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The fully Bayesian (FB) approach for identification of collision black spots has been available for some time. However, little research has been conducted on the performance of the FB method, especially on criteria for ranking sites. A study was done to fill this void by a thorough evaluation of the FB method for black spot identification. First, an investigation compared the FB approach with the now-traditional empirical Bayesian method. It was confirmed that the FB method was superior for key ranking criteria [the posterior Poisson mean (PM) of crash frequency and potential for safety improvement] based on evaluation criteria, including sensitivity and specificity, and the sum of the PM. Next, eight ranking criteria, which included PM, posterior expected, mode and median ranks, and probability of being the worst, were proposed and evaluated for the best of several FB model variations explored. The mode rank of the posterior distribution of the Poisson mean proved to be the most promising because it tended to provide the best results, especially for top-ranked sites. The sum of the Poisson mean was also found to be a solid evaluation criterion, especially for limited numbers of top-ranked sites.

The objective of this study was to develop crash modification factors for four treatment types: rectangular rapid-flashing beacon (RRFB), pedestrian hybrid beacon (PHB), pedestrian refuge island (RI), and advance yield or stop markings and signs (AS). From 14 cities throughout the United States, 975 treatment and comparison sites were selected. Most of the treatment sites were selected at intersections on urban, multilane streets, because these locations present a high risk for pedestrian crashes and are where countermeasures typically are needed most. For each treatment site, relevant data were collected on the treatment characteristics, traffic, geometric, and roadway variables, and the pedestrian crashes and other crash types that occurred at each site. Cross-sectional regression models and before–after empirical Bayesian analysis techniques were used to determine the crash effects of each treatment type. All four of the treatment types were found to be associated with reductions in pedestrian crash risk, compared with the reductions at untreated sites. PHBs were associated with the greatest reduction of pedestrian crash risk (55% reduction), followed by RRFBs (47% reduction), RIs (32% reduction), and AS (25% reduction). The results for RRFBs had their basis in a limited sample and must be used with caution.