Objective: The annual rate of impaired driving crashes in the United States has remained nearly constant over the last decade. While engineers, educators, enforcement, and emergency response personnel have worked diligently in their combined efforts to reduce the loss of life, there is still significant progress to be made. One area of recent interest is the use of data driven enforcement. The basis for data driven enforcement is the use of statistical clustering to identify geographic areas that represent the location of problem identification for various criminal or traffic offenses. In the case of impaired driving fatalities, the clustering represents locations with high rates of impaired driving crashes. Law enforcement officers and supervisors may allocate resources towards more specifically and efficiently addressing problem areas.
Methods: While data driven enforcement has been proven to be an effective tool in addressing crime and traffic safety problems, it has been a slow process for agencies to adopt data driven techniques. This study aims to explore the difference in traffic stops made inside and outside of hotspot identified areas. The study uses data from the Stark County Operating a Vehicle Impaired Task Force between 2013 and 2014.
Results: The analysis determined that stop occurring in hotspot defined areas are more likely to result in impaired driving arrests and seatbelt citations. Additionally it is found that the average cost of impaired driving arrests is significantly cheaper for stops occurring inside of hotspot areas.
Conclusion: Clustering as a means of directing law enforcement efforts are a way to increase the productivity and benefits of law enforcement agencies with limited finances or personnel. From this study it is seen that traffic stops made within defined cluster or hot spot areas are more effective in resulting in OVI arrests. 相似文献
