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Li, H and Yang, Z (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. p. 103171. ISSN 1366-5545
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Incorporation of AIS data based machine learning into unsupervised route planning for maritime autonomous surface ships.pdf - Published Version Available under License Creative Commons Attribution. Download (18MB) | Preview |
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 0102 Applied Mathematics; 0103 Numerical and Computational Mathematics; 1507 Transportation and Freight Services; Logistics & Transportation |
| Subjects: | H Social Sciences > HE Transportation and Communications T Technology > T Technology (General) V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering |
| Divisions: | Engineering |
| Publisher: | Elsevier |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 29 Nov 2023 15:05 |
| Last Modified: | 29 Nov 2023 15:15 |
| DOI or ID number: | 10.1016/j.tre.2023.103171 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/21977 |
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