This study proposes a framework of a model-based hot spot identification method by applying full Bayes (FB) 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 EB 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.
Empirical Evaluation of Alternative Approaches in Identifying Crash Hot Spots
Naive Ranking, Empirical Bayes, Full Bayes Methods
Transportation Research Record
Transportation Research Record: Journal of the Transportation Research Board ; 2103 , 1 ; 32-41
2009-01-01
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Identifying Large Truck Hot Spots Using Crash Counts and PDOEs
Online Contents | 2011
|Identifying Large Truck Hot Spots Using Crash Counts and PDOEs
British Library Online Contents | 2011
|Bayesian Latent Class Safety Performance Function for Identifying Motor Vehicle Crash Black Spots
Transportation Research Record | 2016
|Alternative Approaches to Occupant Response Evaluation in Frontal Impact Crash Testing
SAE Technical Papers | 2016
|