Electrifying Island Ferries : Insights from Interviews and Explainable AI

Le résumé fourni par la source

Electrification is seen as a key pathway toward more sustainable transport systems. This paper examines a Swedish island ferry conversion from diesel to battery electric propulsion by integrating quantitative and qualitative insights: (i) a route-level efficiency assessment to map energy savings across comparable service legs; (ii) an explainable gradient boosting model (SHAP) to quantify the operational and environmental drivers of trip-level energy use; and (iii) interviews with 55 passengers and three captains to capture perceived benefits, operational constraints, and adaptation strategies. The model achieves high predictive accuracy (R2 = 0.97), showing that vessel speed and propulsion type dominate variation in energy intensity, while route geometry and wind contribute smaller but systematic effects. Electrification reduces energy intensity across routes by 10-87%, driven primarily by higher conversion efficiency. Interview results reveal improved onboard comfort, tighter operational margins around charging and schedule adherence, and heating loads as critical constraints in colder seasons. The combined use of EMS data, interpretable ML, and stakeholder interviews provides a rare, system-level perspective on the technical and human factors shaping electric ferry performance. Viewed through a multi level perspective, the findings indicate that electric ferries scale most effectively on short, predictable routes when infrastructure, timetables, and procurement incentives align with the operational characteristics of electric propulsion.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.