使用连接车辆数据识别高风险高速公路段:经验分析
Xueao Li1, Junhua Wang1, Ting Fu1
1The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China; College of Transportation Engineering, Tongji University, 4800 Cao'an Highway, Shanghai 201804, China.
Accident; analysis and prevention
|February 3, 2026
概括
连接车辆 (CV) 警告数据,包括前行监控警告 (HMW) 和前行碰撞警告 (FCW),可以主动识别高风险高速公路段. 这些数据改善了道路安全分析,比传统方法更早地检测出新出现的风险.
科学领域:
- 运输工程 运输工程
- 交通安全分析 交通安全分析
- 数据科学数据科学数据科学
背景情况:
- 传统的道路安全分析依赖于历史事故数据,面临数据稀缺和报告不足等局限性.
- 联网汽车 (CV) 技术提供实时驾驶行为数据,使主动安全评估成为可能.
- CVs产生关键警告,例如前进监控警告 (HMW) 和前进碰撞警告 (FCW).
研究的目的:
- 通过CV警告数据主动识别高风险高速公路段.
- 开发一个综合框架,将空间热点识别和道路安全的统计回归建模结合起来.
- 评估心血管警告频率与碰撞事件之间的关系.
主要方法:
- 利用了上海九条高速公路的简历数据.
- 应用空间热点识别技术 (莫兰的I,Getis-Ord Gi*) 来定位HMW和FCW的集群.
- 采用Poisson,负二项式和仪表变量Poisson模型来分析警告数据和碰撞之间的关联.
主要成果:
- HMW和FCW频率与碰撞事件有显著的积极关联.
- 考虑到交通冲突和碰撞之间的内质性,可以提高估计的可靠性.
- 被确定为CV警告热点的部分,即使没有先前的碰撞数据,也显示出更高的碰撞率,表明早期风险检测.
结论:
- 简历警告数据为及时和主动识别道路安全风险提供了宝贵的工具.
- 开发的框架有效地整合了空间分析和统计建模,以加强交通安全评估.
- 这种方法促进了CV数据的应用,用于在高速公路上主动风险管理.
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