当前位置: X-MOL 学术EPJ Data Sci. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Complete trajectory reconstruction from sparse mobile phone data
EPJ Data Science ( IF 3.6 ) Pub Date : 2019-10-12 , DOI: 10.1140/epjds/s13688-019-0206-8
Guangshuo Chen , Aline Carneiro Viana , Marco Fiore , Carlos Sarraute

Mobile phone data are a popular source of positioning information in many recent studies that have largely improved our understanding of human mobility. These data consist of time-stamped and geo-referenced communication events recorded by network operators, on a per-subscriber basis. They allow for unprecedented tracking of populations of millions of individuals over long periods that span months. Nevertheless, due to the uneven processes that govern mobile communications, the sampling of user locations provided by mobile phone data tends to be sparse and irregular in time, leading to substantial gaps in the resulting trajectory information. In this paper, we illustrate the severity of the problem through an empirical study of a large-scale Call Detail Records (CDR) dataset. We then propose Context-enhanced Trajectory Reconstruction, a new technique that hinges on tensor factorization as a core method to complete individual CDR-based trajectories. The proposed solution infers missing locations with a median displacement within two network cells from the actual position of the user, on an hourly basis and even when as little as 1% of her original mobility is known. Our approach lets us revisit seminal works in the light of complete mobility data, unveiling potential biases that incomplete trajectories obtained from legacy CDR induce on key results about human mobility laws, trajectory uniqueness, and movement predictability.

中文翻译:

根据稀疏的手机数据完成轨迹重建

在许多最近的研究中,移动电话数据是定位信息的流行来源,这些信息大大改善了我们对人类移动性的理解。这些数据由网络运营商在每个订户的基础上记录的带有时间戳和地理参考的通信事件。它们可以在数月之久的长时间内以前所未有的方式跟踪数百万人的人口。然而,由于控制移动通信的过程不平衡,由移动电话数据提供的用户位置的采样在时间上往往是稀疏的和不规则的,从而导致所得到的轨迹信息中存在相当大的差距。在本文中,我们通过对大规模呼叫详细记录(CDR)数据集的经验研究来说明问题的严重性。然后,我们提出上下文增强的轨迹重构,一项以张量分解为核心方法的新技术,可完成基于CDR的各个轨迹。所提出的解决方案以小时为基础,即使已知她的原始移动性只有1%的情况下,也可以从用户的实际位置推断出两个网络单元中的丢失位置的中间位置。我们的方法使我们能够根据完整的流动性数据重新审视开创性工作,揭示从传统CDR获得的不完整轨迹可能引起的偏差,这些偏差会导致有关人类流动性法则,轨迹唯一性和移动可预测性的关键结果。每小时一次,甚至只有她原来的流动性的1%被知道。我们的方法使我们能够根据完整的流动性数据重新审视开创性工作,揭示从传统CDR获得的不完整轨迹可能引起的偏差,这些偏差会导致有关人类流动性法则,轨迹唯一性和移动可预测性的关键结果。每小时一次,甚至只有她原来的流动性的1%被知道。我们的方法使我们能够根据完整的流动性数据重新审视开创性工作,揭示从传统CDR获得的不完整轨迹可能引起的偏差,这些偏差会导致有关人类流动性法则,轨迹唯一性和移动可预测性的关键结果。
更新日期:2019-10-12
down
wechat
bug