基于GM(1,1)残差二次修正的铁路货运量预测
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摘要
铁路货运量是铁路运输能力的重要体现,也是确定铁路交通基础设施建设规模的主要依据。铁路货运量的预测结果是否合理,会对铁路的运输生产及效益产生直接影响。在传统GM(1, 1)灰色预测及GM(1, 1)残差一次修正模型的基础上,建立GM(1, 1)残差二次修正预测模型,从而提高模型预测的精度,并基于残差二次修正后的模型预测未来三年我国铁路的货运量,以期为铁路部门制定未来铁路运输发展战略,合理利用资源,充分发挥铁路运输能力提供参考。
Rail freight volume is an important manifestation of railway transportation capacity and is the primary consideration for determining the scale of railway transportation infrastructure construction. The prediction accuracy of rail freight volume has a direct impact on railway transportation production and benefits. Based on the traditional GM(1,1) grey prediction and GM(1,1) residual correction models, a quadratic modified prediction model of GM(1,1) residual is established to improve the prediction accuracy.Next,the rail freight volume of China is predicted for the next three years based on the model after the second correction of the residuals, to formulate future railway transportation development strategies for railway departments, taking available resources into account and giving full consideration to railway transportation capacity.
Rail freight volume is an important manifestation of railway transportation capacity and is the primary consideration for determining the scale of railway transportation infrastructure construction. The prediction accuracy of rail freight volume has a direct impact on railway transportation production and benefits. Based on the traditional GM(1,1) grey prediction and GM(1,1) residual correction models, a quadratic modified prediction model of GM(1,1) residual is established to improve the prediction accuracy.Next,the rail freight volume of China is predicted for the next three years based on the model after the second correction of the residuals, to formulate future railway transportation development strategies for railway departments, taking available resources into account and giving full consideration to railway transportation capacity.
引文
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[14]冯宇.基于残差修正GM(1,1)模型的包神北线货运量预测[J].交通科技与经济,2018,20(1):20-22, 27.
[15]中华人民共和国国家统计局。http://data.stats.gov. cn/easyquery. htm?cn=C01.
[2]刘倩.铁路货运量近五年来首现增长——一季度同比增15.29%[J].铁合金, 2017, 48(06):30.
[3]彭鹏,李夏苗,高醒.我国铁路货运量演化趋势及其运营对策[J].科技和产业,2017,17(10):5-10.
[4]张岄.铁路货运量预测及影响因素研究[D].北京:北京交通大学, 2016.
[5]左小雨,黄先军.基于灰色预测模型对我国铁路货运量的预测[J].物流科技, 2016, 39(8):82-84.
[6]曹飞.基于灰色残差GM(1,1)模型的中国铁路货运量预测[J].北方经贸, 2012(7):107-109.
[7]张俊勇.2017年:铁路货运企稳向好[J].中国物流与采购, 2018(05):33-35.
[8]姚裕盛,徐开俊,杨泳.基于GM(1,1)残差修正模型的飞行训练量预测[J].西安航空学院学报,2018, 36(1):78-82.
[9]成永刚.灰色GM(1,1)模型预测精度的残差修正及对岩体变形预测的应用[J].四川建筑, 2004,24(02):73-74.
[10]李梦婉,沙秀艳.基于GM(1, 1)灰色预测模型的改进与应用[J].计算机工程与应用, 2016, 52(04):24-30.
[11]杨阳.基于残差修正的GM(1,1)模型的我国人均粮食产量预测[J].统计与决策,2011(17):53-55.
[12]韩姣.山西快递市场的需求预测研究[D].西安:西安建筑科技大学, 2015.
[13]马晓云,陈刚.GM(1,1)模型的残差修正及其在建筑物变形预测中的应用[J].测绘标准化, 2018,34(3):56-58.
[14]冯宇.基于残差修正GM(1,1)模型的包神北线货运量预测[J].交通科技与经济,2018,20(1):20-22, 27.
[15]中华人民共和国国家统计局。http://data.stats.gov. cn/easyquery. htm?cn=C01.