[2603.24054] Hierarchical Spatial-Temporal Graph-Enhanced Model for Map-Matching

Computer Science > Databases arXiv:2603.24054 (cs) [Submitted on 25 Mar 2026] Title:Hierarchical Spatial-Temporal Graph-Enhanced Model for Map-Matching Authors:Anjun Gao, Zhenglin Wan, Pingfu Chao, Shunyu Yao View a PDF of the paper titled Hierarchical Spatial-Temporal Graph-Enhanced Model for Map-Matching, by Anjun Gao and 3 other authors View PDF HTML (experimental) Abstract: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 learning and spatial-temporal supervised learning. We introduce a hierarchical trajectory representation, leveraging both grid cells and geographic tuples to capture moving patterns effectively. The model constructs an Adaptive Trajectory Adjacency Graph to dynamically capture spatial relationships, optimizing GATs for improved efficiency. Furthermore, we incorporate a Spatial-Temporal Factor to extract relevant feat...