在混合交通中对道安全性进行评估的基于的多变量极值框架
Abhijnan Maji1, Indrajit Ghosh1
1Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India.
Accident; analysis and prevention
|August 28, 2025
概括
通过使用无人机录像和先进模型分析交通冲突, 它确定了高风险区域和冲突类型,以指导有针对性的安全干预.
科学领域:
- 运输工程
- 交通安全分析
- 数据驱动的建模
背景情况:
- 低收入和中等收入国家的环形车道面临着独特的安全挑战,原因是非车道交通和混合交通条件.
- 传统的基于碰撞的分析在确定这些复杂环境的前性安全措施方面是有限的.
研究的目的:
- 开发和验证一个数据驱动的框架,以积极识别和分类未经标志的环路上的安全关键冲突事件.
- 整合高分辨率视频分析,先进的统计建模和机器学习以进行全面的安全评估.
主要方法:
- 使用无人机录制的视频来提取车辆轨迹,并使用替代安全评估模型 (SSAM) 计算替代安全措施 (SSM),例如碰撞时间 (TTC) 和入侵后时间 (PET).
- 应用多变量极值理论 (EVT) - 峰值超过值 (POT) 建模,使用Gumbel-Hougaard偶数分析SSM的联合超值,并建立特定环境的值.
- 采用概率集群 (高斯混合模型) 进行空间冲突映射和潜伏配置分析,以按严重程度分层冲突.
主要成果:
- 确定了关键的冲突类型:交换车道 (43%),后端 (38%) 和交叉路口 (19%),具体的热点在靠近的腿部,编织区和行人交叉路口.
- 将冲突严重程度量化为五个级别,从轻微 (29.7%) 到严重 (7.6%),为优先级安全干预提供基础.
- 配合EVT-POT模型有效地捕捉了SSM之间的尾部依赖性,证明了其适用于联合风险评估.
结论:
- 开发的框架提供了可扩展和主动的方法,以提高在各种交通条件下的道安全性.
- 它使实践者能够精确地确定高风险区域并实施有针对性的安全对策,超越了基于碰撞的反应方法.
- 这项研究为改进环路设计和运营提供了宝贵的见解,特别是在资源有限的环境中.
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