从对齐和环境特征的角度预测和分析碰撞风险:在一个丘陵地区的高速公路上进行的一项研究
Pengcheng Qin1, Jie He1, Changjian Zhang2
1School of Transportation, Southeast University, Nanjing, China.
Traffic injury prevention
|February 21, 2025
概括
在丘陵高速公路上的碰撞风险可以使用对齐和环境数据来预测. XGBoost和SHAP模型识别了导致高风险地区的诸如车辆组合和道路几何等因素.
科学领域:
- 道路安全工程工程 道路安全工程
- 交通运输安全运输安全
- 机器学习在土木工程中的应用.
背景情况:
- 在丘陵地形的高速公路存在独特的安全挑战,因为复杂的对齐和环境限制.
- 这些道路上的高碰撞风险威胁着生命和财产,需要先进的预测方法.
研究的目的:
- 通过使用对齐和环境特征,调查山区高速公路的碰撞风险预测.
- 为目标安全改进确定导致高碰撞风险的关键决定因素.
主要方法:
- 利用了中国西南部一条高速公路5年来的车祸数据.
- 使用集群算法进行风险级别分类,使用DeepLabv3进行环境特征提取.
- 开发和评估了四个集体学习模型来预测碰撞风险,包括XGBoost.
主要成果:
- XGBoost模型表现出高精度 (0.9259) 和F1得分 (0.8886) 的卓越性能.
- 与高风险的正相关性包括卡车/汽车比例,建筑和道路存在;负相关性包括植被.
- 特定的对齐特征,如长的坡,紧的曲线和短的过渡曲线,与事故风险的增加有关.
结论:
- 结合XGBoost和SHAP的新方法使得即使没有历史撞车数据,也能够准确地预测山区高速公路上的撞车风险.
- 这种方法增强了对调整和环境因素如何影响碰撞风险的理解,促进了主动安全措施.
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