###### Abstract  
The accurate prediction of highway passenger volume is very important for China's transportation planning and economic development. Based on a neural network, this paper establishes a prediction model by using historical road passenger traffic and related influencing factor data, aiming to provide an accurate road passenger traffic prediction. Firstly, the historical highway passenger volume and the factor data affecting passenger volume are collected. Then, a multilayer perceptron neural network is established by using SPSS software (PASW Statistics 18) to analyze the significant relationship between highway passenger volume and influencing factors. Then, through the training and verification of the model by MATLAB software (R2021a), the reliability of the prediction model is proved. Finally, the model is used to predict the passenger traffic volume in 2020-2022, and the actual passenger traffic volume is compared and analyzed. It is concluded that the highway passenger traffic volume decreased significantly in 2020-2022 due to various factors such as the epidemic situation and policies, which have had an impact on China's economic development. neural network; highway passenger volume; prediction model; multilayer perceptron +
Footnote †: journal: Applied  
0000-0001-9300-2899  
networks in the field of regression analysis and prediction has been paid more and more attention. Neural networks have strong predictive abilities, including nonlinear mapping and self-learning abilities and can deal with complex and nonlinear time series data, so they have significant advantages for passenger volume prediction. Highway passenger volume prediction methods mainly include the exponential smoothing method, elastic coefficient method, regression analysis method, gray prediction method, support vector machine, time series method, Markov prediction method, and so on [2]. For dealing with the problem of highway passenger volume prediction, these models can effectively mine the hidden rules in the data and improve the prediction accuracy. It is of great practical significance to predict the highway passenger volume scientifically and accurately, grasp the trend, characteristics, laws, and quantity of highway passenger transport development, and make full preparations for the developmental planning of highway networks, transportation development strategy, industry management, and decision-making [3]. The primary problem of highway passenger volume prediction is to extract the relevant influencing factors and establish a suitable model. Multiple influencing factors related to highway passenger volume jointly determine the absolute value and spatial distribution of highway passenger volume, such as population, gross domestic product, private car ownership, railway passenger volume, and air passenger volume. Therefore, extracting significant influencing factors and further analyzing their relationship with highway passenger volume is the premise of accurately predicting passenger volume. Secondly, in previous studies, other scholars have evaluated the performance of different models. Based on historical road passenger volume data, some commonly used models have been developed, including the binary logit model, multilayer perceptron, time series model, and BP neural network model. However, the prediction accuracy of existing models is largely affected by the size of historical traffic data sets. Therefore, if the model lacks sufficient data, it is impossible to make accurate predictions using historical data. Highway passenger volume forecast is divided into two parts: (1) extracting significant influencing factors; (2) developing a BP neural network to realize the prediction. Therefore, it is feasible to develop a method based on a neural network to predict highway passenger traffic. Since highways have always been an important mode of transportation, this study takes the highway as the research object.