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author:

Xu, Y. (Xu, Y..) [1] | Peng, P. (Peng, P..) [2] | Lu, F. (Lu, F..) [3] | Claramunt, C. (Claramunt, C..) [4]

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Scopus

Abstract:

Shipping companies are key drivers of maritime trade and crucial in the development of container shipping networks. Each company's strategy shapes differences in port services and shipping routes, creating a complex and interconnected global container shipping network that is difficult to analyze using single-layer or aggregated models. This paper introduces a novel multiplex network modeling approach that leverages a very large set of AIS data and applies a series of structural indices to reveal the unique characteristics and multiple key roles of various shipping company networks. By applying methods such as overlap ratio analysis, HITS analysis, and community detection, the study identifies differences in overlap between ports and routes, hub and authority properties of individual ports, as well as local and regional patterns within the global network. Furthermore, it examines structural differences between the multiplex network, the aggregated network, and individual layers, highlighting the importance of multiplex container networks in understanding ports' roles on both regional and global scales. The findings clarify how different shipping companies shape shipping network patterns, enhance the multiplex network's functional roles, and provide a better understanding of the complex structure of the Global Container Shipping Network (GCSN). Finally, it provides authorities with valuable insights on how to better understand maritime shipping networks. © 2024 Elsevier Ltd

Keyword:

Global container shipping Multiplex network Shipping company Vessel trajectory data

Community:

  • [ 1 ] [Xu Y.]State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
  • [ 2 ] [Xu Y.]University of Chinese Academy of Sciences, Beijing, 100049, China
  • [ 3 ] [Peng P.]State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
  • [ 4 ] [Peng P.]University of Chinese Academy of Sciences, Beijing, 100049, China
  • [ 5 ] [Lu F.]State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
  • [ 6 ] [Lu F.]University of Chinese Academy of Sciences, Beijing, 100049, China
  • [ 7 ] [Lu F.]The Academy of Digital China, Fuzhou University, Fuzhou, 350002, China
  • [ 8 ] [Lu F.]Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China
  • [ 9 ] [Claramunt C.]State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
  • [ 10 ] [Claramunt C.]Naval Academy Research Institute, Lanvéoc, 29240, France

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Source :

Journal of Transport Geography

ISSN: 0966-6923

Year: 2024

Volume: 120

5 . 7 0 0

JCR@2023

Cited Count:

WoS CC Cited Count:

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ESI Highly Cited Papers on the List: 0 Unfold All

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30 Days PV: 0

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