Spatial zero-inflated negative binomial regression models: Application for estimating frequencies of rear-end crashes on Thai highways

Abstract

Objective: Rear-end crashes are a type of road traffic accident that occurs frequently. Currently, the application of advanced statistical models to predict the frequency of accident numbers has increased because such models enable accuracy in predictions. The study focuses on the application of these statistical models to determine the relationship between explanatory variables and the frequency of rear-end crashes. Method: Data used are rear-end collisions occurring on highways throughout Thailand for the years 2011–2018. The number of rear-end collisions was distributed according to road segments with similar physical characteristics. Spatial correlation was utilized by varying according to the jurisdiction of the Department of Highways. Four models, namely, Poisson regression model, negative binomial model, zero-inflated negative binomial model, and spatial zero-inflated negative binomial (SZINB) model were developed. Results: When compared with the conditional Akaike Information Criterion (cAIC), SIZNB was found to be most suitable for data. Regarding random effect results, the effect of the significance was constant for the variables conditional state and zero state, which covered segment length, number of lanes, and traffic volume. Conclusion: This study can serve as a starting point for researchers interested in applying the spatial model to the analysis of rear-end crashes.