Statistical Safety Performance Models considering Pavement and Roadway Characteristics

Abstract

Transportation agencies build statistical models and predict the average crash frequency to identify hazardous road sections and make informed decisions to reduce crashes. In this paper, safety performance models (SPFs) were built and evaluated considering various pavement and roadway characteristics, including pavement friction, which is seldom available for analysis. Four count data models—Poisson model, negative binomial (NB) model, hurdle-NB model (HNB), and zero-inflated NB (ZINB) model—were built based on roadway characteristics and crash data provided by the Oklahoma Department of Transportation (ODOT). Pavement friction, roadway geometry, surface condition characteristics, and traffic exposure were considered the contributing factors to traffic crashes. Established models were compared in terms of the goodness-of-fit, zero inflation, and statistical significance of factors. The HNB model exhibited promising fitting performance with a manageable number of influencing variables. Coefficients in the HNB model suggest that adequate pavement friction and the presence of shoulders can significantly reduce the crash frequency and thus improve roadway safety performance. Potential issues of the statistical models, such as unobserved heterogeneity and multicollinearity, were also discussed. The relation between roadway infrastructure characteristics (including pavement friction) and roadway safety revealed in this study could assist in choosing the proper statistical model for better decision-making and selecting appropriate preventive treatments for improved roadway safety.