Moving Objects Gathering Patterns Retrieving based on Spatio-Temporal Graph

Junming Zhang, Jinglin Li, Zhihan Liu, Quan Yuan and Fangchun Yang
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Junming Zhang: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China
Jinglin Li: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China
Zhihan Liu: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China
Quan Yuan: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China
Fangchun Yang: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China

International Journal of Web Services Research (IJWSR), 2016, vol. 13, issue 3, 88-107

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.

Date: 2016
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