Injury severity analysis of motorcycle crashes: A comparison of latent class clustering and latent segmentation based models with unobserved heterogeneity

The latent class clustering and latent segmentation-based models are employed to account for heterogeneity across different groups. Further, the random parameter variants of these modeling frameworks are employed to consider heterogeneity within the group. Both of these approaches have recently gained significant attention in road safety literature. However, the similarities and differences between these two methods are seldom explained and investigated. To that end, this study proposes to compare the performance of latent class clustering and latent segmentation-based random parameter models in examining crash injury severity outcomes. These models have been developed based on an ordered logit modeling framework to accommodate the ordinal nature of injury severity levels. For examining crash injury severity outcomes, this is the first study to consider the random parameter variant of ordered modeling structure within a latent segmentation modeling scheme. The current study also tests for and incorporates temporal instability of exogenous variables across multiple years of crash data in examining injury severity outcomes. The models have been estimated by using motorcycle crash data of Queensland, Australia, from the year 2012 through 2016. The comparison exercise is also augmented by estimating aggregate level elasticity effects of exogenous variables. The comparison exercise highlights the superiority of the latent segmentation approach in examining injury severity compared to the latent class clustering-based modeling approach. Moreover, the random parameter variants of both frameworks performed better than their fixed-parameter counterparts, which highlights the need to account for both across- and within-group heterogeneity. The temporal stability tests indicate that the effects of exogenous variables on the rider injury severity are different across year-wise models. (C) 2021 Elsevier Ltd. All rights reserved.