Please wait a minute...
文章检索
复杂系统与复杂性科学  2014, Vol. 11 Issue (4): 66-71    DOI: 10.13306/j.1672-3813.2014.04.012
  本期目录 | 过刊浏览 | 高级检索 |
基于预期流优化的集装箱班轮网络引力模型
王杰, 梁金鹏
大连海事大学交通运输管理学院,辽宁 大连 116026
Gravity Model on Container Liner Network Based on Expected Volume Optimization
WANG Jie, LIANG Jinpeng
School of Transportation Management, Dalian Maritime University, Dalian 116026, China
全文: PDF(1065 KB)  
输出: BibTeX | EndNote (RIS)      
摘要 为探究集装箱班轮网络形成机理,首先分析集装箱班轮特点,发现节点间预期流是影响航线开设的关键因素;其次改进经典引力模型,对节点间预期流进行预测,按预期流由大到小顺序连边,构建集装箱班轮网络引力模型;最后对东亚区域班轮网络进行实证研究,计算港口节点适应度,构建仿真网络并分析其拓扑特性演化情况。演化结果表明,随着连边的加入,仿真网络的连通性增强,各项拓扑特性都不断趋近于实际网络。由此,模型的有效性得到验证。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
王杰
梁金鹏
关键词 预期流集装箱班轮网络引力模型节点适应度复杂网络    
Abstract:To probe into the construction mechanism of container liner network, its characteristics is analyzed to find that the expected volume between nodes is a key factor which determines the construction of liner route. The gravity model for container liner network is constructed by connecting the node pair in expected volume order. Then an empirical study on the East Asian container liner network is made in which the port fitness index is calculated to construct a simulative network. The evolution result demonstrates that the connectivity is enhancing and the major topological characteristics in simulative network are approaching those in physical network along with adding of lines. Thus, the model is validated and can be applied in container shipping practice.
Key wordsexpected volume    container liner network    gravity model    node fitness index    complex network
收稿日期: 2013-09-18      出版日期: 2026-06-22
基金资助:教育部哲学社会科学研究重大课题攻关项目(13JZD040);国家社会科学基金项目(09BJL063)
作者简介: 王杰(1962-),男,山东烟台人,博士,教授,主要研究方向为港口与航运经济。
引用本文:   
王杰, 梁金鹏. 基于预期流优化的集装箱班轮网络引力模型[J]. 复杂系统与复杂性科学, 2014, 11(4): 66-71.
WANG Jie, LIANG Jinpeng. Gravity Model on Container Liner Network Based on Expected Volume Optimization[J]. Complex Systems and Complexity Science, 2014, 11(4): 66-71.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2014.04.012      或      https://fzkx.qdu.edu.cn/CN/Y2014/V11/I4/66
[1] Meng Q,Wang S. Liner shipping service network design with empty container repositioning[J]. Transportation Research Part E:Logistics and Transportation Review,2011,47(5):695-708.
[2] Chen C,Zeng Q C. Designing container shipping network under changing demand and freight rates[J]. Transport,2010,25(1):46-57.
[3] Wang S,Meng Q. Sailing speed optimization for container ships in a liner shipping network[J]. Transportation Research Part E:Logistics and Transportation Review,2012,48(3):701-714.
[4] Hu Y H,Zhu D L. Empirical analysis of the worldwide maritime transportation network[J]. Physics A:Statistical Mechanics and Its Applications,2009,388(1):2061-2071.
[5] Ducruet C,Notteboom T. The worldwide maritime network of container shipping:spatial structure and regional dynamics[J]. Global Networks,2012,12(3):395-423.
[6] González L,Jesus F M,Pais M C. Maritime degree,centrality and vulnerability:port hierarchies and emerging areas in containerized transport (2008-2010)[J]. Journal of Transport Geography,2012,24(3):33-44.
[7] Ducruet C,Zaidi F. Maritime constellations:a complex network approach to shipping and ports[J]. Maritime Policy & Management,2012,39(2):151-168.
[8] Pais M C,Freire S J,Gonz L L. General cargo and containership emergent routes:a complex networks description[J]. Transport Policy,2012,24(2):126-140.
[9] 王文. 集装箱班轮航线运营经济模型及发船间隔决策[J]. 交通运输系统工程与信息,2012,12(5):103-109.
Wang Wen. Container liner operation economic model and the departure interval decision-making[J]. Journal of Transportation Systems Engineering and Information Technology,2012,12(5):103-109.
[10] 马伟,王亚华,刘生龙. 交通基础设施与中国人口迁移:基于引力模型分析[J]. 中国软科学, 2012,27(03):69-77.
Ma Wei,Wang Yahua,Liu Shenglong. Transportation infrastructure and population migration in China:an analysis based on gravity model[J]. China Soft Science,2012,27(3):69-77.
[11] 刘辉,申玉铭,孟丹,等. 基于交通可达性的京津冀城市网络集中性及空间结构研究[J]. 经济地理,2013,33(08):37-45.
Liu Hui,Shen Yuming,Meng Dan,et al. The city network centrality and spatial structure in the Beijing-Tianjin-Hebei metropolitan region[J]. Economic Geography,2013,33(08):37-45.
[12] 谢孟军. 基于制度质量视角的我国出口贸易区位选择影响因素研究——扩展引力模型的面板数据实证检验[J]. 国际贸易问题,2013,37(06):3-15.
Xie Mengjun. Study on influencing factors of China’s export location choice from institutional quality perspective:an empirical test of panel data with augmented gravity model[J]. Jouanal of International Trade,2013,37(06):3-15.
[13] Zipf G K. The P1P2/D hypothesis:on the intercity movement of persons[J]. American Sociological Review,1946,11(6):677-686.
[14] Balcan D,Colizza V,Gon A B,et al. Multiscale mobility networks and the spatial spreading of infectious diseases[J]. Proceedings of the National Academy of Sciences,2009,106(51):2484-2489.
[15] Viboud C,Rnstad O N,Smith D L,et al. Synchrony,waves,and spatial hierarchies in the spread of influenza[J]. Science,2006,312(5772):447-451.
[16] Jung W S,Wang F,Stanley H E. Gravity model in the Korean highway[J]. EPL Europhysics Letters,2008,81(4):48-55.
[17] Lloyd’s list intelligence[DB/OL].[2013-05-14]. http://www.lloydslist.com.
[1] 岳芳, 张涵, 樊茂瑞, 戴文慧, 郭剑锋. 开放式交互平台知识协同中的群体观点演化模型与实证[J]. 复杂系统与复杂性科学, 2026, 23(2): 8-18.
[2] 孙艳琴, 吴怀宇, 陈志环. 异维异构多重边复杂网络的广义外同步控制[J]. 复杂系统与复杂性科学, 2026, 23(2): 34-40.
[3] 于海波, 高彦丽, 陈世明, 凤超. 异质耦合下铁路-经济多层网络鲁棒性分析[J]. 复杂系统与复杂性科学, 2026, 23(2): 48-56.
[4] 聂廷远, 王艳伟, 聂晶晶, 刘鹏飞. 基于注意力机制和复杂网络的FPGA可布性预测[J]. 复杂系统与复杂性科学, 2026, 23(1): 53-59.
[5] 户佐安, 杨江浩, 邓锦程. 考虑多元变量的世界航空网络综合鲁棒性研究[J]. 复杂系统与复杂性科学, 2026, 23(1): 60-69.
[6] 孙小慧, 刘毅, 米玉梅, 吕凯. 韧性视角下城市地铁与常规公交网络关键站点及线路识别[J]. 复杂系统与复杂性科学, 2026, 23(1): 26-36.
[7] 牟奇锋, 李晓倩. 基于邻接矩阵的复杂网络演化融合迭代方法[J]. 复杂系统与复杂性科学, 2026, 23(1): 79-86.
[8] 孙文静, 余路粉, 潘文林, 蓝春江. 基于节点影响因子和贡献因子的复杂网络重要节点识别[J]. 复杂系统与复杂性科学, 2026, 23(1): 87-95.
[9] 卢新彪, 刘泽诚, 陈贵允, 杨铁流, 高兴. 基于图卷积网络的复杂网络能控性提升方法[J]. 复杂系统与复杂性科学, 2025, 22(4): 24-28.
[10] 周青, 李依函, 陈文冲. “互联网+”企业创新生态系统网络演化分析[J]. 复杂系统与复杂性科学, 2025, 22(4): 1-7.
[11] 章浩淳, 寇博潇, 张泰杰, 唐智慧. 基于Granger Causality的滑坡机理网络客观权值确定方法[J]. 复杂系统与复杂性科学, 2025, 22(4): 63-70.
[12] 韩世翔, 闫光辉, 裴华艳. 复杂网络上双向免疫对传染病传播的影响[J]. 复杂系统与复杂性科学, 2025, 22(4): 55-62.
[13] 张琦, 汪小帆. 复杂网络观点动力学分析与干预若干研究进展[J]. 复杂系统与复杂性科学, 2025, 22(2): 31-44.
[14] 张明磊, 宋玉蓉, 曲鸿博. 基于图注意力机制的复杂网络关键节点识别[J]. 复杂系统与复杂性科学, 2025, 22(2): 113-119.
[15] 陶昭, 侯忠生. 复杂网络的无模型自适应牵制控制[J]. 复杂系统与复杂性科学, 2025, 22(2): 120-127.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed