AI RESEARCH
Hierarchical Spatial-Temporal Graph-Enhanced Model for Map-Matching
arXiv CS.LG
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ArXi:2603.24054v1 Announce Type: cross 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