Enhancing global maritime traffic network forecasting with gravity-inspired deep learning models

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: Enhancing global maritime traffic network forecasting with gravity-inspired deep learning models ; volume:14 ; number:1 ; day:19 ; month:7 ; year:2024 ; pages:1-16 ; date:12.2024
Scientific reports ; 14, Heft 1 (19.7.2024), 1-16, 12.2024

Creator: Song, Ruixin
Spadon, Gabriel
Pelot, Ronald
Matwin, Stan
Soares, Amilcar

Contributor: SpringerLink (Online service)

DOI: 10.1038/s41598-024-67552-2

URN: urn:nbn:de:101:1-2410052105186.657498870020

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:37 AM CEST

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Song, Ruixin
Spadon, Gabriel
Pelot, Ronald
Matwin, Stan
Soares, Amilcar
SpringerLink (Online service)

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Enhancing global maritime traffic network forecasting with gravity-inspired deep learning models

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