Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2022

Abstract

In the quest to achieve better response times in dense urban environments, law enforcement agencies are seeking AI-driven planning systems to inform their patrol strategies. In this paper, we present a framework, OFFICERS, for deployment planning that learns from historical data to generate deployment schedules on a daily basis. We accurately predict incidents using ST-ResNet, a deep learning technique that captures wide-ranging spatio-temporal dependencies, and solve a large-scale optimization problem to schedule deployment, significantly improving its scalability through a simulated annealing solver. Methodologically, our approach outperforms our previous works where prediction was done using Generative Adversarial Networks, and optimization was performed with the CPLEX solver. Furthermore, we show that our proposed framework is designed to be readily transferable between use cases, handling a wide range of both criminal and non-criminal incidents, with the use of deep learning and a general-purpose efficient solver, reducing dependence on context-specific details. We demonstrate the value of our approach on a police patrol case study, and discuss both the ethical considerations, and operational requirements, for deployment of a lightweight and responsive planning system.

Keywords

Law Enforcement Deployment, Urban Computing, Planning And Scheduling, Simulated Annealing, Incident Prediction, Emergency Response

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 32nd International Conference on Automated Planning and Scheduling, Virtual, 2022 June 13-24

Volume

32

First Page

444

Last Page

452

ISBN

9781577358749

Identifier

10.1609/icaps.v32i1.19830

Publisher

AAAI Press

City or Country

Palo Alto, CA

Additional URL

https://doi.org/10.1609/icaps.v32i1.19830

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