This paper evaluates the performance of Poisson and negative binomial (NB) regression models in establishing the relationship between truck accidents and geometric design of road sections. Three typesof models are considered: Poisson regression, zero-inflated Poisson(ZIP) regression, and NB regression. Maximum likelihood (ML) methodis used to estimate the unknown parameters of these models. Two other feasible estimators for estimating the dispersion parameter in the NB regression model are also examined: a moment estimator and a regession-based estimator. These models and estimators are evaluated based on their (i) estimated regression paarmeters, (ii) overall goodness-of-fit, (iii) estimated relative frequency of truck accident involvements across road sections, (iv) sensitivity to the inclusion of short road sections, and (v) estimated total number of truck accident involvements. Data from the Highway Safety Information System are employed to examine the performance of these models in developing such relationships. (A)
Abstract