Spatial Clustering Approach for Vessel Path Identification
<|reference_start|>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 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.<|reference_end|>
@article{abuella2024spatial,
    title={Spatial Clustering Approach for Vessel Path Identification},
    author={Mohamed Abuella, M. Amine Atoui, Slawomir Nowaczyk, Simon Johansson,
  Ethan Faghan},
    journal={IEEE ACCESS, 2024},
    year={2024},
    doi={10.1109/ACCESS.2024.3399116},
    number={Access-2024-12353},
    archivePrefix={arXiv},
    eprint={2403.05778},
    primaryClass={cs.LG cs.CE}
}