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An Efficient Clustering Algorithm for Massive GPS Trajectory Data
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  • Journal title : Journal of KIISE
  • Volume 43, Issue 1,  2016, pp.40-46
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.1.40
 Title & Authors
An Efficient Clustering Algorithm for Massive GPS Trajectory Data
Kim, Taeyong; Park, Bokuk; Park, Jinkwan; Cho, Hwan-Gue;
Digital road map generation is primarily based on artificial satellite photographing or in-site manual survey work. Therefore, these map generation procedures require a lot of time and a large budget to create and update road maps. Consequently, people have tried to develop automated map generation systems using GPS trajectory data sets obtained by public vehicles. A fundamental problem in this road generation procedure involves the extraction of representative trajectory such as main roads. Extracting a representative trajectory requires the base data set of piecewise line segments(GPS-trajectories), which have close starting and ending points. So, geometrically similar trajectories are selected for clustering before extracting one representative trajectory from among them. This paper proposes a new divide- and-conquer approach by partitioning the whole map region into regular grid sub-spaces. We then try to find similar trajectories by sweeping. Also, we applied the distance measure to compute the similarity between a pair of trajectories. We conducted experiments using a set of real GPS data with more than 500 vehicle trajectories obtained from Gangnam-gu, Seoul. The experiment shows that our grid partitioning approach is fast and stable and can be used in real applications for vehicle trajectory clustering.
trajectory clustering;GPS data;road map generation;line sweeping;
 Cited by
최대 중첩구간을 이용한 새로운 GPS 궤적 클러스터링,김태용;박보국;박진관;조환규;

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