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

Abstract 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. How