  Traffic flow forecasting is a crucial task in transportation management and
planning. The main challenges for traffic flow forecasting are that (1) as the
length of prediction time increases, the accuracy of prediction will decrease;
(2) the predicted results greatly rely on the extraction of temporal and
spatial dependencies from the road networks. To overcome the challenges
mentioned above, we propose a multi-channel spatial-temporal transformer model
for traffic flow forecasting, which improves the accuracy of the prediction by
fusing results from different channels of traffic data. Our approach leverages
graph convolutional network to extract spatial features from each channel while
using a transformer-based architecture to capture temporal dependencies across
channels. We introduce an adaptive adjacency matrix to overcome limitations in
feature extraction from fixed topological structures. Experimental results on
six real-world datasets demonstrate that introducing a multi-channel mechanism
into the temporal model enhances performance and our proposed model outperforms
state-of-the-art models in terms of accuracy.
