Use this url to cite publication: https://hdl.handle.net/20.500.14172/24383
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Investigation of recurrent neural network architectures for prediction of vessel trajectory
Type of publication
Straipsnis konferencijos medžiagoje Web of Science ir Scopus duomenų bazėje / Article in conference proceedings in Web of Science and Scopus database (P1a)
Type of document
type::text::conference output::conference proceedings::conference paper
Author(s)
LT | Vilniaus universitetas | LT | ||
Treigys, Povilas | Vilniaus universitetas | |||
LT | Vilniaus universitetas | LT |
Title
Investigation of recurrent neural network architectures for prediction of vessel trajectory
Publisher
Cham : Springer, 2021
Date Issued
Date Issued | Start Page | End Page |
---|---|---|
2021-10-07 | 194 | 208 |
Is part of
Information and software technologies : 27th international conference, ICIST 2021, Kaunas, Lithuania, October 14-16, 2021 : proceedings
Series/Report no.
Communications in Computer and Information Science book series (CCIS), ISSN 1865-0929, eISSN 1865-0937 ; vol. 1486
Field of Science
Abstract
Modern deep learning algorithms are able to handle large amounts of data and therefore are particularly important in automating vessel movement prediction in intensive shipping. This could be one of the support tools for monitoring, managing the increasing maritime traffic and its participants. Applying deep learning algorithm, a recurrent networks is created that is able to predict the further vessel movement. The developed architectural model is based on sequences when data change over time, therefore the article investigates the most optimal recurrent network structure and network hyper-parameters, which aim to obtain the most accurate prediction results. Different recurrent network architectures were used to compare the results those are: fully-connected (simple) recurrent neural network, basic (vanilla), bidirectional, stacked Long Short-Term Memory network, autoencoder, and gated recurrent unit. The accuracy of the predictions for each architecture is monitored by varying the number of cells size in the hidden layer. The research was performed on a specific sample of data from the Netherlands (North Sea) coastal region and the proposed algorithm can be applied as one of the ways to improve maritime safety. The research showed that the most accurate prediction of the vessel trajectory prediction is achieved with the bidirectional Long Short-Term Memory network architecture in which the variance is less shifting even with the smallest cell selection, and autoenoder network architecture which depends on the choice of the appropriate cell size, because distribution range increasing in 100 and 150 cells.
ISBN (of the container)
9783030883034
9783030883041
WOS
000869711400016
Scopus
2-s2.0-85118179194
eLABa
109036910
Coverage Spatial
Šveicarija / Switzerland (CH)
Language
Anglų / English (en)
Bibliographic Details
15
Date Reporting
2021