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.

潜在类聚类和基于潜在分割的模型用于解释不同组之间的异质性。此外，这些建模框架的随机参数变体用于考虑组内的异质性。这两种方法最近在道路安全文献中都得到了极大的关注。然而，很少解释和研究这两种方法之间的异同。为此，本研究建议比较潜在类别聚类和基于潜在分割的随机参数模型在检查碰撞伤害严重程度结果方面的性能。这些模型是基于有序 logit 建模框架开发的，以适应伤害严重程度的顺序性质。为了检查碰撞伤害严重程度结果，这是第一项在潜在分割建模方案中考虑有序建模结构的随机参数变体的研究。目前的研究还测试并纳入了多年碰撞数据中外生变量的时间不稳定性，以检查伤害严重程度的结果。这些模型是通过使用澳大利亚昆士兰州 2012 年至 2016 年的摩托车碰撞数据进行估计的。还通过估计外生变量的总体水平弹性效应来增强比较练习。与基于潜在类聚类的建模方法相比，比较练习突出了潜在分割方法在检查损伤严重程度方面的优越性。此外，两个框架的随机参数变体都比固定参数的对应物表现得更好，这突出了考虑跨组和组内异质性的必要性。时间稳定性测试表明，外生变量对骑手伤害严重程度的影响在逐年模型中是不同的。