Traffic Signal Timing Optimization Based on Intersection Importance in Vehicle-Road Collaboration

Liu, Pengna; Qiao, Ziyan; Wu, Yalun; Chen, Kang; Hou, Jiasong; Cai, Yingqi; Chu, Liqun; Tong, Endong; Niu, Wenjia; Liu, Jiqiang

doi:10.1007/978-981-97-2458-1_6

Lecture Notes in Computer Science

2024

DOI: 10.1007/978-981-97-2458-1_6

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Traffic Signal Timing Optimization Based on Intersection Importance in Vehicle-Road Collaboration

Pengna Liu,

Ziyan Qiao,

Yalun Wu

et al.

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Deep Reinforcement Learning for Ecological and Distributed Urban Traffic Signal Control with Multi-Agent Equilibrium Decision Making

Yan,

Wang

2024

Electronics

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Multi-Agent Reinforcement Learning (MARL) has shown strong advantages in urban multi-intersection traffic signal control, but it also suffers from the problems of non-smooth environment and inter-agent coordination. However, most of the existing research on MARL traffic signal control has focused on designing efficient communication to solve the environment non-smoothness problem, while neglecting the coordination between agents. In order to coordinate among agents, this paper combines MARL and the regional mixed-strategy Nash equilibrium to construct a Deep Convolutional Nash Policy Gradient Traffic Signal Control (DCNPG-TSC) model, which enables agents to perceive the traffic environment in a wider range and achieves effective agent communication and collaboration. Additionally, a Multi-Agent Distributional Nash Policy Gradient (MADNPG) algorithm is proposed in this model, which is the first time the mixed-strategy Nash equilibrium is used for the improvement in the Multi-Agent Deep Deterministic Policy Gradient algorithm traffic signal control strategy to provide the optimal signal phase for each intersection. In addition, the eco-mobility concept is integrated into MARL traffic signal control to reduce pollutant emissions at intersections. Finally, simulation results in synthetic and real-world traffic road networks show that DCNPG-TSC outperforms other state-of-the-art MARL traffic signal control methods in almost all performance metrics, because it can aggregate the information of neighboring agents and optimize the agent’s decisions through gaming to find an optimal joint equilibrium strategy for the traffic road network.

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Deep Reinforcement Learning for Ecological and Distributed Urban Traffic Signal Control with Multi-Agent Equilibrium Decision Making

Yan,

Wang

2024

Electronics

View full text Add to dashboard Cite

show abstract

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Traffic Signal Timing Optimization Based on Intersection Importance in Vehicle-Road Collaboration

Cited by 1 publication

References 22 publications

Deep Reinforcement Learning for Ecological and Distributed Urban Traffic Signal Control with Multi-Agent Equilibrium Decision Making

Deep Reinforcement Learning for Ecological and Distributed Urban Traffic Signal Control with Multi-Agent Equilibrium Decision Making

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