Analysis and Forecast of Railway Coal Transportation Volume Based on BP Neural Network Combined Forecasting Model

作者：Xu Yongbin^*; Xie Haihong; Wu Liuyi

来源：6th International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation (CDMMS), 2018-04-14 To 2018-04-15.

DOI：10.1063/1.5039126

• coal traffic forecast
• GM (1,1)
• exponential smoothing
• back propagation
• combined forecasting

The share of coal transportation in the total railway freight volume is about 50%. As is widely acknowledged, coal industry is vulnerable to the economic situation and national policies. Coal transportation volume fluctuates significantly under the new economic normal. Grasp the overall development trend of railway coal transportation market, have important reference and guidance significance to the railway and coal industry decision-making. By analyzing the economic indicators and policy implications, this paper expounds the trend of the coal transportation volume, and further combines the economic indicators with the high correlation with the coal transportation volume with the traditional traffic prediction model to establish a combined forecasting model based on the back propagation neural network. The error of the prediction results is tested, which proves that the method has higher accuracy and has practical application.

• 出版日期2018
• 单位北京交通大学