  As one of the important tools for spatial feature extraction, graph
convolution has been applied in a wide range of fields such as traffic flow
prediction. However, current popular works of graph convolution cannot
guarantee spatio-temporal consistency in a long period. The ignorance of
correlational dynamics, convolutional locality and temporal comprehensiveness
would limit predictive accuracy. In this paper, a novel Attention-based Dynamic
Graph Convolutional Recurrent Neural Network (ADGCRNN) is proposed to improve
traffic flow prediction in highway transportation. Three temporal resolutions
of data sequence are effectively integrated by self-attention to extract
characteristics; multi-dynamic graphs and their weights are dynamically created
to compliantly combine the varying characteristics; a dedicated gated kernel
emphasizing highly relative nodes is introduced on these complete graphs to
reduce overfitting for graph convolution operations. Experiments on two public
datasets show our work better than state-of-the-art baselines, and case studies
of a real Web system prove practical benefit in highway transportation.
