走向更安全的高速公路工作区:使用改进的安全潜力现场和机器学习技术对碰撞风险的实证分析
Bo Wang1, Tianyi Chen2, Chi Zhang3
1School of Highway, Chang'An University, Xi'an 710064, China; School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore; Engineering Research Center of Highway Infrastructure Digitalization, Ministry of Education, Xi'an 710000, China.
Accident; analysis and prevention
|October 27, 2023
概括
高速公路工程区过渡区存在高事故风险. 一种新的工作区碰撞风险场 (WCRF) 方法有效地识别这些风险和关键因素,改善交通安全策略.
科学领域:
- 运输工程 运输工程
- 交通安全分析 交通安全分析
- 在土木工程中的机器学习
背景情况:
- 高速公路工程区过渡区由于复杂的交通动态而存在较高的撞车风险.
- 传统的替代安全措施 (SSM) 不足以评估这些复杂的环境中的碰撞风险.
- 确定特定的风险因素对于提高工作区高速公路安全至关重要.
研究的目的:
- 引入一个改进的安全潜力场,工作区碰撞风险场 (WCRF),用于测量高速公路工作区的车辆和道路段碰撞风险.
- 利用机器学习,特别是极端梯度提升 (XGBoost) 与沙普利增量解释 (SHAP),以识别关键风险因素.
- 与传统的SSM相比,评估WCRF的有效性,并分析工作区对高速公路安全的影响.
主要方法:
- 开发工作区碰撞风险场 (WCRF) 以量化单个车辆和细分级别的碰撞风险.
- 训练和验证XGBoost分类器使用基于时间窗口的流量数据和WCRF衍生的碰撞风险标签.
- 将SHAP值与XGBoost集成,以确定导致工作区崩的关键流量特征.
- 使用来自中国高速公路工程区的实时车辆轨迹数据的案例研究.
主要成果:
- 基于WCRF的SSM在识别高速公路工程区过渡区域内的撞车风险方面表现优于传统SSM.
- 分析揭示了工作区实施对高速公路安全的影响,包括车道特定的影响.
- 在不同工作区的风险贡献特征的异质性被确定和调查.
结论:
- 与现有的方法相比,基于WCRF的SSM提供了更实用和更全面的方法来评估高速公路工作区的碰撞风险.
- WCRF技术增强了关键风险贡献特征的识别,支持制定有针对性的安全管理策略.
- 这项研究为改善复杂的高速公路工作区环境中的交通安全工程和管理提供了宝贵的见解.
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