A survey on reinforcement learning-based control for signalized intersections with connected automated vehicles

IF 9.5 1区 工程技术 Q1 TRANSPORTATION
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引用次数: 0

Abstract

Recent advancements in connected automated vehicles (CAVs) and reinforcement learning (RL) hold significant promise for enhancing intelligent traffic control systems. This paper conducts a systematic review of studies on RL-based urban traffic control at signalised intersections, highlighting the significant impact of CAVs on traffic control performance improvement. We first review the fundamental concepts of RL algorithms, establishing a foundational understanding for subsequent RL-based traffic control methods. We then review recent progress in RL-based traffic signal control using CV/CAV trajectory data, RL-based CAV trajectory planning, and the cooperative control of both traffic signals and CAVs at signalised intersections. Our aim is to provide researchers with a comprehensive roadmap for future research in RL-based traffic control at signalised intersections.
基于强化学习的互联自动驾驶车辆信号交叉口控制研究
互联自动驾驶车辆(CAV)和强化学习(RL)的最新进展为增强智能交通控制系统带来了巨大希望。本文系统回顾了信号交叉口基于 RL 的城市交通管制研究,强调了 CAV 对改善交通管制性能的重要影响。我们首先回顾了 RL 算法的基本概念,为后续基于 RL 的交通控制方法奠定了基础。然后,我们回顾了基于 RL 的交通信号控制(使用 CV/CAV 轨迹数据)、基于 RL 的 CAV 轨迹规划以及信号交叉口交通信号和 CAV 协同控制方面的最新进展。我们的目标是为研究人员提供一个全面的路线图,帮助他们在信号交叉路口开展基于 RL 的交通控制的未来研究。
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来源期刊
Transport Reviews
Transport Reviews TRANSPORTATION-
CiteScore
17.70
自引率
1.00%
发文量
32
期刊介绍: Transport Reviews is an international journal that comprehensively covers all aspects of transportation. It offers authoritative and current research-based reviews on transportation-related topics, catering to a knowledgeable audience while also being accessible to a wide readership. Encouraging submissions from diverse disciplinary perspectives such as economics and engineering, as well as various subject areas like social issues and the environment, Transport Reviews welcomes contributions employing different methodological approaches, including modeling, qualitative methods, or mixed-methods. The reviews typically introduce new methodologies, analyses, innovative viewpoints, and original data, although they are not limited to research-based content.
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