基于无偏灰色模糊马尔可夫链法的铁路货运量预测研究

Railway Freight Volume Forecasting Based on Unbiased Grey-Fuzzy-Markov Chain Method

针对传统灰色马尔可夫预测模型存在灰色偏差与抗干扰能力弱的局限性,将无偏灰色理论与模糊集合理论引入预测模型,从趋势曲线灰色拟合与状态分类方式上对传统灰色马尔可夫模型进行改进,提出无偏灰色模糊马尔可夫链预测铁路货运量的方法,并结合"十一五"期间中国铁路货运量和货运市场发展趋势的预测分析,详细阐述了该方法的具体应用,最后使用定性分析对该方法的预测结果进行检验与讨论。理论分析和算例表明,该方法有更可靠的预测结果,并且能够对铁路货运市场的发展趋势进行宏观的把握,有利于决策者的决策行为。

To eliminate the grey bias and improve anti-jamming performance of the standard Grey-Markov forecasting model, the unbiased grey theory and fuzzy classification are introduced into the grey-Markov forecasting model and a new method named the Unbiased Grey-Fuzzy-Markov Chain Method is proposed. The improved prediction method is applied in Chinese railway freight volume forecasting in the Eleventh Five-year Plan period, and the forecast results are verified and discussed by the means of qualitative analysis. It＇s proved in theory and practice that the forecast results are more reliable and can help the decision maker with grasping the general railway freight development tendency and making proper decisions.