Human mobility patterns are associated with many aspects of our life. With the increase of the popularity and pervasiveness of smartphones and portable devices, the Internet of Things (IoT) is turning into a permanent part of our daily routines. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way (data streams). In order to understand human behavior, the detection of important places and the movements between these places is a fundamental task. That said, the proposal of this work is a method for discovering user habits over mobility data without any a priori or external knowledge. Our approach extends a density-based clustering method for spatio-temporal data to identify meaningful places the individuals’ visit. On top of that, a Gaussian mixture model (GMM) is employed over movements between the visits to automatically separate the trajectories accordingly to their key identifiers that may help describe a habit. By regrouping trajectories that look alike by day of the week, length, and starting hour, we discover the individual’s habits. The evaluation of the proposed method is made over three real-world datasets. One dataset contains high-density GPS data and the others use GSM mobile phone data with 15-min sampling rate and Google Location History data with a variable sampling rate. The results show that the proposed pipeline is suitable for this task as other habits rather than just going from home to work and vice versa were found. This method can be used for understanding person behavior and creating their profiles revealing a panorama of human mobility patterns from raw mobility data.

人口流动模式与我们生活的许多方面有关。随着智能手机和便携式设备的普及和普及，物联网（IoT）已成为我们日常工作中不可或缺的一部分。为这些设备提供服务的定位技术，例如蜂窝天线（GSM网络），全球导航卫星系统（GPS）和最近的WiFi定位系统（WPS）以连续方式提供大量时空数据（数据流） 。为了了解人类行为，重要位置的检测以及这些位置之间的移动是一项基本任务。也就是说，这项工作的提议是一种无需任何先验或外部知识即可发现移动数据用户习惯的方法。我们的方法扩展了基于密度的时空数据聚类方法，以识别个人访问的有意义的地点。最重要的是，在访问之间的移动中采用了高斯混合模型（GMM），以自动将轨迹与其可能有助于描述习惯的关键标识符分离开。通过按星期几，时长和开始时间重新组合看起来相似的轨迹，我们发现了个人的习惯。对该方法的评估是在三个真实世界的数据集上进行的。一个数据集包含高密度GPS数据，其他数据集使用15分钟采样率的GSM手机数据和可变采样率的Google定位历史数据。结果表明，拟议的管道适合于其他习惯的工作，而不仅仅是从家中上班，反之亦然。此方法可用于了解人的行为并创建其个人资料，从而根据原始流动性数据揭示出人类流动性模式的全景图。