Mining urban moving trajectory patterns based on multi-scale space partition and road network modeling

In this paper, the problem of discovering moving trajectory patterns in urban environment is studied and the method of integration of moving global pattern and moving local pattern is proposed. Through moving trajectory origin-destination (OD) and moving sequence features, the global patterns and local patterns are mined. In the process of moving global pattern mining, a flexible multi-scale space partition is devised to avoid damage of the dense region edges by hard regular grid division and enhance the ability to distinguish the dense regions and sparse regions. In the process of moving local pattern mining, the modeling method of road network based on moving trajectory is devised to extract the feature of topological relation by key road network nodes. Finally, the raw moving trajectory dataset is converted by partitioned discrete regions and road network model, and the frequent moving trajectory patterns are discovered by a modified sequence pattern mining algorithm. A comprehensive experimental evaluation on Shenzhen taxicabs GPS trajectory dataset is presented, and the evaluation shows that the proposed method outperforms the existing methods in space division, data transform, and interpretability of mined patterns.