摘要
铁路客运量是衡量我国交通需求的重要指标,科学预测铁路客运量是制定交通发展规划的重要依据。鉴于组合模型能克服单一模型的不足并兼具单一模型的优点,基于灰色模型和线性回归模型,根据灰色关联度赋予单一模型相应权重,建立铁路客运量组合预测模型,并选取2006—2015年铁路客运量数据,对我国铁路客运量进行预测。结果表明:组合模型克服了单一模型的预测局限性,能进一步提高预测精度,适用于铁路客运量预测研究。
Railway passenger traffic is an important index to measure the demand of transportation in our country. Scientific forecast of railway passenger traffic is an important basis for the development of transportation development planning. In order to accurately predict the passenger capacity of our country,this paper establishes the grey linear regression combined model for railway passenger traffic,based on the gray model and the linear regression model,to which corresponding weight is given according to the gray correlation degree,and select the of 2006—2015 railway passenger traffic to forecasting our Railway Passenger Traffic. The results show that the combined model overcomes the prediction limitations of the single model and has a wide range of application,which can further improve the prediction accuracy and can be used to predict the railway passenger traffic.
出处
《重庆理工大学学报（自然科学）》
CAS
2017年第11期230-234,240,共6页
Journal of Chongqing University of Technology：Natural Science
基金
天津市应用基础及前沿技术研究计划资助项目"基于感知过程的复杂系统信息融合理论与应用研究"(10JCYBJC07300)
关键词
灰色模型
线性回归模型
组合模型
铁路客运量
grey model
linear regression model
combined model
railway passenger traffic
