分析从两辆汽车碰撞到连锁反应机的过渡:使用随机参数逻辑模型,可解释机器学习和集群的混合方法
Seyed Alireza Samerei1, Kayvan Aghabayk1
1School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Accident; analysis and prevention
|May 3, 2024
概括
连锁反应撞车 (CRC) 是复杂的,从两辆车的事故升级. 本研究引入了一种新的混合模型,以确定关键的风险因素和相互作用,有助于制定针对性预防策略,以提高道路安全性.
科学领域:
- 运输工程 运输工程
- 交通安全 交通安全
- 数据科学在运输中的应用
背景情况:
- 连锁反应碰撞 (CRC) 从最初的两辆车的碰撞中升级,造成更严重的损伤.
- 了解CRC的进展对于开发有效的,但尚未探索的预防策略至关重要.
- 现有的计量经济学和机器学习 (ML) 模型在处理事故数据中未观察到的异质性和复杂相互作用方面存在局限性.
研究的目的:
- 开发和实施一种新的混合建模方法来分析连锁反应崩 (CRC).
- 克服现有的碰撞分析方法的局限性,特别是处理未观察到的异质性和随机参数效应.
- 识别显著的风险因素,它们的关键值,独特的效应和对CRC有助于CRC的相互作用.
主要方法:
- 一种混合方法,将随机参数逻辑模型与可解释的机器学习 (ML) 结合起来.
- 在碰撞分析中首次将先前的潜伏类集群与混合建模集成.
- 利用边际效应和SHAP (夏普利添加式扩展) 来解释风险因素及其跨集群的相互作用.
- 分析了伊朗11条郊区高速公路5年来的车祸,交通和几何数据.
主要成果:
- 在交通拥堵,高交通变化以及与纵向斜坡相结合的水平曲线上发现的CRC风险增加.
- 特定的风险因素表现出不同的或波动的影响,基于条件,如一天的时间,拥堵水平和天气.
- 夜间的风险包括在双车道高速公路上增加的拥堵和出现曲线/斜坡的恶劣天气. 白天的风险涉及拥堵部分的交通变化增加和中等拥堵地区的重型车辆交通.
结论:
- 混合集群和可解释的ML方法提供了对CRC风险因素及其复杂相互作用的更深入的理解.
- 确定的特定条件 (例如,夜间拥堵,白天交通变化,恶劣天气) 可以提高CRC风险.
- 调查结果为决策者提供了有价值的见解,以制定有针对性的和特定条件的策略,以防止连锁反应机.
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