A Survey on Trajectory Data Management, Analytics, and Learning

TL;DR

This survey reviews recent advances in trajectory data management, analysis, and learning, highlighting new techniques, tools, and the integration of deep learning for urban trajectory data processing.

Contribution

It provides a comprehensive overview of recent research trends, including trajectory pre-processing, storage, querying, clustering, real-time analysis, and introduces deep trajectory learning for the first time.

Findings

Deep trajectory learning is emerging as a new research area.

Trajectory querying and clustering techniques have advanced significantly.

Essential qualities for flexible trajectory data management systems are identified.

Abstract

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.