Full Bayes Approach to Before-and-After Safety Evaluation with Matched Comparisons: Case Study of Stop-Sign In-Fill Program

Abstract

The full Bayes (FB) approach has recently been proposed for evaluating road safety treatments in before-and-after studies. In recognition of the advantages of the FB method and because of the lack of sufficient data to develop the safety performance function necessary to conduct an empirical Bayes study, the FB approach is used to determine the effectiveness of the Stop-Sign In-Fill (SSIF) program of the Canadian Insurance Corporation of British Columbia. The SSIF program funds the conversion from uncontrolled residential intersections to two-way stop-controlled intersections in an alternating pattern. This alternating pattern provides consistency in the application of stop signs within a residential neighborhood. Different modeling formulations for the before-and-after evaluation were investigated, and the results were compared with those of the traditional approach. No postprocessing of the results is required to achieve the odds ratio. The FB analysis revealed an overall significant reduction in predicted collision frequency of 51.1% with the credible interval (36.8%, 62.3%) at the 0.95 confidence level. It was also found that incorporating such design features as matched yoked comparison groups in collision prediction models may significantly improve the fit, while reducing the need to account for overdispersion. The results of the traditional technique were compatible with those of the FB approach at the overall level. It seems that the random selection of sites, which reduces the regression-to-the-mean effect, is the reason that both approaches gave relatively similar overall level results. However, the two methods produced quite different results at the zone (site) level.