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Mobility profiler: A framework for discovering mobility profiles of cell phone users

机译:移动性探查器:用于发现手机用户移动性描述文件的框架

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Mobility path information of cell phone users play a crucial role in a wide range of cell phone applications, including context based search and advertising, early warning systems, city-wide sensing applications such as air pollution exposure estimation and traffic planning. However, there is a disconnect between the low level location data logs available from the cell phones and the high level mobility path information required to support these cell phone applications. In this paper, we present formal definitions to capture the cell phone users' mobility patterns and profiles, and provide a complete framework, Mobility Profiler, for discovering mobile cell phone user profiles starting from cell based location data. We use real-world cell phone log data (of over 350 K h of coverage) to demonstrate our framework and perform experiments for discovering frequent mobility patterns and profiles. Our analysis of mobility profiles of cell phone users expose a significant long tail in a user's location-time distribution: A total of 15% of a cell phone user's time is spent on average in locations that each appears with less than 1% of total time.
机译:手机用户的移动路径信息在各种手机应用程序中都起着至关重要的作用,包括基于上下文的搜索和广告,预警系统,城市范围的感应应用程序(如空气污染暴露估算和交通规划)。但是,可从手机获得的低级别位置数据日志与支持这些手机应用程序所需的高级别移动性路径信息之间是脱节的。在本文中,我们提出了正式的定义来捕获手机用户的移动性模式和配置文件,并提供了一个完整的框架Mobility Profiler,用于从基于小区的位置数据中发现移动手机用户配置文件。我们使用现实世界中的手机日志数据(覆盖时间超过350 K h)来演示我们的框架并进行实验,以发现频繁的移动性模式和配置文件。我们对手机用户移动性分析的分析暴露了用户位置时间分布中的一条长尾巴:平均,手机用户平均有15%的时间花费在每个出现时间少于总时间1%的位置。

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