Incorporating AV into Planning Models: Applications

Planning models for autonomous vehicles (AV) are gaining increasing attention from State Transportation Agencies and Metropolitan Planning Organizations. Recent published research, including NCHRP Report 896, have identified significant uncertainties in estimates of deployment rates of different Automated Vehicle technology levels, behavioral responses, and implications for traffic estimates, project prioritization, and supportive land uses such as swapping out parking for pick-up/drop-off locations. Planning responses have emphasized scenario planning (an important strategy identified in USDOT’s AV 3.0 policy document) and risk-based approaches that attempt to increase readiness for anticipated changes in system performance and infrastructure needs. This research effort builds on a project initiated in FY2019 to develop tools for AV scenario evaluation, strategy development and investment prioritization by developing model structures and piloting extensions to the VisionEval strategic modeling framework. The proposed research for FY2020 funding will further develop the VisionEval extensions and related models and to conduct pilot testing with interested agencies who are exploring the ramifications of automated vehicles and related technologies in their planning process.

  • Supplemental Notes:
    • USDOT Research Hub DisplayID 157860


  • English


  • Status: Active
  • Funding: $499191
  • Sponsor Organizations:

    Intelligent Transportation Systems - Joint Program Office

  • Managing Organizations:

    Federal Highway Administration

    Washington, DC  United States 
  • Project Managers:

    Dopart, Kevin

  • Start Date: 20190201
  • Expected Completion Date: 20240516
  • Actual Completion Date: 0
  • USDOT Program: Automation
  • Subprogram: Program Support

Subject/Index Terms

Filing Info

  • Accession Number: 01864200
  • Record Type: Research project
  • Source Agency: Department of Transportation
  • Files: RIP, USDOT
  • Created Date: Nov 17 2022 11:56AM