基于机器学习的自适应性交通预测和控制,使用边缘冲动平台
Manoj Tolani1, G E Saathwik2, Ayush Roy2
1Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India. manoj.tolani@manipal.edu.
Scientific reports
|May 17, 2025
概括
本研究介绍了一种自主交通控制系统,该系统使用传感器和机器学习来动态调整交通信号时间. 这种智能系统可以减少交通拥堵和延误,而无需人类干预.
科学领域:
- 智能运输系统 智能运输系统
- 机器学习应用 机器学习应用
- 自主控制系统 自主控制系统
背景情况:
- 交通拥堵和延误是当前车辆交通控制中的重要问题.
- 传统的固定计时器的交通信号系统对于不可预测的交通条件是不够的.
研究的目的:
- 开发一个自动化交通控制系统,根据实时车辆密度调整信号时间.
- 通过动态信号调整来缓解交通拥堵和减少延误.
主要方法:
- 使用近距离传感器来检测接近的车辆,监控它们的速度和密度.
- 实施基于Edge-Impulse的机器学习模型,用于预测车辆密度和到达时间.
- 根据预测和实时交通数据动态调整交通信号时间.
主要成果:
- 拟议的系统通过优化信号定时,有效减少交通拥堵和延误.
- 机器学习算法可以准确预测交通状况.
- 交通安排过程的自动化可以最大限度地减少人为错误,并提高道路安全.
结论:
- 开发的方法为现代交通控制提供了一个智能,高效和自主解决方案.
- 该系统在真实世界的交通场景中展示了可靠性和准确性.
- 这种方法有可能显著改善现有的交通管理系统.
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