An Approach for the Police Districting Problem Using Artificial Intelligence

Rodríguez‐Jiménez, Jose Manuel

doi:10.1007/978-3-030-01851-1_14

Cited by 2 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The comparative usage statistics and clustering of those algorithms discussed in this paper are depicted in Figure 7. They are mainly categorized into linear programming, 13,16,19,63 genetic algorithm, 15,19 multi-criteria police districting method, 17,20 dynamic programming, 18,19 stochastic programming 16,19 and a polynomial-time approximation scheme. 18 MOLP 13 determines the optimal covering set where Remote Patrol Vehicles (RPV) relocation maximizes police presence and perform allocated activities at each location.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…The system allocates police officers to specific territory under the Police Authority to reduce the crime rate. Police Patrol Districting using an AI‐based approach 17 districts traffic police into their respective zones to serve real‐time incidents instantly. Dynamic Programming‐Based Greedy approach, a grafting mechanism 18 maximizes the police visibility rate with the minimum number of police.…”

Section: Discussionmentioning

confidence: 99%

“…16 Police Districting algorithms focus on reducing the workload and response time of traffic police and Optimization algorithms to perform prompt scheduling of the available traffic police force. 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.…”

Section: Traffic Police Scheduling Policiesmentioning

confidence: 99%

“…J. M. Rodriguez‐Jimenez 17 proposed Police Patrol Districting using an AI‐based approach to distribute traffic police into their respective zones according to real‐time incidents assigned the priority levels with the help of human component. This method takes advantage of the multi‐criteria and Maximum Covering problem.…”

Section: Taxonomymentioning

confidence: 99%

See 4 more Smart Citations

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

Nama

Nath

Bechra

et al. 2021

Int J Communication

View full text Add to dashboard Cite

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.

show abstract