  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.
