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Temporal Difference-Aware Graph Convolutional Reinforcement Learning for Multi-Intersection Traffic Signal Control
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Temporal Difference-Aware Graph Convolutional Reinforcement Learning for Multi-Intersection Traffic Signal Control

Wei-Yu Lin, Yun-Zhu Song, Bo-Kai Ruan, Hong-Han Shuai, Chih-Ya Shen, Li-Chun Wang 和 Yung-Hui Li
IEEE Transactions on Intelligent Transportation Systems
2023
DOI: https://doi.org/10.1109/TITS.2023.3311426

摘要

Adaptation models Convolution graph neural network neural networks Optimal control reinforcement learning Roads Traffic control Traffic light control Turning Urban areas Automotive Engineering Mechanical Engineering Computer Science Applications
Traffic light control plays a crucial role in intelligent transportation systems. This paper introduces Temporal Difference-Aware Graph Convolutional Reinforcement Learning (TeDA-GCRL), a decentralized RL-based method for efficient multi-intersection traffic signal control. Specifically, we put forward a new graph architecture using each lane as a node for considering intersection relations. Additionally, we propose two new rewards by considering temporal information, namely Temporal-Aware Pressure on Incoming Lanes (TAPIL) and Temporal-Aware Action Consistency (TAAC), which enhance learning efficiency and time-interval sensitivity. Experimental results on five datasets show the superiority of TeDA-GCRL over state-of-the-art methods by at least 9.5% in average travel time.

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