Sequence‐based centrality measures in maritime transportation networks-Reference-Cited by-同舟云学术

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Sequence‐based centrality measures in maritime transportation networks

Published:2020-12 Issue:14 Volume:14 Page:2042-2051
ISSN:1751-956X
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET intell. transp. syst

Author:

Li Jing¹,Wang Xuantong²,Zhang Tong³

Affiliation:

1. Department of Geography and the EnvironmentUniversity of DenverDenverCO80208USA

2. Department of Geography and Geographic Information ScienceUniversity of Illinois at Urbana–ChampaignUrbanaIL61801USA

3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan UniversityWuhan430072People's Republic of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Law,Mechanical Engineering,General Environmental Science,Transportation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-its.2020.0301

Reference33 articles.

1. Efficient parallel K best connected trajectory (K‐BCT) query with GPGPU: A combinatorial min‐distance and progressive bounding box approach;Li J.;ISPRS. Int. J. Geoinf.,2018

2. A machine‐learning model for zonal ship flow prediction using AIS data: a case study in the south atlantic states region;Wang X.;J. Mar. Sci. Eng.,2019

3. Dobrkovic A. Iacob M.‐E. vanHillegersberg J.: ‘Using machine learning for unsupervised maritime waypoint discovery from streaming AIS data’.Proc. of the 15th Int. Conf. on Knowledge Technologies and Data‐driven Business Graz Austria October2015 pp.1–8

4. Structure and Dynamics of Transportation Networks: Models, Methods and Applications

5. Centrality in modular networks;Ghalmane Z.;EPJ Data Sci.,2019

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2. Identifying Important Ports in Maritime Silk Road Shipping Network from Local and Global Perspective;Transportation Research Record: Journal of the Transportation Research Board;2022-06-09

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