Hierarchical Spatial-Temporal Graph-Enhanced Model for Map-Matching

2026-03-25

AI Summary

This work proposes HSTGMatch, a novel model addressing key challenges in map matching, including the scarcity of large-scale labeled trajectories, insufficient modeling of spatiotemporal relationships, and distribution shifts between training and testing data. The approach employs a two-stage learning framework combining hierarchical self-supervision and spatiotemporal supervision. It constructs hierarchical trajectory representations through a joint grid-geographic tuple encoding scheme and dynamically captures spatial topology via an adaptive trajectory adjacency graph. Temporal dynamics are integrated by fusing spatiotemporal factors with a decay mechanism. Built upon a graph attention network (GAT) architecture, HSTGMatch enhances model generalization and robustness. Extensive experiments on multiple real-world datasets demonstrate that HSTGMatch significantly outperforms state-of-the-art methods, confirming its high accuracy in complex scenarios and the effectiveness of its modular design.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 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.

Problem

• map-matching
• trajectory data
• spatial-temporal relationships
• data labeling
• distribution discrepancy

Innovation

• Hierarchical Self-Supervised Learning
• Adaptive Trajectory Adjacency Graph
• Spatial-Temporal Factor
• Map-Matching
• Graph Attention Networks