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多种处理的道路安全评估:基于模拟的方法的比较

Yingheng Zhang¹, Haojie Li¹, Gang Ren¹

¹School of Transportation, Southeast University, China; Jiangsu Key Laboratory of Urban ITS, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, China.

Accident; analysis and prevention

|June 18, 2023

概括

本研究评估了使用因果推理的道路安全治疗方法. 通用随机森林 (GRF) 为复杂的,多处理的道路安全评估提供了卓越的性能.

科学领域:

运输科学运输科学
因果推理因果推理
统计建模统计建模

背景情况:

后期道路安全评估对于了解治疗有效性至关重要.
道路安全研究中的多重处理提出了独特的分析挑战.
现有的方法在建模复杂的道路安全干预措施时可能缺乏灵活性.

研究的目的:

引入潜在结果框架,以在多处理道路安全评估中正式确定因果估计.
为了比较各种估计方法的性能,以进行后续道路安全分析.
确定可靠的方法来评估结合的道路安全处理.

主要方法:

模拟实验使用来自伦敦20英里/小时区域的半合成数据.
评估回归,基于倾向分数 (PS) 的方法,以及通用随机森林 (GRF).
在不同的治疗分配标准和效果异质性下评估方法灵活性和性能.

主要成果:

基于倾向分数 (PS) 的方法和GRF在结果模型规范方面表现出更大的灵活性.
通用随机森林 (GRF) 显示出优越的性能,特别是基于标准的治疗分配和异质治疗效果.
潜在结果框架有效地对多种道路安全处理的因果估计进行了正式化.

结论:

潜在结果框架和GRF等先进估计方法对于后续道路安全研究是强烈推的.
通用随机森林 (GRF) 为分析复杂,多处理的道路安全干预提供了强大的工具.
准确评估结合的道路安全处理方法在实践中是有价值的,并且可以通过这些方法实现.

关键词:

因果关系影响因果关系影响.机器学习是机器学习.有多种治疗方法.潜在的结果框架倾向性得分的得分是多少?道路安全评估道路安全评估

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