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Incremental discovery of crowd from evolving trajectory data
Author(s)
Shein, Thi Thi
Puntheeranurak, Sutheera
Imamura, Makoto
Date Issued
August 13, 2018
Type
Conference Paper
Abstract
Discovering the useful knowledge from the evolving trajectory data stream contributes to real-world applications such as animal movement behavior analysis, traffic monitoring, and weather forecasting. Among these explorations, moving objects' crowd detection is a challenging task and enabling to find anomalies in the traffic system. In a real application, a large volume of trajectory data stream arrives continuously for immediate data analysis. Due to the changes of these trajectory data, there is a remaining challenge to discover crowd efficiently. In this paper, we propose incremental crowd discovery framework over evolving data stream to reduce computational time complexity. In our framework, firstly we discover the group by proposing micro-group based clustering, and then we incrementally detect the crowd structure form. Experiments of our proposed system will conduct on real taxi trajectory data and synthetic data.
Citation
Iceast 2018 4th International Conference on Engineering Applied Sciences and Technology Exploring Innovative Solutions for Smart Society, 2018