GNN-RMNet:利用图形神经网络和GPS分析用于物流中的驾驶员行为和路线优化
Eman Ali Aldhahri1, Abdulwahab Ali Almazroi2, Monagi Hassan Alkinani1
1Computer Science and Artificial Intelligence Department, Collage of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.
PloS one
|August 7, 2025
概括
本研究介绍了GNN-RMNet,这是一种用于实时驾驶员行为分析和物流中路线异常检测的深度学习系统. 它通过使用时空GPS和传感器数据来提高车队的安全性和效率.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 复杂的物流网络需要实时车辆数据进行监控.
- 传统的方法无法捕捉动态的时空空间驾驶员行为和路线异常.
- 为了高效的车队管理和安全,需要智能系统.
研究的目的:
- 引入GNN-RMNet,这是一个混合深度学习系统,用于驾驶员行为分析和路线异常检测.
- 为了实现实时分析时空GPS轨迹和车辆传感器数据.
- 提高物流监控的可解释性,可扩展性和效率.
主要方法:
- GNN-RMNet 结合了图形神经网络 (GNN),ResNet 和移动网络.
- 使用时空GPS轨迹和车辆传感器流用于上下文模式学习.
- 采用模块化设计,用于边缘和车载推理.
主要成果:
- 在真实数据集上实现了98%的准确性,97%的回忆率和97.5%的F1分数.
- 证明了高异常检测精度 (96%) 和路线偏差灵敏度 (95%).
- 通过模块化设计,推断延迟时间减少到32ms.
结论:
- 与基线模型相比,GNN-RMNet提供了准确性,效率和概括性的优势.
- 该框架支持实时车队安全,安全物流和智能运输系统.
- 未来的工作包括解决网络安全,数据隐私和多式联网传感器集成问题.
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