Name
Abstract | ||
|---|---|---|
Many domains including policymaking, urban design, and geospatial intelligence benefit from understanding people's mobility behaviors (e.g., work commute, shopping), which can be achieved by clustering massive trajectories using the geo-context around the visiting locations (e.g., sequence of vectors, each describing the geographic environment near a visited location). However, existing clustering approaches on sequential data are not effective for clustering these context sequences based on the contexts' transition patterns. They either rely on traditional pre-defined similarities for specific application requirements or utilize a two-phase autoencoder-based deep learning process, which is not robust to training variations. Thus, we propose a variational approach named VAMBC for clustering context sequences that simultaneously learns the self-supervision and cluster assignments in a single phase to infer moving behaviors from context transitions in trajectories. Our experiments show that VAMBC significantly outperforms the state-of-the-art approaches in robustness and accuracy of clustering mobility behaviors in trajectories. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-86514-6_28 | MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2021: APPLIED DATA SCIENCE TRACK, PT IV |
DocType | Volume | ISSN |
Conference | 12978 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingxuan Yue | 1 | 2 | 1.74 |
| Yao-Yi Chiang | 2 | 360 | 31.33 |
| Cyrus Shahabi | 3 | 5010 | 411.59 |