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.

翻译：本文研究了具有重复航迹、部分重复航迹及新航迹的船舶航迹识别问题。我们提出了一种仅利用位置信息对船舶航迹进行标注的空间聚类方法。我们开发了一个包含两种方法的航迹聚类框架：基于距离的航迹建模方法和似然估计方法。前者通过集成无监督机器学习技术提升了航迹聚类的精度，后者则聚焦于基于似然的航迹建模，并通过引入分段策略实现更细致的分析。实验结果表明，所提出的方法在将船舶航迹聚为五类时均达到了完美的F1分数，充分展现了其卓越的性能与效率。该方法旨在为航线规划提供有价值的参考，从而提升海上运输的安全性与效率。