A Survey on Trajectory Data Management, Analytics, and Learning

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 data management system should possess to maximize flexibility.

Automated summary

This survey comprehensively reviews recent research trends in trajectory data management, covering various aspects such as trajectory pre-processing, storage, common trajectory analytic tools, and related analytical tasks. The study also delves into deep trajectory learning and outlines essential qualities that trajectory data management systems should possess for maximum flexibility.