Optimal Allocation of Police Patrol Resources Using a Continuous-Time Crime Model
Optimal Allocation of Police Patrol Resources Using a Continuous-Time Crime Model
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DOI：
10.1007/978-3-319-47413-7_9
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发表时间：
2016-11
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影响因子：
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通讯作者：
Ayan Mukhopadhyay;Chao Zhang-;Yevgeniy Vorobeychik;Milind Tambe;Kenneth Pence;Paul Speer
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文献类型：
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作者：
Ayan Mukhopadhyay;Chao Zhang-;Yevgeniy Vorobeychik;Milind Tambe;Kenneth Pence;Paul Speer
世界各地的警察部门都急于开发更好的巡逻方法，以管理复杂和不断变化的犯罪格局。令人惊讶的是，先前的研究没有系统地解决空间警察巡逻分配以优化预期犯罪反应时间的问题。我们开发了一个双层优化框架来解决这个问题。我们的框架包括新颖的线性规划巡逻响应公式。然后利用本德尔分解来解决潜在的优化问题。我们遇到的一个关键挑战是，罪犯可能会对警察的巡逻做出反应，从而改变犯罪在空间和时间上的分布。为了解决这个问题，我们开发了一种新的迭代Bender分解方法。我们的验证涉及一个基于生存分析的新的犯罪时空连续时间模型，我们使用田纳西州纳什维尔的真实犯罪和警察巡逻数据学习该模型。我们证明，我们的模型比最先进的替代模型更准确，速度也更快。在双层优化框架中使用该模型，我们证明了我们的决策理论方法的性能优于其他方法，包括实际的警察巡逻策略。
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.