Prediction of road accidents: comparison of two Bayesian methods

Abstract

AbstractIn 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%).Keywords:: Bayesian networksEmpirical Bayesroads and highwaysprediction modelsroad accidentsstatistical analysis AcknowledgementsThe work presented in this paper was conducted with financial support by the Austrian Road Safety Board (KFV Austria) in 2011. The KFV Austria also provided and pre-processed the data.Notes1. Email: schubert@matrisk.com2. Email: adey@ibi.baug.ethz.ch3. It is distinguished between occurrence frequencies and occurrence rates. The first represents the number of accident events per road segment per year, and the latter represents the number of accident events per million vehicle kilometres per year.4. The term safety performance function is herewith equivalent to the term prediction model, because the outcomes, i.e. the estimates of the expected number of accident events, of both are identical.