Railroad freight volume prediction based on grey relation analysis and BP neural network

Railroad transportation is an efficient and economical way of cargo transportation. Due to the imbalance of supply and demand in the railroad freight market, various factors have complex forms and different degrees of influence on the freight volume, which makes the forecasting of the railroad freight volume complexity and non-linear characteristics. This paper combines grey relation analysis and BP neural network to forecast the national railroad freight volume. Firstly, the data of the factors affecting the railroad freight volume from 2012 to 2022 are selected, and the grey relation analysis is used to obtain the relatively high correlation of the four influencing factors, namely the proportion of highway freight volume, the proportion of water freight volume, the mileage of railroad operation and the value added of the primary industry, which are used as the input layer of the BP neural network; then, according to the corresponding relationship between the influencing factors and the volume of railroad freight volume, the model is trained; finally, the model based on the model of railroad freight volume is trained; and the model based on the model of railroad freight volume is trained. Then, the model was trained according to the correspondence between each influential factor and railway freight volume; finally, the grey relation-based BP neural network model was compared with the traditional BP neural network model. Finally, the grey relation-based BP neural network model is compared with the traditional BP neural network model. The results show that the grey relation-based BP neural network can not only get better solutions, but also shorten the training time.