Incorporation of AIS data-based machine learning into unsupervised route planning for maritime autonomous surface ships
Incorporation of AIS data-based machine learning into unsupervised route planning for maritime autonomous surface ships
Maritime Autonomous Surface Ships (MASS) are deemed as the future of maritime transport. Although showing attractiveness in terms of the solutions to emerging challenges such as carbon emission and insufficient labor caused by black swan events such as COVID-19, the applications of MASS have revealed problems in practice, among which MASS navigation safety presents a prioritized concern. To ensure safety, rational route planning for MASS is evident as the most critical step to avoiding any relevant collision accidents. This paper aims to develop a holistic framework for the unsupervised route planning of MASS using machine learning methods based on Automatic Identification System (AIS) data, including the coherent steps of new feature measurement, pattern extraction, and route planning algorithms. Historical AIS data from manned ships are trained to extract and generate movement patterns. The route planning for MASS is derived from the movement patterns according to a dynamic optimization method and a feature extraction algorithm. Numerical experiments are constructed on real AIS data to demonstrate the effectiveness of the proposed method in solving the route planning for different types of MASS.
Li, Huanhuan
5e806b21-10a7-465c-9db3-32e466ae42f1
Yang, Zaili
82d4eebc-4532-4343-8555-35169e79bb6d
7 June 2023
Li, Huanhuan
5e806b21-10a7-465c-9db3-32e466ae42f1
Yang, Zaili
82d4eebc-4532-4343-8555-35169e79bb6d
Li, Huanhuan and Yang, Zaili
(2023)
Incorporation of AIS data-based machine learning into unsupervised route planning for maritime autonomous surface ships.
Transportation Research Part E: Logistics and Transportation Review, 176, [103171].
(doi:10.1016/j.tre.2023.103171).
Abstract
Maritime Autonomous Surface Ships (MASS) are deemed as the future of maritime transport. Although showing attractiveness in terms of the solutions to emerging challenges such as carbon emission and insufficient labor caused by black swan events such as COVID-19, the applications of MASS have revealed problems in practice, among which MASS navigation safety presents a prioritized concern. To ensure safety, rational route planning for MASS is evident as the most critical step to avoiding any relevant collision accidents. This paper aims to develop a holistic framework for the unsupervised route planning of MASS using machine learning methods based on Automatic Identification System (AIS) data, including the coherent steps of new feature measurement, pattern extraction, and route planning algorithms. Historical AIS data from manned ships are trained to extract and generate movement patterns. The route planning for MASS is derived from the movement patterns according to a dynamic optimization method and a feature extraction algorithm. Numerical experiments are constructed on real AIS data to demonstrate the effectiveness of the proposed method in solving the route planning for different types of MASS.
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Accepted/In Press date: 16 May 2023
e-pub ahead of print date: 7 June 2023
Published date: 7 June 2023
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Local EPrints ID: 503687
URI: http://eprints.soton.ac.uk/id/eprint/503687
ISSN: 1366-5545
PURE UUID: 27f0a2a1-a13c-465d-aaf1-a8fc57773522
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Date deposited: 11 Aug 2025 16:30
Last modified: 22 Aug 2025 02:49
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Author:
Huanhuan Li
Author:
Zaili Yang
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