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:: Volume 9, Issue 2 (10-2021) ::
jgit 2021, 9(2): 105-128 Back to browse issues page
Developing a spatial and temporal density-based clustering algorithm to extract stop locations from the user’s trajectory
Negin Masnabadi, Farhad Hosseinali *, Zahra Bahramian
Shahid Rajaee University
Abstract:   (615 Views)
Identifying stopping points of trajectories is a preliminary and necessary step in the study of moving objects and has a major impact on spatial plans and services. In this study we use trajectory clustering to extract stopping points. DBSCAN algorithm (spatial clustering based on density of applications with noise) is the basic algorithm of density-based clustering methods, which despite its advantages has some shortcommings such as difficulty in determining input parameters, inability to detect clusters with different densities and not paying attention to round trip problem. In the proposed method, which is based on density, we use of spatial and temporal indices and several neighborhood radii to extract stop points. Solving the round trip problem, extracting clusters with different densities and reducing the degree of dependence of the results on input parameters are the advantages of the proposed method.In order to evaluate the proposed method, this method was implemented on the data obtained by handheld GPS in Arak city and the data related to the Geolife research project. The obtained results were compared with the results of five other algorithms including DBSCAN, ST-BDSCAN, VDBSCAN, DVBSCAN and K-means. Compared to the manual GPS route data in Arak city, the stop locations extracted by the proposed algorithm and the mentioned algorithms are 100%, 25%, 75%, 50%, 75% and 50%, respectively, which are correctly extracted and show the superiority of the developed method. Also, after extracting the stopping and moving points, indicators from Geolife data were determined to identify working and non-working days (holidays) with which the proposed method was able to act successfully up to 94.06%.The results show a decrease in the dependence of the results on input parameters, the accurate extraction of stopping points, a reduction in the standard deviation within the clusters, and an increase in the distance between the centers of the clusters.
Keywords: Trajectory, Extraction of stop locations, Spatiotemporal clustering, DBSCAN, summer and winter, working and non-working.
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Type of Study: Research | Subject: GIS
Received: 2020/12/11 | Accepted: 2021/10/16 | Published: 2021/10/22
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Masnabadi N, Hosseinali F, Bahramian Z. Developing a spatial and temporal density-based clustering algorithm to extract stop locations from the user’s trajectory. jgit. 2021; 9 (2) :105-128
URL: http://jgit.kntu.ac.ir/article-1-807-en.html


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Volume 9, Issue 2 (10-2021) Back to browse issues page
نشریه علمی-پژوهشی مهندسی فناوری اطلاعات مکانی Engineering Journal of Geospatial Information Technology
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