Vehicular networks have received much attention in recent years as they have emerged as one of the leading data communication solutions for smart cities. At the same time, the popularization of sensing devices has enabled the acquisition of a vast amount of vehicular mobility data (mobility traces). In this sense, a recent trend is to use mobility traces to extract hidden knowledge and apply it to improve solutions for vehicular networks. In this article, we present and discuss a workflow, through a short survey, related to the process of generating mobility traces, preprocessing these datasets, and obtaining knowledge to create intelligent vehicular networks. We describe the main types of mobility data highlighting their strengths and weaknesses. We classify the primary methods for obtaining knowledge from mobility data. Also, we exemplify how these mobility traces and methods can be applied to vehicular networks by reviewing recent contributions. Furthermore, we illustrate through a case study how to obtain knowledge from a specific type of mobility trace. Finally, we point out new research directions that involve mobility traces and intelligent vehicular networks.

中文翻译：

---

从移动轨迹到知识：智能车辆网络的设计指南


近年来，车载网络受到广泛关注，因为它们已成为智慧城市领先的数据通信解决方案之一。同时，传感设备的普及使得能够获取大量的车辆移动数据（移动轨迹）。从这个意义上说，最近的趋势是使用移动轨迹来提取隐藏的知识，并将其应用于改进车辆网络的解决方案。在本文中，我们通过一项简短的调查介绍并讨论了一个工作流程，该工作流程与生成移动轨迹、预处理这些数据集以及获取知识以创建智能车辆网络的过程相关。我们描述了移动数据的主要类型，强调了它们的优点和缺点。我们对从移动数据中获取知识的主要方法进行了分类。此外，我们通过回顾最近的贡献来举例说明如何将这些移动轨迹和方法应用于车辆网络。此外，我们通过案例研究说明如何从特定类型的移动轨迹中获取知识。最后，我们指出了涉及移动轨迹和智能车辆网络的新研究方向。