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.
Highlights
More and more personal daily trajectory data are being recorded with the popularity of smart mobile terminals
How to extract useful information from these trajectory data quickly and accurately in order to provide personalized location services is the primary concern of location-aware computing
The DBSCAN algorithm is very sensitive to the parameters [21] and has a relatively high time complexity. It is not suitable for processing a large amount of data as part of the trajectory data mining. With such concerns about the previously used algorithms, we propose a two-step clustering approach to extract personal locations from individual GPS trajectory data
Summary
More and more personal daily trajectory data are being recorded with the popularity of smart mobile terminals. Zhou et al [13] proposed the DJ-Cluster algorithm based on the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) [14] in order to recognize the location These algorithms do not provide a processing capacity for the points with signal loss and data drift, especially the signal loss caused by a building block. To solve the problems caused by data loss, Ashbrook & Starner [15] proposed to firstly identify the lost points They used the improved K-Means algorithm to cluster the lost points in order to form the locations. The SMoT algorithm [18] did an analysis based on the intersection of trajectory data and candidate stops, in which overlapped and sustained points were considered as locations.
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