Freight volume forecast of China Railway Express based on ES-GA-BP with combined input

Abstract

China Railway Express is an important support that promotes the Belt and Road Initiative. Scientific and reasonable forecast of the train demand is of great significance to the formulation of China Railway Express transportation scheme. Taking the forecast of freight volume of China Railway Express as an object, considering the fluctuation and influence factors, we propose a freight volume forecasting method based on exponential smoothing, genetic algorithm and optimized back propagation (ES-GA-BP) neural network with combined input. Firstly, we analyze the current status of China Railway Express freight transport, and select influencing factors with high correlation as the input of neural network. Then, the exponential smoothing method is used to fit and forecast the historical data of China Railway Express freight volume, so as to optimize the input of neural network. Genetic algorithm is used to optimize the parameters of back propagation neural network to further improve the prediction accuracy. Finally, the prediction of freight container numbers in the international transport channel of 'Hunan Europe Express' is taken as an example to verify the effectiveness of the method. The calculation results show that the combined input ES-GA-BP method is suitable for solving the problem of freight volume forecasting with large fluctuation, and the prediction accuracy is good, which is conducive to the formulation of a reasonable China Railway Express transportation scheme.