一种面向定点稀疏轨迹的密度聚类停留点识别方法

doi:10.11947/j.AGCS.2026.20250284

测绘学报 ›› 2026, Vol. 55 ›› Issue (2): 249-260.doi: 10.11947/j.AGCS.2026.20250284

• 空间智能与智慧城市 • 上一篇

一种面向定点稀疏轨迹的密度聚类停留点识别方法

郭军豪¹(), 吴明治¹(), 王培晓²^,³, 张恒才²^,³

^1.福州大学数字中国研究院（福建），福建　福州　350108
^2.中国科学院地理科学与资源研究所地理信息科学与技术全国重点实验室，北京　100101
^3.中国科学院大学资源与环境学院，北京　100049

收稿日期:2025-07-14 修回日期:2025-10-22 发布日期:2026-03-13
通讯作者: 吴明治 E-mail:245520020@fzu.edu.cn;Mingzhi.wu17@student.xjtlu.edu.cn
作者简介:郭军豪（2002—），男，硕士生，研究方向为地理大数据挖掘。 E-mail：245520020@fzu.edu.cn
基金资助:
国家重点研发计划(2023YFB3906804)

A dual-threshold stay point detection method based on adaptive extended density peak clustering for sparse fixed-point trajectories

Junhao GUO¹(), Mingzhi WU¹(), Peixiao WANG²^,³, Hengcai ZHANG²^,³

^1.Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, China
^2.State Key Laboratory of Geographic Information Science and Technology, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
^3.College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2025-07-14 Revised:2025-10-22 Published:2026-03-13
Contact: Mingzhi WU E-mail:245520020@fzu.edu.cn;Mingzhi.wu17@student.xjtlu.edu.cn
About author:GUO Junhao (2002—), male, postgraduate, majors in geospatial big data mining. E-mail: 245520020@fzu.edu.cn
Supported by:
The National Key Research and Development Program of China(2023YFB3906804)

摘要/Abstract

摘要：

停留点识别作为轨迹数据挖掘的重要前期准备工作，对兴趣点挖掘、移动模式分类等研究具有重要支撑作用。然而，传统识别方法通常用于GPS等稠密轨迹，在面对交通卡口、手机信令等定点稀疏轨迹时难以应对数据密度不均、分布复杂导致的特征挖掘不足、阈值估计偏差等挑战。为此，本文提出一种基于自适应扩展密度峰值聚类（AE-DPC）的双阈值停留点识别方法用于定点稀疏轨迹停留点识别。首先，基于数据整体特征划分全局阈值初步筛选停留点；然后，利用AE-DPC聚类结果设定局部阈值进一步判别，其中AE-DPC通过考虑邻域和改进密度峰值构建初始簇，并经过簇扩展与合并提升聚类性能；最后，结合全局与局部阈值实现精准识别停留点。本文基于开源合成数据集与真实定点稀疏轨迹数据集分别对AE-DPC和双阈值法进行试验。结果表明，AE-DPC聚类结果的ARI、AMI指标均显著优于DBSCAN、HDBSCAN、SNN-DPC等对比算法；基于AE-DPC设定局部阈值的双阈值方法在真实停留点识别中展现出明显优势，与基于HDBSCAN的局部阈值法和动态阈值法相比，该方法在查准率指标上分别提升了14.10%和9.88%。

关键词: 停留点识别, 定点稀疏轨迹, 聚类算法, 密度峰值

Abstract:

Stay point detection serves as a critical preliminary step in trajectory data mining, providing essential support for related research such as point-of-interest mining and movement pattern classification. However, traditional detection methods are typically designed for dense trajectories, such as GPS data, and struggle to address the challenges posed by sparse fixed-point trajectories like those from traffic checkpoints or mobile signaling data. These challenges include insufficient feature extraction and threshold estimation biases due to uneven data density and complex distribution patterns. To tackle these issues, we propose a dual-threshold stay point detection method based on adaptive expanded density peak clustering (AE-DPC) for sparse fixed-point trajectories. First, global thresholds are derived from overall data characteristics to preliminarily identify candidate stay points. Then, local thresholds are further refined based on the clustering results of AE-DPC, which constructs initial clusters by considering neighborhood relationships and improved density peaks, followed by cluster expansion and merging to enhance clustering performance. Finally, the integration of global and local thresholds enables precise stay point detection. Experiments were conducted on both open-source synthetic datasets and real-world sparse fixed-point trajectory datasets to evaluate AE-DPC and the dual-threshold method, respectively. The results demonstrate that AE-DPC significantly outperforms comparative algorithms (such as DBSCAN, HDBSCAN, and SNN-DPC) in terms of the adjusted rand index (ARI) and adjusted mutual information (AMI). Moreover, the dual-threshold method leveraging AE-DPC-based local thresholds shows superior performance in real-world stay point detection tasks, achieving improvements in precision of 14.10% and 9.88% compared to the local threshold method based on HDBSCAN and the dynamic threshold approach, respectively.

Key words: stay point detection, sparse fixed-point trajectory, clustering algorithm, density peak

中图分类号:

P208

郭军豪, 吴明治, 王培晓, 张恒才. 一种面向定点稀疏轨迹的密度聚类停留点识别方法[J]. 测绘学报, 2026, 55(2): 249-260.

Junhao GUO, Mingzhi WU, Peixiao WANG, Hengcai ZHANG. A dual-threshold stay point detection method based on adaptive extended density peak clustering for sparse fixed-point trajectories[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(2): 249-260.

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数据集	样本数	特征数	簇数
Spiral	312	2	3
Ring	500	2	2
Ls3	1741	2	6
Compound	399	2	6
Jain	373	2	2
Jain2	500	2	4

数据集	算法	ARI	AMI	Arg-
Spiral	DBSCAN	1.000	1.000	0.05/3
	HDBSCAN	1.000	1.000	17/2
	SC	1.000	1.000	3/5
	OPTICS	1.000	1.000	4/0.12
	SNN-DPC	1.000	1.000	4/3
	3W-PEDP	1.000	1.000	3
	AE-DPC	1.000	1.000	10/31
Ring	DBSCAN	1.000	1.000	0.16/3
	HDBSCAN	1.000	1.000	2/3
	SC	1.000	1.000	2/8
	OPTICS	1.000	1.000	7/0.08
	SNN-DPC	1.000	1.000	3/2
	3W-PEDP	0.670	0.718	4
	AE-DPC	1.000	1.000	10/50
Ls3	DBSCAN	1.000	1.000	0.05/11
	HDBSCAN	1.000	1.000	9/1
	SC	1.000	1.000	6/25
	OPTICS	0.959	0.950	6/0.03
	SNN-DPC	0.719	0.807	70/6
	3W-PEDP	0.494	0.688	6
	AE-DPC	1.000	1.000	41/174
Compound	DBSCAN	0.949	0.908	0.05/3
	HDBSCAN	0.909	0.896	2/3
	SC	0.804	0.853	4/13
	OPTICS	0.882	0.864	6/0.05
	SNN-DPC	0.875	0.854	3/10
	3W-PEDP	0.820	0.821	6
	AE-DPC	0.969	0.933	9/21
Jain	DBSCAN	0.976	0.865	0.08/2
	HDBSCAN	0.976	0.934	20/2
	SC	1.000	1.000	2/9
	OPTICS	0.976	0.922	4/0.08
	SNN-DPC	0.811	0.832	2/3
	3W-PEDP	0.444	0.601	3
	AE-DPC	1.000	1.000	16/37
Jain2	DBSCAN	0.725	0.814	0.06/5
	HDBSCAN	0.871	0.838	5/4
	SC	0.705	0.688	4/47
	OPTICS	0.799	0.788	9/0.06
	SNN-DPC	0.927	0.921	7/4
	3W-PEDP	0.826	0.838	5
	AE-DPC	0.930	0.913	13/49

车辆	记录时间	监测设备	距离/m	时间/s	速度/（m/s）	簇	簇时间/s	簇速度/（m/s）	标签	结果	簇H	簇D
5f0^**	16：35：36	435^**	0	33	0	2059	600	5.44	－1	0	225	d3
5f0^**	17：26：10	484^**	3799	3034	1.25	4901	1833	5.21	2	2	825	d3
5f0^**	18：57：16	471^**	1421	5466	0.26	1095	2731	5.91	2	2	828	e3
5f0^**	18：58：17	459^**	8	61	0.13	1095	2731	5.91	0	0	828	e3
5f0^**	19：24：10	494^**	11 438	1553	7.367	1095	2731	5.91	1	0	-	f5
13f^**	15：37：31	441^**	0	0	0	1222	764	6.54	－1	0	311	d4
13f^**	15：37：36	441^**	0	5	0	1222	764	6.54	0	0	311	d4
13f^**	15：53：31	455^**	1335	955	1.39	2104	700	6.38	2	2	-	d4
13f^**	15：54：01	456^**	396	30	13.22	2104	700	6.38	0	0	-	d4
13f^**	15：54：23	410^**	181	22	8.26	2104	700	6.38	0	0	368	d4
13f^**	16：05：24	499^**	1831	661	2.77	2307	892	3.99	1	0	369	d4

识别方法	查准率	查全率
本文方法	0.89	0.83
基于HDBSCAN的局部阈值法	0.78	0.71
动态阈值法	0.81	0.82

一种面向定点稀疏轨迹的密度聚类停留点识别方法

A dual-threshold stay point detection method based on adaptive extended density peak clustering for sparse fixed-point trajectories

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