广州铁路枢纽货运资源整合
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摘要
广州铁路枢纽现有的货场分布过密且数量多规模小,这就造成整个枢纽效率的低下;同时,由于不同的货场属于不同的利益团体,造成了各个货场间的竞争加剧从而导致恶性竞争;再者,由于经济的发展,城市的范围不断扩大这就使得原有的许多货场现已处于城市中心,从而给城市交通与货物集散都造成了很大的影响;因此,必须要对枢纽资源进行整合。
本文介绍了广州铁路枢纽的范围、枢纽的货场布局的特点,分析了枢纽内货运市场的特点、货场的能力、货源以及货运现状。
在将枢纽按范围按地理分为枢纽东部、枢纽西部和枢纽北部,货物类型分为散装货物、怕湿货物以及集装箱货物的基础上,利用具有自适应和学习能力的基于径向基(RBF)的神经网络方法,在分析的历史数据的基础上,对广州铁路枢纽各个部分不同类型的货物运量以及枢纽整个运量进行预测。从而对整个枢纽的资源整合以及主中心货场的选择提供必要的数据依据。
通过对广州铁路枢纽内货场布局、专用线、人力资源、货运业务、货运设备、信息资源、客户资源、运价分别进行分析,逐个提出整合方案,来达到对整个铁路枢纽货运资源的有效整合。
在结合广州铁路枢纽的实际情况综合考虑的基础上,给出了三个备选主中心货场。随后采用基于DEA的模糊评价的方法对备选主中心货场进行评价选择,在结合广州铁路枢纽具体情况以及各个货场的技术指标的基础上,最后得出大田货场在三个备选主中心货场中最适合发展成为主中心货场。
There are many small freight yards within Guangzhou hub, which causes the low efficiency of the whole hub. Meanwhile, different freight yards, which belong to different interest groups, caused increasingly competition and resulted in a vicious competition.
In this thesis, the author firstly introduced the scope and layout features of Guangzhou hub. then,the author analyzed the feature, capacity, freight origin and freight activity. Finally,the prospects of the hub are showed in this thesis.
The hub is divided into the eastern hub, western hub and north hub according to scope. Cargoes are classified into bulk cargoes, moisture-proof cargoes and container goods. The adaptive and learning capacity radial-based neural network-based RBF is used on the basis of that. On the basis of analyzing the historical data, freight volume of various parts of the Guangzhou railway hub is forecasted. Thus the Resource Integration of the entire hub and the choice of Center freight yard are formed based on the data.
Furthermore,the author analyzes rail freight yard layout,dedicated line, human resources,cargo businesses,cargo equipments,information resources, customer resources within the hub, and then the integration programs are formed gradually so as to integrate the freight resources of the whole railway hub effectively.
Finally, three options are showed in this thesis on the basis of the actual situation of Guangzhou railway hub, which are evaluated and chosen by using the fuzzy-based DEA evaluation methods. In the end, Da-tian freight yard is considered as the most suitable option for main freight yard center on the basis of the specific conditions of Guangzhou railway hub and technical indicators of each freight yard field.
本文介绍了广州铁路枢纽的范围、枢纽的货场布局的特点,分析了枢纽内货运市场的特点、货场的能力、货源以及货运现状。
在将枢纽按范围按地理分为枢纽东部、枢纽西部和枢纽北部,货物类型分为散装货物、怕湿货物以及集装箱货物的基础上,利用具有自适应和学习能力的基于径向基(RBF)的神经网络方法,在分析的历史数据的基础上,对广州铁路枢纽各个部分不同类型的货物运量以及枢纽整个运量进行预测。从而对整个枢纽的资源整合以及主中心货场的选择提供必要的数据依据。
通过对广州铁路枢纽内货场布局、专用线、人力资源、货运业务、货运设备、信息资源、客户资源、运价分别进行分析,逐个提出整合方案,来达到对整个铁路枢纽货运资源的有效整合。
在结合广州铁路枢纽的实际情况综合考虑的基础上,给出了三个备选主中心货场。随后采用基于DEA的模糊评价的方法对备选主中心货场进行评价选择,在结合广州铁路枢纽具体情况以及各个货场的技术指标的基础上,最后得出大田货场在三个备选主中心货场中最适合发展成为主中心货场。
There are many small freight yards within Guangzhou hub, which causes the low efficiency of the whole hub. Meanwhile, different freight yards, which belong to different interest groups, caused increasingly competition and resulted in a vicious competition.
In this thesis, the author firstly introduced the scope and layout features of Guangzhou hub. then,the author analyzed the feature, capacity, freight origin and freight activity. Finally,the prospects of the hub are showed in this thesis.
The hub is divided into the eastern hub, western hub and north hub according to scope. Cargoes are classified into bulk cargoes, moisture-proof cargoes and container goods. The adaptive and learning capacity radial-based neural network-based RBF is used on the basis of that. On the basis of analyzing the historical data, freight volume of various parts of the Guangzhou railway hub is forecasted. Thus the Resource Integration of the entire hub and the choice of Center freight yard are formed based on the data.
Furthermore,the author analyzes rail freight yard layout,dedicated line, human resources,cargo businesses,cargo equipments,information resources, customer resources within the hub, and then the integration programs are formed gradually so as to integrate the freight resources of the whole railway hub effectively.
Finally, three options are showed in this thesis on the basis of the actual situation of Guangzhou railway hub, which are evaluated and chosen by using the fuzzy-based DEA evaluation methods. In the end, Da-tian freight yard is considered as the most suitable option for main freight yard center on the basis of the specific conditions of Guangzhou railway hub and technical indicators of each freight yard field.
引文
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[6]Niu,Hui-Min.Optimization for the use of the equipment in railway hubs of the America[J]Tiedao Xuebao/Journal of the China Railway Society.2003(2):36-39
[7]Rotter,Harald.New operating concepts for intermoda in transport in America:The mega hub in Hanover/hehrte in Germany[J]Transportation Planning and Technology.2004(5):347-365
[8]姜伟.广州铁路枢纽货运场站布局优化研究[J].铁道货运,2007(1):5-8
[9]李清发.武汉铁路枢纽货场规划探讨[J]铁道运输与经济.2006(7):89-90
[10]徐瑞华,杜世敏,陈百磊.城市铁路枢纽布局的调整优化研究[J].同济大学学报(自然科学版),2005(5):631-635
[11]吕建军.铁路枢纽规划与城市规划的协调[J]铁道运输与经济.2005(10):4-6
[12]马德勇,陈卓.包头铁路枢纽规划改造探讨[J]铁道运输与经济.2005(9):82-84
[13]安晓刚.铁路枢纽参与大都市区城市综合交通运输系统的整合研究[J]科技情报开发与经济.2006(8):167-168
[14]杜进有.大都市区城市交通系统与铁路枢纽整合研究[J].世界科技研究与发展.2006(1):72-76.
[15]魏方华,刘澜,姚琴.铁路枢纽与城市交通系统的整合研究[J]铁路与城市.2005(1):9-11
[16]尹小林.优化运输组织提高广州铁路枢纽运输效率[J]铁路运输.2005(2):40-43
[17]徐行方,向劲松.环形铁路枢纽车流组织方案研究[J]铁道运输与经济.2005(7):45-47
[18]霍建勇,谢辉,胡波.铁路枢纽集装箱中心站装卸设备的选择[J]铁道运输与经济.2006(6):68-70
[19]史跃亚,王慈光,崔丽莉.客运专线引入枢纽后客运站合理布局的探讨[J]交通科技.2006(4):109-112
[20]张学斌.衡茶吉线引入衡阳枢纽方案研究[J]铁道勘查.2006(6):81-84
[21]杨正希.客运专线引入上海铁路枢纽方案研究[J]铁道运输与经济.2006(7):28-30
[22]陈应先.铁路枢纽总图规划有关问题探讨[J]铁道标准设计.2006(增刊):23-29
[23]高丰农,龙顺海.南宁枢纽总图与北部湾经济区铁路建设布局的综合研究[J]铁道工程学报.2006(12):71-74
[24]刘其斌,马桂贞。铁路车站及枢纽[M]。中国铁道出版社。2002
[25]尹小林.优化运输组织提高广州铁路枢纽运输效率[J].中国铁路,2004(12):40-43
[26]孙讯.进一步发展我国铁路快速货物运输的相关问题研究[DB/OL].http://www.wanfangdate.com.cn
[27]杜栋,庞庆华.现代综合评价方法与案例精选.北京清华大学出版社.2005
[28]郑强,孟宪华.浅谈灰色预测法.山西交通科技,2001年6月第3期:14-16
[29]Liu Guangzhong,Li Xiaofeng.The improvement of BP algorithm and self_adjustment of architectural paraneters[J].Journal of Information Management,2000(1):86-92
[30]Hornik K.Approximation capabilities of multiplier feed-forward networks,1993(6):1069-1072
[31]高隽.人工神经网络原理及仿真实例[M]北京:机械工业出版社,2003
[32]Funahashi K.On the approximation realization of continuous mappings by neytral network[J].Neural Networks,2005(2):183-192
[33]刘志杰,季令,叶玉玲等.基于径向基神经网络的铁路货运量预测.铁道学报.2006(5):16-21
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[35]黄国如,胡和平,田富强.用径向基函数神经网络模型预报感潮河洪水位[J].水科学进展,2003(114):158-162.
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