@article{Zhao2026, 
author = {Zitong Zhao and Zixuan Zhang and Zhenxing Niu},
title = {Interactive Dynamic Graph Convolution with Temporal Attention for Traffic Flow Forecasting},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {86},
number = {1},
pages = {1-16},
keywords = {Traffic flow prediction, interactive dynamic graph convolution, graph convolution, temporal multi-head trend-aware attention, self-attention mechanism},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.069752},
doi = {10.32604/cmc.2025.069752},
abstract = {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.}
}