摘要
合理预测铁路货运需求是铁路管理部门建设、运营等决策基础。为应对铁路货运需求的复杂变化,基于Pearson相关性分析方法筛选出铁路货运需求的七个具有关键影响的因素,并结合不确定理论建立不确定多元线性回归模型,相应的铁路货运预测结果由传统单一值变成可能的需求区间范围,更加符合处于不确定环境下的铁路货运需求实际情况。选取国家统计局2004~2016年相关数据进实证研究,并与回归模型以及BP模型的预测结果对比分析,实验表明不确定多元线性回归的预测结果更加精确。
Reasonable prediction of railway freight demand is the decision-making basis for railway management departments to build and operate.Railway freight demand is uncertain.In order to cope with the complex changes of rail"way freight demand,the seven key factors of railway freight demand are selected based on the Pearson correlation analysis,and uncertain multivariate linear regression model is constructed by using the uncertainty theory-The corresponding rail"way freight value is changed from the traditional single value to the possible demand range,which is more in line with the actual demand of railway freight transportation under the uncertain environment.According to the relevant data of the National Bureau of Statistics from 2004 to 2016,the empirical study is carried out,and compared with the prediction results of regression model and BP model.The results show that the prediction results of uncertain multivariate linear regression can deal with the uncertainty well and the accuracy is higher.
作者
刘笑佟
任爽
LIU Xiao-tong;REN Shuang(School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China)
出处
《运筹与管理》
CSSCI
CSCD
北大核心
2020年第3期135-141,共7页
Operations Research and Management Science
基金
国家重点研发计划资助(2018YFB1201401)
中央高校基本科研业务费专项资金资助(2018JBM019)。
