Periodicity aware spatial-temporal adaptive hypergraph neural network for traffic forecasting

Traffic forecasting is the foundation and core task of Intelligent Transportation Systems (ITS). Due to the powerful ability of Graph Neural Network (GNN) to capture topological features, recently, it is commonly used in traffic forecasting to capture spatial features of road networks. Although existing GNN based traffic forecasting methods have achieved satisfactory results, they are still plagued by the following problems: (1) Traffic time-series usually contains complex periodic features, but they only model 1D time features, ignoring multi-periodic information in traffic data. (2) There are multivariate higher-order correlations among nodes in road networks, but they only preserve the pairwise connections by simple graphs, neglecting the higher-order multivariate correlations. (3) They cannot adaptively capture unique patterns of specific areas, only learn the shared patterns of traffic time-series. To solve the above problems, we propose a Periodicity aware spatial-temporal Adaptive Hypergraph Neural Network (PAHNN). Firstly, a temporal multi-periodic block is designed to capture the 2D-variations of traffic time-series to extract multi-periodic features and complex temporal patterns. Then, we propose a spatial adaptive hypergraph block to model spatial multivariate correlations among nodes via hypergraph neural networks. Adaptive selection of hypergraph networks for different data can extract specific spatial patterns of different traffic areas. Finally, extensive experiments are conducted on two types of forecasting tasks to evaluate the effectiveness and accuracy of our model.