基于混合方法调查对交通安全的影响因素:以行人为例
Yue Li1, Yuanyuan Shi1, Huiyuan Xiong1
1Transport Planning and Research Institute, Ministry of Transport, Beijing 100028, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
本研究使用混合模型确定了影响车辆碰撞中行人受伤严重程度的关键因素. 这些发现旨在在智能交通系统 (ITS) 中加强道路安全战略.
科学领域:
- 运输科学 运输科学
- 公共安全公众安全.
- 数据科学数据科学数据科学
背景情况:
- 道路交通安全是全球关注的关键问题,行人安全至关重要.
- 智能交通系统 (ITS) 的进步需要有效的策略来防止行人死亡.
研究的目的:
- 调查影响车辆事故中行人受伤严重程度的因素.
- 提出和验证用于分析事故数据的混合建模方法.
主要方法:
- 使用混合方法,将随机参数逻辑模型和随机森林与SHAP (夏普利增量扩展) 方法结合起来.
- 从多个基于传感器的来源分析了涉及行人撞车数据.
主要成果:
- 确定了8个影响行人受伤严重程度的重要因素.
- 混合模型在因素识别和贡献分析方面展示了互补的优势.
- 某些因素,如"工作日或非工作日","信号控制模式"和"道路安全属性"被认为没有重要意义.
结论:
- 混合方法为行人与车辆碰撞动态提供了强大的洞察力.
- 结果可以为制定有针对性的ITS战略提供信息,以提高行人安全.
- 对于有效的安全干预来说,全面的分析,同时考虑显著和重要的因素至关重要.
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