Depth-based hotspot identification and multivariate ranking using the full Bayes approach.

Although the multivariate structure of traffic accidents has been recognized in the safety literature for over a decade now, univariate identification and ranking of hotspots is still dominant. The present paper advocates the use of multivariate identification and ranking of hotspots based on statistical depth functions, which are useful tools for non-parametric multivariate analysis as they provide center-out ordering of multivariate data. Thus, a depth-based multivariate method is proposed for the identification and ranking of hotspots using the full Bayes (FB) approach. The proposed method is applied to a sample of 236 signalized intersections in the Greater Vancouver Area. Various multivariate Poisson log-normal (MVPLN) models were used for data analysis. For each model, the FB posterior estimates were obtained using the Markov Chains Monte Carlo (MCMC) techniques and several goodness-of-fit measures were used for model selection. Using a depth threshold of 0.025, the proposed method identified 26 intersections (11%) as potential hotspots. The choice of a depth threshold is a delicate decision and it is suggested to determine the threshold according to the amount of funding available for safety improvement, which is the usual practice in univariate hotspot identification (HSID). Also, the results show that the performance of the proposed multivariate depth-based FB HSID method is superior to that of an analogous method based on the depths of accident frequency (AF) in terms of sensitivity, specificity and the sum of norms (lengths) of Poisson mean vectors.