Spatial Clustering Approach for Vessel Path Identification

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 framew...

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Bibliographic Details
Published in:IEEE access 2024-01, Vol.12, p.1-1
Main Authors: Abuella, Mohamed, Atoui, M. Amine, Nowaczyk, Slawomir, Johansson, Simon, Faghani, Ethan
Format: Article
Language:English
Subjects:
average nearest neighbor distance
Clustering
hierarchical clustering
likelihood estimation
Machine learning
Marine transportation
maritime transportation
Modelling
Route planning
Spatial clustering
Unsupervised learning
vessel path identification
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Summary: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 clusters 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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3399116