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A Reinforcement Learning-Based Distributed Control Scheme for Cooperative Intersection Traffic Control
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DOI：10.1109/ACCESS.2023.3283218.png)
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摘要
En 中文
Traffic congestion is a major source of discomfort and economic losses in urban environments. Recently, the proliferation of traffic detectors and the advances in algorithms to efficiently process data have enabled taking a data-driven approach to mitigate congestion. In this context, this work proposes a reinforcement learning (RL) based distributed control scheme that exploits cooperation among intersections. Specifically, a RL controller is synthesized, which manipulates traffic signals using information from neighboring intersections in the form of an embedding obtained from a traffic prediction application. Simulation results using SUMO show that the proposed scheme outperforms classical techniques in terms of waiting time and other key performance indices.
Keyword:
Predictive models
Detectors
Real-time systems
Data models
Urban areas
Graph neural networks
Decentralized control
Reinforcement learning
cyber-physical systems
intersection control
distributed control
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
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