  Traffic prediction is a challenging spatio-temporal forecasting problem that
involves highly complex spatio-temporal correlations. This paper proposes a
Multi-level Multi-view Augmented Spatio-temporal Transformer (LVSTformer) for
traffic prediction. The model aims to capture spatial dependencies from three
different levels: local geographic, global semantic, and pivotal nodes, along
with long- and short-term temporal dependencies. Specifically, we design three
spatial augmented views to delve into the spatial information from the
perspectives of local, global, and pivotal nodes. By combining three spatial
augmented views with three parallel spatial self-attention mechanisms, the
model can comprehensively captures spatial dependencies at different levels. We
design a gated temporal self-attention mechanism to effectively capture long-
and short-term temporal dependencies. Furthermore, a spatio-temporal context
broadcasting module is introduced between two spatio-temporal layers to ensure
a well-distributed allocation of attention scores, alleviating overfitting and
information loss, and enhancing the generalization ability and robustness of
the model. A comprehensive set of experiments is conducted on six well-known
traffic benchmarks, the experimental results demonstrate that LVSTformer
achieves state-of-the-art performance compared to competing baselines, with the
maximum improvement reaching up to 4.32%.
