United States Department of Transportation
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Development of Multimodal Traffic Signal Control
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2019-05-01
[PDF-2.91 MB]
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Abstract:Traffic congestion affects traveler mobility and also has an impact on air quality, which negatively affects public health. Sustainable mobility could enhance air quality and alleviate congestion. Accordingly, optimizing the utilization of the available infrastructure using advanced traffic signal controllers has become necessary to mitigate traffic congestion in a world with growing pressure on financial and physical resources. Hence, this work develops a novel real-time adaptive multi-modal decentralized traffic signal controller that integrates connected vehicles using a Nash bargaining game-theoretic framework by optimizing total queue length. This framework has a flexible phasing sequence and free cycle length, and thus can adapt to dynamic changes in traffic demand. The controller was implemented and evaluated using INTEGRATION microscopic traffic assignment and simulation software. The proposed controller was tested and compared to state-of-the-art isolated and coordinated traffic signal controllers. The developed controller integrates transit signal priority and freight signal priority to maximize flows in real-time using data collected from vehicles through vehicle-to-infrastructure wireless communications. The proposed controller was tested on an isolated intersection, arterial network, and large-scale networks. The simulation results demonstrate that the proposed decentralized controller reduces traffic congestion, fuel consumption and vehicle emission levels, and produces major improvements over other state-of-the-art centralized and de-centralized traffic signal controllers.
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