this section, we briefly review the existing approaches for spatial and temporal dependencies modeling in traffic flow forecasting.

Spatial dependencies modeling
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The earliest traffic flows forecasting models are statistic-based or neural-network-based models. For statistic-based model, such as autoregressive integrated moving average (ARIMA)[@min2011real] and Bayesian networks [@wang2014new], spatial dependencies are modeled from a probabilistic view.Although they help to analyze the uncertainty within traffic flows, their linear natures impedes them to effectively model the highly-nonlinearity within traffic flows. Neural networks based models are more capable to capture the nonlinearity of traffic flows, however, their fully connected structures are expensive in both computational and memory. Furthermore, the lack of assumptions make it impossible to capture the complicated spatial patterns in traffic flows.

With the development of convolutional neural networks (CNNs), they have shown powerful feature extraction abilities in many applications[@gehring2017convolutional] [@Krizhevsky:2012:ICD:2999134.2999257] [@long2015fully], thus attracting much interests to apply them into traffic forecasting area. [@ma2017learning] [@zhang2017deep] adopt CNNs to extract the spatial features in which traffic networks are converted to regular grids. However, traffic networks are inherently irregular, so the conversion will loss the inherent topology information within traffic networks. Graph neural networks (GNNs) [@scarselli2008graph] [@gilmer2017neural] are latter proposed to generalize the deep 