Unsupervised ship trajectory modeling and prediction using compression and clustering

Authors
Publication date 2009
Host editors
  • M. van Erp
  • H. Stehouwer
  • M. van Zaanen
Book title Benelearn 09: the 18th Annual Belgian-Dutch Conference on Machine Learning: proceedings of the conference
Event Belgian-Dutch Conference on Machine Learning (Benelearn 09), Tilburg, the Netherlands
Pages (from-to) 7-12
Publisher Tilburg: Tilburg centre for Creative Computing (TiCC), Tilburg University
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract In this paper we show how to build a model of ship trajectories in a certain maritime region and use this model to predict future ship movements. The presented method is unsupervised and based on existing compression (line-simplification) and clustering techniques. We evaluate the model with a simple prediction task. In this task we compare the performance of our method to two baseline predictors and show that it outperforms them on average. However, it also shows room for improvement, for which we give a number of suggestions.
Document type Conference contribution
Language English
Published at http://benelearn09.uvt.nl/Proceedings_Benelearn_09.pdf
Permalink to this page
Back