Comparative Analysis of Empirical Bayes and Bayesian Hierarchical Models in Hotspot Identification

Abstract

Hotspot identification is an important step in the highway safety management process. Errors in hotspot identification (HSID) may result in an inefficient use of limited resources for safety improvements. The empirical Bayesian (EB) HSID has been widely applied as an effective approach in identifying hotspots. However, there are some limitations with the EB approach. It assumes that the parameter estimates of the safety performance function (SPF) are correct without any uncertainty, and does not consider temporal instability in crashes, which has been reported in recent studies. The Bayesian hierarchical model is an emerging technique that addresses the limitations of the EB method. Thus, the objective of this study is to compare the performance of the standard EB method and the Bayesian hierarchical model in identifying hotspots. Three methods (crash rate, EB, and the Bayesian hierarchical model) were applied to identify risky intersections with different significance levels. Four evaluation tests (site consistency, method consistency, total rank differences, and Poisson mean differences tests) were conducted to assess the performance of these three methods. The testing results suggest that: (1) the Bayesian hierarchical model outperforms the crash rate and the EB methods in most cases, and the Bayesian hierarchical model improves the accuracy of HSID significantly; and (2) hotspots identified with crash rates are generally unreliable. This is significant for roadway agencies and practitioners trying to accurately rank sites in the roadway network to effectively manage safety investments. Roadway agencies and practitioners are encouraged to consider the Bayesian hierarchical model in identifying hotspots.