论文标题
多官方巡逻巡逻高风险领域的路线共同从签入，犯罪和事件响应数据中学到
Multi-officer Routing for Patrolling High Risk Areas Jointly Learned from Check-ins, Crime and Incident Response Data
论文作者
论文摘要
精心设计的警察巡逻路线设计对于提供社会的社区安全至关重要。先前的工作主要集中在使用历史犯罪数据来预测犯罪事件上。从基于位置的社交网络（签到）和利益点（POI）数据（POI）数据中收集的大型移动性数据（用于设计有效的警察巡逻队）的使用情况很大程度上进行了研究。鉴于在现实生活中有多名警察在值班，这使得问题更加复杂。在本文中，我们使用签到，犯罪，事件响应数据和POI信息为多名警察制定了动态犯罪巡逻计划问题。我们提出了一种联合学习和非随机优化方法，用于代表可能的解决方案，其中多名警官同时在高犯罪风险区域巡逻，而不是犯罪风险的低犯罪风险领域。后来，实施了荟萃分析遗传算法（GA）和杜鹃搜索（CS）以找到最佳途径。验证了所提出的解决方案的性能，并与使用现实世界数据集的几种最新方法进行了比较。
A well-crafted police patrol route design is vital in providing community safety and security in the society. Previous works have largely focused on predicting crime events with historical crime data. The usage of large-scale mobility data collected from Location-Based Social Network, or check-ins, and Point of Interests (POI) data for designing an effective police patrol is largely understudied. Given that there are multiple police officers being on duty in a real-life situation, this makes the problem more complex to solve. In this paper, we formulate the dynamic crime patrol planning problem for multiple police officers using check-ins, crime, incident response data, and POI information. We propose a joint learning and non-random optimisation method for the representation of possible solutions where multiple police officers patrol the high crime risk areas simultaneously first rather than the low crime risk areas. Later, meta-heuristic Genetic Algorithm (GA) and Cuckoo Search (CS) are implemented to find the optimal routes. The performance of the proposed solution is verified and compared with several state-of-art methods using real-world datasets.
