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 features and employ a decay coefficient to address variations in trajectory length. Our extensive experiments demonstrate the model's superior performance, module effectiveness, and robustness, providing a promising solution for overcoming the existing limitations in map-matching applications. The source code of HSTGMatch is publicly available on GitHub at https://github.com/Nerooo-g/HSTGMatch.

翻译：全球导航卫星系统（GNSS）数据在便携式设备中的集成产生了海量轨迹数据，这对地图匹配等应用至关重要。为应对基于规则方法的局限性，近期研究探索了深度学习在轨迹相关任务中的应用。然而，现有模型仍面临大规模数据标注困难、时空关系建模效果不佳、训练数据与测试数据分布存在差异等挑战。为此，本文提出HSTGMatch——一种旨在提升地图匹配性能的新模型。该方法采用两阶段流程：层级自监督学习与时空监督学习。我们引入层级轨迹表示，同时利用网格单元与地理元组有效捕捉移动模式。模型构建自适应轨迹邻接图以动态捕获空间关系，并优化图注意力网络（GATs）以提升效率。此外，我们融入时空因子提取相关特征，并采用衰减系数应对轨迹长度变化。大量实验表明，该模型具有优越性能、模块有效性与鲁棒性，为突破地图匹配应用现有局限提供了可行方案。HSTGMatch的源代码已公开于GitHub（https://github.com/Nerooo-g/HSTGMatch）。