Traffic Flow Prediction for Holiday‐Workday Differences: A Deep Model Incorporating Dual‐Graph Structure and Dynamic Sensing of Key Nodes

ABSTRACT In this study, a traffic flow prediction framework based on multigraph structural modeling and dynamic sensing of key nodes is proposed to address the significant differences in traffic patterns between holidays and workdays. Firstly, the original data are categorized using calendar labels to generate holiday‐indication tags for each sample. Then, a sliding‐window sample entropy method is employed to identify highly dynamic key nodes from the workday and holiday datasets to construct the key‐node set. A dynamic masking mechanism based on the calendar labels is introduced during model training to enhance the transformer's attention to core areas. To fully capture the connectivity variations of the traffic system under different contexts, this study adopts a dual adjacency matrix design and dynamically switches the graph structure based on sample labels during forward propagation, enabling context‐aware spatial modeling. Finally, traffic prediction accuracy is effectively improved by integrating a graph convolutional network (GCN) to extract spatial features with an enhanced transformer to dynamically model temporal dependencies. Experimental results demonstrate that this method significantly outperforms traditional single‐graph structure models across various calendar scenarios, validating the effectiveness and potential of multigraph dynamic modeling with a key‐node mechanism for traffic prediction tasks. In addition, ablation experiments are also conducted to compare the effects of different key‐node selection and processing strategies.