## 1 Introduction
In addition, according to different training methods and methods of deep learning, a model can be further divided into generative deep structures, discriminative deep structures, and hybrid deep structures [8]. Very fruitful research results have been achieved in different development periods. The current research direction is mainly derived from a parametric statistical model to a non-parametric model, gradually, and then the combination model appears. Some scholars using the parametric model have used the growth curve to predict new rail transit passenger flow [9]. Among the parametric models, a time-series is the most common, including various time-series models and their variants [10; 11; 12]. The advantages of using parametric models for short-term traffic forecasting are mainly focused on the simplicity of the models and their ease of interpretation. However, this type of model does not reflect the non-linear characteristics of traffic volume. Because, in reality, the characteristics of traffic volume are mostly non-linear, error is larger. Given the limitations of parametric models, non-parametric models provide good solutions. For example, [PERSON] used a support vector regression algorithm (SVR) to predict urban traffic flow. SVR has good results in predicting small-sample, multidimensional data, with non-linear processing and local minimums. Compared with BP, LSTM, and ARIMA, SVR is better than other methods. It has excellent generalization ability, which avoids some problems caused by over-fitting, and it can accurately predict urban short-term traffic flow [13]. [PERSON] used three different variants of an RNN to construct a model to experiment on traffic data and found that a recurrent neural network has a good prediction effect on specific road traffic flow, while LSTM has a less-good prediction error [14]. [PERSON] combined the KNN method with SVM and found that the KNN method helped to significantly reduce the training scale of an SVM, thus speeding up the training process without affecting prediction performance [15]. [PERSON] used a combination of an algorithm (STFSA) and a CNN deep learning framework for a short-term traffic flow prediction two-dimensional matrix. The prediction accuracy was better than an SVR, SARIMA, KNN, ANN, or CNN single model and a combined model (STFSA plus ANN) [16]. [PERSON] scholars adopted the same idea to combine an STFSA and a GRU for a short-term traffic flow prediction model. Compared with the CNN and GRU models, it was found that the combined model was significantly better than the single model in terms of accuracy and stability [17]. [PERSON] constructed a CNN-LSTM multi-step prediction model by combining feature data with an attention mechanism. The results showed that the prediction model provided nearly 99% accuracy, and it applied to peak and non-peak hours, as well as working days and non-working days [18]. [PERSON] proposed three hybrid depth-time models (CL-CN-G, CL-CNG, and G-CN-CL models) based on CNN, GRU, and ConvLSTM models to address the lack of existing studies regarding the prediction of characteristic traffic flows such as holidays and severe weather. The case studies showed that the CL-CN-G, CL-CNG, and G-CN-CL models were accurate and effective in all cases, and the G-CN-CL model performed best [19]. At present, most of the data on highway traffic volume prediction mainly comes from relevant checkpoints [20]. Some scholars also have used the import and export data of toll stations [21]. Among the prediction models after extraction of the spatio-temporal features of traffic volume data, CNN models are commonly used to extract the spatial characteristics of traffic flow because of to their high interpretability [16]. GRU models are commonly used to extract the temporal characteristics of traffic volume due to their ability to effectively solve problems such as gradient disappearance or explosion during temporal extraction [22].