关键词: 铁路客运量, 变权重, 灰色理论, 广义回归神经网络, 组合预测

Abstract: Scientific and accurate short-term forecast of railway passenger traffic is the key to improve the competitiveness and service level of the railway passenger transport system. According to the characteristics of short-term railway passenger traffic, a variable weight combination forecasting model based on grey theory is proposed. In order to obtain the weight coefficient of different models at different times, the dynamic weights are tracked and predicted by using the generalized regression neural network. Taking the railway passenger traffic volume of 1~12 months in 2014 as the research object, the even grey model, the discrete grey model, the grey Verhulst model and the variable weight combination forecasting model are established respectively. The results of case analysis show that the average relative error of the three single model are 17.14%, 12.94% and 16.99% respectively, while the variable weight combination model is 7.01%, The forecasting accuracy of the variable weight combination forecasting model is obviously higher than the single model.

Key words: railway passenger traffic volume, variable weight, grey theory, generalized regression neural network, combination forecasting