Spatial Clustering Approach for Vessel Path Identification

Mohamed Abuella, M. Amine Atoui, Slawomir Nowaczyk, Simon Johansson, Ethan Faghan
3/9/2024
cs.LGcs.CE

No official code found. Best implementation: MohamedAbuella/Path_Clustering (3 stars, last commit 2024-03-27).

Abstract

This paper addresses the challenge of identifying the paths for vessels with operating routes of repetitive paths, partially repetitive paths, and new paths. We propose a spatial clustering approach for labeling the vessel paths by using only position information. We develop a path clustering framework employing two methods: a distance-based path modeling and a likelihood estimation method. The former enhances the accuracy of path clustering through the integration of unsupervised machine learning techniques, while the latter focuses on likelihood-based path modeling and introduces segmentation for a more detailed analysis. The result findings highlight the superior performance and efficiency of the developed approach, as both methods for clustering vessel paths into five classes achieve a perfect F1-score. The approach aims to offer valuable insights for route planning, ultimately contributing to improving safety and efficiency in maritime transportation.

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Code Implementations(1)

MohamedAbuella/Path_ClusteringCommunity100%

Spatial Clustering Approach for Vessel Path Identification

31Jupyter NotebookNov 15, 20232 years ago

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Cite this paper

@article{abuella2024spatial,
  title  = {Spatial Clustering Approach for Vessel Path Identification},
  author = {Mohamed Abuella and M. Amine Atoui and Slawomir Nowaczyk and Simon Johansson and Ethan Faghan},
  year   = {2024},
  eprint = {2403.05778},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.05778v1}
}

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