Multi-vehicle (MV) crashes, which can lead to great damages to society, have always been a serious issue for traffic safety. A further understanding of crash severity can help transportation engineers identify the critical reasons and find effective countermeasures to improve transportation safety. However, studies involving methods of machine learning to predict the possibility of injury-severity of MV crashes are rarely seen. Besides that, previous studies have rarely taken temporal stability into consideration in MV crashes. To bridge these knowledge gaps, two kinds of models: random parameters logit model (RPL), with heterogeneities in the means and variances, and Random Forest (RF) were employed in this research to identify the critical contributing factors and to predict the possibility of MV injury-severity. Three-year (2016–2018) MV data from Washington, United States, extracted from the Highway Safety Information System (HSIS), were applied for crash injury-severity analysis. In addition, a series of likelihood ratio tests were conducted for temporal stability between different years. Four indicators were employed to measure the prediction performance of the selected models, and four categories of crash-related characteristics were specifically investigated based on the RPL model. The results showed that the machine learning-based models performed better than the statistical models did when taking the overall accuracy as an evaluation indicator. However, the statistical models had a better prediction performance than the machine learning models had considering crash costs. Temporal instabilities were present between 2016 and 2017 MV data. The effect of significant factors was elaborated based on the RPL model with heterogeneities in the means and variances.

中文翻译：

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多车事故中伤害严重程度的决定因素和预测

多车（MV）撞车事故可能会严重损害社会，一直是交通安全的一个严重问题。对碰撞严重性的进一步了解可以帮助运输工程师确定关键原因并找到有效的对策，以提高运输安全性。但是，很少涉及涉及机器学习方法来预测MV碰撞严重程度的可能性的研究。除此之外，以前的研究很少在MV碰撞中考虑到时间稳定性。为了弥合这些知识差距，本研究采用两种模型：均值和方差均具有异质性的随机参数对数模型（RPL）和随机森林（RF）来识别关键的贡献因素并预测发生这种情况的可能性。 MV损伤严重程度。从高速公路安全信息系统（HSIS）中提取的美国华盛顿三年（2016-2018）MV数据用于碰撞伤害严重性分析。此外，针对不同年份之间的时间稳定性进行了一系列似然比测试。使用四个指标来衡量所选模型的预测性能，并基于RPL模型专门研究了四类与碰撞相关的特征。结果表明，将整体准确性作为评估指标时，基于机器学习的模型的性能要优于统计模型。但是，与考虑崩溃成本的机器学习模型相比，统计模型的预测性能更好。2016年至2017年MV数据之间存在时间不稳定性。