Operational drivers of fuel performance in Ro–Ro shipping

This study investigates operational drivers of fuel performance in maritime transport using 22 months of high-resolution sensor data from two Ro-Ro sister vessels. A hybrid benchmark model is developed to derive a speed-and draught-independent performance indicator, enabling consistent evaluation of efficiency and environmental impact. Results show that fuel performance is primarily governed by physical operating conditions, while hull condition and human factors are associated with variability in observed performance. Hull degradation leads to efficiency losses through increased resistance and associated operational responses, with implications for fuel consumption and emissions. Variability related to captains and crews are explained by how operating conditions are selected and managed rather than by intrinsic performance differences. A machine learning-based decision support pilot showed a 1.9 percentage-point improvement in fuel performance and reduced variability across operators. Overall, the findings demonstrate how maintenance, operational decisions, and data-driven tools can enhance transport efficiency and support maritime decarbonisation.