On discovery of gathering patterns from trajectories

Zheng, Kai, Zheng, Yu, Yuan, Nicholas Jing and Shang, Shuo (2013). On discovery of gathering patterns from trajectories. In: ICDE 2013 - 29th International Conference on Data Engineering. 29th International Conference on Data Engineering, ICDE 2013, Brisbane, Australia, (242-253). 8-12 April 2013. doi:10.1109/ICDE.2013.6544829

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Author Zheng, Kai
Zheng, Yu
Yuan, Nicholas Jing
Shang, Shuo
Title of paper On discovery of gathering patterns from trajectories
Conference name 29th International Conference on Data Engineering, ICDE 2013
Conference location Brisbane, Australia
Conference dates 8-12 April 2013
Proceedings title ICDE 2013 - 29th International Conference on Data Engineering   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2013
Year available 2013
Sub-type Fully published paper
DOI 10.1109/ICDE.2013.6544829
Open Access Status
ISBN 9781467349086
ISSN 1084-4627
Start page 242
End page 253
Total pages 12
Collection year 2014
Language eng
Abstract/Summary The increasing pervasiveness of location-acquisition technologies has enabled collection of huge amount of trajectories for almost any kind of moving objects. Discovering useful patterns from their movement behaviours can convey valuable knowledge to a variety of critical applications. In this light, we propose a novel concept, called gathering, which is a trajectory pattern modelling various group incidents such as celebrations, parades, protests, traffic jams and so on. A key observation is that these incidents typically involve large congregations of individuals, which form durable and stable areas with high density. Since the process of discovering gathering patterns over large-scale trajectory databases can be quite lengthy, we further develop a set of well thought out techniques to improve the performance. These techniques, including effective indexing structures, fast pattern detection algorithms implemented with bit vectors, and incremental algorithms for handling new trajectory arrivals, collectively constitute an efficient solution for this challenging task. Finally, the effectiveness of the proposed concepts and the efficiency of the approaches are validated by extensive experiments based on a real taxicab trajectory dataset.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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