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Data analytics for fuel consumption management in maritime transportation: Status and perspectives

Citations

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Cited by:

  1. Li, Zhijun & Fei, Jiangang & Du, Yuquan & Ong, Kok-Leong & Arisian, Sobhan, 2024. "A near real-time carbon accounting framework for the decarbonization of maritime transport," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 191(C).
  2. Lan, Tian & Huang, Lianzhong & Ruan, Zhang & Cao, Jianlin & Ma, Ranqi & Wu, Jianyi & Li, Xiaowu & Chen, Li & Wang, Kai, 2025. "Multilevel parallel integration framework for enhancing energy efficiency of wing-assisted ships based on deep learning and intelligent algorithms: Towards a smarter and greener shipping," Applied Energy, Elsevier, vol. 394(C).
  3. Zhang, Zekun & Rong, Wuyue & Liu, Yang & Yang, Ying, 2025. "Port carbon emission estimation: Principles, practices, and machine learning applications," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 199(C).
  4. Bhide, Nitish & Mandal, Jasashwi & Kumar, Ramesh & Tiwari, Manoj K., 2026. "Joint bunker fuel and freight revenue management in liner shipping: A decision-focused learning approach," International Journal of Production Economics, Elsevier, vol. 291(C).
  5. Luo, Xi & Yan, Ran & Xu, Lang & Wang, Shuaian, 2024. "Accuracy and applicability of ship's fuel consumption prediction models: A comprehensive comparative analysis," Energy, Elsevier, vol. 310(C).
  6. Kondratenko, Aleksander A. & Zhang, Mingyang & Tavakoli, Sasan & Altarriba, Elias & Hirdaris, Spyros, 2025. "Existing technologies and scientific advancements to decarbonize shipping by retrofitting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 212(C).
  7. Chua, Celine & Li, Xue & Tan, Kim Hock & Yuen, Kum Fai, 2024. "Building sustainable performance in the maritime industry via digital resources and innovation," Transport Policy, Elsevier, vol. 149(C), pages 282-299.
  8. Fan, Ailong & Wang, Yifu & Yang, Liu & Yang, Zhiyong & Hu, Zhihui, 2025. "A novel grey box model for ship fuel consumption prediction adapted to complex navigating conditions," Energy, Elsevier, vol. 315(C).
  9. Govindan, Kannan & Dua, Rubal & Mehbub Anwar, AHM & Bansal, Prateek, 2024. "Enabling net-zero shipping: An expert review-based agenda for emerging techno-economic and policy research," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 192(C).
  10. Ruan, Zhang & Huang, Lianzhong & Wang, Kai & Ma, Ranqi & Wang, Zhongyi & Zhang, Rui & Zhao, Haoyang & Wang, Cong, 2024. "A novel prediction method of fuel consumption for wing-diesel hybrid vessels based on feature construction," Energy, Elsevier, vol. 286(C).
  11. Ghosh, Indranil & De, Arijit, 2024. "Maritime Fuel Price Prediction of European Ports using Least Square Boosting and Facebook Prophet: Additional Insights from Explainable Artificial Intelligence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 189(C).
  12. Ding, Yanyan & Yang, Dong, 2025. "Should an electric vehicle manufacturer buy its own ship? Investment and pricing strategies under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 195(C).
  13. Xinyu Li & Yi Zuo & Junhao Jiang, 2022. "Application of Regression Analysis Using Broad Learning System for Time-Series Forecast of Ship Fuel Consumption," Sustainability, MDPI, vol. 15(1), pages 1-21, December.
  14. Li, Yiming & Sun, Zhuo & Hong, Soondo, 2024. "An exact algorithm for multiple-equipment integrated scheduling in an automated container terminal using a double-cycling strategy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
  15. Nilu, Tanzila Yeasmin & Wang, Chuanxu & Ahmed, Shek, 2025. "Dynamic spillovers connectedness among carbon trading, shipping freight, bunker oil and crude oil market: Evidence from quantile-frequency analysis," Research in Transportation Economics, Elsevier, vol. 111(C).
  16. Saz-Salazar, Salvador del & Tovar, Beatriz, 2024. "On ferry users’ willingness to pay for improving environmental quality: A case study for the Canary Islands," Transport Policy, Elsevier, vol. 154(C), pages 61-72.
  17. Wang, Huiwen & Yi, Wen & Zhen, Lu, 2024. "Optimal policy for scheduling automated guided vehicles in large-scale intelligent transportation systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
  18. Nguyen, Son & Fu, Xiuju & Zhao, Liangbin & Xu, Haiyan & Zhang, Xiaocai & Li, Ning & Zhang, Wei & Yin, Xiao Feng & Daichi, Ogawa & Koh, Jimmy & Zheng, Qin, 2026. "AI-driven Just-In-Time coordination between port and ships: Advancing maritime decarbonization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 207(C).
  19. Han, Peixiu & Liu, Zhongbo & Li, Chi & Sun, Zhuo & Yan, Chunxin, 2024. "A novel federated learning-based two-stage approach for ship energy consumption optimization considering both shipping data security and statistical heterogeneity," Energy, Elsevier, vol. 309(C).
  20. Niu, Baozhuang & Dong, Jian & Wang, Hongzhi, 2024. "Smart port vs. port integration to mitigate congestion: ESG performance and data validation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 191(C).
  21. Nguyen, Son & Fu, Xiuju & Ogawa, Daichi & Zheng, Qin, 2023. "An application-oriented testing regime and multi-ship predictive modeling for vessel fuel consumption prediction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
  22. Xu, Haonan & Liu, Jiaguo & Xu, Xiaofeng & Chen, Jihong & Yue, Xiaohang, 2024. "The impact of AI technology adoption on operational decision-making in competitive heterogeneous ports☆," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
  23. Ruan, Zhang & Huang, Lianzhong & Li, Daize & Ma, Ranqi & Wang, Kai & Zhang, Rui & Zhao, Haoyang & Wu, Jianyi & Li, Xiaowu, 2025. "A novel dual-stage grey-box stacking method for significantly improving the extrapolation performance of ship fuel consumption prediction models," Energy, Elsevier, vol. 318(C).
  24. Wang, Kai & Li, Zhongwei & Liu, Xing & Hu, Zhiqiang & Huang, Lianzhong & Song, Qiushi & Liang, Hongzhi & Jiang, Xiaoli, 2025. "Wind-assisted propulsion system for shipping decarbonization: Technologies, applications and challenges," Energy, Elsevier, vol. 336(C).
  25. Yang, Ying & Liu, Yang & Li, Guorong & Zhang, Zekun & Liu, Yanbin, 2024. "Harnessing the power of Machine learning for AIS Data-Driven maritime Research: A comprehensive review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
  26. Guomin Li & Wei Li & Yinke Dou & Yigang Wei, 2022. "Antarctic Shipborne Tourism: Carbon Emission and Mitigation Path," Energies, MDPI, vol. 15(21), pages 1-17, October.
  27. Lan, Tian & Huang, Lianzhong & Ma, Ranqi & Wang, Kai & Ruan, Zhang & Wu, Jianyi & Li, Xiaowu & Chen, Li, 2025. "A robust method of dual adaptive prediction for ship fuel consumption based on polymorphic particle swarm algorithm driven," Applied Energy, Elsevier, vol. 379(C).
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