区域特定的高速公路道的实时交通冲突风险建模:CrossTabNet方法
Jieling Jin1, Jipu Li2, Shan Tian2
1Intelligent Transportation System Research Center, Wuhan University of Technology, Wuhan 430063, China; Engineering Research Center of Transportation Information and Safety (ERCTIS), MoE of China, Wuhan 430063, China.
Accident; analysis and prevention
|October 10, 2025
概括
本研究介绍了高速公路道的区域特定交通冲突风险模型,使用深度学习来改善安全预测. 特定区域的模型显著优于全球模型,突显了局部道安全分析的必要性.
科学领域:
- 运输工程 运输工程
- 交通安全 交通安全
- 机器学习 机器学习
背景情况:
- 传统的碰撞数据对于高速公路道安全分析是有限的.
- 实时交通冲突风险建模对于主动安全措施至关重要.
- 现有的道区划框架缺乏精确风险归因的细节性.
研究的目的:
- 为高速公路道开发一个区域特定的,实时的交通冲突风险建模框架.
- 通过整合交通冲突分析,精细的区分和可解释的深度学习来加强道安全评估.
- 通过使用车辆轨迹数据来解决传统碰撞数据的局限性.
主要方法:
- 利用车辆轨迹数据,根据交通冲突推导出替代安全措施.
- 通过了精细的五区道分类 (入口前,入口,内部,出口,后出口).
- 开发了一个CrossTabNet架构,将特征交互层和TabNet编码器结合起来,用于高阶非线性关系建模和可解释性.
主要成果:
- 与既有方法相比,拟议的特定区域模型显示出优越的预测性能.
- 特定区域的模型显著超过了全球模型,强调了本地化建模的必要性.
- 全球敏感性分析确定了上游流量流动的变化,作为所有区域冲突风险的持续积极贡献者.
结论:
- 局部化建模对于有效的高速公路道安全评估至关重要.
- 该框架为适应性,针对区域的交通安全干预提供了有价值的见解.
- 调查结果强调了考虑不同道段内的不同局部动态对于安全管理的重要性.
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