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

Data science in transportation networks (DSTNs) 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 DSTN problems due to their capability to represent spatial correlations between entities. Between 2016 and 2024, the notable applications of GNNs in DSTNs 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 DSTNs, highlighting new progress in prediction and operation from academic and industry perspectives, which are missing in existing reviews. First, we present and analyze various DSTN 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 DSTN studies since 2023, could democratize abundant datasets and efficient GNN methods for various transportation problems including prediction and operation.