Use this url to cite publication: https://hdl.handle.net/20.500.14172/25638
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Application of coordinate systems for vessel trajectory prediction improvement using a recurrent neural networks
Type of publication
Straipsnis Web of Science ir Scopus duomenų bazėje / Article in Web of Science and Scopus database (S1)
Type of document
type::text::journal::journal article::research article
Author(s)
LT | Vilniaus universitetas | LT | ||
LT | Vilniaus universitetas | LT | ||
Treigys, Povilas | Vilniaus universitetas | LT |
Title
Application of coordinate systems for vessel trajectory prediction improvement using a recurrent neural networks
Publisher
Oxford : Pergamon-Elsevier Science Ltd
Date Issued
Date Issued | Volume | Issue | Start Page | End Page |
---|---|---|---|---|
2023-04-15 | vol. 123 | part C, art. no. 106448 | 1 | 10 |
Is part of
Engineering applications of artificial intelligence
Field of Science
Abstract
According to the Global Maritime Insurance annual report, among human and non-human risk factors, the number of accidents in maritime transport remains a significant issue. One of the factors is vessel collisions and anomalies at sea. Massive historical data from automatic identification systems are analyzed, and intelligent transportation systems are being developed to solve the problem of vessel trajectory prediction. The most ordinary attempt to improve accuracy is by evaluating the historical vessel behavior and learning the patterns and similarities of the predicted vessel movements. However, this paper shows that a better forecast also may be reached by choosing a different trajectory calculation strategy. The geographical or polar coordinate system values are used in a classical way, but several modifications, such as Universal Transverse Mercator (UTM), have been proposed in this study as an alternative to Mercator’s projection coordinates. Two main positioning of the vessels motion transformations were tested: by changing the coordinates to measurements of the angular distance (haversine) and displacement angle (azimuth) functions between different time steps; degree coordinates transformation into a Cartesian system using UTM with vector subtraction. The last case improves the accuracy of almost 30% in the available data sample by using the autoencoder architecture, compared to the longitude and latitude predictions even with computed delta features. The research generally compared three recurrent network architectures (with their hyperparameter – cell sizes): Autoencoder Long Short-Term Memory, Bi-directional Long Short-Term Memory, and Gated Recurrent Unit networks. The model calculations are performed in a real historical dataset, exclusively on cargo vessel type trajectories in the Netherlands (North Sea) coastal region. Also, the methods were validated in another dataset of the Baltic Sea Region.
ISSN (of the container)
0952-1976
1873-6769
WOS
001015545000001
Scopus
2-s2.0-85159790251
eLABa
166885893
Coverage Spatial
Jungtinė Karalystė / United Kingdom of Great Britain and Northern Ireland (GB)
Language
Anglų / English (en)
Bibliographic Details
30
Journal | IF | AIF | AIF (min) | AIF (max) | Cat | AV | Year | Quartile |
---|---|---|---|---|---|---|---|---|
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 8 | 4.975 | 3.8 | 6.4 | 4 | 1.686 | 2022 | Q1 |
Journal | IF | AIF | AIF (min) | AIF (max) | Cat | AV | Year | Quartile |
---|---|---|---|---|---|---|---|---|
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 8 | 4.975 | 3.8 | 6.4 | 4 | 1.686 | 2022 | Q1 |
4.975 | ||||||||
4.684 |
Journal | Cite Score | SNIP | SJR | Year | Quartile |
---|---|---|---|---|---|
Engineering Applications of Artificial Intelligence | 12.3 | 2.271 | 1.729 | 2022 | Q1 |