How heterogeneity has been examined in transportation safety analysis: A review of latent class modeling applications

• Published in: Analytic methods in accident research Vol. 40; p. 100292
• Main Author: Kim, Sung Hoo
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2023
• Subjects: Finite mixture; Heterogeneity; Latent class model; Random parameters; Safety analysis; Segmentation; Finite mixture; Heterogeneity; Segmentation; Latent class model; Random parameters; Safety analysis
• ISSN: 2213-6657; 2213-6657
• DOI: 10.1016/j.amar.2023.100292

•Reviewed studies using latent class modeling in the transportation safety field.•Suggested a typology of segmentation-based heterogeneity models.•Examined how the safety literature has treated each of the key elements of the latent class model.•Compared two popular approaches of modeling heterogeneity: random parameters and latent class models.•Discussed further issues of modeling heterogeneity in safety analyses. This study explores how heterogeneity has been examined in transportation safety analyses, specifically focusing on latent class modeling, which has gained popularity and has successfully captured unobserved heterogeneity. The study firstly identifies a large volume of relevant papers in the safety analysis domain and analyzes how models have been used by focusing on key elements of the latent class model (along with the proposed typology of segmentation-based heterogeneity models). In the literature, various class-specific outcome models have been used. They are determined by the type of outcome variable and are also highly associated with the analysis context. For example, crash severity and crash likelihood/frequency analyses are the main applications where crash severity is often treated as binary, nominal, or ordered, whereas crash likelihood/frequency is subject to count data or survival data modeling. The study reviews the number of classes selected in empirical applications and how they were determined. It is found that in safety analyses, it is more common to choose the number of classes based on the judgement of the analyst than quantitative measures (e.g., BIC). This implies that we value interpretability of the latent class model and solutions with many classes (i.e., greater model complexity, many parameters) often hinder the interpretation of models. This paper also covers further discussions about heterogeneity including model comparisons (homogeneity models versus latent class models and random parameters versus latent class models), modeling intra-class heterogeneity, possible alternative model specifications that have been rarely used in the literature, and issues related to temporal instability.