# Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting
## 1 Introduction  
With the continued development of cities in recent years, people's transportation needs have greatly increased, and urban transportation issues are becoming increasingly severe. Scientific and effective management of road congestion has become a challenging problem for traffic management departments. As an effective means of solving traffic problems, the intelligent transport system (ITS) has become a hotspot in the transport field. In smart city and intelligent transport system (ITS) development and operation, traffic status is sensed by sensors installed on the roads (such as loop detectors), transaction logs of subway and bus systems, traffic monitoring videos, and so on. A key component of ITS is traffic flow prediction. Accurately predicting traffic flow ahead of time can help travelers to manage their trips reasonably, avoid rush hour, and reduce travel times and costs. In the case of transportation operators, early intervention may improve network capacity and efficiency and reduce accidents. The mathematical statistics-based model named historical average (HA) and the autoregressive integrated moving average (ARIMA) model have good time-series performance in the early stages, but are not proficient at predicting traffic flows [1, 2]. Subsequently, machine learning and deep-learning techniques were introduced to improve the prediction accuracy. Recurrent neural networks (RNN) derived from natural language processing (NLP), which can capture non-linear relationships in the temporal dimensions of traffic flows, and gated recurrent unit network (GRU), with gate control mechanisms and long short-term memory network (LSTM), were later introduced to predict the speed and throughput of traffic [3, 4]. These methods, however, consider only temporal dependencies and ignore spatial ones. CNN-based convolutional neural networks model citiesas grids and traffic streams as images in order to extract spatial correlations [5], but this method can only be used for Euclidean forms of data and is not optimal for graph-based forms of transportation networks, such as metro systems and the road network [6]. Networks combining LSTM with CNNs will be able to extract spatial-temporal correlations, but RNN-based models do not use parallel computation during training, which requires longer training times, and some of the structural information of the graph is lost during pre-processing [7; 8]. Graph neural networks have become the frontier of deep-learning research in recent years, showing state-of-the-art performance in a variety of data based on graph structures [9]. The existing graph convolution neural network is split into the spectral method and the spatial cube method [10]. The spectral method uses the graph convolution theorem to define graph convolution from the spectral domain, whereas the spatial method converges each central node and its adjacent nodes from the domain of nodes through a defined convolution function. [PERSON]'s spectral CNN (ChebNet) [11] uses the truncated expansion of the Chebyshev polynomial to reach order k in order to make an approximation to the diagonal matrix. GCN [12] is a first-order approximation to ChebNet, using the Chebyshev polynomial approximation filter of the diagonal matrix of eigenvalues. An alternative method is spatial graph convolution, in which graph convolution is defined by propagating information. Diffusion Graph Convolution (DGC), message passing neural network (MPNN), GraphAEGE, and graph attention network (GAT) all follow this approach [13; 14; 15; 16]. With the development of graph neural networks, it has been found that GNN-based models can be used to simulate spatial-temporal correlations in complex traffic networks. The spatial-temporal convolution network (STGCN) [17] is an excellent model based on spectral graph convolution and gated convolution neural network.