Fuel efficiency in ferry services: GPS-based clustering and explainable AI

Enhancing fuel efficiency in ferry operations is essential for reducing emissions and advancing maritime sustainability. This study presents a data-driven framework that uses second-level GPS data enriched with operational and environmental variables to identify and explain fuel consumption patterns. Vessel movements are segmented into trip legs and journeys, and operational metrics such as speed, wind exposure, and fuel use are computed. A hybrid machine learning approach combines unsupervised clustering to detect recurring operational patterns with gradient boosting models and explainable methods to quantify feature impacts. The framework achieves strong performance, with a cluster classification accuracy of 94 percent and a coefficient of determination of 0.97 for fuel prediction. Results indicate that operational speed is the dominant driver of fuel consumption, while analysis of captain assignments reveals the influence of human factors. The proposed framework provides actionable insights for speed management and operational optimization, enabling cost-effective emission reductions in ferry services.