先进的交通冲突分析,用于在混合交通中使用极端价值理论评估环形交叉路口的安全性
Abhijnan Maji1, Indrajit Ghosh1
1Department of Civil Engineering, Indian Institute of Technology (IIT) Roorkee, Roorkee, Uttarakhand 247667, India.
Accident; analysis and prevention
|May 17, 2025
概括
这项研究使用无人机数据和先进的方法来评估中低收入国家的绕道安全. 它通过机器学习识别交通冲突并预测其频率和严重程度,为更安全的道路设计提供了一个新的框架.
科学领域:
- 运输工程 运输工程
- 交通安全 交通安全
- 数据科学数据科学数据科学
背景情况:
- 在低收入中等收入国家评估道安全是具有挑战性的,因为事故数据不可靠.
- 替代安全措施 (SSM) 为安全评估提供了碰撞数据的替代方案.
- 不同质的交通条件和非车道式流量使传统的安全分析变得复杂.
研究的目的:
- 通过先进的图像处理和机器学习,开发一种用于评估环形交通安全的新框架.
- 使用替代安全措施 (SSM) 识别和分类交通冲突.
- 预测非车道为基础的异质交通环境中的交通冲突频率和严重程度.
主要方法:
- 使用高分辨率无人机视频提取车辆轨迹.
- 使用了替代安全措施 (SSM),包括碰撞时间 (TTC) 和碰撞后最大速度差 (MaxDeltaV).
- 极端价值理论 (EVT) 建立了SSM值,机器学习模型 (AdaBoost,Ordered Probit) 预测了冲突的频率和严重程度.
主要成果:
- 交通冲突被分为变更车道 (42%),后车道 (33%) 和交叉路口 (25%) 类型.
- 较宽的圆形道路减少了冲突频率,但随着交通量增加而增加.
- 确定了四个严重程度 (低,中等,高,非常高),其中更大的中央岛径和循环道路宽度降低了严重程度.
结论:
- 该研究提出了一个新的框架,将EVT应用于SSM门,用于混合交通中没有标志的环路.
- 集成了先进的统计和机器学习技术,以积极评估冲突频率和严重程度.
- 调查结果为改进圆圈设计和复杂交通条件下的安全提供了洞察力.
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