Traffic Conflict–Based Before–After Study with Use of Comparison Groups and the Empirical Bayes Method

Abstract

Road safety evaluations mainly rely on the analysis of crash data that are challenged by well-recognized availability and quality issues. The statistical models used to predict the safety level of road sites—that is, safety performance functions—have recently been successfully developed with the use of traffic conflict observations instead of crashes. As such, it is possible to adopt and transfer the statistical techniques used in crash-based road safety analysis to conflict-based analysis. The use of statistically rigorous techniques in crash-based before-and-after (BA) studies is essential for evaluation of the effectiveness of road safety countermeasures. In particular, the use of Bayesian methods, such as the empirical Bayes (EB) technique, is vital to control for confounding factors that can operate simultaneously with the countermeasure and may affect road safety performance. The main objective of this paper was to estimate the treatment effectiveness of two traffic signal (visibility) improvement projects in the city of Edmonton, Alberta, Canada, with a conflict-based BA study using the comparison group and the EB methods. More than 300 h of video data with traffic conflict observations was automatically collected and analyzed by computer vision techniques for two treatment intersections and two control (untreated) intersections before and after the signal improvement projects. The results of the comparison group method showed a statistically significant 24% reduction in the average number of rear-end conflicts per hour, whereas the EB method showed a statistically significant 24.5% reduction in the average number of total conflicts per hour.