Designing Daily Patrol Routes for Policing Based on Ant Colony Algorithm

Abstract

ABSTRACT:In this paper, we address the problem of planning police patrol routes to regularly cover street segments of high crime density (hotspots) with limited police forces. A good patrolling strategy is required to minimise the average time lag between two consecutive visits to hotspots, as well as coordinating multiple patrollers and imparting unpredictability in patrol routes. Previous studies have designed different police patrol strategies for routing police patrol, but these strategies have difficulty in generalising to real patrolling and meeting various requirements. In this research we develop a new police patrolling strategy based on Bayesian method and ant colony algorithm. In this strategy, virtual marker (pheromone) is laid to mark the visiting history of each crime hotspot, and patrollers continuously decide which hotspot to patrol next based on pheromone level and other variables. Simulation results using real data testifies the effective, scalable, unpredictable and extensible nature of this strategy.

Analysing the Police Patrol Routing Problem: A Review

“…Not only the limitations affect the results in the Kansas City preventative patrol experiment, some articles even prove the opposite: random preventive patrol is one of the most important and time-consuming tasks employed on a daily basis by the police [88]. Moreover, high randomness can create a perceived omnipresence of the police and in developing routing strategies it is often mentioned as a prominent characteristic of preventative police patrol [7,8]. Therefore, an efficient and effective allocation of the available resources is crucial.…”

“…The major difference occurs when the vehicles are idle, i.e., not responding to an emergency call. While police vehicles are assigned to a patrol beat and have to patrol streets [6][7][8], the emergency medical services (EMS) and the fire brigade are redeployed to a base station. The redeployment models can be static (the allocation is fixed and a vehicle is sent back to its home base whenever it becomes idle), or dynamic (at the moment of relocation, the state of the system is taken into account), but they neither have to be visible nor have a preventative task [9][10][11][12].…”

“…Currently, there is a poor understanding of how routine day-to-day patrol intervenes with criminal opportunities. Moreover, police patrol routing strategies which incorporate the responsibilities of police officers on patrol are still missing [7]. To address these deficiencies, this paper examines which algorithms can regulate effective police patrol routing, with a focus on overt or visible crime, i.e., crimes occurring in public places, which can be seen and heard by other people and can draw police attention.…”

A Mobile Information System Based on Crowd-Sensed and Official Crime Data for Finding Safe Routes: A Case Study of Mexico City

“…Other proposal has included the position of CCTV cameras to detect crime events [21]. Thus, [22] developed a police patrolling strategy based on the Bayesian method and ant colony algorithm in order to reduce the average time between two consecutive visits to ℎ . In [23], a crime ontology is described by using two scenarios of analysis: (1) the circumstances of a crime, its mechanism, information of criminals, and knowledge about the methods of crime investigation and (2) the penal procedure and other methodological and tactical recommendations of criminality, crime features, and events.…”

Predictive police patrolling to target hotspots and cover response demand

“…A study by Chen et al (2015) looked at how ant colony algorithms can be used to plan patrol routes. Their study developed a patrolling strategy using Bayesian methods and ant colony algorithms.…”