Does Automated Enforcement Presence Impact Collisions and Crime?

Abstract

Enforcement agencies generally operate under a strict budget and with limited resources. For this reason, they are continually searching for new approaches to maximize the efficiency and effectiveness of their deployment. The Data-Driven Approaches to Crime and Traffic Safety approach attempts to identify opportunities where increased visibility of traffic enforcement can lead to a reduction in collision frequencies as well as criminal incidents. Previous research developed functions to model collisions and crime separately, despite evidence suggesting that the two events could be correlated. Additionally, there is little knowledge of the implications of automated enforcement programs on crime. This study developed a Multivariate Poisson-Lognormal model for the city of Edmonton to quantify the correlation between collisions and crime and to determine whether automated enforcement programs can also reduce crime within a neighborhood. The results of this study found a high correlation between collisions and crime of 0.72 which indicates that collision hotspots were also likely to be crime hotspots. The results of this paper also showed that increased enforcement presence resulted in reductions not only in collisions but also in crime. If a single deployment can achieve multiple objectives (e.g., reducing crime and collisions), then optimizing an agency’s deployment strategy would decrease the demand on their resources and allow them to achieve more with less.