# Multi-Factor Highway Freight Volume Prediction Based on Backpropagation Neural Network
## 2 Literature Review  
[PERSON] [7] established a BP (backpropagation neural network, a multi-layer feedforward neural network trained according to error backpropagation algorithm) neural network model and analyzed the influencing factors of freight volume in Xinjiang. Through the training and fitting of the model, historical data were selected as the test samples of the network model, which proved that the prediction of the BP neural network was more accurate than other prediction methods. Taking the national railway freight volume as the research object, [PERSON] [8] uses the methods of multiple linear regression and the BP neural network to obtain a more suitable prediction method for the national railway and conducts research on the prediction of national railway freight volume based on multiple factors. Practice shows that the two prediction methods have their own advantages and the combination of the two can improve the prediction accuracy. [PERSON] [9] analyzed the characteristics of Shandong's freight volume while using several prediction models to make quantitative predictions under the influence of relevant factors and qualitatively corrected the prediction results to obtain the final comprehensive prediction results. [PERSON] [10] established a PSO-SVM (particle swarm optimization-support vector machine) prediction model using a particle swarm support vector machine model and predicted the road freight volume under the influence of industrial transfer in the Yangtze River transport corridor to verify the effectiveness of the model. [PERSON] [11] used the GM (1,1) model direct modeling method to improve and analyze the unbiased grey Verhulst model (single-sequence first-order nonlinear dynamic model) and the traditional grey Verhulst model. Through the simulation and prediction of Lanzhou highway freight volume, the superiority of the GM (1,1), traditional grey Verhulst, and unbiased grey Verhulst models are analyzed and the feasibility and the applicability of the improved unbiased grey Verhulst model are illustrated. On the basis of qualitative and quantitative analysis of the influencing factors of railway transportation, [PERSON] [12] selected some factors affecting China's freight volume to carry out BP neural network prediction research based on multi-factor railway freight volume. Both prediction fitting and the algorithm convergence are good. The complex nonlinear mapping between the influencing factors and the railway freight volume is realized and the prediction results are excellent. The literature research and analysis are shown in Table 1 below.  
Whether it is for the study of highway freight volume, or for the study of railway freight volume, we can receive inspiration from it so as to apply it to the study of highway freight volume. The application of research methods does not distinguish between fields but focuses on reliability. Based on the research of the above scholars, we can find that multiple regression and multi-factor prediction have a good effect on the prediction of freight volume. In summary, based on the comprehensive consideration of the impact indicators of freight volume, this paper selects relevant indicators and collects data. The multi-factor prediction based on the BP neural network is used to predict China's freight volume and compare it with China's actual highway freight volume. The predicted results and reliability are obtained [13].