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Deviation warnings of ferries based on artificial potential field and historical data

Chen, C, Chen, XQ, Ma, F, Chen, YW and Wang, J (2019) Deviation warnings of ferries based on artificial potential field and historical data. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment. ISSN 1475-0902

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Abstract

Ferries are usually used for transporting passengers and vehicles among docks, and any deviation of the course can lead to serious consequences. Therefore, transportation ferries must be watched closely by local maritime administrators, which involves much manpower. With the use of historical data, this article proposes an intelligent method of integrating artificial potential field with Bayesian Network to trigger deviation warnings for a ferry based on its trajectory, speed and course. More specifically, a repulsive potential field-based model is first established to capture a customary waterway of ferries. Subsequently, a method based on non-linear optimisation is introduced to train the coefficients of the proposed repulsive potential field. The deviation of a ferry from the customary route can then be quantified by the potential field. Bayesian Network is further introduced to trigger deviation warnings in accordance with the distribution of deviation values, speeds and courses. Finally, the proposed approach is validated by the historical data of a chosen ferry on a specific route. The testing results show that the approach is capable of providing deviation warnings for ferries accurately and can offer a practical solution for maritime supervision. © IMechE 2019.

Item Type: Article
Uncontrolled Keywords: 0911 Maritime Engineering, 0915 Interdisciplinary Engineering
Subjects: H Social Sciences > HE Transportation and Communications
Divisions: Maritime & Mechanical Engineering
Publisher: SAGE Publications
Date Deposited: 16 Jan 2020 11:37
Last Modified: 16 Jan 2020 11:45
DOI or Identification number: 10.1177/1475090219892736
URI: http://researchonline.ljmu.ac.uk/id/eprint/12042

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