  Traffic forecasting has emerged as a core component of intelligent
transportation systems. However, timely accurate traffic forecasting,
especially long-term forecasting, still remains an open challenge due to the
highly nonlinear and dynamic spatial-temporal dependencies of traffic flows. In
this paper, we propose a novel paradigm of Spatial-Temporal Transformer
Networks (STTNs) that leverages dynamical directed spatial dependencies and
long-range temporal dependencies to improve the accuracy of long-term traffic
forecasting. Specifically, we present a new variant of graph neural networks,
named spatial transformer, by dynamically modeling directed spatial
dependencies with self-attention mechanism to capture realtime traffic
conditions as well as the directionality of traffic flows. Furthermore,
different spatial dependency patterns can be jointly modeled with multi-heads
attention mechanism to consider diverse relationships related to different
factors (e.g. similarity, connectivity and covariance). On the other hand, the
temporal transformer is utilized to model long-range bidirectional temporal
dependencies across multiple time steps. Finally, they are composed as a block
to jointly model the spatial-temporal dependencies for accurate traffic
prediction. Compared to existing works, the proposed model enables fast and
scalable training over a long range spatial-temporal dependencies. Experiment
results demonstrate that the proposed model achieves competitive results
compared with the state-of-the-arts, especially forecasting long-term traffic
flows on real-world PeMS-Bay and PeMSD7(M) datasets.
