## 1 Introduction  
With the rapid development of new technologies and urbanization, the number of vehicles in the global transportation sector has increased sharply, which makes the operation of the urban transportation networks face greater pressure in terms of infrastructure capacity and environmental sustainability. Therefore, accurate traffic prediction in urban areas plays an important role in the management of intelligent transportation systems (TIS) [1, 2] that can guide individuals to avoid congested routes, saving travel time and reducing greenhouse gas emission [3, 4]. The extensive research on traffic flow prediction suggests that a transportation system can be categorized into two scenarios when modelling traffic flow, namely general conditions and abnormal conditions, whereas the prediction models in turn can be classified into statistical models [5], shallow machine learning models [6], and deep learning algorithms [7] in the current transport modeling studies. A thorny issue arising from the statistical theory-based methods [8, 9, 10, 11] and machine learning-based approaches [12, 13, 14] is that these models have struggled to capture the high-dimensional spatiotemporal features in the historical data. With deep learning methods demonstrating advantages in capturing the hidden nonlinear relationships embedded in the existing data, they can be applied to predict the traffic flow with satisfied performance. As the LSTM (Long-Short Term Memory) network has the advantages of dealing with time series data, it has gradually been used for traffic flow prediction. An LSTM Neural Network(LSTM-NN) is developed to extract the nonlinear dynamics in the traffic historical data [15], while a deep bidirectional uni-directional stacked LSTM-NN architecture considers the forward and backward dependencies of the traffic flow data to predict the network flow speed [16]. However, these LSTM-based methods only model the temporal relationships of traffic flow data, without considering the inherent spatial relationship in those data. Therefore, convolution neural networks [17] or deep residual convolutional structures [18] are employed to capture the spatial correlations in the data. However, either spatial or temporal correlations of the data have extensively been considered individually in the existing studies, leading to suboptimal prediction performance in complex traffic flow data. Convolutional Neural Networks (CNN) and LSTM can be combined together for traffic flow prediction, considering both temporal and spatial correlations simultaneously [19]. The city-into-regions and a CNN-based end-to-end spatiotemporal residual network is developed to extract the spatiotemporal correlation among different regions [20]. A multi-view spatiotemporal network is developed where a CNN is employed to learn the local spatial correlations under shared similar temporal patterns [21]. However, the spatial information is easily removed or neglected by the global average pooling layer in the CNN, and the short-term temporal correlation is also twisted and cannot be considered continuously via the long-term dependency of the recursive network. Graph-based deep learning methods have been applied to capture the dependencies in non-Euclidean spaces. Examples include Graph Convolutional Networks (GCN) [22] and Graph Attention [23], which can encode the topological network of road traffic by integrating the graph-based LSTM and CNN structures, thereby enhancing the spatiotemporal correlations. [PERSON] et al. have proposed a traffic graph convolutional LSTM-NN to model the traffic network as a graph and embed the traffic flow network into the graph so as to enhance the prediction accuracy [24].