Deterring Street Crimes Using Aerial Police: Data-Driven Joint Station Deployment and Patrol Path Planning for Policing UAVs

Street crime is a critical public concern, attracting wide social and research attention. Conventional solutions to reduce street crimes are dispatching more police force in patrol and installing more cameras for street surveillance, which, however, suffer from huge manpower and financial consumption and limited performance. Inspired by the wide application of Unmanned Aerial Vehicles (UAVs) in policing and other related missions such as street surveillance, we investigate the use of UAVs in patrolling along high-risk streets to deter street crimes. UAVs significantly outperform police officers and street cameras in terms of cost reduction and deterring performance improvement. Technically, this paper proposes a data-driven framework to schedule the patrol UAVs, including an online patrol path planning module and an offline UAV station siting module. In the first module, the street-level deterring effect of the UAVs is estimated using a prediction-enhanced method, which guides the UAVs to patrol the high-risk streets more efficiently. Evolved from the path planning algorithm, the second module utilizes a data-driven method to estimate the deterring effect of the candidate UAV stations with different numbers of UAVs. Then both the location of the UAV stations and the UAVs at each station are determined. The proposed framework is comprehensively evaluated using a 6-year crime dataset of the Denver city. The results show that the proposed framework improves the deterring effect by 58.49% on average, and up to 157.32% in extreme cases compared to baselines.