Adaptive Graph Convolution Networks for Traffic Flow Forecasting
Zhengdao Li Wei Li Kai Hwang
Traffic flow forecasting is a highly challenging task due to the dynamic
spatial-temporal road conditions. Graph neural networks (GNN) has been widely
applied in this task. However, most of these GNNs ignore the effects of
time-varying road conditions due to the fixed range of the convolution
receptive field. In this paper, we propose a novel Adaptive Graph Convolution
Networks (AGC-net) to address this issue in GNN. The AGC-net is constructed by
the Adaptive Graph Convolution (AGC) based on a novel context attention
mechanism, which consists of a set of graph wavelets with various learnable
scales. The AGC transforms the spatial graph representations into
time-sensitive features considering the temporal context. Moreover, a shifted
graph convolution kernel is designed to enhance the AGC, which attempts to
correct the deviations caused by inaccurate topology. Experimental results on
two public traffic datasets demonstrate the effectiveness of the
AGC-net\footnote{Code is available at: https://github.com/zhengdaoli/AGC-net}
which outperforms other baseline models significantly.