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Use of Adaptive Traffic Management System to Reduce Queue Spillback

Team Members Heading link

  • James Berggren
  • Michelle Calcagno
  • Tresor Moolo
  • Yuna Wang

Advisors: Bobby Gunnells, PE; Dr. Farid Peiravian

Project Description Heading link

Queue spillback is a major contributor to congestion in heavily traversed urban intersections. Drivers who queue in an intersection block cross movements, completely restricting traffic flow and greatly reducing intersection and system efficiency. Traffic congestion does not only cause delay, but also negatively impacts the environment, economy, productivity, and quality of life. The cost of congestion in the City of Chicago, alone, is more than $7 billion annually. Studies performed using computer models and connected vehicles indicate that improved signal coordination and adaptive traffic management systems are effective methods for improving traffic flow in a system and reducing queue spillback. Additional models have shown that the improved traffic signal effectiveness from real-time traffic detection significantly reduces congestion and emissions.

This study seeks to demonstrate to a local department of transportation the benefit of implementing a real-time traffic detection system in a test area bordering the Loop in downtown Chicago. The primary objective is to prevent the spillback that causes congestion by utilizing an adaptive traffic management system. The second objective is to demonstrate the benefits of bringing Chicago up to date technologically as well as improving emergency management capabilities and communications with other municipalities. This is to be achieved with the use of Video Image Processors (VIP), included in the adaptive traffic management system, that can be used to identify and fine motorists that block intersections. The study will compare the current system traffic flow to the traffic flow in an adaptive traffic management system in the study area to determine the impact on the Level of Service (LOS). LOS analysis will be used to estimate the changes in emissions, fossil fuel consumption, and the cost of congestion.

See supporting documentation in the team’s Box drive.