Generalized Sichel Crash Predictive Model: Analysis of Predictor-Dependent Overdispersion in Urban Signalized Intersections

Abstract

Planning to reduce accidents and the resulting costs often require crash prediction processes. Since the distribution of data related to the frequency of crashes often has long tails in addition to overdispersion, studies have shown that sometimes the negative binomial (NB) distribution cannot properly model this type of data. In this study, using the geometric, traffic and crash data of the main intersections in the city of Qazvin, Iran, with the help of NB and Sichel (SI) models in two fixed and predictor-dependent dispersion states of the crash counts, the effect of the two variables of traffic volume and lane width on the number of crashes has been analyzed. According to the analysis, it was found that the full generalized models can better show the effect of predictor variables on the number of crashes. Since the full generalized SI model (-Loglik = 180.03, AIC = 368.06, and BIC = 375.78) has lower goodness of fit criteria than the full generalized NB model (-Loglik = 183.36, AIC = 374.73, and BIC = 383.45), it has better efficiency. The conclusion does not apply for the models with reduced variables. The results show that the dispersion parameter of the SI model can estimate the level of dispersion with more accuracy and confidence.