IPGMVL: based on interactive progressive graph convolution with multi-view learning traffic flow forecasting.

Abstract

The key challenge in traffic flow prediction lies in modeling complex spatio-temporal dependencies effectively. While graph neural networks have shown promise, existing methods face two critical limitations: (1) static graph construction approaches fail to adapt to real-time network dynamics, and (2) prevailing spatio-temporal models neglect both interactive dependency learning and node-specific pattern variations due to spatial heterogeneity. A model based on Interactive Progressive Graph Convolution with Multi-view learning (IPGMVL) is proposed, which introduces three key innovations: First, progressive graph convolution dynamically adjusts edge weights through trend similarity learning, capturing real-time spatial evolution. Second, a multi-view interactive learning mechanism incorporates spatio-temporal heterogeneous patterns for comprehensive dependency modeling. Third, the fast parallel learning (FPL) module is used to realize the synchronous and efficient mining of spatio-temporal features through parameter streamlining, while the serial learning (SL) module expands the serial receptive field and avoid information coverage to further enhance the modeling capability of spatio-temporal dependencies. Experimental results demonstrate IPGMVL's superior performance across four benchmark datasets, establishing new state-of-the-art standards while maintaining computational efficiency. This advancement highlights the importance of dynamic graph adaptation and interactive learning in traffic prediction systems.