Research on the Automatic Classification of Ship’s Navigational Status

  • Jaeyong OhEmail author
  • Hye-Jin Kim
  • Sekil Park
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 536)


Maritime traffic analysis has been attracted increasing attention due to their importance for the safety and efficiency of maritime operations. The first step of maritime traffic analysis is the identification of ships’ navigational status, and various analysis tasks are started based on the status information. It should be considered the complex traffic characteristics of the harbor and ships. These tasks depend on the expert’s experiences, however, it becomes difficult to classify manually as the amount of traffic volume increases. Therefore, in this paper, we proposed a new model to identify the ship’s navigational status automatically. The proposed method generated traffic pattern model using accumulated AIS trajectories and then classified using the clustering algorithm. This method based on semi-supervised machine learning and the proposed clustering method using the pre-classified dataset. Finally, we review experimental results using the actual trajectory data to verify the feasibility of the proposed method.


Navigational status AIS Machine learning Clustering 



This research was supported by a grand from Endowment Project of “Development of core technology for the analysis and reproduction of maritime accidents through simulations” funded by Korea Research Institute of Ships and Ocean Engineering (PES9350).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Korea Research Institute of Ships and Ocean EngineeringDaejeonRepublic of Korea

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