The cellular network is now nearly an almost ubiquitous and real-time sensor with coverage anywhere and anytime for any device. Mobile network data is a rich source for official statistics, such as human mobility. However, unlike GPS tracks, each mobile device in this data is described without precise knowledge of its spatial characteristics. Furthermore, there is no information about the device’s mobility status (i.e., whether it is moving or not) or speed which are important for behavioral analysis. Common mobility and speed estimations rely on precise location and do not consider privacy leakage risk. In this work, we propose two probabilistic approaches that estimate respectively devices’ mobility and devices’ speed from cellular data and connection likelihood maps for each network cell. Every estimation is computed in a short time and with a short history of data (for speed and for mobility). This constraint may be helpful with the most stringent legal frameworks for mobile operators including the combination of ePrivacy Directive and General Data Protection Regulation (GDPR) in Europe. The proposed approaches are the first we are aware of that allows for both mobility and speed estimation in this context. We experimented on two datasets, obtained from a mobile network operator’s signaling data and the associated GPS tracks of many consenting users. Our speed estimations are over 20% more accurate than common ones based on mobile sites and we provide confidence intervals for each estimation. Mainly due to mobile network uncertainty, our approach for speed estimation are relatively inaccurate at low speeds and the movement detection could remain unclear. However our approach for mobility estimation fills this gap.

蜂窝网络现在几乎是一个几乎无处不在的实时传感器，可以随时随地覆盖任何设备。移动网络数据是官方统计数据的丰富来源，例如人员流动性。然而，与 GPS 轨迹不同的是，该数据中的每个移动设备都是在不精确了解其空间特征的情况下进行描述的。此外，没有关于设备移动状态（即是否正在移动）或速度的信息，而这些信息对于行为分析很重要。常见的移动性和速度估计依赖于精确位置，不考虑隐私泄露风险。在这项工作中，我们提出了两种概率方法，分别根据每个网络单元的蜂窝数据和连接可能性图来估计设备的移动性和设备的速度。每个估计都是在短时间内计算的，数据历史也很短（对于速度和移动性）。这一限制可能有助于移动运营商最严格的法律框架，包括欧洲的电子隐私指令和通用数据保护条例 (GDPR) 的结合。所提出的方法是我们所知道的第一个允许在这种情况下进行移动性和速度估计的方法。我们对两个数据集进行了实验，这两个数据集是从移动网络运营商的信令数据以及许多同意用户的相关 GPS 轨迹中获得的。我们的速度估计比基于移动网站的普通速度估计准确 20% 以上，并且我们为每个估计提供了置信区间。主要由于移动网络的不确定性，我们的速度估计方法在低速时相对不准确，并且运动检测可能仍然不清楚。然而，我们的移动性估计方法填补了这一空白。