Long-Term Ship Position Prediction Using Automatic Identification System (AIS) Data and End-to-End Deep Learning

The establishment of maritime safety and security is an important concern. Ship position prediction for maritime situational awareness (MSA), as a critical aspect of maritime safety and security, requires a longer time interval than collision avoidance and maritime traffic monitoring. However, previ...

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Autores principales:	Ibadurrahman, Kunihiro Hamada, Yujiro Wada, Jota Nanao, Daisuke Watanabe, Takahiro Majima
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning AIS ship position prediction long-term end-to-end Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/54f5d7f58dfe4b48aab2182c213efd32
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spelling	oai:doaj.org-article:54f5d7f58dfe4b48aab2182c213efd322021-11-11T19:09:39ZLong-Term Ship Position Prediction Using Automatic Identification System (AIS) Data and End-to-End Deep Learning10.3390/s212171691424-8220https://doaj.org/article/54f5d7f58dfe4b48aab2182c213efd322021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7169https://doaj.org/toc/1424-8220The establishment of maritime safety and security is an important concern. Ship position prediction for maritime situational awareness (MSA), as a critical aspect of maritime safety and security, requires a longer time interval than collision avoidance and maritime traffic monitoring. However, previous studies focused mainly on shorter time-interval predictions ranging from 30 min to 10 h. A longer time-interval ship position prediction is required not only for MSA, but also for efficient allocation of ships by shipping companies in accordance with global freight demand. This study used an end-to-end tracking method that inputs the previous position of a vessel to a trained deep learning model to predict its next position with an average 24-h interval. An AIS dataset with a long-time-interval distribution in a nine-year timespan for capesize bulk carriers worldwide was used. In the first experiment, a deep learning model of the Indian Ocean was examined. Subsequently, the model performance was compared for six different oceans and six primary maritime chokepoints to investigate the influence of each area. In the third experiment, a sample location within the Malacca Strait area was selected, and the number of ships was counted daily. The results indicate that the ship position can be predicted accurately with an average time interval of 24 h using deep learning systems with AIS data.IbadurrahmanKunihiro HamadaYujiro WadaJota NanaoDaisuke WatanabeTakahiro MajimaMDPI AGarticledeep learningAISship position predictionlong-termend-to-endChemical technologyTP1-1185ENSensors, Vol 21, Iss 7169, p 7169 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning AIS ship position prediction long-term end-to-end Chemical technology TP1-1185
spellingShingle	deep learning AIS ship position prediction long-term end-to-end Chemical technology TP1-1185 Ibadurrahman Kunihiro Hamada Yujiro Wada Jota Nanao Daisuke Watanabe Takahiro Majima Long-Term Ship Position Prediction Using Automatic Identification System (AIS) Data and End-to-End Deep Learning
description	The establishment of maritime safety and security is an important concern. Ship position prediction for maritime situational awareness (MSA), as a critical aspect of maritime safety and security, requires a longer time interval than collision avoidance and maritime traffic monitoring. However, previous studies focused mainly on shorter time-interval predictions ranging from 30 min to 10 h. A longer time-interval ship position prediction is required not only for MSA, but also for efficient allocation of ships by shipping companies in accordance with global freight demand. This study used an end-to-end tracking method that inputs the previous position of a vessel to a trained deep learning model to predict its next position with an average 24-h interval. An AIS dataset with a long-time-interval distribution in a nine-year timespan for capesize bulk carriers worldwide was used. In the first experiment, a deep learning model of the Indian Ocean was examined. Subsequently, the model performance was compared for six different oceans and six primary maritime chokepoints to investigate the influence of each area. In the third experiment, a sample location within the Malacca Strait area was selected, and the number of ships was counted daily. The results indicate that the ship position can be predicted accurately with an average time interval of 24 h using deep learning systems with AIS data.
format	article
author	Ibadurrahman Kunihiro Hamada Yujiro Wada Jota Nanao Daisuke Watanabe Takahiro Majima
author_facet	Ibadurrahman Kunihiro Hamada Yujiro Wada Jota Nanao Daisuke Watanabe Takahiro Majima
author_sort	Ibadurrahman
title	Long-Term Ship Position Prediction Using Automatic Identification System (AIS) Data and End-to-End Deep Learning
title_short	Long-Term Ship Position Prediction Using Automatic Identification System (AIS) Data and End-to-End Deep Learning
title_full	Long-Term Ship Position Prediction Using Automatic Identification System (AIS) Data and End-to-End Deep Learning
title_fullStr	Long-Term Ship Position Prediction Using Automatic Identification System (AIS) Data and End-to-End Deep Learning
title_full_unstemmed	Long-Term Ship Position Prediction Using Automatic Identification System (AIS) Data and End-to-End Deep Learning
title_sort	long-term ship position prediction using automatic identification system (ais) data and end-to-end deep learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/54f5d7f58dfe4b48aab2182c213efd32
work_keys_str_mv	AT ibadurrahman longtermshippositionpredictionusingautomaticidentificationsystemaisdataandendtoenddeeplearning AT kunihirohamada longtermshippositionpredictionusingautomaticidentificationsystemaisdataandendtoenddeeplearning AT yujirowada longtermshippositionpredictionusingautomaticidentificationsystemaisdataandendtoenddeeplearning AT jotananao longtermshippositionpredictionusingautomaticidentificationsystemaisdataandendtoenddeeplearning AT daisukewatanabe longtermshippositionpredictionusingautomaticidentificationsystemaisdataandendtoenddeeplearning AT takahiromajima longtermshippositionpredictionusingautomaticidentificationsystemaisdataandendtoenddeeplearning
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Long-Term Ship Position Prediction Using Automatic Identification System (AIS) Data and End-to-End Deep Learning

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