A fine-grained perspective on the robustness of global cargo ship transportation networks
Peng Peng1,2; Cheng Shifen1,2; Chen Jinhai3,4; Liao Mengdi5; Wu Lin6; Liu Xiliang1,7; Lu Feng1,7,8
刊名JOURNAL OF GEOGRAPHICAL SCIENCES
2018-07-01
卷号28期号:7页码:881-899
关键词complex network fine-grained cargo ship transportation network robustness automatic identification system
ISSN号1009-637X
DOI10.1007/s11442-018-1511-z
通讯作者Lu Feng(luf@lreis.ac.cn)
英文摘要The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transportation network based on the most recent Automatic Identification System (AIS) data available. First, we subdivide three typical cargo ship transportation networks (i.e., oil tanker, container ship and bulk carrier) from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, including random attack and three intentional attacks (i.e., degree-based attack, betweenness- based attack and flux-based attack) to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1) compared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation; 2) different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3) small-scale intentional attacks may have significant influence on the integrity of the container network but a minor impact on the bulk carrier and oil tanker transportation networks. These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation system.
资助项目Key Project of the Chinese Academy of Sciences[ZDRW-ZS-2016-6-3] ; National Natural Science Foundation of China[41501490]
WOS关键词SCALE-FREE NETWORKS ; CITY ROAD NETWORKS ; COMPLEX NETWORKS ; CENTRALITY
WOS研究方向Physical Geography
语种英语
出版者SCIENCE PRESS
WOS记录号WOS:000433356100002
资助机构Key Project of the Chinese Academy of Sciences ; National Natural Science Foundation of China
内容类型期刊论文
源URL[http://ir.igsnrr.ac.cn/handle/311030/54782]  
专题中国科学院地理科学与资源研究所
通讯作者Lu Feng
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Jimei Univ, Nav Aids Technol Res Ctr, Xiamen 361021, Fujian, Peoples R China
4.Natl & Local Joint Engn Res Ctr Marine Nav Aids S, Xiamen 361021, Fujian, Peoples R China
5.Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Shandong, Peoples R China
6.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
7.Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, Fujian, Peoples R China
8.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714
Peng Peng,Cheng Shifen,Chen Jinhai,et al. A fine-grained perspective on the robustness of global cargo ship transportation networks[J]. JOURNAL OF GEOGRAPHICAL SCIENCES,2018,28(7):881-899.
APA Peng Peng.,Cheng Shifen.,Chen Jinhai.,Liao Mengdi.,Wu Lin.,...&Lu Feng.(2018).A fine-grained perspective on the robustness of global cargo ship transportation networks.JOURNAL OF GEOGRAPHICAL SCIENCES,28(7),881-899.
MLA Peng Peng,et al."A fine-grained perspective on the robustness of global cargo ship transportation networks".JOURNAL OF GEOGRAPHICAL SCIENCES 28.7(2018):881-899.
个性服务
查看访问统计
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。


©版权所有 ©2017 CSpace - Powered by CSpace