DEVELOPING HIERARCHICAL BAYESIAN SAFETY PERFORMANCE FUNCTIONS USING REAL-TIME WEATHER AND TRAFFIC DATA

Safety Performance Function (SPF) is essential in traffic safety analysis, and it is useful to unveil hazardous factors related to the crash occurrence. Many alternative methodologies have been applied to develop the SPFs by the researchers (generalized linear regression methods, data mining techniques, and nonparametric statistical methods). Recently, Bayesian inference technology has drawn many researchers ’ interest. Among those frequently used models, Hierarchical Bayesian (HB) models are the most popular ones. One reason that HB models are frequently used is that the data structures for crash frequency studies are originally hierarchical (e.g. segment level, seasonal level, and corridor level). Besides, HB models are powerful enough to solve the over-dispersion issue which usually exists in traffic safety data. In this study, four types of HB models are compared (Poisson-gamma model, Random effects Poisson-gamma model, Correlated Random effects Poisson-lognormal model, Uncorrelated Random effects Poisson-lognormal model) with the basic Poisson model. The study focuses on a 15-mile mountainous freeway on I-70 in Colorado. Crash occurrences are aggregated at the homogenous segmentation level and the whole segment was split into 120 homogenous segments. Moreover