Optimizing CAV platoon movements in a signalized road network for travel and energy efficiency

The research team's previous work to optimize CAV platoon movements through an isolated intersection has shown that considerable savings (up to 40%) in both travel time and fuel use can be achieved. In this research, the team attempts to solve the CAV platoon optimization problem for a network of signalized intersections, which presents several challenges that their previous work did not address. In this research, the team proposes a cooperative platoon-trajectory-optimization framework that consists of four components: optimal route planning to identify the CAVs travel paths based on the real-time traffic state, a lane-changing strategy to form platoons for different travel directions, boundary control for generating the initial and final states of optimization, and a platoon-trajectory-optimization method to minimize the fuel consumption and travel time. Simulation studies will be carried out to evaluate the system performance of the proposed framework in reducing fuel consumption and travel delay.


  • English


  • Status: Completed
  • Funding: $180000
  • 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:


    Washington DC,   United States 
  • Project Managers:

    Kline, Robin

  • Performing Organizations:

    University of California, Davis

    1 Shields Ave
    Davis, California  United States  95616
  • Principal Investigators:

    Zhang, Michael

  • Start Date: 20210401
  • Expected Completion Date: 20220331
  • Actual Completion Date: 20220331
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01744086
  • Record Type: Research project
  • Source Agency: Center for Transportation, Environment, and Community Health
  • Contract Numbers: 69A3551747119
  • Files: UTC, RIP
  • Created Date: Jun 25 2020 1:53PM