Adapting Land Use and Infrastructure for Automated Driving

This project is concerned with adapting land use and transportation infrastructure for automated driving. Autonomous vehicles will likely yield a transformation of urban form, its land use and mobility system. We propose to establish quantitative modeling frameworks to analyze these impacts and implications. The frameworks will provide a quantifiable understanding of the tradeoffs, and reveal the underlying mechanism and identify key parameters that could shape the future of mobility systems and urban land use. Moreover, the proposed modeling frameworks will aid planning agencies with infrastructure adaptation planning and optimize a roadmap for shaping highway infrastructure towards automated mobility.


  • English


  • Status: Completed
  • Funding: $190,367
  • Contract Numbers:


  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Center for Connected and Automated Transportation

    University of Michigan Transportation Research Institute
    Ann Arbor, MI  United States  48109
  • Project Managers:

    Tucker-Thomas, Dawn

  • Performing Organizations:

    University of Michigan, Ann Arbor

    Department of Civil and Environmental Engineering
    2350 Hayward
    Ann Arbor, MI  United States  48109-2125

    Purdue University, Lyles School of Civil Engineering

    550 Stadium Mall Drive
    West Lafayette, IN  United States  47907
  • Principal Investigators:

    Yin, Yafeng

    Peeta, Srinivas

  • Start Date: 20180101
  • Expected Completion Date: 20220930
  • Actual Completion Date: 20240105
  • USDOT Program: University Transportation Centers Program
  • Subprogram: Research

Subject/Index Terms

Filing Info

  • Accession Number: 01665936
  • Record Type: Research project
  • Source Agency: Center for Connected and Automated Transportation
  • Contract Numbers: 69A3551747105
  • Files: UTC, RIP
  • Created Date: Apr 12 2018 8:58AM