基于密度的停留点识别方法

李毓瑞; 陈红梅; 王丽珍; 肖清

doi:10.11959/j.issn.2096-0271.2018052

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基于密度的停留点识别方法

研究 | 更新时间：2024-06-03

- 基于密度的停留点识别方法
- Stay point identification based on density
- 大数据 2018年4卷第5期页码：2018052
- 作者机构：
- 作者简介：
  
  [ "李毓瑞（1989-），男，云南大学信息学院硕士生，主要研究方向为空间数据挖掘。" ]
  [ "陈红梅（1976-），女，博士，云南大学信息学院副教授，主要研究方向为数据挖掘、空间数据挖掘等。" ]
  [ "王丽珍（1962-），女，博士，云南大学信息学院教授，博士生导师，主要研究方向为数据库、数据挖掘、计算机算法等。" ]
  [ "肖清（1975-），女，云南大学信息学院讲师，主要研究方向为数据挖掘、空间数据挖掘等。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61662086);国家自然科学基金资助项目;The National Natural Science Foundation of China(61472346);国家自然科学基金资助项目;The National Natural Science Foundation of China(61762090);云南省自然科学基金资助项目;The National Natural Science Foundation of Yunnan Province(2015FB14);云南省自然科学基金资助项目;The National Natural Science Foundation of Yunnan Province(2016FA026);云南大学“东陆中青年骨干教师”培养计划;The Program for Young and Middle-aged Skeleton Teachers of Yunnan University(WX069051)
- DOI：10.11959/j.issn.2096-0271.2018052
  中图分类号： TP311.13
- 网络首发：2018-09，
  
  纸质出版：2018-09-15
- 稿件说明：
移动端阅览
李毓瑞, 陈红梅, 王丽珍, 等. 基于密度的停留点识别方法[J]. 大数据, 2018,4(5):2018052.

Yurui LI, Hongmei CHEN, Lizhen WANG, et al. Stay point identification based on density[J]. Big Data Research, 2018, 4(5): 2018052.
李毓瑞, 陈红梅, 王丽珍, 等. 基于密度的停留点识别方法[J]. 大数据, 2018,4(5):2018052. DOI： 10.11959/j.issn.2096-0271.2018052.

Yurui LI, Hongmei CHEN, Lizhen WANG, et al. Stay point identification based on density[J]. Big Data Research, 2018, 4(5): 2018052. DOI： 10.11959/j.issn.2096-0271.2018052.

摘要

从GPS轨迹点序列中识别停留点，是轨迹分析的重要预处理步骤，是用户行为分析、个性化兴趣点推荐等位置服务的基础，停留点识别方法的识别能力对位置服务的可用性和可靠性有根本性的影响。针对现有方法未考虑轨迹点的时间连续性或仅考虑时间连续性的一个方向所导致的停留点识别能力不足的问题，提出一种新的基于密度的停留点识别方法。该方法考虑了轨迹点的时空聚集，兼顾了轨迹点的时间连续性和方向性。在GeoLife数据集上的实验结果验证了该方法的识别能力强于基准方法，可以进一步识别基准方法不能识别的两类停留点。

Abstract

Identifying stay points from GPS trajectory is an important preprocessing procedure of trajectory analysis and the foundation of location based service such as user behavior analysis and personal POI recommendation

and the capability of the stay point identification method has a fundamental impact on the availability and reliability of location based service.Existing methods for identifying stay points have some shortcomings due to not considering time continuity or only considering one direction of time continuity.A new method called stay point identification based on density (SPID) was proposed.SPID takes into account the spatial-temporal clustering of trajectory points

and the time directions and time continuity of trajectory points.The experimental results on Geolife dataset verify that SPID is better than the baseline methods

and can identify two kinds of stay points which can’t be found by the baseline methods.

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references

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