# Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting
## 1 Introduction
Conv-GCN combines spectral convolution with the three-dimensional convolutional neural network (3 DCNN), which compensates for the inability of spectral convolution to capture spatial correlation in depth, and divides the traffic flows into near-term, daily, and weekly segments in order to extract spatial-temporal correlations [18]. Temporal Graph Convolution Network (T-GCN) combines GCN with GRU in order to extract spatiotemporal features [19]. Diffusion Convolutional Recurrent Neural Network (DCRNN) extracts spatial correlations using the stochastic wandering strategy of GCN [20]. In the multivariate time-series prediction network (MTGNN), a graph learning module is proposed to automatically extract the correlation between segments in order to improve the graph representation and make predictions [21]. Given the inadequacy of expressing a single graph, the spatial-temporal multigraph solution network (ST-MGCN) encodes pairwise non-Euclidean correlations between regions in multiple graphs, then explicitly models these correlations using multi-graph convolution, and uses recurrent contextual-gated neural networks, which augment recursive neural networks to reweight different historical observations using contextual sensor-gated mechanisms [22]. Attention-based graph neural networks are also widely used in traffic flow prediction. An example is the spatial-temporal graph attention network (AST-GAT), which uses multi-head graph attention to capture the spatial correlation between segments of a road traffic network [23]. In general, the existing models have some disadvantages. RNN-based graph neural network models, for example, typically require more loss of training time and lack robustness. Second, a single-graph neural network model cannot extract deep spatial features between the nodes of a graph. While a few studies have proposed neural networks with multiple graphs, or even the relationship between nodes learned by the model itself, the lack of a priori knowledge of the traffic means that the accuracy and robustness of predictions can still be improved. To overcome these shortcomings, in this paper we propose a deep-learning architecture called AMGC-AT, which consists of a multi-view convolutional network and a spatiotemporal attention network. In the multi-view convolutional network module, spatial features are extracted from four different spatial adjacency matrices, which are based on different domain knowledge. In addition, the spatiotemporal attention module is composed of two attention mechanisms, which can better integrate spatiotemporal features across three time patterns. The model is evaluated with the Hangzhou Metro Passenger Flow Data Set and the results show that the model is superior to eight other baseline models in subway passenger flow prediction. The main contributions of this research are as follows:  
1. The AMGC-AT model proposed in this paper is based on the domain knowledge of transport and achieves a better balance between the complexity of the model framework and the comprehensive exploration of this knowledge. 2. The AMGC-AT model is based on three types of traffic flow patterns (recent, daily and weekly), and innovatively combines two kinds of self-attention mechanisms in order to drill deeply into the high-order spatial temporal information of subway patronage. The features learned by the neural network can be fully expressed in a reasonable output layer configuration. 3. In this study, we perform a large number of comparative and ablation experiments, which show that the AMGC-AT model outperforms the other eight base models at all time points at randomly chosen locations. The ablation experiment provides evidence for the validity and rationality of each component of the model framework.