摘要
铁路客运量能够反映一个国家的人口流动,是铁路经济效益计算的重要基础。本文将无偏灰色预测模型与马尔可夫链状态转移矩阵相结合,建立无偏灰色-马尔可夫预测模型。实验表明:无偏灰色-马尔可夫模型预测精度比无偏灰色模型的预测精度高,效果好,模型的参考价值得到了很大提升,为全国铁路各线路的制定、掌握铁路客运量发展规律提供理论参考和数据支撑。
The railway passenger volume can reflect the population flow of a country which is an important basis for the calculation of railway economic benefits.In this paper the unbiased grey forecasting model is combined with Markov chain state transfer matrix to establish an unbiased grey-Markov forecasting model.The results show that the prediction accuracy of the unbiased gray-Markov model is higher than that of the unbiased gray-Markov model and the reference price value of the model has been greatly improved which provides theoretical reference and data support for the formulation of railway lines and the mastery of the development law of railwame can reflect the population flow of a country which is an important basis for the calculation of railway economic benefits.In this paper the unbiased grey forecasting model is combined with Markov chain state transfery passenger volume.
作者
李国平
Li Guoping(Xinhua College of Ningxia University,Yinchuan,China)
出处
《科学技术创新》
2024年第23期207-210,共4页
Scientific and Technological Innovation
基金
宁夏大学新华学院科学研究基金项目(21XHKY04)。
关键词
铁路客运量
无偏灰色预测模型
马尔可夫链
railway passenger volume
unbiased grey prediction model
Markov chain
作者简介
李国平(1982-),男,硕士,副教授,研究方向:随机系统的稳定性与控制。
