# An Urban Traffic Flow Fusion Network Based on a Causal Spatiotemporal Graph Convolution Network
## 1 Introduction
#### Related Work  
* Non-Depth Learning Method  
The early research on traffic flow prediction generally adopted traditional statistical theory and methods, including historical average (HA), the autoregressive integrated moving average model (ARIMA), the Kalman filter, and other methods. [PERSON] et al. [1], according to the periodic change of traffic flow in urban areas, considered that a one week lag seasonal difference applied to traffic data in unrelated sections would produce a weak average transformation. The original traffic data was transformed into a time series with stable mean and variance, and then ARIMA was used to model the transformed time series and make empirical analysis on the data of the intelligent transportation system. The accuracy of the conjecture was verified. The premise of time series modeling is that the data is stable. However, traffic flow data is usually unstable. Although weakly stable data can be obtained after processing, such methods require complex parameter estimation and a large number of high-quality historical data. Therefore, intelligent application in short-term prediction (less than 30 min) cannot meet the needs of medium- and long-term predictions. [PERSON] et al. [2] and [PERSON] et al. [3] used the Kalman filter model to predict traffic flow and commuting time. In their research, they considered not only the road section under investigation, but also the sections related to that, thus introducing more spatial information to the model. The Kalman filter uses a recursive spatial model, which overcomes the limitation that time series modeling can only be applied to stationary series. However, the Kalman filter is a linear model and the reusability of its parameters is poor. With the development of data-driven methods, machine learning methods have also been applied to traffic-flow prediction. [PERSON] et al. [4] proposed application of the local linear regression model to short-term traffic prediction. This study was compared with previous nonparametric methods (such as neighborhood and kernel methods), and it was found that the local linear method was always better than the nearest-neighborhood or kernel-smoothing methods. [PERSON] et al. [5] and [PERSON] et al. [6] used support vector regression (SVR) to fit historical data to predict traffic events. [PERSON] et al. [7] proposed a short-term traffic flow prediction model based on wavelet denoising and a Bayesian network. In addition, machine learning methods to complete the predictions include random forests (RF) [8], gradient boosting decision tree (GBDT) [9], XGBoost (Extreme Gradient Boosting) [10]. Machine learning methods are often better than traditional statistical learning methods, but machine learning methods rely on manual construction of features, and different feature selection has a great impact on the results. On the other hand, the structure of machine learning methods is usually shallow and the expression ability is very limited. * Deep Learning Method  
Deep learning has shown strong learning ability and representation ability in many fields. It can model linear, nonlinear, stationary and non-stationary data, and has high scalability. Deep learning has been applied to traffic flow and other spatial-temporal prediction problems; this has been a gradual process with the development of deep learning. Feedforward neural networks (FNN) were an early deep learning method used in traffic flow prediction. [PERSON] et al. [11] designed a feedforward neural network with a three-layer structure: input layer, hidden layer and output layer. The number of neurons in the hidden layer was dependent on the number of input data roads. When the numbers of input data roads were 1, 3, 4, and 5, the number of neurons were 4, 5, 5, and 6, respectively, which achieved better results. [PERSON] et al. [12] designed an object-oriented neural network model to predict short-term traffic conditions on the Pacific Expressway from Brisbane, Queensland, Australia to the Gold Coast.