基于混沌理论的铁路客货运量预测研究  

Railway Passenger and Freight Volume Forecasting Based on Chaos Theory

作　　者：朱子虎[1] 翁振松 

机构地区：[1]铁道部经济规划研究院运输咨询部,北京100038

出　　处：《铁道学报》2011年第6期1-7,共7页Journal of the China Railway Society

基　　金：铁道部科技研究开发计划(J2009Z005)

摘　　要：应用混沌理论的相空间重构方法,分析与铁路运量相关的12组时间序列,分别计算它们的嵌入延迟时间、嵌入维数、关联维数、最大Lyapunov指数等混沌统计量,并以此为依据判断12组时间序列的混沌特性。结果显示:铁路客货运量及周转量不具有混沌特性,对应的4组时间序列不是混沌的;铁路客货运量、周转量的增量及增长率都具有明显的混沌特性,它们对应的8组时间序列是混沌的。在识别是否混沌的基础上,应用基于最大Lyapunov指数预测方法,对铁路客货运量、周转量进行预测检验及预测结果分析。Applying the phase space reconstruction method of the chaos theory,12 groups of time series associated with rail traffic volumes were analyzed in respect of the chaotic statistics data of the embedding delay time,embedding dimension,correlation dimension and the largest Lyapunov exponent.In accordance,the chaotic characteristics of the 12 groups of time series were identified.The analytical results show as follows： The railway passenger and freight traffic volumes and turnovers do not possess chaotic characteristic,their four groups of corresponding time series are not chaostic sequences;the increments and growth rates of the passenger and freight traffic volumes and turnovers possess significant chaotic characteristic,their eight groups of corresponding time series are chaostic sequences.On the basis of such chaotic judgment,the railway passenger and freight traffic volumes and turnovers are forecasted and analyzed with the largest Lyapunov exponent forecasting model.

关 键 词：铁路运量预测 混沌理论 相空间重构 时间延迟 嵌入维数 最大LYAPUNOV指数 

分 类 号：U293[交通运输工程—交通运输规划与管理] U294[交通运输工程—道路与铁道工程]