The empirical Bayes (EB) approach has now gained wide acceptance among researchers as the much preferred one for the before-after evaluation of road safety treatments. In this approach, the before period crash experience at treated sites is used in conjunction with a crash prediction model for untreated reference sites to estimate the expected number of crashes that would have occurred without treatment. This estimate is compared to the crashes observed after treatment to evaluate the effect of the treatment. This procedure accounts for regression-to-the-mean effects that result from the natural tendency to select for treatment those sites with high observed crash frequencies. Of late, a fully Bayesian (FB) approach has been suggested as a useful, though complex alternative to the empirical Bayes approach in that it is believed to require less data for untreated reference sites, it better accounts for uncertainty in data used, and it provides moredetailed causal inferences and more flexibility in selecting crash count distributions. This paper provides a comparison and discussion of the pros and cons of the two Bayesian approaches, based on, and illustrated with,empirical applications. These applications pertain to the evaluation of two treatments: the conversion of rural intersections from unsignalized to signalized control; and the conversion of road segments from a four-lane to a three-lane cross-section with two-way left turn lanes (road diets). Ineach case the numerical results from the two approaches are comparable.
Samenvatting