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Developing an online cooperative police patrol routing strategy
https://orcid.org/0000-0002-4518-7601
https://orcid.org/0000-0002-5503-9813
AbstractA cooperative routing strategy for daily operations is necessary to maintain the effects of hotspot policing and to reduce crime and disorder. Existing robot patrol routing strategies are not suitable, as they omit the peculiarities and challenges of daily police patrol including minimising the average time lag between two consecutive visits to hotspots, as well as coordinating multiple patrollers and imparting unpredictability to patrol routes. In this research, we propose a set of guidelines for patrol routing strategies to meet the challenges of police patrol. Following these guidelines, we develop an innovative heuristic-based and Bayesian-inspired real-time strategy for cooperative routing police patrols. Using two real-world cases and a benchmark patrol strategy, an online agent-based simulation has been implemented to testify the efficiency, flexibility, scalability, unpredictability, and robustness of the proposed strategy and the usability of the proposed guidelines.
HighlightsComprehensive guidelines and evaluation measures for police patrol routing strategy.An cooperative Bayesian Ant-based Patrol routing Strategy (BAPS)An online implementation of BAPS using agent-based modelling, including emergency situations.Validation of the applicability of BAPS using a benchmark strategy and two case studies.
Developing an online cooperative police patrol routing strategy
https://orcid.org/0000-0002-4518-7601
https://orcid.org/0000-0002-5503-9813
AbstractA cooperative routing strategy for daily operations is necessary to maintain the effects of hotspot policing and to reduce crime and disorder. Existing robot patrol routing strategies are not suitable, as they omit the peculiarities and challenges of daily police patrol including minimising the average time lag between two consecutive visits to hotspots, as well as coordinating multiple patrollers and imparting unpredictability to patrol routes. In this research, we propose a set of guidelines for patrol routing strategies to meet the challenges of police patrol. Following these guidelines, we develop an innovative heuristic-based and Bayesian-inspired real-time strategy for cooperative routing police patrols. Using two real-world cases and a benchmark patrol strategy, an online agent-based simulation has been implemented to testify the efficiency, flexibility, scalability, unpredictability, and robustness of the proposed strategy and the usability of the proposed guidelines.
HighlightsComprehensive guidelines and evaluation measures for police patrol routing strategy.An cooperative Bayesian Ant-based Patrol routing Strategy (BAPS)An online implementation of BAPS using agent-based modelling, including emergency situations.Validation of the applicability of BAPS using a benchmark strategy and two case studies.
Developing an online cooperative police patrol routing strategy
https://orcid.org/0000-0002-4518-7601
https://orcid.org/0000-0002-5503-9813
AbstractA cooperative routing strategy for daily operations is necessary to maintain the effects of hotspot policing and to reduce crime and disorder. Existing robot patrol routing strategies are not suitable, as they omit the peculiarities and challenges of daily police patrol including minimising the average time lag between two consecutive visits to hotspots, as well as coordinating multiple patrollers and imparting unpredictability to patrol routes. In this research, we propose a set of guidelines for patrol routing strategies to meet the challenges of police patrol. Following these guidelines, we develop an innovative heuristic-based and Bayesian-inspired real-time strategy for cooperative routing police patrols. Using two real-world cases and a benchmark patrol strategy, an online agent-based simulation has been implemented to testify the efficiency, flexibility, scalability, unpredictability, and robustness of the proposed strategy and the usability of the proposed guidelines.
HighlightsComprehensive guidelines and evaluation measures for police patrol routing strategy.An cooperative Bayesian Ant-based Patrol routing Strategy (BAPS)An online implementation of BAPS using agent-based modelling, including emergency situations.Validation of the applicability of BAPS using a benchmark strategy and two case studies.
Developing an online cooperative police patrol routing strategy
Chen, Huanfa (author) / Cheng, Tao (author) / Wise, Sarah (author)
Computers, Environments and Urban Systems ; 62 ; 19-29
2016-10-28
11 pages
Article (Journal)
Electronic Resource
English
Developing an online cooperative police patrol routing strategy
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