Spatial Clustering Approach for Vessel Path Identification
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917662023483392 |
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| author | Abuella, Mohamed Atoui, M. Amine Nowaczyk, Slawomir Johansson, Simon Faghan, Ethan |
| author_facet | Abuella, Mohamed Atoui, M. Amine Nowaczyk, Slawomir Johansson, Simon Faghan, Ethan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05778 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spatial Clustering Approach for Vessel Path Identification Abuella, Mohamed Atoui, M. Amine Nowaczyk, Slawomir Johansson, Simon Faghan, Ethan Machine Learning Computational Engineering, Finance, and Science 68T10 (Primary), 68T20 (Secondary) G.1.3; H.3.4 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. |
| title | Spatial Clustering Approach for Vessel Path Identification |
| topic | Machine Learning Computational Engineering, Finance, and Science 68T10 (Primary), 68T20 (Secondary) G.1.3; H.3.4 |
| url | https://arxiv.org/abs/2403.05778 |