Spatiotemporal interactive progressive dynamic graph convolutional networks for traffic flow prediction

Abstract

Accurate traffic flow prediction is essential for mitigating road congestion. However, most existing methods use static or dynamic graphs to model spatial dependencies, often neglecting the dynamic relationships between road nodes that evolve over time, limiting their ability to capture complex spatiotemporal traffic patterns. To address this, we propose the Interactive Progressive Dynamic Spatiotemporal Graph Convolutional Network (STIPDG). The model constructs a progressive adjacency matrix by learning trend similarities between nodes, adapting to dynamic changes in traffic data. Based on this matrix, an interactive dynamic graph convolutional network synchronously captures spatiotemporal dependencies through an interactive learning strategy. Additionally, a progressive dynamic graph generation module accurately captures evolving spatial correlations within the traffic network. Experimental results show that STIPDG significantly outperforms baseline methods in prediction accuracy.