铁道科学与工程学报2018，Vol.15Issue(3)：778-784,7.
基于粗糙集与熵的铁路货物周转量预测
Forecasting railway freight turnover based on rough set and entropy
摘要
Abstract
The simple entropy calculation method was used to discretize the original data, and the decision information system was obtained. The rough set was used to reduce the influencing condition attributes of freight turnover. The rest attributes were taken as explanatory variables, and the explanatory variables and freight turnover were used to establish multiple linear regression model. By using Eviews software to calibrate parameters, the regression significance of the model was tested concurrently. The goods turnover during 2013?2015 was taken as an example to forecast. The predicted growth rate values of goods turnover are 1.9%, 1.0% and ?3.8%, respectively, whereas the turnover values of goods are 29 746.26, 30 054.87 and 29 812.16 tons kilometers, respectively. The average degree of agreement with the actual data is 89.05%, which indicates that the method has certain reliability.关键词
铁路运输/货物周转量/预测/简单熵/属性重要性/多元线性回归Key words
railway transportation/freight turnover/forecasting/simple entropy/attribute importance/multiple linear regression分类
交通工程引用本文复制引用
杨丽蓉,吕红霞..基于粗糙集与熵的铁路货物周转量预测[J].铁道科学与工程学报,2018,15(3):778-784,7.基金项目
国家重点研发计划资助项目(2017YFB1200702,2016YFC0802208) （2017YFB1200702,2016YFC0802208）
国家自然科学基金资助项目(61703351) （61703351）
中国铁路总公司科技研究计划资助项目(2016X006-D) （2016X006-D）
四川省科技计划资助项目(2017ZR0149,2017RZ0007) （2017ZR0149,2017RZ0007）
中央高校基本科研业务费专项资金资助项目(2682017ZDPY 04,2682017CX022,2682017CX018) （2682017ZDPY 04,2682017CX022,2682017CX018）
