Volume II-4/W2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 103-109, 2015
https://doi.org/10.5194/isprsannals-II-4-W2-103-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-4/W2, 103-109, 2015
https://doi.org/10.5194/isprsannals-II-4-W2-103-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  10 Jul 2015

10 Jul 2015

DESIGNING DAILY PATROL ROUTES FOR POLICING BASED ON ANT COLONY ALGORITHM

H. Chen, T. Cheng, and S. Wise H. Chen et al.
  • SpaceTimeLab, Department of Civil, Environmental & Geomatic Engineering, University College London, London WC1E 6BT, UK

Keywords: Police Patrolling Strategy, Hotspot Patrolling, Road Network Patrolling, Ant Colony Algorithm, Unpredictability, Extensibility

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.