  Traffic flow forecasting on graphs has real-world applications in many
fields, such as transportation system and computer networks. Traffic
forecasting can be highly challenging due to complex spatial-temporal
correlations and non-linear traffic patterns. Existing works mostly model such
spatial-temporal dependencies by considering spatial correlations and temporal
correlations separately and fail to model the direct spatial-temporal
correlations. Inspired by the recent success of transformers in the graph
domain, in this paper, we propose to directly model the cross-spatial-temporal
correlations on the spatial-temporal graph using local multi-head
self-attentions. To reduce the time complexity, we set the attention receptive
field to the spatially neighboring nodes, and we also introduce an adaptive
graph to capture the hidden spatial-temporal dependencies. Based on these
attention mechanisms, we propose a novel Adaptive Graph Spatial-Temporal
Transformer Network (ASTTN), which stacks multiple spatial-temporal attention
layers to apply self-attention on the input graph, followed by linear layers
for predictions. Experimental results on public traffic network datasets,
METR-LA PEMS-BAY, PeMSD4, and PeMSD7, demonstrate the superior performance of
our model.
