Developing Hierarchical Bayesian Safety Performance Functions Using Real-Time Weather and Traffic Data

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Rongjie Yu
Mohammed Abdel-Aty

Safety Performance Function (SPF) is essential in traffic safety analysis, and it is useful to unveil hazardous factors related to the crash occurrence.  Recently, Bayesian inference technology has drawn many researchers’ interest as an alternative. 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, real-time traffic data prior to each crash were archived by 30 Remote Traffic Microwave Sensors (RTMS) and real-time weather information are provided by 6 weather stations along the studied roadway. For the model evaluation methods, Deviance Information Criterion (DIC), which recognized as Bayesian generalization of AIC (Akaike information criterion, and standard errors of the estimated coefficients for the independent variables was selected as the evaluation measure to select the best model(s). Comparisons across the models indicate that the Correlated Random effect Poisson model is superior with the smallest DIC values and the least standard errors. Model results indicate that hazardous factors related to the crash occurrence on the roadway segment should be studied by season. For example, the average temperature variable has a distinct coefficient sign for the snow and dry seasons. Moreover, two different sets of parameters have been concluded. Finally, conclusions have shed some lights on designing Active Traffic Management (ATM) strategies: for the dry season, the management strategy should focus on speed control and harmonize, while for the snow season special attentions are needed for the adverse weather conditions.

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