GRAND-VISION: An intelligent system for optimized deployment scheduling of law enforcement agents

• Main Authors: CHASE, Jonathan, PHONG, Tran, LONG, Kang, LE, Tony, LAU, Hoong Chuin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Law enforcement; patrol scheduling; deep learning; artificial intelligence; MITB student; Artificial Intelligence and Robotics; Law Enforcement and Corrections; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/5980; https://ink.library.smu.edu.sg/context/sis_research/article/6983/viewcontent/Chase__J.__Phong__T.__Long__K.__Le__T.____Lau__H._C.__2021__May_._GRAND_VISION_An_Intelligent_System_for_Optimized_Deployment_Scheduling_of_Law_Enforcement_Agents.pdf
• Institution: Singapore Management University

Law enforcement agencies in dense urban environments, faced with a wide range of incidents to handle and limited manpower, are turning to data-driven AI to inform their policing strategy. In this paper we present a patrol scheduling system called GRAND-VISION: Ground Response Allocation and Deployment - Visualization, Simulation, and Optimization. The system employs deep learning to generate incident sets that are used to train a patrol schedule that can accommodate varying manpower, break times, manual pre-allocations, and a variety of spatio-temporal demand features. The complexity of the scenario results in a system with real world applicability, which we demonstrate through simulation on historical data obtained from a large urban law enforcement agency.