An integrated and cooperative architecture for multi-intersection traffic signal control

Qiang Wu; Jianqing Wu; Bojian Kang; Bo Du; Jun Shen; Adriana Simona Mihăiţă

doi:10.48130/DTS-2023-0012

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Digital Transportation and Safety ›› 2023, Vol. 2 ›› Issue (2) : 150-163. DOI: 10.48130/DTS-2023-0012

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An integrated and cooperative architecture for multi-intersection traffic signal control

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Abstract

Traffic signal control (TSC) systems are one essential component in intelligent transport systems. However, relevant studies are usually independent of the urban traffic simulation environment, collaborative TSC algorithms and traffic signal communication. In this paper, we propose (1) an integrated and cooperative Internet-of-Things architecture, namely General City Traffic Computing System (GCTCS), which simultaneously leverages an urban traffic simulation environment, TSC algorithms, and traffic signal communication; and (2) a general multi-agent reinforcement learning algorithm, namely General-MARL, considering cooperation and communication between traffic lights for multi-intersection TSC. In experiments, we demonstrate that the integrated and cooperative architecture of GCTCS is much closer to the real-life traffic environment. The General-MARL increases the average movement speed of vehicles in traffic by 23.2% while decreases the network latency by 11.7%.

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Intelligent transport system / Traffic signal control / Traffic / Deep learning

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Qiang Wu, Jianqing Wu, Bojian Kang, Bo Du, Jun Shen, Adriana Simona Mihăiţă. An integrated and cooperative architecture for multi-intersection traffic signal control. Digital Transportation and Safety, 2023, 2(2): 150‒163 https://doi.org/10.48130/DTS-2023-0012

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This work is supported by the National Natural Science Foundation of China (Grant Nos. 61673150, 11622538). We acknowledge the Science Strength Promotion Programme of UESTC, Chengdu, China.