基于OAS深度Q学习的快速和平稳的控制方法,用于城市动脉潮道的交通信号过渡
Luxi Dong1,2,3, Xiaolan Xie2,3, Jiali Lu4
1College of Earth Sciences, Guilin University of Technology, Guangxi Zhuang Autonomous Region, Guilin 541004, China.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
本研究介绍了一种适应性交通信号控制方法,用于潮车道,使用深度Q学习来确保顺和快速的过渡. 这种方法减少了交通延误和拥堵,提高了整体交通效率.
科学领域:
- 智能运输系统 智能运输系统
- 交通工程是交通工程.
- 控制理论 控制理论
背景情况:
- 潮车道上的交通流动波动对信号控制构成挑战.
- 现有的方法在信号方案更改期间难以保持平稳运行.
研究的目的:
- 开发一种用于控制潮车道上的交通信号转换的新方法.
- 为了使队列长度相等并最大限度地减少车辆在十字路口的延误.
- 提高交通效率,减少与拥堵有关的污染.
主要方法:
- 使用交通流冲突矩阵设计交叉点重叠阶段方案.
- 实施基于流量比率的快速和平稳的过渡方法.
- 使用深度Q学习进行自适应的交通信号转换控制.
- 分析各种潮道场景和相位偏移过渡.
主要成果:
- 与传统方法相比,拟议的方法实现了比传统方法更顺,更快速的交通信号转换.
- 在减少平均车辆延误和队列长度方面取得了明显的改进.
- 通过对交通控制接口的模拟实验进行验证.
结论:
- 适应性控制方法有效地管理潮车道上的交通信号转换.
- 实施为十字路口组提供了显著的好处,包括减少延误和改善空气质量.
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