Maritime Transport Emissions and Efficiency

Neueste Paper

• Time-Aware Validation of Machine Learning Fuel Consumption Models: Evidence from 1\,Hz Operational Data, CCGS \textit{Sir Wilfrid Laurier}

  Samarasimha Reddy Chittamuru, Ayhan Akinturk, Allison Kennedy, Joshua Barnes, Matthew Hamilton · 18. August 2026

  Ship fuel consumption (SFC) prediction supports vessel operation optimisation, emissions estimation, and decision support systems (DSS) for sustainable maritime transportation. Numerous data-driven fuel models have been developed over the past two decades, but a critical and often overlooked limitat…

• Physics-informed offline reinforcement learning eliminates catastrophic fuel waste in maritime routing

  Aniruddha Bora, Julie Chalfant, Chryssostomos Chryssostomidis · 19. März 2026

  International shipping produces approximately 3% of global greenhouse gas emissions, yet voyage routing remains dominated by heuristic methods. We present PIER (Physics-Informed, Energy-efficient, Risk-aware routing), an offline reinforcement learning framework that learns fuel-efficient, safety-awa…

• EcoFair-CH-MARL: Scalable Constrained Hierarchical Multi-Agent RL with Real-Time Emission Budgets and Fairness Guarantees

  Saad Alqithami · 17. März 2026

  Global decarbonisation targets and tightening market pressures demand maritime logistics solutions that are simultaneously efficient, sustainable, and equitable. We introduce EcoFair-CH-MARL, a constrained hierarchical multi-agent reinforcement learning framework that unifies three innovations: (i) …

• Estimation and Optimization of Ship Fuel Consumption in Maritime: Review, Challenges and Future Directions

  Dusica Marijan, Hamza Haruna Mohammed, Bakht Zaman · 26. Februar 2026

  To reduce carbon emissions and minimize shipping costs, improving the fuel efficiency of ships is crucial. Various measures are taken to reduce the total fuel consumption of ships, including optimizing vessel parameters and selecting routes with the lowest fuel consumption. Different estimation meth…

• Physics-Informed Machine Learning for Vessel Shaft Power and Fuel Consumption Prediction: Interpretable KAN-based Approach

  Hamza Haruna Mohammed, Dusica Marijan, Arnbj{\o}rn Maressa · 26. Februar 2026

  Accurate prediction of shaft rotational speed, shaft power, and fuel consumption is crucial for enhancing operational efficiency and sustainability in maritime transportation. Conventional physics-based models provide interpretability but struggle with real-world variability, while purely data-drive…

• Scientific Knowledge-Guided Machine Learning for Vessel Power Prediction: A Comparative Study

  Orfeas Bourchas, George Papalambrou · 23. Februar 2026

  Accurate prediction of main engine power is essential for vessel performance optimization, fuel efficiency, and compliance with emission regulations. Conventional machine learning approaches, such as Support Vector Machines, variants of Artificial Neural Networks (ANNs), and tree-based methods like …

• Realistic Curriculum Reinforcement Learning for Autonomous and Sustainable Marine Vessel Navigation

  Zhang Xiaocai, Xiao Zhe, Liang Maohan, Liu Tao, Li Haijiang, Zhang Wenbin · 19. Januar 2026

  Sustainability is becoming increasingly critical in the maritime transport, encompassing both environmental and social impacts, such as Greenhouse Gas (GHG) emissions and navigational safety. Traditional vessel navigation heavily relies on human experience, often lacking autonomy and emission awaren…

• Physics-guided Neural Network-based Shaft Power Prediction for Vessels

  Dogan Altan, Hamza Haruna Mohammed, Glenn Terje Lines, Dusica Marijan, Arnbj{\o}rn Maressa · 24. Dezember 2025

  Optimizing maritime operations, particularly fuel consumption for vessels, is crucial, considering its significant share in global trade. As fuel consumption is closely related to the shaft power of a vessel, predicting shaft power accurately is a crucial problem that requires careful consideration …