基于Q学习和速度转换矩阵的多代理自适应交通信号控制
Željko Majstorović1, Edouard Ivanjko1, Tonči Carić1
1University of Zagreb, Faculty of Transport and Traffic Sciences, Vukelićeva Street 4, 10000 Zagreb, Croatia.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
互联汽车 (CVs) 为更安全的道路提供实时交通数据. 本研究介绍了使用速度转换矩阵和多代理学习来改善交通流量的自适应信号控制.
科学领域:
- 运输工程 运输工程
- 人工智能的人工智能
- 交通管理系统 交通管理系统
背景情况:
- 互联汽车 (CVs) 提供实时微观交通数据,提高道路容量和安全.
- 车辆到一切 (V2X) 通信使CV能够充当移动传感器.
- 速度转换矩阵 (STM) 可以处理CV数据,同时保留时空特征.
研究的目的:
- 为互联汽车环境提出一种新的自适应交通信号控制战略.
- 为智能交通管理利用STM和合作型多代理学习.
- 在不同的CV透率和合作水平下评估拟议系统的性能.
主要方法:
- 开发一个自适应的交通信号控制系统,集成STM和合作的多代理学习.
- 模拟交叉网络环境,以测试拟议的控制策略.
- 对不同CV透率和代理合作系数的系统性能进行比较分析.
主要成果:
- 拟议的自适应交通信号控制系统在模拟交叉网络中证明了其有效性.
- 通过增加CV透率和更高的代理合作,观察到性能改善.
- 集成STM和多代理学习为交通信号优化提供了一个强大的方法.
结论:
- 使用STM和合作型多代理学习的新型自适应交通信号控制方法对CV环境有效.
- 这项研究强调了CV作为移动传感器的潜力,用于先进的交通管理.
- 未来的工作可以探索拟议系统的现实世界部署和可扩展性.
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