Discovering individual movement patterns from cell-id trajectory data by exploiting handoff features

The discovery of movement patterns from trajectory data is crucial for supporting many location-based applications. Most existing methods require that the trajectories contain explicit location information. However, it is usually difficult to collect such kind of trajectories from mobile phone users. In this paper, we propose a method for mining movement patterns from cell-id trajectories (i.e., sequences of cell tower identifiers) without explicit location information. Specifically, we firstly estimate the spatial closeness between cell towers in a cell-id trajectory dataset by exploiting the handoff features. Then, we propose a novel sequential pattern mining algorithm to mine movement patterns from the cell-id trajectory dataset by taking into account the estimated spatial closeness. We evaluated the proposed method based on a real cell-id trajectory dataset. The experiment results show that the proposed method can adapt to the high uncertainty of cell-id trajectories and it outperforms state-of-the-art methods in terms of efficiency, completeness, and usefulness.