AI-enabled Transportation Network Analysis, Planning and Operations

Vehicle connectivity and automation would make vehicle trajectory data more readily available. The proposed research aims to leverage this dataset and recent advancements in implicit deep learning to develop an end-to-end modeling framework that would transform the way how metropolitan planning organizations (MPO) analyze, plan and manage their transportation networks. The proposed framework can directly take empirical, sampled trajectory data as inputs to learn drivers’ route choice behaviors and estimate traffic flow distribution across an urban traffic network. The proposed framework can further prescribe strategies such as lane direction configuration, parking provision, cordon pricing and perimeter control, to better manage the existing supply of urban traffic networks to reduce congestion


  • English


  • Status: Completed
  • Funding: $137014
  • 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:

    University of Michigan Transportation Research Institute

    2901 Baxter Road
    Ann Arbor, Michigan  United States  48109
  • Project Managers:

    Bezzina, Debra

    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
  • Principal Investigators:

    Yin, Yafeng

  • Start Date: 20220401
  • Expected Completion Date: 20230831
  • Actual Completion Date: 20240219
  • USDOT Program: University Transportation Centers
  • Subprogram: Research

Subject/Index Terms

Filing Info

  • Accession Number: 01842626
  • Record Type: Research project
  • Source Agency: Center for Connected and Automated Transportation
  • Contract Numbers: 69A3551747105
  • Files: UTC, RIP
  • Created Date: Apr 18 2022 11:47AM