Reducing police response times: Optimization and simulation of everyday police patrol

Abstract

AbstractPolice forces around the world are adapting to optimize their current practices through intelligence‐led and evidence‐based policing. This trend towards increasingly data‐driven policing also affects daily police routines. Police patrol is a complex routing problem because of the combination of reactive and proactive tasks. Moreover, a trade‐off exists between these two patrol tasks. In this article, a police patrol algorithm that combines both policing strategies into one strategy and is applicable to everyday policing, is developed. To this end, a discrete event simulation model is built that compares a p‐median redeployment strategy with several benchmark strategies, that is, p‐median deployment, hotspot (re)deployment, and random redeployment. This p‐median redeployment strategy considers the continuous alternation of idle and non‐idle vehicles. The mean response time was lowest for the p‐median deployment strategy, but the redeployment strategy results in better coverage of the area and low mean response times.