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Moving Objects Gathering Patterns Retrieving based on Spatio-Temporal Graph

Moving Objects Gathering Patterns Retrieving based on Spatio-Temporal Graph

Junming Zhang, Jinglin Li, Zhihan Liu, Quan Yuan, Fangchun Yang
Copyright: © 2016 |Volume: 13 |Issue: 3 |Pages: 20
ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781466689060|DOI: 10.4018/IJWSR.2016070105
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MLA

Zhang, Junming, et al. "Moving Objects Gathering Patterns Retrieving based on Spatio-Temporal Graph." IJWSR vol.13, no.3 2016: pp.88-107. http://doi.org/10.4018/IJWSR.2016070105

APA

Zhang, J., Li, J., Liu, Z., Yuan, Q., & Yang, F. (2016). Moving Objects Gathering Patterns Retrieving based on Spatio-Temporal Graph. International Journal of Web Services Research (IJWSR), 13(3), 88-107. http://doi.org/10.4018/IJWSR.2016070105

Chicago

Zhang, Junming, et al. "Moving Objects Gathering Patterns Retrieving based on Spatio-Temporal Graph," International Journal of Web Services Research (IJWSR) 13, no.3: 88-107. http://doi.org/10.4018/IJWSR.2016070105

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Abstract

Moving objects gathering pattern represents a group events or incidents that involve congregation of moving objects, enabling the analysis of traffic system. However, effectively and efficiently discovering the specific gathering pattern turns to be a remaining challenging issue since the large number of moving objects will generate high volume of trajectory data. In order to address this issue, the authors propose a moving object gathering pattern retrieving method that aims to support the retrieving of gathering patterns based on spatio-temporal graph. In this method, firstly the authors use an improved R-tree based density clustering algorithm (RT-DBScan) to index the moving objects and collect clusters. Then, they maintain a spatio-temporal graph rather than storing the spatial coordinates to obtain the spatio-temporal changes in real time. Finally, a gathering retrieving algorithm is developed by searching the maximal complete graphs which meet the spatio-temporal constraints. To the best of their knowledge, effectiveness and efficiency of the proposed methods are outperformed other methods on both real and large trajectory data.

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