ABSTRACT This study presents a novel statistical trajectory-distance metric specialized for nautical route clustering analysis. Based on the dynamic time warping (DTW) metric, one of the most used metrics for trajectory-distance, the statistical trajectory-distance metric was defined by replacing the distance term in DTW with a linear combination of the Jensen–Shannon divergence and Wasserstein distance. Each waypoint from a nautical route was modelled as a discrete and asymmetric binomial normal distribution defined by the cross-track distance (XTD) of the waypoint. The model was then used to compute the statistical distance between waypoints. Nautical route clustering was performed using density-based spatial clustering of applications with noise and the statistical trajectory-distance metric. The nautical route for the clustering analysis, including the XTD information, was extracted from automatic identification system data from the southern sea of the Korean Peninsula. The clustering results were evaluated by comparing them with the results of other popular trajectory-distance metrics. The proposed method was more effective compared to other trajectory-distance when the trajectories pass on both sides of a small island, which is frequent case in coastal route clustering.

中文翻译：

---

使用跨航迹距离的航海路线聚类分析的统计轨迹距离度量

摘要 本研究提出了一种专门用于航海路线聚类分析的新型统计轨迹距离度量。基于动态时间扭曲 (DTW) 度量（轨迹距离最常用的度量之一），统计轨迹距离度量是通过将 DTW 中的距离项替换为 Jensen-Shannon 散度和 Wasserstein 距离的线性组合来定义的. 航路中的每个航路点都被建模为由航路点的跨航迹距离 (XTD) 定义的离散且不对称的二项式正态分布。然后使用该模型计算航路点之间的统计距离。航海路线聚类是使用基于密度的噪声应用空间聚类和统计轨迹距离度量来执行的。聚类分析的航路，包括 XTD 信息，是从朝鲜半岛南部海域的自动识别系统数据中提取的。通过将聚类结果与其他流行的轨迹距离度量的结果进行比较来评估聚类结果。当轨迹通过小岛的两侧时，与其他轨迹距离相比，所提出的方法更有效，这在沿海路线聚类中很常见。