Highway traffic crashes exert a considerable impact on both transportation systems and the economy. In this context, accurate and dependable emergency responses are crucial for effective traffic management. However, the influence of crashes on traffic status varies across diverse factors and may be biased due to selection bias. Therefore, there arises a necessity to accurately estimate the heterogeneous causal effects of crashes, thereby providing essential insights to facilitate individual-level emergency decision-making. This paper proposes a novel causal machine learning framework to estimate the causal effect of different types of crashes on highway speed. The Neyman-Rubin Causal Model (RCM) is employed to formulate this problem from a causal perspective. The Conditional Shapley Value Index (CSVI) is proposed based on causal graph theory to filter adverse variables, and the Structural Causal Model (SCM) is then adopted to define the statistical estimand for causal effects. The treatment effects are estimated by Doubly Robust Learning (DRL) methods, which combine doubly robust causal inference with classification and regression machine learning models. Experimental results from 4815 crashes on Highway Interstate 5 in Washington State reveal the heterogeneous treatment effects of crashes at varying distances and durations. The rear-end crashes cause more severe congestion and longer durations than other types of crashes, and the sideswipe crashes have the longest delayed impact. Additionally, the findings show that rear-end crashes affect traffic greater at night, while crash to objects has the most significant influence during peak hours. Statistical hypothesis tests, error metrics based on matched "counterfactual outcomes", and sensitive analyses are employed for assessment, and the results validate the accuracy and effectiveness of our method.

翻译：交通事故对交通系统和经济产生显著影响。在此背景下，准确且可靠的应急响应对于有效的交通管理至关重要。然而，事故对交通状态的影响因多种因素而异，并可能因选择偏差而产生偏误。因此，有必要精确估计事故的异质性因果效应，从而为个体层面的应急决策提供关键依据。本文提出一种新颖的因果机器学习框架，用于估计不同类型事故对高速公路速度的因果效应。采用Neyman-Rubin因果模型（RCM）从因果视角构建问题，基于因果图理论提出条件沙普利值指数（CSVI）以筛选不良变量，进而采用结构因果模型（SCM）定义因果效应的统计估计量。通过双重稳健学习（DRL）方法估计处理效应，该方法将双重稳健因果推断与分类和回归机器学习模型相结合。基于华盛顿州5号州际高速公路4815起事故的实验结果表明，事故在不同距离和持续时间下存在异质性处理效应。追尾事故较其他类型事故引发更严重的拥堵和更长的持续时间，而侧擦事故的延迟影响最长。此外，追尾事故在夜间对交通影响更大，而碰撞物体事故在高峰时段影响最为显著。采用统计假设检验、基于匹配“反事实结果”的误差指标及敏感性分析进行评估，结果验证了所提方法的准确性与有效性。