School of Rail Transportation, Soochow University, Jiangsu 215131, China; Alabama Transportation Institute, Tuscaloosa, AL 35487, USA.
Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA.
Accid Anal Prev. 2022 Apr;168:106622. doi: 10.1016/j.aap.2022.106622. Epub 2022 Feb 26.
The behavioral pathways in traffic crashes describe the chained linkages among contributing factors, pre-crash road user behaviors, and crash outcomes. Bicyclists are more vulnerable than motorists on road and their pre-crash behaviors play an essential role in the pathways leading to injuries. The objective of this study is to develop a methodological framework that integrates machine learning with path analysis to quantify behavioral pathways in bicycle-motor vehicle crashes. Specifically, two sets of models are developed for predicting: 1) pre-crash behaviors given contributing factors and 2) bicyclist injury severity given contributing factors including pre-crash behaviors. The path analysis chains machine learning models to establish the indirect linkages between contributing factors and injury severities through correlates of pre-crash behaviors. This study explored five machine learning methods, including Random Forest (RF), Categorical Naive Bayes (CNB), Support vector machine (SVM), AdaBoost (Boost), and Neural network (NN). To reduce the bias of any single model, this study proposes a technique to combine model estimates by averaging marginal effects. This study used a dataset containing 9,296 bicycle-motor vehicle crashes to demonstrate the application of the framework. Across five machine learning models, the signs of marginal effects generally agree but their magnitudes vary substantially. The pre-crash behavior of "bicyclist failed to yield" increases bicyclist injury severity by 1.11%. The path analysis results highlighted contributing factors related to risky pre-crash behaviors that lead to severe injuries, such as bicyclist intoxication. The framework is expected to support agencies' decision-making to improve cycling safety by reducing unsafe behaviors on roads.
交通事故中的行为途径描述了导致事故的因素、碰撞前的道路使用者行为和碰撞后果之间的链式联系。与机动车驾驶员相比，自行车骑手在道路上更容易受到伤害，他们的碰撞前行为在导致受伤的途径中起着至关重要的作用。本研究的目的是开发一种将机器学习与路径分析相结合的方法框架，以量化自行车-机动车碰撞中的行为途径。具体来说，开发了两组模型来预测：1）给定导致事故的因素的碰撞前行为，以及 2）给定包括碰撞前行为在内的导致事故的因素的自行车骑手伤害严重程度。路径分析将机器学习模型连接起来，通过碰撞前行为的相关因素建立导致事故的因素与伤害严重程度之间的间接联系。本研究探索了五种机器学习方法，包括随机森林 (RF)、分类朴素贝叶斯 (CNB)、支持向量机 (SVM)、AdaBoost (Boost) 和神经网络 (NN)。为了减少任何单一模型的偏差，本研究提出了一种通过平均边际效应来组合模型估计的技术。本研究使用包含 9296 起自行车-机动车碰撞的数据集来演示该框架的应用。在五个机器学习模型中，边际效应的符号通常一致，但幅度差异很大。“自行车骑手未避让”的碰撞前行为使自行车骑手的伤害严重程度增加了 1.11%。路径分析结果突出了与导致严重伤害的危险碰撞前行为相关的导致事故的因素，例如自行车骑手醉酒。该框架有望通过减少道路上的不安全行为来支持机构的决策，以提高骑行安全性。
