GRAND-VISION: An Intelligent System for Optimized Deployment Scheduling of Law Enforcement Agents
Jonathan Chase, Tran Phong, Kang Long, Tony Le, Hoong Chuin Lau
Singapore Management University, Singapore
Figure 1. Agents patrol their assigned patrol regions. When
an incident occurs, the nearest agent is assigned to attend,
with the aim of meeting the response time QoS requirements.
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.
Conference Presentation
The presentation at ICAPS 2021 is given at this link
Video
