Traffic flow forecasting is crucial for effective urban planning and the development of intelligent transportation systems. However, accurately capturing the complex spatiotemporal dependencies in traffic flow data remains a significant challenge. Current approaches typically face two major limitations: (i) insufficient modeling of long-term historical traffic flow patterns, which hinders the capture of global trends and periodic behaviors; and (ii) inadequate consideration of spatial structures and temporal response heterogeneity, which limits the model’s representational capacity. To tackle these challenges, this paper proposes a novel forecasting framework named Spatiotemporal Memory Decoupled Transformer (STMDFormer). First, a spatiotemporal embedding module is designed to fuse topological node structures with multi-scale periodic information. Then, a spatial memory attention mechanism is introduced, incorporating a learnable historical memory bank and a biased adjacency matrix for dynamic modeling of long-term traffic flow patterns. Furthermore, a decoupled learning module is designed to separate spatial and temporal features through normalization techniques, while employing multi-head attention to better capture spatiotemporal heterogeneity. Extensive experiments on four real-world traffic datasets demonstrate that STMDFormer consistently outperforms various state-of-the-art baselines in forecasting accuracy, validating its effectiveness in dynamic spatiotemporal modeling.
Chen et al. (Tue,) studied this question.