  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 in order to maximize
flexibility.
