Empirical Evaluation of Alternative Approaches in Identifying Crash Hot Spots Naive Ranking, Empirical Bayes, and Full Bayes Methods

This study proposes a framework of a model-based hot spot identification method by applying full Bayes (1713) technique. In comparison with the state-of-the-art approach [i.e., empirical Bayes method (EB)], the advantage of the FB method is the capability to seamlessly integrate prior information and all available data into posterior distributions on which various ranking criteria could be based. With intersection crash data collected in Singapore, an empirical analysis was conducted to evaluate the following six approaches for hot spot identification: (a) naive ranking using raw crash data, (b) standard Ell ranking, (c) FB ranking using a Poisson-gamma model, (d) FB ranking using a Poisson-lognormal model, (e) FB ranking using a hierarchical Poisson model, and (f) FB ranking using a hierarchical Poisson (AR-1) model. The results show that (a) when using the expected crash rate-related decision parameters, all model-based approaches perform significantly better in safety ranking than does the naive ranking method, and (b) the FB approach using hierarchical models significantly outperforms the standard EB approach in correctly identifying hazardous sites.