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Deep Learning Research on Vessel Trajectory Prediction Based on AIS Data with Interpolation Techniques

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(3), pp.1-10
  • DOI : 10.9708/jksci.2024.29.03.001
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : December 1, 2023
  • Accepted : March 13, 2024
  • Published : March 29, 2024

Won-Hee Lee 1 Seung-Won Yoon 1 Da-Hyun Jang 1 Kyu-Chul Lee 1

1충남대학교

Accredited

ABSTRACT

The research on predicting the routes of ships, which constitute the majority of maritime transportation, can detect potential hazards at sea in advance and prevent accidents. Unlike roads, there is no distinct signal system at sea, and traffic management is challenging, making ship route prediction essential for maritime safety. However, the time intervals of the ship route datasets are irregular due to communication disruptions. This study presents a method to adjust the time intervals of data using appropriate interpolation techniques for ship route prediction. Additionally, a deep learning model for predicting ship routes has been developed. This model is an LSTM model that predicts the future GPS coordinates of ships by understanding their movement patterns through real-time route information contained in AIS data. This paper presents a data preprocessing method using linear interpolation and a suitable deep learning model for ship route prediction. The experimental results demonstrate the effectiveness of the proposed method with an MSE of 0.0131 and an Accuracy of 0.9467.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.