Alleviating Traffic Congestion: Developing and Evaluating Novel Multi-Agent Reinforcement Learning Traffic Light Coordination Techniques

Traffic congestion costs American cities tens of billions of dollars per year, not to mention its negative impact on the environment or people’s mental health. Novel Markov game models and advanced reinforcement learning algorithms hold the promise of drastically alleviating congestion through dynamic coordination of traffic signals and adaptive techniques to dynamically re-route traffic. This project involves a collaboration with Econolite, a leading provider of traffic management systems.

• Record URL:
  https://ppms.cit.cmu.edu/projects/detail/407

Language

• English

Project

• Status: Completed
• Funding: $200000
• Contract Numbers:

  69A3551747111

• Sponsor Organizations:

  Carnegie Mellon University

  Mobility21 National USDOT UTC for Mobility of Goods and People
  Pittsburgh, PA  United States  15213

  Office of the Assistant Secretary for Research and Technology

  University Transportation Center Program
  ,    
• Managing Organizations:

  Carnegie Mellon University

  Mobility21 National USDOT UTC for Mobility of Goods and People
  Pittsburgh, PA  United States  15213
• Project Managers:

  Kline, Robin

• Performing Organizations:

  Carnegie Mellon University

  ,    
• Principal Investigators:

  Fang, Fei

• Start Date: 20220701
• Expected Completion Date: 20230630
• Actual Completion Date: 20230707
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Congestion management systems; Coordination; Game theory; Traffic congestion; Traffic signal timing
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01848333
• Record Type: Research project
• Source Agency: National University Transportation Center for Improving Mobility (Mobility21)
• Contract Numbers: 69A3551747111
• Files: UTC, RIP
• Created Date: Jun 10 2022 2:34PM