Use of Objective Safety Evidence to Deploy Automated Enforcement Resources

Abstract

Automated enforcement programs have been an important tool for improving traffic safety. Previous work provides strong evidence supporting the impact that these programs have on increasing safety either on a micro-level (e.g., road segments), or at a macro-level (e.g., neighborhood, city). In both cases, there are many variables that can influence and affect the safety impacts of the enforcement program. Additionally, there is a lack of understanding of how specific deployment parameters (e.g., how often to visit an enforcement site) can influence the overall safety on a macro-level (e.g., traffic analysis zone). The objective of this study is to quantify the impact that automated enforcement has on collisions on a macro-level as well as to develop models that would provide enforcement authorities with an empirical tool to help plan their deployment strategy. The results show that an increase in the number of tickets issued for exceeding the speed limit resulted in a decrease in collisions, for all collision severities. Moreover, the results also showed that collision reductions were also associated with spending a longer time enforcing a site for each visit. Quantifying these safety impacts supports decision makers by providing them with an opportunity to analyze the safety benefits in relation to their deployment strategy to maximize the efficiency of their resources.

Using Bayesian Tobit Models to Understand the Impact of Mobile Automated Enforcement on Collision and Crime Rates

“…While more research has been focused on modeling collision frequencies, recent research has shown that Tobit regression models provide some advantages [30,31]. Instead of using collision counts, this technique considers collision rates which neutralizes the effect of the exposure variable and measures the risk of collision involvement [32][33][34] and was first applied by Anastasopoulos et al [29] in the field of traffic safety. Since then, other studies applied the Tobit model to understanding the influencing factors of collision rates [33,35].…”

“…Recent research identified that a more robust unit of analysis to address the concerns associated with high-level aggregation (i.e., at a national/city level) as well as the microlevel aggregation (i.e., intersection). Ibrahim and Sayed [5,29] investigated the impact of MAE on collisions at a Traffic Analysis Zone (TAZ) level. The authors found that collision reductions for each TAZ were associated with spending a longer time enforcing a site for each visit in a deployment.…”

“…Most of the research on MAE programs has been focused on studying their impact on speed and collisions; one study did investigate their impact on crime [29]. In this study, the authors quantified the correlation between collisions and crime.…”