A contextual hybrid model for vessel movement prediction

Mehri, S., Alesheikh, A. A. and Basiri, A. (2021) A contextual hybrid model for vessel movement prediction. IEEE Access, 9, pp. 45600-45613. (doi: 10.1109/ACCESS.2021.3066463)

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Predicting the movement of the vessels can significantly improve the management of safety. While the movement can be a function of geographic contexts, the current systems and methods rarely incorporate contextual information into the analysis. This paper initially proposes a novel context-aware trajectories’ simplification method to embed the effects of geographic context which guarantees the logical consistency of the compressed trajectories, and further suggests a hybrid method that is built upon a curvilinear model and deep neural networks. The proposed method employs contextual information to check the logical consistency of the curvilinear method and then, constructs a Context-aware Long Short-Term Memory (CLSTM) network that can take into account contextual variables, such as the vessel types. The proposed method can enhance the prediction accuracy while maintaining the logical consistency, through a recursive feedback loop. The implementations of the proposed approach on the Automatic Identification System (AIS) dataset, from the eastern coast of the United States of America which was collected, from November to December 2017, demonstrates the effectiveness and better compression, i.e. 80% compression ratio while maintaining the logical consistency. The estimated compressed trajectories are 23% more similar to their original trajectories compared to currently used simplification methods. Furthermore, the overall accuracy of the implemented hybrid method is 15.68% higher than the ordinary Long Short-Term Memory (LSTM) network which is currently used by various maritime systems and applications, including collision avoidance, vessel route planning, and anomaly detection systems

Item Type:Articles
Additional Information:This work was supported by UK Research and Innovation, funded by the UK Research and Innovation Future Leaders Fellowship under Grant MR/S01795X/2.
Glasgow Author(s) Enlighten ID:Basiri, Professor Ana
Authors: Mehri, S., Alesheikh, A. A., and Basiri, A.
College/School:College of Science and Engineering > School of Geographical and Earth Sciences
Journal Name:IEEE Access
ISSN (Online):2169-3536
Published Online:17 March 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in IEEE Access 9:45600 - 45613
Publisher Policy:Reproduced under a Creative Commons Licence

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
312992Indicative Data: Extracting 3D Models of Cities from Unavailability and Degradation of Global Navigation Satellite Systems (GNSS)Ana BasiriUK Research and Innovation ( UKRI) (UKRI)MR/S01795X/2GES - Geography