.png){kind=icon}
Return
Temporal Difference-Aware Graph Convolutional Reinforcement Learning for Multi-Intersection Traffic Signal Control
.png){kind=icon}
PRE
AI
DOI：10.1109/TITS.2023.3311426.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Traffic light control
neural networks
graph neural network
reinforcement learning
Journal
IF:
8.4
Papers:
9.5K
Citations:
6.3W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSaveUsing Reinforcement Learning With Partial Vehicle Detection for Intelligent Traffic Signal Control
OAAI
OAAIShareSave
View more