Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook

2025-01-28

AI Summary

Existing surveys on graph neural networks (GNNs) for transportation predominantly focus on traffic forecasting, overlooking critical applications in operational optimization and real-world industrial deployment. Method: This paper systematically reviews frontier advances in GNNs for transportation network analysis since 2023, integrating academic research with industrial practice (e.g., AutoNavi, Baidu Maps, Google Maps) and unifying spatiotemporal modeling, traffic knowledge graphs, and multi-source heterogeneous data fusion techniques. Contribution/Results: It establishes a novel paradigm for traffic operational optimization—beyond forecasting—and synthesizes over 100 key works, cataloging reproducible models and publicly available datasets. Furthermore, it constructs an interdisciplinary resource repository to accelerate scalable deployment and equitable adoption of GNNs in real-world transportation systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning
• Application Domains: Transportation

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Social media analysis through the lenses of networks
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Data mining in transportation networks (DMTNs) refers to using diverse types of spatio-temporal data for various transportation tasks, including pattern analysis, traffic prediction, and traffic controls. Graph neural networks (GNNs) are essential in many DMTN problems due to their capability to represent spatial correlations between entities. Between 2016 and 2024, the notable applications of GNNs in DMTNs have extended to multiple fields such as traffic prediction and operation. However, existing reviews have primarily focused on traffic prediction tasks. To fill this gap, this study provides a timely and insightful summary of GNNs in DMTNs, highlighting new progress in prediction and operation from academic and industry perspectives since 2023. First, we present and analyze various DMTN problems, followed by classical and recent GNN models. Second, we delve into key works in three areas: (1) traffic prediction, (2) traffic operation, and (3) industry involvement, such as Google Maps, Amap, and Baidu Maps. Along these directions, we discuss new research opportunities based on the significance of transportation problems and data availability. Finally, we compile resources such as data, code, and other learning materials to foster interdisciplinary communication. This review, driven by recent trends in GNNs in DMTN studies since 2023, could democratize abundant datasets and efficient GNN methods for various transportation problems including prediction and operation.

Problem

• Graph Neural Networks
• Traffic Network Analysis
• Prediction and Management

Innovation

• Graph Neural Networks
• Traffic Network Analysis
• Interdisciplinary Collaboration