著者

志尾 嘉洋 伊藤 博子 川村 恭己 河島 園子

出版者

公益社団法人 日本航海学会

雑誌

日本航海学会論文集 (ISSN:03887405)

巻号頁・発行日

vol.143, pp.77-82, 2020 (Released:2020-12-25)

参考文献数

6

被引用文献数

1

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It is necessary to develop a system that reduces the load on the marine traffic control because its work is manual and heavy. In this study, we created a ship behavior prediction model using Recurrent Neural Network (RNN) to explore the possibility of marine traffic control and ship maneuvering support by machine learning. Specifically, we predicted the position and course of a ship that would go through the bend of the Uraga Channel from 5 items (length, width, course, speed and position) and displayed on a map. It shows that the effectiveness of ship behavior prediction by machine learning has been confirmed.