Investigation of Regression-to-Mean Effect in Traffic Safety Evaluation Methodologies

Observational before-and-after safety evaluations have been commonly used to determine the effectiveness of safety improvements applied to high-crash locations. Such evaluations may typically be affected by regression-to-mean (RTM) bias. This research compares the effectiveness of low-cost safety improvements applied to several high-crash intersections in the cities of Detroit and Grand Rapids, Michigan, and examines the RTM effects by using various safety evaluation methodologies. Some previous studies suggest that before-and-after studies are always biased. It is also claimed that the observed number of crashes cannot be used to determine the effectiveness of safety improvements, and the use of the empirical Bayes (EB) method is suggested. This research examines the RTM effects by using before-and-after studies, before-and-after studies with control sites, and two different variants of the EB method. The research reveals that the expected crash frequencies computed by various evaluation methods do not differ significantly when 3 to 5 years' worth of traffic crash data are used. The deviations of the expected crash frequencies with the before-and-after methods and the EB method are computed to compare the RTM effects for each additional year of traffic crash data used in the evaluation. This research reveals that before-and-after studies produce results similar to those of the EB method, and the RTM effect becomes insignificant when 3 or more years' worth of traffic crash data are used in the evaluation of high-crash locations.