ABSTRACT

As a key area of traffic safety research, crash severity modelling has attracted tremendous attention. Recently, there has been growing interest in applying machine learning (ML) methods in this area. However, the lessons and experience learned so far have not been systematically documented and summarised. This is the first article that surveys studies on ML applications in crash severity modelling and has the following major contributions: (1) it provides a comprehensive and critical review of current research efforts; (2) it summarises the successful experience and main challenges (e.g. data and methodology); and (3) it identifies promising research opportunities towards accurate and reliable crash severity modelling and results interpretation. The review results suggest that imbalanced data remains a major issue. Under- and over-samplings are often used to balance crash severity data despite their limitations. Some studies use local sensitivity analysis (LSA) to interpret ML modelling results but ignore the strict assumptions of LSA and omit the joint effects of risk factors. Moreover, very few studies consider the accuracy and reliability of ML model evaluation metrics. Other issues include spatiotemporal correlations, causality, model transferability and heterogeneity. This paper concludes by providing suggestions on model selection and modification to address the identified issues and recommendations for future research. For example, employing advanced ML methods such as graph convolutional networks (GCN) to model spatiotemporal correlations; exploring innovative ways of applying ML methods; and leveraging new developments in ML (e.g. interpretable ML) to derive causal relationships and interpret modelling results.

摘要

作为交通安全研究的一个关键领域，碰撞严重程度建模引起了极大的关注。最近，人们对在该领域应用机器学习 (ML) 方法越来越感兴趣。然而，迄今为止所吸取的教训和经验还没有被系统地记录和总结。这是第一篇调查碰撞严重性建模中 ML 应用研究的文章，具有以下主要贡献：（1）对当前的研究工作进行了全面和批判性的回顾；(2) 总结成功经验和主要挑战（如数据和方法）；(3) 它为准确可靠的碰撞严重性建模和结果解释确定了有前景的研究机会。审查结果表明，不平衡的数据仍然是一个主要问题。尽管存在局限性，欠采样和过采样通常用于平衡碰撞严重性数据。一些研究使用局部敏感性分析 (LSA) 来解释 ML 建模结果，但忽略了 LSA 的严格假设并忽略了风险因素的联合效应。此外，很少有研究考虑 ML 模型评估指标的准确性和可靠性。其他问题包括时空相关性、因果关系、模型可转移性和异质性。本文最后提供了模型选择和修改的建议，以解决已识别的问题和对未来研究的建议。例如，采用图卷积网络（GCN）等高级机器学习方法对时空相关性进行建模；探索应用 ML 方法的创新方法；并利用机器学习的新发展（例如