Deep Learning-Based Ship Speed Prediction for Intelligent Maritime Traffic Management

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