公路货物运输量统计新试行方案问题及完善思路研究
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摘要
为了研究2014年公路货物运输量统计新试行方案存在的主要问题,提出完善新方案的思路,为以后的运输量统计工作提供可操作的测算方法,基于对公路运输量统计方法的沿革及统计新试行方案的主要特点的分析,发现了新试行方案存在基于车辆的分层抽样调查方法难以持续长久以及利用高速公路计重收费数据推算公路货物运输量代表性不强等问题。基于新试行方案存在的主要问题,应用Excel软件建立了灰色关联分析模型,结合灰色关联分析模型研究了与公路货运量关联度较高的主要统计指标。研究结果表明主要产品产量、货车销量、货车吨位数与公路货运量高度相关,可用于评估公路货运量变化趋势的合理性。趋势变化图显示,公路货运量的数据变动趋势与宏观经济形势相背离且与主要产品产量、货车销量及货车吨位数变化趋势不匹配。基于对方案问题的分析及运用灰色关联模型得出的研究结论,提出了完善公路运输量方案的新思路,并用Eviews6按照新思路建立模型进行实例测算。测算结果表明,通过生铁、水泥、机制纸及纸板、货车吨位数4个指标建立的回归模型不但对样本数据的拟合程度较高,拟合优度检验通过,标准误差值较小,而且在给定的显著性水平上,通过了回归系数t检验,因此可用于测算公路货物运输量。
For the sake of researching the main problems in 2014 highway freight volume statistics trial scheme,putting forward the improving idea,and providing an operational calculation method for the future highway freight volume statistics work,based on the analysis of the evolution of statistical methods and the feature of highway freight volume statistics trial scheme,the problem such as the method of stratified sampling based on vehicles is difficult to last for a long time and using expressway toll-by-weight data to calculate the quantity of highway freight transport is not typical are found. In view of these main problems,a grey relational analysis model is established by Excel software. Using Grey relational analysis model,the main statistical indicators that highly related to the highway freight volume are found. The result shows that product yield,freight vehicle sales and freight vehicle tonnage are highly related to highway freight volume,these indicators can be used to evaluate the rationality of the trend of highway freight volume. The trend map shows that the data change trend of highway freight volume deviates from the macro economic situation and mismatch the trend of product yield,freight vehicle sales and freight vehicle tonnage. Based on the analysis of the problems in the trial scheme and the research conclusions obtained by using grey relational model,a new idea forimproving the trial scheme is proposed,and the example calculation by the built model using Eviews6 according to the new idea is conducted. The calculation result shows that the built regression model using iron,cement,machine-made paper and paperboard,and truck tonnage not only has high fitting degree with the sample data,passed the goodness of fit test and has smaller standard error value,but also passed the regression coefficient t test at a given significance level. Thus,the model can be used for the calculation of highway freight volume.
For the sake of researching the main problems in 2014 highway freight volume statistics trial scheme,putting forward the improving idea,and providing an operational calculation method for the future highway freight volume statistics work,based on the analysis of the evolution of statistical methods and the feature of highway freight volume statistics trial scheme,the problem such as the method of stratified sampling based on vehicles is difficult to last for a long time and using expressway toll-by-weight data to calculate the quantity of highway freight transport is not typical are found. In view of these main problems,a grey relational analysis model is established by Excel software. Using Grey relational analysis model,the main statistical indicators that highly related to the highway freight volume are found. The result shows that product yield,freight vehicle sales and freight vehicle tonnage are highly related to highway freight volume,these indicators can be used to evaluate the rationality of the trend of highway freight volume. The trend map shows that the data change trend of highway freight volume deviates from the macro economic situation and mismatch the trend of product yield,freight vehicle sales and freight vehicle tonnage. Based on the analysis of the problems in the trial scheme and the research conclusions obtained by using grey relational model,a new idea forimproving the trial scheme is proposed,and the example calculation by the built model using Eviews6 according to the new idea is conducted. The calculation result shows that the built regression model using iron,cement,machine-made paper and paperboard,and truck tonnage not only has high fitting degree with the sample data,passed the goodness of fit test and has smaller standard error value,but also passed the regression coefficient t test at a given significance level. Thus,the model can be used for the calculation of highway freight volume.
引文
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[10]张忠民.公路货物运输量统计方案完善与应用研究[J].交通与运输:学术版,2015(1):186-190.ZHANG Zhong-min.Improvement and Application of Road Freight Transportation Volume Statistics Programme[J].Traffic and Transportation,2015(1):186-190.
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[13]王璐,吴群琪,熊锐.公路货物运输发展影响因素分析及预测[J].统计与决策,2015(20):83-85.WANG Lu,WU Qun-qi,XIONG Rui.Analysis and Forecast of Influence Factors on Development of Highway Freight Transportation[J].Statistics and Decision Making,2015(20):83-85.
[14]范碧霞.铁路货物运输总量与其相关影响因素的灰色关联分析[J].物流技术,2015,34(13):157-159.FAN Bi-xia.Analysis of Grey Correlation between Total Railway Freight Transport Volume and Relevant Influence Factors[J].Logistics Technology,2015,34(13):157-159.
[15]杨帆.基于灰色预测模型的山东省公路货运量预测分析[J].现代营销:学苑版,2013(6):156-158.YANG Fan.Prediction and Analysis of Highway Freight Transport Volume in Shandong Province Based on Grey Prediction Model[J].Marketing Management Review,2013(6):156-158.
[16]帅斌,霍娅敏.交通运输经济[M].成都:西南交通大学出版社,2007:19-36.SHUAI Bin,HUO Ya-min.Transportation Economy[M].Chengdu:Southwest Jiaotong University Press,2007:19-36.
[17]郑军伟.基于灰色系统理论的数据关联度建模及其应用[D].浙江:杭州电子科技大学,2011.ZHENG Jun-wei.Modeling and Application of Data Association Degree Based on Grey System Theory[D].Zhejiang:Hangzhou Dianzi University,2011.
[18]刘思峰,杨英杰,吴利丰.灰色系统理论及其应用[M].北京:科学出版社,2014:72-73.LIU Si-feng,YANG Ying-jie,WU Li-feng.Theory and Application of Grey System[M].Beijing:Science Press,2014:72-73.
[2]崔晓飞.区域公路运输量调查与统计方法研究[D].长春:吉林大学,2007.CUI Xiao-fei.Survey and Statistics Methods of Transportation Volume by Highway Traffic[D].Changchun:Jilin University,2007.
[3]王力强.县域公路运输量调查与统计方法研究[D].长春:吉林大学,2011.WANG Li-qiang.Survey and Statistics Methods of County Highway Transportation Volume[D].Changchun:Jilin University,2011.
[4]林成功,段新.公路货物运输与经济发展相关关系研究[J].中国市场,2014(40):134-136.LIN Cheng-gong,DUAN Xin.Study on Relationship between Highway Freight Transportation and Economic Development[J].China Market,2014(40):134-136.
[5]张军君.公路运输对区域经济发展的影响研究[D].北京:中国林业科学研究院,2014.ZHANG Jun-jun.Impact of Road Transport on Regional Economy Development[D].Beijing:Chinese Academy of Forestry,2014.
[6]孙晓飞.改革开放以来中国公路运输政策演变研究[D].长沙:湖南师范大学,2011.SUN Xiao-fei.Research on Evolution of Chinese Highway Transportation Policy Since Reform[D].Changsha:Hunan Normal University,2011.
[7]郭红霞.柳州市公路货物运输统计调查指标体系研究[J].科技资讯,2014,12(14):208-209.GUO Hong-xia.Study on Index System of Liuzhou Highway Freight Transportation Statistical Investigation[J].Science&Technology Information,2014,12(14):208-209.
[8]孙淑勤.关于公路运输量调查统计的思考与建议[J].交通标准化,2011(16):125-129.SUN Shu-qin.Reflection and Suggestion on Highway Transportation Investigation[J].Transportation Standardization,2011(16):125-129.
[9]交通运输部.2009年公路水路运输量统计试行方案[EB/OL].(2013-05-16)[2017-03-15].https://wenku.baidu.com/view/7dc2c3d6d15abe23482f4d55.html.Ministry of Transport.Highway and Waterway Freight Transport Volume Statistics Trial Scheme(2009)[EB/OL].(2013-05-16)[2017-03-15].https://wenku.baidu.com/view/7dc2c3d6d15abe23482 f4d55.html.
[10]张忠民.公路货物运输量统计方案完善与应用研究[J].交通与运输:学术版,2015(1):186-190.ZHANG Zhong-min.Improvement and Application of Road Freight Transportation Volume Statistics Programme[J].Traffic and Transportation,2015(1):186-190.
[11]李坤.常用抽样方法概述[J].市场研究,2012(11):38-39.LI Kun.Overview of Commonly Used Sampling Methods[J].Market Research,2012(11):38-39.
[12]国家发展和改革委员会.国家公路网规划(2013-2030年)[EB/OL].(2014-05-10)[2017-03-15].https://wenku.baidu.com/view/830520a019e8b8f67c1cb9ca.html.National Development and Reform Commission.National Highway Network Planning(2013-2030)[EB/OL].(2014-05-10)[2017-03-15].https://wenku.baidu.com/view/830520a019e8b8f67c1c b9ca.html.
[13]王璐,吴群琪,熊锐.公路货物运输发展影响因素分析及预测[J].统计与决策,2015(20):83-85.WANG Lu,WU Qun-qi,XIONG Rui.Analysis and Forecast of Influence Factors on Development of Highway Freight Transportation[J].Statistics and Decision Making,2015(20):83-85.
[14]范碧霞.铁路货物运输总量与其相关影响因素的灰色关联分析[J].物流技术,2015,34(13):157-159.FAN Bi-xia.Analysis of Grey Correlation between Total Railway Freight Transport Volume and Relevant Influence Factors[J].Logistics Technology,2015,34(13):157-159.
[15]杨帆.基于灰色预测模型的山东省公路货运量预测分析[J].现代营销:学苑版,2013(6):156-158.YANG Fan.Prediction and Analysis of Highway Freight Transport Volume in Shandong Province Based on Grey Prediction Model[J].Marketing Management Review,2013(6):156-158.
[16]帅斌,霍娅敏.交通运输经济[M].成都:西南交通大学出版社,2007:19-36.SHUAI Bin,HUO Ya-min.Transportation Economy[M].Chengdu:Southwest Jiaotong University Press,2007:19-36.
[17]郑军伟.基于灰色系统理论的数据关联度建模及其应用[D].浙江:杭州电子科技大学,2011.ZHENG Jun-wei.Modeling and Application of Data Association Degree Based on Grey System Theory[D].Zhejiang:Hangzhou Dianzi University,2011.
[18]刘思峰,杨英杰,吴利丰.灰色系统理论及其应用[M].北京:科学出版社,2014:72-73.LIU Si-feng,YANG Ying-jie,WU Li-feng.Theory and Application of Grey System[M].Beijing:Science Press,2014:72-73.