Developing safety performance functions for a mountainous freeway

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Rongjie Yu
Mohamed Abdel-Aty

Safety Performance Function (SPF) is essential in traffic safety analysis; it is useful to unveil hazardous factors that related to crash occurrence. Variety data resources have been employed to develop safety performance functions: geometric characteristic features, traffic status information, weather, and surface conditions. This study focuses on a mountainous freeway that features mountainous terrain and adverse weather. Five years crash data (2006-2010), roadway geometry, and traffic characteristics were included in the investigation. As an aggregate analysis, explanatory variables used in the safety performance functions were typically averaged values over a certain time interval. For example, the most applied exposure factor, average annual daily traffic (AADT) is the mean values of the segment annual daily traffic volumes. Moreover, speed limits were included in the models to represent the effects of different traffic characteristics on crash occurrence. However, values of daily volumes and average speeds vary across the whole year, especially for a mountainous freeway suffered from adverse weather and steep slopes. In this study, distributions of daily volume and distributions of 5-min average speed were prepared for each segment. These two distribution variables would be analyzed along with the traditional variables like longitudinal grades, degrees of curvature, and etc to develop SPFs for the mountainous freeway. Data from a 15-mile mountainous freeway on I-70 in Colorado were utilized. Five years crash data have been analyzed along with the Bayesian random effects Poisson model; daily volumes were captured by Remote Traffic Microwave Sensor (RTMS) detectors and segment average speeds were archived by Automatic Vehicle Identification (AVI) systems. Three models have been estimated: (1) ordinal safety performance function with fixed AADT and speed limits; (2) SPF with daily volume distributions; and (3) SPF with both volume and speed distributions. Deviance Information Criterion (DIC), which recognized as Bayesian generalization of AIC (Akaike information criterion) has been select to evaluate the three candidate models. Results indicated that SPFs with distribution variables would improve the model fits significantly.

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