A Periodic Dynamic Graph and Meta-Graph Memory Network for Traffic Flow Prediction

Accurately capturing time-varying spatial dependencies and inherent spatio-temporal heterogeneity remains a significant challenge in traffic flow predic-tion. Traditional methods often rely on predefined static graphs, which fail to adapt to the dynamic and periodic nature of urban traffic. To address these limitations, we propose a novel framework named PDMetaNet (Periodic Dy-namic graph with Meta-graph Memory Network). Specifically, a Periodic Dynamic Graph Generation Module (PDGM) is developed to construct bidi-rectional dynamic adjacency matrices by synergistically fusing real-time traf-fic signals with intra-day and intra-week periodicities. Building upon this, the Periodic Adaptive Graph Convolutional Recurrent Unit (PAGCRU) integrates dynamic graph convolutions with gated recurrent mechanisms to facilitate joint spatiotemporal feature modeling. Furthermore, a Spatio-Temporal Meta-Graph Learner (STMG) is incorporated to maintain a persistent bank of meta-nodes representing prototypical traffic patterns across different periods. This mechanism enables the model to effectively reuse historical knowledge and optimize the dynamic graph topology through an attention-based querying process. To enhance the robustness and generalization of the memory pa-rameters, contrastive and consistency losses are introduced as structural con-straints. Extensive experiments on the PeMS04 and PeMS08 datasets demon-strate that PDMetaNet significantly outperforms ten state-of-the-art baselines, achieving a substantial reduction in prediction error and exhibiting superior capability in capturing complex spatiotemporal dynamics.