自学交通信号和连接自动化车辆的合作控制,以在十字路口优化安全和效率
Gongquan Zhang1, Fengze Li2, Dian Ren3
1School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China; Harvard Medical School, Harvard University, Boston 02138, United States.
Accident; analysis and prevention
|December 20, 2024
概括
本研究引入了深度强化学习 (DRL) 框架,用于协同控制交通信号和连接自动化车辆 (CAV). DRL系统优化了交通信号定时和CAV速度,大大提高了交通安全和效率.
科学领域:
- 智能运输系统 智能运输系统
- 在交通管理中的人工智能.
- 强化学习用于控制系统的强化学习
背景情况:
- 交通信号和互联自动化车辆 (CAV) 的合作控制有可能提高城市环境的安全性和减少拥堵.
- 将CAV集成到智能运输系统中需要先进的控制策略来实现动态适应.
研究的目的:
- 提出基于深度强化学习 (DRL) 的创新合作控制框架,以优化交通信号定时和CAV速度.
- 通过动态控制,提高混合交通环境中的交通安全和效率.
主要方法:
- 一个带有集成信号和速度模块的DRL框架,包含一个交通状态预测模型.
- 一个多目标奖励功能,评估安全性 (交通冲突预测) 和效率 (车辆等待时间).
- 使用双深Q网络 (DDQN) 进行信号控制政策学习和CAV的速度规划模型.
主要成果:
- 拟议的DRL框架在覆盖速度和计算时间方面表现出优于现有的基于DRL的交通信号控制 (DRL-TSC).
- 在各种条件下,与执行式,自适应式和DRL-TSC方法相比,在交通安全和效率方面观察到显著的改善.
- 拟议方法的好处随着CAV透率的提高而大幅增加.
结论:
- 开发的DRL合作控制框架有效优化了交通信号定时和CAV速度,以改善城市交通流动.
- 该框架在各种交叉路口类型,交通需求和时间跨度上显示出强大的性能.
- 这种方法代表了CAV在智能运输系统中集成和管理的重大进步.
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