On Retrieving Moving Objects Gathering Patterns from Trajectory Data via Spatio-temporal Graph
Junming Zhang, Jinglin Li, Shangguang Wang, Zhihan Liu, Quan Sheng Yuan, Fangchun Yang · 2014
Moving object gathering pattern represents a group event or incident that involves congregation of moving objects, enabling the prediction of anomalies in 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, we propose a moving object gathering pattern retrieving method that aims to support the retrieving of gathering patterns by using spatio-temporal graph. In this method, firstly we use a density based clustering algorithm (DBScan) to collect the moving object clusters. Then, we 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 our knowledge, effectiveness and efficiency of the proposed methods are outperformed other methods on both real and large trajectory data.