Forecast of Railway Container Freight Volume by Using a Combinatorial Model

Abstract

Abstract: The forecast of railway container freight volume has significant effect on the operation and development of the railway. The existing forecast models can forecast a single index only, which is not accurate enough. To overcome this disadvantage, the combinatorial forecast model is adopted to forecast railway container freight volume. Based on the historical data, individual index forecast models are derived by using linear polynomial and grey models, respectively. Then, the individual index forecast models are combined by using radial basis function (RBF) neural network. Analysis shows that, in comparison with two single index forecast models, the combinatorial forecast model can improve the forecast result of relative error by 3.19% and 12.76%, respectively. Finally, the combinatorial forecast result is analyzed and modified by Markov chain model.

Key words: railway container, forecast, radial basis function(RBF) neural network, Markov chain

Lin Bing-kun, Cheng Wen-ming, Yu Lan-feng. Forecast of Railway Container Freight Volume by Using a Combinatorial Model[J]. Industrial Engineering Journal , 2012, 15(4): 1-6.

References

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