Accident prediction models for roads with minor junctions

Abstract

The purpose of this study was to develop and validate a method for predicting expected accidents on main roads with minor junctions where traffic counts on the minor approaches are not available. The study was based on data for some 3800 km of highway in the U.K. including more than 5000 minor junctions. The highways consisted of both single and dual-carriageway roads in urban and rural areas. Generalized linear modelling was used to develop regression estimates of expected accidents for six highway categories and an empirical Bayes procedure was used to improve these estimates by combining them with accident counts. Accidents on highway sections were shown to be a non-linear function of exposure and minor junction frequency. For the purposes of estimating expected accidents, while the regression model estimates were shown to be preferable to accident counts, the best results were obtained using the empirical Bayes method. The latter was the only method that produced unbiased estimates of expected accidents for high-risk sites.