Optimal Allocation of Police Patrol Resources Using a Continuous-Time Crime Model
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- 关键词：Decision theoretic policing ; Crime modeling ; Survival analysis ; Bender’s decomposition
- 刊名：Lecture Notes in Computer Science
- 出版年：2016
- 出版时间：2016
- 年：2016
- 卷：9996
- 期：1
- 页码：139-158
- 全文大小：710 KB
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23.Zhang, C., Sinha, A., Tambe, M.: Keeping pace with criminals: designing patrol allocation against adaptive opportunistic criminals. In: International Conference on Autonomous Agents and Multiagent Systems. pp. 1351–1359 (2015) - 作者单位：Ayan Mukhopadhyay (18)
Chao Zhang (19)
Yevgeniy Vorobeychik (18)
Milind Tambe (19)
Kenneth Pence (18)
Paul Speer (18)
18. Vanderbilt University, 2201 West End Ave, Nashville, TN, 37235, USA
19. University of Southern California, Los Angeles, CA, 90089-0894, USA - 丛书名：Decision and Game Theory for Security
- ISBN：978-3-319-47413-7
- 刊物类别：Computer Science
- 刊物主题：Artificial Intelligence and Robotics
Computer Communication Networks
Software Engineering
Data Encryption
Database Management
Computation by Abstract Devices
Algorithm Analysis and Problem Complexity - 出版者：Springer Berlin / Heidelberg
- ISSN：1611-3349
- 卷排序：9996
文摘
Police departments worldwide are eager to develop better patrolling methods to manage the complex and evolving crime landscape. Surprisingly, the problem of spatial police patrol allocation to optimize expected crime response time has not been systematically addressed in prior research. We develop a bi-level optimization framework to address this problem. Our framework includes novel linear programming patrol response formulations. Bender’s decomposition is then utilized to solve the underlying optimization problem. A key challenge we encounter is that criminals may respond to police patrols, thereby shifting the distribution of crime in space and time. To address this, we develop a novel iterative Bender’s decomposition approach. Our validation involves a novel spatio-temporal continuous-time model of crime based on survival analysis, which we learn using real crime and police patrol data for Nashville, TN. We demonstrate that our model is more accurate, and much faster, than state-of-the-art alternatives. Using this model in the bi-level optimization framework, we demonstrate that our decision theoretic approach outperforms alternatives, including actual police patrol policies.