Extract human mobility patterns powered by City Semantic Diagram
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 i...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2022
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/5898 https://ink.library.smu.edu.sg/context/sis_research/article/6901/viewcontent/Extract_Human_Mobility_Patterns_TKDE_av.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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