A multi-task learning-based framework for global maritime trajectory and destination prediction with AIS data

Abstract

Automatic Identification System (AIS) was initially developed for tracking ships and avoiding collisions. As numerous small satellites were launched in recent years, millions of AIS messages are captured by these satellite AIS providers every day. The massive amount of data allow shipping firms and port operators to better predict vessels’ movement. In this study, a computational framework is developed to predict future trajectories and destinations of vessels by applying deep learning to the satellite AIS messages containing self-reporting positioning data. We employed deep learning approaches for large-scale maritime prediction and identifies the most suitable deep neural networks for this specific application. Three prediction models, Convolutional Neural Networks (CNN), Dense Neural Networks (DNN), and Long Short-Term Memory (LSTM) have been tested in the framework. A multi-task learning architecture is implemented based on test results. Through sharing parameters while training, this architecture enables long-term destination predictions by balancing short-term trajectory predictions. The framework is validated globally with the experiments covering five large-scale sea areas. The experimental results demonstrate that the LSTM model with an average accuracy percentage of 85.1% outperforms the CNN and DNN with accuracy percentages of 68.3% and 78.2% respectively. The LSTM model with multi-task architecture can achieve an accuracy of 87.0%. With the proposed computational framework, AIS data can be utilized for maritime predictions, supporting more complex marine applications and covering more expansive geographic areas.