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

Authors :
Lin, Wei-Yu
Song, Yun-Zhu
Ruan, Bo-Kai
Shuai, Hong-Han
Shen, Chih-Ya
Wang, Li-Chun
Li, Yung-Hui
Source :
IEEE Transactions on Intelligent Transportation Systems; January 2024, Vol. 25 Issue: 1 p327-337, 11p
Publication Year :
2024

Abstract

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.

Details

Language :
English
ISSN :
15249050 and 15580016
Volume :
25
Issue :
1
Database :
Supplemental Index
Journal :
IEEE Transactions on Intelligent Transportation Systems
Publication Type :
Periodical
Accession number :
ejs65220782
Full Text :
https://doi.org/10.1109/TITS.2023.3311426