Trajectory Based Traffic Control with Low Penetration of Connected and Automated Vehicles

Traffic control is a critical component of the transportation infrastructure. The state-of-the-practice real-time signal control strategies including vehicle actuated control and adaptive control rely heavily on infrastructure-based sensors, including in-pavement or video based loop detectors for data collection. However, there are significant limitations using the infrastructure based detection. With the advances in CAV technologies, equipped vehicles can communicate with each other (vehicle-to-vehicle, V2V) and with the infrastructure (vehicle-to-infrastructure, V2I) through wireless communications. Therefore, real-time vehicle data can be collected by the infrastructure, from which vehicle trajectories can be constructed. The new source of data provides a much more complete picture of the traffic conditions around the intersection so that traffic controllers should be able to make “smarter” decisions. Meanwhile, trajectories of CAVs can also be controlled along with traffic signals to further improve traffic efficiency and gain environmental benefits. As a result, the control framework is extended from one dimension (temporal) to two dimensions (spatiotemporal). This project aims at developing new science and technology of vehicle trajectory based traffic control, especially under lower penetration of CAVs. Most of the existing models require at least a moderate penetration rate (e.g., 30%) to be effective. How to estimate real-time traffic condition and perform control under lower penetration rate (e.g., <10%) is still an open question. In addition, when the vehicle control is incorporated into signal control, usually a fully CAV environment is assumed. The interactions between CAVs and regular vehicles in a mixed traffic condition are not thoroughly investigated.


  • English


  • Status: Completed
  • Funding: $150,000
  • 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 Transportation Research Institute

    2901 Baxter Road
    Ann Arbor, Michigan  United States  48109

    University of Michigan, Ann Arbor

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

    Feng, Yiheng

    Liu, Henry

  • Start Date: 20180201
  • Expected Completion Date: 20220930
  • Actual Completion Date: 20240306
  • USDOT Program: University Transportation Centers Program
  • Subprogram: Research

Subject/Index Terms

Filing Info

  • Accession Number: 01665947
  • Record Type: Research project
  • Source Agency: Center for Connected and Automated Transportation
  • Contract Numbers: 69A3551747105
  • Files: UTC, RIP
  • Created Date: Apr 12 2018 10:33AM