Framework of Frequently Trajectory Extraction from AIS Data
X. Jin, Y. Yang, X. Qiu
Mining trajectory data has been attracting significant interest in the last years. Emerging technologies like Automatic Identification System (AIS) provides multi-dimensional data which is about voyages and vessels. The maritime area is a free moving space. Unlike the vehicles’ movements are constrained by road networks, there is no such a sea route for ships to follow in maritime area. In this Paper, we propose a framework of frequently voyage extraction from AIS data, which learns frequently voyages using improved dynamic time warping(IDTW) an Adaptive Density Increment Clustering (ADIC). ADIC can adaptively adjust parameters on uneven density data . We conduct the experiments on real maritime trajectories to show the
effectiveness of proposed framework.