Proceedings:
Book One
Volume
Issue:
Proceedings of the International Conference on Automated Planning and Scheduling, 31
Track:
Novel Applications Track
Downloads:
Abstract:
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.
DOI:
10.1609/icaps.v31i1.15992
ICAPS
Proceedings of the International Conference on Automated Planning and Scheduling, 31