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Mémoire
Master thesis

Predicting ships’ path and destination port based on AIS information

SONAR|HES-SO

    2020

75 p.

Mémoire de master: Haute école de gestion de Genève, 2020

AIS data
machine learning
path prediction
destination prediction
voyage recreation
English Maritime transportation plays an important role in the world’s economy, representing 70-80% of all trade. Due its vital status, since 2004 the Automatic Identification System (AIS), provides information on vessel movement by means of electronic data. A ship is required to broadcast a message describing its position, destination, etc. every 2 to 10 seconds while moving. The signal’s high-resolution and the availability of real-time positions provides an abundant source of information, the amount of data generated by the more than 1.9 million vessels worldwide, make processing and analyses of AIS information very challenging. In this thesis, a method is proposed for voyage recreation from raw historical AIS data combined with World’s Ports Index information. Based on these voyages, an algorithm was created to predict a vessel’s path based on its previous positions. Various algorithms were also tested for destination, commodity and draught prediction. Finally, the challenges of working with such as abundant source of information are presented with possible solutions for future improvements of the machine learning models and the quality of AIS data.
Language
  • English
Classification
Information, communication and media sciences
Notes
  • Haute école de gestion Genève
  • Information documentaire
  • hesso:hegge
License
License undefined
Identifiers
  • RERO DOC 329734
Persistent URL
https://sonar.ch/global/documents/315176
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  • Mémoire: 396