## 2 Literature Review  
Advanced traveler information systems and advanced traffic management systems in ITS can actively control traffic signals and actively induce congested traffic flow through predicted traffic flow information [8]; they can aid in passive signal control through real-time traffic flow information [9] when the traffic flow is too large and it is difficult to ensure the smooth flow of the road, and the prediction of the traffic flow for some time in the future can predict the state of the road section, so that traffic managers can control the converging traffic flow in advance, thus reducing the possibility of congestion. For automatic driving technology, the traffic density of the approaching road section can be predicted in advance, and when the density is high, the automatic driving vehicle can reduce the speed and other measures in advance to improve driving safety [10], and can reduce the travel time by predicting the congestion state of the road section in advance and doing route planning. Disturbed by the external environment, the urban traffic system becomes complex and variable, and factors such as weather and morning and evening peaks can affect traffic flow in a nonlinear relationship [11]. Compared with medium-term and long-term traffic flow forecasting, short-term traffic flow forecasting has a shorter time granularity, and the obtained traffic flow data are more random, making short-term traffic flow forecasting more challenging [12, 13]. Forecasting methods are mainly parametric and non-parametric models, and parametric models are statistical class models [14] and Kalman filter models [15]; to capture the seasonal variation of macroscopic traffic flow, [PERSON] et al. [16] proposed the seasonal autoregressive integrated moving average model. However, such shallow models are suitable for relatively smooth and small sample data, which require high data quality and are difficult to accurately fit the changes of complex traffic flow; non-parametric models mainly include tree models and neural network models [17]. [PERSON] et al. [18] applied to the prediction of traffic flow with high accuracy by building a Support Vector Regression (SVR) model and optimizing its parameters. [PERSON] et al. [19] established a combined prediction model of the LSTM and the extreme gradient boosting (XGBoost) for the temporal characteristics of traffic flow data, fused the advantages of the two models, replaced the fully connected layer of LSTM with XGBoost, and validated the model using the data from Shenzhen. The commonly used neural network models are Back Propagation Neural Network [20], radial basis function neural network [21], and wavelet neural network models [22, 23], which essentially use error backpropagation to modify the weights to fit the data, but are highly susceptible to fall into local optimal solutions and have limited feature capture capability. To adapt to the current data era, researchers at home and abroad have favored the use of data-driven models based on deep learning to predict short-time traffic flow [24], and it has been found that the combined models can avoid the problem of inadequate feature capture capability of a single model [25, 26], with higher prediction accuracy and better generalization capability. Considering the spatio-temporal characteristics of traffic flow and the interference of the external environment on traffic flow, [PERSON] et al. [27] propose a One-Dimensional Convolutional Neural Network (1 DCNN) and LSTM model combined with an attention mechanism. The 1 DCNN is used to extract spatial features in the road, then input to LSTM to extract temporal features, and use the attention mechanism to extract key features; the simulation analysis shows that the model has better prediction results. [PERSON] et al. [28] considered the complexity of traffic flow, differenced the reconstructed data to make the data smoother, and used a combined model of recurrent neural network and LSTM for prediction; [PERSON] et al.