Incremental Discovery of Crowd from Evolving Trajectory Data
Thi Thi Shein, Sutheera Puntheeranurak, Makoto Imamura · 2018
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.