The use of prediction models for eliminating effects due to regression-to-the-mean in road accident data

In recent years, various methods have been proposed for estimating the true accident level, i.e. the expected number of accidents m when a total of x accidents have been observed at a junction, road section, etc., during a certain period of time. One such method has been named the Empirical Bayes Method (EB method). A description is given of a variant of the EB method utilizing prediction models for the number of accidents. Input data to the prediction models may consist, for example, of traffic flows in a junction. According to empirical comparisons of accidents in junctions, this variant of the EB method may be preferable in certain cases to the conventional EB method. However, it has not yet been determined how this variant of the EB method should generally take into account the precision of the prediction models. This means, for example, that in a nonexperimental before-and-after study of the effect of a particular action, varying results may be obtained according to the assumptions made concerning the precision of the prediction model.