Towards Optimal Police Patrol Routes with Genetic Algorithms
It is quite consensual that police patrolling can be regarded as one of the best well-known practices for implementing public-safety preventive policies towards the combat of an assortment of urban crimes. However, the specification of successful police patrol routes is by no means a trivial task to pursue, mainly when one considers large demographic areas. In this work, we present the first results achieved with GAPatrol, a novel evolutionary multiagent-based simulation tool devised to assist police managers in the design of effective police patrol route strategies. One particular aspect investigated here relates to the GAPatrol’s facility to automatically discover crime hotspots, that is, high-crime-density regions (or targets) that deserve to be better covered by routine patrol surveillance. In order to testify the potentialities of the novel approach in such regard, simulation results related to two scenarios of study over the same artificial urban territory are presented and discussed here.
KeywordsGenetic Algorithm Multiagent System Crime Prevention Police Resource Urban Crime
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