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Multilevel analysis in road safety research

机译：道路安全研究中的多层次分析

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Hierarchical structures in road safety data are receiving increasing attention in the literature and multilevel (ML) models are proposed for appropriately handling the resulting dependences among the observations. However, so far no empirical synthesis exists of the actual added value of ML modelling techniques as compared to other modelling approaches. This paper summarizes the statistical and conceptual background and motivations for multilevel analyses in road safety research. It then provides a review of several ML analyses applied to aggregate and disaggregate (accident) data. In each case, the relevance of ML modelling techniques is assessed by examining whether ML model formulations (ⅰ) allow improving the fit of the model to the data, (ⅱ) allow identifying and explaining random variation at specific levels of the hierarchy considered, and (ⅲ) yield different (more correct) conclusions than single-level model formulations with respect to the significance of the parameter estimates. The evidence reviewed offers different conclusions depending on whether the analysis concerns aggregate data or disaggregate data. In the first case, the application of ML analysis techniques appears straightforward and relevant. The studies based on disaggregate accident data, on the other hand, offer mixed findings: computational problems can be encountered, and ML applications are not systematically necessary. The general recommendation concerning disaggregate accident data is to proceed to a preliminary investigation of the necessity of ML analyses and of the additional information to be expected from their application.
机译：道路安全数据中的分层结构在文献中受到越来越多的关注，并提出了多级（ML）模型来适当处理观察结果之间的相关性。但是，到目前为止，与其他建模方法相比，还没有关于ML建模技术的实际增加值的经验综合。本文总结了道路安全研究的统计和概念背景以及进行多层次分析的动机。然后，它回顾了应用于合并和分解（意外）数据的几种ML分析。在每种情况下，通过检查ML模型公式（ⅰ）是否允许改善模型对数据的拟合度，（ⅱ）允许识别和解释所考虑层次的特定级别的随机变化，来评估ML建模技术的相关性，以及（ⅲ）关于参数估计的重要性，得出的结论与单层模型公式不同（更正确）。根据分析涉及的是汇总数据还是分类数据，所审查的证据提供了不同的结论。在第一种情况下，机器学习分析技术的应用显得简单而相关。另一方面，基于分类事故数据的研究则得出了不同的结论：可能会遇到计算问题，而系统地应用ML则不是必需的。关于分类事故数据的一般建议是对ML分析的必要性以及从其应用中预期获得的其他信息进行初步调查。

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