GRAND-VISION: An Intelligent System for Optimized Deployment Scheduling of Law Enforcement Agents

Authors

  • Jonathan Chase Singapore Management University
  • Tran Phong Singapore Management University
  • Kang Long Singapore Management University
  • Tony Le Singapore Management University
  • Hoong Chuin Lau Singapore Management University

Keywords:

Description And Modeling Of Novel Application Domains, Industry / Application Challenge Problems, Experiences In Development, Deployment, And Maintenance Of Planning And Scheduling Applications, Integration Of Multiple Planning And Scheduling Techniques, Or Of Planning And Scheduling Techniques With Techniques From Other Areas Or Disciplines

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.

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Published

2021-05-17

How to Cite

Chase, J., Phong, T., Long, K., Le, T., & Lau, H. C. (2021). GRAND-VISION: An Intelligent System for Optimized Deployment Scheduling of Law Enforcement Agents. Proceedings of the International Conference on Automated Planning and Scheduling, 31(1), 459-467. Retrieved from https://ojs.aaai.org/index.php/ICAPS/article/view/15992