智能交通信号灯控制网络物理系统
Siddhesh Deshpande1, Sheng-Jen Hsieh1
1Engineering Technology and Industrial Distribution Department, Texas A&M University, College Station, TX 77843, USA.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了使用摄像头和机器学习的智能交通信号灯系统,以减少车辆和行人延误. 动态交通间隔技术显著减少了十字路口等待时间.
科学领域:
- 智能运输系统 智能运输系统
- 网络物理系统 网络物理系统
- 机器学习应用 机器学习应用
背景情况:
- 现有的智能交通控制系统往往忽视了同时减少车辆和行人延误.
- 优化交通流需要动态适应实时交通条件.
研究的目的:
- 为智能交通信号灯控制提出一个网络物理系统,尽量减少车辆和行人延误.
- 实施动态交通间隔技术,用于自适应性交通信号管理.
主要方法:
- 利用交通检测摄像头,机器学习算法 (CNN,ANN,SVM) 和梯子逻辑进行控制.
- 开发了一种动态流量间隔技术,将流量分为低,中,高和非常高的流量.
- 使用城市移动模拟 (SUMO) 平台验证了系统.
主要成果:
- 动态交通间隔技术显示了显著的效率提高.
- 车辆等待时间减少了12%至27%.
- 与传统方法相比,行人等待时间减少了9%至23%.
结论:
- 拟议的网络物理系统有效地减少了车辆和行人交通延误.
- 动态交通间隔控制优于固定时间和半动态交叉路口管理方法.
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