Large-Scale Traffic Prediction With Hierarchical Hypergraph Message Passing Networks

Abstract

Graph convolutional networks (GCNs) are widely used in social computation such as urban traffic prediction. However, when faced with city-level forecasting challenges, the graph-based deep learning methods struggle to process large-scale multivariate data effectively. To address the challenges of limited scalability, a traffic prediction framework based on a hypergraph message passing network (HMSG) is proposed in this article. The model represents the urban transportation network with hypergraph, where nodes denote transportation hubs and hyperedges represent their relationship at geographical and feature level. Compared with pairwise edges, hyperedges are more scalable and flexible, providing a more descriptive representation of traffic information. The HMSG algorithm updates node and hyperedge features in two steps, facilitating effective and efficient integration of hidden spatial features across layers. The proposed framework is evaluated on large-scale historical datasets and demonstrates its completion of city-scale traffic prediction tasks. The results also show that it matches the accuracy of existing traffic prediction methods on small-scale datasets. This validates the potential of the traffic prediction model based on the HMSG algorithm for intelligent transportation applications.