Does random slope hierarchical modeling always outperform random intercept counterpart? Accounting for unobserved heterogeneity in a real-time empirical analysis of critical crash occurrence

Abstract Traffic crashes impose tremendous socio-economic losses on societies. To alleviate these concerns, countless traffic safety researches have shed light on the cognition of observable crash/crash severity contributing factors. Nonetheless, some influential factors might not be observable or measurable, referred to as unobserved heterogeneity, that could be accounted for by structuring random intercepts and slopes in hierarchical models. With this respect, although it is known random slopes can capture more unobserved heterogeneity, most previous studies utilized random intercepts to simplify result interpretations, indicating an inconsistency in the literature considering the hierarchical modeling specification. This study delves into the mentioned confusion within an empirical real-time clustering critical crashes, involving fatal or incapacitating injuries, versus non-critical crashes throughout 402-miles of Interstate-80 in Wyoming. The crash dataset was conflated with real-time traffic-related and environmental contributing factors. Regarding the inclusion of random intercepts and slopes, eleven Logistic regressions were conducted. As a data-dependent matter, results depicted random slopes, compared to random intercepts, do not necessarily enhance models’ out-of-sample predictive performance because they impose much more complexity on the models’ structure. Besides, considering the type of unobserved heterogeneity, if random slopes are required, random intercepts should be accompanied to allow data showing their true patterns.