a two-step clustering approach to extract locations from individual gps trajectory data

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ID: 157956
2016
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Abstract
High-accuracy location identification is the basis of location awareness and location services. However, because of the influence of GPS signal loss, data drift and repeated access in the individual trajectory data, the efficiency and accuracy of existing algorithms have some deficiencies. Therefore, we propose a two-step clustering approach to extract individuals’ locations according to their GPS trajectory data. Firstly, we defined three different types of stop points; secondly, we extracted these points from the trajectory data by using the spatio-temporal clustering algorithm based on time and distance. The experimental results show that the spatio-temporal clustering algorithm outperformed traditional extraction algorithms. It can avoid the problems caused by repeated access and can substantially reduce the effects of GPS signal loss and data drift. Finally, an improved clustering algorithm based on a fast search and identification of density peaks was applied to discover the trajectory locations. Compared to the existing algorithms, our method shows better performance and accuracy.
Reference Key
fu2016isprsa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Zhongliang Fu;Zongshun Tian;Yanqing Xu;Changjian Qiao
Journal población y desarrollo
Year 2016
DOI
10.3390/ijgi5100166
URL
http://www.mdpi.com/2220-9964/5/10/166
Keywords
Data mining clustering algorithm gps trajectory data personal locationgeography (general)

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