Establishing a Simulation Package and Testbed for Traffic Congestion Reduction Using Deep Reinforcement Learning

Traffic congestion not just causes travel delays but also increases fuel consumption and emissions production [1]. One of the major reasons for congestion in urban areas is traffic accidents. Currently, traffic cameras and video surveillance are some of the ways used to monitor the traffic [2]. However, these methods are capital demanding and do not provide real-time trip information to the travelers. New technologies, such as vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) communication, may be able to greatly reduce congestion. This type of communication allows real-time detection of congestion, which can result in immediate distribution of traffic affected by the congestion and therefore result in a more efficient transportation network. Advances in wireless communication technology, for instance, advanced 5G communication networks will enable this interconnection and will allow users to make better decisions regarding the use of the transportation system. In the foreseen transportation infrastructure, vehicles will communicate with other vehicles, traffic control units, and traffic management centers, to make more efficient trip decisions. All these technologies have paved a solid foundation for autonomous driving, which has been identified as a national priority for future technologies with an expected $488 billion in annual savings from reducing traffic accidents and another $158 billion in savings due to reduced fuel costs [3]. This project aims to develop a simulation package for autonomous driving and route redirection in a designated region using reinforcement learning (RL) algorithms. The developed RL algorithms will determine the motion and routes of vehicles considering the shortest traveling path, shortest traveling time, and traffic conditions to reduce traffic congestion. The research team will further verify the algorithms using a hardware-in-the-loop testbed including scale-down tracks, car-like rovers, and traffic signaling systems. As more and more traffic-related data has been collected and deposited in the past decades, the application of data-driven artificial intelligence to solve traffic congestion is one of the most promising approaches for intelligent transportation systems. This project will advance two national priority areas in research: artificial intelligence and autonomous driving. All outcomes of the project will be shared with DOT and 3rd party stakeholders to benefit the community at large. The proposed project will also seamlessly integrate the booming research on RL with educational activities by training undergraduates with senior design and research experiences for the undergraduate programs at UTSA, training graduate students with thesis and dissertation projects, and high school students with a summer training program to prepare the future workforce for intelligent transportation systems.

  • Supplemental Notes:
    • 22ITSUTSA34


  • English


  • Status: Active
  • Funding: $106000
  • 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:

    Transportation Consortium of South-Central States (Tran-SET)

    Louisiana State University
    Baton Rouge, LA  United States  70803
  • Project Managers:

    Mousa, Momen

  • Performing Organizations:

    University of Texas at San Antonio

    One UTSA Circle
    San Antonio, TX  United States  78249
  • Principal Investigators:

    Jin, Yufang

  • Start Date: 20220401
  • Expected Completion Date: 0
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers Program

Subject/Index Terms

Filing Info

  • Accession Number: 01844944
  • Record Type: Research project
  • Source Agency: Transportation Consortium of South-Central States (Tran-SET)
  • Contract Numbers: 69A3551747106
  • Files: UTC, RIP
  • Created Date: May 9 2022 6:00AM