Prediction of road accidents: comparison of two Bayesian methods

Abstract

In this paper, two Bayesian methods for the development of accident prediction models are compared: the well-acknowledged Empirical Bayes (EB) method and a recently developed method based on Bayesian Probabilistic Networks (BPNs). Brief descriptions of the two methods are provided and their commonalities, differences, advantages and disadvantages are discussed. Both methods can be used to develop models for the multivariate prediction of accident events and can be included in road infrastructure safety management systems such as road safety impact assessments or road safety audits. Using a comprehensive data-set taken from the Austrian rural motorway network, it is shown that the predictions of both models are in good accordance with the data. It is observed that the BPN models show a higher degree of correlation with the data than the models developed using the EB method, as measured through the higher values of the correlation coefficients (∼5–10%).