可靠的车辆路由问题 使用交通传感器 增强信息
Ahmed Almutairi1, Mahmoud Owais2,3
1Department of Civil and Environmental Engineering, Majmaah University, Al-Majmaah 11952, Saudi Arabia.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究引入了一种使用深度学习的新型路由框架,以提高运输网络的可靠性. 它增强了流量预测,并减少了传感器需求,以更好地实时管理流量.
科学领域:
- 运输工程 运输工程
- 网络科学 网络科学
- 数据科学数据科学数据科学
背景情况:
- 随机路由面临不确定的旅行时间和有限的传感器数据带来的挑战.
- 由于动态流量波动,传统的自适应路由可能不足于最佳.
研究的目的:
- 开发一个新的路由框架,集成交通传感器数据增强和深度学习.
- 提高运输网络中路线选择和网络可观测性的可靠性.
主要方法:
- 随机流量分配和多目标路线生成.
- 最佳的交通传感器位置选择.
- 基于深度学习的流量估计,使用堆叠的Sparse自动编码器 (SAE).
主要成果:
- 该框架确定了最小的传感器部署,以准确地估计整个网络的流量.
- 该SAE模型推断了未观察到的链路流,提高了随机流量条件的可观察性.
- 数据驱动的方法大大减少了传感器部署的需求,同时保持了高流量预测准确度.
结论:
- 拟议的框架为实时交通管理提供了一个可扩展和具有成本效益的解决方案.
- 它解决了有限的传感器可用性和完整的网络可观测性之间的差距.
- 这提高了在随机运输网络中的车辆路由优化.
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