Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2021
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.
Keywords
Law enforcement, patrol scheduling, deep learning, artificial intelligence, MITB student
Discipline
Artificial Intelligence and Robotics | Law Enforcement and Corrections | Numerical Analysis and Scientific Computing
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the 31st International Conference on Automated Planning and Scheduling ICAPS 2021: June 7-12
First Page
459
Last Page
467
Publisher
ICAPS
Embargo Period
6-2-2021
Citation
CHASE, Jonathan; PHONG, Tran; LONG, Kang; LE, Tony; and LAU, Hoong Chuin.
GRAND-VISION: An intelligent system for optimized deployment scheduling of law enforcement agents. (2021). Proceedings of the 31st International Conference on Automated Planning and Scheduling ICAPS 2021: June 7-12. 459-467.
Available at: https://ink.library.smu.edu.sg/sis_research/5980
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Artificial Intelligence and Robotics Commons, Law Enforcement and Corrections Commons, Numerical Analysis and Scientific Computing Commons
