HSeq2Seq: Hierarchical graph neural network for accurate mobile traffic forecasting

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Hierarchical graph neural networks (HGNNs) provide a feasible method for modeling complex spatiotemporal dependencies during mobile traffic forecasting. However, most existing studies have adopted spatial node clustering methods to construct a coarsened graph that overlooks the temporal correlation within the original station graph. Furthermore, existing state-of-the-art methods fail to fully exploit the cross-regional feature impacts on stations, which limits their ability to model nonlocal spatial dependency.To overcome these limitations, we proposed a hierarchical sequence-to-sequence (HSeq2Seq) approach that combines HGNNs with a sequence-to-sequence architecture (Seq2Seq) for mobile traffic forecasting. First, a spatiotemporal node clustering method was designed to construct the hierarchical structure. Second, a convolution encoder was employed to extract the local spatiotemporal features from a hierarchical perspective, and a novel attention-based feature fusion module was built to capture the nonlocal spatiotemporal features by identifying both the intra- and cross-regional feature impacts on the stations. Finally, a recurrent decoder is introduced to reweight the spatiotemporal features and recursively produce the final prediction.Extensive experiments on two real-world datasets demonstrate that our model outperforms state-of-the-art methods, while the ablation study also verifies the effectiveness of the spatiotemporal node clustering and the attention-based feature fusion module.