基于无偏灰色残差理论的铁路客运量预测研究

A Research on Railway Transportation Prediction based on Unbiased Grey Residual Theory

铁路客运量作为铁路客运市场体系中的重要指标,反映铁路客运基本情况和发展水平,体现铁路在综合交通运输体系中的地位和作用。为了科学准确地预测"十三五"时期铁路客运量,在分析铁路客运量主要影响因素的基础上,构建基于无偏灰色残差理论的铁路客运量预测模型。以24年铁路客运量数据为例,通过对比支持向量机SVM预测与RBF神经网络预测的结果,验证基于无偏灰色残差理论的铁路客运量预测模型预测精度较高,进而运用此模型预测铁路客运量未来5年的数据,为铁路客运生产组织和发展规划提供理论依据。

Railway passenger volume,as an important index in the railway passenger transport market system,reflects the basic situation and development level of railway passenger transport,as well as its position and role in the comprehensive transportation system.To predict railway passenger volume scientifically and accurately during 13th Five-Year Plan period,a prediction model of railway passenger volume based on unbiased grey residual theory is constructed on the basis of analyzing the main influencing factors of railway passenger volume.Taking the 24-year railway passenger volume data as an example,by comparing the results of SVM prediction with RBF neural network prediction,it is verified that the prediction accuracy of the railway passenger volume prediction model based on unbiased grey residual theory is higher,and then the data of railway passenger volume in the next five years are predicted through this model,which provides a theoretical basis for railway passenger production organization and development planning.