The current state-of-the-art in user mobility research has extensively relied on open-source mobility traces captured from pedestrian and vehicular activity through a variety of communication technologies as users engage in a wide-range of applications, including connected healthcare, localization, social media, e-commerce, etc. Most of these traces are feature-rich and diverse, not only in the information they provide, but also in how they can be used and leveraged. This diversity poses two main challenges for researchers and practitioners who wish to make use of available mobility datasets. First, it is quite difficult to get a bird's eye view of the available traces without spending considerable time looking them up. Second, once they have found the traces, they still need to figure out whether the traces are adequate to their needs. The purpose of this survey is three-fold. It proposes a taxonomy to classify open-source mobility traces including their mobility mode, data source and collection technology. It then uses the proposed taxonomy to classify existing open-source mobility traces and finally, highlights three case studies using popular publicly available datasets to showcase how our taxonomy can tease out feature sets in traces to help determine their applicability to specific use-cases.

翻译：目前,用户流动研究中最先进的是,广泛依赖通过各种通信技术从行人和车辆活动中获取的开放源码流动痕迹,因为用户参与广泛的应用,包括相关的医疗保健、本地化、社交媒体、电子商务等。这些痕迹大多具有丰富多样的特点,不仅在它们提供的信息中,而且在如何使用和利用这些痕迹方面都有差异。这种多样性对希望利用现有流动数据集的研究人员和从业人员提出了两大挑战。首先,在不花大量时间寻找这些痕迹的情况下,很难获得鸟类对现有痕迹的目视。第二,一旦发现这些痕迹,他们仍需要查明这些痕迹是否适合他们的需求。本调查的目的是三重,它建议进行分类,对开放源码流动痕迹进行分类,包括它们的流动性模式、数据来源和收集技术。然后,它利用拟议的分类法对现有开放源流动痕迹进行分类,最后,它强调三个案例研究,利用公众可公开获得的数据集来展示我们的分类系统如何在具体跟踪中使用特征数据集来确定其应用性。