Metro passenger flow prediction is a strategically necessary demand in an intelligent transportation system to alleviate traffic pressure, coordinate operation schedules, and plan future constructions. Graph-based neural networks have been widely used in traffic flow prediction problems. Graph Convolutional Neural Networks (GCN) captures spatial features according to established connections but ignores the high-order relationships between stations and the travel patterns of passengers. In this paper, we utilize a novel representation to tackle this issue - hypergraph. A dynamic spatio-temporal hypergraph neural network to forecast passenger flow is proposed. In the prediction framework, the primary hypergraph is constructed from metro system topology and then extended with advanced hyperedges discovered from pedestrian travel patterns of multiple time spans. Furthermore, hypergraph convolution and spatio-temporal blocks are proposed to extract spatial and temporal features to achieve node-level prediction. Experiments on historical datasets of Beijing and Hangzhou validate the effectiveness of the proposed method, and superior performance of prediction accuracy is achieved compared with the state-of-the-arts.

Traffic prediction methods on a single-source data have achieved excellent results in recent years, especially the Graph Convolutional Networks (GCN) based models with spatio-temporal dependency. In reality, various modes of urban transportation operate simultaneously. They influence and complement each other in common space-time occasions, constituting the transportation system dynamically. Thus, traffic data from multiple sources is ostensibly heterogeneous, but internally correlated. The typical single data driven models are, however, not universally applicable for heterogeneous traffic data. To address this issue, we propose a Multi-task Hypergraph Convolutional Neural Network (MT-HGCN) for the multi-source traffic prediction problem. The framework consists of a main task and a related task. Both tasks are based on Hypergraph Convolutional Neural Networks (HGCN) and are devoted to two prediction problems. Furthermore, the tasks are bridged by a feature compress unit, which models the correlation and shares the latent feature to improve the performance of the main task. The node-level forecasting has been evaluated on historical datasets of Beijing to verify the effectiveness of the proposed method. Compared with the state-of-the-arts, the superior performance of the proposed method can be obtained.

Graph convolutional neural networks, which aim to learn the spatial correlation within multivariate time-series data, have achieved significant advancements in traffic prediction. Despite the proliferation of various spatio-temporal graph neural networks, a fundamental question of the pre-defined graphs still remain to be explored: how can the graph be dynamically optimized as time shifts? Especially for tasks such as rail transit where travel patterns of passengers change periodically. In other words, the spatial correlation of multivariate traffic time-series data is closely intertwined with temporal features, which are tricky to reflect through static graphs with handcrafted association. To adaptively learn the spatial correlation in feature domain, we propose Hierarchical Hypergraph Attention Networks (HHGAT). The hierarchical nature of the framework is reflected in both temporal and spatial dimensions. At different time spans, the model explores the hidden hyperedges with a local hypergraph attention mechanism and optimizes the hypergraph with a global attention mechanism. The node-level prediction results are then fused after the downstream tasks of spatio-temporal hypergraph convolutional networks. The effectiveness and accuracy of the proposed model are evaluated on historical datasets of Beijing and Hangzhou. Compared to the baselines, the proposed approach consistently demonstrates superior performance in various evaluation scenarios.

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.