内蒙古综合交通运量预测研究
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摘要
内蒙古横跨我国三北,在西部大开发和振兴东北老工业基地中具有重要的战略地位和意义。交通运输基础设施是经济和社会发展的基础,内蒙古地区的交通运输发展对这一地区的经济和社会发展乃至对整个西部开发都具有重要的影响。
交通量是确定交通项目建设规模与技术标准的主要依据之一,是提高交通运输建设项目投资效益的重要环节。因此,在交通建设工程可行性研究阶段,如何应用合适的交通量预测法,做好建设项目交通需求量预测工作,具有十分重要的现实意义。
论文将对内蒙古综合运输体系所处的经济和社会环境、发展现状、区位分布以及各主要盟市经济和社会发展情况进行分析,并同时研究总结了国内外常用交通量预测理论与方法。在此基础上,论文根据实地交通调查所获得的项目数据,分别建立了反映自治区交通运输业指标与区内影响因子(GDP)的函数关系的趋势回归模型、灰色系统GM(1,1)模型和三次指数平滑模型,从宏观角度对自治区未来一段时间交通发展情况进行预测,然后将其运用到各区域和节点城市的交通量预测方面。预测结果对内蒙古自治区及主要交通区域综合运输规划和布局具有重要指导意义。
Inner Mongolia which stands across north of China, plays an important role in the policy of West Development and Revitalization of the traditional industrial base of Northeast China. As transportation infrastructure is the foundation of economy and social development, the development of transportation in Inner Mongolia will exert a key influence on this region and the whole West Development.
Transportation volume is one of the basis in ascertaining project scale and technical standard, which can improve project benefits. Therefore in the feasibility research phases, it is crucial to apply appropriate forecast mains to predict the transportation volume.
This paper has given analysis of economy and social environment, development status, regional distributing in both the whole autonomous region and its main cities. On the basis of studying and summarizing the classic theories and approaches of transport volume prediction, the paper has established three prediction models according to the data from traffic investigation in Inner Mongolia and implemented the prediction of transport volume in both the whole autonomous region and its main composed regions, the result of which has a great guiding meaning to plan and layout of the comprehensive transportation system in Inner Mongolia.
交通量是确定交通项目建设规模与技术标准的主要依据之一,是提高交通运输建设项目投资效益的重要环节。因此,在交通建设工程可行性研究阶段,如何应用合适的交通量预测法,做好建设项目交通需求量预测工作,具有十分重要的现实意义。
论文将对内蒙古综合运输体系所处的经济和社会环境、发展现状、区位分布以及各主要盟市经济和社会发展情况进行分析,并同时研究总结了国内外常用交通量预测理论与方法。在此基础上,论文根据实地交通调查所获得的项目数据,分别建立了反映自治区交通运输业指标与区内影响因子(GDP)的函数关系的趋势回归模型、灰色系统GM(1,1)模型和三次指数平滑模型,从宏观角度对自治区未来一段时间交通发展情况进行预测,然后将其运用到各区域和节点城市的交通量预测方面。预测结果对内蒙古自治区及主要交通区域综合运输规划和布局具有重要指导意义。
Inner Mongolia which stands across north of China, plays an important role in the policy of West Development and Revitalization of the traditional industrial base of Northeast China. As transportation infrastructure is the foundation of economy and social development, the development of transportation in Inner Mongolia will exert a key influence on this region and the whole West Development.
Transportation volume is one of the basis in ascertaining project scale and technical standard, which can improve project benefits. Therefore in the feasibility research phases, it is crucial to apply appropriate forecast mains to predict the transportation volume.
This paper has given analysis of economy and social environment, development status, regional distributing in both the whole autonomous region and its main cities. On the basis of studying and summarizing the classic theories and approaches of transport volume prediction, the paper has established three prediction models according to the data from traffic investigation in Inner Mongolia and implemented the prediction of transport volume in both the whole autonomous region and its main composed regions, the result of which has a great guiding meaning to plan and layout of the comprehensive transportation system in Inner Mongolia.
引文
[1]王炜,徐吉谦,杨涛.城市交通规划理论及其应用[M].南京:东南大学出版社,1998.53-55
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[9]斯琴.灰色系统预测方法及应用——基于我国私人汽车拥有量的预测[J].统计教育,2007年1期:50-52
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[11]杭力,韩直,杜益文.BP神经网络和GM(1,1)灰色模型在公路客运量预测中的应用[J].公路交通技术,2006年2期:110-113
[12]彭辉,魏金丽,陈宽明.运输通道公路旅客中长距离OD模型构造及分段客运量预测[J].中国公路学报,2006年2期:101-115
[13]覃频频,牙韩高,黄大明,陆凯平.基于模糊集理论和灰色理论的铁路客运量预测模型[J].统计与决策,2006年2期:29-32
[14]孔光,张海燕.残差修正DGM模型在公路客运量预测中的应用[J].交通与运输,2006年B07期:56-58
[15]Study on time series prediction system and AROP model for transportation demand analysis Tsutsumi,Masafumi(Kyushu Univ);Chishaki,Takeshi Source:Memoirs of the Faculty of Engineering,Kyushu University,v 52,n 2,Jun,1992,p 145-169
[16]Multivariate vehicular traffic flow prediction:Evaluation of ARIMAX modeling Williams,Billy M.(Sch.of Civil and Environmental Eng.,Georgia Institute of Technology)Source:Transportation Research Record,n 1776,2001,p 194-200
[17]Dynamic estimation and prediction of freeway O-D matrices with route switching considerations and time-dependent model parameters Madanat.S.M.(Univ of California at Berkeley);Hu.S.-R.;Krogmeier.J.V.Source:Transportation Research Record,n 1537,Nov,1996,p 98-105
[18]Short-term traffic flow prediction models-a comparison of neural network and nonparametric regression approaches Smith.Brian L.(Virginia Transportation Research Council);Demetsky,Michael J.Source:Proceedings of the IEEE International Conference on Systems,Man and Cybernetics,v 2,1994,p 1706-1709
[2]邓聚龙.灰理论基础[M].武汉:华中科技大学出版社,2002
[3]胡建国,付健.基于四阶段法的交通运输规划中运输量预测方法[J].湖南工程学院学报:自然科学版,2005年3期:74-76,94
[4]姚琴,刘澜.铁路路网规划运量预测研究[J].交通运输工程与信息学报,2005年3卷2期:81-86
[5]施泉,吴中,毛荣昌.轨道交通客流预测四阶段法的改进[J].城市轨道交通研究,2006年9卷9期:44-47
[6]李玉兰.GM(1,1)模型在公路货运量预测中的应用[J].交通标准化,2006年6期:68-70
[7]王慧晶.基于灰色预测模型的铁路客运量预测研究[J].铁道运输与经济,2006年6期:79-81
[8]俞锋.GM(1,1)残差模型在民航客运量预测中的应用[J].西华大学学报,2006年6期:29-30,34
[9]斯琴.灰色系统预测方法及应用——基于我国私人汽车拥有量的预测[J].统计教育,2007年1期:50-52
[10]吴晓磊,王加斌.铁路客运量预测方法探讨[J].运输规划,2007年1期:45-48
[11]杭力,韩直,杜益文.BP神经网络和GM(1,1)灰色模型在公路客运量预测中的应用[J].公路交通技术,2006年2期:110-113
[12]彭辉,魏金丽,陈宽明.运输通道公路旅客中长距离OD模型构造及分段客运量预测[J].中国公路学报,2006年2期:101-115
[13]覃频频,牙韩高,黄大明,陆凯平.基于模糊集理论和灰色理论的铁路客运量预测模型[J].统计与决策,2006年2期:29-32
[14]孔光,张海燕.残差修正DGM模型在公路客运量预测中的应用[J].交通与运输,2006年B07期:56-58
[15]Study on time series prediction system and AROP model for transportation demand analysis Tsutsumi,Masafumi(Kyushu Univ);Chishaki,Takeshi Source:Memoirs of the Faculty of Engineering,Kyushu University,v 52,n 2,Jun,1992,p 145-169
[16]Multivariate vehicular traffic flow prediction:Evaluation of ARIMAX modeling Williams,Billy M.(Sch.of Civil and Environmental Eng.,Georgia Institute of Technology)Source:Transportation Research Record,n 1776,2001,p 194-200
[17]Dynamic estimation and prediction of freeway O-D matrices with route switching considerations and time-dependent model parameters Madanat.S.M.(Univ of California at Berkeley);Hu.S.-R.;Krogmeier.J.V.Source:Transportation Research Record,n 1537,Nov,1996,p 98-105
[18]Short-term traffic flow prediction models-a comparison of neural network and nonparametric regression approaches Smith.Brian L.(Virginia Transportation Research Council);Demetsky,Michael J.Source:Proceedings of the IEEE International Conference on Systems,Man and Cybernetics,v 2,1994,p 1706-1709