# Multi-Factor Highway Freight Volume Prediction Based on Backpropagation Neural Network
## 3 Neural Network Theory  
### MATLAB Multivariate Prediction Theory  
In this paper, we mainly use the neural network regression prediction. MATLAB is a powerful tool for data analysis and numerical calculation. It can use a variety of functions and toolboxes for multivariate prediction. Multivariate prediction generally predicts the value of one or more output variables through a variety of input variables. The steps in multivariate prediction are shown in Figure 1. In MATLAB, we can use a variety of multivariate prediction algorithms to predict. For example, through the construction of multiple decision trees, their results are averaged or voted for by random forest regression prediction; to predict the relationship between independent variables and dependent variables and establish multiple linear regression prediction of linear model, the weighted average is performed according to the nearest neighbor weight to find the K-nearest neighbor regression prediction of the nearest sample point; support vector regression prediction uses the support vector machine idea by finding the optimal hyperplane; the principal component regression predicts the principal component analysis of the independent variables and the principal component regression analysis of the principal component; and the partial least squares regression prediction of the linear model is established after the dimension reduction of the independent variable and the dependent variable. The neural network model is used to establish the neural network regression prediction of the nonlinear mapping relationship between input and output [14; 15; 16; 17; 18]. 1. Data preparation: Collect and organize input and output data. Ensure that the data are clean and complete and perform the necessary preprocessing, such as removing outliers, filling missing values, etc. 2. Feature selection: According to the characteristics of the problem and data, select the appropriate input variables as features. Correlation analysis, principal component analysis, and other methods can be used to assist selection. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Literature Serial Number** & **Research Method** & **Prediction Direction** & **Research Conclusions** \\ \hline \hline [7] & & Xinjiang freight volume & BP neural network prediction is more accurate than other prediction methods. \\ \hline [8] & BP neural network algorithm & National railway freight volume. & The prediction accuracy of multiple linear regression method is higher. \\ \hline [9] & & Railway freight volume. & The algorithm has good convergence and good prediction results. \\ \hline [10] & & Highway freight volume in the Yangtze River comprehensive transport corridor & The prediction results show the effectiveness of the model. \\ \hline \multirow{2}{*}{[11]} & \multirow{2}{*}{Combined model} & \multirow{2}{*}{Freight volume in Shandong Province} & The combination of qualitative prediction and quantitative prediction makes the prediction results more accurate. \\ \cline{3-4}  & & Lanzhou to Zhongchuan highway & The improved model improves the accuracy of simulation prediction. \\ \hline \end{tabular}
\end{table} 
Table 1: Literature research and analysis. Figure 1: Multivariate flow chart. Data partitioning: The data set is divided into training set and test set. Usually, most of the data are used to train the model, and the remaining part is used to evaluate the performance of the model. (4) Model training: Select the appropriate algorithm and use the training set to train the model.