Intelligent vehicular networks emerge as a promising technology to provide efficient data communication in transportation systems and smart cities. At the same time, the popularization of devices with attached sensors has allowed the obtaining of a large volume of data with spatiotemporal information from different entities. In this sense, we are faced with a large volume of vehicular mobility traces being recorded. Those traces provide unprecedented opportunities to understand the dynamics of vehicular mobility and provide data-driven solutions. In this article, we give an overview of the main publicly available vehicular mobility traces; then, we present the main issues for preprocessing these traces. Also, we present the methods used to characterize and model mobility data. Finally, we review existing proposals that apply the hidden knowledge extracted from the mobility trace for vehicular networks. This article provides a survey on studies that use vehicular mobility traces and provides a guideline for the proposition of data-driven solutions in the domain of vehicular networks. Moreover, we discuss open research problems and give some directions to undertake them.

中文翻译：

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智能车载网络的移动轨迹分析

智能车辆网络作为一种有前途的技术出现，可以在交通系统和智能城市中提供高效的数据通信。同时，带有附加传感器的设备的普及使得从不同实体获取大量具有时空信息的数据成为可能。从这个意义上说，我们面临着大量被记录的车辆移动轨迹。这些痕迹提供了前所未有的机会来了解车辆移动的动态并提供数据驱动的解决方案。在本文中，我们概述了主要的公开车辆移动轨迹；然后，我们提出了预处理这些痕迹的主要问题。此外，我们还介绍了用于表征和建模移动数据的方法。最后，我们回顾了现有的提议，这些提议应用从车辆网络的移动轨迹中提取的隐藏知识。本文对使用车辆移动轨迹的研究进行了调查，并为在车辆网络领域提出数据驱动解决方案提供了指导。此外，我们讨论了开放的研究问题，并给出了一些实施这些问题的方向。