patterns beyond predefined graph topology through a learnable embedding for each nodes in graph, which largely improve the forecasting accuracy but their spatial dependencies are still fixed once trained. Unlike these approaches, the proposed model provide a effective and efficient mechanism to model dynamical directed spatial dependencies. We could discover richer hidden dependencies beyond predefined graph structures and local nodes by dynamically computing spatial dependencies in different latent high-dimensional subspace.

Temporal dependencies modeling
------------------------------

As stated in [@ma2015long] [@wu2016short], previous models for temporal dependencies modeling especially RNNs suffer from two main limitations. On one hand, RNNs are limited in capture the long-range dependencies due to gradients exploding or vanishing in model training, resulting in their poor performance when the input sequences are long. On the other hand, the temporal dependencies are also highly dynamical so that it is difficult to determine the optimal sequence length for accurate traffic forecasting. To alleviate these drawbacks, Gated Recurrent Units (GRUs) [@chung2014empirical] [@pan2019urban] or Long-Short Term Memory (LSTM) [@Hochreiter:1997:LSM:1246443.1246450] are thus developed to model long-range dependencies and applied to traffic forecasting [@guo2019attention] [@ma2015long] [@wu2016short]. Unfortunately, they still suffer from their inherent sequential nature, making the training process time-consuming and limiting their scalability to model 