TY - JOUR AU - Chase, Jonathan AU - Phong, Tran AU - Long, Kang AU - Le, Tony AU - Lau, Hoong Chuin PY - 2021/05/17 Y2 - 2024/03/28 TI - GRAND-VISION: An Intelligent System for Optimized Deployment Scheduling of Law Enforcement Agents JF - Proceedings of the International Conference on Automated Planning and Scheduling JA - ICAPS VL - 31 IS - 1 SE - Novel Applications Track DO - 10.1609/icaps.v31i1.15992 UR - https://ojs.aaai.org/index.php/ICAPS/article/view/15992 SP - 459-467 AB - 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. ER -