## 2 Literature Review  
Highway passenger volume was predicted by [PERSON] et al. [4] using a system dynamics model. The results show that the prediction accuracy of the SD model is lower than that of the LV-SD model. [PERSON] and [PERSON] [5] proposed a prediction method for a double hidden layer BP neural network based on the Softplus activation function, which improved the accuracy of the prediction model. While predicting the highway passenger volume in Gansu Province, [PERSON] [6] compared it with the multiple linear regression prediction model. The results show that the BP neural network model is more accurate than the multiple linear regression model. [PERSON] and [PERSON] [7] verified the accuracy and convenience of the BP neural network prediction system and improved the accuracy of highway passenger freight prediction. [PERSON] [8] studied the BP neural network model and concluded that the model had high accuracy. Based on the multilayer perceptron neural network and multiple regression fitting algorithm, [PERSON] et al. [9] established a MLP model, linear regression model, and nonlinear regression model, respectively, for predicting PWV. The results show that the MLP model has better accuracy and adaptability in southern China. [PERSON] and [PERSON] [10] constructed the multilayer perceptron neural network prediction model for the connotation quality of inpatient medical records. The results show that the prediction efficiency of the multilayer perceptron neural network connotation quality prediction model is better, which can provide a theoretical reference for the connotation quality management of inpatient medical records. [PERSON] et al. [11] used the multilayer perceptron neural network regression method to establish the weighted average temperature in Southwest China. The results show that the accuracy and stability of the MLP model in different elevations, latitudes, and seasons in Southwest China are better than those of the Bevis model and the GPT3 model. [PERSON] and [PERSON] [12] used discriminant analysis and multilayer perceptron neural network analysis method to establish models for personal credit and compare and evaluate them. The results show that the classification results of multilayer perceptron are better than those of discriminant analysis. [PERSON] and [PERSON] [13] proposed a fast classification method for multi-spectral images based on integrated fully connected multilayer perceptron. Comparing the classification results of each classifier for Landsat multi-spectral images, it is found that the integrated classifier is better. [PERSON] and [PERSON] [14] used SPSS and MATLAB software to establish multiple regression and BP neural network prediction models, respectively. The results show that the multiple linear regression prediction model is better than the BP neural network prediction model. [PERSON] and [PERSON] [15] used a PCA-BP neural network model to predict highway passenger traffic, and the analysis results prove the effectiveness of the improved PCA-BP neural network model for highway passenger traffic prediction. Aiming at the problem of highway passenger volume prediction, [PERSON] et al. [16] proposed a BP neural network combined prediction model. Given the fuzzy and unpredictable characteristics of urban highway passenger traffic volume, [PERSON] et al. [17] adopted an adaptive neural network model to analyze the relationship between urban highway passenger volume, population, and GDP. Based on the gray prediction, [PERSON] et al. [18] introduced the BP neural network model to establish the GM (1,1) and BP neural network combination model. The research results of the above selected parts of the literature are summarized in a table, as shown in Table 1.  
In summary, most of the above scholars use the neural network model to analyze and predict the highway passenger volume. The results show that the prediction effect of the neural network is still good. Therefore, this paper also uses a neural network to predict the highway passenger volume in China.