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Research on autonomous route generation method based on AIS ship trajectory big data and improved LSTM algorithm.

ChangXi Zhuang1, Chao Chen1

  • 1Maritime School, Zhejiang Ocean University, Zhoushan, China.

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Summary

This study proposes an improved deep learning model for autonomous ship route generation using Automatic Identification System (AIS) data. The enhanced model achieves greater accuracy and efficiency, particularly for shorter generated route trajectories.

Keywords:
AIS ship trajectory big dataLSTMclustering algorithmroute autonomous generationship intelligence

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Area of Science:

  • Maritime technology
  • Artificial intelligence
  • Data science

Background:

  • Autonomous route generation is crucial for ship intelligence.
  • Deep learning on Automatic Identification System (AIS) ship trajectory data is a key enabler.
  • Existing Long Short-Term Memory (LSTM) networks require enhancement for accuracy and efficiency.

Purpose of the Study:

  • To propose an improved multi-task LSTM artificial neural network for autonomous ship route generation.
  • To enhance the accuracy and efficiency of AI-generated ship routes using AIS big data.
  • To introduce an unsupervised trajectory separation mechanism for improved LSTM performance.

Main Methods:

  • Utilized a clustering algorithm to group AIS trajectories based on point density, filtering irrelevant data.
  • Classified ship routes by vessel type to create specialized datasets.
  • Implemented an improved multi-task LSTM artificial neural network incorporating an unsupervised trajectory separation mechanism.

Main Results:

  • The improved LSTM model demonstrated superior performance compared to the standard LSTM for generating shorter route trajectories.
  • An unsupervised trajectory separation mechanism was successfully integrated, enabling fast and accurate separation of similar ship paths.
  • The proposed scheme effectively processed massive AIS data for autonomous route generation.

Conclusions:

  • The enhanced LSTM model offers a more accurate and efficient solution for autonomous ship route generation.
  • The integration of unsupervised trajectory separation significantly improves the handling of complex ship trajectory data.
  • This approach advances the field of ship intelligence through sophisticated deep learning techniques.