# Multi-Factor Highway Freight Volume Prediction Based on Backpropagation Neural Network
## 7 Conclusions  
The innovation of this study lies in the establishment of a multi-layer feedforward neural network to build a road freight volume prediction model based on the BP neural network. A variety of influencing factors such as road diesel consumption, population, gross domestic product, road freight turnover, road freight volume, etc., are used as input variables, and road freight volume is used as the output variable. Using the learning and simulation capabilities of the neural network, a complex mapping relationship between input and output is established. Multi-factor prediction based on the BP neural network can identify the interaction and correlation between different factors so as to predict the output more accurately. It has advantages in dealing with complex multi-factor prediction problems. It can not only achieve good performance on the training set but also effectively predict new and unseen data. In this study, through the establishment of BP neural network, the multi-factor prediction method is used to accurately fit the original data, and finally the predicted value of China's highway freight volume is obtained. An effective prediction of China's freight volume has been made, and a high prediction accuracy has been achieved. The model has a small error, meets the prediction requirements for future freight volume, meets the needs of economic development, meets the needs of regional and social development, and provides a certain reference for correctly predicting the development trend of future freight volume.  
The significance of highway freight volume prediction involves many aspects. It is conducive to enterprises to grasp the development trend of the market, understand the changes in demand, and correctly formulate development strategies to cope with market changes, reduce operating costs, improve transportation efficiency, and gain a foothold in the market. Through the prediction of highway freight volume, it is conducive to the rational planning of highway operation lines, strengthening the planning and management of highway vehicles and rational planning of traffic flow. For the case analysis of road freight volume in this study, we can improve from the following aspects, such as increasing the vertical number of data and horizontal influencing factors and increasing the sample size of the predicted data. While selecting the influencing factors, we should consider the correlation with freight volume. In the future, we can try to enrich the influencing factors of freight volume and increase the spatial dimension characteristics so as to enrich the influencing factors of China's highway freight volume. In addition, we can also adjust the number of training rounds and select the appropriate model.  
Methodology, M.W.; software, C.T.; writing--original draft preparation, Y.Z.; writing--review, Z.Z.; writing--editing, B.G.; writing--polishing. K.M. All authors have read and agreed to the published version of the manuscript.  
This study did not receive any external funding.  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.