@Article{cmc.2025.069752,
AUTHOR = {Zitong Zhao, Zixuan Zhang, Zhenxing Niu},
TITLE = {Interactive Dynamic Graph Convolution with Temporal Attention for Traffic Flow Forecasting},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {86},
YEAR = {2026},
NUMBER = {1},
PAGES = {1--16},
URL = {http://www.techscience.com/cmc/v86n1/64480},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2025.069752}
}