基于灰色理论的铁路客运量预测影响因素优化

Optimal Selection of Factors Influencing Grey-theory-based Forecast of Railway Passenger Traffic Volume

为了更好地反映各种相关因素对客运量的影响,实现铁路客运量预测模型中影响因素的优化选择,采用灰色理论对影响因素进行分析,并针对传统灰关联分析在具体应用中存在的关联评价值趋于均匀化、分辨系数取值影响排序结果等不足,提出一种采用动态分辨系数的铁路客运量灰关联分析方法,从而得到各因素对客运量的关联度,实现铁路客运量预测模型中影响因素的优化选择.仿真实验以河南省铁路客运量为例,结果表明预测精度得到了提高,此方法可行并且有效.

In order to better unfold the influences of related factors on railway passenger traffic volume to optimize the selection of factors influencing railway passenger traffic forecast modeling,this paper analyzes the influencing factors by the grey theory and introduces a novel grey correlation analysis approach to railway passenger traffic volume with dynamic resolution coefficients,considering the weaknesses of traditional correlation analysis embodied in specific applications such as relation appraisal tending to be equalized and sorting results subject to the impact of discrimination coefficient value.Hence the relations between diverse factors and passenger traffic volume are uncovered,which can help to optimize the selection of factors influencing railway traffic volume forecast modeling.With the railway passenger traffic volume in Henan Province taken for instance,the simulation results testify that the accuracy of forecast has been increased,proving that the approach adopted here is feasible and effective.