顾及几何位置和移动特征相似性的船舶轨迹聚类方法

doi:10.11947/j.AGCS.2025.20240384

测绘学报 ›› 2025, Vol. 54 ›› Issue (6): 1107-1121.doi: 10.11947/j.AGCS.2025.20240384

顾及几何位置和移动特征相似性的船舶轨迹聚类方法

安晓亚¹^,²^,³(), 郭伟茹⁴, 张鹏鑫³^,⁴, 李欣欣⁵, 石磊⁵

^1.空间基准全国重点实验室，陕西　西安　710054
^2.智能空间信息国家级重点实验室，北京　100029
^3.西安测绘研究所，陕西　西安　710054
^4.武汉大学资源与环境科学学院，湖北　武汉　430079
^5.星球地图出版社，北京　100086

收稿日期:2024-09-18 修回日期:2025-04-22 出版日期:2025-07-14 发布日期:2025-07-14
作者简介:安晓亚（1982—），男，副研究员，研究方向为地图学与地理信息系统。E-mail：chxyaxy2022@163.com
基金资助:
基础加强计划重点基础研究项目;智慧地球重点实验室基金资助项目(KF2023ZD04-01)

Ship trajectories clustering method considering similarity in geometric position and mobility features

Xiaoya AN¹^,²^,³(), Weiru GUO⁴, Pengxin ZHANG³^,⁴, Xinxin LI⁵, Lei SHI⁵

^1.State Key Laboratory of Spatial Datum, Xi'an 710054, China
^2.National Key Laboratory of Intelligent Geospatial Information, Beijing 100029, China
^3.Xi'an Research Institute of Surveying and Mapping, Xi'an 710054, China
^4.School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
^5.Star Map Press, Beijing 100086, China

Received:2024-09-18 Revised:2025-04-22 Online:2025-07-14 Published:2025-07-14
About author:AN Xiaoya (1982—), male, associate researcher, majors in cartography and geographic information systems. E-mail: chxyaxy2022@163.com
Supported by:
The Key Basic Research Projects of the Foundation Plan of China;Key Laboratory of Smart Earth(KF2023ZD04-01)

摘要/Abstract

摘要：

随着船舶自动识别系统（AIS）在海事管理中的广泛应用，大量船舶轨迹数据被记录。通过聚类分析这些数据，可以有效挖掘船舶的典型航行路线及水域分布信息，为航运安全提供数据支持。然而，现有聚类方法多侧重船舶轨迹的空间形态特征，虽有部分研究涉及船舶移动特征，但对其深层次信息的挖掘仍显不足。本文提出了一种综合考虑几何位置和移动特征相似性的船舶轨迹聚类方法。首先，训练基于一维卷积神经网络U-net的轨迹分类模型，将模型在预测船舶类型前的特征向量作为表征轨迹运动模式的深层次特征。接着，采用欧氏距离度量深层次特征之间的相似性，以量化船舶轨迹在移动特征上的相似性。同时，使用Hausdorff距离来度量不同船舶轨迹之间的几何位置相似性。随后，计算几何位置和移动特征相似性的融合距离，并基于此距离采用自适应的DBSCAN算法进行聚类分析。试验结果表明，本文所提聚类方法在复杂船舶轨迹应用场景下表现出较基线模型更优的聚类效果，且能够有效地将几何形态相似但运动模式不同的轨迹分为不同簇，获得更为细粒度的聚类结果。

关键词: 船舶轨迹聚类, 几何位置相似性, 移动特征相似性, 深度学习

Abstract:

With the widespread application of the automatic identification system (AIS) in maritime management, a large amount of vessel trajectory data is being recorded. By applying clustering analysis to this data, typical navigation routes and maritime area distribution information of vessels can be effectively mined, providing data support for shipping safety. However, existing clustering methods tend to focus on the spatial shape characteristics of vessel trajectories, and while some studies have addressed the movement characteristics of vessels, the exploration of deep-level information remains insufficient. This paper proposes a vessel trajectory clustering method that comprehensively considers both geometric position and movement feature similarity. Firstly, a trajectory classification model based on a one-dimensional convolutional neural network (U-net) is trained, and the feature vectors obtained before the model predicts the vessel type are used as deep-level features representing the vessel's movement pattern. Next, the Euclidean distance is employed to measure the similarity between these deep-level features, quantifying the similarity of vessel trajectories in terms of movement feature. Meanwhile, the Hausdorff distance is used to measure the geometric position similarity between different vessel trajectories. Subsequently, the fusion distance, combining geometric position and movement feature similarity, is calculated. Based on this distance, an adaptive density-based spatial clustering of applications with noise (DBSCAN) algorithm is applied for clustering analysis. Experimental results show that the proposed clustering method achieves superior clustering performance compared to baseline models in complex vessel trajectory application scenarios, effectively classifying trajectories with similar geometric shapes but different motion patterns into different clusters, thus providing more granular clustering results.

Key words: ship trajectory clustering, geometric position similarity, mobility feature similarity, deep learning

中图分类号:

P208

安晓亚, 郭伟茹, 张鹏鑫, 李欣欣, 石磊. 顾及几何位置和移动特征相似性的船舶轨迹聚类方法[J]. 测绘学报, 2025, 54(6): 1107-1121.

Xiaoya AN, Weiru GUO, Pengxin ZHANG, Xinxin LI, Lei SHI. Ship trajectories clustering method considering similarity in geometric position and mobility features[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(6): 1107-1121.

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预测类型	真实类型	拖船	渔船	帆船/游艇	客船	货船	精确率	召回率	F₁值
一维U-net	拖船	160	7	2	1	8	0.94	0.90	0.92
	渔船	1	166	28	0	1	0.89	0.85	0.87
	帆船/游艇	2	13	196	6	2	0.84	0.90	0.87
	客船	0	0	6	176	2	0.92	0.96	0.94
	货船	7	0	0	8	170	0.93	0.92	0.92
LSTM	拖船	136	6	14	3	19	0.87	0.76	0.81
	渔船	5	151	21	9	10	0.87	0.77	0.82
	帆船/游艇	8	15	169	19	8	0.76	0.77	0.77
	客船	1	1	15	160	7	0.72	0.87	0.79
	货船	6	0	2	32	145	0.77	0.78	0.78

船舶轨迹聚类方法	轮廓系数	DBI指数	噪声率/（%）
基于Hausdorff距离的密度聚类方法	0.811	0.927	3.09
基于LCSS距离的密度聚类方法	0.801	0.915	4.45
MD-DBSCAN	0.792	0.965	7.45
顾及几何位置和移动特征相似性的聚类方法	0.815	0.876	4.63

船舶轨迹聚类方法	相似性度量		自适应确定DBSCAN聚类参数	聚类簇数	噪声率/（%）
船舶轨迹聚类方法	几何位置	移动特征	自适应确定DBSCAN聚类参数	聚类簇数	噪声率/（%）
基于Hausdorff距离度量几何位置相似性的聚类方法	k₁=1	k₂=0	ε=0.069	10	2.1
基于Hausdorff距离度量几何位置相似性的聚类方法	k₁=1	k₂=0	MinLns=5	10	2.1
顾及几何位置和移动特征相似性的聚类方法	k₁=0.92	k₂=0.08	ε=0.059	26	6.2
顾及几何位置和移动特征相似性的聚类方法	k₁=0.92	k₂=0.08	MinLns=3	26	6.2

顾及几何位置和移动特征相似性的船舶轨迹聚类方法

Ship trajectories clustering method considering similarity in geometric position and mobility features

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