Estimating vessel arrival times in global supply chains

The global maritime industry, which facilitates around 90% of the world’s trade, faces operational inefficiencies due to inconsistent Automatic Identification System (AIS) data and scalability challenges, limiting the reliability of current tracking systems. This study aims to improve the reliability of both Estimated Time of Arrival (ETA) and vessel destination predictions. We apply Machine Learning (ML) techniques to predict ETAs through both regression and classification models, while leveraging high-order Markov chains for destination prediction based on sequential maritime route patterns. To ensure the practical applicability of these models, we developed an end-to-end pipeline that incorporates waypoint-based trajectory compression, optimizing the handling of satellite-based AIS data (s-AIS). Using a real-world dataset of global shipping routes, our ML models, particularly Support Vector Regression (SVR), achieved a lower mean absolute error than captain-provided ETAs (16.01 vs. 22.15 h). When the time to arrival was more than 75 h, SVR outperformed captain-provided ETAs, whereas captain-provided ETAs were more accurate in the final three days before arrival, due to frequent manual updates. High-order Markov chains achieved a near-perfect accuracy of 99.00% (std: 1.19%) in destination prediction, confirming the regularity of cargo ship routes. These findings demonstrate the potential of combining ML models with Markov chains to enhance the accuracy and reliability of long-term maritime logistics forecasting, transforming raw s-AIS data into actionable insights for improved operational decision-making. • Machine learning models provide more accurate arrival estimates that captins’ in early voyage stages, supporting more reliable long-term maritime logistics planning. • High-order Markov chains predict vessel destinations with near-perfect (99%) accuracy. • An end-to-end pineline with waypoint-based trajectory compression streamlines satellite AIS data processing for real-world application. • Models are validated with global shipping route data.