POLICE PATROL OPTIMIZATION: A REVIEW OF MODELS, CHALLENGES AND FUTURE DIRECTION

Abstract

This review examines contemporary approaches to police patrol optimization, analyzing key developments from 2015 to 2024 through the lenses of district design, route optimization and resource allocation. Due to recent improvements in frameworks such as optimization models, simulations and real-time crime maps, police patrol planning has become more advanced. Studies have claimed that these methods can improve response times by over 40% in some cases. Despite these advancements, major challenges remain, such as unreliable data, computational limits in large cities and the need for dynamic optimization models that can incorporate officer varying skills. The review highlights emerging solutions incorporating adaptive artificial intelligence systems and fairness aware algorithms, while underscoring the critical importance of integrating human factors and community engagement into patrol optimization models. These findings suggest that future progress in the field of police patrol optimization will require balancing technical sophistication with practical implementation concerns, particularly regarding officer varying skills and public trust. The review concludes by identifying promising research directions at the intersection of operations research, criminology, Geography and community policing, offering insights for both academic researchers and law enforcement practitioners seeking to enhance patrol effectiveness while maintaining public accountability.