Traffic Signal Control for Large-Scale Urban Traffic Networks: Real-World Experiments using Vision-Based Sensors

Park, Jiho; Liu, Tong; Wang, Chieh; Wang, Hong; Wang, Qichao; Jiang, Zhong-Ping

doi:10.1109/ICCA62789.2024.10591878

Traffic Signal Control for Large-Scale Urban Traffic Networks: Real-World Experiments using Vision-Based Sensors

Conference · Thu Jul 25 04:00:00 EDT 2024

DOI:https://doi.org/10.1109/ICCA62789.2024.10591878· OSTI ID:2433918

Park, Jiho; Liu, Tong; Wang, Chieh; Wang, Hong; ; Jiang, Zhong-Ping

Effective control of traffic signals plays a critical role in ensuring smooth vehicle flow in urban areas. Expertly engineered traffic signal controllers can considerably minimize travel delays and enhance sustainability. In this paper, the team proposes the Model Predictive Control (MPC) traffic signal control strategy using real-time traffic flow data from a vision-based camera as feedback information. Also, a realistic signal timing plan that considers National Electrical Manufacturers Association (NEMA) constraints has been developed to be applied to real-world scenarios. The primary aim is to reduce the number of vehicles across all links in the controlled area, thereby optimizing traffic flow and reducing energy consumption. To validate the proposed method, several real-life experiments were conducted at 24 intersections in Chattanooga, Tennessee, by collaborating with traffic field engineers. These experiments demonstrated significant performance improvements in comparison to the existing method.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office; National Science Foundation (NSF)

DOE Contract Number:: AC36-08GO28308

OSTI ID:: 2433918

Report Number(s):: NREL/CP-2C00-90975; MainId:92753; UUID:a7f931c0-bd0c-42ef-9f5b-53cc40db5b98; MainAdminId:73427

Country of Publication:: United States

Language:: English

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