交通状态和道路几何形状对高速公路交通事故严重性的非线性影响:一种机器学习方法
Yao Liang1, Hongxia Yuan2, Zhenwu Wang2
1Green and Low Carbon Transport Research Centre, Sichuan Communication Surveying and Design Institute Co., Ltd, Chengdu, China.
PloS one
|November 22, 2024
概括
交通和道路设计显著影响事故严重程度,卡车百分比和道路等级等因素显示出非线性影响. 了解这些关系可以改善高速公路的安全性和设计.
科学领域:
- 运输工程 运输工程
- 交通安全研究 交通安全研究
- 机器学习在土木工程中的应用
背景情况:
- 传统的事故严重程度研究通常使用定性道路描述和平均交通数据.
- 需要分析特定的道路设计元素和实时交通状况,以便更准确地了解事故严重程度.
研究的目的:
- 调查交通条件和道路几何形状对交通事故严重性的非线性和值影响.
- 通过使用先进的机器学习模型,识别影响事故严重性的关键因素及其相互作用.
- 为改善高速公路设计和安全管理提供数据驱动的见解.
主要方法:
- 从G42高速公路收集了道路设计文件,交通数据和事故数据.
- 评估了四种机器学习模型:极端梯度增强,梯度增强决策树,随机森林和轻梯度增强机器.
- 利用最佳的随机森林模型和部分依赖图片来分析对事故严重性的因素影响.
主要成果:
- 确定了卡车百分比,每日交通量,斜坡长度,道路梯度,曲率和曲线长度对事故严重性的显著非线性和值影响.
- 揭示了与不同事故严重程度相关的特定道路和交通特征.
- 证明了机器学习在发现交通安全数据中复杂关系方面的有效性.
结论:
- 特定的道路几何设计元素和交通条件对事故严重程度产生了关键的,往往非线性影响.
- 这些发现支持制定有针对性的安全干预措施和改进高速公路设计标准.
- 这项研究为运输安全的数据驱动决策提供了基础.
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