Publication Type

Journal Article

Version

acceptedVersion

Publication Date

8-2022

Abstract

With widespread deployment of GPS devices, massive spatiotemporal trajectories became more accessible. This booming trend paved the solid data ground for researchers to discover the regularities or patterns of human mobility. However, there are still three challenges in semantic pattern extraction including semantic absence, semantic bias and semantic complexity. In this paper, we invent and apply a novel data structure namely City Semantic Diagram to overcome above three challenges. First, our approach resolves semantic absence by exactly identifying semantic behaviours from raw trajectories. Second, the delicate design of semantic purification helps us to detect semantic complexity from human mobility. Third, we avoid semantic bias using objective data source such as ubiquitous GPS trajectories. Comprehensive and massive experiments have been conducted based on real taxi trajectories and points of interest in Shanghai. Compared with existing approaches, City Semantic Diagram shows its satisfied effectiveness and precision to discover fine-grained semantic patterns.

Keywords

Human mobility, fine-grained semantic pattern, GPS trajectory, Point of Interest

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Knowledge and Data Engineering

Volume

34

Issue

8

First Page

3765

Last Page

3778

ISSN

1041-4347

Identifier

10.1109/TKDE.2020.3026235

Publisher

IEEE

Embargo Period

4-15-2021

Copyright Owner and License

LARC and Authors

Additional URL

https://doi.org/10.1109/TKDE.2020.3026235

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