A Reinforcement Learning-Based Distributed Control Scheme for Cooperative Intersection Traffic Control

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 prop...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Guzman, Jose A., Pizarro, German, Nunez, Felipe
Format: Article
Language:English
Subjects:
Algorithms
Cooperative control
Cyber-physical Systems
Data models
Decentralized control
Detectors
Distributed Control
Economic impact
Graph neural networks
Intersection Control
Performance indices
Predictive models
Real-time systems
Reinforcement Learning
Traffic congestion
Traffic control
Traffic engineering
Traffic information
Traffic intersections
Traffic signals
Urban areas
Urban environments
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3283218