Hybrid Stop Discovery in Trajectory Records

The advance of GPS tracking technique brings a large amount of trajectory data. These data can be used in many application domains such as traffic management, urban planning, tourism, and bird migration. Recently, a semantic model which expresses trajectory as a sequence of stops and moves was introduced and become a hot topic for trajectory data analysis. Stops are important parts of trajectories, such as "working at office", "shopping in a mall", "waiting for the bus". Although several works have been developed to discover stops, they considered the characteristics of the stops separately. Because of this limitation, these approaches only focus on certain well-defined trajectories. They cannot work well for heterogeneous cases like diverse and sparse trajectories. Towards stop discovery in trajectories, in this paper, we propose a comprehensive hybrid feature-based method to discover stops. We also evaluate our approach with real-life GPS datasets, and show that this newly proposed approach can provide a good abstraction on the trajectory, with efficient computation.