Zhang [1] established a PB neural network model and analyzed the influencing fac- 47  
tors of freight volume in Xinjiang. Through the training and fitting of the model, historical  48  
data were selected as the test samples of the network model, which proved that the pre- 49  
diction of BP neural network was more accurate than other prediction methods. Taking  50  
the national railway freight volume as the research object, Luo [3] uses the methods of  51  
multiple linear regression and BP neural network to obtain a more suitable prediction  52  
method for the national railway and conducts research on the prediction of national rail- 53  
way  freight  volume  based  on  multiple  factors.  Practice  shows  that  the  two  prediction  54  
methods have their own advantages and the combination of the two can improve the pre- 55  
diction accuracy. Chen [8] analyzed the characteristics of Shandong's freight volume while  56  
using several prediction models to make quantitative predictions under the influence of  57  
relevant factors and qualitatively corrected the prediction results to obtain the final com- 58  
prehensive prediction results. Yang [7] established a PSO-SVM prediction model using a  59  
particle swarm support vector machine model, and predicted the road freight volume un- 60  
der the influence of industrial transfer in the Yangtze River transport corridor to verify  61  
the effectiveness of the model. An [11] used the GM ( 1,1 ) model direct modeling method  62  
to improve and analyze the unbiased grey Verhulst model and the traditional grey Ver- 63  
hulst model. Through the simulation and prediction of Lanzhou highway freight volume,  64  
the superiority of GM ( 1,1 ), traditional grey Verhulst and unbiased grey Verhulst model  65  
is analyzed and the feasibility as well as the applicability of the improved model unbiased  66  
grey Verhulst model were illustrated. On the basis of qualitative and quantitative analysis  67  
of the influencing factors of railway transportation, Wu [23] selected some factors affect- 68  
ing China 's freight volume to carry out BP neural network prediction research based on  69  
multi-factor  railway  freight  volume.  Both  prediction  fitting  and  the  algorithm  conver- 70  
gence are good. The complex nonlinear mapping between the influencing factors and the  71  
railway freight volume is realized and the prediction results are excellent.    72  
Whether it is for the study of highway freight volume, or for the study of railway  73  
freight volume, we can get inspiration from it, so as to apply it to the study of highway  74  
freight volume. The application of research methods does not distinguish between fields,  75  
but focuses on reliability. Based on the research of the above scholars, we can find that  76  
multiple regression and multi-factor prediction have a good effect on the prediction of  77  
freight volume. In summary, based on the comprehensive consideration of the impact in- 78  
dicators of freight volume, this paper selects relevant indicators and collects data. The  79  
multi-factor prediction based on BP neural network is used to predict China 's freight vol- 80  
ume and compare it with the actual China 's highway freight volume. The predicted re- 81  
sults and reliability are obtained. 

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