基于车辆轨迹的交通冲突预测在尖的水平曲线上
Hao Li1,2, Xiaofei Zhang1,2
1Key Laboratory of Intelligent Health Perception and Ecological Restoration of Rivers and Lakes, Ministry of Education, Hubei University of Technology, Wuhan, China.
Traffic injury prevention
|October 17, 2025
概括
这项研究引入了CNN-LSTM模型,使用车辆轨迹数据预测曲线上的交通冲突. 该模型实现了高精度,提供了更好的安全性和智能运输系统开发.
科学领域:
- 智能运输系统 (ITS) 是一种智能运输系统.
- 交通工程是交通工程.
- 机器学习在运输中的应用
背景情况:
- 利的曲线段带来了严重的交通安全风险.
- 准确预测交通冲突情况对于道路安全至关重要.
- 现有的方法可能不足以准确地捕捉车辆相互作用的复杂时空动态.
研究的目的:
- 开发一种可靠的方法来量化曲线上的交通冲突概率.
- 提出并验证混合卷积神经网络-长期短期记忆 (CNN-LSTM) 模型用于轨迹冲突预测.
- 建立一个全面的评估框架来评估模型性能和概括性.
主要方法:
- 使用无人机和车载记录器获取多源车辆轨迹数据.
- 实施卡尔曼过以减少噪声和估计运动状态.
- 开发一种混合CNN-LSTM模型,集成时空特征提取和时间依赖性建模,与SVM,XGBoost,GNN-LSTM,Vanilla LSTM和Bi-LSTM进行基准测试.
主要成果:
- 在预测曲线上的交通冲突方面,CNN-LSTM模型显著超过了基准模型.
- 实现了高性能指标:准确度>85%,精度>82.7%,回忆>89.9%,F1评分>86.1%.
- 在阶级不平衡的场景中表现出强大的概括,平均AUC-ROC>93.5%.
结论:
- CNN-LSTM模型在提取时空特征和适应车辆冲突风险演变模式方面提供了卓越的能力.
- 为优化利曲线几何设计提供智能决策支持.
- 建立了实时动态风险预警系统的基础,并推进了基于数据的智能运输管理.
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