从移动电话呼叫详细记录推断异常人群事件
Inferring Unusual Crowd Events From Mobile Phone Call Detail Records
摘要：
移动电话数据的普及和可用性提供了发现有关城市环境中人群行为的有用知识的机会。城市可以利用这些知识来提供更好的服务（例如，公共交通规划、优化的资源分配）和更安全的城市。通话详细记录（CDR）数据代表了一个实用的数据源，以检测和监测异常事件，考虑到高水平的移动电话渗透率，与GPS装备和开放式设备相比。在本文中，我们提供了一种方法，能够检测到异常事件的CDR数据，通常具有低精度的空间和时间分辨率。此外，我们引入了一个不寻常的事件，涉及大量的人谁暴露了不寻常的移动行为的概念。我们仔细考虑的问题，来自粗粒度的CDR数据，最终导致一个完全通用的框架，可以有效地检测异常人群事件的CDR数据。通过对非洲一个大城市的真实CDR数据进行广泛的实验，我们证明，与最先进的方法相比，我们的方法可以检测到高出16%的召回率和高出10倍以上的精确度的异常事件。我们实现了一个可视化分析原型系统，以帮助最终用户分析检测到的异常人群事件，以最好地适应不同的应用场景。据我们所知，这是第一次工作的CDR数据与考虑其时间和空间稀疏性和用户不寻常的活动和日常生活之间的区别，从异常事件的检测。
英文摘要：
The pervasiveness and availability of mobile phone data offer the opportunity of discovering usable knowledge about crowd behaviors in urban environments. Cities can leverage such knowledge in order to provide better services (e.g., public transport planning, optimized resource allocation) and safer cities. Call Detail Record (CDR) data represents a practical data source to detect and monitor unusual events considering the high level of mobile phone penetration, compared with GPS equipped and open devices. In this paper, we provide a methodology that is able to detect unusual events from CDR data that typically has low accuracy in terms of space and time resolution. Moreover, we introduce a concept of unusual event that involves a large amount of people who expose an unusual mobility behavior. Our careful consideration of the issues that come from coarse-grained CDR data ultimately leads to a completely general framework that can detect unusual crowd events from CDR data effectively and efficiently. Through extensive experiments on real-world CDR data for a large city in Africa, we demonstrate that our method can detect unusual events with 16% higher recall and over 10 times higher precision, compared to state-of-the-art methods. We implement a visual analytics prototype system to help end users analyze detected unusual crowd events to best suit different application scenarios. To the best of our knowledge, this is the first work on the detection of unusual events from CDR data with considerations of its temporal and spatial sparseness and distinction between user unusual activities and daily routines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
