Analysing the Police Patrol Routing Problem: A Review

Dewinter, Maite; Vandeviver, Christophe; Beken, Tom Vander; Witlox, Frank

doi:10.3390/ijgi9030157

Cited by 42 publications

(35 citation statements)

References 75 publications

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“…The multi‐criteria districting method supports efficient police patrolling and workload distribution, 20 police patrol districting based on real‐time incident occurrence, 17 reduce the incident response time of officers 62 . Modified genetic algorithm eases freeway patrol service 15 and police patrol proactive, reactive routing 19 . The results inferred from one scheduling algorithm or a hybrid of them is in the form of location and timestamp.…”

Section: Taxonomymentioning

confidence: 99%

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Machine learning‐based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges

Nama

Nath

Bechra

et al. 2021

Int J Communication

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Summary The rise in traffic congestion has become a significant concern in the urban city environment. The conventional traffic control systems with inefficient human resources management fail to control the traffic discipline, leading to increased traffic density and road offenses. However, the intelligent transportation system (ITS) can provide safety, efficiency, and sustainability for large‐scale vehicular traffic dilemmas. ITS unites machine learning with the available traffic control force and performs real‐time police scheduling to ensure the smooth flow of traffic. Many researchers have demonstrated notable work in intelligent traffic police scheduling and deployment using various optimization algorithms. However, the compilation of such praiseworthy work as a whole is still missing. Motivated by these facts, we provide a comprehensive review of the machine learning‐based state‐of‐the‐art technologies that can be used to form a three‐tier solution taxonomy. The first tier describes various tools and technologies that can be utilized to collect traffic data. The second tier highlights the machine learning algorithms and their accuracy, which forms a pattern in the collected data and then yields some crucial information about traffic flow, congestion levels, and so forth. In the third tier, the most vital taxonomy layer, various traffic police scheduling strategies are discussed. The proposed survey also presents the use of cases of traffic police scheduling that elaborates this review's applicability in various domains. Finally, some of the key challenges in the subject being reviewed are discussed, which initiates a further scope of improvement.

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Section: Taxonomymentioning

confidence: 99%

“…Dewinter et al 19 discusses Police Patrol Routing Problem (PPRP), specifically within the enormous domain of Dynamic Vehicle Routing Problem (DVRP). This review's main objective is the solution, strategies, and algorithms that solve the police patrol routing problem as a dynamic vehicle routing problem.…”

Section: Taxonomymentioning

confidence: 99%

Machine learning‐based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges

Nama

Nath

Bechra

et al. 2021

Int J Communication

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“…Hence, the review includes studies that were published or available before end of September 2020. The list of variables was derived from examining other research on police and policing (e.g., Sacks 2000, Smith et al 2005, Pullin and Stewart 2006, Staples and Niazi 2007, Braga and Weisburd 2014, Depraetere et al 2020, Dewinter et al 2020.…”

Section: Search Strategies and Databasesmentioning

confidence: 99%

“…Considering that patrol officer compliance was estimated to be around 53 to 67%, evaluations have so far focused on small windows of officer time. Second, police routes can experience novel research, apart from classic framing of routes as shortest paths between response events (Melo et al 2006, Reis et al 2006, Lee et al 2017, Dewinter et al 2020, Wu et al 2020. Perhaps police presence on routes has important effects we do not know anything about yet.…”

Section: Police En Routementioning

confidence: 99%

Policing Directions: A Systematic Review on the Effectiveness of Quantitative Police Presence

Dau¹,

Vandeviver²,

Dewinter³

et al. 2021

Preprint

Self Cite

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This systematic review assesses the efficacy of quantitative police presence. The review also investigates concepts of police presence and differences between reported effects. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and protocols are used to systematically identify and review eligible studies on police presence. Further, quality assessment and findings synthesis are used to map limitations of current research as well as grounds for future avenues. The systematic search strategies yielded 49 studies focusing on testing the effects of police presence or evaluating its measurement. We find evidence that police presence has mostly positive effects on reducing crimes related to motor theft, property, violence, and guns. Police presence also reduces calls for service and improves traffic behaviour. Police presence focused on specific areas, times, and types of crime achieves maximum efficacy. The reviewed studies show a high degree of heterogeneity in reporting, which limits comparability of findings across studies. Research on police presence presents evidence for crime preventative effects of focused police actions. Police forces can be focused on certain areas, times, and types of crimes. We encourage future research to focus on police presence en route and its effects, including crime prevention, traffic regulation, and fear of crime.

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“…The second major contribution of this paper is that it comes with an original application to dynamic police patrol management. Indeed, as highlighted in Dewinter et al (2020)'s review, there is a knowledge gap of solution methods to solve the police patrol routing problem. Thanks to historical data and the field experience provided by the Brussels police department, we empirically show that the SS-VRP-R model can be exploited to enhance the reactivity of mobile units in the Brussels urban area.…”

Section: Introductionmentioning

confidence: 99%

Time-dependent stochastic vehicle routing problem with random requests: Application to online police patrol management in Brussels

Saint-Guillain

Paquay

Limbourg

2021

European Journal of Operational Research

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The Static and Stochastic Vehicle Routing Problem with Random Requests (SS-VRP-R) describes realistic operational contexts in which a fleet of vehicles has to serve customer requests appearing dynamically. Based on a probabilistic knowledge about the appearance of requests, the SS-VRP-R seeks a priori sequences of vehicle relocations, optimizing the expected responsiveness to the requests. In this paper, an existing computational framework, based on recourse strategies, is adapted to meet the objectives of the SS-VRP-R. The resulting models are applied to a real case study of the management of police units in Brussels. In this context, the expected average response time is minimized. To cope with the reality of the urban context, a time-dependent variant is also studied (TD-SS-VRP-R) in which the travel time between two locations is a function that depends on the departure time at the first location. Experiments confirm the contribution and the adaptability of the recourse strategies to a real-life, complex operational context. Provided an adequate solution method, simulation-based results show the high quality of the a priori solutions designed, even when compared to those designed by field experts. Finally, the experiments provide evidence that there is no potential gain in considering time-dependency in such an operational context.

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Analysing the Police Patrol Routing Problem: A Review

Cited by 42 publications

References 75 publications

Machine learning‐based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges

Machine learning‐based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges

Policing Directions: A Systematic Review on the Effectiveness of Quantitative Police Presence

Time-dependent stochastic vehicle routing problem with random requests: Application to online police patrol management in Brussels

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