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Ship navigable route framework extraction using high-density searching from AIS big data

Published online by Cambridge University Press:  20 February 2025

Miao Gao Open the ORCID record for Miao Gao [Opens in a new window] ,
Jinqiang Bi ,
Zhen Kang Open the ORCID record for Zhen Kang [Opens in a new window] ,
Shuai Chen ,
Peiru Shi ,
Xi Zeng  and
Anmin Zhang
Miao Gao*
Affiliation:
Tianjin University, School Marine Science and Technology, Tianjin 300072, People's Republic of China
Jinqiang Bi
Affiliation:
Tianjin University, School Marine Science and Technology, Tianjin 300072, People's Republic of China Ministry of Transport Tianjin Research Institute of Water Transport Engineering, Tianjin 300456, China
Zhen Kang
Affiliation:
Tianjin University, School Marine Science and Technology, Tianjin 300072, People's Republic of China
Shuai Chen
Affiliation:
Tianjin University, School Marine Science and Technology, Tianjin 300072, People's Republic of China
Peiru Shi
Affiliation:
Tianjin University, School Marine Science and Technology, Tianjin 300072, People's Republic of China
Xi Zeng
Affiliation:
Tianjin University, School Marine Science and Technology, Tianjin 300072, People's Republic of China
Anmin Zhang
Affiliation:
Tianjin University, School Marine Science and Technology, Tianjin 300072, People's Republic of China Tianjin Port Environment Monitoring Engineering Center, Tianjin 300072, Peoples Republic of China
*
*Corresponding author. Miao Gao; Email: [email protected]
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Abstract

Research findings based on the data of current automatic identification systems (AISs) can only be applied to some parts of navigation research owing to their insufficient mining depth. Previously, route planning research has been based on the waypoint and corresponding optimised algorithm without considering the actual navigation situation and sailing habits. The planned route considerably differs from the actual sailing route, and the application result is undesirable. A novel solution to support the route planning problem has been introduced owing to the large accumulation of AIS big data. In this study, the ship navigable route framework (SNRF) which is reflected by real data via mining AIS big data serves as the basic network for the planned maritime route. This study uses the concept of manifold distance based on AIS big data to build a maritime SNRF through high-density searching. It can provide basic theoretical support for actual navigation distance calculation, route planning and route accessibility inspection in the future.

Keywords

AISbig data miningship navigable route framework (SNRF)high-density searchingmanifold distance
Type
Research Article
Information
The Journal of Navigation , First View , pp. 1 - 19
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Copyright
Copyright © The Author(s), 2025. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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