Trajectory Prediction for Maritime Vessels Using AIS Data

Abstract

The need for a variety of auxiliary analytical tools to enhance marine safety and marine status awareness has been expressed by various platforms. The information that has been published while cruising is a rich resource for movement analysis of ships. Automatic Identification System (AIS), which is widely used in vessels, broadcasts information including the type of ship, identity number, state, destination, estimated time of arrival (ETA), location, speed, direction, and cargo. In this paper, to aid maritime operators, we work on arrival port, arrival time, and next position prediction on AIS messages, and propose three different approaches for the prediction of marine vessel movement. The experiments conducted against conventional supervised learning approaches reveal the improvement of the proposed solutions.