Before-and-After Empirical Bayes Evaluation of Automated Mobile Speed Enforcement on Urban Arterial Roads

Abstract

Speeding is a leading factor in road collisions and is found to contribute to approximately one-third of all fatal collisions. Speed enforcement is one of the most common countermeasures used to reduce speed. However, a gap exists in the literature regarding the effectiveness of automated mobile photo enforcement on urban arterial roads. This study addresses this gap using the before-and-after empirical Bayes method to account for regression to the mean effects and other confounding factors. Locally developed safety performance functions and yearly calibration factors for different collision severities were obtained by using a reference group of urban arterial roads. The evaluation period covers 8 years. Collision records, deployment information, traffic counts, and road geometric data were collected. The results showed consistent reductions in different collision severities; the reductions ranged from 14% to 20%, with the highest reductions observed for severe collisions. The enforced segments were further categorized according to site selection criteria and deployment hours to examine the effect of enforcement on collision reduction. More reductions were found at segments that had more collisions during the before period and longer deployment hours. The study also compared the safety effects of continuous and discontinuous enforcement strategies on different arterials, and the analysis revealed that continuous enforcement achieved more reductions across all severities and types of collisions. The study also investigated the spillover effects on adjacent unenforced approaches. Significant reductions were found, and these results are discussed with regard to the general and specific deterrence of the enforcement.