A dynamic graph convolutional network with multiscaled attention for traffic prediction

This is the authors' abstract. We don't add key points for this paper.

Purpose Traffic flow prediction is vital for highway road planning and congestion alleviation. Nevertheless, highway traffic forecasting faces difficulties caused by complex spatio-temporal properties. Existing graph-convolution-based prediction approaches cannot sustain stable spatio-temporal consistency for long horizons. They neglect dynamic spatio-temporal correlations, convolution locality and multiresolution long-term dependencies, limiting prediction accuracy. This paper aims to propose attention-based dynamic graph convolutional recurrent neural network (ADGCRNN) for highway traffic flow prediction. Design/methodology/approach This work presents the ADGCRNN. Self-attention integrates three-resolution temporal sequences for feature extraction. Dynamically constructed multidynamic graphs and adaptive weights capture variant traffic properties. A gated kernel focusing on highly correlated nodes is adopted on full graphs to mitigate graph-convolution overfitting. Findings Evaluated on two public data sets, the proposed ADGCRNN outperforms state-of-the-art baseline models. A practical case study based on a real-world web system further validates the practical benefits of this approach for highway-transportation scenarios. Originality/value This model realizes multiresolution temporal fusion via self-attention. It leverages adaptive multidynamic graphs to model time-varying spatial patterns. A gated kernel is introduced to alleviate overfitting for full-graph convolution in traffic forecasting.