Before-and-After Empirical Bayes Evaluation of Citywide Installation of Driver Feedback Signs

Abstract

Speeding is a leading factor that contributes to approximately one-third of all fatal collisions. Over the past decades, various passive/active countermeasures have been adopted to improve drivers’ compliance to posted speed limits to improve traffic safety. The driver feedback sign (DFS) is considered a low-cost innovative intervention that is being widely used, in growing numbers, in urban cities to provide positive guidance for motorists. Despite their documented effectiveness in reducing speeds, limited literature exists on their impact on reducing collisions. This study addresses this gap by designing a before-and-after study using the empirical Bayes method for a large sample of urban road segments. Safety performance functions and yearly calibration factors are developed to quantify the sole effectiveness of DFS using large-scale spatial data and a set of reference road segments within the city of Edmonton, Alberta, Canada. Likewise, the study followed a detailed economic analysis based on three collision-costing criteria to investigate if DFS was indeed a cost-effective intervention. The results showed significant collision reductions that ranged from 32.5% to 44.9%, with the highest reductions observed for severe speed-related collisions. The results further attested that the benefit–cost ratios, combining severe and property-damage-only collisions, ranged from 8.2 to 20.2 indicating that DFS can be an extremely economical countermeasure. The findings from this study can provide transportation agencies in need of implementing cost-efficient countermeasures with a tool they need to design a long-term strategic deployment plan to ensure the safety of traveling public.