Recent advances in sensor and mobile devices have enabled an unprecedented increase in the availability and collection of urban trajectory data, thus increasing the demand for more efficient ways to manage and analyze the data being produced. In this survey, we comprehensively review recent research trends in trajectory data management, ranging from trajectory pre-processing, storage, common trajectory analytic tools, such as querying spatial-only and spatial-textual trajectory data, and trajectory clustering. We also explore four closely related analytical tasks commonly used with trajectory data in interactive or real-time processing. Deep trajectory learning is also reviewed for the first time. Finally, we outline the essential qualities that a trajectory management system should possess in order to maximize flexibility.

中文翻译：

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关于轨迹数据管理、分析和学习的调查

传感器和移动设备的最新进展使城市轨迹数据的可用性和收集空前增加，从而增加了对更有效地管理和分析所产生数据的方法的需求。在本次调查中，我们全面回顾了轨迹数据管理的最新研究趋势，包括轨迹预处理、存储、常用轨迹分析工具，例如查询纯空间和空间文本轨迹数据以及轨迹聚类。我们还探讨了在交互式或实时处理中通常与轨迹数据一起使用的四个密切相关的分析任务。深度轨迹学习也是第一次回顾。最后，我们概述了轨迹管理系统应具备的基本品质，以最大限度地提高灵活性。