Potential Impact of Autonomous Vehicles on Reducing Congestion - Phase 2

Traffic congestion is a major problem in large metropolitan areas in the United States. In 2022, on average, a commuter lost about $1,259 in monetary terms annually due to congestion nationwide, which amounts to 8.7 billion lost hours in total. The lack of coordination among individual users, who make routing decisions independently based on current traffic information without anticipating that others may follow similar decision-making patterns, contributes significantly to the high cost of congestion. The behavior of drivers optimizing their individual routes leads to a state known as the User Equilibrium, leading to travel times that can be significantly higher than travel times from the System Optimal, particularly in congested urban networks where the effects of individual decisions cascade throughout the system. With the future emergence of autonomous vehicles, it is possible that organizations may now own more of the fleet of vehicles and control their routing, providing the organization more options for balancing route selections and thus making it possible to find routing solutions closer to the system optimal. Driverless ride-hailing companies such as Waymo have already begun their service in five major cities across the United States and Tesla has started to test their Robotaxi service in Austin, Texas. In Phase 1, the research team developed the research foundation for this problem. This work includes the literature review and the development of an online dispatch-and-relocation framework for a centrally controlled autonomous vehicle fleet. The Phase 1 framework matches requests to vehicles while accounting for pickup deadlines, near-term vehicle availability, and proactive repositioning toward forecasted demand. Phase 1 also establishes a comparison structure against a traditional human-driver ride-hailing system and an initial simulation capability that traces routes and estimates vehicle miles traveled, deadhead miles, passenger waiting time, revenue, and related performance measures. Phase 2 will build directly on this foundation and is the primary focus of the next stage of the project. In Phase 2, the team will scale the optimization and simulation framework so it can solve problems at the size of major metropolitan areas. This includes extending the model to larger networks and richer demand patterns, improving computational tractability for larger instances, and strengthening the simulation module so it can evaluate passenger-vehicle matches and route decisions under more realistic operating conditions. To make the model scalable, the team will aggregate the service region into zones and solve the resulting problems repeatedly over short rolling horizons. The team will also need to calibrate the demand forecasting and routing inputs for large urban networks and test the algorithms on progressively larger instances to ensure that the solution quality and computation time remain practical. The purpose of Phase 2 is to determine how much centralized control of autonomous fleets can reduce system-wide travel, deadhead mileage, waiting times, and congestion when evaluated on realistic metropolitan-scale settings.

• Record URL:
  • https://metrans.org/research/potential-impact-of-autonomous-vehicles-on-reducing-congestion-phase-2

Language

• English

Project

• Status: Programmed
• Funding: $80,000.00
• Contract Numbers:

  69A3551747109

  PSR-25-SP09

• 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:

  Pacific Southwest Region University Transportation Center

  University of Southern California
  Los Angeles, CA  United States  90089

  METRANS Transportation Consortium

  University of Southern California
  Los Angeles, CA  United States 
• Project Managers:

  Hong, Jennifer

• Performing Organizations:

  University of Southern California, Los Angeles

  University Park Campus
  Los Angeles, CA  United States  90089
• Principal Investigators:

  Dessouky, Maged

• Start Date: 20260915
• Expected Completion Date: 20270915
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Congestion management systems; Metropolitan areas; Ridesourcing; Routes and routing; Traffic simulation; Travel demand; Vehicle fleets
• Subject Areas: Highways; Operations and Traffic Management; Passenger Transportation; Planning and Forecasting;

Filing Info

• Accession Number: 01996390
• Record Type: Research project
• Source Agency: Pacific Southwest Region University Transportation Center
• Contract Numbers: 69A3551747109, PSR-25-SP09
• Files: UTC, RIP
• Created Date: Jul 22 2026 5:37PM