WAY: Estimation of Vessel Destination in Worldwide AIS Trajectory

The Automatic Identification System (AIS) has recorded near-real-time vessel monitoring data over the years, paving the way for data-driven maritime surveillance methods; concurrently, the data suffer from unrefined, reliability issues and irregular intervals. In this paper, we address the problem of vessel destination estimation by exploiting the global-scope AIS data. We propose a differentiated data-driven approach recasting a long sequence of port-to-port international vessel trajectories as a nested sequence structure. Based on spatial grids, this approach mitigates the spatio-temporal bias of AIS data while preserving the detailed resolution of the original. Further, we propose a novel deep learning architecture (WAY) that is designed to effectively process the reformulated trajectory and perform the long-term estimation of the vessel destination ahead of arrival with a horizon of days to weeks. WAY comprises a trajectory representation layer and channel-aggregative sequential processing (CASP) blocks. The representation layer produces the multi-channel vector sequence output based on each kinematic and non-kinematic feature collected from AIS data. Then CASP blocks include multi-headed channeland self-attention architectures, where each processes aggregation and sequential information delivery respectively. Then, a taskspecialized learning technique, Gradient Dropout (GD), is also suggested for adopting many-to-many training along the trajectory progression on single labels. The technique prevents a surge of biased feedback by blocking the gradient flow stochastically using the condition depending on the length of training samples. Experimental results on 5-year accumulated AIS data demonstrated the superiority of WAY with recasting AIS trajectory compared to conventional spatial grid-based approaches, regardless of the trajectory progression steps. Moreover, the data proved that adopting GD