Linking geospatial crash and citation data to inform equitable enforcement decisions

Abstract

Risky and aggressive driving behaviors remain a serious safety concern across the globe. While engineering, education, and emergency medical services can affect the outcome of these behaviors, enforcement is a critical element of a well-rounded safety focus and has been proven to significantly reduce serious injury and fatal crashes. To appropriately and equitably allocate enforcement resources within a region, agencies must understand the relationship of crash locations (e.g., opportunities for improvement) and citation data (e.g., utilization of existing resources). To investigate this relationship, a case study approach was used to develop seemingly unrelated regression equation (SURE) models based on community attributes to predict crash and citation rates in the selected region. Through their application, regions that were overrepresented and underrepresented in their citation and crash rates were revealed. A geographic information system was used to illustrate these relationships using a combination of preliminary geospatial maps and maps based on the applied regression models. The resulting analysis revealed explanatory maps demonstrating potential enforcement gaps. The factors that most influence crash and citation rates were also revealed. This research provides a foundation for officials to improve the allocation of enforcement-related resources to equitably improve safety within a region.