Comparison of modelling methods accounting for temporal correlation in crash counts

Temporal correlation in crash counts is an important issue that road safety researchers face when developing crash prediction models. Several modelling methods have been proposed to date to address this issue. Random effects negative binomial (RENB) model, random parameters negative binomial (RPNB) model, multilevel negative binomial (MLNB) model, negative multinomial (NM) model, and generalized estimating equation (GEE) model are the most frequently used ones. Although those models have been applied successfully, it is not clear to researchers what their differences are and how to make a choice among them. This study attempts to answer these questions through describing the main features of the five models, showing their connections, illustrating their applications to predict the crash frequency on basic freeway segments, and discussing their differences that can guide the choice making. The application results show that the RENB, MLNB, and RPNB models are superior to others in terms of predictability (for the current data set), capability, usability, and interpretability. Three fundamental differences, including the ways to handle temporal correlation, whether to account for other heterogeneities, and the interpretation of estimated coefficients, provide some guidance on the model choice.