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.