A Clustering Regression Approach to Explore the Heterogeneous Effects of Risk Factors Associated with Teen Driver Crash Severity

Abstract

Teen drivers remain one of the most long-standing traffic safety concerns, as they continue to be overrepresented in the fatal and injury crash statistics. No previous studies have explored significant variations in the effect of risk attributes contributing to teen driver collisions in multiple crash circumstances by degrees of crash severity. Therefore, substantial exploration of teen driver crashes is expected to facilitate the strategic employment of countermeasures effectively. This study aimed to analyze teen driver crashes to investigate the heterogeneous effect of contributing factors on crash severity outcomes. Three years (2017 to 2019) of police-investigated crash information was used for the state of Alabama. This research first applied latent class clustering to minimize the heterogeneity in the extracted dataset by dividing the data into meaningful clusters (subgroups of the whole data). Then, multinomial logit models were constructed to illustrate the significant risk factors influencing the severity outcomes in different crash scenarios. Marginal effects were computed to understand the impacts of variable categories better. The findings suggested that the significance and estimated impact of variables varied within clusters and between crash severity levels in the same cluster. The results of latent class segmented submodels represented real-world crash patterns demonstrating the cumulative effect of variable attributes. Such contextual understandings of underlying risk factors could help to strengthen existing teen driver educational interventions. In addition, the study outcomes could assist practitioners and policy makers in developing safety improvement strategies to reduce the causalities associated with teen driver crashes in distinct circumstances.