Open-Source Scenario Planning Tools Can Provide Substantial Flexibility in Terms of the Types of Scenarios Considered At the Expense of Providing Precise Impacts.

State Study Tested Open-Source Scenario Transportation Planning Tool with Forty-Three Scenarios in Three Localities in Northern Virginia.

Date Posted
09/27/2022
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Identifier
2022-L01148

Feasibility of Adapting VisionEval for Scenario Planning

Summary Information

Scenario planning for transportation investments requires the consideration of uncontrolled events such as increases in transit services in conjunction with changes to demographic characteristics such as family size or population growth, vehicle technology, fuel prices, evolution of telecommuting, and also changes in policy responses.  The purpose of this study was to determine the benefits, staff time, and feasibility of applying an open-source scenario planning tool, VisionEval, which was an elasticity-based strategic model, developed as part of a transportation pooled fund (TPF) study led by the Federal Highway Administration, to explore scenarios of interest to Virginia Department of Transportation (VDOT) planning staff and planning partners.

A case study approach where the tool was used to inform policy-level discussions for three jurisdictions: Fairfax County, Fairfax City, and Falls Church. Forty-Three scenarios were evaluated using the tool with respect to their impact on vehicle miles traveled (VMT), energy use, carbon dioxide equivalent (CO2e) emissions, and mode split across vehicles, transit, bicycles, and pedestrians. The tool was deployable with about 500 hours of staff time and covered areas with 1.43 million people and 712 transportation analysis zones. Scenario planning was usually hampered by extensive data requirements; however, this study addressed these major limitations to create a feasible platform.

 

The first task involved developing the base and scenario input files requiring 51 current and forecast year input files. The files spanned across areas of demographics, economy, transportation supply, vehicles fleet, network design, and pricing. Data such as per capita income for Virginia were obtained from U.S. Census Bureau for 2019. The tool incorporated a variety of mathematical relationships among generators of travel demand, transportation service policies and selected performance measures. Varying values for housing prices, employment locations, transit availability and fares, roadway miles, VMT, emissions, and transit use were used to design different types of scenarios. The tool was designed to identify which areas require detailed attention by providing substantial flexibility in terms of the types of scenarios considered at the expense of providing precise impacts. The scenarios were executed in a programming software and were validated by comparing the base model to the regional model. The primary benefits studied were the reduction of CO2 emissions and VMT.

EXAMPLE FINDINGS

  • When two-fifths of the work force participate in telecommuting, heavy truck electrification, and household vehicle electrification could reduce CO2 emissions by 12.9, 6.4, and 4.0 percent in a portion of Northern Virginia (Fairfax County, Fairfax City, and Falls Church City).
  • Table 1 shows, for each scenario, the number of vehicles with high car service, the cost savings relative to total ownership costs (e.g., insurance, depreciation, taxes, and residential parking), and the change in VMT. High carsharing services that was expected to increase access to vehicles could result in greater cost savings relative to costs for lower income zones than for higher income zones.

Table 1. Impact of High Carsharing on Cost Savings and VMT

Scenario (Location of High Carsharing services)

No. of High Carsharing services (relative to total base case household vehicles)

Cost savings (percent)

VMT Change Relative to the Base Scenario (percent)

Base (no zones)

0

0

0

Scenario A (high income)

122,763 (11.4 percent)

4.7

2.2

Scenario B (low income)

186,873 (17.4 percent)

11.1

2.0

Scenario C (all zones)

309,941 (28.8 percent)

17

4.2

  • This case study showed key takeaways such as that the effects on VMT depend on household size. While an eight percent VMT increase was observed for an unforeseen population, larger families expected an increase in household VMT by less than one percent.
  • The study showed that other strategies or events had a lesser impact. For example, electrification of transit vehicles and large fuel tax increases affected emissions by less than one percent.
  • The study also showed that population and employment forecast errors at the traffic analysis zone level, rather than the city or county level, had very little impact on emission estimates.
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