Hierarchical Spatial-Temporal Graph-Enhanced Model for Map-Matching
The integration of GNSS data into portable devices has led to the generation of vast amounts of trajectory data, which is crucial for applications such as map-matching. To tackle the limitations of rule-based methods, recent works in deep learning for trajectory-related tasks occur. However, existing models remain challenging due to issues such as the difficulty of large-scale data labeling, ineffective modeling of spatial-temporal relationships, and discrepancies between training and test data distributions. To tackle these challenges, we propose HSTGMatch, a novel model designed to enhance map-matching performance. Our approach involves a two-stage process: hierarchical self-supervised ...
| Authors | Anjun Gao, Zhenglin Wan, Pingfu Chao et al. |
|---|---|
| Primary category | cs.DB |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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