New Milestones in AIS Research: Vessel Trajectory and Maritime Traffic Forecasting

We are pleased to announce the publication of two new research papers on AIS (Automatic Identification System) research, co-authored by Dr. Gabriel Spadon and his collaborators.

• Enhancing short-term vessel trajectory prediction with clustering for heterogeneous and multi-modal movement patterns.

Authors: Mahbub Alam, Dr. Gabriel Spadon, Mohamed  Etemad, Luis Torgo, Evangelos Milios

This paper presents a novel approach to improving short-term vessel trajectory prediction. The first author, Mahbub Alam, is a PhD student currently supervised by Dr. Gabriel Spadon, and was previously under the supervision of Luis Torgo, Deputy-Director of the Institute for Big Data Analytics.

• Enhancing global maritime traffic network forecasting with gravity-inspired deep learning models.

Authors: Ruixin Song, Dr. Gabriel Spadon, Dr. Ron Pelot, Dr. Stan Matwin, Amilcar Soares

This study introduces gravity-inspired deep learning models to enhance the forecasting of global maritime traffic networks. The first author, Ruixin Song, is a new research assistant working on the AISViz project alongside Dr. Ron Pelot and Dr. Gabriel Spadon.

These publications represent significant advancements in the field of maritime traffic analysis and prediction, showcasing the collaborative efforts and innovative research methodologies of the authors. We are proud of the contributions made by our team and look forward to the continued impact of their work on the maritime industry.