  Long-term traffic prediction is highly challenging due to the complexity of
traffic systems and the constantly changing nature of many impacting factors.
In this paper, we focus on the spatio-temporal factors, and propose a graph
multi-attention network (GMAN) to predict traffic conditions for time steps
ahead at different locations on a road network graph. GMAN adapts an
encoder-decoder architecture, where both the encoder and the decoder consist of
multiple spatio-temporal attention blocks to model the impact of the
spatio-temporal factors on traffic conditions. The encoder encodes the input
traffic features and the decoder predicts the output sequence. Between the
encoder and the decoder, a transform attention layer is applied to convert the
encoded traffic features to generate the sequence representations of future
time steps as the input of the decoder. The transform attention mechanism
models the direct relationships between historical and future time steps that
helps to alleviate the error propagation problem among prediction time steps.
Experimental results on two real-world traffic prediction tasks (i.e., traffic
volume prediction and traffic speed prediction) demonstrate the superiority of
GMAN. In particular, in the 1 hour ahead prediction, GMAN outperforms
state-of-the-art methods by up to 4% improvement in MAE measure. The source
code is available at https://github.com/zhengchuanpan/GMAN.
