Interactive Dynamic Graph Convolution with Temporal Attention for Traffic Flow Forecasting

Reliable traffic flow prediction is crucial for mitigating urban congestion. This paper proposes Attention-based spatiotemporal Interactive Dynamic Graph Convolutional Network (AIDGCN), a novel architecture integrating Interactive Dynamic Graph Convolution Network (IDGCN) with Temporal Multi-Head Trend-Aware Attention. Its core innovation lies in IDGCN, which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs, and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data. For 15- and 60-min forecasting on METR-LA, AIDGCN achieves MAEs of 0.75% and 0.39%, and RMSEs of 1.32% and 0.14%, respectively. In the 60-min long-term forecasting of the PEMS-BAY dataset, the AIDGCN out-performs the MRA-BGCN method by 6.28%, 4.93%, and 7.17% in terms of MAE, RMSE, and MAPE, respectively. Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods.