Dynamic Pattern Matching Network for Traffic Prediction

Due to the inherent complexity of urban road networks and the irregular periodic fluctuations of traffic flow, traffic forecasting remains a challenging spatiotemporal modeling task.Existing studies predominantly focus on capturing spatial dependencies among nodes, while often overlooking the long-term evolutionary patterns and internally stable, recurring flow behaviors at individual nodes. This limitation compromises both the generalization capacity and long-term forecasting performance of current models.To address these issues, we propose a novel Dynamic Pattern Matching Network (DPMNet) that incorporates a memory-augmented architecture to dynamically learn and retrieve historical traffic patterns at each node, thereby enabling efficient modeling of localized flow dynamics. Building upon this foundation, we further develop a comprehensive framework named DPMformer, which integrates daily and weekly temporal embeddings to enhance the modeling of long-term trends and leverages a pattern matching mechanism to improve the representation of complex spatiotemporal structures.Extensive experiments conducted on four real-world traffic datasets demonstrate that the proposed method significantly outperforms mainstream baseline models across multiple forecasting horizons and evaluation metrics.