratio of urbanized area arterial lane-miles to urbanized area population calculated using future (estimated) data
VETransportSupply	CreateFutureAccessibility	TranRevMiPCFuture	Transit revenue miles per capita for the region calculated using future (estimated) data
VETransportSupply	CreateFutureAccessibility	BusRevMiFuture	Bus revenue miles for the region calculated using future (estimated) data
VETransportSupply	CreateFutureAccessibility	RailRevMiFuture	Rail revenue miles for the region calculated using future (estimated) data
VEHouseholdTravel	CalculateTravelDemandFuture	TruckDvmtFuture	Average daily vehicle miles traveled by trucks estimated using model estimations of future year data
VEHouseholdTravel	CalculatePolicyVmt	DvmtPolicy (bzones)	Average daily vehicle miles traveled by place types estimated using model estimations of future year data
VETransportSupplyUse	CalculateCongestionFuture	LtVehDvmtFactorFuture	Light vehicle Dvmt factor adjustment factor

Module Outputs

LtVehDvmtPolicy: Daily vehicle miles travelled by light vehicles in the region calculated using model estimation of future data after adjusting for policies
BusDvmtPolicy: Daily vehicle miles travelled by bus in the region calculated using model estimation of future data after adjusting for policies
MpgAdjLtVehPolicy: Average fuel efficiency adjustment for light vehicles in the region calculated using model estimation of future data after adjusting for policies
MpgAdjBusPolicy: Average fuel efficiency adjustment for buses in the region calculated using model estimation of future data after adjusting for policies
MpgAdjTruckPolicy: Average fuel efficiency adjustment for heavy trucks in the region calculated using model estimation of future data after adjusting for policies
VehHrLtVehPolicy: Total vehicle travel time for light vehicles in the region calculated using model estimation of future data after adjusting for policies
VehHrBusPolicy: Total vehicle travel time for buses in the region calculated using model estimation of future data after adjusting for policies
VehHrTruckPolicy: Total vehicle travel time for heavy trucks in the region calculated using model estimation of future data after adjusting for policies
AveSpeedLtVehPolicy: Average speed for light vehicles in the region calculated using model estimation of future data after adjusting for policies
AveSpeedBusPolicy: Average speed for buses in the region calculated using model estimation of future data after adjusting for policies
AveSpeedTruckPolicy: Average speed for heavy trucks in the region calculated using model estimation of future data after adjusting for policies
FfVehHrLtVehPolicy: Average free-flow travel time for light vehicles in the region calculated using model estimation of future data after adjusting for policies
FfVehHrBusPolicy: Average free-flow travel time for buses in the region calculated using model estimation of future data after adjusting for policies
FfVehHrTruckPolicy: Average free-flow travel time for heavy trucks in the region calculated using model estimation of future data after adjusting for policies
DelayVehHrLtVehPolicy: Total vehicle delay time for light vehicles in the region calculated using model estimation of future data after adjusting for policies
DelayVehHrBusPolicy: Total vehicle delay time for buses in the region calculated using model estimation of future data after adjusting for policies
DelayVehHrTruckPolicy: Total vehicle delay time for heavy trucks in the region calculated using model estimation of future data after adjusting for policies
MpgAdjHhPolicy: Average fuel efficiency adjustment for households in the region calculated using model estimation of future data after adjusting for policies
LtVehDvmtFactorPolicy: Light vehicle Dvmt factor adjustment factor calculated using model estimation of future data after adjusting for policies

Top Definitions Inputs/Outputs

ReportRPATMetrics

This module calculates performance metrics that are designed to address a variety of impacts that are helpful for decision-making. Following table summarizes various performance measures produced by this modules, details of which is discussed in VEReports.

Performance Group	Performance Metric	Units	Temporal Resolution
Direct Travel Impacts	Daily Vehicle Miles Traveled	Miles	Day
Direct Travel Impacts	Daily Vehicle Trips	Trips	Day
Direct Travel Impacts	Daily Transit Trips	Trips	Day
Direct Travel Impacts	Average Travel Speeds by Vehicle Type	MPH	Day
Direct Travel Impacts	Vehicle Hours of Travel Delay	Hours	Day
Environment and Energy Impacts	Fuel Consumption	Gallons	Day
Environment and Energy Impacts	Greenhouse Gas Emissions	Metric Tons	Day
Financial and Economic Impacts	Regional Highway Infrastructure Costs	Dollars	Year
Financial and Economic Impacts	Regional Transit Infrastructure Costs	Dollars	Year
Financial and Economic Impacts	Regional Transit Operating Costs	Dollars	Year
Financial and Economic Impacts	Annual Traveler Cost (Fuel + Charges)	Dollars	Year
Location Impacts	Regional Accessibility	Percent Change From Base	-
Community Impacts	Accident Rates	Count	Year
Community Impacts	Walking Percentage Increase	Percent Change From Base	-
Community Impacts	Job Accessibility by Income Group	Percent Change From Base	-

User Input Files

Transportation Costs (model_transportation_costs.csv): This file contains unit cost rates for transportation infrastructure investments and operating costs and transit fare revenue. The parameters are used in the calculations of the transportation costs performance metrics. The source for transit capital, operating costs, and fare revenue is the NTD, and in particular the National Transit Profile which is available on the NTDB website. Costs are available in a variety of index formats, e.g. cost per revenue mile or hour; cost per passenger trip is used in VERPAT. The source for highway infrastructure costs is FHWA’s Highway Economic Requirements System model, or HERS. Information was obtained from Chapter 6 of the 2005 Technical Report for all US states (FHWA, 2005). Table 8-1 in HERS provides unit costs (per lane mile) for both Rural and Urban highway systems, and distinguishes among three functional classes: interstates, freeways and expressways; other principal arterials; and minor arterials and collectors. Costs estimates are provided for the following improvements:
- Reconstruction and widening
- Reconstruct pavement
- Resurface and widen lanes
- Resurface pavement
- Improve shoulders
Additional choices are offered to distinguish between adding a lane at "normal" vs. "high cost", and also for pavement realignment, also under normal vs. high cost conditions. For practical reasons, it was decided to use only "new construction" (which also includes adding lanes) costs as the basis for the cost estimates, and to ignore the categories of reconstruction, resurfacing, and realignment. These construction costs include right of way, construction, and a "small" allowance for bridges and support facilities. The infrastructure costs focus only on the "urban" system, not rural, which also makes it possible to differentiate by three size classes: Small Urban, Small Urbanized, and Large Urbanized. The numbers from HERS are in 2002 dollars; FHWA advises escalation to current dollars using its NHCCI, which is available online at http://www.fhwa.dot.gov/policyinformation/nhcci.cfm); the report in VERPAT escalates uses this Index to escalate to 2010 values. The parameter values in this file are average values per lane mile, simplifying the calculations of highway costs to just differentiate investments in freeway vs. arterial functional class roads. Here is a snapshot of the file:

SupplyClass CapCosts.2000 OpCosts.2000 Fare.2000

Freeway 17 0 0

Arterial 7 0 0

Bus 0.71 3.4 0.91

Rail 5.11 4.87 2.19
Accident Rates (model_accident_rates.csv): Road safety impacts are calculated by factoring the amount of VMT. The following national average rates, from the Fatality Analysis Reporting System General Estimates System (2009) by US Department of Transportation, are applied to calculate the number of fatal and injury accidents and the value of property damage:
- Fatal: 1.14 per 100 Million Miles Traveled
- Injury: 51.35 per 100 Million Miles Traveled
- Property damage: 133.95 per 100 Million Miles Traveled
Here is a snapshot of the file:

Accident Rate

Fatal 1.14

Injury 51.35

Property 133.95

Internal Module Inputs

Package	Module	Outputs	Description
VESimHouseholds	CreateHouseholds	HhId	Unique household ID
VELandUse	CalculateFuturePlaceTypes	HhPlaceTypes	A place type as assigned to the households
VELandUse	CalculateFuturePlaceTypes	UrbanPop	Total population by place types
VETransportSupply	CreateFutureAccessibility	FwyLaneMiPCFuture	Ratio of urbanized area freeway and expressway lane-miles to urbanized area population calculated using future (estimated) data
VETransportSupply	CreateFutureAccessibility	ArtLaneMiPCFuture	Ratio of urbanized area arterial lane-miles to urbanized area population calculated using future (estimated) data
VETransportSupply	CreateFutureAccessibility	TranRevMiPCFuture	Transit revenue miles per capita for the region calculated using future (estimated) data
VETransportSupply	CreateFutureAccessibility	BusRevMiFuture	Bus revenue miles for the region calculated using future (estimated) data
VETransportSupply	CreateFutureAccessibility	RailRevMiFuture	Rail revenue miles for the region calculated using future (estimated) data
VEHouseholdVehicles	AssignVehicleFeaturesFuture	VehIdFuture	Unique vehicle ID
VEHouseholdVehicles	AssignVehicleFeaturesFuture	TypeFuture	Vehicle body type: Auto = automobile, LtTrk = light trucks (i.e. pickup, SUV, Van)
VEHouseholdVehicles	AssignVehicleFeaturesFuture	MileageFuture	Mileage of vehicles (automobiles and light truck)
VEHouseholdVehicles	AssignVehicleFeaturesFuture	VehiclesFuture	Number of automobiles and light trucks owned or leased by the household
VEHouseholdTravel	CalculateTravelDemandFuture	TruckDvmtFuture	Average daily vehicle miles traveled by trucks estimated using model estimations of future year data
VEHouseholdTravel	CalculateTravelDemandFuture	FuelGallonsFuture	Average daily fuel consumption in gallons by each household estimated using model estimations of future year data
VEHouseholdTravel	CalculateTravelDemandFuture	FuelCo2eFuture	Average daily Co2 equivalent greenhouse gas emissions by each household estimated using model estimations of future year data
VEHouseholdTravel	CalculateInducedDemand	TransitTrips	Policy adjusted transit trips
VEHouseholdTravel	CalculatePolicyVmt	DvmtPolicy (bzones)	Average daily vehicle miles traveled by place types after policy adjustments
VEHouseholdTravel	CalculatePolicyVmt	DvmtPolicy (households)	Average daily vehicle miles traveled by households after policy adjustments
VEHouseholdTravel	CalculatePolicyVmt	DvmtPolicy (vehicles)	Average daily vehicle miles traveled by vehicles after policy adjustments
VEHouseholdTravel	CalculatePolicyVmt	LtVehDvmtPolicy	Average daily vehicle miles traveled by light vehicles by households after policy adjustments
VEHouseholdTravel	CalculatePolicyVmt	DailyParkingCostPolicy	Daily parking cost calculated after making policy adjustments
VETransportSupplyUse	CalculateCongestionPolicy	LtVehDvmtPolicy	Daily vehicle miles travelled by light vehicles in the region calculated using model estimation of future data after adjusting for policies
VETransportSupplyUse	CalculateCongestionPolicy	BusDvmtPolicy	Daily vehicle miles travelled by bus in the region calculated using model estimation of future data after adjusting for policies
VETransportSupplyUse	CalculateCongestionPolicy	MpgAdjLtVehPolicy	Average fuel efficiency adjustment for light vehicles in the region calculated using model estimation of future data after adjusting for policies
VETransportSupplyUse	CalculateCongestionPolicy	MpgAdjBusPolicy	Average fuel efficiency adjustment for buses in the region calculated using model estimation of future data after adjusting for policies
VETransportSupplyUse	CalculateCongestionPolicy	MpgAdjTruckPolicy	Average fuel efficiency adjustment for heavy trucks in the region calculated using model estimation of future data after adjusting for policies
VETransportSupplyUse	CalculateCongestionPolicy	MpgAdjHhPolicy	Average fuel efficiency adjustment for households in the region calculated using model estimation of future data after adjusting for policies

Module Outputs

EmissionsMetric: The amount of greenhouse gas emissions per day by place-types
FuelMetric: The amount of fuel consumed per day by place-types
CostsMetric: The annual traveler cost (fuel + charges)
FuelGallonsMetric: Average daily fuel consumption in gallons after policy
FuelCo2eMetric: Average daily Co2 equivalent greenhouse gas emissions after policy
FutureCostPerMileMetric: Total fuel cost per mile after policy
TotalCostMetric: Total fuel cost after policy
RailPowerMetric: Annual power consumption by rail
TruckFuelMetric: Annual truck fuel consumption
BusFuelMetric: Annual bus fuel consumption
TruckCo2eMetric: Annual greenhouse gas emissions by truck
BusCo2eMetric: Annual greenhouse gas emissions by bus
RailCo2eMetric: Annual greenhouse gas emissions by rail
HighwayCostMetric: Regional highway infrastructure costs
TransitCapCostMetric: Regional transit infrastructure costs
TransitOpCostMetric: Regional transit operating costs
TransitRevenueMetric: Annual fare revenue
FatalIncidentMetric: Number of fatal incidents
InjuryIncidentMetric: Number of incidents with injuries
PropertyDamageMetric: Amount of incidents with property damage
TruckConsumptionMetric: Annual truck fuel consumption by fuel type
BusConsumptionMetric: Annual bus fuel consumption by fuel type

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

Fuel	Intensity
ULSD	77.19
Biodiesel	76.81
RFG	75.65
CARBOB	75.65
Ethanol	74.88
Cng	62.14

VehType	PropDiesel	PropCng	PropGas
Auto	0.007	0	0.993
LtTruck	0.04	0	0.96
Bus	0.995	0.005	0
Truck	0.945	0.005	0.05

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

ModelGeo	Subsidy1	Subsidy2	Subsidy3	Subsidy4
Rur	0	0	0	0
Sub	0.02	0.033	0.079	0.2
CIC	0.034	0.073	0.164	0.2
UC	0.062	0.129	0.2	0.2

Geo	Year	GrpAge0to14	GrpAge15to19	GrpAge20to29	GrpAge30to54	GrpAge55to64	GrpAge65Plus
Multnomah	2005	0	0	0	1	0	0
Multnomah	2035	0	0	0	1	0	0

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

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