Deep learning innovations in South Korean maritime navigation: Enhancing vessel trajectories prediction with AIS data

Predicting ship trajectories can effectively forecast navigation trends and enable the orderly management of ships, which holds immense significance for maritime traffic safety. This paper introduces a novel ship trajectory prediction method utilizing Convolutional Neural Network (CNN), Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). This research comprises two main parts: the first involves preprocessing the large raw Automatic Identification System (AIS) dataset to extract features, and the second focuses on trajectory prediction. The authors emphasize a specialized preprocessing approach tailored for AIS data, including advanced filtering techniques to remove outliers and erroneous data points, and the incorporation of contextual information such as environmental conditions and ship-specific characteristics. The deep learning models utilize trajectory data sourced from the Automatic Identification System (AIS) to train and learn regular patterns within ship trajectory data, enabling them to predict trajectories for the next hour. Experimental results reveal that CNN has substantially reduced the Mean Absolute Error (MAE) and Mean Square Error (MSE) of ship trajectory prediction, showcasing superior performance compared to other deep learning algorithms. Additionally, a comparative analysis with other models-Recurrent Neural Network (RNN), GRU, LSTM, and DBS-LSTM-using metrics such as Average Displacement Error (ADE), Final Displacement Error (FDE), and Non-Linear ADE (NL-ADE), demonstrates the method's robustness and accuracy. This approach not only cleans the data but also enriches it, providing a robust foundation for subsequent deep learning applications in ship trajectory prediction. This improvement effectively enhances the accuracy of trajectory prediction, promising advancements in maritime traffic safety.

• Record URL:
  • http://dx.doi.org/10.1371/journal.pone.0310385
• Supplemental Notes:
  • Copyright: © 2024 Zaman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
• Authors:
  • Zaman, Umar
  • Khan, Junaid
  • Lee, Eunkyu
  • Balobaid, Awatef Salim
  • Aburasain, R Y
  • Kim, Kyungsup
• Publication Date: 2024

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: e0310385
• Serial:
  • PLoS One
  • Volume: 19
  • Issue Number: 10
  • Publisher: Public Library of Science
  • EISSN: 1932-6203
  • Serial URL: https://journals.plos.org/plosone/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Detection and identification systems; Information processing; Machine learning; Neural networks; Predictive models; Vehicle trajectories; Water transportation
• Geographic Terms: South Korea
• Subject Areas: Data and Information Technology; Freight Transportation; Marine Transportation; Planning and Forecasting;

Filing Info

• Accession Number: 01935958
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 6 2024 9:19AM