Deep Learning-Based Ship Speed Prediction for Intelligent Maritime Traffic Management
Sara El Mekkaoui, Loubna Benabbou, Stéphane Caron, Abdelaziz Berrado
Abstract
Improving maritime operations planning and scheduling can play an important role in enhancing the sector’s performance and competitiveness. In this context, accurate ship speed estimation is crucial to ensure efficient maritime traffic management. This study addresses the problem of ship speed prediction from a Maritime Vessel Services perspective in an area of the Saint Lawrence Seaway. The challenge is to build a real-time predictive model that accommodates different routes and vessel types. This study proposes a data-driven solution based on deep learning sequence methods and historical ship trip data to predict ship speeds at different steps of a voyage. It compares three different sequence models and shows that they outperform the baseline ship speed rates used by the VTS. The findings suggest that deep learning models combined with maritime data can leverage the challenge of estimating ship speed. The proposed solution could provide accurate and real-time estimations of ship speed to improve shipping operational efficiency, navigation safety and security, and ship emissions estimation and monitoring.