Spatial Clustering Approach for Vessel Path Identification
Mohamed Abuella M. Amine Atoui Slawomir Nowaczyk Simon Johansson Ethan Faghan
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.