一个多式联网深度融合框架用于高速公路交通异常检测和检测
Mengmeng Duan1,2, Shaowei Sun3,4, Mingzhou Liu5
1Anhui Provincial Key Laboratory of Transportation Information and Security for Universities, Hefei, 230601, China.
Scientific reports
|September 29, 2025
概括
本研究引入了使用异质图神经网络 (HGNN) 和对比悲观概率估计 (CPLE) 进行准确的高速公路交通异常检测的新框架. 多式联运方法提高了智能运输系统的实时预测和稳定性.
科学领域:
- 智能运输系统 智能运输系统
- 深度学习用于流量分析.
- 图形神经网络的神经网络
背景情况:
- 由于依赖单个数据源,传统的交通监控系统在准确性和稳定性方面扎.
- 城市化日益增长,需要先进的解决方案来实时检测和预测交通异常.
- 交通数据中的复杂的时空依赖性对现有模型构成了挑战.
研究的目的:
- 提出一种新的多式联网深度融合框架,用于检测和预测异常交通事件.
- 为了提高交通异常检测的准确性,实时性能和稳定性.
- 解决单一数据源监控系统的局限性.
主要方法:
- 开发了一种多模式深度融合框架,集成异质图形神经网络 (HGNNs).
- 通过对比的悲观概率估计 (CPLE) 算法来增强框架的稳定性.
- 集成多种数据源:视频图像,交通流量,车辆速度和道天气条件.
主要成果:
- 拟议的MHGNN-CPLE模型在静态检测任务中实现了卓越的性能,准确度为0.980和F1得分为0.967.
- 在各种场景中准确识别异常交通事件的高精度和稳定性.
- 在动态交通场景中在不同噪音水平下保持高精度和强大的稳定性.
结论:
- 多式联运框架有效地整合了各种交通数据,捕捉了复杂的时空依赖关系.
- HGNN和CPLE算法为实时交通异常检测提供了可靠和准确的解决方案.
- 代表智能运输系统的重大进步,改善交通管理和安全.
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