Mining navigable water current information from ship-based big automated identification system data

Abstract

High-resolution current information including direction and velocity in navigable water is essential for the safe navigation of ships. There is a lack of effective observation methods for current information in the navigable water environment. In this paper, the Current Information Mining Model (CIMM) is proposed to extract high-resolution current information in the navigable water environment based on on-board sensor data from the Automatic Identification System (AIS). In this model, a deep network based on the Gated Recurrent Unit (GRU) and bidirectional recurrent neural networks (BRNNs) is constructed to retrieval the spatial-temporal features of the vessel track, and an experience matrix (EM) is put forward to generate global attention. The results show that current direction prediction error of 83.5% of the data is less than 20°, current speed prediction error of 86.6% of the data is less than 0.3 m/s, the predicted data of the CIMM can reflect the real current information in navigable waterways. This study proposes a model for extracting current information in navigable waters, and explores a new way to obtain traffic environment information from sensor observation data carried by mobile vehicles.